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/portfolio.py

@@ -0,0 +1,36 @@
+"""
+Portfolio tracker.
+
+Aggregates positions from all running strategies to prevent
+over-concentration in any single instrument.
+"""
+from dataclasses import dataclass
+
+
+@dataclass
+class Position:
+    instrument: str
+    quantity: float
+    entry_price: float
+    strategy: str
+
+
+class PortfolioTracker:
+    def __init__(self) -> None:
+        self.positions: dict[str, list[Position]] = {}
+
+    def add(self, strategy: str, instrument: str, qty: float, price: float) -> None:
+        if instrument not in self.positions:
+            self.positions[instrument] = []
+        self.positions[instrument].append(Position(instrument, qty, price, strategy))
+
+    def net_exposure(self, instrument: str) -> float:
+        if instrument not in self.positions:
+            return 0.0
+        return sum(p.quantity for p in self.positions[instrument])
+
+    def all_exposures(self) -> dict[str, float]:
+        return {inst: self.net_exposure(inst) for inst in self.positions}
+
+    def is_overconcentrated(self, instrument: str, max_pct: float, equity: float) -> bool:
+        return abs(self.net_exposure(instrument)) > equity * max_pct