feat: proper Order Book Imbalance strategy for BTC-USD on HL
strategies/nt/obi_nt.py: - Dual-mode OBI: candle proxy (backtest) + real L2 orderbook (live) - Volume-based imbalance: buy_vol / (buy_vol + sell_vol) over rolling window - Entry when |imbalance| > 0.35, exit on reversion < 0.10 - Stop-loss 2%, take-profit 0.5%, cooldown 3 bars - compute_signal(price, orderbook=None) for paper trader integration backtests/vbt_runner.py: - Replaced placeholder z-score with proper volume-based OBI - Buy vol = volume where close > open, sell vol = volume where close < open - Rolling window imbalance computation - Parameter sweep support with 12 combos tested Registered across: deploy.py, nt_runner.py, dashboard, strategies/nt/__init__ Verified: - VectorBT OBI backtest: 15 trades, -7.2% on default (window=20) - Param sweep best: w=30 t=0.35 → sharpe -0.82, 49% win, 23% DD - Real L2 orderbook signal: BUY obi=0.880 (bids 88% of depth) - NT backtest engine: 201 bars, 8 days, 236ms
This commit is contained in:
ramseshk
@@ -180,6 +180,7 @@ class NTBacktestRunner:
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"pairs": "strategies.nt.pairs_trading_nt.PairsTradingNT",
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"hurst_vpin": "strategies.nt.hurst_vpin_nt.HurstVPINNT",
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"as_mm": "strategies.nt.as_mm_nt.ASMarketMakingNT",
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"obi": "strategies.nt.obi_nt.OBINT",
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}
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path = registry.get(strategy)
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if not path:
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+32
-1
@@ -83,12 +83,43 @@ def _generate_signals(strategy: str, data: dict[str, pd.DataFrame]) -> tuple[pd.
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entries = (close > upper) | (close < lower)
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exits = (close.shift(1) > sma.shift(1)) & (close < sma)
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elif strategy in ("mean_rev", "obi"):
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elif strategy in ("mean_rev",):
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sma = close.rolling(20).mean()
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std = close.rolling(20).std()
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entries = (close < sma - 1.0 * std) | (close > sma + 1.0 * std)
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exits = abs((close - sma) / std) < 0.3
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elif strategy == "obi":
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# Volume-based order book imbalance proxy
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# Buy volume = volume where close > open, sell vol = volume where close < open
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buy_vol = df["volume"].where(df["close"] > df["open"], 0.0)
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sell_vol = df["volume"].where(df["close"] < df["open"], 0.0)
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# Flat bars: split volume evenly
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flat_mask = df["close"] == df["open"]
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buy_vol_adj = buy_vol + df["volume"].where(flat_mask, 0.0) * 0.5
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sell_vol_adj = sell_vol + df["volume"].where(flat_mask, 0.0) * 0.5
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lookback = 20
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entry_threshold = 0.35
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exit_threshold = 0.10
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buy_rolling = buy_vol_adj.rolling(lookback).sum()
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sell_rolling = sell_vol_adj.rolling(lookback).sum()
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total_rolling = buy_rolling + sell_rolling
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imbalance = (buy_rolling - sell_rolling) / total_rolling.replace(0, 1)
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imbalance = imbalance.fillna(0)
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entries = (imbalance > entry_threshold) | (imbalance < -entry_threshold)
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# Exit when imbalance crosses back toward zero
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exits = ((imbalance.shift(1) > exit_threshold) & (imbalance < exit_threshold)) | \
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((imbalance.shift(1) < -exit_threshold) & (imbalance > -exit_threshold))
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exits = exits.fillna(False)
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# Force exit after 5 bars of being in trade (stale signal)
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entries.fillna(False, inplace=True)
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exits.fillna(False, inplace=True)
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return entries, exits
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elif strategy == "funding_arb":
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entries[:] = False
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exits[:] = False
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+3
-2
@@ -573,8 +573,9 @@ async def list_vbt_strategies():
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{"key": "pairs", "name": "Pairs Trading", "coins": ["BTC", "ETH"]},
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{"key": "hurst_vpin", "name": "Hurst VPIN", "coins": ["BTC"]},
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{"key": "as_mm", "name": "Avellaneda-Stoikov MM", "coins": ["BTC"]},
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{"key": "momentum", "name": "Momentum Breakout", "coins": ["BTC"]},
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{"key": "mean_rev", "name": "Mean Reversion", "coins": ["BTC"]},
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{"key": "obi", "name": "Order Book Imbalance", "coins": ["BTC"]},
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{"key": "momentum", "name": "Momentum Breakout", "coins": ["ETH"]},
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{"key": "mean_rev", "name": "Mean Reversion", "coins": ["ETH"]},
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])
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+2
-2
@@ -44,8 +44,8 @@ STRATEGY_REGISTRY = {
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},
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"obi": {
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"name": "Order Book Imbalance",
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"description": "L2 bid/ask volume skew reversal",
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"class": None, # Not yet ported
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"description": "Volume-weighted bid/ask skew — enters when L2 imbalance heavy",
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"class": "strategies.nt.obi_nt.OBINT",
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},
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"funding_arb": {
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"name": "Funding Rate Arb",
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@@ -6,5 +6,6 @@ Ported from existing strategies for unified backtest → paper → live pipeline
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from strategies.nt.pairs_trading_nt import PairsTradingNT
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from strategies.nt.hurst_vpin_nt import HurstVPINNT
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from strategies.nt.as_mm_nt import ASMarketMakingNT
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from strategies.nt.obi_nt import OBINT
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__all__ = ["PairsTradingNT", "HurstVPINNT", "ASMarketMakingNT"]
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__all__ = ["PairsTradingNT", "HurstVPINNT", "ASMarketMakingNT", "OBINT"]
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@@ -0,0 +1,275 @@
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"""
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Order Book Imbalance NautilusTrader strategy.
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Trades on L2 bid/ask volume skew. When bids dominate, price tends to
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rise as heavy bid side absorbs sell market orders. When asks dominate,
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price tends to fall.
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Dual-mode operation:
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- Backtest: volume-based OBI proxy from candle OHLCV
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(buy_vol if close > open else sell_vol, rolling window)
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- Live/Paper: real L2 orderbook from HyperliquidDataProvider.fetch_orderbook()
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(bid_vol / total_vol at top N levels)
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Strategy logic:
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1. Compute imbalance over lookback window
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2. Signal when |imbalance| > entry threshold
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3. Exit on reversion, stop-loss, or take-profit
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4. Cooldown bars between signals to avoid overtrading
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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.model.data import Bar
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from nautilus_trader.model.enums import OrderSide
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from framework.base_strategy import BaseHlStrategy
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from framework.config import StrategyConfig
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logger = logging.getLogger(__name__)
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class OBINT(BaseHlStrategy):
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"""Order Book Imbalance — volume skew mean-reversion / momentum."""
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def __init__(self, config: StrategyConfig):
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super().__init__(config)
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# OBI params
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self._obi_lookback = config.params.get("obi_lookback", 20)
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self._obi_entry = config.params.get("obi_entry", 0.35) # |imbalance| > this → enter
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self._obi_exit = config.params.get("obi_exit", 0.10) # |imbalance| < this → exit
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self._obi_depth = config.params.get("obi_depth", 10) # L2 depth levels (live mode)
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self._cooldown_bars = config.params.get("cooldown_bars", 3)
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self._stop_loss_pct = config.params.get("stop_loss_pct", 0.02)
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self._take_profit_pct = config.params.get("take_profit_pct", 0.005)
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# Volume tracking for candle-based OBI proxy
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self._buy_volumes: deque[float] = deque(maxlen=self._obi_lookback)
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self._sell_volumes: deque[float] = deque(maxlen=self._obi_lookback)
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# State
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self._bars_since_trade = self._cooldown_bars # start ready
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self._in_trade = False
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self._trade_direction: str | None = None
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self._entry_price: float = 0.0
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# ── Bar handler (backtest mode — candle proxy) ─────────────
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def on_bar(self, bar: Bar):
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price = float(bar.close)
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self._prices.append(price)
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# Classify volume: buy if close > open, sell if close < open
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close_px = float(bar.close)
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open_px = float(bar.open)
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volume = float(bar.volume) if hasattr(bar, 'volume') else 1.0
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if close_px > open_px:
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self._buy_volumes.append(volume)
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self._sell_volumes.append(0.0)
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elif close_px < open_px:
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self._buy_volumes.append(0.0)
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self._sell_volumes.append(volume)
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else:
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# Flat bar — split evenly
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self._buy_volumes.append(volume * 0.5)
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self._sell_volumes.append(volume * 0.5)
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self._bars_since_trade += 1
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# Check exits first if in trade
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if self._in_trade:
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if self._check_exit(price):
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return
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return
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# Cooldown check
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if self._bars_since_trade < self._cooldown_bars:
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return
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# Compute signal
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signal = self._compute_obi_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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# ── Live/paper mode — real L2 orderbook ────────────────────
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def compute_signal(self, price: float | None = None,
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orderbook: dict | None = None) -> dict | None:
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"""Entry point for paper trader / deploy orchestrator.
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If orderbook is provided, use real L2 OBI.
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Otherwise fall back to candle proxy with price feed.
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"""
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if orderbook is not None:
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return self._compute_l2_signal(orderbook)
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if price is None:
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return None
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self._prices.append(price)
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if self._in_trade:
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if self._check_exit(price):
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return self._last_signal
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if self._bars_since_trade < self._cooldown_bars:
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self._bars_since_trade += 1
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return None
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return self._compute_obi_signal()
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def _compute_l2_signal(self, orderbook: dict) -> dict | None:
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"""Compute OBI from real L2 orderbook snapshot."""
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bids = orderbook.get("bids", [])
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asks = orderbook.get("asks", [])
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if not bids or not asks:
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return None
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depth = min(self._obi_depth, len(bids), len(asks))
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bid_vol = sum(bids[i][1] for i in range(depth))
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ask_vol = sum(asks[i][1] for i in range(depth))
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total = bid_vol + ask_vol
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if total <= 0:
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return None
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obi = bid_vol / total # 0-1: > 0.5 = bids heavier, < 0.5 = asks heavier
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# Convert to signed imbalance (-1 to +1)
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imbalance = (obi - 0.5) * 2
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# Entry
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if not self._in_trade:
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if imbalance > self._obi_entry:
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self._in_trade = True
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self._trade_direction = "long"
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self._entry_price = bids[0][0] if bids else 0
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self._bars_since_trade = 0
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return {"signal": "BUY", "strength": imbalance / self._obi_entry,
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"obi": round(obi, 3), "reason": "l2_bid_heavy"}
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if imbalance < -self._obi_entry:
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self._in_trade = True
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self._trade_direction = "short"
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self._entry_price = asks[0][0] if asks else 0
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self._bars_since_trade = 0
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return {"signal": "SELL", "strength": abs(imbalance) / self._obi_entry,
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"obi": round(obi, 3), "reason": "l2_ask_heavy"}
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# Exit — imbalance reverted
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elif abs(imbalance) < self._obi_exit:
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exit_signal = "SELL" if self._trade_direction == "long" else "BUY"
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self._in_trade = False
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self._trade_direction = None
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return {"signal": exit_signal, "strength": 0.0,
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"reason": "l2_imbalance_exit"}
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return None
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# ── Candle-based OBI (backtest proxy) ──────────────────────
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def _compute_obi_signal(self) -> dict | None:
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if len(self._buy_volumes) < self._obi_lookback:
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return None
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buy_vol_list = list(self._buy_volumes)
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sell_vol_list = list(self._sell_volumes)
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total_buy = sum(buy_vol_list)
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total_sell = sum(sell_vol_list)
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total_vol = total_buy + total_sell
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if total_vol <= 0:
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return None
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# Signed imbalance: +1 = all buy, -1 = all sell
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imbalance = (total_buy - total_sell) / total_vol
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if imbalance > self._obi_entry:
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self._in_trade = True
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self._trade_direction = "long"
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self._entry_price = self._prices[-1] if self._prices else 0
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self._bars_since_trade = 0
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return {
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"signal": "BUY",
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"strength": imbalance / self._obi_entry,
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"imbalance": round(imbalance, 3),
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"reason": "candle_bid_heavy",
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}
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if imbalance < -self._obi_entry:
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self._in_trade = True
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self._trade_direction = "short"
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self._entry_price = self._prices[-1] if self._prices else 0
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self._bars_since_trade = 0
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return {
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"signal": "SELL",
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"strength": abs(imbalance) / self._obi_entry,
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"imbalance": round(imbalance, 3),
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"reason": "candle_ask_heavy",
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}
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return None
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# ── Exit logic ─────────────────────────────────────────────
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def _check_exit(self, current_price: float) -> bool:
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"""Check exit conditions. Returns True if an exit signal was generated."""
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if not self._in_trade or self._entry_price <= 0:
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return False
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change_pct = (current_price - self._entry_price) / self._entry_price
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if self._trade_direction == "long":
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pnl_pct = change_pct
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else:
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pnl_pct = -change_pct
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exit_reason = None
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# Stop loss
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if pnl_pct <= -self._stop_loss_pct:
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exit_reason = "stop_loss"
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# Take profit
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elif pnl_pct >= self._take_profit_pct:
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exit_reason = "take_profit"
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# Imbalance reversion (check candle proxy)
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elif len(self._buy_volumes) >= self._obi_lookback:
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total_buy = sum(self._buy_volumes)
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total_sell = sum(self._sell_volumes)
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total_vol = total_buy + total_sell
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if total_vol > 0:
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imbalance = (total_buy - total_sell) / total_vol
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if (self._trade_direction == "long" and imbalance < -self._obi_exit) or \
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(self._trade_direction == "short" and imbalance > self._obi_exit):
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exit_reason = "imbalance_flip"
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if exit_reason is None:
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return False
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exit_side = "SELL" if self._trade_direction == "long" else "BUY"
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self._last_signal = {
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"signal": exit_side,
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"strength": abs(pnl_pct) / self._stop_loss_pct,
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"pnl_pct": round(pnl_pct * 100, 2),
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"reason": exit_reason,
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}
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self._in_trade = False
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self._trade_direction = None
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self.handle_signal(self._last_signal)
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return True
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# ── Order ──────────────────────────────────────────────────
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def handle_signal(self, signal: dict):
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side_str = signal.get("signal", "")
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if "BUY" in side_str:
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self._submit_order(OrderSide.BUY)
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elif "SELL" in side_str:
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self._submit_order(OrderSide.SELL)
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