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