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all ¤

Import everything in one. Import some unnecessary but convenient for everyone

BackTestAccount ¤

BackTestAccount(
    risk: float = 0.02,
    balance: float = 10000,
    commission: float = 0.2,
    margin: float = 1,
    leverage: float = 1,
    **kwargs
)

Bases: Account

Parameters:

  • risk (float, default: 0.02 ) –

    description. Defaults to 0.02.

  • balance (float, default: 10000 ) –

    description. Defaults to 10_000.

  • commission (float, default: 0.2 ) –

    Commission fee is percent of size. Defaults to 0.2.

  • margin (float, default: 1 ) –

    description. Defaults to 1.

  • leverage (float, default: 1 ) –

    description. Defaults to 1.

  • **kwargs (dict, default: {} ) –

    Mirror of lettrade.account.Account().

Source code in lettrade/exchange/backtest/account.py 
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def __init__(
    self,
    risk: float = 0.02,
    balance: float = 10_000,
    commission: float = 0.2,
    margin: float = 1,
    leverage: float = 1,
    **kwargs,
) -> None:
    """Account for backtest

    Args:
        risk (float, optional): _description_. Defaults to 0.02.
        balance (float, optional): _description_. Defaults to 10_000.
        commission (float, optional): Commission fee is percent of size. Defaults to 0.2.
        margin (float, optional): _description_. Defaults to 1.
        leverage (float, optional): _description_. Defaults to 1.
        **kwargs (dict, optional): Mirror of [lettrade.account.Account()](site:/reference/account/account/#lettrade.account.account.Account).
    """
    super().__init__(
        risk=risk,
        balance=balance,
        margin=margin,
        leverage=leverage,
        **kwargs,
    )
    self._commission = commission

balance property ¤

balance: float

Balance of account

Returns:

  • float ( float ) –

    description

next ¤

next()

Next account

Source code in lettrade/account/account.py 
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def next(self):
    """Next account"""

next_next ¤

next_next()

Call after strategy.next()

Source code in lettrade/account/account.py 
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def next_next(self):
    """Call after strategy.next()"""
    self._equity_snapshot()

risk ¤

risk(side: TradeSide, size: float, **kwargs) -> float

Risk calculation

Source code in lettrade/account/account.py 
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def risk(self, side: "TradeSide", size: float, **kwargs) -> float:
    """Risk calculation"""
    if size is None:
        return side * abs(self._risk)
    return side * abs(size)

start ¤

start()

Start account

Source code in lettrade/account/account.py 
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def start(self):
    """Start account"""

stop ¤

stop()

Stop account

Source code in lettrade/account/account.py 
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def stop(self):
    """Stop account"""
    try:
        self._equity_snapshot()
    except LetAccountInsufficientException:
        pass

BackTestCommander ¤

Bases: Commander

BackTest Commander to debug commander notify

init ¤

init(
    bot: LetTradeBot,
    brain: Brain,
    exchange: Exchange,
    strategy: Strategy,
)

Init commander dependencies

Parameters:

Source code in lettrade/commander/commander.py 
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def init(
    self,
    bot: "LetTradeBot",
    brain: "Brain",
    exchange: "Exchange",
    strategy: "Strategy",
):
    """Init commander dependencies

    Args:
        bot (LetTradeBot): LetTradeBot object
        brain (Brain): Brain of bot
        exchange (Exchange): Manage bot trading
        strategy (Strategy): Strategy of bot
    """
    self.bot = bot
    self.brain = brain
    self.exchange = exchange
    self.strategy = strategy

    self._name = self.bot._name

BackTestDataFeed ¤

BackTestDataFeed(
    data: DataFrame,
    name: str,
    timeframe: str | int | Timedelta | None = None,
    meta: dict | None = None,
    since: int | str | Timestamp | None = None,
    to: int | str | Timestamp | None = None,
    **kwargs
)

Bases: DataFeed

BackTest DataFeed

Parameters:

  • data (DataFrame) –

    description

  • name (str) –

    description

  • timeframe (str | int | Timedelta | None, default: None ) –

    description. Defaults to None.

  • meta (dict | None, default: None ) –

    description. Defaults to None.

  • since (int | str | Timestamp | None, default: None ) –

    Drop data before since. Defaults to None.

  • to (int | str | Timestamp | None, default: None ) –

    Drop data after to. Defaults to None.

Source code in lettrade/exchange/backtest/data.py 
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def __init__(
    self,
    data: pd.DataFrame,
    name: str,
    timeframe: str | int | pd.Timedelta | None = None,
    meta: dict | None = None,
    since: int | str | pd.Timestamp | None = None,
    to: int | str | pd.Timestamp | None = None,
    **kwargs,
) -> None:
    """_summary_

    Args:
        data (pd.DataFrame): _description_
        name (str): _description_
        timeframe (str | int | pd.Timedelta | None, optional): _description_. Defaults to None.
        meta (dict | None, optional): _description_. Defaults to None.
        since (int | str | pd.Timestamp | None, optional): Drop data before since. Defaults to None.
        to (int | str | pd.Timestamp | None, optional): Drop data after to. Defaults to None.
    """
    if timeframe is None:
        timeframe = self._find_timeframe(data)
        logger.info("DataFeed %s auto detect timeframe %s", name, timeframe)
    super().__init__(
        data=data,
        name=name,
        timeframe=timeframe,
        meta=meta,
        **kwargs,
    )
    if since is not None or to is not None:
        self.drop(since=since, to=to)

i property ¤

i: indicator

Alias to lettrade.indicator and using in DataFeed by call: DataFeed.i.indicator_name()

is_main property ¤

is_main: bool

Property to check DataFeed is main DataFeed or not

l instance-attribute ¤

l: LetDataFeedWrapper

LetTrade DataFeed wrapper using to manage index pointer of DataFeed

meta property ¤

meta: dict

Property to get metadata of DataFeed

name property ¤

name: str

Property to get name of DataFeed

now property ¤

now: Timestamp

Property to get current index value of DataFeed

timeframe property ¤

timeframe: TimeFrame

Property to get timeframe of DataFeed

bar ¤

bar(i: int = 0) -> Timestamp

Get current pd.Timestamp value of DataFeed

Parameters:

  • i (int, default: 0 ) –

    Index. Defaults to 0.

Returns:

  • Timestamp –

    pd.Timestamp: description

Source code in lettrade/data/data.py 
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def bar(self, i: int = 0) -> pd.Timestamp:
    """Get current pd.Timestamp value of DataFeed

    Args:
        i (int, optional): Index. Defaults to 0.

    Returns:
        pd.Timestamp: _description_
    """
    return self.l.index[i]

copy ¤

copy(deep: bool = False, **kwargs) -> DataFeed

summary

Parameters:

  • deep (bool, default: False ) –

    description. Defaults to False.

Returns:

Source code in lettrade/data/data.py 
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def copy(self, deep: bool = False, **kwargs) -> "DataFeed":
    """_summary_

    Args:
        deep (bool, optional): _description_. Defaults to False.

    Returns:
        DataFeed: _description_
    """
    df = super().copy(deep=deep)
    df = self.__class__(
        data=df,
        name=self.name,
        timeframe=self.timeframe,
        meta=self.meta.copy(),
        **kwargs,
    )
    return df

drop ¤

drop(
    *args,
    since: int | str | Timestamp | None = None,
    to: int | str | Timestamp | None = None,
    **kwargs
) -> None | DataFeed

summary

Parameters:

  • since (int | str | Timestamp | None, default: None ) –

    description. Defaults to None.

  • to (int | str | Timestamp | None, default: None ) –

    description. Defaults to None.

Raises:

Source code in lettrade/data/data.py 
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def drop(
    self,
    *args,
    since: int | str | pd.Timestamp | None = None,
    to: int | str | pd.Timestamp | None = None,
    **kwargs,
) -> "None | DataFeed":
    """_summary_

    Args:
        since (int | str | pd.Timestamp | None, optional): _description_. Defaults to None.
        to (int | str | pd.Timestamp | None, optional): _description_. Defaults to None.

    Raises:
        RuntimeError: _description_
    """
    if since is None and to is None:
        return super().drop(*args, **kwargs)

    condiction = None

    # Since
    if since is not None:
        if isinstance(since, int):
            loc = self.l.index[since]
        elif isinstance(since, str):
            loc = pd.to_datetime(since, utc=True)
        elif isinstance(since, pd.Timestamp):
            loc = since
        else:
            raise RuntimeError(f"DataFeed.drop since {since} is invalid")
        condiction = self.index < loc

    # To
    if to is not None:
        if isinstance(to, int):
            loc = self.l.index[to]
        elif isinstance(to, str):
            loc = pd.to_datetime(to, utc=True)
        elif isinstance(to, pd.Timestamp):
            loc = to
        else:
            raise RuntimeError(f"DataFeed.drop to {to} is invalid")

        if condiction is None:
            condiction = self.index > loc
        else:
            condiction = condiction | (self.index > loc)

    index = self[condiction].index
    super().drop(index=index, inplace=True)
    self.l.reset()

    if __debug__:
        logger.debug("BackTestDataFeed %s dropped %s rows", self.name, len(index))

push ¤

push(
    rows: list[list[int | float]],
    unit: str | None = None,
    utc: bool = True,
    **kwargs
)

Push new rows to DataFeed

Parameters:

  • rows (list[list[int | float]]) –

    list of rows [["timestamp", "open price", "high price"...]]

  • unit (str | None, default: None ) –

    pandas.Timestamp parsing unit. Defaults to None.

  • utc (bool, default: True ) –

    description. Defaults to True.

Source code in lettrade/data/data.py 
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def push(
    self,
    rows: list[list[int | float]],
    unit: str | None = None,
    utc: bool = True,
    **kwargs,
):
    """Push new rows to DataFeed

    Args:
        rows (list[list[int | float]]): list of rows `[["timestamp", "open price", "high price"...]]`
        unit (str | None, optional): pandas.Timestamp parsing unit. Defaults to None.
        utc (bool, optional): _description_. Defaults to True.
    """
    for row in rows:
        dt = pd.to_datetime(row[0], unit=unit, utc=utc, **kwargs)
        self.at[
            dt,
            (
                "open",
                "high",
                "low",
                "close",
                "volume",
            ),
        ] = (
            row[1],  # open
            row[2],  # high
            row[3],  # low
            row[4],  # close
            row[5],  # volume
        )

    if __debug__:
        logger.debug("[%s] Update bar: \n%s", self.name, self.tail(len(rows)))

BackTestDataFeeder ¤

BackTestDataFeeder(start_size: int = 500)

Bases: DataFeeder

BackTest DataFeeder

Source code in lettrade/exchange/backtest/feeder.py 
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def __init__(self, start_size: int = 500) -> None:
    super().__init__()
    self._start_size = start_size

is_continous property ¤

is_continous

Flag check is realtime continous datafeeder

BackTestExchange ¤

BackTestExchange(**kwargs)

Bases: Exchange

Source code in lettrade/exchange/backtest/exchange.py 
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def __init__(self, **kwargs):
    super().__init__(**kwargs)

    if self._config.setdefault("use_execution", False):
        self.executions = None

data instance-attribute ¤

data: DataFeed

main DataFeed

datas instance-attribute ¤

datas: list[DataFeed]

List of all available DataFeed

history_orders instance-attribute ¤

history_orders: dict[str, Order] = dict()

History Order dict by Order.id key

history_positions instance-attribute ¤

history_positions: dict[str, Position] = dict()

History Position dict by Position.id key

orders instance-attribute ¤

orders: dict[str, Order] = dict()

Available Order dict by Order.id key

positions instance-attribute ¤

positions: dict[str, Position] = dict()

Available Position dict by Position.id key

init ¤

init(
    brain: Brain,
    feeder: DataFeeder,
    account: Account,
    commander: Commander,
) -> None

Init Exchange dependencies. Set data/datas from feeder. Set state to ExchangeState.Start.

Parameters:

Source code in lettrade/exchange/exchange.py 
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def init(
    self,
    brain: "Brain",
    feeder: DataFeeder,
    account: Account,
    commander: Commander,
) -> None:
    """Init Exchange dependencies.
    Set data/datas from feeder.
    Set state to `ExchangeState.Start`.

    Args:
        brain (Brain): _description_
        feeder (DataFeeder): _description_
        account (Account): _description_
        commander (Commander): _description_
    """
    self._brain = brain
    self._feeder = feeder
    self._account = account
    self._commander = commander

    self.data = self._feeder.data
    self.datas = self._feeder.datas

    self._account.init(exchange=self)

    self._state = ExchangeState.Start

new_order ¤

new_order(
    size: float,
    type: OrderType = OrderType.Market,
    limit: float | None = None,
    stop: float | None = None,
    sl: float | None = None,
    tp: float | None = None,
    tag: object | None = None,
    data: DataFeed | None = None,
    expiration: datetime | None = None,
    **kwargs
) -> OrderResult

Place new order. Then send order events to Brain

Parameters:

  • size (float) –

    description

  • type (OrderType | None, default: Market ) –

    description. Defaults to OrderType.Market.

  • limit (float | None, default: None ) –

    description. Defaults to None.

  • stop (float | None, default: None ) –

    description. Defaults to None.

  • sl (float | None, default: None ) –

    description. Defaults to None.

  • tp (float | None, default: None ) –

    description. Defaults to None.

  • tag (object | None, default: None ) –

    description. Defaults to None.

  • data (DataFeed | None, default: None ) –

    description. Defaults to None.

  • **kwargs (dict | None, default: {} ) –

    Extra-parameters

Returns:

  • OrderResult ( OrderResult ) –

    Result when place new Order

Source code in lettrade/exchange/backtest/exchange.py 
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def new_order(
    self,
    size: float,
    type: OrderType = OrderType.Market,
    limit: float | None = None,
    stop: float | None = None,
    sl: float | None = None,
    tp: float | None = None,
    tag: object | None = None,
    data: DataFeed | None = None,
    expiration: datetime | None = None,
    **kwargs,
) -> OrderResult:
    """Place new order.
    Then send order events to `Brain`

    Args:
        size (float): _description_
        type (OrderType | None, optional): _description_. Defaults to OrderType.Market.
        limit (float | None, optional): _description_. Defaults to None.
        stop (float | None, optional): _description_. Defaults to None.
        sl (float | None, optional): _description_. Defaults to None.
        tp (float | None, optional): _description_. Defaults to None.
        tag (object | None, optional): _description_. Defaults to None.
        data (DataFeed | None, optional): _description_. Defaults to None.
        **kwargs (dict | None, optional): Extra-parameters

    Returns:
        OrderResult: Result when place new `Order`
    """
    if data is None:
        data = self.data

    order = BackTestOrder(
        id=self._id(),
        exchange=self,
        data=data,
        size=size,
        type=type,
        limit_price=limit,
        stop_price=stop,
        sl_price=sl,
        tp_price=tp,
        expiration=expiration,
        tag=tag,
    )
    ok = order.place(at=self.data.bar())

    if type == OrderType.Market:
        # Simulate market order will send event before return order result
        self._simulate_orders()

    return ok

next ¤

next()

Execution when new data feeded

Source code in lettrade/exchange/backtest/exchange.py 
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def next(self):
    """Execution when new data feeded"""
    self._simulate_orders()
    super().next()

next_next ¤

next_next()

Call after strategy.next()

Source code in lettrade/exchange/exchange.py 
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def next_next(self):
    """Call after strategy.next()"""
    self._account.next_next()

on_executions ¤

on_executions(
    executions: list[Execution],
    broadcast: bool | None = True,
    **kwargs
) -> None

Receive Execution event from exchange then store and notify Brain

Source code in lettrade/exchange/exchange.py 
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def on_executions(
    self,
    executions: list[Execution],
    broadcast: bool | None = True,
    **kwargs,
) -> None:
    """
    Receive Execution event from exchange then store and notify Brain
    """
    for execution in executions:
        if not isinstance(execution, Execution):
            raise RuntimeError(f"{execution} is not instance of type Execution")

        if execution.id in self.executions:
            # Merge to keep Execution handler for strategy using
            # when strategy want to store Execution object
            # and object will be automatic update directly
            self.executions[execution.id].merge(execution)
            execution = self.executions[execution.id]
        else:
            self.executions[execution.id] = execution

    if self._state != ExchangeState.Run:
        return

    if broadcast:
        self._brain.on_executions(executions)

on_order ¤

on_order(
    order: Order, broadcast: bool | None = True, **kwargs
) -> None

Receive order event from exchange then store and notify Brain

Parameters:

  • order (Order) –

    description

  • broadcast (bool | None, default: True ) –

    description. Defaults to True.

Source code in lettrade/exchange/exchange.py 
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def on_order(
    self,
    order: Order,
    broadcast: bool | None = True,
    **kwargs,
) -> None:
    """Receive order event from exchange then store and notify Brain

    Args:
        order (Order): _description_
        broadcast (bool | None, optional): _description_. Defaults to True.
    """
    self.on_orders(orders=[order], broadcast=broadcast, **kwargs)

on_orders ¤

on_orders(
    orders: list[Order],
    broadcast: bool | None = True,
    **kwargs
) -> None

Receive orders event from exchange then store and notify Brain

Parameters:

  • orders (list[Order]) –

    description

  • broadcast (bool | None, default: True ) –

    description. Defaults to True.

Raises:

Source code in lettrade/exchange/exchange.py 
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def on_orders(
    self,
    orders: list[Order],
    broadcast: bool | None = True,
    **kwargs,
) -> None:
    """Receive orders event from exchange then store and notify Brain

    Args:
        orders (list[Order]): _description_
        broadcast (bool | None, optional): _description_. Defaults to True.

    Raises:
        RuntimeError: _description_
    """
    for order in orders:
        if not isinstance(order, Order):
            raise RuntimeError(f"{order} is not instance of type Order")

        if order.is_closed:
            if order.id in self.history_orders:
                logger.warning("Order closed recall: %s", order)

            self.history_orders[order.id] = order
            if order.id in self.orders:
                del self.orders[order.id]

            if __debug__:
                logger.info("Order closed: %s", order)
        else:
            if order.id in self.history_orders:
                raise RuntimeError(f"Order {order.id} closed")

            if order.id in self.orders:
                # Merge to keep Order handler for strategy using
                # when strategy want to store Order object
                # and object will be automatic update directly
                self.orders[order.id].merge(order)
                order = self.orders[order.id]

                if __debug__:
                    logger.info("Order updated: %s", order)
            else:
                self.orders[order.id] = order

                if __debug__:
                    logger.info("Order placed: %s", order)

    if self._state != ExchangeState.Run:
        return

    if broadcast:
        self._brain.on_orders(orders)

on_position ¤

on_position(
    position: Position,
    broadcast: bool | None = True,
    **kwargs
) -> None

Receive Position event from exchange then store and notify Brain

Parameters:

  • position (Position) –

    new comming Position

  • broadcast (bool | None, default: True ) –

    Flag notify to Brain. Defaults to True.

Raises:

Source code in lettrade/exchange/exchange.py 
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def on_position(
    self,
    position: Position,
    broadcast: bool | None = True,
    **kwargs,
) -> None:
    """Receive Position event from exchange then store and notify Brain

    Args:
        position (Position): new comming `Position`
        broadcast (bool | None, optional): Flag notify to Brain. Defaults to True.

    Raises:
        RuntimeError: validate `Position` instance
    """
    self.on_positions(positions=[position], broadcast=broadcast, **kwargs)

on_positions ¤

on_positions(
    positions: list[Position],
    broadcast: bool | None = True,
    **kwargs
) -> None

Receive Positions event from exchange then store and notify Brain

Parameters:

  • positions (list[Position]) –

    list of new comming Position

  • broadcast (bool | None, default: True ) –

    Flag notify to Brain. Defaults to True.

Raises:

Source code in lettrade/exchange/exchange.py 
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def on_positions(
    self,
    positions: list[Position],
    broadcast: bool | None = True,
    **kwargs,
) -> None:
    """Receive Positions event from exchange then store and notify Brain

    Args:
        positions (list[Position]): list of new comming `Position`
        broadcast (bool | None, optional): Flag notify to Brain. Defaults to True.

    Raises:
        RuntimeError: validate `Position` instance
    """
    for position in positions:
        if not isinstance(position, Position):
            raise RuntimeError(f"{position} is not instance of type Position")

        if position.is_exited:
            if position.id in self.history_positions:
                logger.warning("Position exited recall: %s", position)

            self.history_positions[position.id] = position
            if position.id in self.positions:
                del self.positions[position.id]

            # self._account._on_position_exit(position)
        else:
            if position.id in self.history_positions:
                raise RuntimeError(f"Position {position.id} closed: {position}")
            if position.id in self.positions:
                # Merge to keep Position handler for strategy using
                # when strategy want to store Position object
                # and object will be automatic update directly
                self.positions[position.id].merge(position)
                position = self.positions[position.id]
            else:
                self.positions[position.id] = position
                # self._account._on_position_entry(position)

    self._account.on_positions(positions)

    if self._state != ExchangeState.Run:
        return

    if broadcast:
        self._brain.on_positions(positions)

start ¤

start() -> None

Start of Exchange by: - Start account. - Set state to ExchangeState.Run ready for next().

Source code in lettrade/exchange/exchange.py 
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def start(self) -> None:
    """Start of Exchange by:
    - Start account.
    - Set state to `ExchangeState.Run` ready for next().
    """
    self._account.start()
    self._state = ExchangeState.Run

stop ¤

stop() -> None

Stop Exchange

Source code in lettrade/exchange/exchange.py 
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def stop(self) -> None:
    """Stop Exchange"""
    self._state = ExchangeState.Stop
    self._account.stop()

BackTestExecution ¤

BackTestExecution(
    exchange: BackTestExchange, *args, **kwargs
)

Bases: Execution

Execution for backtesting

Source code in lettrade/exchange/backtest/trade.py 
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def __init__(self, exchange: "BackTestExchange", *args, **kwargs):
    if exchange.executions is None:
        logger.warning(
            "Execution transaction is disable, enable by flag: show_execution=True"
        )
        return

    super().__init__(*args, exchange=exchange, **kwargs)

is_long property ¤

is_long: bool

True if side is long (size is positive).

Returns:

  • bool ( bool ) –

    True/False

is_short property ¤

is_short: bool

True if side is short (size is negative).

Returns:

  • bool ( bool ) –

    description

side property ¤

side: TradeSide

True if side is short (size is negative).

Returns:

from_order classmethod ¤

from_order(
    order: BackTestOrder,
    price: float,
    at: object,
    size: float | None = None,
) -> BackTestExecution

Method help to build Execution object from Order object

Parameters:

  • price (float) –

    Executed price

  • at (object) –

    Executed bar

  • size (float | None, default: None ) –

    Executed size. Defaults to None.

Returns:

Source code in lettrade/exchange/backtest/trade.py 
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@classmethod
def from_order(
    cls,
    order: "BackTestOrder",
    price: float,
    at: object,
    size: float | None = None,
) -> "BackTestExecution":
    """Method help to build Execution object from Order object

    Args:
        price (float): Executed price
        at (object): Executed bar
        size (float | None, optional): Executed size. Defaults to None.

    Returns:
        BackTestExecution: Execution object
    """
    return cls(
        id=order.id,
        size=size or order.size,
        exchange=order.exchange,
        data=order.data,
        price=price,
        at=at,
        order=order,
    )

merge ¤

merge(other: Execution)

Merge to keep object handler but not overwrite for Strategy using when Strategy want to store object and object will be automatic update directly

Source code in lettrade/exchange/execution.py 
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def merge(self, other: "Execution"):
    """
    Merge to keep object handler but not overwrite for Strategy using when Strategy want to store object and object will be automatic update directly
    """
    if other is self:
        return

    if self.id != other.id:
        raise RuntimeError(f"Merge difference id {self.id} != {other.id} execution")

    self.price = other.price
    self.size = other.size

    if other.at:
        self.at = other.at
    if other.order_id:
        self.order_id = other.order_id
    if other.order:
        self.order = other.order
    if other.position_id:
        self.position_id = other.position_id
    if other.position:
        self.position = other.position

BackTestOrder ¤

BackTestOrder(
    id: str,
    exchange: Exchange,
    data: DataFeed,
    size: float,
    state: OrderState = OrderState.Pending,
    type: OrderType = OrderType.Market,
    limit_price: float | None = None,
    stop_price: float | None = None,
    sl_price: float | None = None,
    tp_price: float | None = None,
    parent: Position | None = None,
    expiration: datetime | None = None,
    tag: str | None = None,
    placed_at: datetime | None = None,
    **kwargs
)

Bases: Order

Order for backtesting

Parameters:

  • id (str) –

    description

  • exchange (Exchange) –

    description

  • data (DataFeed) –

    description

  • size (float) –

    description

  • state (OrderState, default: Pending ) –

    description. Defaults to OrderState.Pending.

  • type (OrderType, default: Market ) –

    description. Defaults to OrderType.Market.

  • limit_price (float | None, default: None ) –

    description. Defaults to None.

  • stop_price (float | None, default: None ) –

    description. Defaults to None.

  • sl_price (float | None, default: None ) –

    description. Defaults to None.

  • tp_price (float | None, default: None ) –

    description. Defaults to None.

  • parent (Position | None, default: None ) –

    description. Defaults to None.

  • expiration (datetime | None, default: None ) –

    description. Defaults to None.

  • tag (str | None, default: None ) –

    description. Defaults to None.

  • placed_at (datetime | None, default: None ) –

    description. Defaults to None.

Source code in lettrade/exchange/order.py 
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def __init__(
    self,
    id: str,
    exchange: "Exchange",
    data: "DataFeed",
    size: float,
    state: OrderState = OrderState.Pending,
    type: OrderType = OrderType.Market,
    limit_price: float | None = None,
    stop_price: float | None = None,
    sl_price: float | None = None,
    tp_price: float | None = None,
    parent: "Position | None" = None,
    expiration: datetime | None = None,
    tag: str | None = None,
    placed_at: datetime | None = None,
    **kwargs,
):
    """_summary_

    Args:
        id (str): _description_
        exchange (Exchange): _description_
        data (DataFeed): _description_
        size (float): _description_
        state (OrderState, optional): _description_. Defaults to OrderState.Pending.
        type (OrderType, optional): _description_. Defaults to OrderType.Market.
        limit_price (float | None, optional): _description_. Defaults to None.
        stop_price (float | None, optional): _description_. Defaults to None.
        sl_price (float | None, optional): _description_. Defaults to None.
        tp_price (float | None, optional): _description_. Defaults to None.
        parent (Position | None, optional): _description_. Defaults to None.
        expiration (datetime | None, optional): _description_. Defaults to None.
        tag (str | None, optional): _description_. Defaults to None.
        placed_at (datetime | None, optional): _description_. Defaults to None.
    """
    super().__init__(
        id=id,
        exchange=exchange,
        data=data,
        size=size,
        **kwargs,
    )

    self.type: OrderType = type
    self.state: OrderState = state
    self.limit_price: float | None = limit_price
    self.stop_price: float | None = stop_price
    self.sl_price: float | None = sl_price
    self.tp_price: float | None = tp_price
    self.parent: "Position | None" = parent
    self.expiration: datetime | None = expiration
    self.tag: str | None = tag
    self.placed_at: pd.Timestamp | None = placed_at
    self.filled_at: pd.Timestamp | None = None
    self.filled_price: float | None = None

is_closed property ¤

is_closed: bool

Flag to check Order closed

Returns:

  • bool ( bool ) –

    True if state in [OrderState.Filled, OrderState.Canceled]

is_long property ¤

is_long: bool

True if side is long (size is positive).

Returns:

  • bool ( bool ) –

    True/False

is_opening property ¤

is_opening: bool

Flag to check Order still alive

Returns:

  • bool ( bool ) –

    True if state in [OrderState.Placed, OrderState.Partial]

is_short property ¤

is_short: bool

True if side is short (size is negative).

Returns:

  • bool ( bool ) –

    description

is_sl_order property ¤

is_sl_order: bool

Order is stop-loss order of a Position

Returns:

  • bool ( bool ) –

    description

is_tp_order property ¤

is_tp_order: bool

Order is take-profit order of a Position

Returns:

  • bool ( bool ) –

    description

limit property ¤

limit: float | None

Getter of limit_price

Returns:

  • float | None –

    float | None: float or None

place_price property ¤

place_price: float | None

Getter of place_price

Returns:

  • float | None –

    float | None: float or None

side property ¤

side: TradeSide

True if side is short (size is negative).

Returns:

sl property ¤

sl: float | None

Getter of sl_price

Returns:

  • float | None –

    float | None: float or None

stop property ¤

stop: float | None

Getter of stop_price

Returns:

  • float | None –

    float | None: float or None

tp property ¤

tp: float | None

Getter of tp_price

Returns:

  • float | None –

    float | None: float or None

cancel ¤

cancel(
    caller: Order | Position | None = None, **kwargs
) -> OrderResult

Cancel the Order and notify Exchange

Source code in lettrade/exchange/backtest/trade.py 
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def cancel(
    self,
    caller: Order | Position | None = None,
    **kwargs,
) -> "OrderResult":
    """Cancel the Order and notify Exchange"""
    if self.state != OrderState.Placed:
        raise RuntimeError(f"Order {self.id} state {self.state} is not Placed")

    if self.parent:
        if self is self.parent.sl_order:
            self.parent.sl_order = None
        elif self is self.parent.tp_order:
            self.parent.tp_order = None

    return super().cancel()

fill ¤

fill(
    price: float, at: object, **kwargs
) -> BackTestExecution

Execution order and notify for Exchange

Parameters:

  • price (float) –

    Executed price

  • at (object) –

    Executed bar

Raises:

Returns:

Source code in lettrade/exchange/backtest/trade.py 
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def fill(self, price: float, at: object, **kwargs) -> BackTestExecution:
    """Execution order and notify for Exchange

    Args:
        price (float): Executed price
        at (object): Executed bar

    Raises:
        RuntimeError: _description_

    Returns:
        BackTestExecution: Execution object
    """
    if self.state != OrderState.Placed:
        raise RuntimeError(f"Execution a {self.state} order")

    # Order
    ok = super().fill(price=price, at=at)

    # Execution is enable
    if self.exchange.executions is not None:
        execution = BackTestExecution.from_order(order=self, price=price, at=at)
        execution._on_execution()

    # Position hit SL/TP
    if self.parent:
        self.parent.exit(price=price, at=at, caller=self)
    else:
        # Position: Place and create new position
        position = BackTestPosition.from_order(order=self)

        position.entry(price=price, at=at)

    return ok

merge ¤

merge(other: Order)

Update current Order variables by other Order

Parameters:

  • other (Order) –

    Merge source Order

Raises:

Source code in lettrade/exchange/order.py 
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def merge(self, other: "Order"):
    """Update current `Order` variables by other `Order`

    Args:
        other (Order): Merge source `Order`

    Raises:
        RuntimeError: Validate same id
    """
    if other is self:
        return

    if self.id != other.id:
        raise RuntimeError(f"Merge difference id {self.id} != {other.id} order")

    self.size = other.size
    self.sl_price = other.sl_price
    self.tp_price = other.tp_price

    if other.limit_price:
        self.limit_price = other.limit_price
    if other.stop_price:
        self.stop_price = other.stop_price
    if other.placed_at:
        self.placed_at = other.placed_at

    if other.filled_price:
        self.filled_price = other.filled_price
    if other.filled_at:
        self.filled_at = other.filled_at

    if other.parent:
        self.parent = other.parent

place ¤

place(
    at: Timestamp, raw: object | None = None
) -> OrderResult

Place Order Set status to OrderState.Placed. Send event to Exchange

Raises:

Returns:

Source code in lettrade/exchange/order.py 
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def place(
    self,
    at: pd.Timestamp,
    raw: object | None = None,
) -> "OrderResult":
    """Place `Order`
    Set `status` to `OrderState.Placed`.
    Send event to `Exchange`

    Raises:
        RuntimeError: _description_

    Returns:
        OrderResult: result of `Order`
    """
    self.validate()

    if self.state != OrderState.Pending:
        raise RuntimeError(f"Order {self.id} state {self.state} is not Pending")

    self.state = OrderState.Placed
    self.placed_at = at

    logger.info("Placing new order: %s", self)

    self.exchange.on_order(self)
    return OrderResultOk(order=self, raw=raw)

update ¤

update(
    limit_price: float | None = None,
    stop_price: float | None = None,
    sl: float | None = None,
    tp: float | None = None,
    raw: object | None = None,
) -> OrderResult

Update Order

Parameters:

  • limit_price (float, default: None ) –

    description. Defaults to None.

  • stop_price (float, default: None ) –

    description. Defaults to None.

  • sl (float, default: None ) –

    description. Defaults to None.

  • tp (float, default: None ) –

    description. Defaults to None.

Raises:

Source code in lettrade/exchange/order.py 
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def update(
    self,
    limit_price: float | None = None,
    stop_price: float | None = None,
    sl: float | None = None,
    tp: float | None = None,
    raw: object | None = None,
) -> "OrderResult":
    """Update Order

    Args:
        limit_price (float, optional): _description_. Defaults to None.
        stop_price (float, optional): _description_. Defaults to None.
        sl (float, optional): _description_. Defaults to None.
        tp (float, optional): _description_. Defaults to None.

    Raises:
        RuntimeError: _description_
    """
    if self.is_closed:
        raise RuntimeError(f"Update a closed order {self}")

    # TODO: validate parameters
    if limit_price is not None:
        self.limit_price = limit_price
    if stop_price is not None:
        self.stop_price = stop_price

    if sl is not None:
        self.sl_price = sl
    if tp is not None:
        self.tp_price = tp

    self.exchange.on_order(self)
    return OrderResultOk(order=self, raw=raw)

BackTestPosition ¤

BackTestPosition(
    id: str,
    exchange: Exchange,
    data: DataFeed,
    size: float,
    parent: Order,
    state: PositionState = PositionState.Open,
    entry_price: float | None = None,
    entry_fee: float = 0.0,
    entry_at: Timestamp | None = None,
    sl_order: Order | None = None,
    tp_order: Order | None = None,
    tag: str | None = None,
    **kwargs
)

Bases: Position

Position for backtesting

Source code in lettrade/exchange/position.py 
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def __init__(
    self,
    id: str,
    exchange: "Exchange",
    data: "DataFeed",
    size: float,
    parent: "Order",
    state: PositionState = PositionState.Open,
    entry_price: float | None = None,
    entry_fee: float = 0.0,
    entry_at: pd.Timestamp | None = None,
    sl_order: Order | None = None,
    tp_order: Order | None = None,
    tag: str | None = None,
    **kwargs,
):
    super().__init__(
        id=id,
        exchange=exchange,
        data=data,
        size=size,
        **kwargs,
    )
    self._account: "Account" = self.exchange._account

    self.state: PositionState = state
    self.parent: "Order" = parent
    self.tag: str | None = tag

    self.entry_price: float | None = entry_price
    self.entry_fee: float = entry_fee
    self.entry_at: pd.Timestamp | None = entry_at

    self.exit_price: float | None = None
    self.exit_fee: float = 0.0
    self.exit_at: pd.Timestamp | None = None
    self.exit_pl: float | None = None

    self.sl_order: Order | None = sl_order
    self.tp_order: Order | None = tp_order

fee property ¤

fee: float

Fee/Estimate Fee for trade

Returns:

  • float ( float ) –

    Fee

is_exited property ¤

is_exited: bool

Flag to check Position state.

Returns:

  • bool ( bool ) –

    True if the trade exited

is_long property ¤

is_long: bool

True if side is long (size is positive).

Returns:

  • bool ( bool ) –

    True/False

is_opening property ¤

is_opening: bool

Flag to check Position state.

Returns:

  • bool ( bool ) –

    True if the trade opening

is_short property ¤

is_short: bool

True if side is short (size is negative).

Returns:

  • bool ( bool ) –

    description

pl property ¤

pl: float

Estimate Profit or Loss of Position

Returns:

  • float ( float ) –

    PnL

side property ¤

side: TradeSide

True if side is short (size is negative).

Returns:

exit ¤

exit(
    price: float | None = None,
    at: Timestamp | None = None,
    caller: Order | Position | None = None,
    **kwargs
) -> PositionResult

Exit Position

Parameters:

  • price (float, default: None ) –

    Exit price

  • at (object, default: None ) –

    Exit bar

  • caller (Order | Position, default: None ) –

    Skip caller to prevent infinite recursion loop. Defaults to None.

Source code in lettrade/exchange/backtest/trade.py 
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def exit(
    self,
    price: float | None = None,
    at: pd.Timestamp | None = None,
    caller: Order | Position | None = None,
    **kwargs,
) -> PositionResult:
    """Exit Position

    Args:
        price (float): Exit price
        at (object): Exit bar
        caller (Order | Position, optional): Skip caller to prevent infinite recursion loop. Defaults to None.
    """
    if self.state == PositionState.Exit:
        if caller is None:
            # Call by user
            raise RuntimeError(f"Call exited position {self}")
        return

    if caller is None:
        # Call by user
        if price is not None:
            raise RuntimeError(f"Price set {price} is not available")
        if at is not None:
            raise RuntimeError(f"At set {at} is not available")

        price = self.data.l.open[0]
        at = self.data.l.index[0]
    else:
        # Call by SL/TP order
        if price is None or at is None:
            raise RuntimeError(f"Caller {caller} with price is None or at is None")

    # PnL
    pl = self._account.pl(
        size=self.size,
        entry_price=self.entry_price,
        exit_price=price,
    )

    # Fee
    fee = self._account.fee(size=self.size)

    # State
    ok = super().exit(price=price, at=at, pl=pl, fee=fee, **kwargs)

    # Caller is position close by tp/sl order
    if caller is None:
        if self.sl_order is not None:
            self.sl_order.cancel(caller=self)
        if self.tp_order is not None:
            self.tp_order.cancel(caller=self)
    else:
        if self.sl_order and self.sl_order is not caller:
            self.sl_order.cancel(caller=caller)
        if self.tp_order and self.tp_order is not caller:
            self.tp_order.cancel(caller=caller)

    return ok

from_order classmethod ¤

from_order(
    order: BackTestOrder,
    size: float | None = None,
    state: PositionState = PositionState.Open,
    **kwargs
) -> BackTestPosition

Build Position object from Order object

Parameters:

  • size (float, default: None ) –

    Size of Position object. Defaults to None.

  • state (PositionState, default: Open ) –

    State of Position object. Defaults to PositionState.Open.

Returns:

Source code in lettrade/exchange/backtest/trade.py 
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@classmethod
def from_order(
    cls,
    order: "BackTestOrder",
    size: float | None = None,
    state: PositionState = PositionState.Open,
    **kwargs,
) -> "BackTestPosition":
    """Build Position object from Order object

    Args:
        size (float, optional): Size of Position object. Defaults to None.
        state (PositionState, optional): State of Position object. Defaults to PositionState.Open.

    Returns:
        BackTestPosition: Position object
    """
    position = cls(
        id=order.id,
        size=size or order.size,
        exchange=order.exchange,
        data=order.data,
        state=state,
        parent=order,
    )
    if order.sl_price:
        position._new_sl_order(stop_price=order.sl_price)
    if order.tp_price:
        position._new_tp_order(limit_price=order.tp_price)
    order.parent = position
    return position

merge ¤

merge(other: Position) -> bool

Merge position from another position has same id

Parameters:

Raises:

Source code in lettrade/exchange/position.py 
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def merge(self, other: "Position") -> bool:
    """Merge position from another position has same id

    Args:
        other (Position): _description_

    Raises:
        RuntimeError: _description_
    """
    if other is self:
        return False

    if self.id != other.id:
        raise RuntimeError(f"Merge difference id {self.id} != {other.id} order")

    self.size = other.size

    if other.sl_order is not None:
        self.sl_order = other.sl_order
        self.sl_order.parent = self
    elif self.sl_order is not None:
        self.sl_order.cancel()
        self.sl_order = None

    if other.tp_order is not None:
        self.tp_order = other.tp_order
        self.tp_order.parent = self
    elif self.tp_order is not None:
        self.tp_order.cancel()
        self.tp_order = None

    if other.entry_at:
        self.entry_at = other.entry_at
    if other.entry_price:
        self.entry_price = other.entry_price

    if other.exit_at:
        self.exit_at = other.exit_at
    if other.exit_price:
        self.exit_price = other.exit_price
    if other.exit_fee:
        self.exit_fee = other.exit_fee
    if other.exit_pl:
        self.exit_pl = other.exit_pl

    if other.parent:
        self.parent = other.parent

    return True

BotPlotter ¤

BotPlotter(bot: LetTradeBot)

Bases: Plotter

Class help to plot bot result

Source code in lettrade/plot/bot.py 
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def __init__(self, bot: "LetTradeBot") -> None:
    self.bot = bot
    self.feeder = bot.feeder
    self.exchange = bot.exchange
    self.account = bot.account
    self.strategy = bot.strategy

    self.datas = self.feeder.datas
    self.data = self.feeder.datas[0]

datas instance-attribute ¤

datas: list[DataFeed] = datas

All plotting datafeeds

jump ¤

jump(
    since: int | str | Timestamp | None = None,
    order_id: str | None = None,
    position_id: str | None = None,
    range: int = 300,
    name: str | None = None,
)

Jump to place on datefeed

Parameters:

  • since (int | str | Timestamp | None, default: None ) –

    Jump to index/datetime. Defaults to None.

  • order_id (str | None, default: None ) –

    Jump to order id. Defaults to None.

  • position_id (str | None, default: None ) –

    Jump to position id. Defaults to None.

  • range (int, default: 300 ) –

    number of candle plot. Defaults to 300.

  • name (str | None, default: None ) –

    description. Defaults to None.

Raises:

Source code in lettrade/plot/bot.py 
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def jump(
    self,
    since: int | str | pd.Timestamp | None = None,
    order_id: str | None = None,
    position_id: str | None = None,
    range: int = 300,
    name: str | None = None,
):
    """Jump to place on datefeed

    Args:
        since (int | str | pd.Timestamp | None, optional): Jump to index/datetime. Defaults to None.
        order_id (str | None, optional): Jump to order id. Defaults to None.
        position_id (str | None, optional): Jump to position id. Defaults to None.
        range (int, optional): number of candle plot. Defaults to 300.
        name (str | None, optional): _description_. Defaults to None.

    Raises:
        RuntimeError: _description_
    """
    if since is None:
        if order_id is not None:  # Jump to order id
            if not isinstance(order_id, str):
                order_id = str(order_id)

            if order_id in self.exchange.orders:
                order = self.exchange.orders[order_id]
            elif order_id in self.exchange.history_orders:
                order = self.exchange.history_orders[order_id]
            else:
                raise RuntimeError(f"Order id {order_id} not found")

            loc = self.data.l.index.get_loc(order.placed_at)
            since = loc - int(range / 2)

        elif position_id is not None:  # Jump to position id
            if not isinstance(position_id, str):
                position_id = str(position_id)

            if position_id in self.exchange.positions:
                position = self.exchange.positions[position_id]
            elif position_id in self.exchange.history_positions:
                position = self.exchange.history_positions[position_id]
            else:
                raise RuntimeError(f"Position id {position_id} not found")

            loc = self.data.l.index.get_loc(position.entry_at)
            since = loc - int(range / 2)
        else:  # Reset
            self.jump_reset()
            return

    elif isinstance(since, str):  # Parse string to pd.Timestamp, then since=index
        since = pd.to_datetime(since, utc=True)
        since = self.data.l.index.get_loc(since)
    elif isinstance(since, pd.Timestamp):  # Get index of Timestamp
        since = self.data.l.index.get_loc(since)

    # Since min at pointer_start
    if since < self.data.l.pointer_start:
        since = self.data.l.pointer_start
    # Since max at pointer_stop
    if since > self.data.l.pointer_stop - range:
        since = self.data.l.pointer_stop - range

    self._jump_since = self.data.l.index[since]
    self._jump_to = self.data.l.index[since + range]

    # Reload data
    self.load()

jump_reset ¤

jump_reset() -> bool

Reset jump datafeeds back to bot datafeeds

Source code in lettrade/plot/bot.py 
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def jump_reset(self) -> bool:
    """Reset jump datafeeds back to bot datafeeds"""
    self._jump_since = None
    self._jump_to = None
    return True

load abstractmethod ¤

load()

Load plot config from Strategy.plot() and setup candlestick/equity

Source code in lettrade/plot/plot.py 
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@abstractmethod
def load(self):
    """Load plot config from `Strategy.plot()` and setup candlestick/equity"""

plot abstractmethod ¤

plot(**kwargs)

Plot equity, orders, and positions then show

Source code in lettrade/plot/plot.py 
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@abstractmethod
def plot(self, **kwargs):
    """Plot `equity`, `orders`, and `positions` then show"""

stop abstractmethod ¤

stop()

stop plotter

Source code in lettrade/plot/plot.py 
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@abstractmethod
def stop(self):
    """stop plotter"""

BotStatistic ¤

BotStatistic(
    feeder: DataFeeder,
    exchange: Exchange,
    strategy: Strategy,
)

Compute strategy result

Source code in lettrade/stats/stats.py 
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def __init__(
    self,
    feeder: DataFeeder,
    exchange: Exchange,
    strategy: Strategy,
) -> None:
    self.feeder: DataFeeder = feeder
    self.exchange: Exchange = exchange
    self.strategy: Strategy = strategy
    self.account: Account = strategy.account

compute ¤

compute()

Calculate strategy report

Source code in lettrade/stats/stats.py 
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def compute(self):
    """Calculate strategy report"""
    data = self.feeder.data

    ### Stats
    self.result = result = pd.Series(dtype=object)

    result.loc["strategy"] = str(self.strategy.__class__)
    result.loc["start"] = data.index[0]
    result.loc["end"] = data.index[-1]
    result.loc["duration"] = result.end - result.start

    ### Equity
    equities_index = pd.DatetimeIndex(self.account._equities.keys())
    equities = pd.Series(self.account._equities.values(), index=equities_index)
    dd = 1 - equities / np.maximum.accumulate(equities)
    dd_dur, dd_peaks = _compute_drawdown_duration_peaks(
        pd.Series(dd, index=equities_index)
    )

    result.loc["start_balance"] = round(equities.iloc[0], 2)
    result.loc["equity"] = round(equities.iloc[-1], 2)
    result.loc["equity_peak"] = round(equities.max(), 2)

    pl = equities.iloc[-1] - equities.iloc[0]
    result.loc["pl"] = round(pl, 2)
    result.loc["pl_percent"] = round(pl / equities.iloc[0] * 100, 2)

    c = data.close.values
    result.loc["buy_hold_pl_percent"] = round(
        (c[-1] - c[0]) / c[0] * 100, 2
    )  # long-only return

    max_dd = -np.nan_to_num(dd.max())
    result.loc["max_drawdown_percent"] = round(max_dd * 100, 2)
    result.loc["avg_drawdown_percent"] = round(-dd_peaks.mean() * 100, 2)
    result.loc["max_drawdown_duration"] = _round_timedelta(
        dd_dur.max(), data.timeframe
    )
    result.loc["avg_drawdown_duration"] = _round_timedelta(
        dd_dur.mean(), data.timeframe
    )

    # Separator
    result.loc[""] = ""

    ### Positions
    positions = list(self.exchange.history_positions.values()) + list(
        self.exchange.positions.values()
    )
    positions_columns = (
        "size",
        "entry_at",
        "exit_at",
        "entry_price",
        "exit_price",
        "pl",
        "fee",
    )
    positions_df = pd.DataFrame(columns=positions_columns)
    for position in positions:
        positions_df.at[position.id, positions_columns] = (
            position.size,
            position.entry_at,
            position.exit_at,
            position.entry_price,
            position.exit_price,
            position.pl,
            position.fee,
        )
    positions_df["duration"] = positions_df["entry_at"] - positions_df["exit_at"]
    positions_total = len(positions)
    pl = positions_df["pl"]

    result.loc["positions"] = positions_total

    win_rate = np.nan if not positions_total else (pl > 0).mean()
    result.loc["win_rate"] = round(win_rate, 2)
    result.loc["fee"] = round(positions_df.fee.sum(), 2)
    result.loc["best_trade_percent"] = round(pl.max(), 2)
    result.loc["worst_trade_percent"] = round(pl.min(), 2)
    result.loc["sqn"] = round(
        np.sqrt(positions_total) * pl.mean() / (pl.std() or np.nan),
        2,
    )
    result.loc["kelly_criterion"] = win_rate - (1 - win_rate) / (
        pl[pl > 0].mean() / -pl[pl < 0].mean()
    )
    result.loc["profit_factor"] = pl[pl > 0].sum() / (
        abs(pl[pl < 0].sum()) or np.nan
    )

    return self.result

show ¤

show()

Show statistic report

Source code in lettrade/stats/stats.py 
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def show(self):
    """
    Show statistic report
    """
    if "Start" not in self.result:
        logger.warning("call compute() before show()")
        self.compute()

    # Show result inside docs session
    if __debug__:
        from lettrade.utils.docs import is_docs_session

        if is_docs_session():
            print(str(self))
            return

    logger.info(
        "\n============= Statistic result =============\n%s\n",
        str(self),
    )

CCXTAPI ¤

CCXTAPI(
    exchange: int,
    key: str,
    secret: str,
    currency: str = "USDT",
    ccxt: CCXTAPIExchange | None = None,
    **kwargs
)

Bases: LiveAPI

CCXT API

Parameters:

  • exchange (int) –

    description

  • key (str) –

    description

  • secret (str) –

    description

  • ccxt (CCXTAPIExchange | None, default: None ) –

    description. Defaults to None.

Source code in lettrade/exchange/ccxt/api.py 
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def __init__(
    self,
    exchange: int,
    key: str,
    secret: str,
    currency: str = "USDT",
    ccxt: CCXTAPIExchange | None = None,
    **kwargs,
):
    """_summary_

    Args:
        exchange (int): _description_
        key (str): _description_
        secret (str): _description_
        ccxt (CCXTAPIExchange | None, optional): _description_. Defaults to None.
    """
    if ccxt is None:
        ccxt = CCXTAPIExchange(exchange=exchange, key=key, secret=secret, **kwargs)
    self._ccxt = ccxt
    self._currency = ccxt

account ¤

account() -> dict
Source code in lettrade/exchange/ccxt/api.py 
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def account(self) -> dict:
    """"""
    raw = self._ccxt.fetch_my_balance()
    currency = raw[self._currency]
    return Box(
        balance=currency["free"],
        equity=currency["total"],
        margin=1,
        leverage=1,
        raw=raw,
    )

execution_get ¤

execution_get(id: str, **kwargs) -> dict
Source code in lettrade/exchange/ccxt/api.py 
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def execution_get(self, id: str, **kwargs) -> dict:
    """"""

executions_get ¤

executions_get(
    position_id: str | None = None,
    search: str | None = None,
    **kwargs
) -> list[dict]
Source code in lettrade/exchange/ccxt/api.py 
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def executions_get(
    self,
    position_id: str | None = None,
    search: str | None = None,
    **kwargs,
) -> list[dict]:
    """"""

executions_total ¤

executions_total(
    since: datetime | None = None,
    to: datetime | None = None,
    **kwargs
) -> int
Source code in lettrade/exchange/ccxt/api.py 
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def executions_total(
    self,
    since: datetime | None = None,
    to: datetime | None = None,
    **kwargs,
) -> int:
    """"""

init ¤

init(**kwargs)
Source code in lettrade/exchange/live/api.py 
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def init(self, **kwargs):
    """"""

order_close ¤

order_close(order: CCXTOrder)
Source code in lettrade/exchange/ccxt/api.py 
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def order_close(self, order: "CCXTOrder"):
    """"""

order_open ¤

order_open(order: CCXTOrder, **kwargs)
Source code in lettrade/exchange/ccxt/api.py 
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def order_open(self, order: "CCXTOrder", **kwargs):
    """"""
    try:
        result = self._ccxt.create_my_order(
            symbol=order.data.symbol,
            type=order.type.lower(),
            side=order.side.lower(),
            amount=abs(order.size),
            price=order.place_price,
            **kwargs,
        )

        print("order_open", order, result)
        return result
    except ccxt.InvalidOrder as e:
        raise LetLiveOrderInvalidException(e.args[0]) from e

orders_get ¤

orders_get(**kwargs)
Source code in lettrade/exchange/ccxt/api.py 
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def orders_get(self, **kwargs):
    """"""

orders_history_get ¤

orders_history_get(
    id: str | None = None,
    since: datetime | None = None,
    to: datetime | None = None,
    **kwargs
) -> list[dict]
Source code in lettrade/exchange/live/api.py 
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def orders_history_get(
    self,
    id: str | None = None,
    since: datetime | None = None,
    to: datetime | None = None,
    **kwargs,
) -> list[dict]:
    """"""

orders_total ¤

orders_total()
Source code in lettrade/exchange/ccxt/api.py 
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def orders_total(self):
    """"""

position_close ¤

position_close(position: CCXTPosition, **kwargs) -> dict
Source code in lettrade/exchange/ccxt/api.py 
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def position_close(self, position: "CCXTPosition", **kwargs) -> dict:
    """"""

position_update ¤

position_update(
    position: CCXTPosition,
    sl: float | None = None,
    tp: float | None = None,
    **kwargs
) -> dict
Source code in lettrade/exchange/ccxt/api.py 
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def position_update(
    self,
    position: "CCXTPosition",
    sl: float | None = None,
    tp: float | None = None,
    **kwargs,
) -> dict:
    """"""

positions_get ¤

positions_get(
    id: str = None, symbol: str = None, **kwargs
) -> list[dict]
Source code in lettrade/exchange/ccxt/api.py 
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def positions_get(self, id: str = None, symbol: str = None, **kwargs) -> list[dict]:
    """"""

positions_total ¤

positions_total(
    since: datetime | None = None,
    to: datetime | None = None,
    **kwargs
) -> int
Source code in lettrade/exchange/ccxt/api.py 
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def positions_total(
    self,
    since: datetime | None = None,
    to: datetime | None = None,
    **kwargs,
) -> int:
    """"""

stop ¤

stop()
Source code in lettrade/exchange/ccxt/api.py 
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def stop(self):
    """"""

CCXTAccount ¤

CCXTAccount(api: LiveAPI, currency: str = 'USDT', **kwargs)

Bases: LiveAccount

Account for CCXT

Source code in lettrade/exchange/ccxt/ccxt.py 
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def __init__(self, api: LiveAPI, currency: str = "USDT", **kwargs) -> None:
    super().__init__(api, **kwargs)
    self._currency = currency

balance property ¤

balance: float

Balance of account

Returns:

  • float ( float ) –

    description

account_refresh ¤

account_refresh()

Refresh account balance

Source code in lettrade/exchange/live/account.py 
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def account_refresh(self):
    """Refresh account balance"""
    self._account = self._api.account()

    if __debug__:
        logger.debug("Account: %s", str(self._account))

    self._balance = self._account.balance
    self._equity = self._account.equity

next ¤

next()

Next account

Source code in lettrade/account/account.py 
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def next(self):
    """Next account"""

next_next ¤

next_next()

Call after strategy.next()

Source code in lettrade/account/account.py 
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def next_next(self):
    """Call after strategy.next()"""
    self._equity_snapshot()

risk ¤

risk(side: TradeSide, size: float, **kwargs) -> float

Risk calculation

Source code in lettrade/account/account.py 
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def risk(self, side: "TradeSide", size: float, **kwargs) -> float:
    """Risk calculation"""
    if size is None:
        return side * abs(self._risk)
    return side * abs(size)

start ¤

start()

Start live account by load account info from API

Source code in lettrade/exchange/live/account.py 
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def start(self):
    """Start live account by load account info from API"""
    self.account_refresh()
    self._margin = self._account.margin
    self._leverage = self._account.leverage

stop ¤

stop()

Stop account

Source code in lettrade/account/account.py 
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def stop(self):
    """Stop account"""
    try:
        self._equity_snapshot()
    except LetAccountInsufficientException:
        pass

CCXTDataFeed ¤

CCXTDataFeed(
    symbol: str,
    timeframe: str | int | Timedelta,
    name: str | None = None,
    columns: list[str] | None = None,
    api: LiveAPI | None = None,
    api_kwargs: dict | None = None,
    **kwargs
)

Bases: LiveDataFeed

DataFeed for CCXT

Parameters:

  • symbol (str) –

    Symbol of DataFeed

  • timeframe (str | int | Timedelta) –

    TimeFrame of DataFeed

  • name (str | None, default: None ) –

    Name of DataFeed, auto generate {symbol}_{timeframe} if none. Defaults to None.

  • columns (list[str] | None, default: None ) –

    List of DataFeed columns. Defaults to None.

  • api (LiveAPI | None, default: None ) –

    Live trading API. Defaults to None.

Source code in lettrade/exchange/live/data.py 
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def __init__(
    self,
    symbol: str,
    timeframe: str | int | pd.Timedelta,
    name: str | None = None,
    columns: list[str] | None = None,
    api: LiveAPI | None = None,
    api_kwargs: dict | None = None,
    **kwargs,
) -> None:
    """_summary_

    Args:
        symbol (str): Symbol of DataFeed
        timeframe (str | int | pd.Timedelta): TimeFrame of DataFeed
        name (str | None, optional): Name of DataFeed, auto generate `{symbol}_{timeframe}` if none. Defaults to None.
        columns (list[str] | None, optional): List of DataFeed columns. Defaults to None.
        api (LiveAPI | None, optional): Live trading API. Defaults to None.
    """
    super().__init__(
        name=name or f"{symbol}_{timeframe}",
        timeframe=timeframe,
        columns=columns or ["open", "high", "low", "close", "volume"],
        **kwargs,
    )

    self.meta.update(symbol=symbol, base_columns=self.columns.copy())

    if api is not None:
        self._api = api
    elif api_kwargs is not None:
        self._api = self._api_cls(**api_kwargs)
    else:
        # raise RuntimeError("Parameter: api or api_kwargs cannot missing")
        logger.info("Parameter: api or api_kwargs cannot missing")

i property ¤

i: indicator

Alias to lettrade.indicator and using in DataFeed by call: DataFeed.i.indicator_name()

is_main property ¤

is_main: bool

Property to check DataFeed is main DataFeed or not

l instance-attribute ¤

l: LetDataFeedWrapper

LetTrade DataFeed wrapper using to manage index pointer of DataFeed

meta property ¤

meta: dict

Property to get metadata of DataFeed

name property ¤

name: str

Property to get name of DataFeed

now property ¤

now: Timestamp

Property to get current index value of DataFeed

symbol property ¤

symbol: str

Property to get symbol of DataFeed

timeframe property ¤

timeframe: TimeFrame

Property to get timeframe of DataFeed

bar ¤

bar(i: int = 0) -> Timestamp

Get current pd.Timestamp value of DataFeed

Parameters:

  • i (int, default: 0 ) –

    Index. Defaults to 0.

Returns:

  • Timestamp –

    pd.Timestamp: description

Source code in lettrade/data/data.py 
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def bar(self, i: int = 0) -> pd.Timestamp:
    """Get current pd.Timestamp value of DataFeed

    Args:
        i (int, optional): Index. Defaults to 0.

    Returns:
        pd.Timestamp: _description_
    """
    return self.l.index[i]

bars ¤

bars(
    since: int | str | Timestamp, to: int | str | Timestamp
) -> list

Get bars from LiveAPI

Parameters:

  • since (int | str | Timestamp) –

    description

  • to (int | str | Timestamp) –

    description

Returns:

  • list ( list ) –

    list of bar

Source code in lettrade/exchange/live/data.py 
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def bars(
    self,
    since: int | str | pd.Timestamp,
    to: int | str | pd.Timestamp,
) -> list:
    """Get bars from LiveAPI

    Args:
        since (int | str | pd.Timestamp): _description_
        to (int | str | pd.Timestamp): _description_

    Returns:
        list: list of bar
    """
    return self._api.bars(
        symbol=self.symbol,
        timeframe=self.timeframe.string,
        since=since,
        to=to,
    )

bars_load ¤

bars_load(
    since: int | str | Timestamp, to: int | str | Timestamp
) -> bool

Get bar from API and push to DataFeed

Parameters:

  • since (int | str | Timestamp) –

    description

  • to (int | str | Timestamp) –

    description

Returns:

  • bool ( bool ) –

    True if has data, False if no data

Source code in lettrade/exchange/live/data.py 
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def bars_load(
    self,
    since: int | str | pd.Timestamp,
    to: int | str | pd.Timestamp,
) -> bool:
    """Get bar from API and push to DataFeed

    Args:
        since (int | str | pd.Timestamp): _description_
        to (int | str | pd.Timestamp): _description_

    Returns:
        bool: True if has data, False if no data
    """
    bars = self.bars(since=since, to=to)

    if bars is None or len(bars) == 0:
        logger.warning("No bars data for %s", self.name)
        return False

    self.push(bars, unit=self._bar_datetime_unit)
    return True

drop ¤

drop(
    *args,
    since: int | str | Timestamp | None = None,
    to: int | str | Timestamp | None = None,
    **kwargs
) -> None | DataFeed

summary

Parameters:

  • since (int | str | Timestamp | None, default: None ) –

    description. Defaults to None.

  • to (int | str | Timestamp | None, default: None ) –

    description. Defaults to None.

Raises:

Source code in lettrade/data/data.py 
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def drop(
    self,
    *args,
    since: int | str | pd.Timestamp | None = None,
    to: int | str | pd.Timestamp | None = None,
    **kwargs,
) -> "None | DataFeed":
    """_summary_

    Args:
        since (int | str | pd.Timestamp | None, optional): _description_. Defaults to None.
        to (int | str | pd.Timestamp | None, optional): _description_. Defaults to None.

    Raises:
        RuntimeError: _description_
    """
    if since is None and to is None:
        return super().drop(*args, **kwargs)

    condiction = None

    # Since
    if since is not None:
        if isinstance(since, int):
            loc = self.l.index[since]
        elif isinstance(since, str):
            loc = pd.to_datetime(since, utc=True)
        elif isinstance(since, pd.Timestamp):
            loc = since
        else:
            raise RuntimeError(f"DataFeed.drop since {since} is invalid")
        condiction = self.index < loc

    # To
    if to is not None:
        if isinstance(to, int):
            loc = self.l.index[to]
        elif isinstance(to, str):
            loc = pd.to_datetime(to, utc=True)
        elif isinstance(to, pd.Timestamp):
            loc = to
        else:
            raise RuntimeError(f"DataFeed.drop to {to} is invalid")

        if condiction is None:
            condiction = self.index > loc
        else:
            condiction = condiction | (self.index > loc)

    index = self[condiction].index
    super().drop(index=index, inplace=True)
    self.l.reset()

    if __debug__:
        logger.debug("BackTestDataFeed %s dropped %s rows", self.name, len(index))

dump_csv ¤

dump_csv(
    path: str | None = None,
    since: int | str | datetime | None = 0,
    to: int | str | datetime | None = 1000,
    **kwargs
)

summary

Parameters:

  • path (str | None, default: None ) –

    description. Defaults to None.

  • since (int | str | datetime | None, default: 0 ) –

    description. Defaults to 0.

  • to (int | str | datetime | None, default: 1000 ) –

    description. Defaults to 1_000.

Source code in lettrade/exchange/live/data.py 
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def dump_csv(
    self,
    path: str | None = None,
    since: int | str | datetime | None = 0,
    to: int | str | datetime | None = 1_000,
    **kwargs,
):
    """_summary_

    Args:
        path (str | None, optional): _description_. Defaults to None.
        since (int  |  str  |  datetime | None, optional): _description_. Defaults to 0.
        to (int  |  str  |  datetime | None, optional): _description_. Defaults to 1_000.
    """
    if self.empty:
        if isinstance(since, str):
            since = pd.to_datetime(since).to_pydatetime()
        if isinstance(to, str):
            to = pd.to_datetime(to).to_pydatetime()

        self.bars_load(since=since, to=to)

    if path is None:
        path = f"data/{self.name}-{since}_{to}.csv"

    from lettrade.data.extra.csv import csv_export

    csv_export(dataframe=self, path=path, **kwargs)

next ¤

next(size=1, tick=0) -> bool

Drop extra columns and load next DataFeed

Parameters:

  • size (int, default: 1 ) –

    description. Defaults to 1.

  • tick (int, default: 0 ) –

    description. Defaults to 0.

Returns:

  • bool ( bool ) –

    description

Source code in lettrade/exchange/live/data.py 
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def next(self, size=1, tick=0) -> bool:
    """Drop extra columns and load next DataFeed

    Args:
        size (int, optional): _description_. Defaults to 1.
        tick (int, optional): _description_. Defaults to 0.

    Returns:
        bool: _description_
    """
    # Drop existed extra columns to skip reusing calculated data
    self.drop(columns=self.columns.difference(self._base_columns), inplace=True)

    self.bars_load(since=0, to=size + 1)
    self.l.go_stop()
    return True

push ¤

push(
    rows: list[list[int | float]],
    unit: str | None = None,
    utc: bool = True,
    **kwargs
)

Push new rows to DataFeed

Parameters:

  • rows (list[list[int | float]]) –

    list of rows [["timestamp", "open price", "high price"...]]

  • unit (str | None, default: None ) –

    pandas.Timestamp parsing unit. Defaults to None.

  • utc (bool, default: True ) –

    description. Defaults to True.

Source code in lettrade/data/data.py 
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def push(
    self,
    rows: list[list[int | float]],
    unit: str | None = None,
    utc: bool = True,
    **kwargs,
):
    """Push new rows to DataFeed

    Args:
        rows (list[list[int | float]]): list of rows `[["timestamp", "open price", "high price"...]]`
        unit (str | None, optional): pandas.Timestamp parsing unit. Defaults to None.
        utc (bool, optional): _description_. Defaults to True.
    """
    for row in rows:
        dt = pd.to_datetime(row[0], unit=unit, utc=utc, **kwargs)
        self.at[
            dt,
            (
                "open",
                "high",
                "low",
                "close",
                "volume",
            ),
        ] = (
            row[1],  # open
            row[2],  # high
            row[3],  # low
            row[4],  # close
            row[5],  # volume
        )

    if __debug__:
        logger.debug("[%s] Update bar: \n%s", self.name, self.tail(len(rows)))

symbol_info ¤

symbol_info()

Get symbol information from API

Source code in lettrade/exchange/live/data.py 
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def symbol_info(self):
    """Get symbol information from API"""
    return self._api.market(symbol=self.symbol)

CCXTDataFeeder ¤

CCXTDataFeeder(
    api: LiveAPI | None = None,
    tick: bool = 5,
    start_size: int = 500,
    api_kwargs: dict | None = None,
    **kwargs
)

Bases: LiveDataFeeder

DataFeeder for CCXT

tick == 0: wait until new bar change value
tick > 0: sleep tick time (in seconds) then update
Source code in lettrade/exchange/live/feeder.py 
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def __init__(
    self,
    api: LiveAPI | None = None,
    tick: bool = 5,
    start_size: int = 500,
    api_kwargs: dict | None = None,
    **kwargs,
) -> None:
    """
    tick:
        tick < 0: no tick, just get completed bar
        tick == 0: wait until new bar change value
        tick > 0: sleep tick time (in seconds) then update
    """
    super().__init__()

    # API init
    if api is None:
        if api_kwargs is None:
            raise RuntimeError("api or api_kwargs cannot missing")
        api = self._api_cls(**api_kwargs)

    self._api = api
    self._tick = tick
    self._start_size = start_size
    self._config = kwargs

    if isinstance(self._tick, int):
        self._wait_timeframe = TimeFrame(f"{self._tick}s")
    else:
        self._wait_timeframe = None

is_continous property ¤

is_continous

Flag check is realtime continous datafeeder

CCXTExchange ¤

CCXTExchange(api: LiveAPI, *args, **kwargs)

Bases: LiveExchange

MetaTrade 5 exchange module for lettrade

Parameters:

  • api (LiveAPI) –

    API connect to rpyc MeTrader 5 Terminal server through module mt5linux

  • *args (list, default: () ) –

    Exchange list parameters

  • **kwargs (dict, default: {} ) –

    Exchange dict parameters

Source code in lettrade/exchange/live/exchange.py 
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def __init__(self, api: LiveAPI, *args, **kwargs):
    """_summary_

    Args:
        api (LiveAPI): API connect to rpyc MeTrader 5 Terminal server through module `mt5linux`
        *args (list): `Exchange` list parameters
        **kwargs (dict): `Exchange` dict parameters
    """
    super().__init__(*args, **kwargs)
    self._api = api

data instance-attribute ¤

data: DataFeed

main DataFeed

datas instance-attribute ¤

datas: list[DataFeed]

List of all available DataFeed

executions instance-attribute ¤

executions: dict[str, Execution] = dict()

Execution dict by Execution.id key

history_orders instance-attribute ¤

history_orders: dict[str, Order] = dict()

History Order dict by Order.id key

history_positions instance-attribute ¤

history_positions: dict[str, Position] = dict()

History Position dict by Position.id key

orders instance-attribute ¤

orders: dict[str, Order] = dict()

Available Order dict by Order.id key

positions instance-attribute ¤

positions: dict[str, Position] = dict()

Available Position dict by Position.id key

init ¤

init(
    brain: Brain,
    feeder: DataFeeder,
    account: Account,
    commander: Commander,
) -> None

Init Exchange dependencies. Set data/datas from feeder. Set state to ExchangeState.Start.

Parameters:

Source code in lettrade/exchange/exchange.py 
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def init(
    self,
    brain: "Brain",
    feeder: DataFeeder,
    account: Account,
    commander: Commander,
) -> None:
    """Init Exchange dependencies.
    Set data/datas from feeder.
    Set state to `ExchangeState.Start`.

    Args:
        brain (Brain): _description_
        feeder (DataFeeder): _description_
        account (Account): _description_
        commander (Commander): _description_
    """
    self._brain = brain
    self._feeder = feeder
    self._account = account
    self._commander = commander

    self.data = self._feeder.data
    self.datas = self._feeder.datas

    self._account.init(exchange=self)

    self._state = ExchangeState.Start

new_order ¤

new_order(
    size: float,
    type: OrderType | None = OrderType.Market,
    limit: float | None = None,
    stop: float | None = None,
    sl: float | None = None,
    tp: float | None = None,
    tag: str | None = None,
    data: DataFeed | None = None,
    **kwargs
) -> OrderResult

Place new order to server

Parameters:

  • size (float) –

    description

  • type (OrderType | None, default: Market ) –

    description. Defaults to OrderType.Market.

  • limit (float | None, default: None ) –

    description. Defaults to None.

  • stop (float | None, default: None ) –

    description. Defaults to None.

  • sl (float | None, default: None ) –

    description. Defaults to None.

  • tp (float | None, default: None ) –

    description. Defaults to None.

  • tag (str | None, default: None ) –

    description. Defaults to None.

  • data (DataFeed | None, default: None ) –

    description. Defaults to None.

Returns:

Source code in lettrade/exchange/live/exchange.py 
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def new_order(
    self,
    size: float,
    type: OrderType | None = OrderType.Market,
    limit: float | None = None,
    stop: float | None = None,
    sl: float | None = None,
    tp: float | None = None,
    tag: str | None = None,
    data: DataFeed | None = None,
    **kwargs,
) -> OrderResult:
    """Place new order to server

    Args:
        size (float): _description_
        type (OrderType | None, optional): _description_. Defaults to OrderType.Market.
        limit (float | None, optional): _description_. Defaults to None.
        stop (float | None, optional): _description_. Defaults to None.
        sl (float | None, optional): _description_. Defaults to None.
        tp (float | None, optional): _description_. Defaults to None.
        tag (str | None, optional): _description_. Defaults to None.
        data (DataFeed | None, optional): _description_. Defaults to None.

    Returns:
        OrderResult: _description_
    """
    if data is None:
        data = self.data

    if type == OrderType.Market:
        limit = 0
        stop = 0

    order = self._order_cls(
        id=-1,
        exchange=self,
        data=data,
        size=size,
        type=type,
        limit_price=limit,
        stop_price=stop,
        sl_price=sl,
        tp_price=tp,
        tag=tag,
        **kwargs,
    )
    ok = order.place()

    if __debug__:
        logger.info("New order %s at %s", order, data.now)

    return ok

next_next ¤

next_next()

Call after strategy.next()

Source code in lettrade/exchange/exchange.py 
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def next_next(self):
    """Call after strategy.next()"""
    self._account.next_next()

on_execution ¤

on_execution(
    execution: Execution,
    broadcast: bool | None = True,
    **kwargs
) -> None

Receive Execution event from exchange then store and notify Brain

Parameters:

  • execution (Execution) –

    description

  • broadcast (bool | None, default: True ) –

    description. Defaults to True.

Source code in lettrade/exchange/exchange.py 
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def on_execution(
    self,
    execution: Execution,
    broadcast: bool | None = True,
    **kwargs,
) -> None:
    """Receive Execution event from exchange then store and notify Brain

    Args:
        execution (Execution): _description_
        broadcast (bool | None, optional): _description_. Defaults to True.
    """
    self.on_executions(executions=[execution], broadcast=broadcast, **kwargs)

on_executions ¤

on_executions(
    executions: list[Execution],
    broadcast: bool | None = True,
    **kwargs
) -> None

Receive Execution event from exchange then store and notify Brain

Source code in lettrade/exchange/exchange.py 
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def on_executions(
    self,
    executions: list[Execution],
    broadcast: bool | None = True,
    **kwargs,
) -> None:
    """
    Receive Execution event from exchange then store and notify Brain
    """
    for execution in executions:
        if not isinstance(execution, Execution):
            raise RuntimeError(f"{execution} is not instance of type Execution")

        if execution.id in self.executions:
            # Merge to keep Execution handler for strategy using
            # when strategy want to store Execution object
            # and object will be automatic update directly
            self.executions[execution.id].merge(execution)
            execution = self.executions[execution.id]
        else:
            self.executions[execution.id] = execution

    if self._state != ExchangeState.Run:
        return

    if broadcast:
        self._brain.on_executions(executions)

on_order ¤

on_order(
    order: Order, broadcast: bool | None = True, **kwargs
) -> None

Receive order event from exchange then store and notify Brain

Parameters:

  • order (Order) –

    description

  • broadcast (bool | None, default: True ) –

    description. Defaults to True.

Source code in lettrade/exchange/exchange.py 
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def on_order(
    self,
    order: Order,
    broadcast: bool | None = True,
    **kwargs,
) -> None:
    """Receive order event from exchange then store and notify Brain

    Args:
        order (Order): _description_
        broadcast (bool | None, optional): _description_. Defaults to True.
    """
    self.on_orders(orders=[order], broadcast=broadcast, **kwargs)

on_orders ¤

on_orders(
    orders: list[Order],
    broadcast: bool | None = True,
    **kwargs
) -> None

Receive orders event from exchange then store and notify Brain

Parameters:

  • orders (list[Order]) –

    description

  • broadcast (bool | None, default: True ) –

    description. Defaults to True.

Raises:

Source code in lettrade/exchange/exchange.py 
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def on_orders(
    self,
    orders: list[Order],
    broadcast: bool | None = True,
    **kwargs,
) -> None:
    """Receive orders event from exchange then store and notify Brain

    Args:
        orders (list[Order]): _description_
        broadcast (bool | None, optional): _description_. Defaults to True.

    Raises:
        RuntimeError: _description_
    """
    for order in orders:
        if not isinstance(order, Order):
            raise RuntimeError(f"{order} is not instance of type Order")

        if order.is_closed:
            if order.id in self.history_orders:
                logger.warning("Order closed recall: %s", order)

            self.history_orders[order.id] = order
            if order.id in self.orders:
                del self.orders[order.id]

            if __debug__:
                logger.info("Order closed: %s", order)
        else:
            if order.id in self.history_orders:
                raise RuntimeError(f"Order {order.id} closed")

            if order.id in self.orders:
                # Merge to keep Order handler for strategy using
                # when strategy want to store Order object
                # and object will be automatic update directly
                self.orders[order.id].merge(order)
                order = self.orders[order.id]

                if __debug__:
                    logger.info("Order updated: %s", order)
            else:
                self.orders[order.id] = order

                if __debug__:
                    logger.info("Order placed: %s", order)

    if self._state != ExchangeState.Run:
        return

    if broadcast:
        self._brain.on_orders(orders)

on_position ¤

on_position(
    position: Position,
    broadcast: bool | None = True,
    **kwargs
) -> None

Receive Position event from exchange then store and notify Brain

Parameters:

  • position (Position) –

    new comming Position

  • broadcast (bool | None, default: True ) –

    Flag notify to Brain. Defaults to True.

Raises:

Source code in lettrade/exchange/exchange.py 
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def on_position(
    self,
    position: Position,
    broadcast: bool | None = True,
    **kwargs,
) -> None:
    """Receive Position event from exchange then store and notify Brain

    Args:
        position (Position): new comming `Position`
        broadcast (bool | None, optional): Flag notify to Brain. Defaults to True.

    Raises:
        RuntimeError: validate `Position` instance
    """
    self.on_positions(positions=[position], broadcast=broadcast, **kwargs)

on_positions ¤

on_positions(
    positions: list[Position],
    broadcast: bool | None = True,
    **kwargs
) -> None

Receive Positions event from exchange then store and notify Brain

Parameters:

  • positions (list[Position]) –

    list of new comming Position

  • broadcast (bool | None, default: True ) –

    Flag notify to Brain. Defaults to True.

Raises:

Source code in lettrade/exchange/exchange.py 
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def on_positions(
    self,
    positions: list[Position],
    broadcast: bool | None = True,
    **kwargs,
) -> None:
    """Receive Positions event from exchange then store and notify Brain

    Args:
        positions (list[Position]): list of new comming `Position`
        broadcast (bool | None, optional): Flag notify to Brain. Defaults to True.

    Raises:
        RuntimeError: validate `Position` instance
    """
    for position in positions:
        if not isinstance(position, Position):
            raise RuntimeError(f"{position} is not instance of type Position")

        if position.is_exited:
            if position.id in self.history_positions:
                logger.warning("Position exited recall: %s", position)

            self.history_positions[position.id] = position
            if position.id in self.positions:
                del self.positions[position.id]

            # self._account._on_position_exit(position)
        else:
            if position.id in self.history_positions:
                raise RuntimeError(f"Position {position.id} closed: {position}")
            if position.id in self.positions:
                # Merge to keep Position handler for strategy using
                # when strategy want to store Position object
                # and object will be automatic update directly
                self.positions[position.id].merge(position)
                position = self.positions[position.id]
            else:
                self.positions[position.id] = position
                # self._account._on_position_entry(position)

    self._account.on_positions(positions)

    if self._state != ExchangeState.Run:
        return

    if broadcast:
        self._brain.on_positions(positions)

start ¤

start() -> None

Start Live exchange by: Sync positions from server

Source code in lettrade/exchange/live/exchange.py 
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def start(self) -> None:
    """Start Live exchange by: Sync positions from server"""
    self._api.start(exchange=self)
    return super().start()

stop ¤

stop() -> None

Stop Exchange

Source code in lettrade/exchange/exchange.py 
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def stop(self) -> None:
    """Stop Exchange"""
    self._state = ExchangeState.Stop
    self._account.stop()

CCXTExecution ¤

CCXTExecution(
    id: str,
    exchange: LiveExchange,
    data: LiveDataFeed,
    size: float,
    price: float,
    at: float,
    order_id: str | None = None,
    order: Order | None = None,
    position_id: str | None = None,
    position: Position | None = None,
    api: LiveAPI | None = None,
    raw: object | None = None,
    **kwargs
)

Bases: LiveExecution

Execution for CCXT

Source code in lettrade/exchange/live/trade.py 
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def __init__(
    self,
    id: str,
    exchange: "LiveExchange",
    data: "LiveDataFeed",
    size: float,
    price: float,
    at: float,
    order_id: str | None = None,
    order: "Order | None" = None,
    position_id: str | None = None,
    position: "Position | None" = None,
    # tag: str | None = None,
    api: LiveAPI | None = None,
    raw: object | None = None,
    **kwargs,
):
    super().__init__(
        id=id,
        exchange=exchange,
        data=data,
        size=size,
        price=price,
        at=at,
        order_id=order_id,
        order=order,
        position_id=position_id,
        position=position,
        api=api,
        raw=raw,
        **kwargs,
    )

is_long property ¤

is_long: bool

True if side is long (size is positive).

Returns:

  • bool ( bool ) –

    True/False

is_short property ¤

is_short: bool

True if side is short (size is negative).

Returns:

  • bool ( bool ) –

    description

side property ¤

side: TradeSide

True if side is short (size is negative).

Returns:

from_raw classmethod ¤

from_raw(
    raw, exchange: LiveExchange, api: CCXTAPI = None
) -> CCXTExecution

Building new CCXTExecution from live api raw object

Parameters:

  • raw (_type_) –

    description

  • exchange (LiveExchange) –

    description

Returns:

Source code in lettrade/exchange/ccxt/trade.py 
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@classmethod
def from_raw(
    cls,
    raw,
    exchange: "LiveExchange",
    api: CCXTAPI = None,
) -> "CCXTExecution":
    """Building new CCXTExecution from live api raw object

    Args:
        raw (_type_): _description_
        exchange (LiveExchange): _description_

    Returns:
        CCXTExecution: _description_
    """

    return cls(
        exchange=exchange,
        id=raw.ticket,
        # TODO: Fix by get data from symbol
        data=exchange.data,
        # TODO: size and type from raw.type
        size=raw.volume,
        price=raw.price,
        # TODO: set bar time
        at=None,
        order_id=raw.order,
        position_id=raw.position_id,
        tag=raw.comment,
        api=api,
        raw=raw,
    )

merge ¤

merge(other: Execution)

Merge to keep object handler but not overwrite for Strategy using when Strategy want to store object and object will be automatic update directly

Source code in lettrade/exchange/execution.py 
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def merge(self, other: "Execution"):
    """
    Merge to keep object handler but not overwrite for Strategy using when Strategy want to store object and object will be automatic update directly
    """
    if other is self:
        return

    if self.id != other.id:
        raise RuntimeError(f"Merge difference id {self.id} != {other.id} execution")

    self.price = other.price
    self.size = other.size

    if other.at:
        self.at = other.at
    if other.order_id:
        self.order_id = other.order_id
    if other.order:
        self.order = other.order
    if other.position_id:
        self.position_id = other.position_id
    if other.position:
        self.position = other.position

CCXTOrder ¤

CCXTOrder(is_real: bool = True, **kwargs)

Bases: LiveOrder

Order for CCXT

Source code in lettrade/exchange/ccxt/trade.py 
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def __init__(self, is_real: bool = True, **kwargs):
    super().__init__(**kwargs)

    self.is_real: bool = is_real
    """Flag to check `Order` is real, cannot duplicate id, cannot recall from history"""

is_closed property ¤

is_closed: bool

Flag to check Order closed

Returns:

  • bool ( bool ) –

    True if state in [OrderState.Filled, OrderState.Canceled]

is_long property ¤

is_long: bool

True if side is long (size is positive).

Returns:

  • bool ( bool ) –

    True/False

is_opening property ¤

is_opening: bool

Flag to check Order still alive

Returns:

  • bool ( bool ) –

    True if state in [OrderState.Placed, OrderState.Partial]

is_real instance-attribute ¤

is_real: bool = is_real

Flag to check Order is real, cannot duplicate id, cannot recall from history

is_short property ¤

is_short: bool

True if side is short (size is negative).

Returns:

  • bool ( bool ) –

    description

is_sl_order property ¤

is_sl_order: bool

Order is stop-loss order of a Position

Returns:

  • bool ( bool ) –

    description

is_tp_order property ¤

is_tp_order: bool

Order is take-profit order of a Position

Returns:

  • bool ( bool ) –

    description

limit property ¤

limit: float | None

Getter of limit_price

Returns:

  • float | None –

    float | None: float or None

place_price property ¤

place_price: float | None

Getter of place_price

Returns:

  • float | None –

    float | None: float or None

side property ¤

side: TradeSide

True if side is short (size is negative).

Returns:

sl property ¤

sl: float | None

Getter of sl_price

Returns:

  • float | None –

    float | None: float or None

stop property ¤

stop: float | None

Getter of stop_price

Returns:

  • float | None –

    float | None: float or None

tp property ¤

tp: float | None

Getter of tp_price

Returns:

  • float | None –

    float | None: float or None

cancel ¤

cancel(**kwargs) -> OrderResult

Cancel order

Returns:

Source code in lettrade/exchange/ccxt/trade.py 
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def cancel(self, **kwargs) -> OrderResult:
    """Cancel order

    Returns:
        OrderResult: _description_
    """
    if not self.parent:
        # Abandon order
        result = self._api.order_close(order=self, **kwargs)
    else:
        # Virtual SL/TP order of trade
        result = None

    return super(LiveOrder, self).cancel(raw=result)

fill ¤

fill(
    price: float, at: Timestamp, raw: object | None = None
) -> OrderResult

Fill Order. Set status to OrderState.Executed. Send event to Exchange

Parameters:

  • price (float) –

    Executed price

  • at (Timestamp) –

    Executed bar

Raises:

Returns:

Source code in lettrade/exchange/order.py 
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def fill(
    self,
    price: float,
    at: pd.Timestamp,
    raw: object | None = None,
) -> "OrderResult":
    """Fill `Order`.
    Set `status` to `OrderState.Executed`.
    Send event to `Exchange`

    Args:
        price (float): Executed price
        at (pd.Timestamp): Executed bar

    Raises:
        RuntimeError: _description_

    Returns:
        OrderResult: result of `Order`
    """
    if self.state != OrderState.Placed:
        raise RuntimeError(f"Order {self.id} state {self.state} is not Placed")

    self.filled_at = at
    self.filled_price = price
    self.state = OrderState.Filled
    self.exchange.on_order(self)
    return OrderResultOk(order=self, raw=raw)

from_position classmethod ¤

from_position(
    position: CCXTPosition,
    sl: float | None = None,
    tp: float | None = None,
) -> CCXTOrder

summary

Parameters:

  • position (CCXTPosition) –

    description

  • sl (float | None, default: None ) –

    description. Defaults to None.

  • tp (float | None, default: None ) –

    description. Defaults to None.

Raises:

Returns:

Source code in lettrade/exchange/ccxt/trade.py 
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@classmethod
def from_position(
    cls,
    position: "CCXTPosition",
    sl: float | None = None,
    tp: float | None = None,
) -> "CCXTOrder":
    """_summary_

    Args:
        position (CCXTPosition): _description_
        sl (float | None, optional): _description_. Defaults to None.
        tp (float | None, optional): _description_. Defaults to None.

    Raises:
        RuntimeError: _description_

    Returns:
        CCXTOrder: _description_
    """
    if not sl and not tp:
        raise RuntimeError("not sl and not tp")
    return cls(
        id=f"{position.id}-{'sl' if sl else 'tp'}",
        exchange=position.exchange,
        data=position.data,
        state=OrderState.Placed,
        type=OrderType.Stop if sl else OrderType.Limit,
        size=-position.size,
        limit_price=tp,
        stop_price=sl,
        parent=position,
        placed_at=position.entry_at,
        is_real=False,
    )

from_raw classmethod ¤

from_raw(
    raw: Any,
    exchange: LiveExchange,
    api: CCXTAPI | None = None,
) -> CCXTOrder | None

summary

Parameters:

  • raw (Any) –

    description.

  • exchange (LiveExchange) –

    description.

  • api (CCXTAPI | None, default: None ) –

    description. Defaults to None.

Raises:

Returns:

  • CCXTOrder ( CCXTOrder | None ) –

    description

Source code in lettrade/exchange/ccxt/trade.py 
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@classmethod
def from_raw(
    cls,
    raw: Any,
    exchange: "LiveExchange",
    api: CCXTAPI | None = None,
) -> "CCXTOrder | None":
    """_summary_

    Args:
        raw (Any): _description_.
        exchange (LiveExchange): _description_.
        api (CCXTAPI | None, optional): _description_. Defaults to None.

    Raises:
        NotImplementedError: _description_

    Returns:
        CCXTOrder: _description_
    """
    # DataFeed
    data = None
    for d in exchange.datas:
        if d.symbol == raw.symbol:
            data = d
            break
    if data is None:
        logger.warning("Raw order %s is not handling %s", raw.symbol, raw)
        return

    # Prices & Side & Type
    limit_price = None
    stop_price = None
    match raw.type:
        case MT5.ORDER_TYPE_BUY:
            side = TradeSide.Buy
            type = OrderType.Market
        case MT5.ORDER_TYPE_SELL:
            side = TradeSide.Sell
            type = OrderType.Market
        case MT5.ORDER_TYPE_BUY_LIMIT:
            side = TradeSide.Buy
            type = OrderType.Limit
            limit_price = raw.price_open
        case MT5.ORDER_TYPE_SELL_LIMIT:
            side = TradeSide.Sell
            type = OrderType.Limit
            limit_price = raw.price_open
        case MT5.ORDER_TYPE_BUY_STOP:
            side = TradeSide.Buy
            type = OrderType.Stop
            stop_price = raw.price_open
        case MT5.ORDER_TYPE_SELL_STOP:
            side = TradeSide.Sell
            type = OrderType.Stop
            stop_price = raw.price_open
        # case MT5.ORDER_TYPE_BUY_STOP_LIMIT:
        #     side = TradeSide.Buy
        #     type = OrderType.StopLimit
        #     # TODO
        #     limit_price = raw.price_open
        #     stop_price = raw.price_open
        # case MT5.ORDER_TYPE_SELL_STOP_LIMIT:
        #     side = TradeSide.Sell
        #     type = OrderType.StopLimit
        #     # TODO
        #     limit_price = raw.price_open
        #     stop_price = raw.price_open
        # case MT5.ORDER_TYPE_CLOSE_BY:
        case _:
            raise NotImplementedError(
                f"Order type {raw.type} is not implement",
                raw,
            )
    # State
    match raw.state:
        case MT5.ORDER_STATE_STARTED:
            state = OrderState.Pending
        case MT5.ORDER_STATE_PLACED:
            state = OrderState.Placed
        case MT5.ORDER_STATE_CANCELED:
            state = OrderState.Canceled
        case MT5.ORDER_STATE_PARTIAL:
            state = OrderState.Partial
        case MT5.ORDER_STATE_FILLED:
            state = OrderState.Filled
        case MT5.ORDER_STATE_REJECTED:
            state = OrderState.Canceled
        case MT5.ORDER_STATE_EXPIRED:
            state = OrderState.Canceled
        case MT5.ORDER_STATE_REQUEST_ADD:
            state = OrderState.Placed
        case MT5.ORDER_STATE_REQUEST_MODIFY:
            state = OrderState.Placed
        case MT5.ORDER_STATE_REQUEST_CANCEL:
            state = OrderState.Canceled
        case _:
            raise NotImplementedError(
                f"Raw order state {raw.state} is not implement"
            )

    order = cls(
        exchange=exchange,
        id=raw.ticket,
        state=state,
        data=data,
        size=side * (raw.volume_current or raw.volume_initial),
        type=type,
        limit_price=limit_price,
        stop_price=stop_price,
        sl_price=raw.sl or None,
        tp_price=raw.tp or None,
        tag=raw.comment,
        placed_at=pd.to_datetime(raw.time_setup_msc, unit="ms", utc=True),
        api=api,
        raw=raw,
    )

    if hasattr(raw, "time_done_msc"):
        order.filled_price = raw.price_current
        order.filled_at = pd.to_datetime(raw.time_done_msc, unit="ms", utc=True)

    return order

merge ¤

merge(other: Order)

Update current Order variables by other Order

Parameters:

  • other (Order) –

    Merge source Order

Raises:

Source code in lettrade/exchange/order.py 
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def merge(self, other: "Order"):
    """Update current `Order` variables by other `Order`

    Args:
        other (Order): Merge source `Order`

    Raises:
        RuntimeError: Validate same id
    """
    if other is self:
        return

    if self.id != other.id:
        raise RuntimeError(f"Merge difference id {self.id} != {other.id} order")

    self.size = other.size
    self.sl_price = other.sl_price
    self.tp_price = other.tp_price

    if other.limit_price:
        self.limit_price = other.limit_price
    if other.stop_price:
        self.stop_price = other.stop_price
    if other.placed_at:
        self.placed_at = other.placed_at

    if other.filled_price:
        self.filled_price = other.filled_price
    if other.filled_at:
        self.filled_at = other.filled_at

    if other.parent:
        self.parent = other.parent

place ¤

place() -> OrderResult

summary

Raises:

Returns:

Source code in lettrade/exchange/ccxt/trade.py 
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def place(self) -> OrderResult:
    """_summary_

    Raises:
        RuntimeError: _description_

    Returns:
        OrderResult: _description_
    """
    if self.state != OrderState.Pending:
        raise RuntimeError(f"Order {self.id} state {self.state} is not Pending")

    try:
        result = self._api.order_open(self)

        self.raw = result
        self.id = result.order

        # TODO: get current order time
        return super(LiveOrder, self).place(at=self.data.l.index[0], raw=result)
    except LetLiveOrderInvalidException as e:
        error = OrderResultError(
            error=e.message,
            order=self,
            raw=e.raw,
        )
        logger.error("Place order %s", str(error))
        self.exchange.on_notify(error=error)
        return error

update ¤

update(
    limit_price: float | None = None,
    stop_price: float | None = None,
    sl: float | None = None,
    tp: float | None = None,
    caller: float | None = None,
    **kwargs
) -> OrderResult

summary

Parameters:

  • limit_price (float | None, default: None ) –

    description. Defaults to None.

  • stop_price (float | None, default: None ) –

    description. Defaults to None.

  • sl (float | None, default: None ) –

    description. Defaults to None.

  • tp (float | None, default: None ) –

    description. Defaults to None.

  • caller (float | None, default: None ) –

    description. Defaults to None.

Raises:

Returns:

Source code in lettrade/exchange/ccxt/trade.py 
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def update(
    self,
    limit_price: float | None = None,
    stop_price: float | None = None,
    sl: float | None = None,
    tp: float | None = None,
    caller: float | None = None,
    **kwargs,
) -> OrderResult:
    """_summary_

    Args:
        limit_price (float | None, optional): _description_. Defaults to None.
        stop_price (float | None, optional): _description_. Defaults to None.
        sl (float | None, optional): _description_. Defaults to None.
        tp (float | None, optional): _description_. Defaults to None.
        caller (float | None, optional): _description_. Defaults to None.

    Raises:
        RuntimeError: _description_

    Returns:
        OrderResult: _description_
    """
    if caller is self:
        raise RuntimeError(f"Order recusive update {self}")

    if self.parent is None:
        result = self._api.order_update(
            order=self,
            limit_price=limit_price,
            stop_price=stop_price,
            sl=sl,
            tp=tp,
            **kwargs,
        )
        return super(LiveOrder, self).update(
            limit_price=result.limit_price,
            stop_price=result.stop_price,
            sl=result.sl,
            tp=result.tp,
        )
    else:
        # SL/TP Order just a virtual order
        if caller is not self.parent:
            if self.is_sl_order:
                self.parent.update(sl=stop_price, caller=self)
            elif self.is_tp_order:
                self.parent.update(tp=limit_price, caller=self)
            else:
                raise RuntimeError(f"Abandon order {self}")

        return super(LiveOrder, self).update(
            limit_price=limit_price,
            stop_price=stop_price,
        )

CCXTPosition ¤

CCXTPosition(
    id: str,
    exchange: LiveExchange,
    data: LiveDataFeed,
    size: float,
    parent: Order,
    tag: str | None = None,
    state: PositionState = PositionState.Open,
    entry_price: float | None = None,
    entry_fee: float = 0.0,
    entry_at: int | None = None,
    sl_order: Order | None = None,
    tp_order: Order | None = None,
    api: LiveAPI | None = None,
    raw: object | None = None,
    **kwargs
)

Bases: LivePosition

Position for CCXT

Source code in lettrade/exchange/live/trade.py 
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def __init__(
    self,
    id: str,
    exchange: "LiveExchange",
    data: "LiveDataFeed",
    size: float,
    parent: Order,
    tag: str | None = None,
    state: PositionState = PositionState.Open,
    entry_price: float | None = None,
    entry_fee: float = 0.0,
    entry_at: int | None = None,
    sl_order: Order | None = None,
    tp_order: Order | None = None,
    api: LiveAPI | None = None,
    raw: object | None = None,
    **kwargs,
):
    super().__init__(
        id=id,
        exchange=exchange,
        data=data,
        size=size,
        parent=parent,
        tag=tag,
        state=state,
        entry_price=entry_price,
        entry_fee=entry_fee,
        entry_at=entry_at,
        sl_order=sl_order,
        tp_order=tp_order,
        api=api,
        raw=raw,
        **kwargs,
    )

fee property ¤

fee: float

Fee/Estimate Fee for trade

Returns:

  • float ( float ) –

    Fee

is_exited property ¤

is_exited: bool

Flag to check Position state.

Returns:

  • bool ( bool ) –

    True if the trade exited

is_long property ¤

is_long: bool

True if side is long (size is positive).

Returns:

  • bool ( bool ) –

    True/False

is_opening property ¤

is_opening: bool

Flag to check Position state.

Returns:

  • bool ( bool ) –

    True if the trade opening

is_short property ¤

is_short: bool

True if side is short (size is negative).

Returns:

  • bool ( bool ) –

    description

pl property ¤

pl: float

Estimate Profit or Loss of Position

Returns:

  • float ( float ) –

    PnL

side property ¤

side: TradeSide

True if side is short (size is negative).

Returns:

exit ¤

exit() -> PositionResult

summary

Returns:

Source code in lettrade/exchange/ccxt/trade.py 
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def exit(self) -> PositionResult:
    """_summary_

    Returns:
        PositionResult: _description_
    """
    result = self._api.position_close(position=self)
    if result.code != 0:
        logger.error("Update position %s", str(result))
        error = PositionResultError(
            error=result.error,
            position=self,
            raw=result,
        )
        self.exchange.on_notify(error=error)
        return error

    execution_raw = self._api.execution_get(id=result.execution_id)

    # TODO: execution object and event
    result.execution_raw = execution_raw

    return super(LivePosition, self).exit(
        price=result.price,
        at=pd.to_datetime(execution_raw.time_msc, unit="ms", utc=True),
        pl=execution_raw.profit,
        fee=execution_raw.fee + execution_raw.swap + execution_raw.commission,
        raw=result,
    )

from_raw classmethod ¤

from_raw(
    raw,
    exchange: LiveExchange,
    state: PositionState = PositionState.Open,
    data: LiveDataFeed = None,
    api: CCXTAPI = None,
) -> CCXTPosition

summary

Parameters:

  • raw (_type_) –

    description

  • exchange (LiveExchange) –

    description

  • data (LiveDataFeed, default: None ) –

    description. Defaults to None.

  • api (CCXTAPI, default: None ) –

    description. Defaults to None.

Raises:

Returns:

Source code in lettrade/exchange/ccxt/trade.py 
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@classmethod
def from_raw(
    cls,
    raw,
    exchange: "LiveExchange",
    state: PositionState = PositionState.Open,
    data: "LiveDataFeed" = None,
    api: CCXTAPI = None,
) -> "CCXTPosition":
    """_summary_

    Args:
        raw (_type_): _description_
        exchange (LiveExchange): _description_
        data (LiveDataFeed, optional): _description_. Defaults to None.
        api (CCXTAPI, optional): _description_. Defaults to None.

    Raises:
        NotImplementedError: _description_

    Returns:
        CCXTPosition: _description_
    """
    # DataFeed
    if data is None:
        for d in exchange.datas:
            if d.symbol == raw.symbol:
                data = d
                break
        if data is None:
            logger.warning("Raw position %s is not handling %s", raw.symbol, raw)
            return

    # Side
    match raw.type:
        case MT5.POSITION_TYPE_BUY:
            side = TradeSide.Buy
        case MT5.POSITION_TYPE_SELL:
            side = TradeSide.Sell
        case _:
            raise NotImplementedError(
                f"Position type {raw.type} is not implement",
                raw,
            )

    position = cls(
        exchange=exchange,
        id=raw.ticket,
        data=data,
        state=state,
        size=side * raw.volume,
        entry_price=raw.price_open,
        entry_fee=raw.swap,
        entry_at=pd.to_datetime(raw.time_msc, unit="ms", utc=True),
        parent=None,
        tag=raw.comment,
        api=api,
        raw=raw,
    )

    # SL
    if raw.sl > 0.0:
        position.sl_order = exchange._order_cls.from_position(
            position=position, sl=raw.sl
        )
        exchange.on_order(position.sl_order)

    # TP
    if raw.tp > 0.0:
        position.tp_order = exchange._order_cls.from_position(
            position=position, tp=raw.tp
        )
        exchange.on_order(position.tp_order)

    return position

merge ¤

merge(other: LivePosition) -> bool

Merge LivePosition from another

Parameters:

Returns:

  • bool ( bool ) –

    description

Source code in lettrade/exchange/live/trade.py 
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def merge(self, other: "LivePosition") -> bool:
    """Merge LivePosition from another

    Args:
        other (LivePosition): _description_

    Returns:
        bool: _description_
    """
    if not super().merge(other):
        return False
    self.raw = other.raw
    return True

update ¤

update(
    sl: float | None = None,
    tp: float | None = None,
    caller: float | None = None,
    **kwargs
) -> PositionResult

summary

Parameters:

  • sl (float | None, default: None ) –

    description. Defaults to None.

  • tp (float | None, default: None ) –

    description. Defaults to None.

  • caller (float | None, default: None ) –

    description. Defaults to None.

Raises:

Returns:

Source code in lettrade/exchange/ccxt/trade.py 
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def update(
    self,
    sl: float | None = None,
    tp: float | None = None,
    caller: float | None = None,
    **kwargs,
) -> PositionResult:
    """_summary_

    Args:
        sl (float | None, optional): _description_. Defaults to None.
        tp (float | None, optional): _description_. Defaults to None.
        caller (float | None, optional): _description_. Defaults to None.

    Raises:
        RuntimeError: _description_

    Returns:
        PositionResult: _description_
    """
    if not sl and not tp:
        raise RuntimeError("Update sl=None and tp=None")
    if caller is self:
        raise RuntimeError(f"Position recusive update {self}")

    result = self._api.position_update(position=self, sl=sl, tp=tp)
    if result.code != 0:
        logger.error("Update position %s", str(result))
        error = PositionResultError(
            error=result.error,
            position=self,
            raw=result,
        )
        self.exchange.on_notify(error=error)
        return error

    if sl is not None:
        if self.sl_order:
            if caller is not self.sl_order:
                self.sl_order.update(stop_price=sl, caller=self)
        else:
            self.sl_order = self.exchange._order_cls.from_position(
                position=self, sl=sl
            )

    if tp is not None:
        if self.tp_order:
            if caller is not self.tp_order:
                self.tp_order.update(limit_price=tp, caller=self)
        else:
            self.tp_order = self.exchange._order_cls.from_position(
                position=self, tp=tp
            )

    return super(LivePosition, self).update(raw=result)

CSVBackTestDataFeed ¤

CSVBackTestDataFeed(
    path: str | None = None,
    csv: dict | None = None,
    name: str | None = None,
    timeframe: str | int | Timedelta | None = None,
    meta: dict | None = None,
    data: DataFeed | None = None,
    **kwargs: dict
)

Bases: BackTestDataFeed

Implement help to load DataFeed from csv file

Parameters:

  • path (str | None, default: None ) –

    Path to csv file. Defaults to None.

  • csv (dict | None, default: None ) –

    Reflect of pandas.read_csv() parameters. Defaults to None.

  • name (str | None, default: None ) –

    description. Defaults to None.

  • timeframe (str | int | Timedelta | None, default: None ) –

    description. Defaults to None.

  • meta (dict | None, default: None ) –

    description. Defaults to None.

  • data (DataFeed | None, default: None ) –

    description. Defaults to None.

  • **kwargs (dict, default: {} ) –

    DataFeed dict parameters

Source code in lettrade/exchange/backtest/data.py 
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def __init__(
    self,
    path: str | None = None,
    csv: dict | None = None,
    name: str | None = None,
    timeframe: str | int | pd.Timedelta | None = None,
    meta: dict | None = None,
    data: DataFeed | None = None,
    **kwargs: dict,
) -> None:
    """_summary_

    Args:
        path (str | None, optional): Path to csv file. Defaults to None.
        csv (dict | None, optional): Reflect of `pandas.read_csv()` parameters. Defaults to None.
        name (str | None, optional): _description_. Defaults to None.
        timeframe (str | int | pd.Timedelta | None, optional): _description_. Defaults to None.
        meta (dict | None, optional): _description_. Defaults to None.
        data (DataFeed | None, optional): _description_. Defaults to None.
        **kwargs (dict): [DataFeed](../../data/data.md#lettrade.data.data.DataFeed) dict parameters
    """
    if name is None:
        name = _path_to_name(path)

    if data is None:
        csv_params = dict(
            index_col=0,
            parse_dates=["datetime"],
            delimiter=",",
            header=0,
        )
        if csv is not None:
            csv_params.update(**csv)

        data = pd.read_csv(path, **csv_params)

        if not isinstance(data.index, pd.DatetimeIndex):
            data.index = data.index.astype("datetime64[ns, UTC]")

        data.sort_index(inplace=True)

    super().__init__(
        data=data,
        name=name,
        timeframe=timeframe,
        meta=meta,
        **kwargs,
    )

i property ¤

i: indicator

Alias to lettrade.indicator and using in DataFeed by call: DataFeed.i.indicator_name()

is_main property ¤

is_main: bool

Property to check DataFeed is main DataFeed or not

l instance-attribute ¤

l: LetDataFeedWrapper

LetTrade DataFeed wrapper using to manage index pointer of DataFeed

meta property ¤

meta: dict

Property to get metadata of DataFeed

name property ¤

name: str

Property to get name of DataFeed

now property ¤

now: Timestamp

Property to get current index value of DataFeed

timeframe property ¤

timeframe: TimeFrame

Property to get timeframe of DataFeed

bar ¤

bar(i: int = 0) -> Timestamp

Get current pd.Timestamp value of DataFeed

Parameters:

  • i (int, default: 0 ) –

    Index. Defaults to 0.

Returns:

  • Timestamp –

    pd.Timestamp: description

Source code in lettrade/data/data.py 
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def bar(self, i: int = 0) -> pd.Timestamp:
    """Get current pd.Timestamp value of DataFeed

    Args:
        i (int, optional): Index. Defaults to 0.

    Returns:
        pd.Timestamp: _description_
    """
    return self.l.index[i]

copy ¤

copy(deep: bool = False, **kwargs) -> DataFeed

summary

Parameters:

  • deep (bool, default: False ) –

    description. Defaults to False.

Returns:

Source code in lettrade/data/data.py 
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def copy(self, deep: bool = False, **kwargs) -> "DataFeed":
    """_summary_

    Args:
        deep (bool, optional): _description_. Defaults to False.

    Returns:
        DataFeed: _description_
    """
    df = super().copy(deep=deep)
    df = self.__class__(
        data=df,
        name=self.name,
        timeframe=self.timeframe,
        meta=self.meta.copy(),
        **kwargs,
    )
    return df

drop ¤

drop(
    *args,
    since: int | str | Timestamp | None = None,
    to: int | str | Timestamp | None = None,
    **kwargs
) -> None | DataFeed

summary

Parameters:

  • since (int | str | Timestamp | None, default: None ) –

    description. Defaults to None.

  • to (int | str | Timestamp | None, default: None ) –

    description. Defaults to None.

Raises:

Source code in lettrade/data/data.py 
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def drop(
    self,
    *args,
    since: int | str | pd.Timestamp | None = None,
    to: int | str | pd.Timestamp | None = None,
    **kwargs,
) -> "None | DataFeed":
    """_summary_

    Args:
        since (int | str | pd.Timestamp | None, optional): _description_. Defaults to None.
        to (int | str | pd.Timestamp | None, optional): _description_. Defaults to None.

    Raises:
        RuntimeError: _description_
    """
    if since is None and to is None:
        return super().drop(*args, **kwargs)

    condiction = None

    # Since
    if since is not None:
        if isinstance(since, int):
            loc = self.l.index[since]
        elif isinstance(since, str):
            loc = pd.to_datetime(since, utc=True)
        elif isinstance(since, pd.Timestamp):
            loc = since
        else:
            raise RuntimeError(f"DataFeed.drop since {since} is invalid")
        condiction = self.index < loc

    # To
    if to is not None:
        if isinstance(to, int):
            loc = self.l.index[to]
        elif isinstance(to, str):
            loc = pd.to_datetime(to, utc=True)
        elif isinstance(to, pd.Timestamp):
            loc = to
        else:
            raise RuntimeError(f"DataFeed.drop to {to} is invalid")

        if condiction is None:
            condiction = self.index > loc
        else:
            condiction = condiction | (self.index > loc)

    index = self[condiction].index
    super().drop(index=index, inplace=True)
    self.l.reset()

    if __debug__:
        logger.debug("BackTestDataFeed %s dropped %s rows", self.name, len(index))

push ¤

push(
    rows: list[list[int | float]],
    unit: str | None = None,
    utc: bool = True,
    **kwargs
)

Push new rows to DataFeed

Parameters:

  • rows (list[list[int | float]]) –

    list of rows [["timestamp", "open price", "high price"...]]

  • unit (str | None, default: None ) –

    pandas.Timestamp parsing unit. Defaults to None.

  • utc (bool, default: True ) –

    description. Defaults to True.

Source code in lettrade/data/data.py 
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def push(
    self,
    rows: list[list[int | float]],
    unit: str | None = None,
    utc: bool = True,
    **kwargs,
):
    """Push new rows to DataFeed

    Args:
        rows (list[list[int | float]]): list of rows `[["timestamp", "open price", "high price"...]]`
        unit (str | None, optional): pandas.Timestamp parsing unit. Defaults to None.
        utc (bool, optional): _description_. Defaults to True.
    """
    for row in rows:
        dt = pd.to_datetime(row[0], unit=unit, utc=utc, **kwargs)
        self.at[
            dt,
            (
                "open",
                "high",
                "low",
                "close",
                "volume",
            ),
        ] = (
            row[1],  # open
            row[2],  # high
            row[3],  # low
            row[4],  # close
            row[5],  # volume
        )

    if __debug__:
        logger.debug("[%s] Update bar: \n%s", self.name, self.tail(len(rows)))

Commander ¤

Bases: ABC

Abstract class for strategy commander. Help to manage and report strategy real-time

init ¤

init(
    bot: LetTradeBot,
    brain: Brain,
    exchange: Exchange,
    strategy: Strategy,
)

Init commander dependencies

Parameters:

Source code in lettrade/commander/commander.py 
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def init(
    self,
    bot: "LetTradeBot",
    brain: "Brain",
    exchange: "Exchange",
    strategy: "Strategy",
):
    """Init commander dependencies

    Args:
        bot (LetTradeBot): LetTradeBot object
        brain (Brain): Brain of bot
        exchange (Exchange): Manage bot trading
        strategy (Strategy): Strategy of bot
    """
    self.bot = bot
    self.brain = brain
    self.exchange = exchange
    self.strategy = strategy

    self._name = self.bot._name

DataFeed ¤

DataFeed(
    *args,
    name: str,
    timeframe: TimeFrame,
    meta: dict | None = None,
    **kwargs
)

Bases: DataFrame

Data for Strategy. A implement of pandas.DataFrame

Parameters:

  • name (str) –

    description

  • timeframe (TimeFrame) –

    description

  • meta (dict | None, default: None ) –

    description. Defaults to None.

Raises:

Source code in lettrade/data/data.py 
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def __init__(
    self,
    *args,
    name: str,
    timeframe: TimeFrame,
    meta: dict | None = None,
    **kwargs,
) -> None:
    """_summary_

    Args:
        name (str): _description_
        timeframe (TimeFrame): _description_
        meta (dict | None, optional): _description_. Defaults to None.

    Raises:
        RuntimeError: _description_
    """
    # Validate
    if not _data_name_pattern.match(name):
        raise RuntimeError(
            f"Bot name {name} is not valid format {_data_name_pattern}"
        )

    # Init
    super().__init__(*args, **kwargs)
    self._init_index()

    # Metadata
    if not meta:
        meta = dict()
    meta["name"] = name
    meta["timeframe"] = TimeFrame(timeframe)
    self.attrs = {"lt_meta": meta}

    # LetWrapper
    object.__setattr__(self, "l", LetDataFeedWrapper(self))

i property ¤

i: indicator

Alias to lettrade.indicator and using in DataFeed by call: DataFeed.i.indicator_name()

is_main property ¤

is_main: bool

Property to check DataFeed is main DataFeed or not

l instance-attribute ¤

l: LetDataFeedWrapper

LetTrade DataFeed wrapper using to manage index pointer of DataFeed

meta property ¤

meta: dict

Property to get metadata of DataFeed

name property ¤

name: str

Property to get name of DataFeed

now property ¤

now: Timestamp

Property to get current index value of DataFeed

timeframe property ¤

timeframe: TimeFrame

Property to get timeframe of DataFeed

bar ¤

bar(i: int = 0) -> Timestamp

Get current pd.Timestamp value of DataFeed

Parameters:

  • i (int, default: 0 ) –

    Index. Defaults to 0.

Returns:

  • Timestamp –

    pd.Timestamp: description

Source code in lettrade/data/data.py 
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def bar(self, i: int = 0) -> pd.Timestamp:
    """Get current pd.Timestamp value of DataFeed

    Args:
        i (int, optional): Index. Defaults to 0.

    Returns:
        pd.Timestamp: _description_
    """
    return self.l.index[i]

copy ¤

copy(deep: bool = False, **kwargs) -> DataFeed

summary

Parameters:

  • deep (bool, default: False ) –

    description. Defaults to False.

Returns:

Source code in lettrade/data/data.py 
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def copy(self, deep: bool = False, **kwargs) -> "DataFeed":
    """_summary_

    Args:
        deep (bool, optional): _description_. Defaults to False.

    Returns:
        DataFeed: _description_
    """
    df = super().copy(deep=deep)
    df = self.__class__(
        data=df,
        name=self.name,
        timeframe=self.timeframe,
        meta=self.meta.copy(),
        **kwargs,
    )
    return df

drop ¤

drop(
    *args,
    since: int | str | Timestamp | None = None,
    to: int | str | Timestamp | None = None,
    **kwargs
) -> None | DataFeed

summary

Parameters:

  • since (int | str | Timestamp | None, default: None ) –

    description. Defaults to None.

  • to (int | str | Timestamp | None, default: None ) –

    description. Defaults to None.

Raises:

Source code in lettrade/data/data.py 
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def drop(
    self,
    *args,
    since: int | str | pd.Timestamp | None = None,
    to: int | str | pd.Timestamp | None = None,
    **kwargs,
) -> "None | DataFeed":
    """_summary_

    Args:
        since (int | str | pd.Timestamp | None, optional): _description_. Defaults to None.
        to (int | str | pd.Timestamp | None, optional): _description_. Defaults to None.

    Raises:
        RuntimeError: _description_
    """
    if since is None and to is None:
        return super().drop(*args, **kwargs)

    condiction = None

    # Since
    if since is not None:
        if isinstance(since, int):
            loc = self.l.index[since]
        elif isinstance(since, str):
            loc = pd.to_datetime(since, utc=True)
        elif isinstance(since, pd.Timestamp):
            loc = since
        else:
            raise RuntimeError(f"DataFeed.drop since {since} is invalid")
        condiction = self.index < loc

    # To
    if to is not None:
        if isinstance(to, int):
            loc = self.l.index[to]
        elif isinstance(to, str):
            loc = pd.to_datetime(to, utc=True)
        elif isinstance(to, pd.Timestamp):
            loc = to
        else:
            raise RuntimeError(f"DataFeed.drop to {to} is invalid")

        if condiction is None:
            condiction = self.index > loc
        else:
            condiction = condiction | (self.index > loc)

    index = self[condiction].index
    super().drop(index=index, inplace=True)
    self.l.reset()

    if __debug__:
        logger.debug("BackTestDataFeed %s dropped %s rows", self.name, len(index))

next ¤

next(size=1)

Load next data

Parameters:

  • size (int, default: 1 ) –

    description. Defaults to 1.

Source code in lettrade/data/data.py 
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def next(self, size=1):
    """Load next data

    Args:
        size (int, optional): _description_. Defaults to 1.
    """
    self.l.next(size)

push ¤

push(
    rows: list[list[int | float]],
    unit: str | None = None,
    utc: bool = True,
    **kwargs
)

Push new rows to DataFeed

Parameters:

  • rows (list[list[int | float]]) –

    list of rows [["timestamp", "open price", "high price"...]]

  • unit (str | None, default: None ) –

    pandas.Timestamp parsing unit. Defaults to None.

  • utc (bool, default: True ) –

    description. Defaults to True.

Source code in lettrade/data/data.py 
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def push(
    self,
    rows: list[list[int | float]],
    unit: str | None = None,
    utc: bool = True,
    **kwargs,
):
    """Push new rows to DataFeed

    Args:
        rows (list[list[int | float]]): list of rows `[["timestamp", "open price", "high price"...]]`
        unit (str | None, optional): pandas.Timestamp parsing unit. Defaults to None.
        utc (bool, optional): _description_. Defaults to True.
    """
    for row in rows:
        dt = pd.to_datetime(row[0], unit=unit, utc=utc, **kwargs)
        self.at[
            dt,
            (
                "open",
                "high",
                "low",
                "close",
                "volume",
            ),
        ] = (
            row[1],  # open
            row[2],  # high
            row[3],  # low
            row[4],  # close
            row[5],  # volume
        )

    if __debug__:
        logger.debug("[%s] Update bar: \n%s", self.name, self.tail(len(rows)))

ForexBackTestAccount ¤

ForexBackTestAccount(
    risk: float = 0.02,
    balance: float = 10000,
    commission: float = 0.2,
    margin: float = 1,
    leverage: float = 1,
    **kwargs
)

Bases: BackTestAccount

Forex backtest account helps to handle lot size

Parameters:

  • risk (float, default: 0.02 ) –

    description. Defaults to 0.02.

  • balance (float, default: 10000 ) –

    description. Defaults to 10_000.

  • commission (float, default: 0.2 ) –

    Commission fee is percent of size. Defaults to 0.2.

  • margin (float, default: 1 ) –

    description. Defaults to 1.

  • leverage (float, default: 1 ) –

    description. Defaults to 1.

  • **kwargs (dict, default: {} ) –

    Mirror of lettrade.account.Account().

Source code in lettrade/exchange/backtest/account.py 
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def __init__(
    self,
    risk: float = 0.02,
    balance: float = 10_000,
    commission: float = 0.2,
    margin: float = 1,
    leverage: float = 1,
    **kwargs,
) -> None:
    """Account for backtest

    Args:
        risk (float, optional): _description_. Defaults to 0.02.
        balance (float, optional): _description_. Defaults to 10_000.
        commission (float, optional): Commission fee is percent of size. Defaults to 0.2.
        margin (float, optional): _description_. Defaults to 1.
        leverage (float, optional): _description_. Defaults to 1.
        **kwargs (dict, optional): Mirror of [lettrade.account.Account()](site:/reference/account/account/#lettrade.account.account.Account).
    """
    super().__init__(
        risk=risk,
        balance=balance,
        margin=margin,
        leverage=leverage,
        **kwargs,
    )
    self._commission = commission

balance property ¤

balance: float

Balance of account

Returns:

  • float ( float ) –

    description

next ¤

next()

Next account

Source code in lettrade/account/account.py 
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def next(self):
    """Next account"""

next_next ¤

next_next()

Call after strategy.next()

Source code in lettrade/account/account.py 
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def next_next(self):
    """Call after strategy.next()"""
    self._equity_snapshot()

risk ¤

risk(side: TradeSide, size: float, **kwargs) -> float

Risk calculation

Source code in lettrade/account/account.py 
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def risk(self, side: "TradeSide", size: float, **kwargs) -> float:
    """Risk calculation"""
    if size is None:
        return side * abs(self._risk)
    return side * abs(size)

start ¤

start()

Start account

Source code in lettrade/account/account.py 
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def start(self):
    """Start account"""

stop ¤

stop()

Stop account

Source code in lettrade/account/account.py 
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def stop(self):
    """Stop account"""
    try:
        self._equity_snapshot()
    except LetAccountInsufficientException:
        pass

IndicatorPlotter ¤

IndicatorPlotter(
    dataframe: DataFrame,
    plotter: Callable,
    filter: Callable | Series | None = None,
    push: bool = True,
    **kwargs
)

Add indicator plotter to DataFrame

Source code in lettrade/indicator/plot.py 
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def __init__(
    self,
    dataframe: pd.DataFrame,
    plotter: Callable,
    filter: Callable | pd.Series | None = None,
    push: bool = True,
    **kwargs,
) -> None:
    # self.dataframe: pd.DataFrame = dataframe
    self.plotter: Callable = plotter
    self.filter: Callable | pd.Series | None = filter
    self.kwargs = kwargs

    if push:
        indicator_push_plotter(dataframe=dataframe, ip=self)

LetTrade ¤

LetTrade(
    datas: DataFeed | list[DataFeed] | str | list[str],
    strategy: type[Strategy],
    feeder: type[DataFeeder],
    exchange: type[Exchange],
    account: type[Account],
    commander: type[Commander] | Commander | None = None,
    stats: type[BotStatistic] = BotStatistic,
    plotter: type[Plotter] | None = None,
    name: str | None = None,
    bot: type[LetTradeBot] = LetTradeBot,
    **kwargs
)

Building new bot object and handle multiprocessing

Parameters:

Source code in lettrade/lettrade.py 
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def __init__(
    self,
    datas: DataFeed | list[DataFeed] | str | list[str],
    strategy: type[Strategy],
    feeder: type[DataFeeder],
    exchange: type[Exchange],
    account: type[Account],
    commander: type[Commander] | Commander | None = None,
    stats: type[BotStatistic] = BotStatistic,
    plotter: type[Plotter] | None = None,
    name: str | None = None,
    bot: type[LetTradeBot] = LetTradeBot,
    **kwargs,
) -> None:
    """_summary_

    Args:
        datas (DataFeed | list[DataFeed] | str | list[str]): _description_
        strategy (type[Strategy]): _description_
        feeder (type[DataFeeder]): _description_
        exchange (type[Exchange]): _description_
        account (type[Account]): _description_
        commander (type[Commander] | Commander | None, optional): _description_. Defaults to None.
        stats (type[BotStatistic], optional): _description_. Defaults to BotStatistic.
        plotter (type[Plotter] | None, optional): _description_. Defaults to None.
        name (str | None, optional): _description_. Defaults to None.
        bot (type[LetTradeBot], optional): _description_. Defaults to LetTradeBot.
    """
    self._kwargs = kwargs
    self._kwargs["strategy_cls"] = strategy
    self._kwargs["feeder_cls"] = feeder
    self._kwargs["exchange_cls"] = exchange
    self._kwargs["account_cls"] = account
    self._kwargs["commander_cls"] = commander
    self._kwargs["stats_cls"] = stats
    self._kwargs["plotter_cls"] = plotter
    self._kwargs["bot_cls"] = bot
    self._kwargs["name"] = name

    self._kwargs["datas"] = self._init_datafeeds(datas)

plot ¤

plot(*args, jump: dict | None = None, **kwargs)

Plot strategy/optimize result

BotPlotter

Miror of BotPlotter.plot(). Plotly implement PlotlyBotPlotter.plot().

Args: jump (dict | None, optional): Miror of BotPlotter.jump()

Example: Jump to position_id 

lt.plot(
    jump=dict(position_id=1, range=300),
    layout=dict(height=2000),
)

OptimizePlotter

Miror of OptimizePlotter.plot(). Plotly implement PlotlyOptimizePlotter.plot().

Example: 

lt.plot(layout=dict(height=2000))

Source code in lettrade/lettrade.py 
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def plot(self, *args, jump: dict | None = None, **kwargs):
    """Plot strategy/optimize result

    BotPlotter:
        Miror of [BotPlotter.plot()](site:/reference/plot/bot/#lettrade.plot.bot.BotPlotter.plot).
        Plotly implement [PlotlyBotPlotter.plot()](site:/reference/plot/plotly/plotly/#lettrade.plot.plotly.plotly.PlotlyBotPlotter.plot).

        Args:
            `jump` (dict | None, optional): Miror of [BotPlotter.jump()](site:/reference/plot/bot/#lettrade.plot.bot.BotPlotter.jump)

        Example:
            Jump to position_id
                ```python
                lt.plot(
                    jump=dict(position_id=1, range=300),
                    layout=dict(height=2000),
                )
                ```

    OptimizePlotter:
        Miror of [OptimizePlotter.plot()](site:/reference/plot/optimize/#lettrade.plot.optimize.OptimizePlotter.plot).
        Plotly implement [PlotlyOptimizePlotter.plot()](site:/reference/exchange/backtest/plotly/optimize/#lettrade.exchange.backtest.plotly.optimize.PlotlyOptimizePlotter.plot).

        Example:
                ```python
                lt.plot(layout=dict(height=2000))
                ```
    """
    if __debug__:
        from .utils.docs import is_docs_session

        if is_docs_session():
            return

    self.plotter.plot(*args, jump=jump, **kwargs)

run ¤

run(worker: int | None = None, **kwargs)

Run LetTrade in single or multiple processing

Parameters:

  • worker (int | None, default: None ) –

    Number of processing. Defaults to None.

Source code in lettrade/lettrade.py 
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def run(self, worker: int | None = None, **kwargs):
    """Run LetTrade in single or multiple processing

    Args:
        worker (int | None, optional): Number of processing. Defaults to None.
    """
    if worker or isinstance(self.data, list):
        if not isinstance(self.data, list):
            # self.data = self.datas
            self.datas = [self.data]

        worker = self._max_workers(worker)

        self._multiprocess()

        datas_source = self._kwargs.pop("datas")
        with ProcessPoolExecutor(max_workers=worker) as executor:
            futures = [
                executor.submit(
                    self._bot_cls.run_bot,
                    datas=datas,
                    id=i,
                    result="str",
                    **self._kwargs,
                    **kwargs,
                )
                for i, datas in enumerate(datas_source)
            ]
            for future in futures:
                result = future.result()
                print(result)
    else:
        self._bot = self._bot_cls.run_bot(
            bot=self._bot,
            result="bot",
            **self._kwargs,
            **kwargs,
        )
        print(str(self._bot.stats))

start ¤

start(force: bool = False)

Start LetTrade by init bot object and loading datafeeds

Parameters:

  • force (bool, default: False ) –

    description. Defaults to False.

Source code in lettrade/lettrade.py 
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def start(self, force: bool = False):
    """Start LetTrade by init bot object and loading datafeeds

    Args:
        force (bool, optional): _description_. Defaults to False.
    """
    if force and self._bot is not None:
        self._bot = None

    self._bot = self._bot_cls.start_bot(bot=self._bot, **self._kwargs)

stop ¤

stop()

Stop LetTrade

Source code in lettrade/lettrade.py 
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def stop(self):
    """Stop LetTrade"""
    if self._bot is not None:
        return self._bot.stop()
    if self.stats:
        self.stats.stop()

LetTradeBackTest ¤

LetTradeBackTest(
    datas: DataFeed | list[DataFeed] | str | list[str],
    strategy: type[Strategy],
    feeder: type[DataFeeder],
    exchange: type[Exchange],
    account: type[Account],
    commander: type[Commander] | Commander | None = None,
    stats: type[BotStatistic] = BotStatistic,
    plotter: type[Plotter] | None = None,
    name: str | None = None,
    bot: type[LetTradeBot] = LetTradeBot,
    **kwargs
)

Bases: LetTrade

Parameters:

Source code in lettrade/lettrade.py 
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def __init__(
    self,
    datas: DataFeed | list[DataFeed] | str | list[str],
    strategy: type[Strategy],
    feeder: type[DataFeeder],
    exchange: type[Exchange],
    account: type[Account],
    commander: type[Commander] | Commander | None = None,
    stats: type[BotStatistic] = BotStatistic,
    plotter: type[Plotter] | None = None,
    name: str | None = None,
    bot: type[LetTradeBot] = LetTradeBot,
    **kwargs,
) -> None:
    """_summary_

    Args:
        datas (DataFeed | list[DataFeed] | str | list[str]): _description_
        strategy (type[Strategy]): _description_
        feeder (type[DataFeeder]): _description_
        exchange (type[Exchange]): _description_
        account (type[Account]): _description_
        commander (type[Commander] | Commander | None, optional): _description_. Defaults to None.
        stats (type[BotStatistic], optional): _description_. Defaults to BotStatistic.
        plotter (type[Plotter] | None, optional): _description_. Defaults to None.
        name (str | None, optional): _description_. Defaults to None.
        bot (type[LetTradeBot], optional): _description_. Defaults to LetTradeBot.
    """
    self._kwargs = kwargs
    self._kwargs["strategy_cls"] = strategy
    self._kwargs["feeder_cls"] = feeder
    self._kwargs["exchange_cls"] = exchange
    self._kwargs["account_cls"] = account
    self._kwargs["commander_cls"] = commander
    self._kwargs["stats_cls"] = stats
    self._kwargs["plotter_cls"] = plotter
    self._kwargs["bot_cls"] = bot
    self._kwargs["name"] = name

    self._kwargs["datas"] = self._init_datafeeds(datas)

optimize ¤

optimize(
    multiprocessing: Literal["auto", "fork"] = "auto",
    workers: int | None = None,
    process_bar: bool = True,
    cache: str = "data/optimize",
    **kwargs
)

Backtest optimization

Parameters:

  • multiprocessing (str | None, default: 'auto' ) –

    description. Defaults to "auto".

Source code in lettrade/exchange/backtest/backtest.py 
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def optimize(
    self,
    multiprocessing: Literal["auto", "fork"] = "auto",
    workers: int | None = None,
    process_bar: bool = True,
    cache: str = "data/optimize",
    **kwargs,
):
    """Backtest optimization

    Args:
        multiprocessing (str | None, optional): _description_. Defaults to "auto".
    """
    if self.data.l.pointer != 0:
        # TODO: Can drop unnecessary columns by snapshort data.columns from init time
        raise RuntimeError(
            "Optimize datas is not clean, don't run() backtest before optimize()"
        )

    # optimizes = list(product(*(zip(repeat(k), v) for k, v in kwargs.items())))
    optimizes = list(
        dict(zip(kwargs.keys(), values)) for values in product(*kwargs.values())
    )

    self._optimize_init(cache=cache, total=len(optimizes), process_bar=process_bar)

    # Run optimize in multiprocessing
    self._optimizes_multiproccess(
        optimizes=optimizes,
        multiprocessing=multiprocessing,
        workers=workers,
    )

    self.optimize_done()

optimize_cache ¤

optimize_cache(cache: str = 'data/optimize')

Load optimize results from cache

Parameters:

  • cache (str, default: 'data/optimize' ) –

    Cache directory. Defaults to "data/optimize".

Source code in lettrade/exchange/backtest/backtest.py 
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def optimize_cache(self, cache: str = "data/optimize"):
    """Load optimize results from cache

    Args:
        cache (str, optional): Cache directory. Defaults to "data/optimize".
    """
    import json

    self._optimize_init(cache=cache, total=0, process_bar=False)
    cache_dir = self._kwargs["cache"]
    queue = self._kwargs["queue"]

    logger.info("Load caches from: %s", cache)

    for cache_file in os.listdir(cache_dir):
        if cache_file == "info.json":
            continue

        try:
            cache_path = f"{cache_dir}/{cache_file}"
            data = json.load(open(cache_path, encoding="utf-8"))

            queue.put(
                dict(
                    index=cache_file,
                    optimize=data["optimize"],
                    result=data["result"],
                )
            )
        except Exception as e:
            logger.warning("Loading cache %s error %s", cache_path, e)

    logger.info("Loaded %s caches", len(self._stats.results))

optimize_done ¤

optimize_done()

Clean and close optimize handlers

Source code in lettrade/exchange/backtest/backtest.py 
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def optimize_done(self):
    """Clean and close optimize handlers"""
    self._stats.done()

optimize_model ¤

optimize_model(
    params_parser: Callable[
        [Any], list[set[str, Any]]
    ] = None,
    result_parser: Callable[[Series], float] = None,
    total: int = 0,
    cache: str = "data/optimize",
    process_bar: bool = False,
    dumper: (
        Callable[[dict, LetTradeBackTest], None] | None
    ) = None,
) -> Callable[[Any], Any]

Optimize function help to integrated with external optimize trainer

Parameters:

  • params_parser (Callable[[Any], list[set[str, Any]]], default: None ) –

    Parse external parameters to bot parameters dict. Defaults to None.

  • result_parser (Callable[[Series], float], default: None ) –

    Parse bot result to external score. Defaults to None.

  • total (int, default: 0 ) –

    Total number of optimize if possible. Defaults to 0.

  • cache (str, default: 'data/optimize' ) –

    Cache directory to store optimize result. Defaults to "data/optimize".

  • process_bar (bool, default: False ) –

    Enable/Disable process bar. Defaults to False.

Raises:

Returns:

  • Callable[[Any], Any] –

    Callable[[Any], Any]: Optimize model function

Source code in lettrade/exchange/backtest/backtest.py 
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def optimize_model(
    self,
    params_parser: Callable[[Any], list[set[str, Any]]] = None,
    result_parser: Callable[[pd.Series], float] = None,
    total: int = 0,
    cache: str = "data/optimize",
    process_bar: bool = False,
    dumper: Callable[[dict, "LetTradeBackTest"], None] | None = None,
) -> Callable[[Any], Any]:
    """Optimize function help to integrated with external optimize trainer

    Args:
        params_parser (Callable[[Any], list[set[str, Any]]], optional): Parse external parameters to bot parameters dict. Defaults to None.
        result_parser (Callable[[pd.Series], float], optional): Parse bot result to external score. Defaults to None.
        total (int, optional): Total number of optimize if possible. Defaults to 0.
        cache (str, optional): Cache directory to store optimize result. Defaults to "data/optimize".
        process_bar (bool, optional): Enable/Disable process bar. Defaults to False.

    Raises:
        RuntimeError: _description_

    Returns:
        Callable[[Any], Any]: Optimize model function
    """
    if self.data.l.pointer != 0:
        raise RuntimeError(
            "Optimize datas is not clean, don't run() backtest before optimize()"
        )

    self._optimize_init(cache=cache, total=total, process_bar=process_bar)

    # Optimize parameters
    optimizer_kwargs = dict(
        main_pid=os.getpid(),
        params_parser=params_parser,
        result_parser=result_parser,
        kwargs=self._kwargs,
    )

    if dumper is not None:
        dumper(optimizer_kwargs, self)
    else:
        self.__class__._optimize_model_kwargs(optimizer_kwargs)

    return self.__class__._optimize_model

plot ¤

plot(*args, jump: dict | None = None, **kwargs)

Plot strategy/optimize result

BotPlotter

Miror of BotPlotter.plot(). Plotly implement PlotlyBotPlotter.plot().

Args: jump (dict | None, optional): Miror of BotPlotter.jump()

Example: Jump to position_id 

lt.plot(
    jump=dict(position_id=1, range=300),
    layout=dict(height=2000),
)

OptimizePlotter

Miror of OptimizePlotter.plot(). Plotly implement PlotlyOptimizePlotter.plot().

Example: 

lt.plot(layout=dict(height=2000))

Source code in lettrade/lettrade.py 
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def plot(self, *args, jump: dict | None = None, **kwargs):
    """Plot strategy/optimize result

    BotPlotter:
        Miror of [BotPlotter.plot()](site:/reference/plot/bot/#lettrade.plot.bot.BotPlotter.plot).
        Plotly implement [PlotlyBotPlotter.plot()](site:/reference/plot/plotly/plotly/#lettrade.plot.plotly.plotly.PlotlyBotPlotter.plot).

        Args:
            `jump` (dict | None, optional): Miror of [BotPlotter.jump()](site:/reference/plot/bot/#lettrade.plot.bot.BotPlotter.jump)

        Example:
            Jump to position_id
                ```python
                lt.plot(
                    jump=dict(position_id=1, range=300),
                    layout=dict(height=2000),
                )
                ```

    OptimizePlotter:
        Miror of [OptimizePlotter.plot()](site:/reference/plot/optimize/#lettrade.plot.optimize.OptimizePlotter.plot).
        Plotly implement [PlotlyOptimizePlotter.plot()](site:/reference/exchange/backtest/plotly/optimize/#lettrade.exchange.backtest.plotly.optimize.PlotlyOptimizePlotter.plot).

        Example:
                ```python
                lt.plot(layout=dict(height=2000))
                ```
    """
    if __debug__:
        from .utils.docs import is_docs_session

        if is_docs_session():
            return

    self.plotter.plot(*args, jump=jump, **kwargs)

stop ¤

stop()

Stop LetTrade

Source code in lettrade/lettrade.py 
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def stop(self):
    """Stop LetTrade"""
    if self._bot is not None:
        return self._bot.stop()
    if self.stats:
        self.stats.stop()

LetTradeBackTestBot ¤

LetTradeBackTestBot(
    datas: DataFeed | list[DataFeed] | list[str] | str,
    strategy: type[Strategy],
    feeder: type[DataFeeder],
    exchange: type[Exchange],
    account: type[Account],
    commander: type[Commander] | None = None,
    plotter: type[Plotter] | None = None,
    stats: type[BotStatistic] | None = None,
    name: str | None = None,
    **kwargs
)

Bases: LetTradeBot

LetTradeBot for backtest

Parameters:

Source code in lettrade/bot.py 
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def __init__(
    self,
    datas: DataFeed | list[DataFeed] | list[str] | str,
    strategy: type[Strategy],
    feeder: type[DataFeeder],
    exchange: type[Exchange],
    account: type[Account],
    commander: type[Commander] | None = None,
    plotter: type[Plotter] | None = None,
    stats: type[BotStatistic] | None = None,
    name: str | None = None,
    **kwargs,
) -> None:
    """_summary_

    Args:
        datas (DataFeed | list[DataFeed] | list[str] | str): _description_
        strategy (type[Strategy]): _description_
        feeder (type[DataFeeder]): _description_
        exchange (type[Exchange]): _description_
        account (type[Account]): _description_
        commander (type[Commander] | None, optional): _description_. Defaults to None.
        plotter (type[Plotter] | None, optional): _description_. Defaults to None.
        stats (type[BotStatistic] | None, optional): _description_. Defaults to None.
        name (str | None, optional): _description_. Defaults to None.
    """
    logger.info("New bot: %s", name)

    self._strategy_cls = strategy
    self._feeder_cls = feeder
    self._exchange_cls = exchange
    self._account_cls = account
    self._commander_cls = commander
    self._plotter_cls = plotter
    self._stats_cls = stats

    self._name = name
    self._kwargs = kwargs

    # DataFeeds
    self.datas = datas
    self.data = self.datas[0]

account instance-attribute ¤

account: Account

Trading account handler

brain instance-attribute ¤

brain: Brain

Brain of bot

commander class-attribute instance-attribute ¤

commander: Commander | None = None

Control the bot

data instance-attribute ¤

data: DataFeed = datas[0]

Main DataFeed for bot

datas instance-attribute ¤

datas: list[DataFeed] = datas

DataFeed list for bot

exchange instance-attribute ¤

exchange: Exchange

Trading exchange and events

feeder instance-attribute ¤

feeder: DataFeeder

DataFeeder help to handle datas

plotter class-attribute instance-attribute ¤

plotter: Plotter | None = None

Plot graphic results

strategy instance-attribute ¤

strategy: Strategy

Strategy

new_bot classmethod ¤

new_bot(
    datas: list[DataFeed],
    strategy_cls: type[Strategy],
    feeder_cls: type[DataFeeder],
    exchange_cls: type[Exchange],
    account_cls: type[Account],
    commander_cls: type[Commander],
    plotter_cls: type[Plotter],
    stats_cls: type[BotStatistic],
    name: str | None = None,
    **kwargs
) -> LetTradeBot

Create new bot object

Parameters:

Returns:

Source code in lettrade/bot.py 
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@classmethod
def new_bot(
    cls,
    datas: list[DataFeed],
    strategy_cls: type[Strategy],
    feeder_cls: type[DataFeeder],
    exchange_cls: type[Exchange],
    account_cls: type[Account],
    commander_cls: type[Commander],
    plotter_cls: type[Plotter],
    stats_cls: type[BotStatistic],
    name: str | None = None,
    **kwargs,
) -> "LetTradeBot":
    """Create new bot object

    Args:
        datas (list[DataFeed]): _description_
        strategy_cls (type[Strategy]): _description_
        feeder_cls (type[DataFeeder]): _description_
        exchange_cls (type[Exchange]): _description_
        account_cls (type[Account]): _description_
        commander_cls (type[Commander]): _description_
        plotter_cls (type[Plotter]): _description_
        stats_cls (type[BotStatistic]): _description_
        name (str | None, optional): _description_. Defaults to None.

    Returns:
        LetTradeBot: _description_
    """
    bot = cls(
        strategy=strategy_cls,
        datas=datas,
        feeder=feeder_cls,
        exchange=exchange_cls,
        account=account_cls,
        commander=commander_cls,
        plotter=plotter_cls,
        stats=stats_cls,
        name=name,
        **kwargs,
    )
    return bot

plot ¤

plot(*args, **kwargs)

Plot bot result

Source code in lettrade/bot.py 
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def plot(self, *args, **kwargs):
    """Plot bot result"""
    if __debug__:
        from .utils.docs import is_docs_session

        if is_docs_session():
            return

    self.plotter.plot(*args, **kwargs)

run ¤

run()

Run bot

Source code in lettrade/bot.py 
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def run(self):
    """Run bot"""
    if self.commander:
        self.commander.start()

    self.brain.run()

    if self.commander:
        self.commander.stop()

    if self._stats_cls:
        self.stats.compute()

run_bot classmethod ¤

run_bot(
    bot: LetTradeBot | None = None,
    datas: list[DataFeed] | None = None,
    id: int | None = None,
    name: str | None = None,
    result: Literal["str", "stats", "bot", None] = "str",
    **kwargs
) -> LetTradeBot | BotStatistic | str | None

Run bot object

Parameters:

  • bot (LetTradeBot | None, default: None ) –

    description. Defaults to None.

  • datas (list[DataFeed] | None, default: None ) –

    description. Defaults to None.

  • id (int | None, default: None ) –

    description. Defaults to None.

  • name (str | None, default: None ) –

    description. Defaults to None.

  • result (Literal['str', 'stats', 'bot', None], default: 'str' ) –

    description. Defaults to "str".

Returns:

  • LetTradeBot | BotStatistic | str | None –

    LetTradeBot | BotStatistic | str | None: Return value will be pickle across multiprocessing. The cost higher from str, stats object, bot object

Source code in lettrade/bot.py 
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@classmethod
def run_bot(
    cls,
    bot: "LetTradeBot | None" = None,
    datas: list[DataFeed] | None = None,
    id: int | None = None,
    name: str | None = None,
    result: Literal["str", "stats", "bot", None] = "str",
    **kwargs,
) -> "LetTradeBot | BotStatistic | str | None":
    """Run bot object

    Args:
        bot (LetTradeBot | None, optional): _description_. Defaults to None.
        datas (list[DataFeed] | None, optional): _description_. Defaults to None.
        id (int | None, optional): _description_. Defaults to None.
        name (str | None, optional): _description_. Defaults to None.
        result (Literal["str", "stats", "bot", None], optional): _description_. Defaults to "str".

    Returns:
        LetTradeBot | BotStatistic | str | None: Return value will be pickle across multiprocessing.
            The cost higher from `str`, `stats` object, `bot` object
    """
    # Set name for current processing
    if name is None:
        d = datas[0] if datas else bot.data
        name = f"{id}-{os.getpid()}-{d.name}"

    if bot is None:
        bot = cls.start_bot(
            datas=datas,
            name=name,
            **kwargs,
        )

    # bot
    bot.run(**kwargs.get("run_kwargs", {}))

    # Return type
    if result == "stats":
        return bot.stats
    if result == "str":
        return str(bot.stats)

    return bot

start ¤

start(**kwargs)

Start bot

Source code in lettrade/bot.py 
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def start(self, **kwargs):
    """Start bot"""
    if not hasattr(self, "brain"):
        self.init()

    self.brain.start(**kwargs)

    if __debug__:
        logger.debug("Bot %s started with %d datas", self._name, len(self.datas))

start_bot classmethod ¤

start_bot(
    bot: LetTradeBot | None = None, **kwargs
) -> LetTradeBot

Init and start bot object

Parameters:

  • bot (LetTradeBot | None, default: None ) –

    description. Defaults to None.

Returns:

Source code in lettrade/bot.py 
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@classmethod
def start_bot(
    cls,
    bot: "LetTradeBot | None" = None,
    **kwargs,
) -> "LetTradeBot":
    """Init and start bot object

    Args:
        bot (LetTradeBot | None, optional): _description_. Defaults to None.

    Returns:
        LetTradeBot: _description_
    """
    if bot is None:
        bot = cls.new_bot(**kwargs)
    bot.init()
    bot.start(**kwargs.get("start_kwargs", {}))
    return bot

stop ¤

stop()

Stop bot

Source code in lettrade/bot.py 
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def stop(self):
    """Stop bot"""
    self.brain.stop()
    if self.plotter is not None:
        self.plotter.stop()

LetTradeBot ¤

LetTradeBot(
    datas: DataFeed | list[DataFeed] | list[str] | str,
    strategy: type[Strategy],
    feeder: type[DataFeeder],
    exchange: type[Exchange],
    account: type[Account],
    commander: type[Commander] | None = None,
    plotter: type[Plotter] | None = None,
    stats: type[BotStatistic] | None = None,
    name: str | None = None,
    **kwargs
)

Bot object helps to manage actions of bot

Parameters:

Source code in lettrade/bot.py 
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def __init__(
    self,
    datas: DataFeed | list[DataFeed] | list[str] | str,
    strategy: type[Strategy],
    feeder: type[DataFeeder],
    exchange: type[Exchange],
    account: type[Account],
    commander: type[Commander] | None = None,
    plotter: type[Plotter] | None = None,
    stats: type[BotStatistic] | None = None,
    name: str | None = None,
    **kwargs,
) -> None:
    """_summary_

    Args:
        datas (DataFeed | list[DataFeed] | list[str] | str): _description_
        strategy (type[Strategy]): _description_
        feeder (type[DataFeeder]): _description_
        exchange (type[Exchange]): _description_
        account (type[Account]): _description_
        commander (type[Commander] | None, optional): _description_. Defaults to None.
        plotter (type[Plotter] | None, optional): _description_. Defaults to None.
        stats (type[BotStatistic] | None, optional): _description_. Defaults to None.
        name (str | None, optional): _description_. Defaults to None.
    """
    logger.info("New bot: %s", name)

    self._strategy_cls = strategy
    self._feeder_cls = feeder
    self._exchange_cls = exchange
    self._account_cls = account
    self._commander_cls = commander
    self._plotter_cls = plotter
    self._stats_cls = stats

    self._name = name
    self._kwargs = kwargs

    # DataFeeds
    self.datas = datas
    self.data = self.datas[0]

account instance-attribute ¤

account: Account

Trading account handler

brain instance-attribute ¤

brain: Brain

Brain of bot

commander class-attribute instance-attribute ¤

commander: Commander | None = None

Control the bot

data instance-attribute ¤

data: DataFeed = datas[0]

Main DataFeed for bot

datas instance-attribute ¤

datas: list[DataFeed] = datas

DataFeed list for bot

exchange instance-attribute ¤

exchange: Exchange

Trading exchange and events

feeder instance-attribute ¤

feeder: DataFeeder

DataFeeder help to handle datas

plotter class-attribute instance-attribute ¤

plotter: Plotter | None = None

Plot graphic results

strategy instance-attribute ¤

strategy: Strategy

Strategy

init ¤

init()

Init objects from classes

Source code in lettrade/bot.py 
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def init(self):
    """Init objects from classes"""
    # Feeder
    self.feeder = self._feeder_cls(**self._kwargs.get("feeder_kwargs", {}))
    self.feeder.init(self.datas)

    # Account
    self.account = self._account_cls(**self._kwargs.get("account_kwargs", {}))

    # Exchange
    self.exchange = self._exchange_cls(**self._kwargs.get("exchange_kwargs", {}))

    # Commander
    if self._commander_cls:
        if isinstance(self._commander_cls, Commander):
            self.commander = self._commander_cls
        else:
            self.commander = self._commander_cls(
                **self._kwargs.get("commander_kwargs", {})
            )

    # Strategy
    self.strategy = self._strategy_cls(
        feeder=self.feeder,
        exchange=self.exchange,
        account=self.account,
        commander=self.commander,
        **self._kwargs.get("strategy_kwargs", {}),
    )

    # Brain
    self.brain = Brain(
        strategy=self.strategy,
        exchange=self.exchange,
        feeder=self.feeder,
        commander=self.commander,
        **self._kwargs.get("brain_kwargs", {}),
    )

    # Init
    if self.commander:
        self.commander.init(
            bot=self,
            brain=self.brain,
            exchange=self.exchange,
            strategy=self.strategy,
        )
    self.exchange.init(
        brain=self.brain,
        feeder=self.feeder,
        account=self.account,
        commander=self.commander,
    )

    # Stats
    self.stats = self._stats_cls(
        feeder=self.feeder,
        exchange=self.exchange,
        strategy=self.strategy,
        **self._kwargs.get("stats_kwargs", {}),
    )

    # Plotter
    if self._plotter_cls:
        self.plotter = self._plotter_cls(
            self,
            **self._kwargs.get("plotter_kwargs", {}),
        )

    if __debug__:
        logger.debug("Bot %s inited with %d datas", self._name, len(self.datas))

new_bot classmethod ¤

new_bot(
    datas: list[DataFeed],
    strategy_cls: type[Strategy],
    feeder_cls: type[DataFeeder],
    exchange_cls: type[Exchange],
    account_cls: type[Account],
    commander_cls: type[Commander],
    plotter_cls: type[Plotter],
    stats_cls: type[BotStatistic],
    name: str | None = None,
    **kwargs
) -> LetTradeBot

Create new bot object

Parameters:

Returns:

Source code in lettrade/bot.py 
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@classmethod
def new_bot(
    cls,
    datas: list[DataFeed],
    strategy_cls: type[Strategy],
    feeder_cls: type[DataFeeder],
    exchange_cls: type[Exchange],
    account_cls: type[Account],
    commander_cls: type[Commander],
    plotter_cls: type[Plotter],
    stats_cls: type[BotStatistic],
    name: str | None = None,
    **kwargs,
) -> "LetTradeBot":
    """Create new bot object

    Args:
        datas (list[DataFeed]): _description_
        strategy_cls (type[Strategy]): _description_
        feeder_cls (type[DataFeeder]): _description_
        exchange_cls (type[Exchange]): _description_
        account_cls (type[Account]): _description_
        commander_cls (type[Commander]): _description_
        plotter_cls (type[Plotter]): _description_
        stats_cls (type[BotStatistic]): _description_
        name (str | None, optional): _description_. Defaults to None.

    Returns:
        LetTradeBot: _description_
    """
    bot = cls(
        strategy=strategy_cls,
        datas=datas,
        feeder=feeder_cls,
        exchange=exchange_cls,
        account=account_cls,
        commander=commander_cls,
        plotter=plotter_cls,
        stats=stats_cls,
        name=name,
        **kwargs,
    )
    return bot

plot ¤

plot(*args, **kwargs)

Plot bot result

Source code in lettrade/bot.py 
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def plot(self, *args, **kwargs):
    """Plot bot result"""
    if __debug__:
        from .utils.docs import is_docs_session

        if is_docs_session():
            return

    self.plotter.plot(*args, **kwargs)

run ¤

run()

Run bot

Source code in lettrade/bot.py 
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def run(self):
    """Run bot"""
    if self.commander:
        self.commander.start()

    self.brain.run()

    if self.commander:
        self.commander.stop()

    if self._stats_cls:
        self.stats.compute()

run_bot classmethod ¤

run_bot(
    bot: LetTradeBot | None = None,
    datas: list[DataFeed] | None = None,
    id: int | None = None,
    name: str | None = None,
    result: Literal["str", "stats", "bot", None] = "str",
    **kwargs
) -> LetTradeBot | BotStatistic | str | None

Run bot object

Parameters:

  • bot (LetTradeBot | None, default: None ) –

    description. Defaults to None.

  • datas (list[DataFeed] | None, default: None ) –

    description. Defaults to None.

  • id (int | None, default: None ) –

    description. Defaults to None.

  • name (str | None, default: None ) –

    description. Defaults to None.

  • result (Literal['str', 'stats', 'bot', None], default: 'str' ) –

    description. Defaults to "str".

Returns:

  • LetTradeBot | BotStatistic | str | None –

    LetTradeBot | BotStatistic | str | None: Return value will be pickle across multiprocessing. The cost higher from str, stats object, bot object

Source code in lettrade/bot.py 
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@classmethod
def run_bot(
    cls,
    bot: "LetTradeBot | None" = None,
    datas: list[DataFeed] | None = None,
    id: int | None = None,
    name: str | None = None,
    result: Literal["str", "stats", "bot", None] = "str",
    **kwargs,
) -> "LetTradeBot | BotStatistic | str | None":
    """Run bot object

    Args:
        bot (LetTradeBot | None, optional): _description_. Defaults to None.
        datas (list[DataFeed] | None, optional): _description_. Defaults to None.
        id (int | None, optional): _description_. Defaults to None.
        name (str | None, optional): _description_. Defaults to None.
        result (Literal["str", "stats", "bot", None], optional): _description_. Defaults to "str".

    Returns:
        LetTradeBot | BotStatistic | str | None: Return value will be pickle across multiprocessing.
            The cost higher from `str`, `stats` object, `bot` object
    """
    # Set name for current processing
    if name is None:
        d = datas[0] if datas else bot.data
        name = f"{id}-{os.getpid()}-{d.name}"

    if bot is None:
        bot = cls.start_bot(
            datas=datas,
            name=name,
            **kwargs,
        )

    # bot
    bot.run(**kwargs.get("run_kwargs", {}))

    # Return type
    if result == "stats":
        return bot.stats
    if result == "str":
        return str(bot.stats)

    return bot

start ¤

start(**kwargs)

Start bot

Source code in lettrade/bot.py 
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def start(self, **kwargs):
    """Start bot"""
    if not hasattr(self, "brain"):
        self.init()

    self.brain.start(**kwargs)

    if __debug__:
        logger.debug("Bot %s started with %d datas", self._name, len(self.datas))

start_bot classmethod ¤

start_bot(
    bot: LetTradeBot | None = None, **kwargs
) -> LetTradeBot

Init and start bot object

Parameters:

  • bot (LetTradeBot | None, default: None ) –

    description. Defaults to None.

Returns:

Source code in lettrade/bot.py 
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@classmethod
def start_bot(
    cls,
    bot: "LetTradeBot | None" = None,
    **kwargs,
) -> "LetTradeBot":
    """Init and start bot object

    Args:
        bot (LetTradeBot | None, optional): _description_. Defaults to None.

    Returns:
        LetTradeBot: _description_
    """
    if bot is None:
        bot = cls.new_bot(**kwargs)
    bot.init()
    bot.start(**kwargs.get("start_kwargs", {}))
    return bot

stop ¤

stop()

Stop bot

Source code in lettrade/bot.py 
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def stop(self):
    """Stop bot"""
    self.brain.stop()
    if self.plotter is not None:
        self.plotter.stop()

LetTradeCCXT ¤

LetTradeCCXT(
    feeder: type[CCXTDataFeeder] = CCXTDataFeeder,
    exchange: type[CCXTExchange] = CCXTExchange,
    account: type[CCXTAccount] = CCXTAccount,
    **kwargs
)

Bases: LetTradeLive

Help to maintain CCXT bots

Parameters:

Source code in lettrade/exchange/ccxt/ccxt.py 
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def __init__(
    self,
    feeder: type[CCXTDataFeeder] = CCXTDataFeeder,
    exchange: type[CCXTExchange] = CCXTExchange,
    account: type[CCXTAccount] = CCXTAccount,
    **kwargs,
) -> None:
    """_summary_

    Args:
        feeder (Type[CCXTDataFeeder], optional): _description_. Defaults to CCXTDataFeeder.
        exchange (Type[CCXTExchange], optional): _description_. Defaults to CCXTExchange.
        account (Type[CCXTAccount], optional): _description_. Defaults to CCXTAccount.
    """
    super().__init__(
        feeder=feeder,
        exchange=exchange,
        account=account,
        **kwargs,
    )

plot ¤

plot(*args, jump: dict | None = None, **kwargs)

Plot strategy/optimize result

BotPlotter

Miror of BotPlotter.plot(). Plotly implement PlotlyBotPlotter.plot().

Args: jump (dict | None, optional): Miror of BotPlotter.jump()

Example: Jump to position_id 

lt.plot(
    jump=dict(position_id=1, range=300),
    layout=dict(height=2000),
)

OptimizePlotter

Miror of OptimizePlotter.plot(). Plotly implement PlotlyOptimizePlotter.plot().

Example: 

lt.plot(layout=dict(height=2000))

Source code in lettrade/lettrade.py 
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def plot(self, *args, jump: dict | None = None, **kwargs):
    """Plot strategy/optimize result

    BotPlotter:
        Miror of [BotPlotter.plot()](site:/reference/plot/bot/#lettrade.plot.bot.BotPlotter.plot).
        Plotly implement [PlotlyBotPlotter.plot()](site:/reference/plot/plotly/plotly/#lettrade.plot.plotly.plotly.PlotlyBotPlotter.plot).

        Args:
            `jump` (dict | None, optional): Miror of [BotPlotter.jump()](site:/reference/plot/bot/#lettrade.plot.bot.BotPlotter.jump)

        Example:
            Jump to position_id
                ```python
                lt.plot(
                    jump=dict(position_id=1, range=300),
                    layout=dict(height=2000),
                )
                ```

    OptimizePlotter:
        Miror of [OptimizePlotter.plot()](site:/reference/plot/optimize/#lettrade.plot.optimize.OptimizePlotter.plot).
        Plotly implement [PlotlyOptimizePlotter.plot()](site:/reference/exchange/backtest/plotly/optimize/#lettrade.exchange.backtest.plotly.optimize.PlotlyOptimizePlotter.plot).

        Example:
                ```python
                lt.plot(layout=dict(height=2000))
                ```
    """
    if __debug__:
        from .utils.docs import is_docs_session

        if is_docs_session():
            return

    self.plotter.plot(*args, jump=jump, **kwargs)

run ¤

run(worker: int | None = None, **kwargs)

Run LetTrade in single or multiple processing

Parameters:

  • worker (int | None, default: None ) –

    Number of processing. Defaults to None.

Source code in lettrade/lettrade.py 
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def run(self, worker: int | None = None, **kwargs):
    """Run LetTrade in single or multiple processing

    Args:
        worker (int | None, optional): Number of processing. Defaults to None.
    """
    if worker or isinstance(self.data, list):
        if not isinstance(self.data, list):
            # self.data = self.datas
            self.datas = [self.data]

        worker = self._max_workers(worker)

        self._multiprocess()

        datas_source = self._kwargs.pop("datas")
        with ProcessPoolExecutor(max_workers=worker) as executor:
            futures = [
                executor.submit(
                    self._bot_cls.run_bot,
                    datas=datas,
                    id=i,
                    result="str",
                    **self._kwargs,
                    **kwargs,
                )
                for i, datas in enumerate(datas_source)
            ]
            for future in futures:
                result = future.result()
                print(result)
    else:
        self._bot = self._bot_cls.run_bot(
            bot=self._bot,
            result="bot",
            **self._kwargs,
            **kwargs,
        )
        print(str(self._bot.stats))

start ¤

start(force: bool = False)

Start LetTrade by init bot object and loading datafeeds

Parameters:

  • force (bool, default: False ) –

    description. Defaults to False.

Source code in lettrade/lettrade.py 
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def start(self, force: bool = False):
    """Start LetTrade by init bot object and loading datafeeds

    Args:
        force (bool, optional): _description_. Defaults to False.
    """
    if force and self._bot is not None:
        self._bot = None

    self._bot = self._bot_cls.start_bot(bot=self._bot, **self._kwargs)

stop ¤

stop()

Stop LetTrade

Source code in lettrade/lettrade.py 
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def stop(self):
    """Stop LetTrade"""
    if self._bot is not None:
        return self._bot.stop()
    if self.stats:
        self.stats.stop()

LetTradeCCXTBot ¤

LetTradeCCXTBot(api: LiveAPI | None = LiveAPI, **kwargs)

Bases: LetTradeLiveBot

LetTradeBot for CCXT

Parameters:

  • api (LiveAPI | None, default: LiveAPI ) –

    description. Defaults to LiveAPI.

Source code in lettrade/exchange/live/live.py 
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def __init__(self, api: LiveAPI | None = LiveAPI, **kwargs) -> None:
    """_summary_

    Args:
        api (LiveAPI | None, optional): _description_. Defaults to LiveAPI.
    """
    super().__init__(**kwargs)

    if issubclass(api, LiveAPI):
        self._api = api(**self._kwargs.get("api_kwargs", {}))
    else:
        self._api = api

    self._kwargs.setdefault("feeder_kwargs", dict()).update(api=self._api)
    self._kwargs.setdefault("exchange_kwargs", dict()).update(api=self._api)
    self._kwargs.setdefault("account_kwargs", dict()).update(api=self._api)

    for data in self.datas:
        data._api = self._api

account instance-attribute ¤

account: Account

Trading account handler

brain instance-attribute ¤

brain: Brain

Brain of bot

commander class-attribute instance-attribute ¤

commander: Commander | None = None

Control the bot

data instance-attribute ¤

data: DataFeed = datas[0]

Main DataFeed for bot

exchange instance-attribute ¤

exchange: Exchange

Trading exchange and events

feeder instance-attribute ¤

feeder: DataFeeder

DataFeeder help to handle datas

plotter class-attribute instance-attribute ¤

plotter: Plotter | None = None

Plot graphic results

strategy instance-attribute ¤

strategy: Strategy

Strategy

new_bot classmethod ¤

new_bot(
    datas: list[DataFeed],
    strategy_cls: type[Strategy],
    feeder_cls: type[DataFeeder],
    exchange_cls: type[Exchange],
    account_cls: type[Account],
    commander_cls: type[Commander],
    plotter_cls: type[Plotter],
    stats_cls: type[BotStatistic],
    name: str | None = None,
    **kwargs
) -> LetTradeBot

Create new bot object

Parameters:

Returns:

Source code in lettrade/bot.py 
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@classmethod
def new_bot(
    cls,
    datas: list[DataFeed],
    strategy_cls: type[Strategy],
    feeder_cls: type[DataFeeder],
    exchange_cls: type[Exchange],
    account_cls: type[Account],
    commander_cls: type[Commander],
    plotter_cls: type[Plotter],
    stats_cls: type[BotStatistic],
    name: str | None = None,
    **kwargs,
) -> "LetTradeBot":
    """Create new bot object

    Args:
        datas (list[DataFeed]): _description_
        strategy_cls (type[Strategy]): _description_
        feeder_cls (type[DataFeeder]): _description_
        exchange_cls (type[Exchange]): _description_
        account_cls (type[Account]): _description_
        commander_cls (type[Commander]): _description_
        plotter_cls (type[Plotter]): _description_
        stats_cls (type[BotStatistic]): _description_
        name (str | None, optional): _description_. Defaults to None.

    Returns:
        LetTradeBot: _description_
    """
    bot = cls(
        strategy=strategy_cls,
        datas=datas,
        feeder=feeder_cls,
        exchange=exchange_cls,
        account=account_cls,
        commander=commander_cls,
        plotter=plotter_cls,
        stats=stats_cls,
        name=name,
        **kwargs,
    )
    return bot

plot ¤

plot(*args, **kwargs)

Plot bot result

Source code in lettrade/bot.py 
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def plot(self, *args, **kwargs):
    """Plot bot result"""
    if __debug__:
        from .utils.docs import is_docs_session

        if is_docs_session():
            return

    self.plotter.plot(*args, **kwargs)

run ¤

run()

Run bot

Source code in lettrade/bot.py 
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def run(self):
    """Run bot"""
    if self.commander:
        self.commander.start()

    self.brain.run()

    if self.commander:
        self.commander.stop()

    if self._stats_cls:
        self.stats.compute()

run_bot classmethod ¤

run_bot(
    bot: LetTradeBot | None = None,
    datas: list[DataFeed] | None = None,
    id: int | None = None,
    name: str | None = None,
    result: Literal["str", "stats", "bot", None] = "str",
    **kwargs
) -> LetTradeBot | BotStatistic | str | None

Run bot object

Parameters:

  • bot (LetTradeBot | None, default: None ) –

    description. Defaults to None.

  • datas (list[DataFeed] | None, default: None ) –

    description. Defaults to None.

  • id (int | None, default: None ) –

    description. Defaults to None.

  • name (str | None, default: None ) –

    description. Defaults to None.

  • result (Literal['str', 'stats', 'bot', None], default: 'str' ) –

    description. Defaults to "str".

Returns:

  • LetTradeBot | BotStatistic | str | None –

    LetTradeBot | BotStatistic | str | None: Return value will be pickle across multiprocessing. The cost higher from str, stats object, bot object

Source code in lettrade/bot.py 
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@classmethod
def run_bot(
    cls,
    bot: "LetTradeBot | None" = None,
    datas: list[DataFeed] | None = None,
    id: int | None = None,
    name: str | None = None,
    result: Literal["str", "stats", "bot", None] = "str",
    **kwargs,
) -> "LetTradeBot | BotStatistic | str | None":
    """Run bot object

    Args:
        bot (LetTradeBot | None, optional): _description_. Defaults to None.
        datas (list[DataFeed] | None, optional): _description_. Defaults to None.
        id (int | None, optional): _description_. Defaults to None.
        name (str | None, optional): _description_. Defaults to None.
        result (Literal["str", "stats", "bot", None], optional): _description_. Defaults to "str".

    Returns:
        LetTradeBot | BotStatistic | str | None: Return value will be pickle across multiprocessing.
            The cost higher from `str`, `stats` object, `bot` object
    """
    # Set name for current processing
    if name is None:
        d = datas[0] if datas else bot.data
        name = f"{id}-{os.getpid()}-{d.name}"

    if bot is None:
        bot = cls.start_bot(
            datas=datas,
            name=name,
            **kwargs,
        )

    # bot
    bot.run(**kwargs.get("run_kwargs", {}))

    # Return type
    if result == "stats":
        return bot.stats
    if result == "str":
        return str(bot.stats)

    return bot

start ¤

start(**kwargs)

Start bot

Source code in lettrade/bot.py 
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def start(self, **kwargs):
    """Start bot"""
    if not hasattr(self, "brain"):
        self.init()

    self.brain.start(**kwargs)

    if __debug__:
        logger.debug("Bot %s started with %d datas", self._name, len(self.datas))

start_bot classmethod ¤

start_bot(
    bot: LetTradeBot | None = None, **kwargs
) -> LetTradeBot

Init and start bot object

Parameters:

  • bot (LetTradeBot | None, default: None ) –

    description. Defaults to None.

Returns:

Source code in lettrade/bot.py 
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@classmethod
def start_bot(
    cls,
    bot: "LetTradeBot | None" = None,
    **kwargs,
) -> "LetTradeBot":
    """Init and start bot object

    Args:
        bot (LetTradeBot | None, optional): _description_. Defaults to None.

    Returns:
        LetTradeBot: _description_
    """
    if bot is None:
        bot = cls.new_bot(**kwargs)
    bot.init()
    bot.start(**kwargs.get("start_kwargs", {}))
    return bot

stop ¤

stop()

Stop bot

Source code in lettrade/bot.py 
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def stop(self):
    """Stop bot"""
    self.brain.stop()
    if self.plotter is not None:
        self.plotter.stop()

LetTradeLive ¤

LetTradeLive(
    datas: DataFeed | list[DataFeed] | str | list[str],
    strategy: type[Strategy],
    feeder: type[DataFeeder],
    exchange: type[Exchange],
    account: type[Account],
    commander: type[Commander] | Commander | None = None,
    stats: type[BotStatistic] = BotStatistic,
    plotter: type[Plotter] | None = None,
    name: str | None = None,
    bot: type[LetTradeBot] = LetTradeBot,
    **kwargs
)

Bases: LetTrade

Help to maintain live bots

Parameters:

Source code in lettrade/lettrade.py 
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def __init__(
    self,
    datas: DataFeed | list[DataFeed] | str | list[str],
    strategy: type[Strategy],
    feeder: type[DataFeeder],
    exchange: type[Exchange],
    account: type[Account],
    commander: type[Commander] | Commander | None = None,
    stats: type[BotStatistic] = BotStatistic,
    plotter: type[Plotter] | None = None,
    name: str | None = None,
    bot: type[LetTradeBot] = LetTradeBot,
    **kwargs,
) -> None:
    """_summary_

    Args:
        datas (DataFeed | list[DataFeed] | str | list[str]): _description_
        strategy (type[Strategy]): _description_
        feeder (type[DataFeeder]): _description_
        exchange (type[Exchange]): _description_
        account (type[Account]): _description_
        commander (type[Commander] | Commander | None, optional): _description_. Defaults to None.
        stats (type[BotStatistic], optional): _description_. Defaults to BotStatistic.
        plotter (type[Plotter] | None, optional): _description_. Defaults to None.
        name (str | None, optional): _description_. Defaults to None.
        bot (type[LetTradeBot], optional): _description_. Defaults to LetTradeBot.
    """
    self._kwargs = kwargs
    self._kwargs["strategy_cls"] = strategy
    self._kwargs["feeder_cls"] = feeder
    self._kwargs["exchange_cls"] = exchange
    self._kwargs["account_cls"] = account
    self._kwargs["commander_cls"] = commander
    self._kwargs["stats_cls"] = stats
    self._kwargs["plotter_cls"] = plotter
    self._kwargs["bot_cls"] = bot
    self._kwargs["name"] = name

    self._kwargs["datas"] = self._init_datafeeds(datas)

plot ¤

plot(*args, jump: dict | None = None, **kwargs)

Plot strategy/optimize result

BotPlotter

Miror of BotPlotter.plot(). Plotly implement PlotlyBotPlotter.plot().

Args: jump (dict | None, optional): Miror of BotPlotter.jump()

Example: Jump to position_id 

lt.plot(
    jump=dict(position_id=1, range=300),
    layout=dict(height=2000),
)

OptimizePlotter

Miror of OptimizePlotter.plot(). Plotly implement PlotlyOptimizePlotter.plot().

Example: 

lt.plot(layout=dict(height=2000))

Source code in lettrade/lettrade.py 
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def plot(self, *args, jump: dict | None = None, **kwargs):
    """Plot strategy/optimize result

    BotPlotter:
        Miror of [BotPlotter.plot()](site:/reference/plot/bot/#lettrade.plot.bot.BotPlotter.plot).
        Plotly implement [PlotlyBotPlotter.plot()](site:/reference/plot/plotly/plotly/#lettrade.plot.plotly.plotly.PlotlyBotPlotter.plot).

        Args:
            `jump` (dict | None, optional): Miror of [BotPlotter.jump()](site:/reference/plot/bot/#lettrade.plot.bot.BotPlotter.jump)

        Example:
            Jump to position_id
                ```python
                lt.plot(
                    jump=dict(position_id=1, range=300),
                    layout=dict(height=2000),
                )
                ```

    OptimizePlotter:
        Miror of [OptimizePlotter.plot()](site:/reference/plot/optimize/#lettrade.plot.optimize.OptimizePlotter.plot).
        Plotly implement [PlotlyOptimizePlotter.plot()](site:/reference/exchange/backtest/plotly/optimize/#lettrade.exchange.backtest.plotly.optimize.PlotlyOptimizePlotter.plot).

        Example:
                ```python
                lt.plot(layout=dict(height=2000))
                ```
    """
    if __debug__:
        from .utils.docs import is_docs_session

        if is_docs_session():
            return

    self.plotter.plot(*args, jump=jump, **kwargs)

run ¤

run(worker: int | None = None, **kwargs)

Run LetTrade in single or multiple processing

Parameters:

  • worker (int | None, default: None ) –

    Number of processing. Defaults to None.

Source code in lettrade/lettrade.py 
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def run(self, worker: int | None = None, **kwargs):
    """Run LetTrade in single or multiple processing

    Args:
        worker (int | None, optional): Number of processing. Defaults to None.
    """
    if worker or isinstance(self.data, list):
        if not isinstance(self.data, list):
            # self.data = self.datas
            self.datas = [self.data]

        worker = self._max_workers(worker)

        self._multiprocess()

        datas_source = self._kwargs.pop("datas")
        with ProcessPoolExecutor(max_workers=worker) as executor:
            futures = [
                executor.submit(
                    self._bot_cls.run_bot,
                    datas=datas,
                    id=i,
                    result="str",
                    **self._kwargs,
                    **kwargs,
                )
                for i, datas in enumerate(datas_source)
            ]
            for future in futures:
                result = future.result()
                print(result)
    else:
        self._bot = self._bot_cls.run_bot(
            bot=self._bot,
            result="bot",
            **self._kwargs,
            **kwargs,
        )
        print(str(self._bot.stats))

start ¤

start(force: bool = False)

Start LetTrade by init bot object and loading datafeeds

Parameters:

  • force (bool, default: False ) –

    description. Defaults to False.

Source code in lettrade/lettrade.py 
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def start(self, force: bool = False):
    """Start LetTrade by init bot object and loading datafeeds

    Args:
        force (bool, optional): _description_. Defaults to False.
    """
    if force and self._bot is not None:
        self._bot = None

    self._bot = self._bot_cls.start_bot(bot=self._bot, **self._kwargs)

stop ¤

stop()

Stop LetTrade

Source code in lettrade/lettrade.py 
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def stop(self):
    """Stop LetTrade"""
    if self._bot is not None:
        return self._bot.stop()
    if self.stats:
        self.stats.stop()

LetTradeLiveBot ¤

LetTradeLiveBot(api: LiveAPI | None = LiveAPI, **kwargs)

Bases: LetTradeBot

Live bot object

Parameters:

  • api (LiveAPI | None, default: LiveAPI ) –

    description. Defaults to LiveAPI.

Source code in lettrade/exchange/live/live.py 
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def __init__(self, api: LiveAPI | None = LiveAPI, **kwargs) -> None:
    """_summary_

    Args:
        api (LiveAPI | None, optional): _description_. Defaults to LiveAPI.
    """
    super().__init__(**kwargs)

    if issubclass(api, LiveAPI):
        self._api = api(**self._kwargs.get("api_kwargs", {}))
    else:
        self._api = api

    self._kwargs.setdefault("feeder_kwargs", dict()).update(api=self._api)
    self._kwargs.setdefault("exchange_kwargs", dict()).update(api=self._api)
    self._kwargs.setdefault("account_kwargs", dict()).update(api=self._api)

    for data in self.datas:
        data._api = self._api

account instance-attribute ¤

account: Account

Trading account handler

brain instance-attribute ¤

brain: Brain

Brain of bot

commander class-attribute instance-attribute ¤

commander: Commander | None = None

Control the bot

data instance-attribute ¤

data: DataFeed = datas[0]

Main DataFeed for bot

exchange instance-attribute ¤

exchange: Exchange

Trading exchange and events

feeder instance-attribute ¤

feeder: DataFeeder

DataFeeder help to handle datas

plotter class-attribute instance-attribute ¤

plotter: Plotter | None = None

Plot graphic results

strategy instance-attribute ¤

strategy: Strategy

Strategy

new_bot classmethod ¤

new_bot(
    datas: list[DataFeed],
    strategy_cls: type[Strategy],
    feeder_cls: type[DataFeeder],
    exchange_cls: type[Exchange],
    account_cls: type[Account],
    commander_cls: type[Commander],
    plotter_cls: type[Plotter],
    stats_cls: type[BotStatistic],
    name: str | None = None,
    **kwargs
) -> LetTradeBot

Create new bot object

Parameters:

Returns:

Source code in lettrade/bot.py 
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@classmethod
def new_bot(
    cls,
    datas: list[DataFeed],
    strategy_cls: type[Strategy],
    feeder_cls: type[DataFeeder],
    exchange_cls: type[Exchange],
    account_cls: type[Account],
    commander_cls: type[Commander],
    plotter_cls: type[Plotter],
    stats_cls: type[BotStatistic],
    name: str | None = None,
    **kwargs,
) -> "LetTradeBot":
    """Create new bot object

    Args:
        datas (list[DataFeed]): _description_
        strategy_cls (type[Strategy]): _description_
        feeder_cls (type[DataFeeder]): _description_
        exchange_cls (type[Exchange]): _description_
        account_cls (type[Account]): _description_
        commander_cls (type[Commander]): _description_
        plotter_cls (type[Plotter]): _description_
        stats_cls (type[BotStatistic]): _description_
        name (str | None, optional): _description_. Defaults to None.

    Returns:
        LetTradeBot: _description_
    """
    bot = cls(
        strategy=strategy_cls,
        datas=datas,
        feeder=feeder_cls,
        exchange=exchange_cls,
        account=account_cls,
        commander=commander_cls,
        plotter=plotter_cls,
        stats=stats_cls,
        name=name,
        **kwargs,
    )
    return bot

plot ¤

plot(*args, **kwargs)

Plot bot result

Source code in lettrade/bot.py 
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def plot(self, *args, **kwargs):
    """Plot bot result"""
    if __debug__:
        from .utils.docs import is_docs_session

        if is_docs_session():
            return

    self.plotter.plot(*args, **kwargs)

run ¤

run()

Run bot

Source code in lettrade/bot.py 
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def run(self):
    """Run bot"""
    if self.commander:
        self.commander.start()

    self.brain.run()

    if self.commander:
        self.commander.stop()

    if self._stats_cls:
        self.stats.compute()

run_bot classmethod ¤

run_bot(
    bot: LetTradeBot | None = None,
    datas: list[DataFeed] | None = None,
    id: int | None = None,
    name: str | None = None,
    result: Literal["str", "stats", "bot", None] = "str",
    **kwargs
) -> LetTradeBot | BotStatistic | str | None

Run bot object

Parameters:

  • bot (LetTradeBot | None, default: None ) –

    description. Defaults to None.

  • datas (list[DataFeed] | None, default: None ) –

    description. Defaults to None.

  • id (int | None, default: None ) –

    description. Defaults to None.

  • name (str | None, default: None ) –

    description. Defaults to None.

  • result (Literal['str', 'stats', 'bot', None], default: 'str' ) –

    description. Defaults to "str".

Returns:

  • LetTradeBot | BotStatistic | str | None –

    LetTradeBot | BotStatistic | str | None: Return value will be pickle across multiprocessing. The cost higher from str, stats object, bot object

Source code in lettrade/bot.py 
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@classmethod
def run_bot(
    cls,
    bot: "LetTradeBot | None" = None,
    datas: list[DataFeed] | None = None,
    id: int | None = None,
    name: str | None = None,
    result: Literal["str", "stats", "bot", None] = "str",
    **kwargs,
) -> "LetTradeBot | BotStatistic | str | None":
    """Run bot object

    Args:
        bot (LetTradeBot | None, optional): _description_. Defaults to None.
        datas (list[DataFeed] | None, optional): _description_. Defaults to None.
        id (int | None, optional): _description_. Defaults to None.
        name (str | None, optional): _description_. Defaults to None.
        result (Literal["str", "stats", "bot", None], optional): _description_. Defaults to "str".

    Returns:
        LetTradeBot | BotStatistic | str | None: Return value will be pickle across multiprocessing.
            The cost higher from `str`, `stats` object, `bot` object
    """
    # Set name for current processing
    if name is None:
        d = datas[0] if datas else bot.data
        name = f"{id}-{os.getpid()}-{d.name}"

    if bot is None:
        bot = cls.start_bot(
            datas=datas,
            name=name,
            **kwargs,
        )

    # bot
    bot.run(**kwargs.get("run_kwargs", {}))

    # Return type
    if result == "stats":
        return bot.stats
    if result == "str":
        return str(bot.stats)

    return bot

start ¤

start(**kwargs)

Start bot

Source code in lettrade/bot.py 
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def start(self, **kwargs):
    """Start bot"""
    if not hasattr(self, "brain"):
        self.init()

    self.brain.start(**kwargs)

    if __debug__:
        logger.debug("Bot %s started with %d datas", self._name, len(self.datas))

start_bot classmethod ¤

start_bot(
    bot: LetTradeBot | None = None, **kwargs
) -> LetTradeBot

Init and start bot object

Parameters:

  • bot (LetTradeBot | None, default: None ) –

    description. Defaults to None.

Returns:

Source code in lettrade/bot.py 
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@classmethod
def start_bot(
    cls,
    bot: "LetTradeBot | None" = None,
    **kwargs,
) -> "LetTradeBot":
    """Init and start bot object

    Args:
        bot (LetTradeBot | None, optional): _description_. Defaults to None.

    Returns:
        LetTradeBot: _description_
    """
    if bot is None:
        bot = cls.new_bot(**kwargs)
    bot.init()
    bot.start(**kwargs.get("start_kwargs", {}))
    return bot

stop ¤

stop()

Stop bot

Source code in lettrade/bot.py 
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def stop(self):
    """Stop bot"""
    self.brain.stop()
    if self.plotter is not None:
        self.plotter.stop()

LetTradeMetaTrader ¤

LetTradeMetaTrader(
    feeder: type[
        MetaTraderDataFeeder
    ] = MetaTraderDataFeeder,
    exchange: type[MetaTraderExchange] = MetaTraderExchange,
    account: type[MetaTraderAccount] = MetaTraderAccount,
    **kwargs
)

Bases: LetTradeLive

Help to maintain MetaTrader bots

Parameters:

Source code in lettrade/exchange/metatrader/metatrader.py 
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def __init__(
    self,
    feeder: type[MetaTraderDataFeeder] = MetaTraderDataFeeder,
    exchange: type[MetaTraderExchange] = MetaTraderExchange,
    account: type[MetaTraderAccount] = MetaTraderAccount,
    **kwargs,
) -> None:
    """_summary_

    Args:
        feeder (Type[MetaTraderDataFeeder], optional): _description_. Defaults to MetaTraderDataFeeder.
        exchange (Type[MetaTraderExchange], optional): _description_. Defaults to MetaTraderExchange.
        account (Type[MetaTraderAccount], optional): _description_. Defaults to MetaTraderAccount.
    """
    super().__init__(
        feeder=feeder,
        exchange=exchange,
        account=account,
        **kwargs,
    )

plot ¤

plot(*args, jump: dict | None = None, **kwargs)

Plot strategy/optimize result

BotPlotter

Miror of BotPlotter.plot(). Plotly implement PlotlyBotPlotter.plot().

Args: jump (dict | None, optional): Miror of BotPlotter.jump()

Example: Jump to position_id 

lt.plot(
    jump=dict(position_id=1, range=300),
    layout=dict(height=2000),
)

OptimizePlotter

Miror of OptimizePlotter.plot(). Plotly implement PlotlyOptimizePlotter.plot().

Example: 

lt.plot(layout=dict(height=2000))

Source code in lettrade/lettrade.py 
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def plot(self, *args, jump: dict | None = None, **kwargs):
    """Plot strategy/optimize result

    BotPlotter:
        Miror of [BotPlotter.plot()](site:/reference/plot/bot/#lettrade.plot.bot.BotPlotter.plot).
        Plotly implement [PlotlyBotPlotter.plot()](site:/reference/plot/plotly/plotly/#lettrade.plot.plotly.plotly.PlotlyBotPlotter.plot).

        Args:
            `jump` (dict | None, optional): Miror of [BotPlotter.jump()](site:/reference/plot/bot/#lettrade.plot.bot.BotPlotter.jump)

        Example:
            Jump to position_id
                ```python
                lt.plot(
                    jump=dict(position_id=1, range=300),
                    layout=dict(height=2000),
                )
                ```

    OptimizePlotter:
        Miror of [OptimizePlotter.plot()](site:/reference/plot/optimize/#lettrade.plot.optimize.OptimizePlotter.plot).
        Plotly implement [PlotlyOptimizePlotter.plot()](site:/reference/exchange/backtest/plotly/optimize/#lettrade.exchange.backtest.plotly.optimize.PlotlyOptimizePlotter.plot).

        Example:
                ```python
                lt.plot(layout=dict(height=2000))
                ```
    """
    if __debug__:
        from .utils.docs import is_docs_session

        if is_docs_session():
            return

    self.plotter.plot(*args, jump=jump, **kwargs)

run ¤

run(worker: int | None = None, **kwargs)

Run LetTrade in single or multiple processing

Parameters:

  • worker (int | None, default: None ) –

    Number of processing. Defaults to None.

Source code in lettrade/lettrade.py 
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def run(self, worker: int | None = None, **kwargs):
    """Run LetTrade in single or multiple processing

    Args:
        worker (int | None, optional): Number of processing. Defaults to None.
    """
    if worker or isinstance(self.data, list):
        if not isinstance(self.data, list):
            # self.data = self.datas
            self.datas = [self.data]

        worker = self._max_workers(worker)

        self._multiprocess()

        datas_source = self._kwargs.pop("datas")
        with ProcessPoolExecutor(max_workers=worker) as executor:
            futures = [
                executor.submit(
                    self._bot_cls.run_bot,
                    datas=datas,
                    id=i,
                    result="str",
                    **self._kwargs,
                    **kwargs,
                )
                for i, datas in enumerate(datas_source)
            ]
            for future in futures:
                result = future.result()
                print(result)
    else:
        self._bot = self._bot_cls.run_bot(
            bot=self._bot,
            result="bot",
            **self._kwargs,
            **kwargs,
        )
        print(str(self._bot.stats))

start ¤

start(force: bool = False)

Start LetTrade by init bot object and loading datafeeds

Parameters:

  • force (bool, default: False ) –

    description. Defaults to False.

Source code in lettrade/lettrade.py 
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def start(self, force: bool = False):
    """Start LetTrade by init bot object and loading datafeeds

    Args:
        force (bool, optional): _description_. Defaults to False.
    """
    if force and self._bot is not None:
        self._bot = None

    self._bot = self._bot_cls.start_bot(bot=self._bot, **self._kwargs)

stop ¤

stop()

Stop LetTrade

Source code in lettrade/lettrade.py 
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def stop(self):
    """Stop LetTrade"""
    if self._bot is not None:
        return self._bot.stop()
    if self.stats:
        self.stats.stop()

LetTradeMetaTraderBot ¤

LetTradeMetaTraderBot(
    api: LiveAPI | None = LiveAPI, **kwargs
)

Bases: LetTradeLiveBot

LetTradeBot for MetaTrader

Parameters:

  • api (LiveAPI | None, default: LiveAPI ) –

    description. Defaults to LiveAPI.

Source code in lettrade/exchange/live/live.py 
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def __init__(self, api: LiveAPI | None = LiveAPI, **kwargs) -> None:
    """_summary_

    Args:
        api (LiveAPI | None, optional): _description_. Defaults to LiveAPI.
    """
    super().__init__(**kwargs)

    if issubclass(api, LiveAPI):
        self._api = api(**self._kwargs.get("api_kwargs", {}))
    else:
        self._api = api

    self._kwargs.setdefault("feeder_kwargs", dict()).update(api=self._api)
    self._kwargs.setdefault("exchange_kwargs", dict()).update(api=self._api)
    self._kwargs.setdefault("account_kwargs", dict()).update(api=self._api)

    for data in self.datas:
        data._api = self._api

account instance-attribute ¤

account: Account

Trading account handler

brain instance-attribute ¤

brain: Brain

Brain of bot

commander class-attribute instance-attribute ¤

commander: Commander | None = None

Control the bot

data instance-attribute ¤

data: DataFeed = datas[0]

Main DataFeed for bot

exchange instance-attribute ¤

exchange: Exchange

Trading exchange and events

feeder instance-attribute ¤

feeder: DataFeeder

DataFeeder help to handle datas

plotter class-attribute instance-attribute ¤

plotter: Plotter | None = None

Plot graphic results

strategy instance-attribute ¤

strategy: Strategy

Strategy

new_bot classmethod ¤

new_bot(
    datas: list[DataFeed],
    strategy_cls: type[Strategy],
    feeder_cls: type[DataFeeder],
    exchange_cls: type[Exchange],
    account_cls: type[Account],
    commander_cls: type[Commander],
    plotter_cls: type[Plotter],
    stats_cls: type[BotStatistic],
    name: str | None = None,
    **kwargs
) -> LetTradeBot

Create new bot object

Parameters:

Returns:

Source code in lettrade/bot.py 
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@classmethod
def new_bot(
    cls,
    datas: list[DataFeed],
    strategy_cls: type[Strategy],
    feeder_cls: type[DataFeeder],
    exchange_cls: type[Exchange],
    account_cls: type[Account],
    commander_cls: type[Commander],
    plotter_cls: type[Plotter],
    stats_cls: type[BotStatistic],
    name: str | None = None,
    **kwargs,
) -> "LetTradeBot":
    """Create new bot object

    Args:
        datas (list[DataFeed]): _description_
        strategy_cls (type[Strategy]): _description_
        feeder_cls (type[DataFeeder]): _description_
        exchange_cls (type[Exchange]): _description_
        account_cls (type[Account]): _description_
        commander_cls (type[Commander]): _description_
        plotter_cls (type[Plotter]): _description_
        stats_cls (type[BotStatistic]): _description_
        name (str | None, optional): _description_. Defaults to None.

    Returns:
        LetTradeBot: _description_
    """
    bot = cls(
        strategy=strategy_cls,
        datas=datas,
        feeder=feeder_cls,
        exchange=exchange_cls,
        account=account_cls,
        commander=commander_cls,
        plotter=plotter_cls,
        stats=stats_cls,
        name=name,
        **kwargs,
    )
    return bot

plot ¤

plot(*args, **kwargs)

Plot bot result

Source code in lettrade/bot.py 
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def plot(self, *args, **kwargs):
    """Plot bot result"""
    if __debug__:
        from .utils.docs import is_docs_session

        if is_docs_session():
            return

    self.plotter.plot(*args, **kwargs)

run ¤

run()

Run bot

Source code in lettrade/bot.py 
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def run(self):
    """Run bot"""
    if self.commander:
        self.commander.start()

    self.brain.run()

    if self.commander:
        self.commander.stop()

    if self._stats_cls:
        self.stats.compute()

run_bot classmethod ¤

run_bot(
    bot: LetTradeBot | None = None,
    datas: list[DataFeed] | None = None,
    id: int | None = None,
    name: str | None = None,
    result: Literal["str", "stats", "bot", None] = "str",
    **kwargs
) -> LetTradeBot | BotStatistic | str | None

Run bot object

Parameters:

  • bot (LetTradeBot | None, default: None ) –

    description. Defaults to None.

  • datas (list[DataFeed] | None, default: None ) –

    description. Defaults to None.

  • id (int | None, default: None ) –

    description. Defaults to None.

  • name (str | None, default: None ) –

    description. Defaults to None.

  • result (Literal['str', 'stats', 'bot', None], default: 'str' ) –

    description. Defaults to "str".

Returns:

  • LetTradeBot | BotStatistic | str | None –

    LetTradeBot | BotStatistic | str | None: Return value will be pickle across multiprocessing. The cost higher from str, stats object, bot object

Source code in lettrade/bot.py 
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@classmethod
def run_bot(
    cls,
    bot: "LetTradeBot | None" = None,
    datas: list[DataFeed] | None = None,
    id: int | None = None,
    name: str | None = None,
    result: Literal["str", "stats", "bot", None] = "str",
    **kwargs,
) -> "LetTradeBot | BotStatistic | str | None":
    """Run bot object

    Args:
        bot (LetTradeBot | None, optional): _description_. Defaults to None.
        datas (list[DataFeed] | None, optional): _description_. Defaults to None.
        id (int | None, optional): _description_. Defaults to None.
        name (str | None, optional): _description_. Defaults to None.
        result (Literal["str", "stats", "bot", None], optional): _description_. Defaults to "str".

    Returns:
        LetTradeBot | BotStatistic | str | None: Return value will be pickle across multiprocessing.
            The cost higher from `str`, `stats` object, `bot` object
    """
    # Set name for current processing
    if name is None:
        d = datas[0] if datas else bot.data
        name = f"{id}-{os.getpid()}-{d.name}"

    if bot is None:
        bot = cls.start_bot(
            datas=datas,
            name=name,
            **kwargs,
        )

    # bot
    bot.run(**kwargs.get("run_kwargs", {}))

    # Return type
    if result == "stats":
        return bot.stats
    if result == "str":
        return str(bot.stats)

    return bot

start ¤

start(**kwargs)

Start bot

Source code in lettrade/bot.py 
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def start(self, **kwargs):
    """Start bot"""
    if not hasattr(self, "brain"):
        self.init()

    self.brain.start(**kwargs)

    if __debug__:
        logger.debug("Bot %s started with %d datas", self._name, len(self.datas))

start_bot classmethod ¤

start_bot(
    bot: LetTradeBot | None = None, **kwargs
) -> LetTradeBot

Init and start bot object

Parameters:

  • bot (LetTradeBot | None, default: None ) –

    description. Defaults to None.

Returns:

Source code in lettrade/bot.py 
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@classmethod
def start_bot(
    cls,
    bot: "LetTradeBot | None" = None,
    **kwargs,
) -> "LetTradeBot":
    """Init and start bot object

    Args:
        bot (LetTradeBot | None, optional): _description_. Defaults to None.

    Returns:
        LetTradeBot: _description_
    """
    if bot is None:
        bot = cls.new_bot(**kwargs)
    bot.init()
    bot.start(**kwargs.get("start_kwargs", {}))
    return bot

stop ¤

stop()

Stop bot

Source code in lettrade/bot.py 
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def stop(self):
    """Stop bot"""
    self.brain.stop()
    if self.plotter is not None:
        self.plotter.stop()

LiveAPI ¤

LiveAPI(**kwargs)

Bases: ABC

Source code in lettrade/exchange/live/api.py 
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def __init__(self, **kwargs):
    """"""

account abstractmethod ¤

account() -> dict
Source code in lettrade/exchange/live/api.py 
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@abstractmethod
def account(self) -> dict:
    """"""

bars abstractmethod ¤

bars(
    symbol,
    timeframe,
    since: int | datetime | None = 0,
    to: int | datetime | None = 1000,
) -> list[list]
Source code in lettrade/exchange/live/api.py 
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@abstractmethod
def bars(
    self,
    symbol,
    timeframe,
    since: int | datetime | None = 0,
    to: int | datetime | None = 1_000,
) -> list[list]:
    """"""

execution_get abstractmethod ¤

execution_get(id: str, **kwargs) -> dict
Source code in lettrade/exchange/live/api.py 
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@abstractmethod
def execution_get(self, id: str, **kwargs) -> dict:
    """"""

executions_get abstractmethod ¤

executions_get(
    position_id: str | None = None,
    search: str | None = None,
    **kwargs
) -> list[dict]
Source code in lettrade/exchange/live/api.py 
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@abstractmethod
def executions_get(
    self,
    position_id: str | None = None,
    search: str | None = None,
    **kwargs,
) -> list[dict]:
    """"""

executions_total abstractmethod ¤

executions_total(
    since: datetime | None = None,
    to: datetime | None = None,
    **kwargs
) -> int
Source code in lettrade/exchange/live/api.py 
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@abstractmethod
def executions_total(
    self,
    since: datetime | None = None,
    to: datetime | None = None,
    **kwargs,
) -> int:
    """"""

heartbeat ¤

heartbeat() -> bool
Source code in lettrade/exchange/live/api.py 
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def heartbeat(self) -> bool:
    """"""
    return True

init ¤

init(**kwargs)
Source code in lettrade/exchange/live/api.py 
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def init(self, **kwargs):
    """"""

market abstractmethod ¤

market(symbol: str) -> dict
Source code in lettrade/exchange/live/api.py 
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@abstractmethod
def market(self, symbol: str) -> dict:
    """"""

markets abstractmethod ¤

markets(symbols: list[str]) -> dict
Source code in lettrade/exchange/live/api.py 
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@abstractmethod
def markets(self, symbols: list[str]) -> dict:
    """"""

multiprocess classmethod ¤

multiprocess(kwargs: dict, **other_kwargs)
Source code in lettrade/exchange/live/api.py 
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@classmethod
def multiprocess(cls, kwargs: dict, **other_kwargs):
    """"""

next ¤

next()
Source code in lettrade/exchange/live/api.py 
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def next(self):
    """"""

order_close abstractmethod ¤

order_close(order: LiveOrder, **kwargs) -> dict
Source code in lettrade/exchange/live/api.py 
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@abstractmethod
def order_close(self, order: "LiveOrder", **kwargs) -> dict:
    """"""

order_open abstractmethod ¤

order_open(**kwargs) -> dict
Source code in lettrade/exchange/live/api.py 
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@abstractmethod
def order_open(self, **kwargs) -> dict:
    """"""

order_update abstractmethod ¤

order_update(
    order: LiveOrder, sl=None, tp=None, **kwargs
) -> dict
Source code in lettrade/exchange/live/api.py 
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@abstractmethod
def order_update(self, order: "LiveOrder", sl=None, tp=None, **kwargs) -> dict:
    """"""

orders_get abstractmethod ¤

orders_get(
    id: str | None = None,
    symbol: str | None = None,
    **kwargs
)
Source code in lettrade/exchange/live/api.py 
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@abstractmethod
def orders_get(
    self,
    id: str | None = None,
    symbol: str | None = None,
    **kwargs,
):
    """"""

orders_history_get ¤

orders_history_get(
    id: str | None = None,
    since: datetime | None = None,
    to: datetime | None = None,
    **kwargs
) -> list[dict]
Source code in lettrade/exchange/live/api.py 
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def orders_history_get(
    self,
    id: str | None = None,
    since: datetime | None = None,
    to: datetime | None = None,
    **kwargs,
) -> list[dict]:
    """"""

orders_total abstractmethod ¤

orders_total(
    since: datetime | None = None,
    to: datetime | None = None,
    **kwargs
) -> int
Source code in lettrade/exchange/live/api.py 
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@abstractmethod
def orders_total(
    self,
    since: datetime | None = None,
    to: datetime | None = None,
    **kwargs,
) -> int:
    """"""

position_close abstractmethod ¤

position_close(position: LivePosition, **kwargs) -> dict
Source code in lettrade/exchange/live/api.py 
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@abstractmethod
def position_close(self, position: "LivePosition", **kwargs) -> dict:
    """"""

position_update abstractmethod ¤

position_update(
    position: LivePosition,
    sl: float | None = None,
    tp: float | None = None,
    **kwargs
) -> dict
Source code in lettrade/exchange/live/api.py 
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@abstractmethod
def position_update(
    self,
    position: "LivePosition",
    sl: float | None = None,
    tp: float | None = None,
    **kwargs,
) -> dict:
    """"""

positions_get abstractmethod ¤

positions_get(
    id: str = None, symbol: str = None, **kwargs
) -> list[dict]
Source code in lettrade/exchange/live/api.py 
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@abstractmethod
def positions_get(self, id: str = None, symbol: str = None, **kwargs) -> list[dict]:
    """"""

positions_total abstractmethod ¤

positions_total(
    since: datetime | None = None,
    to: datetime | None = None,
    **kwargs
) -> int
Source code in lettrade/exchange/live/api.py 
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@abstractmethod
def positions_total(
    self,
    since: datetime | None = None,
    to: datetime | None = None,
    **kwargs,
) -> int:
    """"""

start ¤

start(exchange: LiveExchange)
Source code in lettrade/exchange/live/api.py 
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def start(self, exchange: "LiveExchange"):
    """"""

stop ¤

stop()
Source code in lettrade/exchange/live/api.py 
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def stop(self):
    """"""

tick_get abstractmethod ¤

tick_get(symbol: str) -> dict
Source code in lettrade/exchange/live/api.py 
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@abstractmethod
def tick_get(self, symbol: str) -> dict:
    """"""

LiveAccount ¤

LiveAccount(api: LiveAPI, **kwargs)

Bases: Account

Parameters:

Source code in lettrade/exchange/live/account.py 
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def __init__(self, api: LiveAPI, **kwargs) -> None:
    """Account for live trading

    Args:
        api (LiveAPI): _description_
        **kwargs (dict, optional): Mirror of [lettrade.account.Account()](site:/reference/account/account/#lettrade.account.account.Account).
    """
    super().__init__(**kwargs)
    self._api = api

    self._balance = 0.0
    self._equity = 0.0

balance property ¤

balance: float

Balance of account

Returns:

  • float ( float ) –

    description

account_refresh ¤

account_refresh()

Refresh account balance

Source code in lettrade/exchange/live/account.py 
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def account_refresh(self):
    """Refresh account balance"""
    self._account = self._api.account()

    if __debug__:
        logger.debug("Account: %s", str(self._account))

    self._balance = self._account.balance
    self._equity = self._account.equity

next ¤

next()

Next account

Source code in lettrade/account/account.py 
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def next(self):
    """Next account"""

next_next ¤

next_next()

Call after strategy.next()

Source code in lettrade/account/account.py 
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def next_next(self):
    """Call after strategy.next()"""
    self._equity_snapshot()

risk ¤

risk(side: TradeSide, size: float, **kwargs) -> float

Risk calculation

Source code in lettrade/account/account.py 
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def risk(self, side: "TradeSide", size: float, **kwargs) -> float:
    """Risk calculation"""
    if size is None:
        return side * abs(self._risk)
    return side * abs(size)

start ¤

start()

Start live account by load account info from API

Source code in lettrade/exchange/live/account.py 
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def start(self):
    """Start live account by load account info from API"""
    self.account_refresh()
    self._margin = self._account.margin
    self._leverage = self._account.leverage

stop ¤

stop()

Stop account

Source code in lettrade/account/account.py 
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def stop(self):
    """Stop account"""
    try:
        self._equity_snapshot()
    except LetAccountInsufficientException:
        pass

LiveDataFeed ¤

LiveDataFeed(
    symbol: str,
    timeframe: str | int | Timedelta,
    name: str | None = None,
    columns: list[str] | None = None,
    api: LiveAPI | None = None,
    api_kwargs: dict | None = None,
    **kwargs
)

Bases: DataFeed

Live trading DataFeed

Parameters:

  • symbol (str) –

    Symbol of DataFeed

  • timeframe (str | int | Timedelta) –

    TimeFrame of DataFeed

  • name (str | None, default: None ) –

    Name of DataFeed, auto generate {symbol}_{timeframe} if none. Defaults to None.

  • columns (list[str] | None, default: None ) –

    List of DataFeed columns. Defaults to None.

  • api (LiveAPI | None, default: None ) –

    Live trading API. Defaults to None.

Source code in lettrade/exchange/live/data.py 
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def __init__(
    self,
    symbol: str,
    timeframe: str | int | pd.Timedelta,
    name: str | None = None,
    columns: list[str] | None = None,
    api: LiveAPI | None = None,
    api_kwargs: dict | None = None,
    **kwargs,
) -> None:
    """_summary_

    Args:
        symbol (str): Symbol of DataFeed
        timeframe (str | int | pd.Timedelta): TimeFrame of DataFeed
        name (str | None, optional): Name of DataFeed, auto generate `{symbol}_{timeframe}` if none. Defaults to None.
        columns (list[str] | None, optional): List of DataFeed columns. Defaults to None.
        api (LiveAPI | None, optional): Live trading API. Defaults to None.
    """
    super().__init__(
        name=name or f"{symbol}_{timeframe}",
        timeframe=timeframe,
        columns=columns or ["open", "high", "low", "close", "volume"],
        **kwargs,
    )

    self.meta.update(symbol=symbol, base_columns=self.columns.copy())

    if api is not None:
        self._api = api
    elif api_kwargs is not None:
        self._api = self._api_cls(**api_kwargs)
    else:
        # raise RuntimeError("Parameter: api or api_kwargs cannot missing")
        logger.info("Parameter: api or api_kwargs cannot missing")

i property ¤

i: indicator

Alias to lettrade.indicator and using in DataFeed by call: DataFeed.i.indicator_name()

is_main property ¤

is_main: bool

Property to check DataFeed is main DataFeed or not

l instance-attribute ¤

l: LetDataFeedWrapper

LetTrade DataFeed wrapper using to manage index pointer of DataFeed

meta property ¤

meta: dict

Property to get metadata of DataFeed

name property ¤

name: str

Property to get name of DataFeed

now property ¤

now: Timestamp

Property to get current index value of DataFeed

symbol property ¤

symbol: str

Property to get symbol of DataFeed

timeframe property ¤

timeframe: TimeFrame

Property to get timeframe of DataFeed

bar ¤

bar(i: int = 0) -> Timestamp

Get current pd.Timestamp value of DataFeed

Parameters:

  • i (int, default: 0 ) –

    Index. Defaults to 0.

Returns:

  • Timestamp –

    pd.Timestamp: description

Source code in lettrade/data/data.py 
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def bar(self, i: int = 0) -> pd.Timestamp:
    """Get current pd.Timestamp value of DataFeed

    Args:
        i (int, optional): Index. Defaults to 0.

    Returns:
        pd.Timestamp: _description_
    """
    return self.l.index[i]

bars ¤

bars(
    since: int | str | Timestamp, to: int | str | Timestamp
) -> list

Get bars from LiveAPI

Parameters:

  • since (int | str | Timestamp) –

    description

  • to (int | str | Timestamp) –

    description

Returns:

  • list ( list ) –

    list of bar

Source code in lettrade/exchange/live/data.py 
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def bars(
    self,
    since: int | str | pd.Timestamp,
    to: int | str | pd.Timestamp,
) -> list:
    """Get bars from LiveAPI

    Args:
        since (int | str | pd.Timestamp): _description_
        to (int | str | pd.Timestamp): _description_

    Returns:
        list: list of bar
    """
    return self._api.bars(
        symbol=self.symbol,
        timeframe=self.timeframe.string,
        since=since,
        to=to,
    )

bars_load ¤

bars_load(
    since: int | str | Timestamp, to: int | str | Timestamp
) -> bool

Get bar from API and push to DataFeed

Parameters:

  • since (int | str | Timestamp) –

    description

  • to (int | str | Timestamp) –

    description

Returns:

  • bool ( bool ) –

    True if has data, False if no data

Source code in lettrade/exchange/live/data.py 
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def bars_load(
    self,
    since: int | str | pd.Timestamp,
    to: int | str | pd.Timestamp,
) -> bool:
    """Get bar from API and push to DataFeed

    Args:
        since (int | str | pd.Timestamp): _description_
        to (int | str | pd.Timestamp): _description_

    Returns:
        bool: True if has data, False if no data
    """
    bars = self.bars(since=since, to=to)

    if bars is None or len(bars) == 0:
        logger.warning("No bars data for %s", self.name)
        return False

    self.push(bars, unit=self._bar_datetime_unit)
    return True

drop ¤

drop(
    *args,
    since: int | str | Timestamp | None = None,
    to: int | str | Timestamp | None = None,
    **kwargs
) -> None | DataFeed

summary

Parameters:

  • since (int | str | Timestamp | None, default: None ) –

    description. Defaults to None.

  • to (int | str | Timestamp | None, default: None ) –

    description. Defaults to None.

Raises:

Source code in lettrade/data/data.py 
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def drop(
    self,
    *args,
    since: int | str | pd.Timestamp | None = None,
    to: int | str | pd.Timestamp | None = None,
    **kwargs,
) -> "None | DataFeed":
    """_summary_

    Args:
        since (int | str | pd.Timestamp | None, optional): _description_. Defaults to None.
        to (int | str | pd.Timestamp | None, optional): _description_. Defaults to None.

    Raises:
        RuntimeError: _description_
    """
    if since is None and to is None:
        return super().drop(*args, **kwargs)

    condiction = None

    # Since
    if since is not None:
        if isinstance(since, int):
            loc = self.l.index[since]
        elif isinstance(since, str):
            loc = pd.to_datetime(since, utc=True)
        elif isinstance(since, pd.Timestamp):
            loc = since
        else:
            raise RuntimeError(f"DataFeed.drop since {since} is invalid")
        condiction = self.index < loc

    # To
    if to is not None:
        if isinstance(to, int):
            loc = self.l.index[to]
        elif isinstance(to, str):
            loc = pd.to_datetime(to, utc=True)
        elif isinstance(to, pd.Timestamp):
            loc = to
        else:
            raise RuntimeError(f"DataFeed.drop to {to} is invalid")

        if condiction is None:
            condiction = self.index > loc
        else:
            condiction = condiction | (self.index > loc)

    index = self[condiction].index
    super().drop(index=index, inplace=True)
    self.l.reset()

    if __debug__:
        logger.debug("BackTestDataFeed %s dropped %s rows", self.name, len(index))

dump_csv ¤

dump_csv(
    path: str | None = None,
    since: int | str | datetime | None = 0,
    to: int | str | datetime | None = 1000,
    **kwargs
)

summary

Parameters:

  • path (str | None, default: None ) –

    description. Defaults to None.

  • since (int | str | datetime | None, default: 0 ) –

    description. Defaults to 0.

  • to (int | str | datetime | None, default: 1000 ) –

    description. Defaults to 1_000.

Source code in lettrade/exchange/live/data.py 
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def dump_csv(
    self,
    path: str | None = None,
    since: int | str | datetime | None = 0,
    to: int | str | datetime | None = 1_000,
    **kwargs,
):
    """_summary_

    Args:
        path (str | None, optional): _description_. Defaults to None.
        since (int  |  str  |  datetime | None, optional): _description_. Defaults to 0.
        to (int  |  str  |  datetime | None, optional): _description_. Defaults to 1_000.
    """
    if self.empty:
        if isinstance(since, str):
            since = pd.to_datetime(since).to_pydatetime()
        if isinstance(to, str):
            to = pd.to_datetime(to).to_pydatetime()

        self.bars_load(since=since, to=to)

    if path is None:
        path = f"data/{self.name}-{since}_{to}.csv"

    from lettrade.data.extra.csv import csv_export

    csv_export(dataframe=self, path=path, **kwargs)

next ¤

next(size=1, tick=0) -> bool

Drop extra columns and load next DataFeed

Parameters:

  • size (int, default: 1 ) –

    description. Defaults to 1.

  • tick (int, default: 0 ) –

    description. Defaults to 0.

Returns:

  • bool ( bool ) –

    description

Source code in lettrade/exchange/live/data.py 
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def next(self, size=1, tick=0) -> bool:
    """Drop extra columns and load next DataFeed

    Args:
        size (int, optional): _description_. Defaults to 1.
        tick (int, optional): _description_. Defaults to 0.

    Returns:
        bool: _description_
    """
    # Drop existed extra columns to skip reusing calculated data
    self.drop(columns=self.columns.difference(self._base_columns), inplace=True)

    self.bars_load(since=0, to=size + 1)
    self.l.go_stop()
    return True

push ¤

push(
    rows: list[list[int | float]],
    unit: str | None = None,
    utc: bool = True,
    **kwargs
)

Push new rows to DataFeed

Parameters:

  • rows (list[list[int | float]]) –

    list of rows [["timestamp", "open price", "high price"...]]

  • unit (str | None, default: None ) –

    pandas.Timestamp parsing unit. Defaults to None.

  • utc (bool, default: True ) –

    description. Defaults to True.

Source code in lettrade/data/data.py 
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def push(
    self,
    rows: list[list[int | float]],
    unit: str | None = None,
    utc: bool = True,
    **kwargs,
):
    """Push new rows to DataFeed

    Args:
        rows (list[list[int | float]]): list of rows `[["timestamp", "open price", "high price"...]]`
        unit (str | None, optional): pandas.Timestamp parsing unit. Defaults to None.
        utc (bool, optional): _description_. Defaults to True.
    """
    for row in rows:
        dt = pd.to_datetime(row[0], unit=unit, utc=utc, **kwargs)
        self.at[
            dt,
            (
                "open",
                "high",
                "low",
                "close",
                "volume",
            ),
        ] = (
            row[1],  # open
            row[2],  # high
            row[3],  # low
            row[4],  # close
            row[5],  # volume
        )

    if __debug__:
        logger.debug("[%s] Update bar: \n%s", self.name, self.tail(len(rows)))

symbol_info ¤

symbol_info()

Get symbol information from API

Source code in lettrade/exchange/live/data.py 
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def symbol_info(self):
    """Get symbol information from API"""
    return self._api.market(symbol=self.symbol)

LiveDataFeeder ¤

LiveDataFeeder(
    api: LiveAPI | None = None,
    tick: bool = 5,
    start_size: int = 500,
    api_kwargs: dict | None = None,
    **kwargs
)

Bases: DataFeeder

tick == 0: wait until new bar change value
tick > 0: sleep tick time (in seconds) then update
Source code in lettrade/exchange/live/feeder.py 
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def __init__(
    self,
    api: LiveAPI | None = None,
    tick: bool = 5,
    start_size: int = 500,
    api_kwargs: dict | None = None,
    **kwargs,
) -> None:
    """
    tick:
        tick < 0: no tick, just get completed bar
        tick == 0: wait until new bar change value
        tick > 0: sleep tick time (in seconds) then update
    """
    super().__init__()

    # API init
    if api is None:
        if api_kwargs is None:
            raise RuntimeError("api or api_kwargs cannot missing")
        api = self._api_cls(**api_kwargs)

    self._api = api
    self._tick = tick
    self._start_size = start_size
    self._config = kwargs

    if isinstance(self._tick, int):
        self._wait_timeframe = TimeFrame(f"{self._tick}s")
    else:
        self._wait_timeframe = None

is_continous property ¤

is_continous

Flag check is realtime continous datafeeder

LiveExchange ¤

LiveExchange(api: LiveAPI, *args, **kwargs)

Bases: Exchange

MetaTrade 5 exchange module for lettrade

Parameters:

  • api (LiveAPI) –

    API connect to rpyc MeTrader 5 Terminal server through module mt5linux

  • *args (list, default: () ) –

    Exchange list parameters

  • **kwargs (dict, default: {} ) –

    Exchange dict parameters

Source code in lettrade/exchange/live/exchange.py 
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def __init__(self, api: LiveAPI, *args, **kwargs):
    """_summary_

    Args:
        api (LiveAPI): API connect to rpyc MeTrader 5 Terminal server through module `mt5linux`
        *args (list): `Exchange` list parameters
        **kwargs (dict): `Exchange` dict parameters
    """
    super().__init__(*args, **kwargs)
    self._api = api

data instance-attribute ¤

data: DataFeed

main DataFeed

datas instance-attribute ¤

datas: list[DataFeed]

List of all available DataFeed

executions instance-attribute ¤

executions: dict[str, Execution] = dict()

Execution dict by Execution.id key

history_orders instance-attribute ¤

history_orders: dict[str, Order] = dict()

History Order dict by Order.id key

history_positions instance-attribute ¤

history_positions: dict[str, Position] = dict()

History Position dict by Position.id key

orders instance-attribute ¤

orders: dict[str, Order] = dict()

Available Order dict by Order.id key

positions instance-attribute ¤

positions: dict[str, Position] = dict()

Available Position dict by Position.id key

init ¤

init(
    brain: Brain,
    feeder: DataFeeder,
    account: Account,
    commander: Commander,
) -> None

Init Exchange dependencies. Set data/datas from feeder. Set state to ExchangeState.Start.

Parameters:

Source code in lettrade/exchange/exchange.py 
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def init(
    self,
    brain: "Brain",
    feeder: DataFeeder,
    account: Account,
    commander: Commander,
) -> None:
    """Init Exchange dependencies.
    Set data/datas from feeder.
    Set state to `ExchangeState.Start`.

    Args:
        brain (Brain): _description_
        feeder (DataFeeder): _description_
        account (Account): _description_
        commander (Commander): _description_
    """
    self._brain = brain
    self._feeder = feeder
    self._account = account
    self._commander = commander

    self.data = self._feeder.data
    self.datas = self._feeder.datas

    self._account.init(exchange=self)

    self._state = ExchangeState.Start

new_order ¤

new_order(
    size: float,
    type: OrderType | None = OrderType.Market,
    limit: float | None = None,
    stop: float | None = None,
    sl: float | None = None,
    tp: float | None = None,
    tag: str | None = None,
    data: DataFeed | None = None,
    **kwargs
) -> OrderResult

Place new order to server

Parameters:

  • size (float) –

    description

  • type (OrderType | None, default: Market ) –

    description. Defaults to OrderType.Market.

  • limit (float | None, default: None ) –

    description. Defaults to None.

  • stop (float | None, default: None ) –

    description. Defaults to None.

  • sl (float | None, default: None ) –

    description. Defaults to None.

  • tp (float | None, default: None ) –

    description. Defaults to None.

  • tag (str | None, default: None ) –

    description. Defaults to None.

  • data (DataFeed | None, default: None ) –

    description. Defaults to None.

Returns:

Source code in lettrade/exchange/live/exchange.py 
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def new_order(
    self,
    size: float,
    type: OrderType | None = OrderType.Market,
    limit: float | None = None,
    stop: float | None = None,
    sl: float | None = None,
    tp: float | None = None,
    tag: str | None = None,
    data: DataFeed | None = None,
    **kwargs,
) -> OrderResult:
    """Place new order to server

    Args:
        size (float): _description_
        type (OrderType | None, optional): _description_. Defaults to OrderType.Market.
        limit (float | None, optional): _description_. Defaults to None.
        stop (float | None, optional): _description_. Defaults to None.
        sl (float | None, optional): _description_. Defaults to None.
        tp (float | None, optional): _description_. Defaults to None.
        tag (str | None, optional): _description_. Defaults to None.
        data (DataFeed | None, optional): _description_. Defaults to None.

    Returns:
        OrderResult: _description_
    """
    if data is None:
        data = self.data

    if type == OrderType.Market:
        limit = 0
        stop = 0

    order = self._order_cls(
        id=-1,
        exchange=self,
        data=data,
        size=size,
        type=type,
        limit_price=limit,
        stop_price=stop,
        sl_price=sl,
        tp_price=tp,
        tag=tag,
        **kwargs,
    )
    ok = order.place()

    if __debug__:
        logger.info("New order %s at %s", order, data.now)

    return ok

next_next ¤

next_next()

Call after strategy.next()

Source code in lettrade/exchange/exchange.py 
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def next_next(self):
    """Call after strategy.next()"""
    self._account.next_next()

on_execution ¤

on_execution(
    execution: Execution,
    broadcast: bool | None = True,
    **kwargs
) -> None

Receive Execution event from exchange then store and notify Brain

Parameters:

  • execution (Execution) –

    description

  • broadcast (bool | None, default: True ) –

    description. Defaults to True.

Source code in lettrade/exchange/exchange.py 
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def on_execution(
    self,
    execution: Execution,
    broadcast: bool | None = True,
    **kwargs,
) -> None:
    """Receive Execution event from exchange then store and notify Brain

    Args:
        execution (Execution): _description_
        broadcast (bool | None, optional): _description_. Defaults to True.
    """
    self.on_executions(executions=[execution], broadcast=broadcast, **kwargs)

on_executions ¤

on_executions(
    executions: list[Execution],
    broadcast: bool | None = True,
    **kwargs
) -> None

Receive Execution event from exchange then store and notify Brain

Source code in lettrade/exchange/exchange.py 
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def on_executions(
    self,
    executions: list[Execution],
    broadcast: bool | None = True,
    **kwargs,
) -> None:
    """
    Receive Execution event from exchange then store and notify Brain
    """
    for execution in executions:
        if not isinstance(execution, Execution):
            raise RuntimeError(f"{execution} is not instance of type Execution")

        if execution.id in self.executions:
            # Merge to keep Execution handler for strategy using
            # when strategy want to store Execution object
            # and object will be automatic update directly
            self.executions[execution.id].merge(execution)
            execution = self.executions[execution.id]
        else:
            self.executions[execution.id] = execution

    if self._state != ExchangeState.Run:
        return

    if broadcast:
        self._brain.on_executions(executions)

on_order ¤

on_order(
    order: Order, broadcast: bool | None = True, **kwargs
) -> None

Receive order event from exchange then store and notify Brain

Parameters:

  • order (Order) –

    description

  • broadcast (bool | None, default: True ) –

    description. Defaults to True.

Source code in lettrade/exchange/exchange.py 
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def on_order(
    self,
    order: Order,
    broadcast: bool | None = True,
    **kwargs,
) -> None:
    """Receive order event from exchange then store and notify Brain

    Args:
        order (Order): _description_
        broadcast (bool | None, optional): _description_. Defaults to True.
    """
    self.on_orders(orders=[order], broadcast=broadcast, **kwargs)

on_orders ¤

on_orders(
    orders: list[Order],
    broadcast: bool | None = True,
    **kwargs
) -> None

Receive orders event from exchange then store and notify Brain

Parameters:

  • orders (list[Order]) –

    description

  • broadcast (bool | None, default: True ) –

    description. Defaults to True.

Raises:

Source code in lettrade/exchange/exchange.py 
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def on_orders(
    self,
    orders: list[Order],
    broadcast: bool | None = True,
    **kwargs,
) -> None:
    """Receive orders event from exchange then store and notify Brain

    Args:
        orders (list[Order]): _description_
        broadcast (bool | None, optional): _description_. Defaults to True.

    Raises:
        RuntimeError: _description_
    """
    for order in orders:
        if not isinstance(order, Order):
            raise RuntimeError(f"{order} is not instance of type Order")

        if order.is_closed:
            if order.id in self.history_orders:
                logger.warning("Order closed recall: %s", order)

            self.history_orders[order.id] = order
            if order.id in self.orders:
                del self.orders[order.id]

            if __debug__:
                logger.info("Order closed: %s", order)
        else:
            if order.id in self.history_orders:
                raise RuntimeError(f"Order {order.id} closed")

            if order.id in self.orders:
                # Merge to keep Order handler for strategy using
                # when strategy want to store Order object
                # and object will be automatic update directly
                self.orders[order.id].merge(order)
                order = self.orders[order.id]

                if __debug__:
                    logger.info("Order updated: %s", order)
            else:
                self.orders[order.id] = order

                if __debug__:
                    logger.info("Order placed: %s", order)

    if self._state != ExchangeState.Run:
        return

    if broadcast:
        self._brain.on_orders(orders)

on_position ¤

on_position(
    position: Position,
    broadcast: bool | None = True,
    **kwargs
) -> None

Receive Position event from exchange then store and notify Brain

Parameters:

  • position (Position) –

    new comming Position

  • broadcast (bool | None, default: True ) –

    Flag notify to Brain. Defaults to True.

Raises:

Source code in lettrade/exchange/exchange.py 
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def on_position(
    self,
    position: Position,
    broadcast: bool | None = True,
    **kwargs,
) -> None:
    """Receive Position event from exchange then store and notify Brain

    Args:
        position (Position): new comming `Position`
        broadcast (bool | None, optional): Flag notify to Brain. Defaults to True.

    Raises:
        RuntimeError: validate `Position` instance
    """
    self.on_positions(positions=[position], broadcast=broadcast, **kwargs)

on_positions ¤

on_positions(
    positions: list[Position],
    broadcast: bool | None = True,
    **kwargs
) -> None

Receive Positions event from exchange then store and notify Brain

Parameters:

  • positions (list[Position]) –

    list of new comming Position

  • broadcast (bool | None, default: True ) –

    Flag notify to Brain. Defaults to True.

Raises:

Source code in lettrade/exchange/exchange.py 
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def on_positions(
    self,
    positions: list[Position],
    broadcast: bool | None = True,
    **kwargs,
) -> None:
    """Receive Positions event from exchange then store and notify Brain

    Args:
        positions (list[Position]): list of new comming `Position`
        broadcast (bool | None, optional): Flag notify to Brain. Defaults to True.

    Raises:
        RuntimeError: validate `Position` instance
    """
    for position in positions:
        if not isinstance(position, Position):
            raise RuntimeError(f"{position} is not instance of type Position")

        if position.is_exited:
            if position.id in self.history_positions:
                logger.warning("Position exited recall: %s", position)

            self.history_positions[position.id] = position
            if position.id in self.positions:
                del self.positions[position.id]

            # self._account._on_position_exit(position)
        else:
            if position.id in self.history_positions:
                raise RuntimeError(f"Position {position.id} closed: {position}")
            if position.id in self.positions:
                # Merge to keep Position handler for strategy using
                # when strategy want to store Position object
                # and object will be automatic update directly
                self.positions[position.id].merge(position)
                position = self.positions[position.id]
            else:
                self.positions[position.id] = position
                # self._account._on_position_entry(position)

    self._account.on_positions(positions)

    if self._state != ExchangeState.Run:
        return

    if broadcast:
        self._brain.on_positions(positions)

start ¤

start() -> None

Start Live exchange by: Sync positions from server

Source code in lettrade/exchange/live/exchange.py 
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def start(self) -> None:
    """Start Live exchange by: Sync positions from server"""
    self._api.start(exchange=self)
    return super().start()

stop ¤

stop() -> None

Stop Exchange

Source code in lettrade/exchange/exchange.py 
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def stop(self) -> None:
    """Stop Exchange"""
    self._state = ExchangeState.Stop
    self._account.stop()

MetaTraderAPI ¤

MetaTraderAPI(
    login: int,
    password: str,
    server: str,
    timeout: int = 60,
    retry: int = 20,
    host: str = "localhost",
    port: int = 18812,
    wine: str | None = None,
    path: str | None = None,
    magic: int = 88888888,
    **kwargs
)

Bases: LiveAPI

API to connect MetaTrader 5 Terminal

Parameters:

  • login (int) –

    description

  • password (str) –

    description

  • server (str) –

    description

  • timeout (int, default: 60 ) –

    description. Defaults to 60.

  • retry (int, default: 20 ) –

    description. Defaults to 20.

  • host (str, default: 'localhost' ) –

    description. Defaults to "localhost".

  • port (int, default: 18812 ) –

    description. Defaults to 18812.

  • wine (str | None, default: None ) –

    description. Defaults to None.

  • magic (int, default: 88888888 ) –

    description. Defaults to 88888888.

Raises:

Source code in lettrade/exchange/metatrader/api.py 
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def __init__(
    self,
    login: int,
    password: str,
    server: str,
    timeout: int = 60,
    retry: int = 20,
    host: str = "localhost",
    port: int = 18812,
    wine: str | None = None,
    path: str | None = None,
    magic: int = 88888888,
    **kwargs,
):
    """_summary_

    Args:
        login (int): _description_
        password (str): _description_
        server (str): _description_
        timeout (int, optional): _description_. Defaults to 60.
        retry (int, optional): _description_. Defaults to 20.
        host (str, optional): _description_. Defaults to "localhost".
        port (int, optional): _description_. Defaults to 18812.
        wine (str | None, optional): _description_. Defaults to None.
        magic (int, optional): _description_. Defaults to 88888888.

    Raises:
        ConnectionRefusedError: _description_
        RuntimeError: _description_
    """
    # Parameters
    self._config = kwargs
    self._magic = magic

    self._load_history_since = datetime.now() - timedelta(days=7)
    self._deal_time_checked = datetime.now() - timedelta(days=1)
    self._orders_stored = dict()
    self._executions_stored = dict()
    self._positions_stored = dict()

    # Update config
    self._config.update(
        host=host,
        port=port,
        login=int(login),
        password=password,
        server=server,
        wine=wine,
        path=path,
        retry=retry,
    )

    # Start enviroments
    self._refresh_environments()

account ¤

account(**kwargs) -> dict

Metatrader 5 account information

Returns:

  • dict ( dict ) –

    description

Source code in lettrade/exchange/metatrader/api.py 
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@mt5_connection
def account(self, **kwargs) -> dict:
    """Metatrader 5 account information

    Returns:
        dict: _description_
    """
    raw = self._mt5.account_info()
    if raw is None:
        raise _RetryException()
    return raw

bars ¤

bars(
    symbol,
    timeframe,
    since: int | datetime | None = 0,
    to: int | datetime | None = 1000,
    **kwargs
) -> list[list]

summary

Parameters:

  • symbol (_type_) –

    description

  • timeframe (_type_) –

    description

  • since (int | datetime | None, default: 0 ) –

    description. Defaults to 0.

  • to (int | datetime | None, default: 1000 ) –

    description. Defaults to 1_000.

Returns:

  • list[list] –

    list[list]: description

Source code in lettrade/exchange/metatrader/api.py 
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@mt5_connection
def bars(
    self,
    symbol,
    timeframe,
    since: int | datetime | None = 0,
    to: int | datetime | None = 1_000,
    **kwargs,
) -> list[list]:
    """_summary_

    Args:
        symbol (_type_): _description_
        timeframe (_type_): _description_
        since (int | datetime | None, optional): _description_. Defaults to 0.
        to (int | datetime | None, optional): _description_. Defaults to 1_000.

    Returns:
        list[list]: _description_
    """
    timeframe = TIMEFRAME_L2M[timeframe]

    if isinstance(since, int):
        raw = self._mt5.copy_rates_from_pos(symbol, timeframe, since, to)

    elif isinstance(to, int):
        raw = self._mt5.copy_rates_from(symbol, timeframe, since, to)
    else:
        raw = self._mt5.copy_rates_range(symbol, timeframe, since, to)

    if raw is None:
        raise _RetryException()
    return raw

do_position_update ¤

do_position_update(
    id: int,
    sl: float | None = None,
    tp: float | None = None,
    **kwargs
) -> dict

summary

Parameters:

  • id (int) –

    description

  • sl (float | None, default: None ) –

    description. Defaults to None.

  • tp (float | None, default: None ) –

    description. Defaults to None.

Returns:

  • dict ( dict ) –

    description

Source code in lettrade/exchange/metatrader/api.py 
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@mt5_connection
def do_position_update(
    self,
    id: int,
    # symbol: str,
    sl: float | None = None,
    tp: float | None = None,
    **kwargs,
) -> dict:
    """_summary_

    Args:
        id (int): _description_
        sl (float | None, optional): _description_. Defaults to None.
        tp (float | None, optional): _description_. Defaults to None.

    Returns:
        dict: _description_
    """
    request = self._parse_trade_request(
        position=id,
        # symbol=symbol,
        sl=sl,
        tp=tp,
        action=MT5.TRADE_ACTION_SLTP,
        **kwargs,
    )
    raw = self._mt5.order_send(request)

    # Retry
    if raw is None:
        raise _RetryException()

    return self._parse_trade_send_response(raw)

execution_get ¤

execution_get(id: str, **kwargs) -> dict

summary

Parameters:

  • id (str) –

    description

Returns:

  • dict ( dict ) –

    description

Source code in lettrade/exchange/metatrader/api.py 
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@mt5_connection
def execution_get(self, id: str, **kwargs) -> dict:
    """_summary_

    Args:
        id (str): _description_

    Returns:
        dict: _description_
    """
    if id is not None:
        kwargs["ticket"] = int(id)

    raws = self._mt5.history_deals_get(**kwargs)

    # Retry
    if raws is None:
        raise _RetryException()

    if __debug__:
        logger.debug("Raw execution: %s", raws)

    return self._execution_parse_response(raws[0])

executions_get ¤

executions_get(
    position_id: str | None = None,
    search: str | None = None,
    **kwargs
) -> list[dict]

summary

Parameters:

  • position_id (str | None, default: None ) –

    description. Defaults to None.

  • search (str | None, default: None ) –

    description. Defaults to None.

Returns:

  • list[dict] –

    list[dict]: description

Source code in lettrade/exchange/metatrader/api.py 
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@mt5_connection
def executions_get(
    self,
    position_id: str | None = None,
    search: str | None = None,
    **kwargs,
) -> list[dict]:
    """_summary_

    Args:
        position_id (str | None, optional): _description_. Defaults to None.
        search (str | None, optional): _description_. Defaults to None.

    Returns:
        list[dict]: _description_
    """
    if position_id is not None:
        kwargs["position"] = int(position_id)
    if search is not None:
        kwargs["group"] = search

    raws = self._mt5.history_deals_get(**kwargs)

    # Retry
    if raws is None:
        raise _RetryException()

    # May be wrong account when position exist but no execution
    if not raws and position_id is not None:
        logger.warning(
            "Execution retry check connection when position=%s exist but no execution",
            position_id,
        )

        # Retry check mt5 connection
        raise _RetryException()

    if __debug__:
        logger.debug("Raw executions: %s", raws)

    return [self._execution_parse_response(raw) for raw in raws]

executions_total ¤

executions_total(
    since: datetime | None = None,
    to: datetime | None = None,
    **kwargs
) -> int

summary

Parameters:

  • since (datetime | None, default: None ) –

    description. Defaults to None.

  • to (datetime | None, default: None ) –

    description. Defaults to None.

Returns:

  • int ( int ) –

    description

Source code in lettrade/exchange/metatrader/api.py 
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@mt5_connection
def executions_total(
    self,
    since: datetime | None = None,
    to: datetime | None = None,
    **kwargs,
) -> int:
    """_summary_

    Args:
        since (datetime | None, optional): _description_. Defaults to None.
        to (datetime | None, optional): _description_. Defaults to None.

    Returns:
        int: _description_
    """
    if since is not None:
        kwargs["date_from"] = since
    if to is not None:
        kwargs["date_to"] = to

    raw = self._mt5.history_deals_total(**kwargs)
    if raw is None:
        raise _RetryException()
    return raw

heartbeat ¤

heartbeat() -> bool

Heartbeat

Returns:

  • bool ( bool ) –

    description

Source code in lettrade/exchange/metatrader/api.py 
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def heartbeat(self) -> bool:
    """Heartbeat

    Returns:
        bool: _description_
    """
    return True

init ¤

init(**kwargs)
Source code in lettrade/exchange/live/api.py 
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def init(self, **kwargs):
    """"""

market ¤

market(symbol: str, **kwargs) -> dict

summary

Parameters:

  • symbol (str) –

    description

Returns:

  • dict ( dict ) –

    description

Source code in lettrade/exchange/metatrader/api.py 
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@mt5_connection
def market(self, symbol: str, **kwargs) -> dict:
    """_summary_

    Args:
        symbol (str): _description_

    Returns:
        dict: _description_
    """
    raw = self._mt5.symbol_info(symbol)
    if raw is None:
        raise _RetryException()
    return self._market_parse_response(raw)

markets ¤

markets(search: str | None = '*', **kwargs) -> list[dict]

The filter for arranging a group of necessary symbols. If the group is specified, the function returns only symbols meeting a specified criteria.

Search example

Get symbols whose names do not contain USD, EUR, JPY and GBP search="*,!*USD*,!*EUR*,!*JPY*,!*GBP*"

Parameters:

  • search (str | None, default: '*' ) –

    description. Defaults to None.

Returns:

  • list[dict] –

    list[dict]: description

Source code in lettrade/exchange/metatrader/api.py 
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@mt5_connection
def markets(self, search: str | None = "*", **kwargs) -> list[dict]:
    """The filter for arranging a group of necessary symbols.
    If the group is specified, the function returns only symbols meeting a specified criteria.

    Search example:
        Get symbols whose names do not contain USD, EUR, JPY and GBP
        `search="*,!*USD*,!*EUR*,!*JPY*,!*GBP*"`

    Args:
        search (str | None, optional): _description_. Defaults to None.

    Returns:
        list[dict]: _description_
    """
    raws = self._mt5.symbols_get(search)
    if raws is None:
        raise _RetryException()
    return [self._market_parse_response(raw) for raw in raws]

next ¤

next()

Next tick action

Source code in lettrade/exchange/metatrader/api.py 
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def next(self):
    """Next tick action"""
    self._check_transaction_events()

order_open ¤

order_open(order: MetaTraderOrder) -> dict

summary

Parameters:

Raises:

Returns:

  • dict ( dict ) –

    description

Source code in lettrade/exchange/metatrader/api.py 
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@mt5_connection
def order_open(self, order: "MetaTraderOrder") -> dict:
    """_summary_

    Args:
        order (MetaTraderOrder): _description_

    Raises:
        NotImplementedError: _description_

    Returns:
        dict: _description_
    """
    match order.type:
        case OrderType.Limit:
            action = MT5.TRADE_ACTION_PENDING
            price = order.limit_price
            type = (
                MT5.ORDER_TYPE_BUY_LIMIT
                if order.is_long
                else MT5.ORDER_TYPE_SELL_LIMIT
            )
        case OrderType.Stop:
            action = MT5.TRADE_ACTION_PENDING
            price = order.stop_price
            type = (
                MT5.ORDER_TYPE_BUY_STOP
                if order.is_long
                else MT5.ORDER_TYPE_SELL_STOP
            )
        case OrderType.Market:
            action = MT5.TRADE_ACTION_DEAL
            tick = self.tick_get(order.data.symbol)
            price = tick.ask if order.is_long else tick.bid
            type = MT5.ORDER_TYPE_BUY if order.is_long else MT5.ORDER_TYPE_SELL
        case _:
            raise NotImplementedError(
                f"Open order type {order.type} is not implement yet"
            )

    # Request
    request = self._parse_trade_request(
        action=action,
        symbol=order.data.symbol,
        size=order.size,
        type=type,
        price=price,
        sl=order.sl,
        tp=order.tp,
        tag=order.tag,
        expiration=order.expiration,
    )
    raw = self._mt5.order_send(request)

    # Retry
    if raw is None:
        raise _RetryException()

    raw = self._parse_trade_send_response(raw)
    if raw.code != 0:
        raise LetLiveOrderInvalidException(raw.error, raw=raw)

    return raw

orders_get ¤

orders_get(
    id: str | None = None,
    symbol: str | None = None,
    **kwargs
) -> list[dict]

summary

Parameters:

  • id (str | None, default: None ) –

    description. Defaults to None.

  • symbol (str | None, default: None ) –

    description. Defaults to None.

Returns:

  • list[dict] –

    list[dict]: description

Source code in lettrade/exchange/metatrader/api.py 
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@mt5_connection
def orders_get(
    self,
    id: str | None = None,
    symbol: str | None = None,
    **kwargs,
) -> list[dict]:
    """_summary_

    Args:
        id (str | None, optional): _description_. Defaults to None.
        symbol (str | None, optional): _description_. Defaults to None.

    Returns:
        list[dict]: _description_
    """
    if id is not None:
        kwargs["ticket"] = int(id)
    if symbol is not None:
        kwargs["symbol"] = symbol

    raws = self._mt5.orders_get(**kwargs)

    # Return None to retry
    if raws is None:
        raise _RetryException()

    return [self._order_parse_response(raw) for raw in raws]

orders_history_get ¤

orders_history_get(
    id: str | None = None,
    position_id: str | None = None,
    since: datetime | None = None,
    to: datetime | None = None,
    **kwargs
) -> list[dict]

summary

Parameters:

  • id (str | None, default: None ) –

    description. Defaults to None.

  • position_id (str | None, default: None ) –

    description. Defaults to None.

  • since (datetime | None, default: None ) –

    description. Defaults to None.

  • to (datetime | None, default: None ) –

    description. Defaults to None.

Returns:

  • list[dict] –

    list[dict]: description

Source code in lettrade/exchange/metatrader/api.py 
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@mt5_connection
def orders_history_get(
    self,
    id: str | None = None,
    position_id: str | None = None,
    since: datetime | None = None,
    to: datetime | None = None,
    **kwargs,
) -> list[dict]:
    """_summary_

    Args:
        id (str | None, optional): _description_. Defaults to None.
        position_id (str | None, optional): _description_. Defaults to None.
        since (datetime | None, optional): _description_. Defaults to None.
        to (datetime | None, optional): _description_. Defaults to None.

    Returns:
        list[dict]: _description_
    """
    if id is not None:
        kwargs["ticket"] = int(id)
    if position_id is not None:
        kwargs["position"] = int(position_id)
    if since is not None:
        kwargs["date_from"] = since
    if to is not None:
        kwargs["date_to"] = to

    raws = self._mt5.history_orders_get(**kwargs)

    # Retry
    if raws is None:
        raise _RetryException()

    return [self._order_parse_response(raw) for raw in raws]

orders_history_total ¤

orders_history_total(
    since: datetime | None = None,
    to: datetime | None = None,
    **kwargs
) -> int

summary

Parameters:

  • since (datetime | None, default: None ) –

    description. Defaults to None.

  • to (datetime | None, default: None ) –

    description. Defaults to None.

Returns:

  • int ( int ) –

    description

Source code in lettrade/exchange/metatrader/api.py 
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@mt5_connection
def orders_history_total(
    self,
    since: datetime | None = None,
    to: datetime | None = None,
    **kwargs,
) -> int:
    """_summary_

    Args:
        since (datetime | None, optional): _description_. Defaults to None.
        to (datetime | None, optional): _description_. Defaults to None.

    Returns:
        int: _description_
    """
    if since is not None:
        kwargs["date_from"] = since
    if to is not None:
        kwargs["date_to"] = to

    raw = self._mt5.history_orders_get(**kwargs)
    if raw is None:
        raise _RetryException()
    return raw

orders_total ¤

orders_total(
    since: datetime | None = None,
    to: datetime | None = None,
    **kwargs
) -> int

summary

Parameters:

  • since (datetime | None, default: None ) –

    description. Defaults to None.

  • to (datetime | None, default: None ) –

    description. Defaults to None.

Returns:

  • int ( int ) –

    description

Source code in lettrade/exchange/metatrader/api.py 
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@mt5_connection
def orders_total(
    self,
    since: datetime | None = None,
    to: datetime | None = None,
    **kwargs,
) -> int:
    """_summary_

    Args:
        since (datetime | None, optional): _description_. Defaults to None.
        to (datetime | None, optional): _description_. Defaults to None.

    Returns:
        int: _description_
    """
    if since is not None:
        kwargs["date_from"] = since
    if to is not None:
        kwargs["date_to"] = to

    raw = self._mt5.orders_total(**kwargs)
    if raw is None:
        raise _RetryException()

    return raw

position_close ¤

position_close(
    position: MetaTraderPosition, **kwargs
) -> dict

Close a position

Parameters:

Returns:

  • dict ( dict ) –

    description

Source code in lettrade/exchange/metatrader/api.py 
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def position_close(self, position: "MetaTraderPosition", **kwargs) -> dict:
    """Close a position

    Args:
        position (MetaTraderPosition): _description_

    Returns:
        dict: _description_
    """
    tick = self.tick_get(position.data.symbol)
    price = tick.ask if position.is_long else tick.bid

    # Opposite with position side
    type = MT5.ORDER_TYPE_SELL if position.is_long else MT5.ORDER_TYPE_BUY

    return self.do_position_close(
        id=int(position.id),
        symbol=position.data.symbol,
        type=type,
        size=position.size,
        price=price,
        **kwargs,
    )

position_update ¤

position_update(
    position: MetaTraderPosition,
    sl: float | None = None,
    tp: float | None = None,
    **kwargs
) -> dict

summary

Parameters:

  • position (MetaTraderPosition) –

    description

  • sl (float | None, default: None ) –

    description. Defaults to None.

  • tp (float | None, default: None ) –

    description. Defaults to None.

Returns:

  • dict ( dict ) –

    description

Source code in lettrade/exchange/metatrader/api.py 
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def position_update(
    self,
    position: "MetaTraderPosition",
    sl: float | None = None,
    tp: float | None = None,
    **kwargs,
) -> dict:
    """_summary_

    Args:
        position (MetaTraderPosition): _description_
        sl (float | None, optional): _description_. Defaults to None.
        tp (float | None, optional): _description_. Defaults to None.

    Returns:
        dict: _description_
    """
    return self.do_position_update(
        id=int(position.id),
        symbol=position.data.symbol,
        sl=sl,
        tp=tp,
        **kwargs,
    )

positions_get ¤

positions_get(
    id: str = None, symbol: str = None, **kwargs
) -> list[dict]

summary

Parameters:

  • id (str, default: None ) –

    description. Defaults to None.

  • symbol (str, default: None ) –

    description. Defaults to None.

Returns:

  • list[dict] –

    list[dict]: description

Source code in lettrade/exchange/metatrader/api.py 
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@mt5_connection
def positions_get(
    self,
    id: str = None,
    symbol: str = None,
    **kwargs,
) -> list[dict]:
    """_summary_

    Args:
        id (str, optional): _description_. Defaults to None.
        symbol (str, optional): _description_. Defaults to None.

    Returns:
        list[dict]: _description_
    """
    if id is not None:
        kwargs.update(ticket=int(id))
    if symbol is not None:
        kwargs.update(symbol=symbol)

    raws = self._mt5.positions_get(**kwargs)

    # Retry
    if raws is None:
        raise _RetryException()

    return [self._position_parse_response(raw) for raw in raws]

positions_total ¤

positions_total(
    since: datetime | None = None,
    to: datetime | None = None,
    **kwargs
) -> int

summary

Parameters:

  • since (datetime | None, default: None ) –

    description. Defaults to None.

  • to (datetime | None, default: None ) –

    description. Defaults to None.

Returns:

  • int ( int ) –

    description

Source code in lettrade/exchange/metatrader/api.py 
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@mt5_connection
def positions_total(
    self,
    since: datetime | None = None,
    to: datetime | None = None,
    **kwargs,
) -> int:
    """_summary_

    Args:
        since (datetime | None, optional): _description_. Defaults to None.
        to (datetime | None, optional): _description_. Defaults to None.

    Returns:
        int: _description_
    """
    if since is not None:
        kwargs["date_from"] = since
    if to is not None:
        kwargs["date_to"] = to

    raw = self._mt5.positions_total(**kwargs)
    if raw is None:
        raise _RetryException()
    return raw

start ¤

start(exchange: MetaTraderExchange)

summary

Parameters:

Source code in lettrade/exchange/metatrader/api.py 
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def start(self, exchange: "MetaTraderExchange"):
    """_summary_

    Args:
        exchange (MetaTraderExchange): _description_
    """
    self._exchange = exchange

    self._load_history_transactions()
    self._check_transaction_events()

stop ¤

stop()

Stop MetaTrader 5 Terminal

Source code in lettrade/exchange/metatrader/api.py 
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def stop(self):
    """Stop MetaTrader 5 Terminal"""
    self._mt5.shutdown()

tick_get ¤

tick_get(symbol: str, **kwargs) -> dict

summary

Parameters:

  • symbol (str) –

    description

Returns:

  • dict ( dict ) –

    description

Source code in lettrade/exchange/metatrader/api.py 
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@mt5_connection
def tick_get(self, symbol: str, **kwargs) -> dict:
    """_summary_

    Args:
        symbol (str): _description_

    Returns:
        dict: _description_
    """
    raw = self._mt5.symbol_info_tick(symbol)
    if raw is None:
        raise _RetryException()
    return raw

MetaTraderAccount ¤

MetaTraderAccount(api: LiveAPI, **kwargs)

Bases: LiveAccount

Account for MetaTrader

Parameters:

Source code in lettrade/exchange/live/account.py 
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def __init__(self, api: LiveAPI, **kwargs) -> None:
    """Account for live trading

    Args:
        api (LiveAPI): _description_
        **kwargs (dict, optional): Mirror of [lettrade.account.Account()](site:/reference/account/account/#lettrade.account.account.Account).
    """
    super().__init__(**kwargs)
    self._api = api

    self._balance = 0.0
    self._equity = 0.0

balance property ¤

balance: float

Balance of account

Returns:

  • float ( float ) –

    description

account_refresh ¤

account_refresh()

Refresh account balance

Source code in lettrade/exchange/live/account.py 
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def account_refresh(self):
    """Refresh account balance"""
    self._account = self._api.account()

    if __debug__:
        logger.debug("Account: %s", str(self._account))

    self._balance = self._account.balance
    self._equity = self._account.equity

next ¤

next()

Next account

Source code in lettrade/account/account.py 
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def next(self):
    """Next account"""

next_next ¤

next_next()

Call after strategy.next()

Source code in lettrade/account/account.py 
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def next_next(self):
    """Call after strategy.next()"""
    self._equity_snapshot()

risk ¤

risk(side: TradeSide, size: float, **kwargs) -> float

Risk calculation

Source code in lettrade/account/account.py 
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def risk(self, side: "TradeSide", size: float, **kwargs) -> float:
    """Risk calculation"""
    if size is None:
        return side * abs(self._risk)
    return side * abs(size)

start ¤

start()

Start live account by load account info from API

Source code in lettrade/exchange/live/account.py 
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def start(self):
    """Start live account by load account info from API"""
    self.account_refresh()
    self._margin = self._account.margin
    self._leverage = self._account.leverage

stop ¤

stop()

Stop account

Source code in lettrade/account/account.py 
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def stop(self):
    """Stop account"""
    try:
        self._equity_snapshot()
    except LetAccountInsufficientException:
        pass

MetaTraderDataFeed ¤

MetaTraderDataFeed(
    symbol: str,
    timeframe: str | int | Timedelta,
    name: str | None = None,
    columns: list[str] | None = None,
    api: LiveAPI | None = None,
    api_kwargs: dict | None = None,
    **kwargs
)

Bases: LiveDataFeed

DataFeed for MetaTrader

Parameters:

  • symbol (str) –

    Symbol of DataFeed

  • timeframe (str | int | Timedelta) –

    TimeFrame of DataFeed

  • name (str | None, default: None ) –

    Name of DataFeed, auto generate {symbol}_{timeframe} if none. Defaults to None.

  • columns (list[str] | None, default: None ) –

    List of DataFeed columns. Defaults to None.

  • api (LiveAPI | None, default: None ) –

    Live trading API. Defaults to None.

Source code in lettrade/exchange/live/data.py 
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def __init__(
    self,
    symbol: str,
    timeframe: str | int | pd.Timedelta,
    name: str | None = None,
    columns: list[str] | None = None,
    api: LiveAPI | None = None,
    api_kwargs: dict | None = None,
    **kwargs,
) -> None:
    """_summary_

    Args:
        symbol (str): Symbol of DataFeed
        timeframe (str | int | pd.Timedelta): TimeFrame of DataFeed
        name (str | None, optional): Name of DataFeed, auto generate `{symbol}_{timeframe}` if none. Defaults to None.
        columns (list[str] | None, optional): List of DataFeed columns. Defaults to None.
        api (LiveAPI | None, optional): Live trading API. Defaults to None.
    """
    super().__init__(
        name=name or f"{symbol}_{timeframe}",
        timeframe=timeframe,
        columns=columns or ["open", "high", "low", "close", "volume"],
        **kwargs,
    )

    self.meta.update(symbol=symbol, base_columns=self.columns.copy())

    if api is not None:
        self._api = api
    elif api_kwargs is not None:
        self._api = self._api_cls(**api_kwargs)
    else:
        # raise RuntimeError("Parameter: api or api_kwargs cannot missing")
        logger.info("Parameter: api or api_kwargs cannot missing")

i property ¤

i: indicator

Alias to lettrade.indicator and using in DataFeed by call: DataFeed.i.indicator_name()

is_main property ¤

is_main: bool

Property to check DataFeed is main DataFeed or not

l instance-attribute ¤

l: LetDataFeedWrapper

LetTrade DataFeed wrapper using to manage index pointer of DataFeed

meta property ¤

meta: dict

Property to get metadata of DataFeed

name property ¤

name: str

Property to get name of DataFeed

now property ¤

now: Timestamp

Property to get current index value of DataFeed

symbol property ¤

symbol: str

Property to get symbol of DataFeed

timeframe property ¤

timeframe: TimeFrame

Property to get timeframe of DataFeed

bar ¤

bar(i: int = 0) -> Timestamp

Get current pd.Timestamp value of DataFeed

Parameters:

  • i (int, default: 0 ) –

    Index. Defaults to 0.

Returns:

  • Timestamp –

    pd.Timestamp: description

Source code in lettrade/data/data.py 
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def bar(self, i: int = 0) -> pd.Timestamp:
    """Get current pd.Timestamp value of DataFeed

    Args:
        i (int, optional): Index. Defaults to 0.

    Returns:
        pd.Timestamp: _description_
    """
    return self.l.index[i]

bars ¤

bars(
    since: int | str | Timestamp, to: int | str | Timestamp
) -> list

Get bars from LiveAPI

Parameters:

  • since (int | str | Timestamp) –

    description

  • to (int | str | Timestamp) –

    description

Returns:

  • list ( list ) –

    list of bar

Source code in lettrade/exchange/live/data.py 
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def bars(
    self,
    since: int | str | pd.Timestamp,
    to: int | str | pd.Timestamp,
) -> list:
    """Get bars from LiveAPI

    Args:
        since (int | str | pd.Timestamp): _description_
        to (int | str | pd.Timestamp): _description_

    Returns:
        list: list of bar
    """
    return self._api.bars(
        symbol=self.symbol,
        timeframe=self.timeframe.string,
        since=since,
        to=to,
    )

bars_load ¤

bars_load(
    since: int | str | Timestamp, to: int | str | Timestamp
) -> bool

Get bar from API and push to DataFeed

Parameters:

  • since (int | str | Timestamp) –

    description

  • to (int | str | Timestamp) –

    description

Returns:

  • bool ( bool ) –

    True if has data, False if no data

Source code in lettrade/exchange/live/data.py 
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def bars_load(
    self,
    since: int | str | pd.Timestamp,
    to: int | str | pd.Timestamp,
) -> bool:
    """Get bar from API and push to DataFeed

    Args:
        since (int | str | pd.Timestamp): _description_
        to (int | str | pd.Timestamp): _description_

    Returns:
        bool: True if has data, False if no data
    """
    bars = self.bars(since=since, to=to)

    if bars is None or len(bars) == 0:
        logger.warning("No bars data for %s", self.name)
        return False

    self.push(bars, unit=self._bar_datetime_unit)
    return True

drop ¤

drop(
    *args,
    since: int | str | Timestamp | None = None,
    to: int | str | Timestamp | None = None,
    **kwargs
) -> None | DataFeed

summary

Parameters:

  • since (int | str | Timestamp | None, default: None ) –

    description. Defaults to None.

  • to (int | str | Timestamp | None, default: None ) –

    description. Defaults to None.

Raises:

Source code in lettrade/data/data.py 
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def drop(
    self,
    *args,
    since: int | str | pd.Timestamp | None = None,
    to: int | str | pd.Timestamp | None = None,
    **kwargs,
) -> "None | DataFeed":
    """_summary_

    Args:
        since (int | str | pd.Timestamp | None, optional): _description_. Defaults to None.
        to (int | str | pd.Timestamp | None, optional): _description_. Defaults to None.

    Raises:
        RuntimeError: _description_
    """
    if since is None and to is None:
        return super().drop(*args, **kwargs)

    condiction = None

    # Since
    if since is not None:
        if isinstance(since, int):
            loc = self.l.index[since]
        elif isinstance(since, str):
            loc = pd.to_datetime(since, utc=True)
        elif isinstance(since, pd.Timestamp):
            loc = since
        else:
            raise RuntimeError(f"DataFeed.drop since {since} is invalid")
        condiction = self.index < loc

    # To
    if to is not None:
        if isinstance(to, int):
            loc = self.l.index[to]
        elif isinstance(to, str):
            loc = pd.to_datetime(to, utc=True)
        elif isinstance(to, pd.Timestamp):
            loc = to
        else:
            raise RuntimeError(f"DataFeed.drop to {to} is invalid")

        if condiction is None:
            condiction = self.index > loc
        else:
            condiction = condiction | (self.index > loc)

    index = self[condiction].index
    super().drop(index=index, inplace=True)
    self.l.reset()

    if __debug__:
        logger.debug("BackTestDataFeed %s dropped %s rows", self.name, len(index))

dump_csv ¤

dump_csv(
    path: str | None = None,
    since: int | str | datetime | None = 0,
    to: int | str | datetime | None = 1000,
    **kwargs
)

summary

Parameters:

  • path (str | None, default: None ) –

    description. Defaults to None.

  • since (int | str | datetime | None, default: 0 ) –

    description. Defaults to 0.

  • to (int | str | datetime | None, default: 1000 ) –

    description. Defaults to 1_000.

Source code in lettrade/exchange/live/data.py 
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def dump_csv(
    self,
    path: str | None = None,
    since: int | str | datetime | None = 0,
    to: int | str | datetime | None = 1_000,
    **kwargs,
):
    """_summary_

    Args:
        path (str | None, optional): _description_. Defaults to None.
        since (int  |  str  |  datetime | None, optional): _description_. Defaults to 0.
        to (int  |  str  |  datetime | None, optional): _description_. Defaults to 1_000.
    """
    if self.empty:
        if isinstance(since, str):
            since = pd.to_datetime(since).to_pydatetime()
        if isinstance(to, str):
            to = pd.to_datetime(to).to_pydatetime()

        self.bars_load(since=since, to=to)

    if path is None:
        path = f"data/{self.name}-{since}_{to}.csv"

    from lettrade.data.extra.csv import csv_export

    csv_export(dataframe=self, path=path, **kwargs)

next ¤

next(size=1, tick=0) -> bool

Drop extra columns and load next DataFeed

Parameters:

  • size (int, default: 1 ) –

    description. Defaults to 1.

  • tick (int, default: 0 ) –

    description. Defaults to 0.

Returns:

  • bool ( bool ) –

    description

Source code in lettrade/exchange/live/data.py 
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def next(self, size=1, tick=0) -> bool:
    """Drop extra columns and load next DataFeed

    Args:
        size (int, optional): _description_. Defaults to 1.
        tick (int, optional): _description_. Defaults to 0.

    Returns:
        bool: _description_
    """
    # Drop existed extra columns to skip reusing calculated data
    self.drop(columns=self.columns.difference(self._base_columns), inplace=True)

    self.bars_load(since=0, to=size + 1)
    self.l.go_stop()
    return True

push ¤

push(
    rows: list[list[int | float]],
    unit: str | None = None,
    utc: bool = True,
    **kwargs
)

Push new rows to DataFeed

Parameters:

  • rows (list[list[int | float]]) –

    list of rows [["timestamp", "open price", "high price"...]]

  • unit (str | None, default: None ) –

    pandas.Timestamp parsing unit. Defaults to None.

  • utc (bool, default: True ) –

    description. Defaults to True.

Source code in lettrade/data/data.py 
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def push(
    self,
    rows: list[list[int | float]],
    unit: str | None = None,
    utc: bool = True,
    **kwargs,
):
    """Push new rows to DataFeed

    Args:
        rows (list[list[int | float]]): list of rows `[["timestamp", "open price", "high price"...]]`
        unit (str | None, optional): pandas.Timestamp parsing unit. Defaults to None.
        utc (bool, optional): _description_. Defaults to True.
    """
    for row in rows:
        dt = pd.to_datetime(row[0], unit=unit, utc=utc, **kwargs)
        self.at[
            dt,
            (
                "open",
                "high",
                "low",
                "close",
                "volume",
            ),
        ] = (
            row[1],  # open
            row[2],  # high
            row[3],  # low
            row[4],  # close
            row[5],  # volume
        )

    if __debug__:
        logger.debug("[%s] Update bar: \n%s", self.name, self.tail(len(rows)))

symbol_info ¤

symbol_info()

Get symbol information from API

Source code in lettrade/exchange/live/data.py 
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def symbol_info(self):
    """Get symbol information from API"""
    return self._api.market(symbol=self.symbol)

MetaTraderDataFeeder ¤

MetaTraderDataFeeder(
    api: LiveAPI | None = None,
    tick: bool = 5,
    start_size: int = 500,
    api_kwargs: dict | None = None,
    **kwargs
)

Bases: LiveDataFeeder

DataFeeder for MetaTrader

tick == 0: wait until new bar change value
tick > 0: sleep tick time (in seconds) then update
Source code in lettrade/exchange/live/feeder.py 
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def __init__(
    self,
    api: LiveAPI | None = None,
    tick: bool = 5,
    start_size: int = 500,
    api_kwargs: dict | None = None,
    **kwargs,
) -> None:
    """
    tick:
        tick < 0: no tick, just get completed bar
        tick == 0: wait until new bar change value
        tick > 0: sleep tick time (in seconds) then update
    """
    super().__init__()

    # API init
    if api is None:
        if api_kwargs is None:
            raise RuntimeError("api or api_kwargs cannot missing")
        api = self._api_cls(**api_kwargs)

    self._api = api
    self._tick = tick
    self._start_size = start_size
    self._config = kwargs

    if isinstance(self._tick, int):
        self._wait_timeframe = TimeFrame(f"{self._tick}s")
    else:
        self._wait_timeframe = None

is_continous property ¤

is_continous

Flag check is realtime continous datafeeder

MetaTraderExchange ¤

MetaTraderExchange(api: LiveAPI, *args, **kwargs)

Bases: LiveExchange

MetaTrade 5 exchange module for lettrade

Parameters:

  • api (LiveAPI) –

    API connect to rpyc MeTrader 5 Terminal server through module mt5linux

  • *args (list, default: () ) –

    Exchange list parameters

  • **kwargs (dict, default: {} ) –

    Exchange dict parameters

Source code in lettrade/exchange/live/exchange.py 
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def __init__(self, api: LiveAPI, *args, **kwargs):
    """_summary_

    Args:
        api (LiveAPI): API connect to rpyc MeTrader 5 Terminal server through module `mt5linux`
        *args (list): `Exchange` list parameters
        **kwargs (dict): `Exchange` dict parameters
    """
    super().__init__(*args, **kwargs)
    self._api = api

data instance-attribute ¤

data: DataFeed

main DataFeed

datas instance-attribute ¤

datas: list[DataFeed]

List of all available DataFeed

executions instance-attribute ¤

executions: dict[str, Execution] = dict()

Execution dict by Execution.id key

history_orders instance-attribute ¤

history_orders: dict[str, Order] = dict()

History Order dict by Order.id key

history_positions instance-attribute ¤

history_positions: dict[str, Position] = dict()

History Position dict by Position.id key

orders instance-attribute ¤

orders: dict[str, Order] = dict()

Available Order dict by Order.id key

positions instance-attribute ¤

positions: dict[str, Position] = dict()

Available Position dict by Position.id key

init ¤

init(
    brain: Brain,
    feeder: DataFeeder,
    account: Account,
    commander: Commander,
) -> None

Init Exchange dependencies. Set data/datas from feeder. Set state to ExchangeState.Start.

Parameters:

Source code in lettrade/exchange/exchange.py 
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def init(
    self,
    brain: "Brain",
    feeder: DataFeeder,
    account: Account,
    commander: Commander,
) -> None:
    """Init Exchange dependencies.
    Set data/datas from feeder.
    Set state to `ExchangeState.Start`.

    Args:
        brain (Brain): _description_
        feeder (DataFeeder): _description_
        account (Account): _description_
        commander (Commander): _description_
    """
    self._brain = brain
    self._feeder = feeder
    self._account = account
    self._commander = commander

    self.data = self._feeder.data
    self.datas = self._feeder.datas

    self._account.init(exchange=self)

    self._state = ExchangeState.Start

new_order ¤

new_order(
    size: float,
    type: OrderType | None = OrderType.Market,
    limit: float | None = None,
    stop: float | None = None,
    sl: float | None = None,
    tp: float | None = None,
    tag: str | None = None,
    data: DataFeed | None = None,
    **kwargs
) -> OrderResult

Place new order to server

Parameters:

  • size (float) –

    description

  • type (OrderType | None, default: Market ) –

    description. Defaults to OrderType.Market.

  • limit (float | None, default: None ) –

    description. Defaults to None.

  • stop (float | None, default: None ) –

    description. Defaults to None.

  • sl (float | None, default: None ) –

    description. Defaults to None.

  • tp (float | None, default: None ) –

    description. Defaults to None.

  • tag (str | None, default: None ) –

    description. Defaults to None.

  • data (DataFeed | None, default: None ) –

    description. Defaults to None.

Returns:

Source code in lettrade/exchange/live/exchange.py 
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def new_order(
    self,
    size: float,
    type: OrderType | None = OrderType.Market,
    limit: float | None = None,
    stop: float | None = None,
    sl: float | None = None,
    tp: float | None = None,
    tag: str | None = None,
    data: DataFeed | None = None,
    **kwargs,
) -> OrderResult:
    """Place new order to server

    Args:
        size (float): _description_
        type (OrderType | None, optional): _description_. Defaults to OrderType.Market.
        limit (float | None, optional): _description_. Defaults to None.
        stop (float | None, optional): _description_. Defaults to None.
        sl (float | None, optional): _description_. Defaults to None.
        tp (float | None, optional): _description_. Defaults to None.
        tag (str | None, optional): _description_. Defaults to None.
        data (DataFeed | None, optional): _description_. Defaults to None.

    Returns:
        OrderResult: _description_
    """
    if data is None:
        data = self.data

    if type == OrderType.Market:
        limit = 0
        stop = 0

    order = self._order_cls(
        id=-1,
        exchange=self,
        data=data,
        size=size,
        type=type,
        limit_price=limit,
        stop_price=stop,
        sl_price=sl,
        tp_price=tp,
        tag=tag,
        **kwargs,
    )
    ok = order.place()

    if __debug__:
        logger.info("New order %s at %s", order, data.now)

    return ok

next_next ¤

next_next()

Call after strategy.next()

Source code in lettrade/exchange/exchange.py 
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def next_next(self):
    """Call after strategy.next()"""
    self._account.next_next()

on_execution ¤

on_execution(
    execution: Execution,
    broadcast: bool | None = True,
    **kwargs
) -> None

Receive Execution event from exchange then store and notify Brain

Parameters:

  • execution (Execution) –

    description

  • broadcast (bool | None, default: True ) –

    description. Defaults to True.

Source code in lettrade/exchange/exchange.py 
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def on_execution(
    self,
    execution: Execution,
    broadcast: bool | None = True,
    **kwargs,
) -> None:
    """Receive Execution event from exchange then store and notify Brain

    Args:
        execution (Execution): _description_
        broadcast (bool | None, optional): _description_. Defaults to True.
    """
    self.on_executions(executions=[execution], broadcast=broadcast, **kwargs)

on_executions ¤

on_executions(
    executions: list[Execution],
    broadcast: bool | None = True,
    **kwargs
) -> None

Receive Execution event from exchange then store and notify Brain

Source code in lettrade/exchange/exchange.py 
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def on_executions(
    self,
    executions: list[Execution],
    broadcast: bool | None = True,
    **kwargs,
) -> None:
    """
    Receive Execution event from exchange then store and notify Brain
    """
    for execution in executions:
        if not isinstance(execution, Execution):
            raise RuntimeError(f"{execution} is not instance of type Execution")

        if execution.id in self.executions:
            # Merge to keep Execution handler for strategy using
            # when strategy want to store Execution object
            # and object will be automatic update directly
            self.executions[execution.id].merge(execution)
            execution = self.executions[execution.id]
        else:
            self.executions[execution.id] = execution

    if self._state != ExchangeState.Run:
        return

    if broadcast:
        self._brain.on_executions(executions)

on_orders ¤

on_orders(
    orders: list[Order],
    broadcast: bool | None = True,
    **kwargs
) -> None

Receive orders event from exchange then store and notify Brain

Parameters:

  • orders (list[Order]) –

    description

  • broadcast (bool | None, default: True ) –

    description. Defaults to True.

Raises:

Source code in lettrade/exchange/exchange.py 
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def on_orders(
    self,
    orders: list[Order],
    broadcast: bool | None = True,
    **kwargs,
) -> None:
    """Receive orders event from exchange then store and notify Brain

    Args:
        orders (list[Order]): _description_
        broadcast (bool | None, optional): _description_. Defaults to True.

    Raises:
        RuntimeError: _description_
    """
    for order in orders:
        if not isinstance(order, Order):
            raise RuntimeError(f"{order} is not instance of type Order")

        if order.is_closed:
            if order.id in self.history_orders:
                logger.warning("Order closed recall: %s", order)

            self.history_orders[order.id] = order
            if order.id in self.orders:
                del self.orders[order.id]

            if __debug__:
                logger.info("Order closed: %s", order)
        else:
            if order.id in self.history_orders:
                raise RuntimeError(f"Order {order.id} closed")

            if order.id in self.orders:
                # Merge to keep Order handler for strategy using
                # when strategy want to store Order object
                # and object will be automatic update directly
                self.orders[order.id].merge(order)
                order = self.orders[order.id]

                if __debug__:
                    logger.info("Order updated: %s", order)
            else:
                self.orders[order.id] = order

                if __debug__:
                    logger.info("Order placed: %s", order)

    if self._state != ExchangeState.Run:
        return

    if broadcast:
        self._brain.on_orders(orders)

on_position ¤

on_position(
    position: Position,
    broadcast: bool | None = True,
    **kwargs
) -> None

Receive Position event from exchange then store and notify Brain

Parameters:

  • position (Position) –

    new comming Position

  • broadcast (bool | None, default: True ) –

    Flag notify to Brain. Defaults to True.

Raises:

Source code in lettrade/exchange/exchange.py 
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def on_position(
    self,
    position: Position,
    broadcast: bool | None = True,
    **kwargs,
) -> None:
    """Receive Position event from exchange then store and notify Brain

    Args:
        position (Position): new comming `Position`
        broadcast (bool | None, optional): Flag notify to Brain. Defaults to True.

    Raises:
        RuntimeError: validate `Position` instance
    """
    self.on_positions(positions=[position], broadcast=broadcast, **kwargs)

on_positions ¤

on_positions(
    positions: list[Position],
    broadcast: bool | None = True,
    **kwargs
) -> None

Receive Positions event from exchange then store and notify Brain

Parameters:

  • positions (list[Position]) –

    list of new comming Position

  • broadcast (bool | None, default: True ) –

    Flag notify to Brain. Defaults to True.

Raises:

Source code in lettrade/exchange/exchange.py 
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def on_positions(
    self,
    positions: list[Position],
    broadcast: bool | None = True,
    **kwargs,
) -> None:
    """Receive Positions event from exchange then store and notify Brain

    Args:
        positions (list[Position]): list of new comming `Position`
        broadcast (bool | None, optional): Flag notify to Brain. Defaults to True.

    Raises:
        RuntimeError: validate `Position` instance
    """
    for position in positions:
        if not isinstance(position, Position):
            raise RuntimeError(f"{position} is not instance of type Position")

        if position.is_exited:
            if position.id in self.history_positions:
                logger.warning("Position exited recall: %s", position)

            self.history_positions[position.id] = position
            if position.id in self.positions:
                del self.positions[position.id]

            # self._account._on_position_exit(position)
        else:
            if position.id in self.history_positions:
                raise RuntimeError(f"Position {position.id} closed: {position}")
            if position.id in self.positions:
                # Merge to keep Position handler for strategy using
                # when strategy want to store Position object
                # and object will be automatic update directly
                self.positions[position.id].merge(position)
                position = self.positions[position.id]
            else:
                self.positions[position.id] = position
                # self._account._on_position_entry(position)

    self._account.on_positions(positions)

    if self._state != ExchangeState.Run:
        return

    if broadcast:
        self._brain.on_positions(positions)

start ¤

start() -> None

Start Live exchange by: Sync positions from server

Source code in lettrade/exchange/live/exchange.py 
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def start(self) -> None:
    """Start Live exchange by: Sync positions from server"""
    self._api.start(exchange=self)
    return super().start()

stop ¤

stop() -> None

Stop Exchange

Source code in lettrade/exchange/exchange.py 
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def stop(self) -> None:
    """Stop Exchange"""
    self._state = ExchangeState.Stop
    self._account.stop()

MetaTraderExecution ¤

MetaTraderExecution(
    pl: float | None = None,
    fee: float | None = None,
    tag: str | None = None,
    **kwargs
)

Bases: LiveExecution

Execution for MetaTrader

Source code in lettrade/exchange/metatrader/trade.py 
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def __init__(
    self,
    pl: float | None = None,
    fee: float | None = None,
    tag: str | None = None,
    **kwargs,
):
    super().__init__(**kwargs)
    self.pl: float = pl
    self.fee: float = fee
    self.tag: str | None = tag

is_long property ¤

is_long: bool

True if side is long (size is positive).

Returns:

  • bool ( bool ) –

    True/False

is_short property ¤

is_short: bool

True if side is short (size is negative).

Returns:

  • bool ( bool ) –

    description

side property ¤

side: TradeSide

True if side is short (size is negative).

Returns:

from_raw classmethod ¤

from_raw(
    raw,
    exchange: MetaTraderExchange,
    data: MetaTraderDataFeed | None = None,
    api: MetaTraderAPI | None = None,
) -> MetaTraderExecution | None

Building new MetaTraderExecution from live api raw object

Parameters:

Returns:

Source code in lettrade/exchange/metatrader/trade.py 
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@classmethod
def from_raw(
    cls,
    raw,
    exchange: "MetaTraderExchange",
    data: "MetaTraderDataFeed | None" = None,
    api: MetaTraderAPI | None = None,
) -> "MetaTraderExecution | None":
    """Building new MetaTraderExecution from live api raw object

    Args:
        raw (_type_): _description_
        exchange (MetaTraderExchange): _description_

    Returns:
        MetaTraderExecution: _description_
    """
    # DataFeed
    if data is None:
        for d in exchange.datas:
            if d.symbol == raw.symbol:
                data = d
                break
        if data is None:
            logger.warning("Raw execution %s is not handling %s", raw.symbol, raw)
            return
    # Side
    match raw.type:
        case MT5.DEAL_TYPE_BUY:
            side = TradeSide.Buy
        case MT5.DEAL_TYPE_SELL:
            side = TradeSide.Sell
        case _:
            logger.warning(
                "Raw execution %s type %s is not handling %s",
                raw.symbol,
                raw.type,
                raw,
            )
            return

    return cls(
        exchange=exchange,
        id=raw.ticket,
        data=data,
        order_id=raw.order,
        position_id=raw.position_id,
        size=side * raw.volume,
        price=raw.price,
        pl=raw.profit,
        fee=raw.fee + raw.swap + raw.commission,
        at=pd.to_datetime(raw.time_msc, unit="ms", utc=True),
        tag=raw.comment,
        api=api,
        raw=raw,
    )

merge ¤

merge(other: Execution)

Merge to keep object handler but not overwrite for Strategy using when Strategy want to store object and object will be automatic update directly

Source code in lettrade/exchange/execution.py 
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def merge(self, other: "Execution"):
    """
    Merge to keep object handler but not overwrite for Strategy using when Strategy want to store object and object will be automatic update directly
    """
    if other is self:
        return

    if self.id != other.id:
        raise RuntimeError(f"Merge difference id {self.id} != {other.id} execution")

    self.price = other.price
    self.size = other.size

    if other.at:
        self.at = other.at
    if other.order_id:
        self.order_id = other.order_id
    if other.order:
        self.order = other.order
    if other.position_id:
        self.position_id = other.position_id
    if other.position:
        self.position = other.position

MetaTraderOrder ¤

MetaTraderOrder(is_real: bool = True, **kwargs)

Bases: LiveOrder

Order for MetaTrader

Source code in lettrade/exchange/metatrader/trade.py 
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def __init__(self, is_real: bool = True, **kwargs):
    super().__init__(**kwargs)

    self.is_real: bool = is_real
    """Flag to check `Order` is real, cannot duplicate id, cannot recall from history"""

is_closed property ¤

is_closed: bool

Flag to check Order closed

Returns:

  • bool ( bool ) –

    True if state in [OrderState.Filled, OrderState.Canceled]

is_long property ¤

is_long: bool

True if side is long (size is positive).

Returns:

  • bool ( bool ) –

    True/False

is_opening property ¤

is_opening: bool

Flag to check Order still alive

Returns:

  • bool ( bool ) –

    True if state in [OrderState.Placed, OrderState.Partial]

is_real instance-attribute ¤

is_real: bool = is_real

Flag to check Order is real, cannot duplicate id, cannot recall from history

is_short property ¤

is_short: bool

True if side is short (size is negative).

Returns:

  • bool ( bool ) –

    description

is_sl_order property ¤

is_sl_order: bool

Order is stop-loss order of a Position

Returns:

  • bool ( bool ) –

    description

is_tp_order property ¤

is_tp_order: bool

Order is take-profit order of a Position

Returns:

  • bool ( bool ) –

    description

limit property ¤

limit: float | None

Getter of limit_price

Returns:

  • float | None –

    float | None: float or None

place_price property ¤

place_price: float | None

Getter of place_price

Returns:

  • float | None –

    float | None: float or None

side property ¤

side: TradeSide

True if side is short (size is negative).

Returns:

sl property ¤

sl: float | None

Getter of sl_price

Returns:

  • float | None –

    float | None: float or None

stop property ¤

stop: float | None

Getter of stop_price

Returns:

  • float | None –

    float | None: float or None

tp property ¤

tp: float | None

Getter of tp_price

Returns:

  • float | None –

    float | None: float or None

cancel ¤

cancel(**kwargs) -> OrderResult

Cancel order

Returns:

Source code in lettrade/exchange/metatrader/trade.py 
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def cancel(self, **kwargs) -> OrderResult:
    """Cancel order

    Returns:
        OrderResult: _description_
    """
    if not self.parent:
        # Abandon order
        result = self._api.order_close(order=self, **kwargs)
    else:
        # Virtual SL/TP order of trade
        result = None

    return super(LiveOrder, self).cancel(raw=result)

fill ¤

fill(
    price: float, at: Timestamp, raw: object | None = None
) -> OrderResult

Fill Order. Set status to OrderState.Executed. Send event to Exchange

Parameters:

  • price (float) –

    Executed price

  • at (Timestamp) –

    Executed bar

Raises:

Returns:

Source code in lettrade/exchange/order.py 
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def fill(
    self,
    price: float,
    at: pd.Timestamp,
    raw: object | None = None,
) -> "OrderResult":
    """Fill `Order`.
    Set `status` to `OrderState.Executed`.
    Send event to `Exchange`

    Args:
        price (float): Executed price
        at (pd.Timestamp): Executed bar

    Raises:
        RuntimeError: _description_

    Returns:
        OrderResult: result of `Order`
    """
    if self.state != OrderState.Placed:
        raise RuntimeError(f"Order {self.id} state {self.state} is not Placed")

    self.filled_at = at
    self.filled_price = price
    self.state = OrderState.Filled
    self.exchange.on_order(self)
    return OrderResultOk(order=self, raw=raw)

from_position classmethod ¤

from_position(
    position: MetaTraderPosition,
    sl: float | None = None,
    tp: float | None = None,
) -> MetaTraderOrder

summary

Parameters:

  • position (MetaTraderPosition) –

    description

  • sl (float | None, default: None ) –

    description. Defaults to None.

  • tp (float | None, default: None ) –

    description. Defaults to None.

Raises:

Returns:

Source code in lettrade/exchange/metatrader/trade.py 
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@classmethod
def from_position(
    cls,
    position: "MetaTraderPosition",
    sl: float | None = None,
    tp: float | None = None,
) -> "MetaTraderOrder":
    """_summary_

    Args:
        position (MetaTraderPosition): _description_
        sl (float | None, optional): _description_. Defaults to None.
        tp (float | None, optional): _description_. Defaults to None.

    Raises:
        RuntimeError: _description_

    Returns:
        MetaTraderOrder: _description_
    """
    if not sl and not tp:
        raise RuntimeError("not sl and not tp")
    return cls(
        id=f"{position.id}-{'sl' if sl else 'tp'}",
        exchange=position.exchange,
        data=position.data,
        state=OrderState.Placed,
        type=OrderType.Stop if sl else OrderType.Limit,
        size=-position.size,
        limit_price=tp,
        stop_price=sl,
        parent=position,
        placed_at=position.entry_at,
        is_real=False,
    )

from_raw classmethod ¤

from_raw(
    raw: Any,
    exchange: MetaTraderExchange,
    data: MetaTraderDataFeed | None = None,
    api: MetaTraderAPI | None = None,
) -> MetaTraderOrder | None

summary

Parameters:

Raises:

Returns:

Source code in lettrade/exchange/metatrader/trade.py 
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@classmethod
def from_raw(
    cls,
    raw: Any,
    exchange: "MetaTraderExchange",
    data: "MetaTraderDataFeed | None" = None,
    api: MetaTraderAPI | None = None,
) -> "MetaTraderOrder | None":
    """_summary_

    Args:
        raw (Any): _description_
        exchange (MetaTraderExchange): _description_
        data (MetaTraderDataFeed | None, optional): _description_. Defaults to None.
        api (MetaTraderAPI | None, optional): _description_. Defaults to None.

    Raises:
        NotImplementedError: _description_

    Returns:
        MetaTraderOrder | None: _description_
    """
    # DataFeed
    if data is None:
        for d in exchange.datas:
            if d.symbol == raw.symbol:
                data = d
                break
        if data is None:
            logger.warning("Raw order %s is not handling %s", raw.symbol, raw)
            return

    # Prices & Side & Type
    limit_price = None
    stop_price = None
    match raw.type:
        case MT5.ORDER_TYPE_BUY:
            side = TradeSide.Buy
            type = OrderType.Market
        case MT5.ORDER_TYPE_SELL:
            side = TradeSide.Sell
            type = OrderType.Market
        case MT5.ORDER_TYPE_BUY_LIMIT:
            side = TradeSide.Buy
            type = OrderType.Limit
            limit_price = raw.price_open
        case MT5.ORDER_TYPE_SELL_LIMIT:
            side = TradeSide.Sell
            type = OrderType.Limit
            limit_price = raw.price_open
        case MT5.ORDER_TYPE_BUY_STOP:
            side = TradeSide.Buy
            type = OrderType.Stop
            stop_price = raw.price_open
        case MT5.ORDER_TYPE_SELL_STOP:
            side = TradeSide.Sell
            type = OrderType.Stop
            stop_price = raw.price_open
        # case MT5.ORDER_TYPE_BUY_STOP_LIMIT:
        #     side = TradeSide.Buy
        #     type = OrderType.StopLimit
        #     # TODO
        #     limit_price = raw.price_open
        #     stop_price = raw.price_open
        # case MT5.ORDER_TYPE_SELL_STOP_LIMIT:
        #     side = TradeSide.Sell
        #     type = OrderType.StopLimit
        #     # TODO
        #     limit_price = raw.price_open
        #     stop_price = raw.price_open
        # case MT5.ORDER_TYPE_CLOSE_BY:
        case _:
            raise NotImplementedError(
                f"Order type {raw.type} is not implement",
                raw,
            )
    # State
    match raw.state:
        case MT5.ORDER_STATE_STARTED:
            state = OrderState.Pending
        case MT5.ORDER_STATE_PLACED:
            state = OrderState.Placed
        case MT5.ORDER_STATE_CANCELED:
            state = OrderState.Canceled
        case MT5.ORDER_STATE_PARTIAL:
            state = OrderState.Partial
        case MT5.ORDER_STATE_FILLED:
            state = OrderState.Filled
        case MT5.ORDER_STATE_REJECTED:
            state = OrderState.Canceled
        case MT5.ORDER_STATE_EXPIRED:
            state = OrderState.Canceled
        case MT5.ORDER_STATE_REQUEST_ADD:
            state = OrderState.Placed
        case MT5.ORDER_STATE_REQUEST_MODIFY:
            state = OrderState.Placed
        case MT5.ORDER_STATE_REQUEST_CANCEL:
            state = OrderState.Canceled
        case _:
            raise NotImplementedError(
                f"Raw order state {raw.state} is not implement"
            )

    order = cls(
        exchange=exchange,
        id=raw.ticket,
        state=state,
        data=data,
        size=side * (raw.volume_current or raw.volume_initial),
        type=type,
        limit_price=limit_price,
        stop_price=stop_price,
        sl_price=raw.sl or None,
        tp_price=raw.tp or None,
        tag=raw.comment,
        placed_at=pd.to_datetime(raw.time_setup_msc, unit="ms", utc=True),
        api=api,
        raw=raw,
    )

    if hasattr(raw, "time_done_msc"):
        order.filled_price = raw.price_current
        order.filled_at = pd.to_datetime(raw.time_done_msc, unit="ms", utc=True)

    return order

merge ¤

merge(other: Order)

Update current Order variables by other Order

Parameters:

  • other (Order) –

    Merge source Order

Raises:

Source code in lettrade/exchange/order.py 
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def merge(self, other: "Order"):
    """Update current `Order` variables by other `Order`

    Args:
        other (Order): Merge source `Order`

    Raises:
        RuntimeError: Validate same id
    """
    if other is self:
        return

    if self.id != other.id:
        raise RuntimeError(f"Merge difference id {self.id} != {other.id} order")

    self.size = other.size
    self.sl_price = other.sl_price
    self.tp_price = other.tp_price

    if other.limit_price:
        self.limit_price = other.limit_price
    if other.stop_price:
        self.stop_price = other.stop_price
    if other.placed_at:
        self.placed_at = other.placed_at

    if other.filled_price:
        self.filled_price = other.filled_price
    if other.filled_at:
        self.filled_at = other.filled_at

    if other.parent:
        self.parent = other.parent

place ¤

place() -> OrderResult

summary

Raises:

Returns:

Source code in lettrade/exchange/metatrader/trade.py 
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def place(self) -> OrderResult:
    """_summary_

    Raises:
        RuntimeError: _description_

    Returns:
        OrderResult: _description_
    """
    if self.state != OrderState.Pending:
        raise RuntimeError(f"Order {self.id} state {self.state} is not Pending")

    try:
        result = self._api.order_open(self)

        self.raw = result
        self.id = result.order

        # TODO: get current order time
        return super(LiveOrder, self).place(at=self.data.l.index[0], raw=result)
    except LetLiveOrderInvalidException as e:
        error = OrderResultError(
            error=e.message,
            order=self,
            raw=e.raw,
        )
        logger.error("Place order %s", str(error))
        self.exchange.on_notify(error=error)
        return error

update ¤

update(
    limit_price: float | None = None,
    stop_price: float | None = None,
    sl: float | None = None,
    tp: float | None = None,
    caller: float | None = None,
    **kwargs
) -> OrderResult

summary

Parameters:

  • limit_price (float | None, default: None ) –

    description. Defaults to None.

  • stop_price (float | None, default: None ) –

    description. Defaults to None.

  • sl (float | None, default: None ) –

    description. Defaults to None.

  • tp (float | None, default: None ) –

    description. Defaults to None.

  • caller (float | None, default: None ) –

    description. Defaults to None.

Raises:

Returns:

Source code in lettrade/exchange/metatrader/trade.py 
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def update(
    self,
    limit_price: float | None = None,
    stop_price: float | None = None,
    sl: float | None = None,
    tp: float | None = None,
    caller: float | None = None,
    **kwargs,
) -> OrderResult:
    """_summary_

    Args:
        limit_price (float | None, optional): _description_. Defaults to None.
        stop_price (float | None, optional): _description_. Defaults to None.
        sl (float | None, optional): _description_. Defaults to None.
        tp (float | None, optional): _description_. Defaults to None.
        caller (float | None, optional): _description_. Defaults to None.

    Raises:
        RuntimeError: _description_

    Returns:
        OrderResult: _description_
    """
    if caller is self:
        raise RuntimeError(f"Order recusive update {self}")

    if self.parent is None:
        result = self._api.order_update(
            order=self,
            limit_price=limit_price,
            stop_price=stop_price,
            sl=sl,
            tp=tp,
            **kwargs,
        )
        return super(LiveOrder, self).update(
            limit_price=result.limit_price,
            stop_price=result.stop_price,
            sl=result.sl,
            tp=result.tp,
        )
    else:
        # SL/TP Order just a virtual order
        if caller is not self.parent:
            if self.is_sl_order:
                self.parent.update(sl=stop_price, caller=self)
            elif self.is_tp_order:
                self.parent.update(tp=limit_price, caller=self)
            else:
                raise RuntimeError(f"Abandon order {self}")

        return super(LiveOrder, self).update(
            limit_price=limit_price,
            stop_price=stop_price,
        )

MetaTraderPosition ¤

MetaTraderPosition(
    id: str,
    exchange: LiveExchange,
    data: LiveDataFeed,
    size: float,
    parent: Order,
    tag: str | None = None,
    state: PositionState = PositionState.Open,
    entry_price: float | None = None,
    entry_fee: float = 0.0,
    entry_at: int | None = None,
    sl_order: Order | None = None,
    tp_order: Order | None = None,
    api: LiveAPI | None = None,
    raw: object | None = None,
    **kwargs
)

Bases: LivePosition

Position for MetaTrader

Source code in lettrade/exchange/live/trade.py 
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def __init__(
    self,
    id: str,
    exchange: "LiveExchange",
    data: "LiveDataFeed",
    size: float,
    parent: Order,
    tag: str | None = None,
    state: PositionState = PositionState.Open,
    entry_price: float | None = None,
    entry_fee: float = 0.0,
    entry_at: int | None = None,
    sl_order: Order | None = None,
    tp_order: Order | None = None,
    api: LiveAPI | None = None,
    raw: object | None = None,
    **kwargs,
):
    super().__init__(
        id=id,
        exchange=exchange,
        data=data,
        size=size,
        parent=parent,
        tag=tag,
        state=state,
        entry_price=entry_price,
        entry_fee=entry_fee,
        entry_at=entry_at,
        sl_order=sl_order,
        tp_order=tp_order,
        api=api,
        raw=raw,
        **kwargs,
    )

fee property ¤

fee: float

Fee/Estimate Fee for trade

Returns:

  • float ( float ) –

    Fee

is_exited property ¤

is_exited: bool

Flag to check Position state.

Returns:

  • bool ( bool ) –

    True if the trade exited

is_long property ¤

is_long: bool

True if side is long (size is positive).

Returns:

  • bool ( bool ) –

    True/False

is_opening property ¤

is_opening: bool

Flag to check Position state.

Returns:

  • bool ( bool ) –

    True if the trade opening

is_short property ¤

is_short: bool

True if side is short (size is negative).

Returns:

  • bool ( bool ) –

    description

pl property ¤

pl: float

Estimate Profit or Loss of Position

Returns:

  • float ( float ) –

    PnL

side property ¤

side: TradeSide

True if side is short (size is negative).

Returns:

exit ¤

exit() -> PositionResult

summary

Returns:

Source code in lettrade/exchange/metatrader/trade.py 
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def exit(self) -> PositionResult:
    """_summary_

    Returns:
        PositionResult: _description_
    """
    result = self._api.position_close(position=self)
    if result.code != 0:
        logger.error("Update position %s", str(result))
        error = PositionResultError(
            error=result.error,
            position=self,
            raw=result,
        )
        self.exchange.on_notify(error=error)
        return error

    execution_raw = self._api.execution_get(id=result.execution_id)

    # TODO: execution object and event
    result.execution_raw = execution_raw

    return super(LivePosition, self).exit(
        price=result.price,
        at=pd.to_datetime(execution_raw.time_msc, unit="ms", utc=True),
        pl=execution_raw.profit,
        fee=execution_raw.fee + execution_raw.swap + execution_raw.commission,
        raw=result,
    )

from_raw classmethod ¤

from_raw(
    raw,
    exchange: MetaTraderExchange,
    state: PositionState = PositionState.Open,
    data: MetaTraderDataFeed | None = None,
    api: MetaTraderAPI = None,
) -> MetaTraderPosition

summary

Parameters:

Raises:

Returns:

Source code in lettrade/exchange/metatrader/trade.py 
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@classmethod
def from_raw(
    cls,
    raw,
    exchange: "MetaTraderExchange",
    state: PositionState = PositionState.Open,
    data: "MetaTraderDataFeed | None" = None,
    api: MetaTraderAPI = None,
) -> "MetaTraderPosition":
    """_summary_

    Args:
        raw (_type_): _description_
        exchange (MetaTraderExchange): _description_
        data (MetaTraderDataFeed, optional): _description_. Defaults to None.
        api (MetaTraderAPI, optional): _description_. Defaults to None.

    Raises:
        NotImplementedError: _description_

    Returns:
        MetaTraderPosition: _description_
    """
    # DataFeed
    if data is None:
        for d in exchange.datas:
            if d.symbol == raw.symbol:
                data = d
                break
        if data is None:
            logger.warning("Raw position %s is not handling %s", raw.symbol, raw)
            return

    # Side
    match raw.type:
        case MT5.POSITION_TYPE_BUY:
            side = TradeSide.Buy
        case MT5.POSITION_TYPE_SELL:
            side = TradeSide.Sell
        case _:
            raise NotImplementedError(
                f"Position type {raw.type} is not implement",
                raw,
            )
    # API
    if api is None:
        api = exchange._api

    position = cls(
        exchange=exchange,
        id=raw.ticket,
        data=data,
        state=state,
        size=side * raw.volume,
        entry_price=raw.price_open,
        entry_fee=raw.swap,
        entry_at=pd.to_datetime(raw.time_msc, unit="ms", utc=True),
        parent=None,
        tag=raw.comment,
        api=api,
        raw=raw,
    )

    # SL
    if raw.sl > 0.0:
        position.sl_order = exchange._order_cls.from_position(
            position=position, sl=raw.sl
        )
        exchange.on_order(position.sl_order)

    # TP
    if raw.tp > 0.0:
        position.tp_order = exchange._order_cls.from_position(
            position=position, tp=raw.tp
        )
        exchange.on_order(position.tp_order)

    if position.state == PositionState.Exit:
        if position.exit_price is None:
            if not hasattr(raw, "executions"):
                raw.executions = api.executions_get(position_id=position.id)

            exchange.on_executions_event(raw.executions, broadcast=False)

            execution = exchange.executions[raw.executions[-1].ticket]
            position.exit_at = execution.at
            position.exit_price = execution.price
            position.exit_pl = execution.pl
            position.exit_fee = execution.fee

    return position

merge ¤

merge(other: LivePosition) -> bool

Merge LivePosition from another

Parameters:

Returns:

  • bool ( bool ) –

    description

Source code in lettrade/exchange/live/trade.py 
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def merge(self, other: "LivePosition") -> bool:
    """Merge LivePosition from another

    Args:
        other (LivePosition): _description_

    Returns:
        bool: _description_
    """
    if not super().merge(other):
        return False
    self.raw = other.raw
    return True

update ¤

update(
    sl: float | None = None,
    tp: float | None = None,
    caller: float | None = None,
    **kwargs
) -> PositionResult

summary

Parameters:

  • sl (float | None, default: None ) –

    description. Defaults to None.

  • tp (float | None, default: None ) –

    description. Defaults to None.

  • caller (float | None, default: None ) –

    description. Defaults to None.

Raises:

Returns:

Source code in lettrade/exchange/metatrader/trade.py 
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def update(
    self,
    sl: float | None = None,
    tp: float | None = None,
    caller: float | None = None,
    **kwargs,
) -> PositionResult:
    """_summary_

    Args:
        sl (float | None, optional): _description_. Defaults to None.
        tp (float | None, optional): _description_. Defaults to None.
        caller (float | None, optional): _description_. Defaults to None.

    Raises:
        RuntimeError: _description_
        RuntimeError: _description_

    Returns:
        PositionResult: _description_
    """
    if not sl and not tp:
        raise RuntimeError("Update sl=None and tp=None")
    if caller is self:
        raise RuntimeError(f"Position recusive update {self}")

    result = self._api.position_update(position=self, sl=sl, tp=tp)
    if result.code != 0:
        logger.error("Update position %s", str(result))
        error = PositionResultError(
            error=result.error,
            position=self,
            raw=result,
        )
        self.exchange.on_notify(error=error)
        return error

    if sl is not None:
        if self.sl_order:
            if caller is not self.sl_order:
                self.sl_order.update(stop_price=sl, caller=self)
        else:
            self.sl_order = self.exchange._order_cls.from_position(
                position=self, sl=sl
            )

    if tp is not None:
        if self.tp_order:
            if caller is not self.tp_order:
                self.tp_order.update(limit_price=tp, caller=self)
        else:
            self.tp_order = self.exchange._order_cls.from_position(
                position=self, tp=tp
            )

    return super(LivePosition, self).update(raw=result)

OptimizePlotter ¤

OptimizePlotter()

Bases: Plotter

Class help to plot lettrade

Source code in lettrade/exchange/backtest/plot.py 
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def __init__(self) -> None:
    super().__init__()

contour ¤

contour(x: str, y: str, z: str = 'equity', **kwargs)
Source code in lettrade/exchange/backtest/plot.py 
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def contour(self, x: str, y: str, z: str = "equity", **kwargs):
    """"""

heatmap ¤

heatmap(x: str, y: str, z: str = 'equity', **kwargs)
Source code in lettrade/exchange/backtest/plot.py 
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def heatmap(self, x: str, y: str, z: str = "equity", **kwargs):
    """"""

load abstractmethod ¤

load()

Load plot config from Strategy.plot() and setup candlestick/equity

Source code in lettrade/plot/plot.py 
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@abstractmethod
def load(self):
    """Load plot config from `Strategy.plot()` and setup candlestick/equity"""

on_done ¤

on_done()
Source code in lettrade/exchange/backtest/plot.py 
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def on_done(self):
    """"""

on_result ¤

on_result(result)
Source code in lettrade/exchange/backtest/plot.py 
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def on_result(self, result):
    """"""

plot abstractmethod ¤

plot(**kwargs)

Plot equity, orders, and positions then show

Source code in lettrade/plot/plot.py 
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@abstractmethod
def plot(self, **kwargs):
    """Plot `equity`, `orders`, and `positions` then show"""

stop abstractmethod ¤

stop()

stop plotter

Source code in lettrade/plot/plot.py 
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@abstractmethod
def stop(self):
    """stop plotter"""

PlotColor ¤

Bases: str, Enum

Enum plot color

AMBER class-attribute instance-attribute ¤

AMBER = '#fa0'

BLUE class-attribute instance-attribute ¤

BLUE = '#4287ff'

CYAN class-attribute instance-attribute ¤

CYAN = '#00bad6'

DEEP_ORANGE class-attribute instance-attribute ¤

DEEP_ORANGE = '#ff6e42'

DEEP_PURPLE class-attribute instance-attribute ¤

DEEP_PURPLE = '#7c4dff'

GREEN class-attribute instance-attribute ¤

GREEN = '#00c753'

INDIGO class-attribute instance-attribute ¤

INDIGO = '#526cfe'

LIGHT_BLUE class-attribute instance-attribute ¤

LIGHT_BLUE = '#0091eb'

LIGHT_GREEN class-attribute instance-attribute ¤

LIGHT_GREEN = '#63de17'

LIGHT_PINK class-attribute instance-attribute ¤

LIGHT_PINK = '#f06292'

LIGHT_RED class-attribute instance-attribute ¤

LIGHT_RED = '#e6695b'

LIGHT_YELLOW class-attribute instance-attribute ¤

LIGHT_YELLOW = '#fff176'

LIME class-attribute instance-attribute ¤

LIME = '#b0eb00'

ORANGE class-attribute instance-attribute ¤

ORANGE = '#ff9100'

PINK class-attribute instance-attribute ¤

PINK = '#f50056'

PURPLE class-attribute instance-attribute ¤

PURPLE = '#df41fb'

RED class-attribute instance-attribute ¤

RED = '#ff1947'

TEAL class-attribute instance-attribute ¤

TEAL = '#00bda4'

YELLOW class-attribute instance-attribute ¤

YELLOW = '#ffd500'

PlotlyBotPlotter ¤

PlotlyBotPlotter(bot: LetTradeBot)

Bases: BotPlotter

Class help to plot lettrade

Source code in lettrade/plot/bot.py 
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def __init__(self, bot: "LetTradeBot") -> None:
    self.bot = bot
    self.feeder = bot.feeder
    self.exchange = bot.exchange
    self.account = bot.account
    self.strategy = bot.strategy

    self.datas = self.feeder.datas
    self.data = self.feeder.datas[0]

datas instance-attribute ¤

datas: list[DataFeed] = datas

All plotting datafeeds

jump ¤

jump(
    since: int | str | Timestamp | None = None,
    order_id: str | None = None,
    position_id: str | None = None,
    range: int = 300,
    name: str | None = None,
)

Jump to place on datefeed

Parameters:

  • since (int | str | Timestamp | None, default: None ) –

    Jump to index/datetime. Defaults to None.

  • order_id (str | None, default: None ) –

    Jump to order id. Defaults to None.

  • position_id (str | None, default: None ) –

    Jump to position id. Defaults to None.

  • range (int, default: 300 ) –

    number of candle plot. Defaults to 300.

  • name (str | None, default: None ) –

    description. Defaults to None.

Raises:

Source code in lettrade/plot/bot.py 
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def jump(
    self,
    since: int | str | pd.Timestamp | None = None,
    order_id: str | None = None,
    position_id: str | None = None,
    range: int = 300,
    name: str | None = None,
):
    """Jump to place on datefeed

    Args:
        since (int | str | pd.Timestamp | None, optional): Jump to index/datetime. Defaults to None.
        order_id (str | None, optional): Jump to order id. Defaults to None.
        position_id (str | None, optional): Jump to position id. Defaults to None.
        range (int, optional): number of candle plot. Defaults to 300.
        name (str | None, optional): _description_. Defaults to None.

    Raises:
        RuntimeError: _description_
    """
    if since is None:
        if order_id is not None:  # Jump to order id
            if not isinstance(order_id, str):
                order_id = str(order_id)

            if order_id in self.exchange.orders:
                order = self.exchange.orders[order_id]
            elif order_id in self.exchange.history_orders:
                order = self.exchange.history_orders[order_id]
            else:
                raise RuntimeError(f"Order id {order_id} not found")

            loc = self.data.l.index.get_loc(order.placed_at)
            since = loc - int(range / 2)

        elif position_id is not None:  # Jump to position id
            if not isinstance(position_id, str):
                position_id = str(position_id)

            if position_id in self.exchange.positions:
                position = self.exchange.positions[position_id]
            elif position_id in self.exchange.history_positions:
                position = self.exchange.history_positions[position_id]
            else:
                raise RuntimeError(f"Position id {position_id} not found")

            loc = self.data.l.index.get_loc(position.entry_at)
            since = loc - int(range / 2)
        else:  # Reset
            self.jump_reset()
            return

    elif isinstance(since, str):  # Parse string to pd.Timestamp, then since=index
        since = pd.to_datetime(since, utc=True)
        since = self.data.l.index.get_loc(since)
    elif isinstance(since, pd.Timestamp):  # Get index of Timestamp
        since = self.data.l.index.get_loc(since)

    # Since min at pointer_start
    if since < self.data.l.pointer_start:
        since = self.data.l.pointer_start
    # Since max at pointer_stop
    if since > self.data.l.pointer_stop - range:
        since = self.data.l.pointer_stop - range

    self._jump_since = self.data.l.index[since]
    self._jump_to = self.data.l.index[since + range]

    # Reload data
    self.load()

jump_reset ¤

jump_reset() -> bool

Reset jump datafeeds back to bot datafeeds

Source code in lettrade/plot/bot.py 
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def jump_reset(self) -> bool:
    """Reset jump datafeeds back to bot datafeeds"""
    self._jump_since = None
    self._jump_to = None
    return True

load ¤

load()

Load plot config from Strategy.plot() and setup candlestick/equity

Source code in lettrade/plot/plotly/plotly.py 
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def load(self):
    """Load plot config from `Strategy.plot()` and setup candlestick/equity"""

    config = self._config_default()
    config = self._config_strategy(config)
    config = self._config_standard(config)

    params: dict = config.setdefault("params", dict())
    self.figure = make_subplots(**params)

    self._plot_config(config)

    self.figure.update_layout(**config["layout"])

plot ¤

plot(jump: dict | None = None, **kwargs)

Plotly show figure

Parameters:

  • jump (dict | None, default: None ) –

    description. Defaults to None.

Source code in lettrade/plot/plotly/plotly.py 
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def plot(self, jump: dict | None = None, **kwargs):
    """Plotly show figure

    Args:
        jump (dict | None, optional): _description_. Defaults to None.
    """
    if jump is not None:
        self.jump(**jump)
    elif self.figure is None:
        self.load()
    else:
        if self.jump_reset():
            self.load()

    params = dict(layout_xaxis_rangeslider_visible=False)
    params.update(**kwargs)
    self.figure.update(**params)

    self.figure.show()

stop ¤

stop()

stop plotter

Source code in lettrade/plot/plotly/plotly.py 
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def stop(self):
    """stop plotter"""

PlotlyOptimizePlotter ¤

PlotlyOptimizePlotter(total=None, process_bar: bool = True)

Bases: OptimizePlotter

Source code in lettrade/exchange/backtest/plotly/optimize.py 
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def __init__(self, total=None, process_bar: bool = True) -> None:
    super().__init__()

    self._total = total

    if process_bar:
        from rich.progress import (
            BarColumn,
            Console,
            Progress,
            SpinnerColumn,
            TaskProgressColumn,
            TextColumn,
            TimeElapsedColumn,
            TimeRemainingColumn,
        )

        console = Console(record=True, force_jupyter=False)
        self._process_bar = Progress(
            SpinnerColumn(),
            # *Progress.get_default_columns(),
            TextColumn("[progress.description]{task.description}"),
            BarColumn(),
            TaskProgressColumn(),
            TextColumn("[bold blue][{task.completed}/{task.total}]"),
            TimeRemainingColumn(),
            TimeElapsedColumn(),
            console=console,
            # transient=False,
        )
        self._process_bar.add_task("[cyan2]Optimizing", total=total)
        self._process_bar.start()
    else:
        self._process_bar = None

contour ¤

contour(
    x: str = None,
    y: str = None,
    z: str = "equity",
    histfunc="max",
    **kwargs
)

Plot optimize contour

Parameters:

  • x (str, default: None ) –

    description. Defaults to None.

  • y (str, default: None ) –

    description. Defaults to None.

  • z (str, default: 'equity' ) –

    description. Defaults to "equity".

  • histfunc (str, default: 'max' ) –

    description. Defaults to "max".

Source code in lettrade/exchange/backtest/plotly/optimize.py 
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def contour(
    self,
    x: str = None,
    y: str = None,
    z: str = "equity",
    histfunc="max",
    **kwargs,
):
    """Plot optimize contour

    Args:
        x (str, optional): _description_. Defaults to None.
        y (str, optional): _description_. Defaults to None.
        z (str, optional): _description_. Defaults to "equity".
        histfunc (str, optional): _description_. Defaults to "max".
    """
    x, y, z = self._xyz_default(x, y, z)
    df = pd.DataFrame(self._xyzs(x=x, y=y, z=z))
    fig = px.density_contour(
        df,
        x=x,
        y=y,
        z=z,
        nbinsx=int(df[x].max() - df[x].min() + 1),
        nbinsy=int(df[y].max() - df[y].min() + 1),
        histfunc=histfunc,
        **kwargs,
    )
    fig.update_traces(contours_coloring="fill", contours_showlabels=True)
    fig.show()

heatmap ¤

heatmap(
    x: str = None,
    y: str = None,
    z: str = "equity",
    histfunc="max",
    **kwargs
)

Plot optimize heatmap

Parameters:

  • x (str, default: None ) –

    description. Defaults to None.

  • y (str, default: None ) –

    description. Defaults to None.

  • z (str, default: 'equity' ) –

    description. Defaults to "equity".

  • histfunc (str, default: 'max' ) –

    description. Defaults to "max".

Source code in lettrade/exchange/backtest/plotly/optimize.py 
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def heatmap(
    self,
    x: str = None,
    y: str = None,
    z: str = "equity",
    histfunc="max",
    **kwargs,
):
    """Plot optimize heatmap

    Args:
        x (str, optional): _description_. Defaults to None.
        y (str, optional): _description_. Defaults to None.
        z (str, optional): _description_. Defaults to "equity".
        histfunc (str, optional): _description_. Defaults to "max".
    """
    x, y, z = self._xyz_default(x, y, z)
    df = pd.DataFrame(self._xyzs(x=x, y=y, z=z))
    fig = px.density_heatmap(
        df,
        x=x,
        y=y,
        z=z,
        nbinsx=int(df[x].max() - df[x].min() + 1),
        nbinsy=int(df[y].max() - df[y].min() + 1),
        histfunc=histfunc,
        color_continuous_scale="Viridis",
        **kwargs,
    )
    fig.show()

plot ¤

plot(**kwargs)

Plot optimize result

Source code in lettrade/exchange/backtest/plotly/optimize.py 
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def plot(self, **kwargs):
    """Plot optimize result"""
    ids = []
    equities = []
    for result in self.results:
        ids.append(result["index"])
        equities.append(result["result"]["equity"])

    df = pd.DataFrame({"id": ids, "equity": equities})

    fig = px.scatter(df, x="id", y="equity")
    fig.show()

Plotter ¤

Bases: ABC

Base class help to plot strategy

load abstractmethod ¤

load()

Load plot config from Strategy.plot() and setup candlestick/equity

Source code in lettrade/plot/plot.py 
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@abstractmethod
def load(self):
    """Load plot config from `Strategy.plot()` and setup candlestick/equity"""

plot abstractmethod ¤

plot(**kwargs)

Plot equity, orders, and positions then show

Source code in lettrade/plot/plot.py 
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@abstractmethod
def plot(self, **kwargs):
    """Plot `equity`, `orders`, and `positions` then show"""

stop abstractmethod ¤

stop()

stop plotter

Source code in lettrade/plot/plot.py 
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@abstractmethod
def stop(self):
    """stop plotter"""

StorageBackTestCommander ¤

Bases: Commander

BackTest Commander to debug commander notify

init ¤

init(
    bot: LetTradeBot,
    brain: Brain,
    exchange: Exchange,
    strategy: Strategy,
)

Init commander dependencies

Parameters:

Source code in lettrade/commander/commander.py 
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def init(
    self,
    bot: "LetTradeBot",
    brain: "Brain",
    exchange: "Exchange",
    strategy: "Strategy",
):
    """Init commander dependencies

    Args:
        bot (LetTradeBot): LetTradeBot object
        brain (Brain): Brain of bot
        exchange (Exchange): Manage bot trading
        strategy (Strategy): Strategy of bot
    """
    self.bot = bot
    self.brain = brain
    self.exchange = exchange
    self.strategy = strategy

    self._name = self.bot._name

Strategy ¤

Strategy(
    feeder: DataFeeder,
    exchange: Exchange,
    account: Account,
    commander: Commander,
    is_optimize: bool = False,
    **kwargs
)

Base class to implement a strategy

Parameters:

  • feeder (DataFeeder) –

    DataFeeder for strategy

  • exchange (Exchange) –

    Trading exchange

  • account (Account) –

    Account manager

  • commander (Commander) –

    Event/Command manager

  • is_optimize (bool, default: False ) –

    flag validate optimize condiction. Defaults to False.

Raises:

Example

Set custom variable to strategy: lt = let_backtest(strategy_kwargs=dict(custom_key=custom_value)

Source code in lettrade/strategy/strategy.py 
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def __init__(
    self,
    feeder: DataFeeder,
    exchange: Exchange,
    account: Account,
    commander: Commander,
    is_optimize: bool = False,
    **kwargs,
):
    """_summary_

    Args:
        feeder (DataFeeder): DataFeeder for strategy
        exchange (Exchange): Trading exchange
        account (Account): Account manager
        commander (Commander): Event/Command manager
        is_optimize (bool, optional): flag validate optimize condiction. Defaults to False.

    Raises:
        RuntimeError: Validate valid is_optimize flag

    Example:
        Set custom variable to strategy:
            ```lt = let_backtest(strategy_kwargs=dict(custom_key=custom_value)```

    """
    self.__feeder: DataFeeder = feeder
    self.__exchange: Exchange = exchange
    self.__account: Account = account
    self.__commander: Commander = commander

    self.__datas: list[DataFeed] = self.__feeder.datas
    self.__data: DataFeed = self.__feeder.data

    if is_optimize and self.is_live:
        raise RuntimeError("Optimize a live datafeeder")
    self.__is_optimize: bool = is_optimize

    # Set parameters
    if kwargs:
        logger.info("Update strategy parameters %s", kwargs)
        for k, v in kwargs.items():
            setattr(self, k, v)

account property ¤

account: Account

Getter of Account

Returns:

  • Account ( Account ) –

    description

commander property ¤

commander: Commander

Getter of Commander

Returns:

data property ¤

data: DataFeed

Getter of main DataFeed

Returns:

datas property ¤

datas: list[DataFeed]

Getter of all DataFeed

Returns:

exchange property ¤

exchange: Exchange

Getter of Exchange

Returns:

feeder property ¤

feeder: DataFeeder

Getter of DataFeeder

Returns:

history_orders property ¤

history_orders: dict[str, Order | BackTestOrder]

Getter of history Order dict

Returns:

history_positions property ¤

history_positions: dict[str, Position | BackTestPosition]

Getter of history Position dict

Returns:

is_backtest property ¤

is_backtest: bool

Flag to check strategy is running in backtest DataFeeder

Returns:

  • bool ( bool ) –

    description

is_live property ¤

is_live: bool

Flag to check strategy is running in live DataFeeder

Returns:

  • bool ( bool ) –

    description

is_optimize property ¤

is_optimize: bool

Flag to check strategy is running in optimize session

Returns:

  • bool ( bool ) –

    description

now property ¤

now: datetime

Getter of current datetime

Returns:

  • datetime ( datetime ) –

    current datetime of bar

orders property ¤

orders: dict[str, Order | BackTestOrder]

Getter of Order dict

Returns:

positions property ¤

positions: dict[str, Position | BackTestPosition]

Getter of Position dict

Returns:

indicators ¤

indicators(df: DataFeed) -> None

All indicator and signal should implement here to cacheable. Because of lettrade will cache/pre-load all DataFeed.

To add indicators for a special DataFeed, define a new function name indicators_<DataFeed name>.

Usage 
df_eurusd = DataFeed(name="eurusd")
df_gbpusd = DataFeed(name="gbpusd")

# This function will load indicators for df_eurusd
def indicators(self, df: DataFeed):
    df['ema'] = df.i.ema(window=21)

# This function will load indicators for df_gbpusd
def indicators_gbpusd(self, df: DataFeed):
    df['ema'] = df.i.ema(window=25)

Parameters:

  • df (DataFeed) –

    DataFeed need to load indicators value

Source code in lettrade/strategy/strategy.py 
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def indicators(self, df: DataFeed) -> None:
    """All indicator and signal should implement here to cacheable.
    Because of `lettrade` will cache/pre-load all `DataFeed`.

    To add indicators for a special DataFeed, define a new function name
    `indicators_<DataFeed name>`.

    Usage:
        ```python
        df_eurusd = DataFeed(name="eurusd")
        df_gbpusd = DataFeed(name="gbpusd")

        # This function will load indicators for df_eurusd
        def indicators(self, df: DataFeed):
            df['ema'] = df.i.ema(window=21)

        # This function will load indicators for df_gbpusd
        def indicators_gbpusd(self, df: DataFeed):
            df['ema'] = df.i.ema(window=25)
        ```

    Args:
        df (DataFeed): DataFeed need to load indicators value
    """

init ¤

init() -> None

Init strategy variables

Source code in lettrade/strategy/strategy.py 
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def init(self) -> None:
    """Init strategy variables"""

next ¤

next(df: DataFeed, *others: list[DataFeed]) -> None

Next bar event

Source code in lettrade/strategy/strategy.py 
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def next(self, df: DataFeed, *others: list[DataFeed]) -> None:
    """Next bar event"""

on_executions ¤

on_executions(executions: list[Execution])

Listen for Execution event since next() begin

Parameters:

Source code in lettrade/strategy/strategy.py 
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def on_executions(self, executions: list[Execution]):
    """Listen for `Execution` event since `next()` begin

    Args:
        executions (list[Execution]): _description_
    """
    if hasattr(self, "on_execution"):
        for execution in executions:
            self.on_execution(execution)

on_notify ¤

on_notify(*args, **kwargs) -> None

Listen for notify event since next() begin

Returns:

  • _type_ ( None ) –

    None

Source code in lettrade/strategy/strategy.py 
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def on_notify(self, *args, **kwargs) -> None:
    """Listen for `notify` event since `next()` begin

    Returns:
        _type_: `None`
    """

on_orders ¤

on_orders(orders: list[Order])

Listen for Order event

Parameters:

Source code in lettrade/strategy/strategy.py 
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def on_orders(self, orders: list[Order]):
    """Listen for `Order` event

    Args:
        orders (list[Order]): _description_
    """
    if hasattr(self, "on_order"):
        for order in orders:
            self.on_order(order)

on_positions ¤

on_positions(positions: list[Position])

Listen for Position event since next() begin

Parameters:

Source code in lettrade/strategy/strategy.py 
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def on_positions(self, positions: list[Position]):
    """Listen for `Position` event since `next()` begin

    Args:
        positions (list[Position]): _description_
    """
    if hasattr(self, "on_position"):
        for position in positions:
            self.on_position(position)

on_transactions ¤

on_transactions(trans: list[Execution | Order | Position])

Listen for transaction events since next() begin

Parameters:

Source code in lettrade/strategy/strategy.py 
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def on_transactions(self, trans: list[Execution | Order | Position]):
    """Listen for transaction events since `next()` begin

    Args:
        trans (list[Execution  |  Order  |  Position]): _description_
    """
    if hasattr(self, "on_transaction"):
        for tran in trans:
            self.on_transaction(tran)

place_buy_order ¤

place_buy_order(
    size: float | None = None,
    type: OrderType = OrderType.Market,
    limit: float | None = None,
    stop: float | None = None,
    sl: float | None = None,
    tp: float | None = None,
    expiration: (
        int
        | datetime
        | timedelta
        | Timestamp
        | Timedelta
        | None
    ) = None,
    tag: str | None = None,
    **kwargs
) -> OrderResult

Place a new long order.

Parameters:

  • size (float | None, default: None ) –

    description. Defaults to None.

  • type (OrderType, default: Market ) –

    description. Defaults to OrderType.Market.

  • limit (float | None, default: None ) –

    description. Defaults to None.

  • stop (float | None, default: None ) –

    description. Defaults to None.

  • sl (float | None, default: None ) –

    description. Defaults to None.

  • tp (float | None, default: None ) –

    description. Defaults to None.

  • tag (str | None, default: None ) –

    description. Defaults to None.

  • **kwargs (dict | None, default: {} ) –

    Extra-parameters send to Exchange.new_order

Returns:

  • OrderResult ( OrderResult ) –

    Order result information

Source code in lettrade/strategy/strategy.py 
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@final
def place_buy_order(
    self,
    size: float | None = None,
    type: OrderType = OrderType.Market,
    limit: float | None = None,
    stop: float | None = None,
    sl: float | None = None,
    tp: float | None = None,
    expiration: int
    | datetime
    | timedelta
    | pd.Timestamp
    | pd.Timedelta
    | None = None,
    tag: str | None = None,
    **kwargs,
) -> OrderResult:
    """Place a new long order.

    Args:
        size (float | None, optional): _description_. Defaults to None.
        type (OrderType, optional): _description_. Defaults to OrderType.Market.
        limit (float | None, optional): _description_. Defaults to None.
        stop (float | None, optional): _description_. Defaults to None.
        sl (float | None, optional): _description_. Defaults to None.
        tp (float | None, optional): _description_. Defaults to None.
        tag (str | None, optional): _description_. Defaults to None.
        **kwargs (dict | None, optional): Extra-parameters send to `Exchange.new_order`

    Returns:
        OrderResult: Order result information
    """
    return self.place_order(
        side=TradeSide.Buy,
        size=size,
        type=type,
        limit=limit,
        stop=stop,
        sl=sl,
        tp=tp,
        expiration=expiration,
        tag=tag,
        **kwargs,
    )

place_order ¤

place_order(
    side: TradeSide,
    size: float | None = None,
    type: OrderType = OrderType.Market,
    limit: float | None = None,
    stop: float | None = None,
    sl: float | None = None,
    tp: float | None = None,
    expiration: (
        int
        | datetime
        | timedelta
        | Timestamp
        | Timedelta
        | None
    ) = None,
    tag: str | None = None,
    data: DataFeed | None = None,
    **kwargs
) -> OrderResult

summary

Parameters:

  • side (TradeSide) –

    description

  • size (float | None, default: None ) –

    description. Defaults to None.

  • type (OrderType, default: Market ) –

    description. Defaults to OrderType.Market.

  • limit (float | None, default: None ) –

    description. Defaults to None.

  • stop (float | None, default: None ) –

    description. Defaults to None.

  • sl (float | None, default: None ) –

    description. Defaults to None.

  • tp (float | None, default: None ) –

    description. Defaults to None.

  • expiration (int | datetime | timedelta | Timestamp | Timedelta | None, default: None ) –

    description. Defaults to None.

  • tag (str | None, default: None ) –

    description. Defaults to None.

  • data (DataFeed | None, default: None ) –

    description. Defaults to None.

Returns:

Source code in lettrade/strategy/strategy.py 
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@final
def place_order(
    self,
    side: TradeSide,
    size: float | None = None,
    type: OrderType = OrderType.Market,
    limit: float | None = None,
    stop: float | None = None,
    sl: float | None = None,
    tp: float | None = None,
    expiration: int
    | datetime
    | timedelta
    | pd.Timestamp
    | pd.Timedelta
    | None = None,
    tag: str | None = None,
    data: DataFeed | None = None,
    **kwargs,
) -> OrderResult:
    """_summary_

    Args:
        side (TradeSide): _description_
        size (float | None, optional): _description_. Defaults to None.
        type (OrderType, optional): _description_. Defaults to OrderType.Market.
        limit (float | None, optional): _description_. Defaults to None.
        stop (float | None, optional): _description_. Defaults to None.
        sl (float | None, optional): _description_. Defaults to None.
        tp (float | None, optional): _description_. Defaults to None.
        expiration (int | datetime | timedelta | pd.Timestamp | pd.Timedelta | None, optional): _description_. Defaults to None.
        tag (str | None, optional): _description_. Defaults to None.
        data (DataFeed | None, optional): _description_. Defaults to None.

    Returns:
        OrderResult: _description_
    """
    if data is None:
        data = self.data

    if isinstance(expiration, int):
        expiration = self.now + data.timeframe.delta * expiration
    elif isinstance(expiration, timedelta):
        expiration = self.now + expiration

    params = dict(
        size=size,
        type=type,
        limit=limit,
        stop=stop,
        sl=sl,
        tp=tp,
        expiration=expiration,
        tag=tag,
        data=data,
        **kwargs,
    )
    params["size"] = side * abs(self.__account.risk(side=side, **params))
    return self.__exchange.new_order(**params)

place_sell_order ¤

place_sell_order(
    size: float | None = None,
    type: OrderType = OrderType.Market,
    limit: float | None = None,
    stop: float | None = None,
    sl: float | None = None,
    tp: float | None = None,
    expiration: (
        int
        | datetime
        | timedelta
        | Timestamp
        | Timedelta
        | None
    ) = None,
    tag: str | None = None,
    **kwargs
) -> OrderResult

Place a new short order.

Parameters:

  • size (float | None, default: None ) –

    description. Defaults to None.

  • type (OrderType, default: Market ) –

    description. Defaults to OrderType.Market.

  • limit (float | None, default: None ) –

    description. Defaults to None.

  • stop (float | None, default: None ) –

    description. Defaults to None.

  • sl (float | None, default: None ) –

    description. Defaults to None.

  • tp (float | None, default: None ) –

    description. Defaults to None.

  • tag (str | None, default: None ) –

    description. Defaults to None.

  • **kwargs (dict | None, default: {} ) –

    Extra-parameters send to Exchange.new_order

Returns:

  • OrderResult ( OrderResult ) –

    Order result information

Source code in lettrade/strategy/strategy.py 
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@final
def place_sell_order(
    self,
    size: float | None = None,
    type: OrderType = OrderType.Market,
    limit: float | None = None,
    stop: float | None = None,
    sl: float | None = None,
    tp: float | None = None,
    expiration: int
    | datetime
    | timedelta
    | pd.Timestamp
    | pd.Timedelta
    | None = None,
    tag: str | None = None,
    **kwargs,
) -> OrderResult:
    """Place a new short order.

    Args:
        size (float | None, optional): _description_. Defaults to None.
        type (OrderType, optional): _description_. Defaults to OrderType.Market.
        limit (float | None, optional): _description_. Defaults to None.
        stop (float | None, optional): _description_. Defaults to None.
        sl (float | None, optional): _description_. Defaults to None.
        tp (float | None, optional): _description_. Defaults to None.
        tag (str | None, optional): _description_. Defaults to None.
        **kwargs (dict | None, optional): Extra-parameters send to `Exchange.new_order`

    Returns:
        OrderResult: Order result information
    """
    return self.place_order(
        side=TradeSide.Sell,
        size=size,
        type=type,
        limit=limit,
        stop=stop,
        sl=sl,
        tp=tp,
        expiration=expiration,
        tag=tag,
        **kwargs,
    )

plot ¤

plot(df: DataFeed, *others: list[DataFeed]) -> dict

Custom config of plot

Example 
{
    # Global items is main datafeed item
    "items": [],

    # DataFeed plot config
    "datafeed_name_eurusd": {
        "items": [
            {
                "type": "scatter",
                "x": dataframe.index,
                "y": dataframe["close"],
                "name": "EMA",
                "mode": "lines",
                "line": { "color": '#00bad6', "width": 1 },
            }
        ]
    },

    # Layout config
    {
        "hovermode": "x unified"
    }
}

Parameters:

Returns:

  • dict ( dict ) –

    config

Source code in lettrade/strategy/strategy.py 
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def plot(self, df: DataFeed, *others: list[DataFeed]) -> dict:
    """Custom config of plot

    Example:
        ```json
        {
            # Global items is main datafeed item
            "items": [],

            # DataFeed plot config
            "datafeed_name_eurusd": {
                "items": [
                    {
                        "type": "scatter",
                        "x": dataframe.index,
                        "y": dataframe["close"],
                        "name": "EMA",
                        "mode": "lines",
                        "line": { "color": '#00bad6', "width": 1 },
                    }
                ]
            },

            # Layout config
            {
                "hovermode": "x unified"
            }
        }
        ```

    Args:
        df (DataFeed): plot DataFeed

    Returns:
        dict: config
    """
    return dict()

send_notify ¤

send_notify(msg: str, **kwargs) -> Any

Send message to commander

Parameters:

  • msg (str) –

    message string

Returns:

  • Any ( Any ) –

    description

Source code in lettrade/strategy/strategy.py 
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@final
def send_notify(self, msg: str, **kwargs) -> Any:
    """Send message to commander

    Args:
        msg (str): message string

    Returns:
        Any: _description_
    """
    return self.commander.send_message(msg=msg, **kwargs)

start ¤

start(df: DataFeed, *others: list[DataFeed]) -> None

call after init() and before first next() is called

Parameters:

Returns:

  • _type_ ( None ) –

    None

Source code in lettrade/strategy/strategy.py 
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def start(self, df: DataFeed, *others: list[DataFeed]) -> None:
    """call after `init()` and before first `next()` is called

    Args:
        df (DataFeed): _description_

    Returns:
        _type_: `None`
    """

stop ¤

stop(df: DataFeed, *others: list[DataFeed]) -> None

Call when strategy run completed

Parameters:

  • df (DataFeed) –

    main data of strategy

Source code in lettrade/strategy/strategy.py 
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def stop(self, df: DataFeed, *others: list[DataFeed]) -> None:
    """Call when strategy run completed

    Args:
        df (DataFeed): main data of strategy
    """

TelegramAPI ¤

TelegramAPI(token: str, chat_id: int, *args, **kwargs)

Singleton object communicate across multipprocessing

Source code in lettrade/commander/telegram.py 
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def __init__(self, token: str, chat_id: int, *args, **kwargs) -> None:
    self._token: str = token
    self._chat_id: int = int(chat_id)
    self._bots_queue = dict()
    self._bot_selected = None

cleanup ¤

cleanup() -> None

Stops all running telegram threads.

Source code in lettrade/commander/telegram.py 
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def cleanup(self) -> None:
    """Stops all running telegram threads."""
    # This can take up to `timeout` from the call to `start_polling`.
    asyncio.run_coroutine_threadsafe(self._cleanup_telegram(), self._loop)
    self._thread.join()

send_message ¤

send_message(msg: str, pname: str, **kwargs) -> None

Send message to Telegram Bot

Parameters:

  • msg (str) –

    Message

Returns:

  • _type_ ( None ) –

    None

Source code in lettrade/commander/telegram.py 
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def send_message(self, msg: str, pname: str, **kwargs) -> None:
    """Send message to Telegram Bot

    Args:
        msg (str): Message

    Returns:
        _type_: `None`
    """
    msg = f"*[Process: {pname}]*\n\n{escape_markdown(msg)}"
    asyncio.run_coroutine_threadsafe(self._send_msg(msg, **kwargs), self._loop)

start ¤

start(pname: str, action_queue: Queue)

Start

Source code in lettrade/commander/telegram.py 
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def start(self, pname: str, action_queue: Queue):
    """Start"""
    if pname in self._bots_queue:
        logger.warning("Process name %s override existed action queue", pname)
    self._bots_queue[pname] = action_queue

    logger.info("New join process: %s", pname)

    # TODO: Lock for safe multipleprocessing
    if hasattr(self, "_keyboard"):
        return

    self._init_keyboard()
    self._start_thread()

TelegramCommander ¤

TelegramCommander(
    token: str,
    chat_id: int,
    api: TelegramAPI | None = None,
    *args,
    **kwargs
)

Bases: Commander

Send notify and receive command from Telegram Bot

Parameters:

  • token (str) –

    Telegram Bot token

  • chat_id (int) –

    Telegram chat_id

Source code in lettrade/commander/telegram.py 
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def __init__(
    self,
    token: str,
    chat_id: int,
    api: TelegramAPI | None = None,
    *args,
    **kwargs,
) -> None:
    """_summary_

    Args:
        token (str): Telegram Bot token
        chat_id (int): Telegram chat_id
    """
    super().__init__(*args, **kwargs)
    self._api = api or TelegramAPI(token=token, chat_id=chat_id)
    self._is_running = True

init ¤

init(
    bot: LetTradeBot,
    brain: Brain,
    exchange: Exchange,
    strategy: Strategy,
)

Init commander dependencies

Parameters:

Source code in lettrade/commander/commander.py 
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def init(
    self,
    bot: "LetTradeBot",
    brain: "Brain",
    exchange: "Exchange",
    strategy: "Strategy",
):
    """Init commander dependencies

    Args:
        bot (LetTradeBot): LetTradeBot object
        brain (Brain): Brain of bot
        exchange (Exchange): Manage bot trading
        strategy (Strategy): Strategy of bot
    """
    self.bot = bot
    self.brain = brain
    self.exchange = exchange
    self.strategy = strategy

    self._name = self.bot._name

start ¤

start()

Start

Source code in lettrade/commander/telegram.py 
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def start(self):
    """Start"""
    logger.info("TelegramCommander start %s", self._name)
    q = self._t_action()
    self._api.start(pname=self._name, action_queue=q)

stop ¤

stop()

Stop

Source code in lettrade/commander/telegram.py 
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def stop(self):
    """Stop"""
    logger.info("TelegramCommander stop %s", self._name)
    self._api.cleanup()
    self._is_running = False

YFBackTestDataFeed ¤

YFBackTestDataFeed(
    name: str,
    ticker: str,
    since: str,
    to: str | None = None,
    period: str | None = None,
    interval: str = "1d",
    **kwargs
)

Bases: BackTestDataFeed

YahooFinance DataFeed

Parameters:

  • name (str) –

    description

  • ticker (str) –

    description

  • since (str) –

    description

  • to (str | None, default: None ) –

    description. Defaults to None.

  • period (str | None, default: None ) –

    description. Defaults to None.

  • interval (str, default: '1d' ) –

    description. Defaults to "1d".

Source code in lettrade/exchange/backtest/data.py 
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def __init__(
    self,
    name: str,
    ticker: str,
    since: str,
    to: str | None = None,
    period: str | None = None,
    interval: str = "1d",
    **kwargs,
) -> None:
    """_summary_

    Args:
        name (str): _description_
        ticker (str): _description_
        since (str): _description_
        to (str | None, optional): _description_. Defaults to None.
        period (str | None, optional): _description_. Defaults to None.
        interval (str, optional): _description_. Defaults to "1d".
    """
    params = dict(
        start=since,
        end=to,
        period=period,
        interval=interval,
    )

    # Download
    import yfinance as yf

    df = yf.download(ticker, **params)

    # Parse to lettrade datafeed
    from .extra.yfinance import yf_parse

    df = yf_parse(df)

    # Metadata
    meta = dict(yf=dict(ticker=ticker, **params))

    super().__init__(
        name=name,
        meta=meta,
        data=df,
        **kwargs,
    )

i property ¤

i: indicator

Alias to lettrade.indicator and using in DataFeed by call: DataFeed.i.indicator_name()

is_main property ¤

is_main: bool

Property to check DataFeed is main DataFeed or not

l instance-attribute ¤

l: LetDataFeedWrapper

LetTrade DataFeed wrapper using to manage index pointer of DataFeed

meta property ¤

meta: dict

Property to get metadata of DataFeed

name property ¤

name: str

Property to get name of DataFeed

now property ¤

now: Timestamp

Property to get current index value of DataFeed

timeframe property ¤

timeframe: TimeFrame

Property to get timeframe of DataFeed

bar ¤

bar(i: int = 0) -> Timestamp

Get current pd.Timestamp value of DataFeed

Parameters:

  • i (int, default: 0 ) –

    Index. Defaults to 0.

Returns:

  • Timestamp –

    pd.Timestamp: description

Source code in lettrade/data/data.py 
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def bar(self, i: int = 0) -> pd.Timestamp:
    """Get current pd.Timestamp value of DataFeed

    Args:
        i (int, optional): Index. Defaults to 0.

    Returns:
        pd.Timestamp: _description_
    """
    return self.l.index[i]

copy ¤

copy(deep: bool = False, **kwargs) -> DataFeed

summary

Parameters:

  • deep (bool, default: False ) –

    description. Defaults to False.

Returns:

Source code in lettrade/data/data.py 
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def copy(self, deep: bool = False, **kwargs) -> "DataFeed":
    """_summary_

    Args:
        deep (bool, optional): _description_. Defaults to False.

    Returns:
        DataFeed: _description_
    """
    df = super().copy(deep=deep)
    df = self.__class__(
        data=df,
        name=self.name,
        timeframe=self.timeframe,
        meta=self.meta.copy(),
        **kwargs,
    )
    return df

drop ¤

drop(
    *args,
    since: int | str | Timestamp | None = None,
    to: int | str | Timestamp | None = None,
    **kwargs
) -> None | DataFeed

summary

Parameters:

  • since (int | str | Timestamp | None, default: None ) –

    description. Defaults to None.

  • to (int | str | Timestamp | None, default: None ) –

    description. Defaults to None.

Raises:

Source code in lettrade/data/data.py 
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def drop(
    self,
    *args,
    since: int | str | pd.Timestamp | None = None,
    to: int | str | pd.Timestamp | None = None,
    **kwargs,
) -> "None | DataFeed":
    """_summary_

    Args:
        since (int | str | pd.Timestamp | None, optional): _description_. Defaults to None.
        to (int | str | pd.Timestamp | None, optional): _description_. Defaults to None.

    Raises:
        RuntimeError: _description_
    """
    if since is None and to is None:
        return super().drop(*args, **kwargs)

    condiction = None

    # Since
    if since is not None:
        if isinstance(since, int):
            loc = self.l.index[since]
        elif isinstance(since, str):
            loc = pd.to_datetime(since, utc=True)
        elif isinstance(since, pd.Timestamp):
            loc = since
        else:
            raise RuntimeError(f"DataFeed.drop since {since} is invalid")
        condiction = self.index < loc

    # To
    if to is not None:
        if isinstance(to, int):
            loc = self.l.index[to]
        elif isinstance(to, str):
            loc = pd.to_datetime(to, utc=True)
        elif isinstance(to, pd.Timestamp):
            loc = to
        else:
            raise RuntimeError(f"DataFeed.drop to {to} is invalid")

        if condiction is None:
            condiction = self.index > loc
        else:
            condiction = condiction | (self.index > loc)

    index = self[condiction].index
    super().drop(index=index, inplace=True)
    self.l.reset()

    if __debug__:
        logger.debug("BackTestDataFeed %s dropped %s rows", self.name, len(index))

push ¤

push(
    rows: list[list[int | float]],
    unit: str | None = None,
    utc: bool = True,
    **kwargs
)

Push new rows to DataFeed

Parameters:

  • rows (list[list[int | float]]) –

    list of rows [["timestamp", "open price", "high price"...]]

  • unit (str | None, default: None ) –

    pandas.Timestamp parsing unit. Defaults to None.

  • utc (bool, default: True ) –

    description. Defaults to True.

Source code in lettrade/data/data.py 
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def push(
    self,
    rows: list[list[int | float]],
    unit: str | None = None,
    utc: bool = True,
    **kwargs,
):
    """Push new rows to DataFeed

    Args:
        rows (list[list[int | float]]): list of rows `[["timestamp", "open price", "high price"...]]`
        unit (str | None, optional): pandas.Timestamp parsing unit. Defaults to None.
        utc (bool, optional): _description_. Defaults to True.
    """
    for row in rows:
        dt = pd.to_datetime(row[0], unit=unit, utc=utc, **kwargs)
        self.at[
            dt,
            (
                "open",
                "high",
                "low",
                "close",
                "volume",
            ),
        ] = (
            row[1],  # open
            row[2],  # high
            row[3],  # low
            row[4],  # close
            row[5],  # volume
        )

    if __debug__:
        logger.debug("[%s] Update bar: \n%s", self.name, self.tail(len(rows)))

above ¤

above(
    series1: Series | str,
    series2: Series | str,
    dataframe: DataFrame = None,
    name: str | None = None,
    prefix: str = "",
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_kwargs: dict | None = None,
) -> Series

Check a Series is above another Series

Parameters:

  • series1 (Series) –

    first Series

  • series2 (Series) –

    second Series

Returns:

  • Series –

    pd.Series: True series1 is above series2 else False

Source code in lettrade/indicator/series.py 
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def above(
    series1: pd.Series | str,
    series2: pd.Series | str,
    dataframe: pd.DataFrame = None,
    name: str | None = None,
    prefix: str = "",
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_kwargs: dict | None = None,
    # **kwargs,
) -> pd.Series:
    """Check a Series is above another Series

    Args:
        series1 (pd.Series): first Series
        series2 (pd.Series): second Series

    Returns:
        pd.Series: True series1 is above series2 else False
    """
    if __debug__:
        if plot and not inplace:
            raise RuntimeError("Cannot plot when inplace=False")

    if isinstance(series1, str):
        series1 = dataframe[series1]
    if isinstance(series2, str):
        series2 = dataframe[series2]

    i = s_above(series1=series1, series2=series2)

    if inplace:
        name = name or f"{prefix}above"
        dataframe[name] = i

        if plot:
            _plot_mark(dataframe=dataframe, name=name, plot_kwargs=plot_kwargs)

    return i

authorized_only ¤

authorized_only(
    command_handler: Callable[
        ..., Coroutine[Any, Any, None]
    ]
)

Decorator to check if the message comes from the correct chat_id

Parameters:

Source code in lettrade/commander/telegram.py 
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def authorized_only(command_handler: Callable[..., Coroutine[Any, Any, None]]):
    """Decorator to check if the message comes from the correct chat_id

    Args:
        command_handler (Callable[..., Coroutine[Any, Any, None]]): Telegram CommandHandler
    """

    @wraps(command_handler)
    async def wrapper(self: "TelegramCommander", *args, **kwargs):
        """Decorator logic"""
        update = kwargs.get("update") or args[0]

        # Reject unauthorized messages
        if update.callback_query:
            cchat_id = int(update.callback_query.message.chat.id)
        else:
            cchat_id = int(update.message.chat_id)

        if cchat_id != self._chat_id:
            logger.info(f"Rejected unauthorized message from: {update.message.chat_id}")
            return wrapper

        logger.debug(
            "Executing handler: %s for chat_id: %s",
            command_handler.__name__,
            self._chat_id,
        )
        try:
            return await command_handler(self, *args, **kwargs)
        except Exception as e:
            await self._send_msg(str(e))
            # except BaseException:
            logger.exception("Exception occurred within Telegram module", exc_info=e)

    return wrapper

below ¤

below(
    series1: Series | str,
    series2: Series | str,
    dataframe: DataFrame = None,
    name: str | None = None,
    prefix: str = "",
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_kwargs: dict | None = None,
) -> Series

Check a Series is below another Series

Parameters:

  • series1 (Series) –

    first Series

  • series2 (Series) –

    second Series

Returns:

  • Series –

    pd.Series: -100 series1 is below series2 else 0

Source code in lettrade/indicator/series.py 
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def below(
    series1: pd.Series | str,
    series2: pd.Series | str,
    dataframe: pd.DataFrame = None,
    name: str | None = None,
    prefix: str = "",
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_kwargs: dict | None = None,
    # **kwargs,
) -> pd.Series:
    """Check a Series is below another Series

    Args:
        series1 (pd.Series): first Series
        series2 (pd.Series): second Series

    Returns:
        pd.Series: -100 series1 is below series2 else 0
    """
    if __debug__:
        if plot and not inplace:
            raise RuntimeError("Cannot plot when inplace=False")

    if isinstance(series1, str):
        series1 = dataframe[series1]
    if isinstance(series2, str):
        series2 = dataframe[series2]

    i = s_below(series1, series2)

    if inplace:
        name = name or f"{prefix}below"
        dataframe[name] = i

        if plot:
            _plot_mark(dataframe=dataframe, name=name, plot_kwargs=plot_kwargs)

    return i

cdl_3blackcrows ¤

cdl_3blackcrows(
    dataframe: DataFrame,
    name: str | None = None,
    prefix: str = "cdl_",
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_type: Literal[
        "candlestick", "mark"
    ] = "candlestick",
    plot_kwargs: dict | None = None,
    **kwargs
) -> Series | DataFrame

summary

Parameters:

  • dataframe (DataFrame) –

    description

  • name (str | None, default: None ) –

    description. Defaults to None.

  • prefix (str, default: 'cdl_' ) –

    description. Defaults to "cdl_".

  • inplace (bool, default: False ) –

    description. Defaults to False.

Returns:

  • Series | DataFrame –

    pd.Series | pd.DataFrame: description

Source code in lettrade/indicator/candlestick/talib.py 
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def cdl_3blackcrows(
    dataframe: pd.DataFrame,
    name: str | None = None,
    prefix: str = "cdl_",
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_type: Literal["candlestick", "mark"] = "candlestick",
    plot_kwargs: dict | None = None,
    **kwargs,
) -> pd.Series | pd.DataFrame:
    """_summary_

    Args:
        dataframe (pd.DataFrame): _description_
        name (str | None, optional): _description_. Defaults to None.
        prefix (str, optional): _description_. Defaults to "cdl_".
        inplace (bool, optional): _description_. Defaults to False.

    Returns:
        pd.Series | pd.DataFrame: _description_
    """
    return cdl_pattern(
        dataframe=dataframe,
        pattern="3blackcrows",
        name=name,
        prefix=prefix,
        inplace=inplace,
        plot=plot,
        plot_type=plot_type,
        plot_kwargs=plot_kwargs,
        **kwargs,
    )

cdl_3inside ¤

cdl_3inside(
    dataframe: DataFrame,
    name: str | None = None,
    prefix: str = "cdl_",
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_type: Literal[
        "candlestick", "mark"
    ] = "candlestick",
    plot_kwargs: dict | None = None,
    **kwargs
) -> Series | DataFrame

summary

Parameters:

  • dataframe (DataFrame) –

    description

  • name (str | None, default: None ) –

    description. Defaults to None.

  • prefix (str, default: 'cdl_' ) –

    description. Defaults to "cdl_".

  • inplace (bool, default: False ) –

    description. Defaults to False.

Returns:

  • Series | DataFrame –

    pd.Series | pd.DataFrame: description

Source code in lettrade/indicator/candlestick/talib.py 
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def cdl_3inside(
    dataframe: pd.DataFrame,
    name: str | None = None,
    prefix: str = "cdl_",
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_type: Literal["candlestick", "mark"] = "candlestick",
    plot_kwargs: dict | None = None,
    **kwargs,
) -> pd.Series | pd.DataFrame:
    """_summary_

    Args:
        dataframe (pd.DataFrame): _description_
        name (str | None, optional): _description_. Defaults to None.
        prefix (str, optional): _description_. Defaults to "cdl_".
        inplace (bool, optional): _description_. Defaults to False.

    Returns:
        pd.Series | pd.DataFrame: _description_
    """
    return cdl_pattern(
        dataframe=dataframe,
        pattern="3inside",
        name=name,
        prefix=prefix,
        inplace=inplace,
        plot=plot,
        plot_type=plot_type,
        plot_kwargs=plot_kwargs,
        **kwargs,
    )

cdl_3whitesoldiers ¤

cdl_3whitesoldiers(
    dataframe: DataFrame,
    name: str | None = None,
    prefix: str = "cdl_",
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_type: Literal[
        "candlestick", "mark"
    ] = "candlestick",
    plot_kwargs: dict | None = None,
    **kwargs
) -> Series | DataFrame

summary

Parameters:

  • dataframe (DataFrame) –

    description

  • name (str | None, default: None ) –

    description. Defaults to None.

  • prefix (str, default: 'cdl_' ) –

    description. Defaults to "cdl_".

  • inplace (bool, default: False ) –

    description. Defaults to False.

Returns:

  • Series | DataFrame –

    pd.Series | pd.DataFrame: description

Source code in lettrade/indicator/candlestick/talib.py 
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def cdl_3whitesoldiers(
    dataframe: pd.DataFrame,
    name: str | None = None,
    prefix: str = "cdl_",
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_type: Literal["candlestick", "mark"] = "candlestick",
    plot_kwargs: dict | None = None,
    **kwargs,
) -> pd.Series | pd.DataFrame:
    """_summary_

    Args:
        dataframe (pd.DataFrame): _description_
        name (str | None, optional): _description_. Defaults to None.
        prefix (str, optional): _description_. Defaults to "cdl_".
        inplace (bool, optional): _description_. Defaults to False.

    Returns:
        pd.Series | pd.DataFrame: _description_
    """
    return cdl_pattern(
        dataframe=dataframe,
        pattern="3whitesoldiers",
        name=name,
        prefix=prefix,
        inplace=inplace,
        plot=plot,
        plot_type=plot_type,
        plot_kwargs=plot_kwargs,
        **kwargs,
    )

cdl_direction ¤

cdl_direction(
    dataframe: DataFrame,
    name: str | None = None,
    prefix: str = "cdl_",
    inplace: bool = False,
) -> Series | DataFrame

Direction of candle

Parameters:

  • dataframe (DataFrame) –

    description

  • name (str | None, default: None ) –

    description. Defaults to None.

  • prefix (str, default: 'cdl_' ) –

    description. Defaults to "cdl_".

  • inplace (bool, default: False ) –

    description. Defaults to False.

Returns:

  • Series | DataFrame –

    pd.Series | pd.DataFrame: pd.Series return [100, 0 , -100] - 100 for bull bar - -100 for bear bar - 0 for None

Source code in lettrade/indicator/candlestick/candlestick.py 
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def cdl_direction(
    dataframe: pd.DataFrame,
    name: str | None = None,
    prefix: str = "cdl_",
    inplace: bool = False,
) -> pd.Series | pd.DataFrame:
    """Direction of candle

    Args:
        dataframe (pd.DataFrame): _description_
        name (str | None, optional): _description_. Defaults to None.
        prefix (str, optional): _description_. Defaults to "cdl_".
        inplace (bool, optional): _description_. Defaults to False.

    Returns:
        pd.Series | pd.DataFrame: `pd.Series` return [100, 0 , -100]
            - `100` for bull bar
            - `-100` for bear bar
            - `0` for None
    """
    if __debug__:
        if not isinstance(dataframe, pd.DataFrame):
            raise RuntimeError(
                f"dataframe type '{type(dataframe)}' "
                "is not instance of pandas.DataFrame"
            )
    i = dataframe.apply(
        lambda r: 100 if r.open < r.close else -100 if r.open > r.close else 0,
        axis=1,
    ).astype(int)

    if inplace:
        dataframe[name or f"{prefix}direction"] = i
        return dataframe

    return i

cdl_doji ¤

cdl_doji(
    dataframe: DataFrame,
    name: str | None = None,
    prefix: str = "cdl_",
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_type: Literal[
        "candlestick", "mark"
    ] = "candlestick",
    plot_kwargs: dict | None = None,
    **kwargs
) -> Series | DataFrame

summary

Parameters:

  • dataframe (DataFrame) –

    description

  • name (str | None, default: None ) –

    description. Defaults to None.

  • prefix (str, default: 'cdl_' ) –

    description. Defaults to "cdl_".

  • inplace (bool, default: False ) –

    description. Defaults to False.

Returns:

  • Series | DataFrame –

    pd.Series | pd.DataFrame: description

Source code in lettrade/indicator/candlestick/talib.py 
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def cdl_doji(
    dataframe: pd.DataFrame,
    name: str | None = None,
    prefix: str = "cdl_",
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_type: Literal["candlestick", "mark"] = "candlestick",
    plot_kwargs: dict | None = None,
    **kwargs,
) -> pd.Series | pd.DataFrame:
    """_summary_

    Args:
        dataframe (pd.DataFrame): _description_
        name (str | None, optional): _description_. Defaults to None.
        prefix (str, optional): _description_. Defaults to "cdl_".
        inplace (bool, optional): _description_. Defaults to False.

    Returns:
        pd.Series | pd.DataFrame: _description_
    """
    return cdl_pattern(
        dataframe=dataframe,
        pattern="doji",
        name=name,
        prefix=prefix,
        inplace=inplace,
        plot=plot,
        plot_type=plot_type,
        plot_kwargs=plot_kwargs,
        **kwargs,
    )

cdl_engulfing ¤

cdl_engulfing(
    dataframe: DataFrame,
    name: str | None = None,
    prefix: str = "cdl_",
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_type: Literal[
        "candlestick", "mark"
    ] = "candlestick",
    plot_kwargs: dict | None = None,
    **kwargs
) -> Series | DataFrame

summary

Parameters:

  • dataframe (DataFrame) –

    description

  • name (str | None, default: None ) –

    description. Defaults to None.

  • prefix (str, default: 'cdl_' ) –

    description. Defaults to "cdl_".

  • inplace (bool, default: False ) –

    description. Defaults to False.

Returns:

  • Series | DataFrame –

    pd.Series | pd.DataFrame: description

Source code in lettrade/indicator/candlestick/talib.py 
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def cdl_engulfing(
    dataframe: pd.DataFrame,
    name: str | None = None,
    prefix: str = "cdl_",
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_type: Literal["candlestick", "mark"] = "candlestick",
    plot_kwargs: dict | None = None,
    **kwargs,
) -> pd.Series | pd.DataFrame:
    """_summary_

    Args:
        dataframe (pd.DataFrame): _description_
        name (str | None, optional): _description_. Defaults to None.
        prefix (str, optional): _description_. Defaults to "cdl_".
        inplace (bool, optional): _description_. Defaults to False.

    Returns:
        pd.Series | pd.DataFrame: _description_
    """
    return cdl_pattern(
        dataframe=dataframe,
        pattern="engulfing",
        name=name,
        prefix=prefix,
        inplace=inplace,
        plot=plot,
        plot_type=plot_type,
        plot_kwargs=plot_kwargs,
        **kwargs,
    )

cdl_eveningstar ¤

cdl_eveningstar(
    dataframe: DataFrame,
    name: str | None = None,
    prefix: str = "cdl_",
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_type: Literal[
        "candlestick", "mark"
    ] = "candlestick",
    plot_kwargs: dict | None = None,
    **kwargs
) -> Series | DataFrame

summary

Parameters:

  • dataframe (DataFrame) –

    description

  • name (str | None, default: None ) –

    description. Defaults to None.

  • prefix (str, default: 'cdl_' ) –

    description. Defaults to "cdl_".

  • inplace (bool, default: False ) –

    description. Defaults to False.

Returns:

  • Series | DataFrame –

    pd.Series | pd.DataFrame: description

Source code in lettrade/indicator/candlestick/talib.py 
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def cdl_eveningstar(
    dataframe: pd.DataFrame,
    name: str | None = None,
    prefix: str = "cdl_",
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_type: Literal["candlestick", "mark"] = "candlestick",
    plot_kwargs: dict | None = None,
    **kwargs,
) -> pd.Series | pd.DataFrame:
    """_summary_

    Args:
        dataframe (pd.DataFrame): _description_
        name (str | None, optional): _description_. Defaults to None.
        prefix (str, optional): _description_. Defaults to "cdl_".
        inplace (bool, optional): _description_. Defaults to False.

    Returns:
        pd.Series | pd.DataFrame: _description_
    """
    return cdl_pattern(
        dataframe=dataframe,
        pattern="eveningstar",
        name=name,
        prefix=prefix,
        inplace=inplace,
        plot=plot,
        plot_type=plot_type,
        plot_kwargs=plot_kwargs,
        **kwargs,
    )

cdl_morningstar ¤

cdl_morningstar(
    dataframe: DataFrame,
    name: str | None = None,
    prefix: str = "cdl_",
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_type: Literal[
        "candlestick", "mark"
    ] = "candlestick",
    plot_kwargs: dict | None = None,
    **kwargs
) -> Series | DataFrame

summary

Parameters:

  • dataframe (DataFrame) –

    description

  • name (str | None, default: None ) –

    description. Defaults to None.

  • prefix (str, default: 'cdl_' ) –

    description. Defaults to "cdl_".

  • inplace (bool, default: False ) –

    description. Defaults to False.

Returns:

  • Series | DataFrame –

    pd.Series | pd.DataFrame: description

Source code in lettrade/indicator/candlestick/talib.py 
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def cdl_morningstar(
    dataframe: pd.DataFrame,
    name: str | None = None,
    prefix: str = "cdl_",
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_type: Literal["candlestick", "mark"] = "candlestick",
    plot_kwargs: dict | None = None,
    **kwargs,
) -> pd.Series | pd.DataFrame:
    """_summary_

    Args:
        dataframe (pd.DataFrame): _description_
        name (str | None, optional): _description_. Defaults to None.
        prefix (str, optional): _description_. Defaults to "cdl_".
        inplace (bool, optional): _description_. Defaults to False.

    Returns:
        pd.Series | pd.DataFrame: _description_
    """
    return cdl_pattern(
        dataframe=dataframe,
        pattern="morningstar",
        name=name,
        prefix=prefix,
        inplace=inplace,
        plot=plot,
        plot_type=plot_type,
        plot_kwargs=plot_kwargs,
        **kwargs,
    )

cdl_pattern ¤

cdl_pattern(
    dataframe: DataFrame,
    pattern: str,
    name: str | None = None,
    prefix: str = "cdl_",
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_type: Literal[
        "candlestick", "mark"
    ] = "candlestick",
    plot_kwargs: dict | None = None,
    **kwargs
) -> Series | DataFrame

summary

Parameters:

  • dataframe (DataFrame) –

    pandas.DataFrame with ohlcv

  • pattern (str) –

    TA-Lib candle pattern name. Ex: 3whitesoldiers, 3blackcrows

  • name (str | None, default: None ) –

    description. Defaults to None.

  • prefix (str, default: 'cdl_' ) –

    description. Defaults to "cdl_".

  • inplace (bool, default: False ) –

    description. Defaults to False.

Raises:

Returns:

  • Series | DataFrame –

    pd.Series | pd.DataFrame: description

Source code in lettrade/indicator/candlestick/talib.py 
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def cdl_pattern(
    dataframe: pd.DataFrame,
    pattern: str,
    name: str | None = None,
    prefix: str = "cdl_",
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_type: Literal["candlestick", "mark"] = "candlestick",
    plot_kwargs: dict | None = None,
    **kwargs,
) -> pd.Series | pd.DataFrame:
    """_summary_

    Args:
        dataframe (pd.DataFrame): pandas.DataFrame with ohlcv
        pattern (str): TA-Lib candle pattern name. Ex: `3whitesoldiers`, `3blackcrows`
        name (str | None, optional): _description_. Defaults to None.
        prefix (str, optional): _description_. Defaults to "cdl_".
        inplace (bool, optional): _description_. Defaults to False.

    Raises:
        RuntimeError: _description_

    Returns:
        pd.Series | pd.DataFrame: _description_
    """
    if __debug__:
        if not isinstance(dataframe, pd.DataFrame):
            raise RuntimeError(
                f"dataframe type '{type(dataframe)}' "
                "is not instance of pandas.DataFrame"
            )
        if plot and not inplace:
            raise RuntimeError("Cannot plot when inplace=False")

    i = getattr(ta, f"CDL{pattern.upper()}")(dataframe, **kwargs)

    if inplace:
        name = name or f"{prefix}{pattern.lower()}"
        dataframe[name] = i

        # Plot
        if plot:
            _plot_pattern(
                dataframe=dataframe,
                indicator=i,
                name=name,
                plot_type=plot_type,
                plot_kwargs=plot_kwargs,
            )

        return dataframe

    return i

crossover ¤

crossover(
    series1: Series | str,
    series2: Series | str,
    dataframe: DataFrame = None,
    name: str | None = None,
    prefix: str = "",
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_kwargs: dict | None = None,
) -> Series

Check if a Series cross over another Series

Parameters:

  • series1 (Series) –

    first Series

  • series2 (Series) –

    second Series

Returns:

  • Series –

    pd.Series: 100 if series1 cross over series2 else 0

Source code in lettrade/indicator/series.py 
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def crossover(
    series1: pd.Series | str,
    series2: pd.Series | str,
    dataframe: pd.DataFrame = None,
    name: str | None = None,
    prefix: str = "",
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_kwargs: dict | None = None,
    # **kwargs,
) -> pd.Series:
    """Check if a Series cross over another Series

    Args:
        series1 (pd.Series): first Series
        series2 (pd.Series): second Series

    Returns:
        pd.Series: 100 if series1 cross over series2 else 0
    """
    if __debug__:
        if plot and not inplace:
            raise RuntimeError("Cannot plot when inplace=False")

    if isinstance(series1, str):
        series1 = dataframe[series1]
    if isinstance(series2, str):
        series2 = dataframe[series2]

    below1 = s_below(series1, series2).shift(1)
    above0 = s_above(series1, series2)
    i = (-below1 + above0).apply(lambda v: 100 if v >= 200 else 0)

    if inplace:
        name = name or f"{prefix}crossover"
        dataframe[name] = i

        if plot:
            _plot_mark(dataframe=dataframe, name=name, plot_kwargs=plot_kwargs)

    return i

crossunder ¤

crossunder(
    series1: Series | str,
    series2: Series | str,
    dataframe: DataFrame = None,
    name: str | None = None,
    prefix: str = "",
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_kwargs: dict | None = None,
) -> Series

Check if a Series cross under another Series

Parameters:

  • series1 (Series) –

    first Series

  • series2 (Series) –

    second Series

Returns:

  • Series –

    pd.Series: -100 if series1 cross under series2 else 0

Source code in lettrade/indicator/series.py 
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def crossunder(
    series1: pd.Series | str,
    series2: pd.Series | str,
    dataframe: pd.DataFrame = None,
    name: str | None = None,
    prefix: str = "",
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_kwargs: dict | None = None,
    # **kwargs,
) -> pd.Series:
    """Check if a Series cross under another Series

    Args:
        series1 (pd.Series): first Series
        series2 (pd.Series): second Series

    Returns:
        pd.Series: -100 if series1 cross under series2 else 0
    """
    if __debug__:
        if plot and not inplace:
            raise RuntimeError("Cannot plot when inplace=False")

    if isinstance(series1, str):
        series1 = dataframe[series1]
    if isinstance(series2, str):
        series2 = dataframe[series2]

    above1 = s_above(series1, series2).shift(1)
    below0 = s_below(series1, series2)
    i = (below0 - above1).apply(lambda v: -100 if v <= -200 else 0)

    if inplace:
        name = name or f"{prefix}crossunder"
        dataframe[name] = i

        if plot:
            _plot_mark(dataframe=dataframe, name=name, plot_kwargs=plot_kwargs)

    return i

diff ¤

diff(
    series1: Series | str,
    series2: Series | str,
    dataframe: DataFrame = None,
    name: str | None = None,
    prefix: str = "",
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_kwargs: dict | None = None,
) -> Series

Difference between 2 series

Parameters:

  • series1 (Series) –

    description

  • series2 (Series) –

    description

  • dataframe (DataFrame, default: None ) –

    description. Defaults to None.

  • name (str | None, default: None ) –

    description. Defaults to None.

  • prefix (str, default: '' ) –

    description. Defaults to "".

  • inplace (bool, default: False ) –

    description. Defaults to False.

  • plot (bool | list, default: False ) –

    description. Defaults to False.

  • plot_kwargs (dict | None, default: None ) –

    description. Defaults to None.

Raises:

Returns:

  • Series –

    pd.Series: Difference of 2 series (series1 - series2)

Source code in lettrade/indicator/series.py 
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def diff(
    series1: pd.Series | str,
    series2: pd.Series | str,
    dataframe: pd.DataFrame = None,
    name: str | None = None,
    prefix: str = "",
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_kwargs: dict | None = None,
    # **kwargs,
) -> pd.Series:
    """Difference between 2 series

    Args:
        series1 (pd.Series): _description_
        series2 (pd.Series): _description_
        dataframe (pd.DataFrame, optional): _description_. Defaults to None.
        name (str | None, optional): _description_. Defaults to None.
        prefix (str, optional): _description_. Defaults to "".
        inplace (bool, optional): _description_. Defaults to False.
        plot (bool | list, optional): _description_. Defaults to False.
        plot_kwargs (dict | None, optional): _description_. Defaults to None.

    Raises:
        RuntimeError: _description_

    Returns:
        pd.Series: Difference of 2 series (series1 - series2)
    """
    if __debug__:
        if plot and not inplace:
            raise RuntimeError("Cannot plot when inplace=False")

    if isinstance(series1, str):
        series1 = dataframe[series1]
    if isinstance(series2, str):
        series2 = dataframe[series2]

    i = series1 - series2

    if inplace:
        name = name or f"{prefix}diff"
        dataframe[name] = i

        if plot:
            _plot_mark(dataframe=dataframe, name=name, plot_kwargs=plot_kwargs)

    return i

direction ¤

direction(
    series1: Series | str,
    series2: Series | str,
    dataframe: DataFrame = None,
    name: str | None = None,
    prefix: str = "",
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_kwargs: dict | None = None,
) -> Series

Check a Series is direction another Series

Parameters:

  • series1 (Series) –

    first Series

  • series2 (Series) –

    second Series

Returns:

  • Series –

    pd.Series: -100 if series1 < series2, 100 if series1 > series2, else 0

Source code in lettrade/indicator/series.py 
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def direction(
    series1: pd.Series | str,
    series2: pd.Series | str,
    dataframe: pd.DataFrame = None,
    name: str | None = None,
    prefix: str = "",
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_kwargs: dict | None = None,
    # **kwargs,
) -> pd.Series:
    """Check a Series is direction another Series

    Args:
        series1 (pd.Series): first Series
        series2 (pd.Series): second Series

    Returns:
        pd.Series: -100 if series1 < series2, 100 if series1 > series2, else 0
    """
    if __debug__:
        if plot and not inplace:
            raise RuntimeError("Cannot plot when inplace=False")

    if isinstance(series1, str):
        series1 = dataframe[series1]
    if isinstance(series2, str):
        series2 = dataframe[series2]

    i = s_direction(series1, series2)

    if inplace:
        name = name or f"{prefix}direction"
        dataframe[name] = i

        if plot:
            _plot_mark(dataframe=dataframe, name=name, plot_kwargs=plot_kwargs)

    return i

ema ¤

ema(
    series: Series | str = "close",
    window: int = None,
    dataframe: DataFrame = None,
    name: str | None = None,
    prefix: str = "",
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_kwargs: dict | None = None,
    **kwargs
) -> Series | DataFrame

Exponential Moving Average

Parameters:

  • series (Series | str, default: 'close' ) –

    description. Defaults to "close".

  • window (int, default: None ) –

    description. Defaults to None.

  • dataframe (DataFrame, default: None ) –

    description. Defaults to None.

  • name (str | None, default: None ) –

    description. Defaults to None.

  • prefix (str, default: '' ) –

    description. Defaults to "".

  • inplace (bool, default: False ) –

    description. Defaults to False.

  • plot (bool | list, default: False ) –

    description. Defaults to False.

  • plot_kwargs (dict | None, default: None ) –

    description. Defaults to None.

Example 
df.i.ema(window=21, name="ema", inplace=True, plot=True)

Returns:

  • Series | DataFrame –

    pd.Series | pd.DataFrame: description

Source code in lettrade/indicator/trend/ma.py 
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def ema(
    series: pd.Series | str = "close",
    window: int = None,
    dataframe: pd.DataFrame = None,
    name: str | None = None,
    prefix: str = "",
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_kwargs: dict | None = None,
    **kwargs,
) -> pd.Series | pd.DataFrame:
    """Exponential Moving Average

    Args:
        series (pd.Series | str, optional): _description_. Defaults to "close".
        window (int, optional): _description_. Defaults to None.
        dataframe (pd.DataFrame, optional): _description_. Defaults to None.
        name (str | None, optional): _description_. Defaults to None.
        prefix (str, optional): _description_. Defaults to "".
        inplace (bool, optional): _description_. Defaults to False.
        plot (bool | list, optional): _description_. Defaults to False.
        plot_kwargs (dict | None, optional): _description_. Defaults to None.

    Example:
        ```python
        df.i.ema(window=21, name="ema", inplace=True, plot=True)
        ```

    Returns:
        pd.Series | pd.DataFrame: _description_
    """
    return ma(
        series=series,
        window=window,
        mode="ema",
        dataframe=dataframe,
        name=name,
        prefix=prefix,
        inplace=inplace,
        plot=plot,
        plot_kwargs=plot_kwargs,
        **kwargs,
    )

indicators_inject_pandas ¤

indicators_inject_pandas(obj: object | None = None)

Inject indicators to Pandas

Source code in lettrade/indicator/__init__.py 
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def indicators_inject_pandas(obj: object | None = None):
    """Inject indicators to Pandas"""
    if obj is None:
        from pandas.core.base import PandasObject

        obj = PandasObject

    # Flag to mark indicators injected
    if hasattr(obj, "_lt_indicators_injected"):
        if __debug__:
            print("[WARN] Re-injected indicators")
        return

    from .candlestick import pandas_inject as candlestick_pandas_inject
    from .dataframe import pandas_inject as dataframe_pandas_inject
    from .momentum import pandas_inject as momentum_pandas_inject
    from .series import pandas_inject as series_pandas_inject
    from .trend import pandas_inject as trend_pandas_inject
    from .volatility import pandas_inject as volatility_pandas_inject

    series_pandas_inject(obj)
    dataframe_pandas_inject(obj)

    candlestick_pandas_inject(obj)
    trend_pandas_inject(obj)
    volatility_pandas_inject(obj)
    momentum_pandas_inject(obj)

    # Flag to mark indicators injected
    obj._lt_indicators_injected = True

let_backtest ¤

let_backtest(
    datas: DataFeed | list[DataFeed] | str | list[str],
    strategy: type[Strategy],
    *,
    feeder: type[DataFeeder] = BackTestDataFeeder,
    exchange: type[Exchange] = BackTestExchange,
    account: type[Account] = BackTestAccount,
    commander: (
        type[Commander] | Commander | None
    ) = BackTestCommander,
    stats: type[BotStatistic] | None = BotStatistic,
    optimize_stats: (
        type[OptimizeStatistic] | None
    ) = OptimizeStatistic,
    plotter: type[BotPlotter] | None = "PlotlyBotPlotter",
    optimize_plotter: (
        type[OptimizePlotter] | None
    ) = "PlotlyOptimizePlotter",
    bot: (
        type[LetTradeBackTestBot] | None
    ) = LetTradeBackTestBot,
    name: str | None = None,
    balance: float | None = 10000,
    commission: float | None = 0.2,
    leverage: float | None = 20,
    **kwargs
) -> LetTradeBackTest

Complete lettrade backtest depenencies

Parameters:

Returns:

Source code in lettrade/exchange/backtest/backtest.py 
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def let_backtest(
    datas: DataFeed | list[DataFeed] | str | list[str],
    strategy: type[Strategy],
    *,
    feeder: type[DataFeeder] = BackTestDataFeeder,
    exchange: type[Exchange] = BackTestExchange,
    account: type[Account] = BackTestAccount,
    commander: type[Commander] | Commander | None = BackTestCommander,
    stats: type[BotStatistic] | None = BotStatistic,
    optimize_stats: type[OptimizeStatistic] | None = OptimizeStatistic,
    plotter: type[BotPlotter] | None = "PlotlyBotPlotter",
    optimize_plotter: type[OptimizePlotter] | None = "PlotlyOptimizePlotter",
    bot: type[LetTradeBackTestBot] | None = LetTradeBackTestBot,
    name: str | None = None,
    # Account kwargs
    balance: float | None = 10_000,
    commission: float | None = 0.2,
    leverage: float | None = 20,
    **kwargs,
) -> "LetTradeBackTest":
    """Complete `lettrade` backtest depenencies

    Args:
        datas (DataFeed | list[DataFeed] | str | list[str]): _description_
        strategy (type[Strategy]): The Strategy implement class
        feeder (type[DataFeeder], optional): _description_. Defaults to BackTestDataFeeder.
        exchange (type[Exchange], optional): _description_. Defaults to BackTestExchange.
        account (type[Account], optional): _description_. Defaults to BackTestAccount.
        commander (type[Commander] | Commander | None, optional): _description_. Defaults to BackTestCommander.
        stats (type[BotStatistic] | None, optional): _description_. Defaults to BotStatistic.
        optimize_stats (type[OptimizeStatistic] | None, optional): _description_. Defaults to OptimizeStatistic.
        plotter (type[BotPlotter] | None, optional): _description_. Defaults to "PlotlyBotPlotter".
        optimize_plotter (type[OptimizePlotter] | None, optional): _description_. Defaults to "PlotlyOptimizePlotter".
        bot (type[LetTradeBackTestBot] | None, optional): _description_. Defaults to LetTradeBackTestBot.
        name (str | None, optional): _description_. Defaults to None.
        balance (float | None, optional): _description_. Defaults to 10_000.
        commission (float | None, optional): _description_. Defaults to 0.2.
        leverage (float | None, optional): _description_. Defaults to 20.

    Returns:
        LetTradeBackTest: The LetTrade backtesting object
    """

    account_kwargs: dict = kwargs.setdefault("account_kwargs", {})
    account_kwargs.update(
        balance=balance,
        commission=commission,
        leverage=leverage,
    )

    return LetTradeBackTest(
        strategy=strategy,
        datas=datas,
        feeder=feeder,
        exchange=exchange,
        commander=commander,
        account=account,
        stats=stats,
        plotter=plotter,
        bot=bot,
        name=name,
        # Backtest
        optimize_stats_cls=optimize_stats,
        optimize_plotter_cls=optimize_plotter,
        **kwargs,
    )

let_ccxt ¤

let_ccxt(
    datas: set[set[str]],
    strategy: type[Strategy],
    *,
    ccxt_exchange: str,
    ccxt_key: str,
    ccxt_secret: str,
    ccxt_type: Literal["spot", "margin", "future"] = "spot",
    ccxt_verbose: bool = False,
    feeder: type[CCXTDataFeeder] = CCXTDataFeeder,
    exchange: type[CCXTExchange] = CCXTExchange,
    account: type[CCXTAccount] = CCXTAccount,
    commander: type[Commander] | None = None,
    stats: type[BotStatistic] | None = BotStatistic,
    plotter: type[Plotter] | None = None,
    bot: type[LetTradeCCXTBot] | None = LetTradeCCXTBot,
    lettrade: type[LetTradeCCXT] | None = LetTradeCCXT,
    api: type[CCXTAPI] | None = CCXTAPI,
    **kwargs
) -> LetTradeCCXT

Help to build LetTradeCCXT

Parameters:

  • datas (set[set[str]]) –

    description

  • strategy (Type[Strategy]) –

    description

  • ccxt_exchange (str) –

    description

  • ccxt_key (str) –

    description

  • ccxt_secret (str) –

    description

  • ccxt_type (Literal['spot', 'margin', 'future'], default: 'spot' ) –

    description. Defaults to "spot".

  • ccxt_verbose (bool, default: False ) –

    description. Defaults to False.

  • feeder (Type[CCXTDataFeeder], default: CCXTDataFeeder ) –

    description. Defaults to CCXTDataFeeder.

  • exchange (Type[CCXTExchange], default: CCXTExchange ) –

    description. Defaults to CCXTExchange.

  • account (Type[CCXTAccount], default: CCXTAccount ) –

    description. Defaults to CCXTAccount.

  • commander (Type[Commander] | None, default: None ) –

    description. Defaults to None.

  • stats (Type[BotStatistic] | None, default: BotStatistic ) –

    description. Defaults to BotStatistic.

  • plotter (Type[Plotter] | None, default: None ) –

    description. Defaults to None.

  • bot (Type[LetTradeCCXTBot] | None, default: LetTradeCCXTBot ) –

    description. Defaults to LetTradeCCXTBot.

  • lettrade (Type[LetTradeCCXT] | None, default: LetTradeCCXT ) –

    description. Defaults to LetTradeCCXT.

  • api (Type[CCXTAPI] | None, default: CCXTAPI ) –

    description. Defaults to CCXTAPI.

  • **kwargs (dict, default: {} ) –

    All remaining properties are passed to the constructor of LetTradeLive

Returns:

Source code in lettrade/exchange/ccxt/ccxt.py 
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def let_ccxt(
    datas: set[set[str]],
    strategy: type[Strategy],
    *,
    ccxt_exchange: str,
    ccxt_key: str,
    ccxt_secret: str,
    ccxt_type: Literal["spot", "margin", "future"] = "spot",
    ccxt_verbose: bool = False,
    feeder: type[CCXTDataFeeder] = CCXTDataFeeder,
    exchange: type[CCXTExchange] = CCXTExchange,
    account: type[CCXTAccount] = CCXTAccount,
    commander: type[Commander] | None = None,
    stats: type[BotStatistic] | None = BotStatistic,
    plotter: type[Plotter] | None = None,
    bot: type[LetTradeCCXTBot] | None = LetTradeCCXTBot,
    lettrade: type[LetTradeCCXT] | None = LetTradeCCXT,
    api: type[CCXTAPI] | None = CCXTAPI,
    **kwargs,
) -> LetTradeCCXT:
    """Help to build `LetTradeCCXT`

    Args:
        datas (set[set[str]]): _description_
        strategy (Type[Strategy]): _description_
        ccxt_exchange (str): _description_
        ccxt_key (str): _description_
        ccxt_secret (str): _description_
        ccxt_type (Literal["spot", "margin", "future"], optional): _description_. Defaults to "spot".
        ccxt_verbose (bool, optional): _description_. Defaults to False.
        feeder (Type[CCXTDataFeeder], optional): _description_. Defaults to CCXTDataFeeder.
        exchange (Type[CCXTExchange], optional): _description_. Defaults to CCXTExchange.
        account (Type[CCXTAccount], optional): _description_. Defaults to CCXTAccount.
        commander (Type[Commander] | None, optional): _description_. Defaults to None.
        stats (Type[BotStatistic] | None, optional): _description_. Defaults to BotStatistic.
        plotter (Type[Plotter] | None, optional): _description_. Defaults to None.
        bot (Type[LetTradeCCXTBot] | None, optional): _description_. Defaults to LetTradeCCXTBot.
        lettrade (Type[LetTradeCCXT] | None, optional): _description_. Defaults to LetTradeCCXT.
        api (Type[CCXTAPI] | None, optional): _description_. Defaults to CCXTAPI.
        **kwargs (dict): All remaining properties are passed to the constructor of `LetTradeLive`

    Returns:
        LetTradeCCXT: _description_
    """
    api_kwargs: dict = kwargs.setdefault("api_kwargs", {})
    api_kwargs.update(
        exchange=ccxt_exchange,
        key=ccxt_key,
        secret=ccxt_secret,
        type=ccxt_type,
        verbose=ccxt_verbose,
    )

    return let_live(
        strategy=strategy,
        datas=datas,
        feeder=feeder,
        exchange=exchange,
        account=account,
        commander=commander,
        plotter=plotter,
        stats=stats,
        bot=bot,
        lettrade=lettrade,
        api=api,
        **kwargs,
    )

let_live ¤

let_live(
    datas: set[set[str]],
    strategy: type[Strategy],
    *,
    commander: Commander | None = None,
    stats: type[BotStatistic] = BotStatistic,
    plotter: type[Plotter] | None = None,
    bot: type[LetTradeLiveBot] = LetTradeLiveBot,
    lettrade: type[LetTradeLive] = LetTradeLive,
    api: type[LiveAPI] = LiveAPI,
    **kwargs
) -> LetTradeLive

Help to build LetTradeLive

Parameters:

Returns:

Source code in lettrade/exchange/live/live.py 
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def let_live(
    datas: set[set[str]],
    strategy: type[Strategy],
    *,
    commander: Commander | None = None,
    stats: type[BotStatistic] = BotStatistic,
    plotter: type[Plotter] | None = None,
    bot: type[LetTradeLiveBot] = LetTradeLiveBot,
    lettrade: type[LetTradeLive] = LetTradeLive,
    api: type[LiveAPI] = LiveAPI,
    **kwargs,
) -> "LetTradeLive":
    """Help to build `LetTradeLive`

    Args:
        datas (set[set[str]]): _description_
        strategy (type[Strategy]): _description_
        commander (Commander | None, optional): _description_. Defaults to None.
        stats (type[BotStatistic], optional): _description_. Defaults to BotStatistic.
        plotter (type[Plotter] | None, optional): _description_. Defaults to None.
        bot (type[LetTradeLiveBot], optional): _description_. Defaults to LetTradeLiveBot.
        lettrade (type[LetTradeLive], optional): _description_. Defaults to LetTradeLive.
        api (type[LiveAPI], optional): _description_. Defaults to LiveAPI.

    Returns:
        LetTradeLive: _description_
    """
    return lettrade(
        strategy=strategy,
        datas=datas,
        commander=commander,
        plotter=plotter,
        stats=stats,
        bot=bot,
        api=api,
        **kwargs,
    )

let_metatrader ¤

let_metatrader(
    datas: set[set[str]],
    strategy: type[Strategy],
    *,
    mt5_login: int,
    mt5_password: str,
    mt5_server: str,
    mt5_wine: str | None = None,
    mt5_path: str | None = None,
    feeder: type[
        MetaTraderDataFeeder
    ] = MetaTraderDataFeeder,
    exchange: type[MetaTraderExchange] = MetaTraderExchange,
    account: type[MetaTraderAccount] = MetaTraderAccount,
    commander: type[Commander] | None = None,
    stats: type[BotStatistic] = BotStatistic,
    plotter: type[Plotter] | None = None,
    bot: type[
        LetTradeMetaTraderBot
    ] = LetTradeMetaTraderBot,
    lettrade: type[LetTradeMetaTrader] = LetTradeMetaTrader,
    api: type[MetaTraderAPI] = MetaTraderAPI,
    **kwargs
) -> LetTradeMetaTrader

Help to build LetTradeMetaTrader

Parameters:

Returns:

Source code in lettrade/exchange/metatrader/metatrader.py 
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def let_metatrader(
    datas: set[set[str]],
    strategy: type[Strategy],
    *,
    mt5_login: int,
    mt5_password: str,
    mt5_server: str,
    mt5_wine: str | None = None,
    mt5_path: str | None = None,
    feeder: type[MetaTraderDataFeeder] = MetaTraderDataFeeder,
    exchange: type[MetaTraderExchange] = MetaTraderExchange,
    account: type[MetaTraderAccount] = MetaTraderAccount,
    commander: type[Commander] | None = None,
    stats: type[BotStatistic] = BotStatistic,
    plotter: type[Plotter] | None = None,
    bot: type[LetTradeMetaTraderBot] = LetTradeMetaTraderBot,
    lettrade: type[LetTradeMetaTrader] = LetTradeMetaTrader,
    api: type[MetaTraderAPI] = MetaTraderAPI,
    **kwargs,
) -> LetTradeMetaTrader:
    """Help to build `LetTradeMetaTrader`

    Args:
        datas (set[set[str]]): _description_
        strategy (type[Strategy]): _description_
        mt5_login (int): _description_
        mt5_password (str): _description_
        mt5_server (str): _description_
        mt5_wine (str | None, optional): WineHQ execute path. Defaults to None.
        feeder (type[MetaTraderDataFeeder], optional): _description_. Defaults to MetaTraderDataFeeder.
        exchange (type[MetaTraderExchange], optional): _description_. Defaults to MetaTraderExchange.
        account (type[MetaTraderAccount], optional): _description_. Defaults to MetaTraderAccount.
        commander (type[Commander] | None, optional): _description_. Defaults to None.
        stats (type[BotStatistic], optional): _description_. Defaults to BotStatistic.
        plotter (type[Plotter] | None, optional): _description_. Defaults to None.
        bot (type[LetTradeMetaTraderBot], optional): _description_. Defaults to LetTradeMetaTraderBot.
        lettrade (type[LetTradeMetaTrader], optional): _description_. Defaults to LetTradeMetaTrader.
        api (type[MetaTraderAPI], optional): _description_. Defaults to MetaTraderAPI.

    Returns:
        LetTradeMetaTrader: _description_
    """
    api_kwargs: dict = kwargs.setdefault("api_kwargs", {})
    api_kwargs.update(
        login=int(mt5_login),
        password=mt5_password,
        server=mt5_server,
        wine=mt5_wine,
        path=mt5_path,
    )

    return let_live(
        strategy=strategy,
        datas=datas,
        feeder=feeder,
        exchange=exchange,
        account=account,
        commander=commander,
        stats=stats,
        plotter=plotter,
        bot=bot,
        lettrade=lettrade,
        api=api,
        **kwargs,
    )

plot_bollinger_bands ¤

plot_bollinger_bands(
    dataframe: DataFrame,
    upper: str | None = "upper",
    basis: str | None = "basis",
    lower: str | None = "lower",
    width: int = 1,
    upper_color: str = "#33BDFF",
    basis_color: str = "#D105F5",
    lower_color: str = "#33BDFF",
    filter: Series | None = None,
) -> dict

summary

Parameters:

  • dataframe (DataFrame) –

    description

  • upper (str, default: 'upper' ) –

    Column name. None mean skip plot. Defaults to "upper".

  • basis (str, default: 'basis' ) –

    Column name. None mean skip plot. Defaults to "basis".

  • lower (str, default: 'lower' ) –

    Column name. None mean skip plot. Defaults to "lower".

  • width (int, default: 1 ) –

    description. Defaults to 1.

  • upper_color (str, default: '#33BDFF' ) –

    Color code. Defaults to "#33BDFF".

  • lower_color (str, default: '#33BDFF' ) –

    Color code. Defaults to "#33BDFF".

  • basis_color (str, default: '#D105F5' ) –

    Color code. Defaults to "#D105F5".

  • filter (Series | None, default: None ) –

    description. Defaults to None.

Returns:

  • dict ( dict ) –

    description

Source code in lettrade/plot/plotly/indicator.py 
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def plot_bollinger_bands(
    dataframe: pd.DataFrame,
    upper: str | None = "upper",
    basis: str | None = "basis",
    lower: str | None = "lower",
    width: int = 1,
    upper_color: str = "#33BDFF",
    basis_color: str = "#D105F5",
    lower_color: str = "#33BDFF",
    filter: pd.Series | None = None,
) -> dict:
    """_summary_

    Args:
        dataframe (pd.DataFrame): _description_
        upper (str, optional): Column name. `None` mean skip plot. Defaults to "upper".
        basis (str, optional): Column name. `None` mean skip plot. Defaults to "basis".
        lower (str, optional): Column name. `None` mean skip plot. Defaults to "lower".
        width (int, optional): _description_. Defaults to 1.
        upper_color (str, optional): Color code. Defaults to "#33BDFF".
        lower_color (str, optional): Color code. Defaults to "#33BDFF".
        basis_color (str, optional): Color code. Defaults to "#D105F5".
        filter (pd.Series | None, optional): _description_. Defaults to None.

    Returns:
        dict: _description_
    """
    if filter is not None:
        df_name = dataframe.name
        dataframe = dataframe.loc[filter]
        object.__setattr__(dataframe, "name", df_name)

    items = []

    if upper is not None:
        items.append(
            dict(
                type="scatter",
                x=dataframe.index,
                y=dataframe[upper],
                name=upper,
                mode="lines",
                line=dict(color=upper_color, width=width),
            )
        )
    if basis is not None:
        items.append(
            dict(
                type="scatter",
                x=dataframe.index,
                y=dataframe[basis],
                name=basis,
                mode="lines",
                line=dict(color=basis_color, width=width),
            )
        )
    if lower is not None:
        items.append(
            dict(
                type="scatter",
                x=dataframe.index,
                y=dataframe[lower],
                name=lower,
                mode="lines",
                line=dict(color=lower_color, width=width),
            )
        )
    return {f"{dataframe.name}": dict(items=items)}

plot_candlestick ¤

plot_candlestick(
    dataframe: DataFrame,
    name: str = "Candlestick",
    width: int = 1,
    increasing_line_color=PlotColor.LIGHT_GREEN,
    decreasing_line_color=PlotColor.LIGHT_RED,
    row: int = 1,
    col: int = 1,
    filter: Series | None = None,
    **kwargs
) -> dict

summary

Parameters:

  • dataframe (DataFrame) –

    description

  • name (str, default: 'Candlestick' ) –

    description. Defaults to "Candlestick".

  • width (int, default: 1 ) –

    description. Defaults to 1.

  • increasing_line_color (str, default: LIGHT_GREEN ) –

    description. Defaults to PlotColor.LIGHT_GREEN.

  • decreasing_line_color (str, default: LIGHT_RED ) –

    description. Defaults to PlotColor.LIGHT_RED.

  • row (int, default: 1 ) –

    description. Defaults to 1.

  • col (int, default: 1 ) –

    description. Defaults to 1.

  • filter (Series | None, default: None ) –

    description. Defaults to None.

Returns:

  • dict ( dict ) –

    description

Source code in lettrade/plot/plotly/indicator.py 
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def plot_candlestick(
    dataframe: pd.DataFrame,
    name: str = "Candlestick",
    width: int = 1,
    increasing_line_color=PlotColor.LIGHT_GREEN,
    decreasing_line_color=PlotColor.LIGHT_RED,
    row: int = 1,
    col: int = 1,
    filter: pd.Series | None = None,
    **kwargs,
) -> dict:
    """_summary_

    Args:
        dataframe (pd.DataFrame): _description_
        name (str, optional): _description_. Defaults to "Candlestick".
        width (int, optional): _description_. Defaults to 1.
        increasing_line_color (str, optional): _description_. Defaults to PlotColor.LIGHT_GREEN.
        decreasing_line_color (str, optional): _description_. Defaults to PlotColor.LIGHT_RED.
        row (int, optional): _description_. Defaults to 1.
        col (int, optional): _description_. Defaults to 1.
        filter (pd.Series | None, optional): _description_. Defaults to None.

    Returns:
        dict: _description_
    """
    if filter is not None:
        df_name = dataframe.name
        dataframe = dataframe.loc[filter]
        object.__setattr__(dataframe, "name", df_name)

    config = dict(
        items=[
            dict(
                type="candlestick",
                x=dataframe.index,
                open=dataframe["open"],
                high=dataframe["high"],
                low=dataframe["low"],
                close=dataframe["close"],
                name=name,
                line=dict(width=width),
                increasing_line_color=increasing_line_color,
                decreasing_line_color=decreasing_line_color,
                hoverinfo="text",
                hovertext=name,
                row=row,
                col=col,
                **kwargs,
            ),
        ]
    )

    return {f"{dataframe.name}": config}

plot_ichimoku ¤

plot_ichimoku(
    dataframe: DataFrame,
    tenkan_sen="tenkan_sen",
    kijun_sen="kijun_sen",
    senkou_span_a="senkou_span_a",
    senkou_span_b="senkou_span_b",
    chikou_span="chikou_span",
    width=1,
    tenkan_sen_color="#33BDFF",
    kijun_sen_color="#D105F5",
    senkou_span_a_color="#228B22",
    senkou_span_b_color="#FF3342",
    chikou_span_color="#F1F316",
    filter: Series | None = None,
) -> dict

summary

Parameters:

  • dataframe (DataFrame) –

    description

  • tenkan_sen (str, default: 'tenkan_sen' ) –

    description. Defaults to "tenkan_sen".

  • kijun_sen (str, default: 'kijun_sen' ) –

    description. Defaults to "kijun_sen".

  • senkou_span_a (str, default: 'senkou_span_a' ) –

    description. Defaults to "senkou_span_a".

  • senkou_span_b (str, default: 'senkou_span_b' ) –

    description. Defaults to "senkou_span_b".

  • chikou_span (str, default: 'chikou_span' ) –

    description. Defaults to "chikou_span".

  • width (int, default: 1 ) –

    description. Defaults to 1.

  • tenkan_sen_color (str, default: '#33BDFF' ) –

    description. Defaults to "#33BDFF".

  • kijun_sen_color (str, default: '#D105F5' ) –

    description. Defaults to "#D105F5".

  • senkou_span_a_color (str, default: '#228B22' ) –

    description. Defaults to "#228B22".

  • senkou_span_b_color (str, default: '#FF3342' ) –

    description. Defaults to "#FF3342".

  • chikou_span_color (str, default: '#F1F316' ) –

    description. Defaults to "#F1F316".

  • filter (Series | None, default: None ) –

    description. Defaults to None.

Returns:

  • dict ( dict ) –

    description

Source code in lettrade/plot/plotly/indicator.py 
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def plot_ichimoku(
    dataframe: pd.DataFrame,
    tenkan_sen="tenkan_sen",
    kijun_sen="kijun_sen",
    senkou_span_a="senkou_span_a",
    senkou_span_b="senkou_span_b",
    chikou_span="chikou_span",
    width=1,
    tenkan_sen_color="#33BDFF",
    kijun_sen_color="#D105F5",
    senkou_span_a_color="#228B22",
    senkou_span_b_color="#FF3342",
    chikou_span_color="#F1F316",
    filter: pd.Series | None = None,
) -> dict:
    """_summary_

    Args:
        dataframe (pd.DataFrame): _description_
        tenkan_sen (str, optional): _description_. Defaults to "tenkan_sen".
        kijun_sen (str, optional): _description_. Defaults to "kijun_sen".
        senkou_span_a (str, optional): _description_. Defaults to "senkou_span_a".
        senkou_span_b (str, optional): _description_. Defaults to "senkou_span_b".
        chikou_span (str, optional): _description_. Defaults to "chikou_span".
        width (int, optional): _description_. Defaults to 1.
        tenkan_sen_color (str, optional): _description_. Defaults to "#33BDFF".
        kijun_sen_color (str, optional): _description_. Defaults to "#D105F5".
        senkou_span_a_color (str, optional): _description_. Defaults to "#228B22".
        senkou_span_b_color (str, optional): _description_. Defaults to "#FF3342".
        chikou_span_color (str, optional): _description_. Defaults to "#F1F316".
        filter (pd.Series | None, optional): _description_. Defaults to None.

    Returns:
        dict: _description_
    """
    if filter is not None:
        df_name = dataframe.name
        dataframe = dataframe.loc[filter]
        object.__setattr__(dataframe, "name", df_name)

    items = []

    if tenkan_sen is not None:
        items.append(
            dict(
                type="scatter",
                x=dataframe.index,
                y=dataframe[tenkan_sen],
                name=tenkan_sen,
                mode="lines",
                line=dict(color=tenkan_sen_color, width=width),
            )
        )
    if kijun_sen is not None:
        items.append(
            dict(
                type="scatter",
                x=dataframe.index,
                y=dataframe[kijun_sen],
                name=kijun_sen,
                mode="lines",
                line=dict(color=kijun_sen_color, width=width),
            )
        )
    if senkou_span_a is not None:
        items.append(
            dict(
                type="scatter",
                x=dataframe.index,
                y=dataframe[senkou_span_a],
                name=senkou_span_a,
                mode="lines",
                line=dict(color=senkou_span_a_color, width=width),
            )
        )
    if senkou_span_b is not None:
        items.append(
            dict(
                type="scatter",
                x=dataframe.index,
                y=dataframe[senkou_span_b],
                name=senkou_span_b,
                mode="lines",
                fill="tonexty",
                line=dict(color=senkou_span_b_color, width=width),
            )
        )
    if chikou_span is not None:
        items.append(
            dict(
                type="scatter",
                x=dataframe.index,
                y=dataframe[chikou_span],
                name=chikou_span,
                mode="lines",
                line=dict(color=chikou_span_color, width=width),
            )
        )

    return {f"{dataframe.name}": dict(items=items)}

plot_line ¤

plot_line(
    series: Series | str,
    color: str = PlotColor.PURPLE,
    width: int = 1,
    name: str | None = None,
    mode: str = "lines",
    fullfill: bool = False,
    dataframe: DataFrame | None = None,
    filter: Series | None = None,
    **kwargs
) -> dict

summary

Parameters:

  • series (Series | str) –

    description

  • color (str, default: PURPLE ) –

    description. Defaults to PlotColor.PURPLE.

  • width (int, default: 1 ) –

    description. Defaults to 1.

  • name (str | None, default: None ) –

    description. Defaults to None.

  • mode (str, default: 'lines' ) –

    description. Defaults to "lines".

  • fullfill (bool, default: False ) –

    description. Defaults to False.

  • dataframe (DataFrame | None, default: None ) –

    description. Defaults to None.

  • filter (Series | None, default: None ) –

    description. Defaults to None.

Returns:

  • dict ( dict ) –

    description

Source code in lettrade/plot/plotly/indicator.py 
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def plot_line(
    series: pd.Series | str,
    color: str = PlotColor.PURPLE,
    width: int = 1,
    name: str | None = None,
    mode: str = "lines",
    fullfill: bool = False,
    dataframe: pd.DataFrame | None = None,
    filter: pd.Series | None = None,
    **kwargs,
) -> dict:
    """_summary_

    Args:
        series (pd.Series | str): _description_
        color (str, optional): _description_. Defaults to PlotColor.PURPLE.
        width (int, optional): _description_. Defaults to 1.
        name (str | None, optional): _description_. Defaults to None.
        mode (str, optional): _description_. Defaults to "lines".
        fullfill (bool, optional): _description_. Defaults to False.
        dataframe (pd.DataFrame | None, optional): _description_. Defaults to None.
        filter (pd.Series | None, optional): _description_. Defaults to None.

    Returns:
        dict: _description_
    """
    if isinstance(series, str):
        series = dataframe[series]

    if filter is not None:
        series = series.loc[filter]

    config = dict(
        items=[
            dict(
                type="scatter",
                x=series.index,
                y=series,
                line=dict(color=color, width=width),
                name=name or series.name,
                mode=mode,
                fullfill=fullfill,
                **kwargs,
            )
        ]
    )
    if dataframe is None:
        return config

    return {f"{dataframe.name}": config}

plot_lines ¤

plot_lines(
    serieses: list[Series | str],
    color: str = PlotColor.PURPLE,
    width: int = 1,
    name: str | None = None,
    mode: str = "lines",
    fullfill: bool = False,
    dataframe: DataFrame | None = None,
    plots_kwargs: list[dict] | None = None,
    **kwargs
) -> dict

summary

Parameters:

  • serieses (list[Series | str]) –

    description

  • color (str, default: PURPLE ) –

    description. Defaults to PlotColor.PURPLE.

  • width (int, default: 1 ) –

    description. Defaults to 1.

  • name (str | None, default: None ) –

    description. Defaults to None.

  • mode (str, default: 'lines' ) –

    description. Defaults to "lines".

  • fullfill (bool, default: False ) –

    description. Defaults to False.

  • dataframe (DataFrame | None, default: None ) –

    description. Defaults to None.

Returns:

  • dict ( dict ) –

    description

Source code in lettrade/plot/plotly/indicator.py 
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def plot_lines(
    serieses: list[pd.Series | str],
    color: str = PlotColor.PURPLE,
    width: int = 1,
    name: str | None = None,
    mode: str = "lines",
    fullfill: bool = False,
    dataframe: pd.DataFrame | None = None,
    plots_kwargs: list[dict] | None = None,
    **kwargs,
) -> dict:
    """_summary_

    Args:
        serieses (list[pd.Series  |  str]): _description_
        color (str, optional): _description_. Defaults to PlotColor.PURPLE.
        width (int, optional): _description_. Defaults to 1.
        name (str | None, optional): _description_. Defaults to None.
        mode (str, optional): _description_. Defaults to "lines".
        fullfill (bool, optional): _description_. Defaults to False.
        dataframe (pd.DataFrame | None, optional): _description_. Defaults to None.

    Returns:
        dict: _description_
    """
    kwargs.update(
        color=color,
        width=width,
        name=name,
        mode=mode,
        fullfill=fullfill,
        dataframe=dataframe,
    )

    result = {}
    for i, series in enumerate(serieses):
        series_kwargs = kwargs.copy()
        if i < len(plots_kwargs):
            series_kwargs.update(**plots_kwargs[i])

        plot_merge(result, plot_line(series=series, **series_kwargs))
    return result

plot_mark ¤

plot_mark(
    series: Series | str,
    color: str = PlotColor.YELLOW,
    width: int = 1,
    mode: str = "markers",
    name: str | None = None,
    fullfill: bool = False,
    dataframe: DataFrame | None = None,
    filter: Series | None = None,
    **kwargs
) -> dict

summary

Parameters:

  • series (Series | str) –

    description

  • color (str, default: YELLOW ) –

    description. Defaults to PlotColor.YELLOW.

  • width (int, default: 1 ) –

    description. Defaults to 1.

  • mode (str, default: 'markers' ) –

    description. Defaults to "markers".

  • name (str | None, default: None ) –

    description. Defaults to None.

  • fullfill (bool, default: False ) –

    description. Defaults to False.

  • dataframe (DataFrame | None, default: None ) –

    description. Defaults to None.

  • filter (Series | None, default: None ) –

    description. Defaults to None.

Returns:

  • dict ( dict ) –

    description

Source code in lettrade/plot/plotly/indicator.py 
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def plot_mark(
    series: pd.Series | str,
    color: str = PlotColor.YELLOW,
    width: int = 1,
    mode: str = "markers",
    name: str | None = None,
    fullfill: bool = False,
    dataframe: pd.DataFrame | None = None,
    filter: pd.Series | None = None,
    **kwargs,
) -> dict:
    """_summary_

    Args:
        series (pd.Series | str): _description_
        color (str, optional): _description_. Defaults to PlotColor.YELLOW.
        width (int, optional): _description_. Defaults to 1.
        mode (str, optional): _description_. Defaults to "markers".
        name (str | None, optional): _description_. Defaults to None.
        fullfill (bool, optional): _description_. Defaults to False.
        dataframe (pd.DataFrame | None, optional): _description_. Defaults to None.
        filter (pd.Series | None, optional): _description_. Defaults to None.

    Returns:
        dict: _description_
    """

    return plot_line(
        series=series,
        color=color,
        width=width,
        mode=mode,
        name=name,
        fullfill=fullfill,
        dataframe=dataframe,
        filter=filter,
        **kwargs,
    )

plot_merge ¤

plot_merge(
    source: dict,
    *updates: list[dict],
    recursive: int = 0,
    recursive_max: int = 1
) -> dict

Merge multiple update plot config to source config

Parameters:

  • source (dict) –

    description

  • *updates (list[dict], default: () ) –

    description

Returns:

  • dict ( dict ) –

    Merged config

Source code in lettrade/plot/helper.py 
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def plot_merge(
    source: dict,
    *updates: list[dict],
    recursive: int = 0,
    recursive_max: int = 1,
) -> dict:
    """Merge multiple update plot config to source config

    Args:
        source (dict): _description_
        *updates (list[dict]): _description_

    Returns:
        dict: Merged config
    """
    for update in updates:
        for key, value in update.items():
            if key not in source:
                source[key] = value
                continue

            if isinstance(source[key], list):
                source[key].extend(value)
                continue
            if isinstance(source[key], dict):
                if recursive >= recursive_max:
                    source[key] = value
                    continue

                sub = source[key]
                plot_merge(
                    sub,
                    value,
                    recursive=recursive + 1,
                    recursive_max=recursive_max,
                )
                continue
    return source

plot_parabolic_sar ¤

plot_parabolic_sar(
    dataframe: DataFrame,
    long: str | None = "long",
    short: str | None = "short",
    width: int = 1,
    long_color: str = "#33BDFF",
    short_color: str = "#D105F5",
    filter: Series | None = None,
) -> dict

summary

Parameters:

  • dataframe (DataFrame) –

    description

  • long (str | None, default: 'long' ) –

    description. Defaults to "long".

  • short (str | None, default: 'short' ) –

    description. Defaults to "short".

  • width (int, default: 1 ) –

    description. Defaults to 1.

  • long_color (str, default: '#33BDFF' ) –

    description. Defaults to "#33BDFF".

  • short_color (str, default: '#D105F5' ) –

    description. Defaults to "#D105F5".

  • filter (Series | None, default: None ) –

    description. Defaults to None.

Returns:

  • dict ( dict ) –

    description

Source code in lettrade/plot/plotly/indicator.py 
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def plot_parabolic_sar(
    dataframe: pd.DataFrame,
    long: str | None = "long",
    short: str | None = "short",
    width: int = 1,
    long_color: str = "#33BDFF",
    short_color: str = "#D105F5",
    filter: pd.Series | None = None,
) -> dict:
    """_summary_

    Args:
        dataframe (pd.DataFrame): _description_
        long (str | None, optional): _description_. Defaults to "long".
        short (str | None, optional): _description_. Defaults to "short".
        width (int, optional): _description_. Defaults to 1.
        long_color (str, optional): _description_. Defaults to "#33BDFF".
        short_color (str, optional): _description_. Defaults to "#D105F5".
        filter (pd.Series | None, optional): _description_. Defaults to None.

    Returns:
        dict: _description_
    """
    if filter is not None:
        df_name = dataframe.name
        dataframe = dataframe.loc[filter]
        object.__setattr__(dataframe, "name", df_name)

    config = dict()

    if long is not None:
        plot_merge(
            config,
            plot_mark(
                series=long,
                color=long_color,
                name="psar_long",
                width=width,
                dataframe=dataframe,
            ),
        )
    if short is not None:
        plot_merge(
            config,
            plot_mark(
                series=short,
                color=short_color,
                name="psar_short",
                width=width,
                dataframe=dataframe,
            ),
        )
    return config

rolling_above ¤

rolling_above(
    series1: Series,
    series2: Series,
    window: int = 20,
    min_periods: int | None = None,
    dataframe: DataFrame = None,
    name: str | None = None,
    prefix: str = "",
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_kwargs: dict | None = None,
) -> Series

Check a Series is rolling above another Series

Parameters:

  • series1 (Series) –

    first Series

  • series2 (Series) –

    second Series

Returns:

  • Series –

    pd.Series: 100 mean series1 is rolling above series2 else 0

Source code in lettrade/indicator/series.py 
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def rolling_above(
    series1: pd.Series,
    series2: pd.Series,
    window: int = 20,
    min_periods: int | None = None,
    dataframe: pd.DataFrame = None,
    name: str | None = None,
    prefix: str = "",
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_kwargs: dict | None = None,
    # **kwargs,
) -> pd.Series:
    """Check a Series is rolling above another Series

    Args:
        series1 (pd.Series): first Series
        series2 (pd.Series): second Series

    Returns:
        pd.Series: 100 mean series1 is rolling above series2 else 0
    """
    if __debug__:
        if plot and not inplace:
            raise RuntimeError("Cannot plot when inplace=False")

    if isinstance(series1, str):
        series1 = dataframe[series1]
    if isinstance(series2, str):
        series2 = dataframe[series2]

    min_periods = window if min_periods is None else min_periods

    i = s_above(series1, series2)
    i = i.rolling(window=window, min_periods=min_periods).min()
    i = i.apply(lambda v: 100 if v >= 100 else 0).astype(int)

    if inplace:
        name = name or f"{prefix}rolling_above"
        dataframe[name] = i

        if plot:
            _plot_mark(dataframe=dataframe, name=name, plot_kwargs=plot_kwargs)

    return i

rolling_below ¤

rolling_below(
    series1: Series,
    series2: Series,
    window: int = 20,
    min_periods: int | None = None,
    dataframe: DataFrame = None,
    name: str | None = None,
    prefix: str = "",
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_kwargs: dict | None = None,
) -> Series

Check a Series is rolling below another Series

Parameters:

  • series1 (Series) –

    first Series

  • series2 (Series) –

    second Series

Returns:

  • Series –

    pd.Series: -100 mean series1 is rolling below series2 else 0

Source code in lettrade/indicator/series.py 
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def rolling_below(
    series1: pd.Series,
    series2: pd.Series,
    window: int = 20,
    min_periods: int | None = None,
    dataframe: pd.DataFrame = None,
    name: str | None = None,
    prefix: str = "",
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_kwargs: dict | None = None,
    # **kwargs,
) -> pd.Series:
    """Check a Series is rolling below another Series

    Args:
        series1 (pd.Series): first Series
        series2 (pd.Series): second Series

    Returns:
        pd.Series: -100 mean series1 is rolling below series2 else 0
    """
    if __debug__:
        if plot and not inplace:
            raise RuntimeError("Cannot plot when inplace=False")

    if isinstance(series1, str):
        series1 = dataframe[series1]
    if isinstance(series2, str):
        series2 = dataframe[series2]

    min_periods = window if min_periods is None else min_periods

    i = s_below(series1, series2)
    i = i.rolling(window=window, min_periods=min_periods).max()
    i = i.apply(lambda v: -100 if v <= -100 else 0).astype(int)

    if inplace:
        name = name or f"{prefix}rolling_below"
        dataframe[name] = i

        if plot:
            _plot_mark(dataframe=dataframe, name=name, plot_kwargs=plot_kwargs)

    return i

rolling_direction ¤

rolling_direction(
    series1: Series,
    series2: Series,
    window: int = 20,
    min_periods: int | None = None,
    dataframe: DataFrame = None,
    name: str | None = None,
    prefix: str = "",
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_kwargs: dict | None = None,
) -> Series

Check a Series is rolling on one side with another Series

Parameters:

  • series1 (Series) –

    first Series

  • series2 (Series) –

    second Series

Returns:

  • Series –

    pd.Series: 100 mean series1 keep consistences above series2 at least window bars -100 mean series1 keep consistences below series2 at least window bars 0 else cases

Source code in lettrade/indicator/series.py 
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def rolling_direction(
    series1: pd.Series,
    series2: pd.Series,
    window: int = 20,
    min_periods: int | None = None,
    dataframe: pd.DataFrame = None,
    name: str | None = None,
    prefix: str = "",
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_kwargs: dict | None = None,
    # **kwargs,
) -> pd.Series:
    """Check a Series is rolling on one side with another Series

    Args:
        series1 (pd.Series): first Series
        series2 (pd.Series): second Series

    Returns:
        pd.Series:  100 mean series1 keep consistences above series2 at least `window` bars
                    -100 mean series1 keep consistences below series2 at least `window` bars
                    0 else cases
    """
    if __debug__:
        if plot and not inplace:
            raise RuntimeError("Cannot plot when inplace=False")

    if isinstance(series1, str):
        series1 = dataframe[series1]
    if isinstance(series2, str):
        series2 = dataframe[series2]

    min_periods = window if min_periods is None else min_periods

    i = s_direction(series1, series2)
    i = i.rolling(window=window, min_periods=min_periods).mean()
    i = i.apply(lambda v: -100 if v <= -100 else 100 if v >= 100 else 0).astype(int)

    if inplace:
        name = name or f"{prefix}rolling_direction"
        dataframe[name] = i

        if plot:
            _plot_mark(dataframe=dataframe, name=name, plot_kwargs=plot_kwargs)

    return i

signal_condiction ¤

signal_condiction(
    dataframe: DataFrame,
    condictions: list[dict],
    name: str,
    value: int | float = np.nan,
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_type: Literal["line", "mark"] = "mark",
    **kwargs
) -> Series | DataFrame

Define a signal with multiple condiction

Parameters:

  • dataframe (DataFrame) –

    description

  • condictions (list[list[Series | Any]]) –

    Pairs of condiction [<pandas.Series condiction>, <value>]

  • name (str) –

    Name of signal, column name when add to DataFrame with inplace=True.

  • value (int, default: nan ) –

    Default value when condiction is not matched. Defaults to 0.

  • inplace (bool, default: False ) –

    description. Defaults to False.

  • plot (bool | list, default: False ) –

    description. Defaults to False.

  • plot_type (Literal['line', 'mark'], default: 'mark' ) –

    description. Defaults to "line".

Usage 
df.i.signal_condiction(
    [df["close"] > df["open"], 100],
    [df["close"] < df["open"], -100],
    name="cdl_direction",
    inplace=True,
    plot=True,
)

Raises:

Returns:

  • Series | DataFrame –

    pd.Series | pd.DataFrame: description

Source code in lettrade/indicator/dataframe.py 
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def signal_condiction(
    dataframe: pd.DataFrame,
    condictions: list[dict],
    name: str,
    value: int | float = np.nan,
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_type: Literal["line", "mark"] = "mark",
    **kwargs,
) -> pd.Series | pd.DataFrame:
    """Define a signal with multiple condiction

    Args:
        dataframe (pd.DataFrame): _description_
        condictions (list[list[pd.Series | Any]]): Pairs of condiction [`<pandas.Series condiction>`, `<value>`]
        name (str): Name of signal, column name when add to DataFrame with inplace=True.
        value (int, optional): Default value when condiction is not matched. Defaults to 0.
        inplace (bool, optional): _description_. Defaults to False.
        plot (bool | list, optional): _description_. Defaults to False.
        plot_type (Literal["line", "mark"], optional): _description_. Defaults to "line".

    Usage:
        ```python
        df.i.signal_condiction(
            [df["close"] > df["open"], 100],
            [df["close"] < df["open"], -100],
            name="cdl_direction",
            inplace=True,
            plot=True,
        )
        ```

    Raises:
        RuntimeError: _description_

    Returns:
        pd.Series | pd.DataFrame: _description_
    """
    if __debug__:
        if not isinstance(dataframe, pd.DataFrame):
            raise RuntimeError(
                f"dataframe type '{type(dataframe)}' "
                "is not instance of pandas.DataFrame"
            )

    # Series
    indicators = []
    plots = {}
    for condiction in condictions:
        series_value = pd.to_numeric(condiction["value"], errors="coerce")
        series_index = dataframe.loc[condiction["series"]].index
        series = pd.Series(
            series_value,
            index=series_index,
            name=condiction["name"],
            **kwargs,
        )
        indicators.append(series)

        if plot and "plot_kwargs" in condiction:
            plots[condiction["name"]] = condiction["plot_kwargs"]

    # Plot
    if plot:
        from lettrade.indicator.plot import IndicatorPlotter
        from lettrade.plot.plotly import plot_line, plot_mark

        for series in indicators:
            if series.name not in plots:
                continue

            plot_kwargs = plots[series.name]
            plot_kwargs.update(series=series, name=series.name)

            plotter = plot_mark if plot_type == "mark" else plot_line
            IndicatorPlotter(dataframe=dataframe, plotter=plotter, **plot_kwargs)

    # Merge
    series = pd.Series(value, index=dataframe.index, name=name, **kwargs)
    for s in indicators:
        series.update(s)

    if inplace:
        dataframe[name] = series
        return dataframe

    return series

signal_direction ¤

signal_direction(
    dataframe: DataFrame,
    up: Series,
    down: Series,
    name: str = "direction",
    value: int = 0,
    value_up: int = 100,
    value_down: int = -100,
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_type: Literal["line", "mark"] = "mark",
    plot_up_kwargs: dict | None = None,
    plot_down_kwargs: dict | None = None,
    **kwargs
) -> Series | DataFrame

Define a signal with 2 direction Up and Down with fixed value

Parameters:

  • dataframe (DataFrame) –

    description

  • up (Series) –

    description

  • down (Series) –

    description

  • name (str, default: 'direction' ) –

    Name of signal, column name when add to DataFrame with inplace=True.

  • value (int, default: 0 ) –

    Default value when condiction is not matched. Defaults to 0.

  • value_up (int, default: 100 ) –

    description. Defaults to 100.

  • value_down (int, default: -100 ) –

    description. Defaults to -100.

  • inplace (bool, default: False ) –

    Whether to add to the DataFrame and return DataFrame rather than return result. Defaults to False.

  • plot (bool | list[str], default: False ) –

    description. Defaults to False.

  • plot_type (Literal['line', 'mark'], default: 'mark' ) –

    description. Defaults to "mark".

  • plot_up_kwargs (dict | None, default: None ) –

    description. Defaults to None.

  • plot_down_kwargs (dict | None, default: None ) –

    description. Defaults to None.

Returns:

  • Series | DataFrame –

    pd.Series | pd.DataFrame: description

Source code in lettrade/indicator/dataframe.py 
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def signal_direction(
    dataframe: pd.DataFrame,
    up: pd.Series,
    down: pd.Series,
    name: str = "direction",
    value: int = 0,
    value_up: int = 100,
    value_down: int = -100,
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_type: Literal["line", "mark"] = "mark",
    plot_up_kwargs: dict | None = None,
    plot_down_kwargs: dict | None = None,
    **kwargs,
) -> pd.Series | pd.DataFrame:
    """Define a signal with 2 direction Up and Down with fixed value

    Args:
        dataframe (pd.DataFrame): _description_
        up (pd.Series): _description_
        down (pd.Series): _description_
        name (str): Name of signal, column name when add to DataFrame with inplace=True.
        value (int, optional): Default value when condiction is not matched. Defaults to 0.
        value_up (int, optional): _description_. Defaults to 100.
        value_down (int, optional): _description_. Defaults to -100.
        inplace (bool, optional): Whether to add to the DataFrame and return DataFrame rather than return result. Defaults to False.
        plot (bool | list[str], optional): _description_. Defaults to False.
        plot_type (Literal["line", "mark"], optional): _description_. Defaults to "mark".
        plot_up_kwargs (dict | None, optional): _description_. Defaults to None.
        plot_down_kwargs (dict | None, optional): _description_. Defaults to None.

    Returns:
        pd.Series | pd.DataFrame: _description_
    """

    # Up
    if plot_up_kwargs is None:
        plot_up_kwargs = dict()
    plot_up_kwargs.setdefault("color", PlotColor.CYAN)

    # Down
    if plot_down_kwargs is None:
        plot_down_kwargs = dict()
    plot_down_kwargs.setdefault("color", PlotColor.ORANGE)

    return signal_condiction(
        dataframe=dataframe,
        condictions=[
            dict(
                series=up,
                value=value_up,
                name=f"{name}_up",
                plot_kwargs=plot_up_kwargs,
            ),
            dict(
                series=down,
                value=value_down,
                name=f"{name}_down",
                plot_kwargs=plot_down_kwargs,
            ),
        ],
        name=name,
        value=value,
        inplace=inplace,
        plot=plot,
        plot_type=plot_type,
        **kwargs,
    )

signal_exist ¤

signal_exist(
    dataframe: DataFrame,
    series: Series,
    window: int,
    value_exist_up: int = 100,
    value_exist_down: int = -100,
    name: str = "exist",
    value_up: int = 100,
    value_down: int = -100,
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_up_kwargs: dict | None = None,
    plot_down_kwargs: dict | None = None,
    **kwargs
) -> Series | DataFrame

summary

Parameters:

  • dataframe (DataFrame) –

    description

  • series (Series) –

    description

  • window (int) –

    description

  • value_exist_up (int, default: 100 ) –

    description. Defaults to 100.

  • value_exist_down (int, default: -100 ) –

    description. Defaults to -100.

  • name (str, default: 'exist' ) –

    description. Defaults to "exist".

  • value_up (int, default: 100 ) –

    description. Defaults to 100.

  • value_down (int, default: -100 ) –

    description. Defaults to -100.

  • inplace (bool, default: False ) –

    description. Defaults to False.

  • plot (bool | list[str], default: False ) –

    description. Defaults to False.

  • plot_up_kwargs (dict | None, default: None ) –

    description. Defaults to None.

  • plot_down_kwargs (dict | None, default: None ) –

    description. Defaults to None.

Returns:

  • Series | DataFrame –

    pd.Series | pd.DataFrame: description

Source code in lettrade/indicator/dataframe.py 
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def signal_exist(
    dataframe: pd.DataFrame,
    series: pd.Series,
    window: int,
    value_exist_up: int = 100,
    value_exist_down: int = -100,
    name: str = "exist",
    value_up: int = 100,
    value_down: int = -100,
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_up_kwargs: dict | None = None,
    plot_down_kwargs: dict | None = None,
    **kwargs,
) -> pd.Series | pd.DataFrame:
    """_summary_

    Args:
        dataframe (pd.DataFrame): _description_
        series (pd.Series): _description_
        window (int): _description_
        value_exist_up (int, optional): _description_. Defaults to 100.
        value_exist_down (int, optional): _description_. Defaults to -100.
        name (str, optional): _description_. Defaults to "exist".
        value_up (int, optional): _description_. Defaults to 100.
        value_down (int, optional): _description_. Defaults to -100.
        inplace (bool, optional): _description_. Defaults to False.
        plot (bool | list[str], optional): _description_. Defaults to False.
        plot_up_kwargs (dict | None, optional): _description_. Defaults to None.
        plot_down_kwargs (dict | None, optional): _description_. Defaults to None.

    Returns:
        pd.Series | pd.DataFrame: _description_
    """
    up_rolling_max = series.rolling(window=window).max()
    down_rolling_min = series.rolling(window=window).min()

    signal = signal_direction(
        dataframe=dataframe,
        up=(up_rolling_max >= value_exist_up),
        down=(down_rolling_min <= value_exist_down),
        name=name,
        value_up=value_up,
        value_down=value_down,
        inplace=inplace,
        plot=plot,
        plot_up_kwargs=plot_up_kwargs,
        plot_down_kwargs=plot_down_kwargs,
        **kwargs,
    )
    return signal

signal_repeat ¤

signal_repeat(
    dataframe: DataFrame,
    series: Series,
    window: int,
    value_repeat_up: int = 100,
    value_repeat_down: int = -100,
    name: str = "repeat",
    value_up: int = 100,
    value_down: int = -100,
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_up_kwargs: dict | None = None,
    plot_down_kwargs: dict | None = None,
    **kwargs
) -> Series | DataFrame

summary

Parameters:

  • dataframe (DataFrame) –

    description

  • series (Series) –

    description

  • window (int) –

    description

  • value_repeat_up (int, default: 100 ) –

    description. Defaults to 100.

  • value_repeat_down (int, default: -100 ) –

    description. Defaults to -100.

  • name (str, default: 'repeat' ) –

    description. Defaults to "repeat".

  • value_up (int, default: 100 ) –

    description. Defaults to 100.

  • value_down (int, default: -100 ) –

    description. Defaults to -100.

  • inplace (bool, default: False ) –

    description. Defaults to False.

  • plot (bool | list[str], default: False ) –

    description. Defaults to False.

  • plot_up_kwargs (dict | None, default: None ) –

    description. Defaults to None.

  • plot_down_kwargs (dict | None, default: None ) –

    description. Defaults to None.

Returns:

  • Series | DataFrame –

    pd.Series | pd.DataFrame: description

Source code in lettrade/indicator/dataframe.py 
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def signal_repeat(
    dataframe: pd.DataFrame,
    series: pd.Series,
    window: int,
    value_repeat_up: int = 100,
    value_repeat_down: int = -100,
    name: str = "repeat",
    value_up: int = 100,
    value_down: int = -100,
    inplace: bool = False,
    plot: bool | list[str] = False,
    plot_up_kwargs: dict | None = None,
    plot_down_kwargs: dict | None = None,
    **kwargs,
) -> pd.Series | pd.DataFrame:
    """_summary_

    Args:
        dataframe (pd.DataFrame): _description_
        series (pd.Series): _description_
        window (int): _description_
        value_repeat_up (int, optional): _description_. Defaults to 100.
        value_repeat_down (int, optional): _description_. Defaults to -100.
        name (str, optional): _description_. Defaults to "repeat".
        value_up (int, optional): _description_. Defaults to 100.
        value_down (int, optional): _description_. Defaults to -100.
        inplace (bool, optional): _description_. Defaults to False.
        plot (bool | list[str], optional): _description_. Defaults to False.
        plot_up_kwargs (dict | None, optional): _description_. Defaults to None.
        plot_down_kwargs (dict | None, optional): _description_. Defaults to None.

    Returns:
        pd.Series | pd.DataFrame: _description_
    """
    up_rolling_min = series.rolling(window=window).min()
    down_rolling_max = series.rolling(window=window).max()

    signal = signal_direction(
        up=(up_rolling_min >= value_repeat_up),
        down=(down_rolling_max <= value_repeat_down),
        dataframe=dataframe,
        name=name,
        value_up=value_up,
        value_down=value_down,
        inplace=inplace,
        plot=plot,
        plot_up_kwargs=plot_up_kwargs,
        plot_down_kwargs=plot_down_kwargs,
        **kwargs,
    )
    return signal