plot_mae_mfe

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function backtide.analysis.mae_mfe.plot_mae_mfe(run, interval=None, symbols=None, title=None, legend="upper left", figsize=(900, 600), filename=None, display=True)[source]

Plot Maximum Adverse Excursion vs Maximum Favourable Excursion per trade.

For each closed trade, compute the maximum unrealized loss (MAE) and gain (MFE) versus the entry price. Markers are colored by final PnL sign (green = winner, red = loser). The diagonal reference line marks mfe == mae.

Parameters

run : RunResult The strategy run whose trades will be analyzed. interval : str | Interval | None, default=None Bar interval to load (e.g., "1d", "1h"). When None, the function uses the first available interval in the database. symbols : list[str] | None, default=None List of symbols to include in the plot. If None or empty, all traded symbols are included. title : str | dict | None, default=None Title for the plot. If None, no title is shown. If str, text for the title. If dict, title configuration. legend : str | dict | None, default="upper left" Legend for the plot. See the user guide for an extended description of the choices. If None: No legend is shown. If str: Position to display the legend. If dict: Legend configuration. figsize : tuple[int, int], default=(900, 600) Figure's size in pixels, format as (x, y). filename : str | Path | None, default=None Save the plot using this name. The type of the file depends on the provided name (.html, .png, .pdf, etc...). If filename has no file type, the plot is saved as .html. If None, the plot isn't saved. display : bool | None, default=True Whether to render the plot. If None, it returns the figure.

Returns

Figure | None The Plotly figure object. Only returned if display=None.

See Also

plot_trade_pnl

Create a per-trade PnL over time plot for one or more strategy runs.

Trade

A single round-trip trade (open + close of a position).

query_bars

Return stored OHLCV bars as a dataframe.

Example

>>> from backtide.analysis import plot_mae_mfe
>>> from backtide.storage import query_experiments, query_strategy_runs

>>> exp = query_experiments().iloc[0]
>>> run = query_strategy_runs(exp.id)[-1]
>>> plot_mae_mfe(run)