Initial project scaffold: five quant strategies for Hyperliquid Testnet

Set up the directory structure and wrote placeholder logic for:

- Order Book Imbalance: trades on L2 bid/ask skew
- Iceberg/TWAP detection: follows whale accumulation patterns
- Funding rate arbitrage: delta-neutral carry on perp funding
- Pairs trading: BTC/ETH spread mean reversion
- Avellaneda-Stoikov market making: optimal bid/ask quoting

Also added shared risk manager, portfolio tracker, and a plain-language strategy walkthrough in docs/.

common/metrics.py

@@ -0,0 +1,43 @@
+"""
+Performance metrics.
+
+Sharpe ratio, Sortino ratio, max drawdown, win rate.
+Standard toolbox for evaluating a trading strategy.
+"""
+import numpy as np
+
+
+def sharpe(returns: list[float], rf: float = 0.0, periods: int = 365) -> float:
+    if len(returns) < 2:
+        return 0.0
+    excess = np.mean(returns) - rf
+    std = np.std(returns, ddof=1)
+    return (excess / std) * np.sqrt(periods) if std > 0 else 0.0
+
+
+def sortino(returns: list[float], rf: float = 0.0, periods: int = 365) -> float:
+    if len(returns) < 2:
+        return 0.0
+    excess = np.mean(returns) - rf
+    downside = [r for r in returns if r < 0]
+    d_std = np.std(downside, ddof=1) if downside else 0.0
+    return (excess / d_std) * np.sqrt(periods) if d_std > 0 else 0.0
+
+
+def max_drawdown(equity: list[float]) -> float:
+    if not equity:
+        return 0.0
+    peak = equity[0]
+    worst = 0.0
+    for v in equity:
+        if v > peak:
+            peak = v
+        dd = (peak - v) / peak if peak > 0 else 0.0
+        worst = max(worst, dd)
+    return worst
+
+
+def win_rate(trades: list[dict]) -> float:
+    if not trades:
+        return 0.0
+    return sum(1 for t in trades if t.get("pnl", 0) > 0) / len(trades)