Initial public release

hydra/backtest/result.py

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+# hydra/backtest/result.py
+from dataclasses import dataclass, field
+
+
+@dataclass
+class Trade:
+    market: str
+    symbol: str
+    entry_price: float
+    exit_price: float
+    qty: float
+    pnl_usd: float
+    pnl_pct: float
+    entry_ts: int
+    exit_ts: int
+    entry_reason: str
+    exit_reason: str
+
+
+@dataclass
+class BacktestResult:
+    market: str
+    symbol: str
+    timeframe: str
+    since: int
+    until: int
+    initial_capital: float
+    final_equity: float
+    trades: list[Trade] = field(default_factory=list)
+    equity_curve: list[dict] = field(default_factory=list)
+    metrics: dict = field(default_factory=dict)
+
+
+def compute_metrics(
+    trades: list[Trade],
+    equity_curve: list[dict],
+    initial_capital: float,
+    final_equity: float,
+) -> dict:
+    total_return_pct = (final_equity - initial_capital) / initial_capital * 100
+    total_trades = len(trades)
+
+    if total_trades == 0:
+        return {
+            "total_return_pct": round(total_return_pct, 4),
+            "total_trades": 0,
+            "win_rate": 0.0,
+            "max_drawdown_pct": 0.0,
+            "sharpe_ratio": 0.0,
+            "avg_pnl_usd": 0.0,
+        }
+
+    wins = sum(1 for t in trades if t.pnl_usd > 0)
+    win_rate = wins / total_trades * 100
+    avg_pnl_usd = sum(t.pnl_usd for t in trades) / total_trades
+
+    # Max drawdown from equity curve
+    max_drawdown_pct = 0.0
+    if equity_curve:
+        peak = equity_curve[0]["equity"]
+        for point in equity_curve:
+            eq = point["equity"]
+            if eq > peak:
+                peak = eq
+            dd = (peak - eq) / peak * 100 if peak > 0 else 0.0
+            if dd > max_drawdown_pct:
+                max_drawdown_pct = dd
+
+    # Sharpe ratio (annualized, trade-based)
+    sharpe_ratio = 0.0
+    if total_trades >= 2:
+        import math
+        pnls = [t.pnl_usd for t in trades]
+        mean = sum(pnls) / len(pnls)
+        variance = sum((p - mean) ** 2 for p in pnls) / (len(pnls) - 1)
+        std = math.sqrt(variance)
+        if std > 0:
+            sharpe_ratio = round((mean / std) * math.sqrt(252), 4)
+
+    return {
+        "total_return_pct": round(total_return_pct, 4),
+        "total_trades": total_trades,
+        "win_rate": round(win_rate, 2),
+        "max_drawdown_pct": round(max_drawdown_pct, 4),
+        "sharpe_ratio": sharpe_ratio,
+        "avg_pnl_usd": round(avg_pnl_usd, 4),
+    }