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optimize ¤

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()