Skip to content

trading.analyzer.graphing.risk_return_overview

trading.analyzer.graphing.risk_return_overview

build_risk_return_overview 

build_risk_return_overview(df: DataFrame, color_by: str = 'calmar_ratio') -> go.Figure

Builds the risk/return overview scatter plot.

Parameters:

Name Type Description Default
df DataFrame

Experiment DataFrame from data_prep.results_to_dataframe.

 required
color_by str

Column to color points by. Defaults to "calmar_ratio" so warmer colors highlight the best risk-adjusted performers. Pass a parameter column name instead to color by a swept parameter.

 'calmar_ratio'

Returns:

Type Description
Figure

A plotly Figure.
Source code in src\contango\trading\analyzer\graphing\risk_return_overview.py 
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
def build_risk_return_overview(
    df: pd.DataFrame,
    color_by: str = "calmar_ratio",
) -> go.Figure:
    """
    Builds the risk/return overview scatter plot.

    Args:
        df: Experiment DataFrame from `data_prep.results_to_dataframe`.
        color_by: Column to color points by. Defaults to "calmar_ratio" so
                  warmer colors highlight the best risk-adjusted performers. Pass a
                  parameter column name instead to color by a swept parameter.

    Returns:
        A plotly Figure.
    """
    plot_df = df.dropna(subset=["annual_return", "sharpe_ratio", "max_drawdown", color_by]).copy()
    plot_df["drawdown_severity"] = plot_df["max_drawdown"].abs()

    is_numeric_color = pd.api.types.is_numeric_dtype(plot_df[color_by])

    fig = px.scatter(   # type: ignore[unknownMemberType]
        plot_df,
        x="annual_return",
        y="sharpe_ratio",
        size="drawdown_severity",
        color=color_by,
        hover_name="experiment_id",
        hover_data={
            "max_drawdown": ":.2%",
            "calmar_ratio": ":.2f",
            "trade_count": True,
            "drawdown_severity": False,
        },
        color_continuous_scale="Viridis" if is_numeric_color else None,
        title="Risk/Return Overview — Annual Return vs. Sharpe Ratio (bubble size = drawdown severity)",
        labels={"annual_return": "Annual Return (CAGR)", "sharpe_ratio": "Sharpe Ratio"},
    )
    fig.update_layout(xaxis_tickformat=".1%")  # type: ignore[unknownMemberType]
    return fig