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/__init__.py

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+# Package init files (make these importable)

strategies/avellaneda_stoikov.py

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+"""
+Avellaneda-Stoikov Market Making strategy.
+
+A mathematical model for optimal market making based on
+stochastic optimal control. Computes optimal bid/ask quotes
+considering current inventory, risk aversion, volatility,
+and time horizon.
+
+Key formulas:
+  Reservation price:  r = s - q * gamma * sigma^2 * tau
+  Optimal spread:     delta = gamma * sigma^2 * tau + (2/gamma) * ln(1 + gamma/k)
+
+where:
+  s = mid price, q = inventory, gamma = risk aversion
+  sigma = volatility, tau = remaining time, k = order intensity
+"""
+import math
+from nautilus_trader.trading.strategy import Strategy
+from nautilus_trader.config import StrategyConfig
+from datetime import datetime, timezone
+
+
+class AvellanedaStoikovConfig(StrategyConfig, frozen=True):
+    instrument_id: str
+    gamma: float = 0.1
+    sigma: float = 0.02
+    T: float = 1.0
+    k: float = 1.5
+    min_spread: float = 0.0001
+    max_inventory: float = 0.01
+
+
+class AvellanedaStoikov(Strategy):
+    """
+    A-S optimal market making.
+
+    Instead of predicting direction, this strategy provides
+    liquidity by continuously quoting bid/ask prices at an
+    optimal distance from the mid price. The spread widens
+    as inventory builds up (to discourage further accumulation)
+    and tightens as the time horizon approaches.
+    """
+
+    def __init__(self, config: AvellanedaStoikovConfig) -> None:
+        super().__init__(config)
+        self.config = config
+        self.start_time: datetime | None = None
+
+    def on_start(self) -> None:
+        self.start_time = self.clock.utc_now()
+        self.subscribe_quote_ticks(self.config.instrument_id)
+        self.log.info(
+            f"A-S MM on {self.config.instrument_id} "
+            f"(gamma={self.config.gamma})"
+        )
+
+    def on_quote_tick(self, tick) -> None:
+        self.cancel_all_orders(self.config.instrument_id)
+
+        elapsed = (self.clock.utc_now() - self.start_time).total_seconds() / 3600
+        tau = max(self.config.T - elapsed, 0.01)
+
+        q = float(self.portfolio.net_position(self.config.instrument_id))
+        if abs(q) >= self.config.max_inventory:
+            return
+
+        g = self.config.gamma
+        s = self.config.sigma
+        k = self.config.k
+
+        mid = (tick.bid + tick.ask) / 2
+        reservation = mid - q * g * s**2 * tau
+        spread = g * s**2 * tau + (2 / g) * math.log(1 + g / k)
+        spread = max(spread, self.config.min_spread)
+
+        self.submit_order(self.order_factory.limit(
+            instrument_id=self.config.instrument_id,
+            order_side="BUY",
+            quantity=self.config.max_inventory / 10,
+            price=reservation - spread / 2,
+        ))
+        self.submit_order(self.order_factory.limit(
+            instrument_id=self.config.instrument_id,
+            order_side="SELL",
+            quantity=self.config.max_inventory / 10,
+            price=reservation + spread / 2,
+        ))

strategies/funding_rate_arb.py

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+"""
+Funding Rate Arbitrage strategy.
+
+Hyperliquid pays funding every 8 hours. When the rate is positive,
+longs pay shorts. This strategy:
+
+1. Goes LONG spot (no funding payments)
+2. Goes SHORT perp (collects funding)
+3. Maintains delta neutrality
+
+The profit comes from funding, not price direction.
+"""
+from nautilus_trader.trading.strategy import Strategy
+from nautilus_trader.config import StrategyConfig
+
+
+class FundingRateArbConfig(StrategyConfig, frozen=True):
+    spot_instrument: str
+    perp_instrument: str
+    min_funding_rate: float = 0.0001
+    rebalance_threshold: float = 0.05
+    position_size: float = 0.01
+
+
+class FundingRateArb(Strategy):
+    """
+    Delta-neutral funding rate carry trade.
+
+    Key idea: funding rate IS the edge. Stay neutral, collect
+    the payments.
+    """
+
+    def __init__(self, config: FundingRateArbConfig) -> None:
+        super().__init__(config)
+        self.config = config
+        self.position_open = False
+
+    def on_start(self) -> None:
+        bar_type = f"{self.config.perp_instrument}-1-MINUTE-LAST-INTERNAL"
+        self.subscribe_bars(bar_type)
+        self.log.info(
+            f"Funding arb: {self.config.spot_instrument} / {self.config.perp_instrument}"
+        )
+
+    def on_bar(self, bar) -> None:
+        funding_rate = self._get_funding_rate()
+        if funding_rate is None:
+            return
+
+        spot_pos = self.portfolio.net_position(self.config.spot_instrument)
+
+        if funding_rate > self.config.min_funding_rate and spot_pos == 0:
+            self._open()
+            self.position_open = True
+        elif funding_rate < self.config.min_funding_rate / 2 and self.position_open:
+            self._close()
+            self.position_open = False
+
+    def _get_funding_rate(self) -> float | None:
+        # TODO: fetch from Hyperliquid API
+        return 0.0001
+
+    def _open(self) -> None:
+        self.submit_order(self.order_factory.market(
+            instrument_id=self.config.spot_instrument,
+            order_side="BUY",
+            quantity=self.config.position_size,
+        ))
+        self.submit_order(self.order_factory.market(
+            instrument_id=self.config.perp_instrument,
+            order_side="SELL",
+            quantity=self.config.position_size,
+        ))
+
+    def _close(self) -> None:
+        self.close_all_positions(self.config.spot_instrument)
+        self.close_all_positions(self.config.perp_instrument)

strategies/iceberg_detection.py

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+"""
+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

strategies/orderbook_imbalance.py

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+"""
+Order Book Imbalance strategy.
+
+Enters positions when bid/ask volume at the top of the order book
+shows a significant directional skew. The idea: when one side of
+the book is much heavier, price tends to move toward the thinner
+side as the heavy side absorbs market orders.
+"""
+from nautilus_trader.trading.strategy import Strategy
+from nautilus_trader.config import StrategyConfig
+
+
+class OrderBookImbalanceConfig(StrategyConfig, frozen=True):
+    instrument_id: str
+    depth: int = 10
+    imbalance_threshold: float = 0.6
+    trade_size: float = 0.001
+    max_position: float = 0.003
+    cooldown_bars: int = 5
+
+
+class OrderBookImbalance(Strategy):
+    """
+    Trades on L2 order book imbalance.
+
+    - imbalance > threshold  -> bid side heavy -> buy
+    - imbalance < 1-threshold -> ask side heavy -> sell
+    """
+
+    def __init__(self, config: OrderBookImbalanceConfig) -> None:
+        super().__init__(config)
+        self.config = config
+        self.bars_since_last_trade = 0
+
+    def on_start(self) -> None:
+        self.subscribe_order_book_deltas(
+            self.config.instrument_id,
+            depth=self.config.depth,
+        )
+        self.log.info(
+            f"OFI started on {self.config.instrument_id} "
+            f"(depth={self.config.depth})"
+        )
+
+    def on_order_book_deltas(self, deltas) -> None:
+        self.bars_since_last_trade += 1
+        if self.bars_since_last_trade < self.config.cooldown_bars:
+            return
+
+        book = self.cache.order_book(self.config.instrument_id)
+        if not book or len(book.bids) == 0 or len(book.asks) == 0:
+            return
+
+        depth = min(self.config.depth, len(book.bids), len(book.asks))
+        bid_vol = sum(book.bids[i].size for i in range(depth))
+        ask_vol = sum(book.asks[i].size for i in range(depth))
+        total = bid_vol + ask_vol
+        if total == 0:
+            return
+
+        imbalance = bid_vol / total
+        pos = self.portfolio.net_position(self.config.instrument_id)
+
+        if imbalance > self.config.imbalance_threshold and pos <= 0:
+            self._enter("BUY")
+            self.bars_since_last_trade = 0
+        elif imbalance < (1 - self.config.imbalance_threshold) and pos >= 0:
+            self._enter("SELL")
+            self.bars_since_last_trade = 0
+
+    def _enter(self, side: str) -> None:
+        self.submit_order(self.order_factory.market(
+            instrument_id=self.config.instrument_id,
+            order_side=side,
+            quantity=self.config.trade_size,
+        ))

strategies/pairs_trading.py

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+"""
+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,
+        ))