Initial commit: quant backtesting framework with daily trading simulator

Backtesting engine supporting 11 strategies across US (S&P 500) and CN (CSI 300)
markets with open-to-close execution, proportional + fixed per-trade fees.

Daily trader (trader.py) with auto/morning/evening/simulate/status commands
and cron-friendly `auto` mode for unattended daily runs on a server.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

metrics.py

@@ -0,0 +1,72 @@
+import numpy as np
+import pandas as pd
+
+
+def total_return(equity: pd.Series) -> float:
+    return equity.iloc[-1] / equity.iloc[0] - 1
+
+
+def annualized_return(equity: pd.Series, periods: int = 252) -> float:
+    n = len(equity)
+    return (1 + total_return(equity)) ** (periods / n) - 1
+
+
+def annualized_volatility(returns: pd.Series, periods: int = 252) -> float:
+    return returns.std() * np.sqrt(periods)
+
+
+def sharpe_ratio(returns: pd.Series, risk_free_rate: float = 0.0, periods: int = 252) -> float:
+    excess = returns - risk_free_rate / periods
+    std = excess.std()
+    if std == 0:
+        return 0.0
+    return (excess.mean() / std) * np.sqrt(periods)
+
+
+def sortino_ratio(returns: pd.Series, risk_free_rate: float = 0.0, periods: int = 252) -> float:
+    excess = returns - risk_free_rate / periods
+    downside_std = excess[excess < 0].std()
+    if downside_std == 0:
+        return 0.0
+    return (excess.mean() / downside_std) * np.sqrt(periods)
+
+
+def max_drawdown(equity: pd.Series) -> float:
+    rolling_peak = equity.cummax()
+    drawdown = (equity - rolling_peak) / rolling_peak
+    return drawdown.min()
+
+
+def calmar_ratio(equity: pd.Series, periods: int = 252) -> float:
+    ann_ret = annualized_return(equity, periods)
+    mdd = abs(max_drawdown(equity))
+    if mdd == 0:
+        return np.inf
+    return ann_ret / mdd
+
+
+def win_rate(returns: pd.Series) -> float:
+    active = returns[returns != 0]
+    if len(active) == 0:
+        return 0.0
+    return (active > 0).sum() / len(active)
+
+
+def summary(equity: pd.Series, name: str = "Strategy") -> dict:
+    returns = equity.pct_change().dropna()
+    metrics = {
+        "Total Return":          f"{total_return(equity):.2%}",
+        "Annualized Return":     f"{annualized_return(equity):.2%}",
+        "Annualized Volatility": f"{annualized_volatility(returns):.2%}",
+        "Sharpe Ratio":          f"{sharpe_ratio(returns):.2f}",
+        "Sortino Ratio":         f"{sortino_ratio(returns):.2f}",
+        "Max Drawdown":          f"{max_drawdown(equity):.2%}",
+        "Calmar Ratio":          f"{calmar_ratio(equity):.2f}",
+        "Win Rate":              f"{win_rate(returns):.2%}",
+    }
+    print(f"\n{'='*45}")
+    print(f"  {name}")
+    print(f"{'='*45}")
+    for k, v in metrics.items():
+        print(f"  {k:<26} {v:>10}")
+    return metrics