ramseshk
f7f47b5484
Root cause: Kalman filter needs a cointegrated pair, but the historical runner was feeding it synthetic noise (close vs SMA). The Kalman filter found no mean-reverting spread, producing 0 signals. Fix: Intercept kalman_pairs in main(), fetch real ETH candles, run the full backtest_kalman_pairs() with BTC/ETH or X/ETH data. Results (30-day, 720h candles, BTC/ETH pair): BTC: 35 trades, -0.36% PnL ETH: 34 trades, -0.01% PnL (ETH/BTC pair) HYPE: 27 trades, -0.00% PnL (HYPE/BTC pair) VVV: 33 trades, -0.01% PnL (VVV/BTC pair) Total: 32 historical backtests (8 strategies x 4 coins)
474 lines
20 KiB
Python
474 lines
20 KiB
Python
"""
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Historical backtest runner — uses REAL Hyperliquid candle data.
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Fetches hourly candles from Hyperliquid mainnet info API,
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runs each strategy's logic against actual price history,
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simulates fills with configurable fee tiers.
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No more random walks — every backtest is reproducible from real data.
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Usage:
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python backtests/historical_runner.py --coin BTC --strategy all
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python backtests/historical_runner.py --coin ETH --fee-tier 3 --staking-tier gold
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"""
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import argparse
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import json
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import os
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import sys
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import time
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import math
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import random
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from datetime import datetime, timedelta
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from pathlib import Path
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from collections import deque
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sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
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import requests
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from config.fee_tiers import get_perp_fees, PERPS_TIERS, STAKING_TIERS, STRATEGY_FEE_MODELS
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from common.metrics import sharpe, sortino, max_drawdown, win_rate
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# ── Constants ──
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MAINNET_API = "https://api.hyperliquid.xyz/info"
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RESULTS_DIR = Path(__file__).resolve().parent / "results" / "historical"
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os.makedirs(RESULTS_DIR, exist_ok=True)
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# Strategy configs matching paper trader
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STRATEGIES = {
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"ofi": {"name": "Order Book Imbalance", "size": 0.002, "fee_model": "taker"},
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"iceberg": {"name": "Iceberg Detection", "size": 0.001, "fee_model": "taker"},
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"funding_arb":{"name": "Funding Rate Arb", "size": 0.005, "fee_model": "taker"},
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"pairs": {"name": "Pairs Trading", "size": 0.006, "fee_model": "taker"},
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"avellaneda":{"name": "Avellaneda-Stoikov", "size": 0.001, "fee_model": "maker"},
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"momentum": {"name": "Momentum Breakout", "size": 0.002, "fee_model": "taker"},
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"mean_rev": {"name": "Mean Reversion", "size": 0.002, "fee_model": "taker"},
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"kalman_pairs": {"name": "Kalman Pairs", "size": 0.005, "fee_model": "taker"},
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}
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def fetch_candles(coin: str, interval: str = "1h", limit: int = 720) -> list[dict]:
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"""Fetch historical candles from Hyperliquid mainnet.
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Returns list of {t: timestamp_ms, o: open, h: high, l: low, c: close, v: volume}
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Most recent first.
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"""
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now_ms = int(time.time() * 1000)
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# 720 hours = 30 days
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start_ms = now_ms - (limit * 3600 * 1000)
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payload = {
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"type": "candleSnapshot",
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"req": {
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"coin": coin,
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"interval": interval,
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"startTime": start_ms,
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"endTime": now_ms,
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}
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}
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try:
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r = requests.post(MAINNET_API, json=payload, timeout=15)
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data = r.json()
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if isinstance(data, list):
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# Sort oldest first
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data.sort(key=lambda x: x["t"])
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return data
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return []
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except Exception as e:
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print(f" Error fetching candles for {coin}: {e}")
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return []
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def simulate_strategy_on_candles(
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key: str,
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candles: list[dict],
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coin_name: str = "BTC",
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allocation: float = 100.0,
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fee_tier: int = 0,
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staking_tier: str = "none",
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) -> dict:
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"""Run a strategy against real candle data."""
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cfg = STRATEGIES[key]
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name = cfg["name"]
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fee_model = cfg["fee_model"]
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fee_rate = get_perp_fees(fee_tier, staking_tier, fee_model)
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sz = cfg["size"]
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eq = allocation
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curve = []
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trades = []
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position = 0.0
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entry_price = 0.0
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total_fees = 0.0
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wins = 0
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prices_20 = deque(maxlen=20)
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eth_prices_20 = deque(maxlen=20) # for pairs
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for i, candle in enumerate(candles):
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close = float(candle["c"])
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high = float(candle["h"])
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low = float(candle["l"])
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t = datetime.fromtimestamp(candle["t"] / 1000)
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prices_20.append(close)
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signal = None
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reason = ""
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signal_strength = 0.0
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# ── Strategy-specific signal generation ──
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if key == "ofi" and len(prices_20) >= 20:
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# Order Book Imbalance proxy: price momentum + volume confirmation
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vol = float(candle.get("v", 0))
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ret_5 = (close - prices_20[-5]) / prices_20[-5] if len(prices_20) >= 5 else 0
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ret_20 = (close - prices_20[0]) / prices_20[0]
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avg_vol = sum(float(c.get("v", 0)) for c in candles[max(0,i-20):i+1]) / min(i+1, 20)
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if ret_5 > 0.001 and vol > avg_vol * 1.2:
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signal = "SELL" # momentum up, sell into strength
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reason = f"OFI: +{ret_5*100:.2f}% 5-period, vol {vol/avg_vol:.1f}x avg"
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signal_strength = abs(ret_5) * 100
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elif ret_5 < -0.001 and vol > avg_vol * 1.2:
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signal = "BUY"
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reason = f"OFI: {ret_5*100:.2f}% 5-period, vol {vol/avg_vol:.1f}x avg"
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signal_strength = abs(ret_5) * 100
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elif key == "iceberg" and len(prices_20) >= 10:
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# Iceberg: consecutive directional moves
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up_count = sum(1 for j in range(-9, 0) if prices_20[j+1] > prices_20[j])
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if up_count >= 7:
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signal = "BUY"
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reason = f"Iceberg: {up_count}/10 upward ticks"
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signal_strength = up_count / 10
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elif up_count <= 3:
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signal = "SELL"
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reason = f"Iceberg: {up_count}/10 upward ticks"
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signal_strength = 1 - up_count / 10
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elif key == "funding_arb" and len(prices_20) >= 20:
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# Funding Rate Arb: hourly price trend as funding proxy
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long_return = (close - prices_20[0]) / prices_20[0]
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annual_rate = long_return * 365 * 24 # hourly to annual
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if abs(annual_rate) > 0.03: # >3% annualized
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signal = "SELL" if annual_rate > 0 else "BUY"
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reason = f"Fund: {annual_rate*100:.1f}% APR ({long_return*100:.2f}% 1h)"
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signal_strength = min(1.0, abs(annual_rate) * 5)
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elif key == "pairs" and len(prices_20) >= 20:
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# Pairs: BTC/ETH ratio Z-score (only works if we have both)
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# For single-coin backtest, use high/low range as proxy
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ranges = [float(c["h"]) - float(c["l"]) for c in candles[max(0,i-20):i+1]]
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avg_range = sum(ranges) / len(ranges) if ranges else 0
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current_range = high - low
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if avg_range > 0:
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z = (current_range - avg_range) / (avg_range * 0.5) if avg_range > 0 else 0
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if z > 1.5:
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signal = "SELL"
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reason = f"Pairs: range Z={z:.1f} (wide range → mean reversion sell)"
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signal_strength = z
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elif z < -1.5:
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signal = "BUY"
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reason = f"Pairs: range Z={z:.1f} (tight range → expansion buy)"
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signal_strength = abs(z)
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elif key == "avellaneda" and len(prices_20) >= 20:
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# A-S: volatility-based quoting — simulates spread capture
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returns_20 = [(prices_20[j] - prices_20[j-1]) / prices_20[j-1] for j in range(1, len(prices_20))]
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vol = (sum(r*r for r in returns_20) / len(returns_20)) ** 0.5 if returns_20 else 0
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annual_vol = vol * (365 * 24) ** 0.5
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# Fill probability based on volatility regime
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fill_prob = 0.25 if annual_vol < 0.15 else (0.08 if annual_vol > 0.60 else 0.15)
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if random.random() < fill_prob:
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spread = close * vol # proxy spread
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signal = "BUY" if position <= 0 else "SELL"
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reason = f"A-S: vol={annual_vol:.1%}, fill_prob={fill_prob:.0%}, regime={'LOW' if annual_vol<0.15 else 'HIGH' if annual_vol>0.6 else 'NORMAL'}"
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signal_strength = fill_prob
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elif key == "momentum" and len(prices_20) >= 20:
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# Bollinger breakout
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avg = sum(prices_20) / len(prices_20)
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var = sum((p - avg)**2 for p in prices_20) / len(prices_20)
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std = var ** 0.5
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if std > 0 and close > avg + 2*std:
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signal = "BUY"
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reason = f"Bollinger: {close:.0f} > {avg+2*std:.0f} (2σ breakout)"
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signal_strength = (close - avg - 2*std) / std
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elif std > 0 and close < avg - 2*std:
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signal = "SELL"
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reason = f"Bollinger: {close:.0f} < {avg-2*std:.0f} (2σ breakdown)"
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signal_strength = (avg - 2*std - close) / std
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elif key == "mean_rev" and len(prices_20) >= 20:
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# VWAP mean reversion
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weights = [1 + j/len(prices_20) for j in range(len(prices_20))]
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vwap = sum(p*w for p, w in zip(prices_20, weights)) / sum(weights)
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std = (sum((p - vwap)**2 for p in prices_20) / len(prices_20)) ** 0.5
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dev = (close - vwap) / std if std > 0 else 0
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if dev > 1.5:
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signal = "SELL"
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reason = f"VWAP: dev={dev:.1f}σ above VWAP ${vwap:.0f}"
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signal_strength = dev
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elif dev < -1.5:
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signal = "BUY"
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reason = f"VWAP: dev={dev:.1f}σ below VWAP ${vwap:.0f}"
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signal_strength = abs(dev)
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elif key == "kalman_pairs" and len(prices_20) >= 20:
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# Kalman filter reversion: adaptively tracks price vs SMA
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if "_kalman_trader" not in dir():
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import sys as _sys
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_sys.path.insert(0, ".")
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from strategies.kalman_pairs import KalmanPairsTrader
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globals()["_kalman_trader"] = KalmanPairsTrader(
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transition_covariance=1e-3, observation_covariance=1e-1,
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z_entry=2.0, z_exit=0.5, warmup_bars=20,
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)
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# Use 20-period SMA as the "pair" asset X, price as Y
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sma_20 = sum(prices_20) / len(prices_20)
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result = globals()["_kalman_trader"].step(sma_20, close)
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if result["signal"] != 0:
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signal = "BUY" if result["signal"] > 0 else "SELL"
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reason = f"K-pairs z={result['z_score']:.2f} b={result['beta']:.3f}"
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signal_strength = abs(result["z_score"]) / 4.0
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# ── Execute signal ──
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if signal and signal_strength > 0.15: # minimum strength filter
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notional = sz * close
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fee = notional * fee_rate * 2 # entry + exit
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total_fees += fee
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if signal.startswith("BUY"):
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if position < 0:
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# Close short
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close_pnl = abs(position) * (entry_price - close)
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eq += close_pnl
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if close_pnl > 0:
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wins += 1
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trades.append({
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"time": t.strftime("%Y-%m-%d %H:%M"),
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"side": "BUY (close short)",
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"size": abs(position),
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"price": close,
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"pnl_gross": round(close_pnl, 4),
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"pnl_net": round(close_pnl - fee, 4),
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"fee": round(fee, 6),
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"reason": reason,
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})
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position = 0
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if position == 0:
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entry_price = close
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position = sz
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eq -= fee
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else:
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entry_price = (entry_price * position + close * sz) / (position + sz)
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position += sz
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eq -= fee
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else: # SELL
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if position > 0:
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close_pnl = position * (close - entry_price)
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eq += close_pnl
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if close_pnl > 0:
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wins += 1
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trades.append({
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"time": t.strftime("%Y-%m-%d %H:%M"),
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"side": "SELL (close long)",
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"size": position,
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"price": close,
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"pnl_gross": round(close_pnl, 4),
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"pnl_net": round(close_pnl - fee, 4),
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"fee": round(fee, 6),
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"reason": reason,
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})
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position = 0
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if position == 0:
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entry_price = close
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position = -sz
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eq -= fee
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else:
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entry_price = (entry_price * abs(position) + close * sz) / (abs(position) + sz)
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position -= sz
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eq -= fee
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# Track equity curve
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unrealized = position * (close - entry_price) if position != 0 else 0
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curve.append({"t": t.isoformat(), "v": round(eq + unrealized, 4)})
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# Close any open position at last price
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if position != 0 and candles:
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last_close = float(candles[-1]["c"])
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close_pnl = abs(position) * (last_close - entry_price) * (1 if position > 0 else -1)
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eq += close_pnl
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if close_pnl > 0:
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wins += 1
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# ── Compute metrics ──
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pnl_net = eq - allocation
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pnl_gross = pnl_net + total_fees
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returns = []
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for i in range(1, len(curve)):
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if curve[i-1]["v"] > 0:
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returns.append((curve[i]["v"] - curve[i-1]["v"]) / curve[i-1]["v"])
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padded_equity = [allocation] * 10 + [c["v"] for c in curve]
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return {
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"strategy": name,
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"strategy_key": key,
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"coin": coin_name, # actual ticker (BTC, ETH, etc.)
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"allocation": allocation,
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"start_time": curve[0]["t"] if curve else "",
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"end_time": curve[-1]["t"] if curve else "",
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"start_equity": allocation,
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"end_equity": round(eq, 4),
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"pnl": round(pnl_net, 4),
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"pnl_pct": round(pnl_net / allocation * 100, 2),
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"pnl_gross": round(pnl_gross, 4),
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"pnl_gross_pct": round(pnl_gross / allocation * 100, 2),
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"fees_total": round(total_fees, 4),
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"fee_tier": fee_tier,
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"staking_tier": staking_tier,
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"fee_model": fee_model,
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"sharpe": round(sharpe(returns) if returns else 0, 4),
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"sortino": round(sortino(returns) if returns else 0, 4),
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"max_dd": round(max_drawdown(padded_equity), 4),
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"max_dd_pct": round(max_drawdown(padded_equity) * 100, 2),
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"win_rate": round(win_rate(trades), 4),
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"total_trades": len(trades),
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"equity_curve": curve,
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"trades": trades[-100:],
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"num_periods": len(candles),
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"data_source": "Hyperliquid Mainnet",
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"generated_at": datetime.now().isoformat(),
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}
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def main():
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p = argparse.ArgumentParser(description="FTDT Historical Backtest Runner")
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p.add_argument("--coin", default="BTC", choices=["BTC", "ETH", "SOL", "HYPE", "VVV"], help="Coin to backtest")
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p.add_argument("--strategy", "-s", choices=list(STRATEGIES) + ["all"], default="all")
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p.add_argument("--fee-tier", type=int, default=0, choices=range(7))
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p.add_argument("--staking-tier", default="none", choices=list(STAKING_TIERS.keys()))
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p.add_argument("--hours", type=int, default=720, help="Hours of history (default: 720 = 30 days)")
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a = p.parse_args()
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ft_info = PERPS_TIERS[a.fee_tier]
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st_info = STAKING_TIERS[a.staking_tier]
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print("=" * 60)
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print(f" FTDT Quant Lab — HISTORICAL Backtest Runner")
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print(f" Coin: {a.coin} | Period: {a.hours}h ({a.hours//24} days)")
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print(f" Fee Tier: {ft_info['name']} | Staking: {st_info['name']}")
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print("=" * 60)
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# Fetch real candles
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print(f"\n Fetching {a.coin} candles from Hyperliquid mainnet...")
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candles = fetch_candles(a.coin, interval="1h", limit=a.hours)
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if not candles:
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print(" ERROR: No candle data returned. Check API connectivity.")
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sys.exit(1)
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print(f" Got {len(candles)} candles: "
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f"{datetime.fromtimestamp(candles[0]['t']/1000).strftime('%Y-%m-%d')} → "
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f"{datetime.fromtimestamp(candles[-1]['t']/1000).strftime('%Y-%m-%d')}")
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print(f" Price range: ${float(candles[0]['c']):.0f} → ${float(candles[-1]['c']):.0f}")
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keys = list(STRATEGIES) if a.strategy == "all" else [a.strategy]
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for key in keys:
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cfg = STRATEGIES[key]
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print(f"\n Running: {cfg['name']} on {a.coin}...")
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# Kalman Pairs: use real BTC/ETH pair data
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if key == "kalman_pairs":
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try:
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sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
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from strategies.kalman_pairs import KalmanPairsTrader, backtest_kalman_pairs
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# Fetch ETH candles
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if a.coin != "ETH":
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eth_candles = fetch_candles("ETH", interval="1h", limit=a.hours)
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else:
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eth_candles = fetch_candles("BTC", interval="1h", limit=a.hours)
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if eth_candles:
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X = [float(c["c"]) for c in eth_candles]
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Y = [float(c["c"]) for c in candles]
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n = min(len(X), len(Y))
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X, Y = X[:n], Y[:n]
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trader = KalmanPairsTrader(
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transition_covariance=1e-4, observation_covariance=1e-2,
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z_entry=2.0, z_exit=0.5, warmup_bars=20,
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)
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bt = backtest_kalman_pairs(
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X, Y, trader,
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trade_size_usd=50.0,
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transaction_cost_bps=2.5,
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)
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# Convert to standard format expected by the dashboard
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result = {
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"strategy": cfg["name"],
|
||
"strategy_key": key,
|
||
"coin": a.coin,
|
||
"allocation": 100.0,
|
||
"start_time": str(bt["equity_curve"][0]["t"]) if bt["equity_curve"] else "",
|
||
"end_time": str(bt["equity_curve"][-1]["t"]) if bt["equity_curve"] else "",
|
||
"start_equity": 100.0,
|
||
"end_equity": round(bt["final_equity"], 4),
|
||
"pnl": round(bt["total_pnl"], 4),
|
||
"pnl_pct": round(bt["pnl_pct"], 2),
|
||
"pnl_gross": round(bt["total_pnl"] + bt["transaction_costs"], 4),
|
||
"pnl_gross_pct": round(bt["pnl_pct"], 2),
|
||
"fees_total": round(bt["transaction_costs"], 4),
|
||
"fee_tier": a.fee_tier,
|
||
"staking_tier": a.staking_tier,
|
||
"fee_model": cfg["fee_model"],
|
||
"sharpe": round(bt["sharpe"], 4),
|
||
"sortino": round(bt["sortino"], 4),
|
||
"max_dd": round(bt["max_drawdown"], 4),
|
||
"max_dd_pct": round(bt["max_drawdown"] * 100, 2),
|
||
"win_rate": round(bt["win_rate"], 4),
|
||
"total_trades": bt["total_trades"],
|
||
"equity_curve": [{"t": e["t"], "v": e["equity"]} for e in bt["equity_curve"]],
|
||
"trades": bt["trades"][-100:],
|
||
"num_periods": n,
|
||
"data_source": "Hyperliquid Mainnet (BTC/ETH pair)",
|
||
"generated_at": datetime.now().isoformat(),
|
||
}
|
||
else:
|
||
result = {} # Skip
|
||
except Exception as e:
|
||
print(f" Kalman pairs error: {e}")
|
||
result = {}
|
||
else:
|
||
result = simulate_strategy_on_candles(
|
||
key, candles, a.coin,
|
||
fee_tier=a.fee_tier,
|
||
staking_tier=a.staking_tier,
|
||
)
|
||
|
||
# Save
|
||
ts = datetime.now().strftime("%Y%m%d-%H%M%S")
|
||
filename = f"{key}_{a.coin}_{ts}.json"
|
||
filepath = RESULTS_DIR / filename
|
||
with open(filepath, "w") as f:
|
||
json.dump(result, f, indent=2, default=str)
|
||
|
||
print(f" Saved: {filepath}")
|
||
print(f" Net PnL: {result['pnl_pct']:+.2f}% | "
|
||
f"Gross: {result['pnl_gross_pct']:+.2f}% | "
|
||
f"Fees: ${result['fees_total']:.2f} | "
|
||
f"Sharpe: {result['sharpe']:.2f} | "
|
||
f"Trades: {result['total_trades']} | "
|
||
f"Win: {result['win_rate']:.0%}")
|
||
|
||
print("\n" + "=" * 60)
|
||
print(f" Results in backtests/results/historical/")
|
||
print(f" View at: https://ftdt.io/cv")
|
||
print("=" * 60)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|