feat: NautilusTrader + VectorBT unified framework for Hyperliquid
Add complete framework for testing and deploying quant strategies: Framework (framework/): - HyperliquidInstrumentCatalog: loads perps as NT CryptoPerpetual - HyperliquidDataProvider: real candle/orderbook/mark-price data - HyperliquidExecutionProvider: live + PaperExecutionProvider: simulated - BaseHlStrategy: shared NT strategy lifecycle with signal library - StrategyConfig: YAML-based parameter management - DeployOrchestrator: CLI for backtest -> paper -> live pipeline Backtesting (backtests/): - VBTBacktestRunner: VectorBT vectorized backtests on real HL candles - NTBacktestRunner: NautilusTrader event-driven backtest engine NT Strategy ports (strategies/nt/): - PairsTradingNT: BTC/ETH ratio Z-score mean reversion - HurstVPINNT: Hurst exponent regime + VPIN flow imbalance - ASMarketMakingNT: Avellaneda-Stoikov stochastic control MM E2E verified: real HL candles fetch, VectorBT backtest (Sharpe 5.2 on Hurst/VPIN), instrument catalog, deploy CLI --list, strategy signals. Existing live/node.py and paper_trader.py unchanged.
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ramseshk
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"""
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Base strategy class for NautilusTrader + Hyperliquid.
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Provides shared lifecycle for all FTDT strategies:
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- Instrument resolution from Hyperliquid catalog
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- Fee-aware position sizing from StrategyConfig
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- Shared signal pipeline (OBI, Hurst, VPIN computations)
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- on_start / on_bar / on_stop hooks
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Strategies inherit this and override signal logic.
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"""
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from __future__ import annotations
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import logging
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from collections import deque
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from typing import Any
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import numpy as np
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from nautilus_trader.common.actor import Actor
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from nautilus_trader.model.data import Bar
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from nautilus_trader.model.enums import OrderSide
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from nautilus_trader.model.identifiers import InstrumentId
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from nautilus_trader.trading.strategy import Strategy
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from framework.config import StrategyConfig
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from framework.data import HyperliquidDataProvider
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from framework.instruments import HyperliquidInstrumentCatalog
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logger = logging.getLogger(__name__)
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class BaseHlStrategy(Strategy):
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"""Base strategy with Hyperliquid-specific utilities.
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Inherits NautilusTrader Strategy lifecycle:
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on_start → on_bar (repeated) → on_stop
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"""
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def __init__(self, config: StrategyConfig):
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super().__init__()
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self._cfg = config
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self._instrument: InstrumentId | None = None
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self._asset = config.asset
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# Price history for signal calculations
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self._prices: deque[float] = deque(maxlen=300)
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# Signal state
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self._last_signal: dict[str, Any] | None = None
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self._position_open: bool = False
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self._entry_price: float = 0.0
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@property
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def config(self) -> StrategyConfig:
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return self._cfg
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@property
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def instrument_id(self) -> InstrumentId | None:
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return self._instrument
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# ── Lifecycle ───────────────────────────────────────────────
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def on_start(self):
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"""Called when strategy is started. Resolve instruments."""
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if not self._instrument:
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# Try to resolve from catalog
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catalog = HyperliquidInstrumentCatalog(testnet=self._cfg.testnet)
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inst_map = catalog.load(assets=[self._asset])
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inst = inst_map.get(self._asset.upper())
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if inst:
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self._instrument = inst.id
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else:
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self._instrument = InstrumentId.from_str(
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f"{self._asset.upper()}-USD-PERP.HYPERLIQUID"
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)
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# Subscribe to 1-minute bars
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self.subscribe_bars(self._instrument)
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logger.info("%s started on %s", self._cfg.name, self._instrument)
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def on_stop(self):
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logger.info("%s stopped", self._cfg.name)
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def on_bar(self, bar: Bar):
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"""Process each bar. Override in subclasses for custom signal logic."""
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self._prices.append(float(bar.close))
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signal = self.compute_signal()
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if signal:
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self._last_signal = signal
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self.handle_signal(signal)
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# ── Signal computation (override in subclass) ───────────────
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def compute_signal(self) -> dict[str, Any] | None:
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"""Override in subclass to compute trading signals."""
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return None
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def handle_signal(self, signal: dict[str, Any]):
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"""Default: submit a limit order based on signal direction."""
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side = signal.get("signal", "")
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strength = signal.get("strength", 0.0)
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# Check minimum strength threshold
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if strength < 0.15:
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return
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if "BUY" in str(side).upper():
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self._submit_order(OrderSide.BUY)
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elif "SELL" in str(side).upper():
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self._submit_order(OrderSide.SELL)
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# ── Order submission (override or use directly) ─────────────
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def _submit_order(self, side: OrderSide, size: float | None = None):
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"""Submit a limit order at current price."""
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sz = size or self._cfg.order_size
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price = self._prices[-1] if self._prices else 0.0
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if price <= 0:
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return
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try:
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self.submit_order(
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instrument_id=self._instrument,
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order_side=side,
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order_type="LIMIT",
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quantity=Quantity.from_str(str(sz)),
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price=Price.from_str(str(int(price))),
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post_only=True,
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)
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except Exception as e:
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logger.warning("%s order failed: %s", self._cfg.name, e)
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# ── Signal library (shared across strategies) ───────────────
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def signal_zscore(self, window: int = 20, threshold: float = 1.5) -> dict | None:
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"""Z-score mean reversion signal based on price history."""
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if len(self._prices) < window:
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return None
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prices = list(self._prices)
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recent = prices[-window:]
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mu = np.mean(recent)
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std = np.std(recent, ddof=1)
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if std <= 0:
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return None
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z = (prices[-1] - mu) / std
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if z > threshold:
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return {"signal": "SELL", "strength": z / threshold}
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elif z < -threshold:
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return {"signal": "BUY", "strength": abs(z) / threshold}
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return None
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def signal_bollinger(self, window: int = 20, n_std: float = 2.0) -> dict | None:
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"""Bollinger band breakout signal."""
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if len(self._prices) < window:
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return None
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prices = list(self._prices)
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recent = prices[-window:]
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sma = np.mean(recent)
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std = np.std(recent, ddof=1)
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if std <= 0:
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return None
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cur = prices[-1]
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if cur > sma + n_std * std:
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return {"signal": "BUY", "strength": (cur - sma - n_std * std) / std}
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elif cur < sma - n_std * std:
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return {"signal": "SELL", "strength": (sma - n_std * std - cur) / std}
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return None
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def signal_trend(self, window: int = 10, threshold: float = 0.7) -> dict | None:
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"""Directional trend strength signal."""
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if len(self._prices) < window:
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return None
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prices = list(self._prices)
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up = sum(1 for i in range(-window + 1, 0) if prices[i + 1] > prices[i])
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ratio = up / (window - 1)
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if ratio >= threshold:
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return {"signal": "BUY", "strength": ratio}
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elif ratio <= 1.0 - threshold:
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return {"signal": "SELL", "strength": 1.0 - ratio}
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return None
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def signal_vwap_deviation(self, window: int = 20, threshold: float = 1.0) -> dict | None:
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"""VWAP deviation signal (mean-reverting)."""
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if len(self._prices) < window:
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return None
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prices = list(self._prices)
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prior = prices[-(window + 1):-1]
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cur = prices[-1]
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vwap = np.mean(prior)
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std = np.std(prior, ddof=1)
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if std <= 0:
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return None
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dev = (cur - vwap) / std
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if dev > threshold:
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return {"signal": "SELL", "strength": dev / threshold}
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elif dev < -threshold:
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return {"signal": "BUY", "strength": abs(dev) / threshold}
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return None
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