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/iceberg_detection.py

@@ -0,0 +1,66 @@
+"""
+Iceberg / TWAP detection strategy.
+
+Large traders often split big orders into small slices to avoid
+slippage. This strategy detects those patterns by watching for
+recurring same-sized trades above average volume, then enters
+in the same direction.
+"""
+from collections import deque
+from nautilus_trader.trading.strategy import Strategy
+from nautilus_trader.config import StrategyConfig
+
+
+class IcebergDetectorConfig(StrategyConfig, frozen=True):
+    instrument_id: str
+    lookback_seconds: int = 300
+    volume_spike_mult: float = 3.0
+    min_slices: int = 4
+    trade_size: float = 0.001
+
+
+class IcebergDetector(Strategy):
+    """
+    Detects iceberg/TWAP execution patterns.
+
+    Logic:
+    1. Track trade sizes in a rolling window
+    2. When a trade is much larger than average, flag it
+    3. If same size repeats N times -> confirmed iceberg
+    4. Trade in the same direction
+    """
+
+    def __init__(self, config: IcebergDetectorConfig) -> None:
+        super().__init__(config)
+        self.config = config
+        self.recent_sizes: deque[float] = deque(maxlen=100)
+        self.slice_count = 0
+        self.last_flagged_size: float | None = None
+
+    def on_start(self) -> None:
+        self.subscribe_trade_ticks(self.config.instrument_id)
+        self.log.info(f"Iceberg detector started on {self.config.instrument_id}")
+
+    def on_trade_tick(self, tick) -> None:
+        self.recent_sizes.append(tick.size)
+        avg = sum(self.recent_sizes) / len(self.recent_sizes) if self.recent_sizes else 0
+
+        if tick.size > avg * self.config.volume_spike_mult:
+            if tick.size == self.last_flagged_size:
+                self.slice_count += 1
+            else:
+                self.slice_count = 1
+                self.last_flagged_size = tick.size
+        else:
+            self.slice_count = 0
+
+        if self.slice_count >= self.config.min_slices:
+            self.log.info(
+                f"Iceberg: {self.slice_count} slices of size {self.last_flagged_size}"
+            )
+            self.submit_order(self.order_factory.market(
+                instrument_id=self.config.instrument_id,
+                order_side="BUY" if tick.is_buyer_maker else "SELL",
+                quantity=self.config.trade_size,
+            ))
+            self.slice_count = 0