Crypto trading bot: engine, brokers, backtester, dashboard, deploy

FastAPI backend with a single-strategy async engine, paper and Binance
brokers, pure-python indicators, a backtester that shares the engine's
portfolio math, and a Vue dashboard with a live WebSocket feed. Adds the
Gitea Actions CI/CD workflow that deploys to Traefik via docker_default.

backend/app/backtest/metrics.py

@@ -0,0 +1,83 @@
+from __future__ import annotations
+
+from collections.abc import Sequence
+
+from app.schemas import BacktestMetricsOut, BacktestPointOut, BacktestTradeOut
+
+# Bars per year for annualising the Sharpe ratio. Unknown timeframes -> None.
+BARS_PER_YEAR: dict[str, float] = {
+    "1m": 525_600,
+    "3m": 175_200,
+    "5m": 105_120,
+    "15m": 35_040,
+    "30m": 17_520,
+    "1h": 8_760,
+    "2h": 4_380,
+    "4h": 2_190,
+    "6h": 1_460,
+    "12h": 730,
+    "1d": 365,
+    "3d": 121,
+    "1w": 52,
+}
+
+
+def max_drawdown_pct(equities: Sequence[float]) -> float:
+    peak = equities[0] if equities else 0.0
+    worst = 0.0
+    for value in equities:
+        peak = max(peak, value)
+        if peak > 0:
+            worst = max(worst, (peak - value) / peak)
+    return worst * 100
+
+
+def sharpe_ratio(equities: Sequence[float], timeframe: str) -> float | None:
+    if len(equities) < 3:
+        return None
+    returns = [
+        equities[i] / equities[i - 1] - 1 for i in range(1, len(equities)) if equities[i - 1] > 0
+    ]
+    if len(returns) < 2:
+        return None
+    mean = sum(returns) / len(returns)
+    variance = sum((r - mean) ** 2 for r in returns) / (len(returns) - 1)
+    std = variance**0.5
+    periods = BARS_PER_YEAR.get(timeframe)
+    if std == 0 or not periods:
+        return None
+    return mean / std * (periods**0.5)
+
+
+def compute_metrics(
+    curve: Sequence[BacktestPointOut],
+    trades: Sequence[BacktestTradeOut],
+    starting_balance: float,
+    timeframe: str,
+) -> BacktestMetricsOut:
+    equities = [point.equity for point in curve] or [starting_balance]
+    final_equity = equities[-1]
+    total_return = (final_equity / starting_balance - 1) * 100 if starting_balance else 0.0
+    buy_hold = (
+        (curve[-1].buy_hold / starting_balance - 1) * 100 if curve and starting_balance else 0.0
+    )
+
+    returns = [trade.return_pct for trade in trades]
+    gross_profit = sum(trade.pnl for trade in trades if trade.pnl > 0)
+    gross_loss = -sum(trade.pnl for trade in trades if trade.pnl < 0)
+    wins = sum(1 for trade in trades if trade.pnl > 0)
+
+    return BacktestMetricsOut(
+        initial_equity=starting_balance,
+        final_equity=final_equity,
+        total_return_pct=total_return,
+        buy_hold_return_pct=buy_hold,
+        max_drawdown_pct=max_drawdown_pct(equities),
+        num_trades=len(trades),
+        win_rate_pct=(wins / len(trades) * 100) if trades else 0.0,
+        profit_factor=(gross_profit / gross_loss) if gross_loss > 0 else None,
+        avg_trade_pct=(sum(returns) / len(returns)) if returns else 0.0,
+        best_trade_pct=max(returns) if returns else 0.0,
+        worst_trade_pct=min(returns) if returns else 0.0,
+        sharpe=sharpe_ratio(equities, timeframe),
+    )