ramseshk
37da46a016
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
367 lines
13 KiB
Python
367 lines
13 KiB
Python
"""
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VectorBT backtest runner — fast vectorized backtesting on Hyperliquid candle data.
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Fetches real candles from Hyperliquid, converts to signals, and runs
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through VectorBT's Portfolio simulator for instant results.
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Supports parameter sweeps, walk-forward optimization, and full metrics.
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"""
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from __future__ import annotations
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import json
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import logging
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import os
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from datetime import datetime, timezone
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from pathlib import Path
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from typing import Any
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import numpy as np
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import pandas as pd
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import vectorbt as vbt
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sys_path = str(Path(__file__).resolve().parent.parent)
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if sys_path not in __import__("sys").path:
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__import__("sys").path.insert(0, sys_path)
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from framework.data import HyperliquidDataProvider, INTERVAL_MAP
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logger = logging.getLogger(__name__)
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RESULTS_DIR = Path(__file__).resolve().parent / "results"
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RESULTS_DIR.mkdir(parents=True, exist_ok=True)
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# ═══════════════════════════════════════════════════════════════
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# Strategy signal generators
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# ═══════════════════════════════════════════════════════════════
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def _generate_signals(strategy: str, data: dict[str, pd.DataFrame]) -> tuple[pd.Series, pd.Series]:
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"""Generate entry/exit signals for a strategy from candle data.
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Returns (entries, exits) as boolean pandas Series.
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Each strategy uses the primary coin's close prices.
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"""
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main_coin = {"pairs": "ETH", "hurst_vpin": "BTC", "as_mm": "BTC",
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"obi": "BTC", "funding_arb": "BTC", "momentum": "BTC",
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"mean_rev": "BTC"}.get(strategy, "BTC")
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df = data.get(main_coin)
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if df is None or df.empty:
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return pd.Series(dtype=bool), pd.Series(dtype=bool)
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close = df["close"]
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entries = pd.Series(False, index=close.index)
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exits = pd.Series(False, index=close.index)
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if strategy == "pairs":
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btc_df = data.get("BTC")
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if btc_df is not None and not btc_df.empty:
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ratio = btc_df["close"] / close
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mu = ratio.rolling(20).mean()
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std = ratio.rolling(20).std()
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z = (ratio - mu) / std
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entries = z < -1.5
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exits = z.shift(1) >= -0.5
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elif strategy == "hurst_vpin":
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returns = close.pct_change().dropna()
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hurst = returns.rolling(64).apply(_hurst_rs_series, raw=False)
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entries = hurst > 0.55
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exits = hurst.shift(1) < 0.45
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elif strategy == "as_mm":
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spread = (df["high"] - df["low"]) / df["close"]
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vol = close.pct_change().rolling(20).std()
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favorable = (spread > spread.rolling(100).mean()) & (vol < 0.02)
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entries = favorable
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exits = favorable.shift(3)
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elif strategy == "momentum":
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sma = close.rolling(20).mean()
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std = close.rolling(20).std()
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upper = sma + 2 * std
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lower = sma - 2 * std
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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",):
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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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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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def _hurst_rs_series(returns_series: pd.Series) -> float:
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"""Hurst exponent via R/S on a window of log returns."""
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rets = returns_series.dropna().values
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if len(rets) < 32:
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return 0.5
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n = len(rets)
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max_lag = min(n // 2, 64)
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lags = []
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rs_vals = []
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for lag in range(4, max_lag):
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segs = n // lag
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if segs < 2:
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continue
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vals = []
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for s in range(segs):
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seg = rets[s * lag:(s + 1) * lag]
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mean = np.mean(seg)
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dev = np.cumsum(seg - mean)
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r = float(np.max(dev) - np.min(dev))
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sd = float(np.std(seg, ddof=1))
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if sd > 1e-12:
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vals.append(r / sd)
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if vals:
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lags.append(np.log(lag))
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rs_vals.append(np.log(np.mean(vals)))
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if len(lags) < 4:
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return 0.5
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slope = float(np.polyfit(lags, rs_vals, 1)[0])
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return max(0.2, min(0.8, slope))
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# ═══════════════════════════════════════════════════════════════
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# VBT Backtest Runner
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# ═══════════════════════════════════════════════════════════════
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class VBTBacktestRunner:
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"""VectorBT-powered backtesting on Hyperliquid candle data."""
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def __init__(self, fee_rate: float = 0.0005):
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self._provider = HyperliquidDataProvider()
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self._fee_rate = fee_rate
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def run_strategy(
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self,
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strategy: str = "pairs",
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interval: str = "1h",
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testnet: bool = False,
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limit: int = 5000,
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) -> dict[str, Any] | None:
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"""Fetch candles, generate signals, run VBT backtest, return metrics."""
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coins = self._get_coins(strategy)
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provider = HyperliquidDataProvider(testnet=testnet)
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data = {}
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for coin in coins:
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try:
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df = provider.fetch_candles(coin, interval=interval, limit=limit)
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if not df.empty:
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data[coin] = df
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except Exception as e:
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logger.warning("Failed to fetch %s: %s", coin, e)
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if not data:
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logger.error("No candle data fetched for strategy: %s", strategy)
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return None
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entries, exits = _generate_signals(strategy, data)
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primary = list(data.values())[0]
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close = primary["close"]
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# Align indices
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common_idx = entries.index.intersection(close.index)
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entries = entries.reindex(common_idx).fillna(False)
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exits = exits.reindex(common_idx).fillna(False)
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close = close.reindex(common_idx)
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if entries.sum() == 0:
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logger.warning("No signals generated for %s", strategy)
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return self._empty_result(strategy, interval)
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try:
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pf = vbt.Portfolio.from_signals(
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close=close,
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entries=entries,
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exits=exits,
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fees=self._fee_rate,
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slippage=0.001,
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freq=INTERVAL_MAP.get(interval, "1h"),
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init_cash=10000.0,
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)
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except Exception as e:
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logger.error("VBT portfolio error: %s", e)
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return self._empty_result(strategy, interval)
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stats = pf.stats()
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result = self._extract_metrics(pf, stats, strategy, interval, len(close))
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# Save equity curve
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eq_curve = pf.value().dropna()
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result["equity_curve"] = [
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{"t": idx.isoformat(), "v": round(float(v), 2)}
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for idx, v in eq_curve.to_dict().items()
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]
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result["total_trades"] = int(pf.trades.count())
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result["generated_at"] = datetime.now(timezone.utc).isoformat()
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return result
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def param_sweep(
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self,
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strategy: str = "pairs",
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param_grid: dict[str, list] | None = None,
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) -> pd.DataFrame | None:
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"""Grid search over parameters using VBT."""
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coins = self._get_coins(strategy)
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data = {}
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for coin in coins:
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df = self._provider.fetch_candles(coin, interval="1h", limit=2000)
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if not df.empty:
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data[coin] = df
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if not data:
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return None
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primary = list(data.values())[0]
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close = primary["close"]
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if param_grid is None:
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param_grid = {
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"window": [10, 20, 30, 50],
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"threshold": [1.0, 1.5, 2.0, 2.5],
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}
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results_rows = []
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for window in param_grid.get("window", [20]):
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for threshold in param_grid.get("threshold", [1.5]):
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entries, exits = _generate_signals_sweep(strategy, data, window, threshold)
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try:
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pf = vbt.Portfolio.from_signals(
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close=close,
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entries=entries,
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exits=exits,
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fees=self._fee_rate,
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init_cash=10000.0,
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)
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stats = pf.stats()
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results_rows.append({
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"window": window,
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"threshold": threshold,
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"sharpe": stats.get("Sharpe Ratio", 0),
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"total_return": stats.get("Total Return [%]", 0),
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"max_drawdown": stats.get("Max Drawdown [%]", 0),
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"win_rate": stats.get("Win Rate [%]", 0),
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"trades": int(pf.trades.count()),
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})
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except Exception:
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pass
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return pd.DataFrame(results_rows) if results_rows else None
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# ── Helpers ─────────────────────────────────────────────────
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def _get_coins(self, strategy: str) -> list[str]:
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coin_map = {
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"pairs": ["BTC", "ETH"],
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"hurst_vpin": ["BTC"],
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"as_mm": ["BTC"],
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"obi": ["BTC"],
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"funding_arb": ["BTC"],
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"momentum": ["BTC"],
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"mean_rev": ["BTC"],
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}
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return coin_map.get(strategy, ["BTC"])
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def _extract_metrics(self, pf, stats, strategy, interval, n_bars) -> dict:
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return {
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"strategy": strategy,
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"interval": interval,
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"n_bars": n_bars,
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"start_equity": 10000.0,
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"end_equity": round(float(pf.value().iloc[-1]), 2),
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"total_return_pct": round(float(stats.get("Total Return [%]", 0)), 2),
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"pnl": round(float(pf.value().iloc[-1]) - 10000, 2),
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"sharpe": round(float(stats.get("Sharpe Ratio", 0)), 3),
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"sortino": round(float(stats.get("Sortino Ratio", 0)), 3),
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"max_drawdown_pct": round(float(stats.get("Max Drawdown [%]", 0)), 2),
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"win_rate": round(float(stats.get("Win Rate [%]", 0)) / 100, 3),
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"profit_factor": round(float(stats.get("Profit Factor", 0)), 3),
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"expectancy": round(float(stats.get("Expectancy", 0)), 3),
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}
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def _empty_result(self, strategy: str, interval: str) -> dict:
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return {
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"strategy": strategy,
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"interval": interval,
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"n_bars": 0,
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"start_equity": 10000.0,
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"end_equity": 10000.0,
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"total_return_pct": 0.0,
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"pnl": 0.0,
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"sharpe": 0.0,
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"sortino": 0.0,
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"max_drawdown_pct": 0.0,
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"win_rate": 0.0,
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"total_trades": 0,
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"generated_at": datetime.now(timezone.utc).isoformat(),
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}
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def _generate_signals_sweep(
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strategy: str,
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data: dict[str, pd.DataFrame],
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window: int,
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threshold: float,
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) -> tuple[pd.Series, pd.Series]:
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"""Variant of signal generator for parameter sweeps with configurable params."""
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main_coin = {"pairs": "ETH", "hurst_vpin": "BTC", "as_mm": "BTC"}.get(strategy, "BTC")
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df = data.get(main_coin)
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if df is None or df.empty:
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return pd.Series(dtype=bool), pd.Series(dtype=bool)
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close = df["close"]
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entries = pd.Series(False, index=close.index)
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exits = pd.Series(False, index=close.index)
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sma = close.rolling(window).mean()
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std = close.rolling(window).std()
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entries = (close < sma - threshold * std) | (close > sma + threshold * std)
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exits = abs((close - sma) / (std + 1e-10)) < 0.3 * threshold
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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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