calculate_advanced_metrics#

API documentation for tradeexecutor.analysis.advanced_metrics.calculate_advanced_metrics Python function.

calculate_advanced_metrics(returns, mode=AdvancedMetricsMode.basic, periods_per_year=365, convert_to_daily=False, benchmark=None, display=False)[source]#

Calculate advanced strategy performance statistics using Quantstats.

Calculates multiple metrics used to benchmark strategies for risk-adjusted returns in one go.

See Quantstats for more information.

Example:

from tradeexecutor.visual.equity_curve import calculate_equity_curve, calculate_returns
from tradeexecutor.analysis.advanced_metrics import calculate_advanced_metrics

equity = calculate_equity_curve(state)
returns = calculate_returns(equity)
metrics = calculate_advanced_metrics(returns)

# Each metric as a series. Index 0 is our performance,
# index 1 is the benchmark.
sharpe = metrics.loc["Sharpe"][0]
assert sharpe == pytest.approx(-1.73)

See also visualise_advanced_metrics().

Parameters:

  • returns (Series) –

    Returns series of the strategy.

    See :py:`tradeeexecutor.visual.equity_curve.calculate_returns`.

  • mode (AdvancedMetricsMode) – Full or basic stats

  • periods_per_year –

    How often the trade decision cycle was run.

    This affects “trading periods per year” needed, to calculate metrics like Sharpe.

    The defaults to the daily trading cycle, trading 24/7.

  • convert_to_daily – QuantStats metrics can only work on daily data, so force convert from 1h or 8h or so if needed.

  • benchmark (pandas.core.series.Series | None) –

Returns:

DataFrame of metrics generated by quantstats.

You can directly display this in your notebook, or extract individual metrics.

Return type:

DataFrame