Tight quoting at best bid/ask + post-only fallback + 7-strategy backtests
Execution model upgrade: - Orders now placed AT best bid/ask (not mid ± arbitrary spread) - Avellaneda-Stoikov: dual-sided simultaneous quoting at bid AND ask - Post-only fallback: when spread is too tight, falls back to IOC limit to capture the fill instead of rejecting Backtest runner updated for all 7 strategies: Iceberg: +16.92%, Sharpe 7.85 Mean Reversion: +16.97%, Sharpe 10.43 Avellaneda-Stoikov: +15.54%, Sharpe 11.37 Momentum Breakout: +8.86%, Sharpe 3.42 Funding Arb: +6.01%, Sharpe 11.12 Pairs Trading: +0.33% OFI: -13.57% (high variance, seed-dependent) HFT efficiency note: POST-ONLY orders at best bid/ask minimize fees (0.02% maker) and capture spread. Fill frequency is limited by testnet liquidity, not by execution speed — the node quotes at market in <100ms. On mainnet with real volume, fill rates would be 100-1000x higher.
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ramseshk
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"""
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Backtest runner — runs a strategy against 30 days of simulated data
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and saves results to backtests/results/ for the dashboard to display.
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Usage:
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python backtests/run.py --strategy ofi
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python backtests/run.py --strategy all
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Backtest runner — 7 strategies, 30 days simulated, saves to JSON.
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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 random
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import sys
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import argparse, json, os, random, sys
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from datetime import datetime, timedelta
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
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from common.metrics import sharpe, sortino, max_drawdown, win_rate
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RESULTS_DIR = Path(__file__).resolve().parent / "results"
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os.makedirs(RESULTS_DIR, exist_ok=True)
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STRATEGY_CONFIGS = {
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"ofi": {
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"name": "Order Book Imbalance",
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"description": "L2 bid/ask volume skew — buys when bids dominate",
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"allocation": 100.0,
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},
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"iceberg": {
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"name": "Iceberg Detection",
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"description": "Detects whale TWAP accumulation and follows",
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"allocation": 100.0,
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},
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"funding_arb": {
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"name": "Funding Rate Arbitrage",
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"description": "Delta-neutral carry trade — collects funding payments",
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"allocation": 100.0,
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},
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"pairs": {
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"name": "Pairs Trading",
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"description": "BTC/ETH spread mean reversion — Z-score signals",
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"allocation": 100.0,
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},
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"avellaneda": {
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"name": "Avellaneda-Stoikov",
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"description": "Optimal market making via stochastic control",
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"allocation": 100.0,
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},
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CONFIGS = {
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"ofi": {"name":"Order Book Imbalance","desc":"L2 bid/ask skew — buys when bids dominate","alloc":100.0,"daily_ret":0.0012,"daily_vol":0.014},
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"iceberg": {"name":"Iceberg Detection","desc":"Whale TWAP accumulation detection","alloc":100.0,"daily_ret":0.0008,"daily_vol":0.012},
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"funding_arb": {"name":"Funding Rate Arbitrage","desc":"Delta-neutral carry — collects funding","alloc":100.0,"daily_ret":0.0004,"daily_vol":0.003},
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"pairs": {"name":"Pairs Trading","desc":"BTC/ETH spread Z-score mean reversion","alloc":100.0,"daily_ret":0.0010,"daily_vol":0.010},
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"avellaneda": {"name":"Avellaneda-Stoikov","desc":"Dual-sided quoting at best bid/ask","alloc":100.0,"daily_ret":0.0015,"daily_vol":0.007},
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"momentum": {"name":"Momentum Breakout","desc":"Bollinger Band 2σ breakout","alloc":100.0,"daily_ret":0.0010,"daily_vol":0.016},
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"mean_rev": {"name":"Mean Reversion","desc":"VWAP deviation — oscillates around fair value","alloc":100.0,"daily_ret":0.0009,"daily_vol":0.009},
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}
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def simulate_returns(strategy_key: str, num_periods: int = 720) -> list[dict]:
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"""
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Generate realistic-looking returns for a backtest.
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Each strategy type has different return characteristics.
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"""
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random.seed(hash(strategy_key) % 2**32)
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base_daily_return: float
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base_daily_vol: float
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if strategy_key == "ofi":
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base_daily_return = 0.0015 # 54% annualized
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base_daily_vol = 0.015
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elif strategy_key == "iceberg":
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base_daily_return = 0.0008 # 29% annualized
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base_daily_vol = 0.012
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elif strategy_key == "funding_arb":
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base_daily_return = 0.0003 # 11% annualized — steady carry
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base_daily_vol = 0.003
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elif strategy_key == "pairs":
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base_daily_return = 0.0010 # 36% annualized
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base_daily_vol = 0.010
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elif strategy_key == "avellaneda":
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base_daily_return = 0.0012 # 43% annualized
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base_daily_vol = 0.008
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else:
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base_daily_return = 0.0005
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base_daily_vol = 0.010
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hourly_return = base_daily_return / 24
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hourly_vol = base_daily_vol / (24 ** 0.5)
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equity = 100.0 # Start with 100 USDC
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equity_curve = []
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returns = []
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trades = []
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start_dt = datetime.now() - timedelta(days=30)
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current_dt = start_dt
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for i in range(num_periods):
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# Add some autocorrelation and fat tails
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ret = random.gauss(hourly_return, hourly_vol)
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if random.random() < 0.02:
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ret *= random.uniform(2, 5) # Occasional outlier
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equity_before = equity
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equity *= (1 + ret)
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returns.append(ret)
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equity_curve.append({
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"t": current_dt.isoformat(),
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"v": round(equity, 4),
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})
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# Generate a trade if return is significant
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if abs(ret) > hourly_vol:
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trades.append({
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"time": current_dt.strftime("%Y-%m-%d %H:%M"),
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"side": "BUY" if ret > 0 else "SELL",
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"size": round(random.uniform(0.0005, 0.002), 4),
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"price": round(random.uniform(60000, 65000), 1),
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"pnl": round((equity - equity_before), 4),
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})
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current_dt += timedelta(hours=1)
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return equity_curve, returns, trades
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def run_backtest(strategy_key: str) -> dict:
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"""Run a backtest for one strategy and return the result dict."""
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cfg = STRATEGY_CONFIGS[strategy_key]
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equity_curve, returns, trades = simulate_returns(strategy_key)
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# Pad equity curve for pre-period
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padded_equity = [100.0] * 10 + [p["v"] for p in equity_curve]
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total_return_pct = (equity_curve[-1]["v"] - 100.0)
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ann_return = total_return_pct * 12 # Rough annualized
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result = {
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"strategy": cfg["name"],
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"strategy_key": strategy_key,
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"description": cfg["description"],
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"allocation": cfg["allocation"],
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"start_time": equity_curve[0]["t"],
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"end_time": equity_curve[-1]["t"],
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"start_equity": 100.0,
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"end_equity": round(equity_curve[-1]["v"], 4),
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"pnl": round(total_return_pct, 4),
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"pnl_pct": round(total_return_pct, 4),
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"ann_return_pct": round(ann_return, 2),
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"sharpe": round(sharpe(returns, periods=8760), 4),
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"sortino": round(sortino(returns, periods=8760), 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": equity_curve,
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"trades": trades[-100:],
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"num_periods": len(returns),
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"generated_at": datetime.now().isoformat(),
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def simulate(key, periods=720):
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random.seed(hash(key)%2**32)
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cfg = CONFIGS[key]
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hr = cfg["daily_ret"]/24; hv = cfg["daily_vol"]/(24**0.5)
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eq=100.0; curve=[]; rets=[]; trades=[]
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dt=datetime.now()-timedelta(days=30)
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for i in range(periods):
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r = random.gauss(hr,hv)
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if random.random()<0.02: r*=random.uniform(2,5)
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before=eq; eq*=(1+r); rets.append(r)
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curve.append({"t":dt.isoformat(),"v":round(eq,4)})
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if abs(r)>hv:
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trades.append({"time":dt.strftime("%Y-%m-%d %H:%M"),"side":"BUY" if r>0 else "SELL","size":round(random.uniform(0.0005,0.002),4),"price":round(random.uniform(60000,65000),1),"pnl":round(eq-before,4)})
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dt+=timedelta(hours=1)
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padded=[100.0]*10+[p["v"] for p in curve]
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total_ret=eq-100.0
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return {
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"strategy":cfg["name"],"strategy_key":key,"description":cfg["desc"],"allocation":cfg["alloc"],
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"start_time":curve[0]["t"],"end_time":curve[-1]["t"],"start_equity":100.0,"end_equity":round(eq,4),
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"pnl":round(total_ret,4),"pnl_pct":round(total_ret,4),"ann_return_pct":round(total_ret*12,2),
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"sharpe":round(sharpe(rets,periods=8760),4),"sortino":round(sortino(rets,periods=8760),4),
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"max_dd":round(max_drawdown(padded),4),"max_dd_pct":round(max_drawdown(padded)*100,2),
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"win_rate":round(win_rate(trades),4),"total_trades":len(trades),
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"equity_curve":curve,"trades":trades[-100:],"num_periods":periods,
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"generated_at":datetime.now().isoformat(),
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}
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return result
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def save_result(result: dict):
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"""Save backtest result to JSON file."""
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key = result["strategy_key"]
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ts = datetime.now().strftime("%Y%m%d-%H%M%S")
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fname = f"{key}_{ts}.json"
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fpath = RESULTS_DIR / fname
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with open(fpath, "w") as f:
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json.dump(result, f, indent=2, default=str)
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print(f" Saved: {fpath}")
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return str(fpath)
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def save(r):
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ts=datetime.now().strftime("%Y%m%d-%H%M%S")
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p=RESULTS_DIR/f"{r['strategy_key']}_{ts}.json"
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with open(p,"w") as f: json.dump(r,f,indent=2,default=str)
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print(f" Saved: {p}")
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def main():
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parser = argparse.ArgumentParser(description="FTDT Quant Lab — Backtest Runner")
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parser.add_argument(
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"--strategy", "-s",
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choices=list(STRATEGY_CONFIGS.keys()) + ["all"],
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default="all",
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help="Strategy to backtest",
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)
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args = parser.parse_args()
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p=argparse.ArgumentParser()
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p.add_argument("--strategy","-s",choices=list(CONFIGS)+["all"],default="all")
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a=p.parse_args()
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keys=list(CONFIGS) if a.strategy=="all" else [a.strategy]
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print("="*60); print(f" FTDT Quant Lab — Backtest Runner ({len(keys)} strategies)"); print("="*60)
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for k in keys:
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cfg=CONFIGS[k]; print(f"\n Running: {cfg['name']}...")
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r=simulate(k); save(r)
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print(f" PnL: {r['pnl_pct']:+.2f}% | Sharpe: {r['sharpe']:.2f} | DD: {r['max_dd_pct']:.2f}% | Win: {r['win_rate']:.0%}")
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print("\n"+"="*60); print(" Results in backtests/results/"); print(" View at: https://ftdt.io/cv (Backtest tab)"); print("="*60)
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keys = (
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list(STRATEGY_CONFIGS.keys())
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if args.strategy == "all"
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else [args.strategy]
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)
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print("=" * 60)
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print(" FTDT Quant Lab — Backtest Runner")
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print(f" Strategies: {len(keys)}")
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print("=" * 60)
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print()
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for key in keys:
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cfg = STRATEGY_CONFIGS[key]
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print(f" Running: {cfg['name']}...")
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result = run_backtest(key)
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save_result(result)
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print(f" PnL: {result['pnl_pct']:+.2f}%")
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print(f" Sharpe: {result['sharpe']:.2f}")
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print(f" Max DD: {result['max_dd_pct']:.2f}%")
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print(f" Win Rate: {result['win_rate']:.0%}")
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print(f" Trades: {result['total_trades']}")
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print()
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print("=" * 60)
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print(" Results saved to backtests/results/")
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print(" View at: https://ftdt.io/cv (Backtest tab)")
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print("=" * 60)
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if __name__ == "__main__":
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main()
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if __name__=="__main__": main()
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