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/.

strategies/pairs_trading.py

@@ -0,0 +1,95 @@
+"""
+Pairs Trading strategy (BTC-PERP / ETH-PERP).
+
+Computes the Z-score of the BTC-ETH spread over a rolling window.
+When the spread moves beyond a threshold, trades mean reversion.
+
+- Z > +2: BTC expensive -> short BTC, long ETH
+- Z < -2: BTC cheap -> long BTC, short ETH
+"""
+import numpy as np
+from nautilus_trader.trading.strategy import Strategy
+from nautilus_trader.config import StrategyConfig
+
+
+class PairsTradingConfig(StrategyConfig, frozen=True):
+    pair: tuple[str, str]
+    z_entry: float = 2.0
+    z_exit: float = 0.5
+    lookback_hours: int = 24
+    trade_size: float = 0.001
+    hedge_ratio: float = 0.05
+
+
+class PairsTrading(Strategy):
+    """
+    Statistical arbitrage on BTC/ETH spread.
+
+    Assumes BTC and ETH are cointegrated — the spread between
+    them tends to revert to a mean. Trades the deviations.
+    """
+
+    def __init__(self, config: PairsTradingConfig) -> None:
+        super().__init__(config)
+        self.config = config
+        self.price_history: dict[str, list[float]] = {
+            self.config.pair[0]: [],
+            self.config.pair[1]: [],
+        }
+        self.position_open = False
+
+    def on_start(self) -> None:
+        for inst in self.config.pair:
+            self.subscribe_bars(f"{inst}-1-MINUTE-LAST-INTERNAL")
+        self.log.info(f"Pairs trading: {self.config.pair[0]} / {self.config.pair[1]}")
+
+    def on_bar(self, bar) -> None:
+        inst_id = str(bar.bar_type.instrument_id)
+        if inst_id not in self.price_history:
+            return
+
+        self.price_history[inst_id].append(bar.close.as_double())
+
+        a_hist = self.price_history[self.config.pair[0]]
+        b_hist = self.price_history[self.config.pair[1]]
+        if len(a_hist) < 100 or len(b_hist) < 100:
+            return
+
+        maxlen = self.config.lookback_hours * 60
+        self.price_history[self.config.pair[0]] = a_hist[-maxlen:]
+        self.price_history[self.config.pair[1]] = b_hist[-maxlen:]
+
+        a = np.array(a_hist[-100:])
+        b = np.array(b_hist[-100:])
+        spread = a - self.config.hedge_ratio * b
+
+        std = spread.std()
+        z = (spread[-1] - spread.mean()) / std if std > 0 else 0
+
+        self._signal(z)
+
+    def _signal(self, z: float) -> None:
+        btc_pos = self.portfolio.net_position(self.config.pair[0])
+
+        if z > self.config.z_entry and btc_pos <= 0:
+            self._trade("SELL", "BUY")
+            self.position_open = True
+        elif z < -self.config.z_entry and btc_pos >= 0:
+            self._trade("BUY", "SELL")
+            self.position_open = True
+        elif abs(z) < self.config.z_exit and self.position_open:
+            self.close_all_positions(self.config.pair[0])
+            self.close_all_positions(self.config.pair[1])
+            self.position_open = False
+
+    def _trade(self, a_side: str, b_side: str) -> None:
+        self.submit_order(self.order_factory.market(
+            instrument_id=self.config.pair[0],
+            order_side=a_side,
+            quantity=self.config.trade_size,
+        ))
+        self.submit_order(self.order_factory.market(
+            instrument_id=self.config.pair[1],
+            order_side=b_side,
+            quantity=self.config.trade_size / self.config.hedge_ratio,
+        ))