ramseshk
b59dcc3629
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/.
96 lines
3.2 KiB
Python
96 lines
3.2 KiB
Python
"""
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Pairs Trading strategy (BTC-PERP / ETH-PERP).
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Computes the Z-score of the BTC-ETH spread over a rolling window.
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When the spread moves beyond a threshold, trades mean reversion.
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- Z > +2: BTC expensive -> short BTC, long ETH
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- Z < -2: BTC cheap -> long BTC, short ETH
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"""
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import numpy as np
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from nautilus_trader.trading.strategy import Strategy
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from nautilus_trader.config import StrategyConfig
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class PairsTradingConfig(StrategyConfig, frozen=True):
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pair: tuple[str, str]
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z_entry: float = 2.0
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z_exit: float = 0.5
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lookback_hours: int = 24
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trade_size: float = 0.001
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hedge_ratio: float = 0.05
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class PairsTrading(Strategy):
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"""
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Statistical arbitrage on BTC/ETH spread.
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Assumes BTC and ETH are cointegrated — the spread between
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them tends to revert to a mean. Trades the deviations.
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"""
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def __init__(self, config: PairsTradingConfig) -> None:
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super().__init__(config)
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self.config = config
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self.price_history: dict[str, list[float]] = {
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self.config.pair[0]: [],
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self.config.pair[1]: [],
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}
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self.position_open = False
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def on_start(self) -> None:
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for inst in self.config.pair:
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self.subscribe_bars(f"{inst}-1-MINUTE-LAST-INTERNAL")
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self.log.info(f"Pairs trading: {self.config.pair[0]} / {self.config.pair[1]}")
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def on_bar(self, bar) -> None:
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inst_id = str(bar.bar_type.instrument_id)
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if inst_id not in self.price_history:
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return
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self.price_history[inst_id].append(bar.close.as_double())
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a_hist = self.price_history[self.config.pair[0]]
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b_hist = self.price_history[self.config.pair[1]]
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if len(a_hist) < 100 or len(b_hist) < 100:
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return
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maxlen = self.config.lookback_hours * 60
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self.price_history[self.config.pair[0]] = a_hist[-maxlen:]
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self.price_history[self.config.pair[1]] = b_hist[-maxlen:]
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a = np.array(a_hist[-100:])
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b = np.array(b_hist[-100:])
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spread = a - self.config.hedge_ratio * b
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std = spread.std()
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z = (spread[-1] - spread.mean()) / std if std > 0 else 0
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self._signal(z)
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def _signal(self, z: float) -> None:
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btc_pos = self.portfolio.net_position(self.config.pair[0])
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if z > self.config.z_entry and btc_pos <= 0:
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self._trade("SELL", "BUY")
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self.position_open = True
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elif z < -self.config.z_entry and btc_pos >= 0:
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self._trade("BUY", "SELL")
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self.position_open = True
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elif abs(z) < self.config.z_exit and self.position_open:
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self.close_all_positions(self.config.pair[0])
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self.close_all_positions(self.config.pair[1])
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self.position_open = False
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def _trade(self, a_side: str, b_side: str) -> None:
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self.submit_order(self.order_factory.market(
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instrument_id=self.config.pair[0],
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order_side=a_side,
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quantity=self.config.trade_size,
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))
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self.submit_order(self.order_factory.market(
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instrument_id=self.config.pair[1],
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order_side=b_side,
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quantity=self.config.trade_size / self.config.hedge_ratio,
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))
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