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plotly ¤

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

data property writable ¤

data: DataFeed

Get plotting main datafeed

Returns:

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 self._datas_stored is None:
        self._datas_stored = self.datas.copy()

    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_stored.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_stored.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_stored.l.index.get_loc(since)
    elif isinstance(since, pd.Timestamp):  # Get index of Timestamp
        since = self._data_stored.l.index.get_loc(since)

    if name is None:
        name = self._data_stored.name

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

    # Jump
    jump_start_dt = None
    jump_stop_dt = None
    for i, data in enumerate(self._datas_stored):
        if i == 0:
            self.datas[i] = data.__class__(
                data=data.l[since : since + range],
                name=data.name,
                timeframe=data.timeframe,
            )
            jump_start_dt = self.data.index[0]
            jump_stop_dt = self.data.index[-1]
        else:
            self.datas[i] = data.__class__(
                data=data.loc[
                    (data.index >= jump_start_dt) & (data.index <= jump_stop_dt)
                ],
                name=data.name,
                timeframe=data.timeframe,
            )

        if hasattr(data, DATAFRAME_PLOTTERS_NAME):
            object.__setattr__(
                self.datas[i],
                DATAFRAME_PLOTTERS_NAME,
                getattr(data, DATAFRAME_PLOTTERS_NAME),
            )

    # 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"""
    if not self._datas_stored or self.data is self._data_stored:
        return False

    self.datas = self._datas_stored.copy()
    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"""

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

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:
    """_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:
        super().drop(*args, **kwargs)
        return

    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)))

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

data property writable ¤

data: DataFeed

Get plotting main datafeed

Returns:

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 self._datas_stored is None:
        self._datas_stored = self.datas.copy()

    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_stored.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_stored.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_stored.l.index.get_loc(since)
    elif isinstance(since, pd.Timestamp):  # Get index of Timestamp
        since = self._data_stored.l.index.get_loc(since)

    if name is None:
        name = self._data_stored.name

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

    # Jump
    jump_start_dt = None
    jump_stop_dt = None
    for i, data in enumerate(self._datas_stored):
        if i == 0:
            self.datas[i] = data.__class__(
                data=data.l[since : since + range],
                name=data.name,
                timeframe=data.timeframe,
            )
            jump_start_dt = self.data.index[0]
            jump_stop_dt = self.data.index[-1]
        else:
            self.datas[i] = data.__class__(
                data=data.loc[
                    (data.index >= jump_start_dt) & (data.index <= jump_stop_dt)
                ],
                name=data.name,
                timeframe=data.timeframe,
            )

        if hasattr(data, DATAFRAME_PLOTTERS_NAME):
            object.__setattr__(
                self.datas[i],
                DATAFRAME_PLOTTERS_NAME,
                getattr(data, DATAFRAME_PLOTTERS_NAME),
            )

    # 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"""
    if not self._datas_stored or self.data is self._data_stored:
        return False

    self.datas = self._datas_stored.copy()
    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"""

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="#26c6da",
    decreasing_line_color="#ab47bc",
    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: '#26c6da' ) –

    description. Defaults to "#26c6da".

  • decreasing_line_color (str, default: '#ab47bc' ) –

    description. Defaults to "#ab47bc".

  • 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="#26c6da",
    decreasing_line_color="#ab47bc",
    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 "#26c6da".
        decreasing_line_color (str, optional): _description_. Defaults to "#ab47bc".
        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 = "#ffee58",
    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: '#ffee58' ) –

    description. Defaults to "#ffee58".

  • 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 = "#ffee58",
    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 "#ffee58".
        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 = "#ffee58",
    width: int = 1,
    name: str | None = None,
    mode: str = "lines",
    fullfill: bool = False,
    dataframe: DataFrame | None = None,
    **kwargs
) -> dict

summary

Parameters:

  • color (str, default: '#ffee58' ) –

    description. Defaults to "#ffee58".

  • 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 = "#ffee58",
    width: int = 1,
    name: str | None = None,
    mode: str = "lines",
    fullfill: bool = False,
    dataframe: pd.DataFrame | None = None,
    **kwargs,
) -> dict:
    """_summary_

    Args:
        color (str, optional): _description_. Defaults to "#ffee58".
        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_
    """
    result = {}
    for series in serieses:
        plot_merge(
            result,
            plot_line(
                series=series,
                color=color,
                width=width,
                name=name,
                mode=mode,
                fullfill=fullfill,
                dataframe=dataframe,
                **kwargs,
            ),
        )
    return result

plot_mark ¤

plot_mark(
    series: Series | str,
    color: str = "#ffee58",
    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: '#ffee58' ) –

    description. Defaults to "#ffee58".

  • 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 = "#ffee58",
    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 "#ffee58".
        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_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