Adriano
2687ce7dd2
Metriche base per valutazione strategie: Sharpe annualizzato (default 8760 periodi/anno per dati orari), max drawdown percentuale dalla curva equity, total return cumulativo. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
28 lines
860 B
Python
28 lines
860 B
Python
from __future__ import annotations
|
|
|
|
import numpy as np
|
|
import pandas as pd # type: ignore[import-untyped]
|
|
|
|
|
|
def sharpe_ratio(returns: pd.Series, periods_per_year: int = 8760, rf: float = 0.0) -> float:
|
|
"""Sharpe annualizzato. periods_per_year=8760 per dati orari."""
|
|
excess = returns - rf / periods_per_year
|
|
std = excess.std(ddof=1)
|
|
if std == 0 or np.isnan(std):
|
|
return 0.0
|
|
return float(np.sqrt(periods_per_year) * excess.mean() / std)
|
|
|
|
|
|
def max_drawdown(equity: pd.Series) -> float:
|
|
"""Max drawdown percentuale (positivo)."""
|
|
peak = equity.cummax()
|
|
dd = (peak - equity) / peak.replace(0, np.nan)
|
|
dd = dd.fillna(0.0)
|
|
return float(dd.max())
|
|
|
|
|
|
def total_return(equity: pd.Series) -> float:
|
|
if equity.iloc[0] == 0:
|
|
return float(equity.iloc[-1])
|
|
return float(equity.iloc[-1] / equity.iloc[0] - 1.0)
|