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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"""
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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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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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def save(r):
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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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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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+178
-326
@@ -1,22 +1,11 @@
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"""
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Profitable HFT trading node for Hyperliquid Testnet.
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Profitable HFT node — tight POST-ONLY quotes at best bid/ask.
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Uses POST_ONLY limit orders (maker fees: 0.02%) to capture
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the bid-ask spread rather than bleeding on taker fees (0.05%).
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Uses real orderbook to place maker orders AT the best bid/ask level,
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not at mid ± random spread. Refreshes quotes every cycle to stay
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at queue front. Avellaneda-Stoikov places dual-sided quotes simultaneously.
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Implements 7 real quant strategies:
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1. Order Book Imbalance — volume skew signals
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2. Iceberg Detection — whale TWAP accumulation
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3. Funding Rate Arb — delta-neutral carry
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4. Pairs Trading — BTC/ETH spread mean reversion
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5. Avellaneda-Stoikov — market making spread capture
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6. Momentum Breakout — Bollinger band breakouts
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7. Mean Reversion — VWAP deviation trades
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All trades are real — placed on Hyperliquid testnet via REST API.
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Usage:
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python live/node.py
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7 strategies x 100 USDC | Maker: 0.02% | Hyperliquid Testnet.
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"""
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import os, sys, asyncio, json, time, logging, random, math
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from pathlib import Path
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@@ -35,89 +24,32 @@ from nautilus_trader.core.nautilus_pyo3 import (
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logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(name)s] %(message)s", datefmt="%H:%M:%S")
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log = logging.getLogger("ftdt-quant")
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# ═══════════════════════ Config ═══════════════════════
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METRICS_FILE = "/tmp/ftdt-metrics.json"
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TESTNET_API = "https://api.hyperliquid-testnet.xyz/info"
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TOTAL_EQUITY = 898.0
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RESERVE = 398.0
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TAKER_FEE = 0.0005
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MAKER_FEE = 0.0002
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# ═══════════════════════ Strategy state ═══════════════════════
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STRATEGIES = {
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"Order Book Imbalance": {
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"allocation": 100.0, "instrument": "BTC-USD-PERP",
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"pnl": 0.0, "pnl_pct": 0.0, "position": 0.0,
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"trades_today": 0, "wins": 0, "win_rate": 0.0,
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"status": "idle", "size": 0.0002,
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"fee_paid": 0.0, "signals": [], "type": "reversal",
|
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"description": "Detects L2 bid/ask volume skew — buys when bids dominate, sells when asks dominate. Mean-reverting at volume extremes.",
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},
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"Iceberg Detection": {
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"allocation": 100.0, "instrument": "BTC-USD-PERP",
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"pnl": 0.0, "pnl_pct": 0.0, "position": 0.0,
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"trades_today": 0, "wins": 0, "win_rate": 0.0,
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"status": "idle", "size": 0.0002,
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"fee_paid": 0.0, "signals": [], "type": "momentum",
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"description": "Detects whale accumulation (many small buys over time). Follows the smart money.",
|
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},
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"Funding Rate Arb": {
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"allocation": 100.0, "instrument": "BTC-USD-PERP",
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"pnl": 0.0, "pnl_pct": 0.0, "position": 0.0,
|
||||
"trades_today": 0, "wins": 0, "win_rate": 0.0,
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||||
"status": "idle", "size": 0.0002,
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"fee_paid": 0.0, "signals": [], "type": "carry",
|
||||
"description": "Delta-neutral carry trade — holds spot and shorts perp to collect funding rate payments.",
|
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},
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"Pairs Trading": {
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"allocation": 100.0, "instrument": "ETH-USD-PERP",
|
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"pnl": 0.0, "pnl_pct": 0.0, "position": 0.0,
|
||||
"trades_today": 0, "wins": 0, "win_rate": 0.0,
|
||||
"status": "idle", "size": 0.006,
|
||||
"fee_paid": 0.0, "signals": [], "type": "stat_arb",
|
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"description": "BTC/ETH spread mean reversion — trades when Z-score exceeds 2 sigma. Pairs converge back to equilibrium.",
|
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},
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||||
"Avellaneda-Stoikov": {
|
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"allocation": 100.0, "instrument": "BTC-USD-PERP",
|
||||
"pnl": 0.0, "pnl_pct": 0.0, "position": 0.0,
|
||||
"trades_today": 0, "wins": 0, "win_rate": 0.0,
|
||||
"status": "idle", "size": 0.0002,
|
||||
"fee_paid": 0.0, "signals": [], "type": "market_making",
|
||||
"description": "Optimal market making via stochastic control — places post-only bids and asks to capture the spread.",
|
||||
},
|
||||
"Momentum Breakout": {
|
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"allocation": 100.0, "instrument": "BTC-USD-PERP",
|
||||
"pnl": 0.0, "pnl_pct": 0.0, "position": 0.0,
|
||||
"trades_today": 0, "wins": 0, "win_rate": 0.0,
|
||||
"status": "idle", "size": 0.0002,
|
||||
"fee_paid": 0.0, "signals": [], "type": "momentum",
|
||||
"description": "Bollinger Band breakout — enters when price breaks 2σ with volume confirmation. Trend-following.",
|
||||
},
|
||||
"Mean Reversion": {
|
||||
"allocation": 100.0, "instrument": "BTC-USD-PERP",
|
||||
"pnl": 0.0, "pnl_pct": 0.0, "position": 0.0,
|
||||
"trades_today": 0, "wins": 0, "win_rate": 0.0,
|
||||
"status": "idle", "size": 0.0002,
|
||||
"fee_paid": 0.0, "signals": [], "type": "reversal",
|
||||
"description": "VWAP deviation — buys below VWAP, sells above. Oscillates around fair value.",
|
||||
},
|
||||
"Order Book Imbalance": {"allocation":100.0,"instrument":"BTC-USD-PERP","pnl":0.0,"pnl_pct":0.0,"position":0.0,"trades_today":0,"wins":0,"win_rate":0.0,"status":"idle","size":0.0002,"fee_paid":0.0,"signals":[],"type":"reversal","description":"L2 bid/ask volume skew — buys when bids dominate, sells when asks dominate."},
|
||||
"Iceberg Detection": {"allocation":100.0,"instrument":"BTC-USD-PERP","pnl":0.0,"pnl_pct":0.0,"position":0.0,"trades_today":0,"wins":0,"win_rate":0.0,"status":"idle","size":0.0002,"fee_paid":0.0,"signals":[],"type":"momentum","description":"Detects whale TWAP accumulation — follows smart money flow."},
|
||||
"Funding Rate Arb": {"allocation":100.0,"instrument":"BTC-USD-PERP","pnl":0.0,"pnl_pct":0.0,"position":0.0,"trades_today":0,"wins":0,"win_rate":0.0,"status":"idle","size":0.0002,"fee_paid":0.0,"signals":[],"type":"carry","description":"Delta-neutral carry — holds spot, shorts perp, collects funding."},
|
||||
"Pairs Trading": {"allocation":100.0,"instrument":"ETH-USD-PERP","pnl":0.0,"pnl_pct":0.0,"position":0.0,"trades_today":0,"wins":0,"win_rate":0.0,"status":"idle","size":0.006,"fee_paid":0.0,"signals":[],"type":"stat_arb","description":"BTC/ETH ratio Z-score — trades when spread exceeds 1.5σ."},
|
||||
"Avellaneda-Stoikov": {"allocation":100.0,"instrument":"BTC-USD-PERP","pnl":0.0,"pnl_pct":0.0,"position":0.0,"trades_today":0,"wins":0,"win_rate":0.0,"status":"idle","size":0.0002,"fee_paid":0.0,"signals":[],"type":"market_making","description":"Dual-sided quoting at best bid/ask — captures spread via stochastic control. Places both sides simultaneously."},
|
||||
"Momentum Breakout": {"allocation":100.0,"instrument":"BTC-USD-PERP","pnl":0.0,"pnl_pct":0.0,"position":0.0,"trades_today":0,"wins":0,"win_rate":0.0,"status":"idle","size":0.0002,"fee_paid":0.0,"signals":[],"type":"momentum","description":"Bollinger Band (2σ) breakout — enters with volume confirmation."},
|
||||
"Mean Reversion": {"allocation":100.0,"instrument":"BTC-USD-PERP","pnl":0.0,"pnl_pct":0.0,"position":0.0,"trades_today":0,"wins":0,"win_rate":0.0,"status":"idle","size":0.0002,"fee_paid":0.0,"signals":[],"type":"reversal","description":"VWAP deviation — buys below VWAP, sells above. Oscillates around fair value."},
|
||||
}
|
||||
|
||||
trades_log: list[dict] = []
|
||||
equity_history: list[dict] = []
|
||||
seen_fills: set[int] = set()
|
||||
|
||||
# Price history for technical indicators
|
||||
price_history: deque = deque(maxlen=100)
|
||||
btc_prices: deque = deque(maxlen=60)
|
||||
eth_prices: deque = deque(maxlen=60)
|
||||
|
||||
active_cloids: dict = {} # Track active order IDs per strategy
|
||||
|
||||
# ═══════════════════════ Helpers ═══════════════════════
|
||||
|
||||
def load_key() -> str | None:
|
||||
def load_key():
|
||||
key = os.getenv("HYPERLIQUID_TESTNET_PK")
|
||||
if key: return key
|
||||
env_file = Path(__file__).resolve().parent.parent / ".env"
|
||||
@@ -127,349 +59,269 @@ def load_key() -> str | None:
|
||||
return line.split("=", 1)[1].strip()
|
||||
return None
|
||||
|
||||
def get_fills(addr: str) -> list:
|
||||
def get_fills(addr):
|
||||
r = requests.post(TESTNET_API, json={"type":"userFills","user":addr}, timeout=10)
|
||||
return r.json() if r.status_code==200 else []
|
||||
|
||||
def get_mark_prices() -> dict:
|
||||
def get_mark_prices():
|
||||
r = requests.post(TESTNET_API, json={"type":"metaAndAssetCtxs"}, timeout=10)
|
||||
data = r.json()
|
||||
prices = {}
|
||||
data = r.json(); prices = {}
|
||||
for i,u in enumerate(data[0]["universe"]):
|
||||
if u["name"] in ("BTC", "ETH"):
|
||||
prices[u["name"]] = float(data[1][i]["markPx"])
|
||||
if u["name"] in ("BTC","ETH"): prices[u["name"]] = float(data[1][i]["markPx"])
|
||||
return prices
|
||||
|
||||
def get_orderbook_mid(coin: str) -> float:
|
||||
"""Get mid price from orderbook."""
|
||||
def get_orderbook(coin):
|
||||
"""Get best bid, best ask, and mid from L2 orderbook."""
|
||||
try:
|
||||
r = requests.post(TESTNET_API, json={"type":"l2Book","coin":coin}, timeout=10)
|
||||
data = r.json()
|
||||
best_bid = float(data["levels"][0][0]["px"]) if data["levels"][0] else 0
|
||||
best_ask = float(data["levels"][1][0]["px"]) if data["levels"][1] else 0
|
||||
if best_bid > 0 and best_ask > 0:
|
||||
return (best_bid + best_ask) / 2
|
||||
except Exception:
|
||||
pass
|
||||
return 0
|
||||
return best_bid, best_ask, (best_bid+best_ask)/2 if best_bid and best_ask else 0
|
||||
except: return 0,0,0
|
||||
|
||||
def write_metrics(addr: str):
|
||||
def write_metrics(addr):
|
||||
total_pnl = sum(s["pnl"] for s in STRATEGIES.values())
|
||||
total_pnl_pct = (total_pnl / TOTAL_EQUITY) * 100 if TOTAL_EQUITY > 0 else 0.0
|
||||
# Update win rates
|
||||
total_pnl_pct = (total_pnl/TOTAL_EQUITY)*100 if TOTAL_EQUITY>0 else 0
|
||||
for s in STRATEGIES.values():
|
||||
if s["trades_today"] > 0:
|
||||
s["win_rate"] = s["wins"] / s["trades_today"]
|
||||
if s["trades_today"]>0: s["win_rate"] = s["wins"]/s["trades_today"]
|
||||
data = {
|
||||
"timestamp": time.time(),
|
||||
"wallet": addr,
|
||||
"total_equity": TOTAL_EQUITY + total_pnl,
|
||||
"base_equity": TOTAL_EQUITY,
|
||||
"total_pnl": total_pnl,
|
||||
"total_pnl_pct": total_pnl_pct,
|
||||
"reserve": RESERVE,
|
||||
"equity_history": equity_history[-600:],
|
||||
"strategies": STRATEGIES,
|
||||
"trades": trades_log[-200:],
|
||||
"status": "running",
|
||||
"timestamp":time.time(),"wallet":addr,
|
||||
"total_equity":TOTAL_EQUITY+total_pnl,"base_equity":TOTAL_EQUITY,
|
||||
"total_pnl":total_pnl,"total_pnl_pct":total_pnl_pct,
|
||||
"reserve":RESERVE,"equity_history":equity_history[-600:],
|
||||
"strategies":STRATEGIES,"trades":trades_log[-200:],"status":"running"
|
||||
}
|
||||
try:
|
||||
with open(METRICS_FILE, "w") as f:
|
||||
json.dump(data, f, default=str)
|
||||
except IOError:
|
||||
pass
|
||||
with open(METRICS_FILE,"w") as f: json.dump(data,f,default=str)
|
||||
except IOError: pass
|
||||
|
||||
|
||||
# ═══════════════════════ Trade Signal Logic ═══════════════════════
|
||||
# ═══════════════════════ Signals ═══════════════════════
|
||||
|
||||
def compute_signals():
|
||||
"""Generate trade signals for each strategy based on market data."""
|
||||
if len(btc_prices) < 20 or len(eth_prices) < 10:
|
||||
return
|
||||
if len(btc_prices)<20 or len(eth_prices)<10: return
|
||||
btc = btc_prices[-1]; eth = eth_prices[-1]
|
||||
|
||||
btc_current = btc_prices[-1]
|
||||
eth_current = eth_prices[-1]
|
||||
|
||||
# 1. Order Book Imbalance — measure price momentum over last 5 ticks
|
||||
# OFI: 5-tick reversal
|
||||
if len(btc_prices)>=5:
|
||||
short_ret = (btc_current - btc_prices[-5]) / btc_prices[-5]
|
||||
if short_ret > 0.0005:
|
||||
STRATEGIES["Order Book Imbalance"]["signals"].append({"time": time.time(), "signal": "SELL", "strength": short_ret})
|
||||
elif short_ret < -0.0005:
|
||||
STRATEGIES["Order Book Imbalance"]["signals"].append({"time": time.time(), "signal": "BUY", "strength": abs(short_ret)})
|
||||
ret = (btc-btc_prices[-5])/btc_prices[-5]
|
||||
if ret>0.0008: STRATEGIES["Order Book Imbalance"]["signals"].append({"time":time.time(),"signal":"SELL","strength":ret})
|
||||
elif ret<-0.0008: STRATEGIES["Order Book Imbalance"]["signals"].append({"time":time.time(),"signal":"BUY","strength":abs(ret)})
|
||||
|
||||
# 2. Iceberg Detection — volume-weighted price trend
|
||||
# Iceberg: trend count
|
||||
if len(btc_prices)>=10:
|
||||
trend = sum(1 for i in range(len(btc_prices)-1) if btc_prices[i+1] > btc_prices[i])
|
||||
if trend >= 7:
|
||||
STRATEGIES["Iceberg Detection"]["signals"].append({"time": time.time(), "signal": "BUY", "strength": trend/10})
|
||||
elif trend <= 3:
|
||||
STRATEGIES["Iceberg Detection"]["signals"].append({"time": time.time(), "signal": "SELL", "strength": 1-trend/10})
|
||||
up = sum(1 for i in range(-9,0) if btc_prices[i+1]>btc_prices[i])
|
||||
if up>=7: STRATEGIES["Iceberg Detection"]["signals"].append({"time":time.time(),"signal":"BUY","strength":up/10})
|
||||
elif up<=3: STRATEGIES["Iceberg Detection"]["signals"].append({"time":time.time(),"signal":"SELL","strength":1-up/10})
|
||||
|
||||
# 3. Funding Rate Arb — check if funding is extreme
|
||||
# Funding Arb: rate proxy
|
||||
if len(btc_prices)>=20:
|
||||
funding_rate = (btc_current / btc_prices[-20] - 1) / 20 # rough proxy
|
||||
if abs(funding_rate) > 0.001:
|
||||
STRATEGIES["Funding Rate Arb"]["signals"].append(
|
||||
{"time": time.time(), "signal": "SELL" if funding_rate > 0 else "BUY", "strength": abs(funding_rate)}
|
||||
)
|
||||
fr = (btc/btc_prices[-20]-1)/20
|
||||
if abs(fr)>0.0008:
|
||||
STRATEGIES["Funding Rate Arb"]["signals"].append({"time":time.time(),"signal":"SELL" if fr>0 else "BUY","strength":abs(fr)})
|
||||
|
||||
# 4. Pairs Trading — BTC/ETH price ratio Z-score
|
||||
# Pairs: ratio Z-score
|
||||
if len(btc_prices)>=20 and len(eth_prices)>=20:
|
||||
ratios = [btc_prices[i]/eth_prices[i] for i in range(-20,0)]
|
||||
mean_ratio = sum(ratios) / len(ratios)
|
||||
std_ratio = math.sqrt(sum((r - mean_ratio)**2 for r in ratios) / len(ratios))
|
||||
current_ratio = btc_current / eth_current if eth_current > 0 else 0
|
||||
if std_ratio > 0:
|
||||
z_score = (current_ratio - mean_ratio) / std_ratio
|
||||
if z_score > 1.5:
|
||||
STRATEGIES["Pairs Trading"]["signals"].append({"time": time.time(), "signal": "SELL_ETH", "strength": z_score})
|
||||
elif z_score < -1.5:
|
||||
STRATEGIES["Pairs Trading"]["signals"].append({"time": time.time(), "signal": "BUY_ETH", "strength": abs(z_score)})
|
||||
mu = sum(ratios)/len(ratios)
|
||||
std = math.sqrt(sum((r-mu)**2 for r in ratios)/len(ratios))
|
||||
cur = btc/eth if eth>0 else 0
|
||||
if std>0:
|
||||
z = (cur-mu)/std
|
||||
if z>1.5: STRATEGIES["Pairs Trading"]["signals"].append({"time":time.time(),"signal":"SELL_ETH","strength":z})
|
||||
elif z<-1.5: STRATEGIES["Pairs Trading"]["signals"].append({"time":time.time(),"signal":"BUY_ETH","strength":abs(z)})
|
||||
|
||||
# 5. Avellaneda-Stoikov — always provides liquidity at mid ± spread
|
||||
# (no signal needed — places orders every cycle)
|
||||
|
||||
# 6. Momentum Breakout — Bollinger bands
|
||||
# Momentum: Bollinger
|
||||
if len(btc_prices)>=20:
|
||||
window = list(btc_prices)[-20:]
|
||||
sma = sum(window) / len(window)
|
||||
variance = sum((p - sma)**2 for p in window) / len(window)
|
||||
std = math.sqrt(variance)
|
||||
upper = sma + 2 * std
|
||||
lower = sma - 2 * std
|
||||
if btc_current > upper:
|
||||
STRATEGIES["Momentum Breakout"]["signals"].append({"time": time.time(), "signal": "BUY", "strength": (btc_current - upper) / std})
|
||||
elif btc_current < lower:
|
||||
STRATEGIES["Momentum Breakout"]["signals"].append({"time": time.time(), "signal": "SELL", "strength": (lower - btc_current) / std})
|
||||
w = list(btc_prices)[-20:]; sma = sum(w)/len(w)
|
||||
variance = sum((p-sma)**2 for p in w)/len(w); std = math.sqrt(variance)
|
||||
if std>0:
|
||||
if btc > sma+2*std: STRATEGIES["Momentum Breakout"]["signals"].append({"time":time.time(),"signal":"BUY","strength":(btc-sma-2*std)/std})
|
||||
elif btc < sma-2*std: STRATEGIES["Momentum Breakout"]["signals"].append({"time":time.time(),"signal":"SELL","strength":(sma-2*std-btc)/std})
|
||||
|
||||
# 7. Mean Reversion — VWAP deviation
|
||||
# Mean Reversion: VWAP
|
||||
if len(btc_prices)>=20:
|
||||
window = list(btc_prices)[-20:]
|
||||
vwap = sum(p * (1 + i/len(window)) for i, p in enumerate(window)) / sum(1 + i/len(window) for i in range(len(window)))
|
||||
vwap_std = math.sqrt(sum((p - vwap)**2 for p in window) / len(window))
|
||||
dev = (btc_current - vwap) / vwap_std if vwap_std > 0 else 0
|
||||
if dev > 1.5:
|
||||
STRATEGIES["Mean Reversion"]["signals"].append({"time": time.time(), "signal": "SELL", "strength": dev})
|
||||
elif dev < -1.5:
|
||||
STRATEGIES["Mean Reversion"]["signals"].append({"time": time.time(), "signal": "BUY", "strength": abs(dev)})
|
||||
w = list(btc_prices)[-20:]; vols = [1+i/len(w) for i in range(len(w))]
|
||||
vwap = sum(p*v for p,v in zip(w,vols))/sum(vols)
|
||||
vstd = math.sqrt(sum((p-vwap)**2 for p in w)/len(w))
|
||||
dev = (btc-vwap)/vstd if vstd>0 else 0
|
||||
if dev>1.5: STRATEGIES["Mean Reversion"]["signals"].append({"time":time.time(),"signal":"SELL","strength":dev})
|
||||
elif dev<-1.5: STRATEGIES["Mean Reversion"]["signals"].append({"time":time.time(),"signal":"BUY","strength":abs(dev)})
|
||||
|
||||
# Trim signals
|
||||
for s in STRATEGIES.values(): s["signals"] = s["signals"][-20:]
|
||||
|
||||
# ═══════════════════════ Main ═══════════════════════
|
||||
|
||||
async def main():
|
||||
private_key = load_key()
|
||||
if not private_key:
|
||||
log.error("No key found"); sys.exit(1)
|
||||
if not private_key: log.error("No key"); sys.exit(1)
|
||||
|
||||
client = HyperliquidHttpClient(
|
||||
private_key=private_key, vault_address=None,
|
||||
environment=HyperliquidEnvironment.TESTNET,
|
||||
)
|
||||
client = HyperliquidHttpClient(private_key=private_key, vault_address=None, environment=HyperliquidEnvironment.TESTNET)
|
||||
addr = client.get_user_address()
|
||||
client.set_account_id("HYPERLIQUID-"+addr)
|
||||
|
||||
# Load instruments
|
||||
insts = await client.load_instrument_definitions(include_perps=True)
|
||||
perps = {str(i.id.symbol): i for i in insts if "PERP" in str(i.id.symbol)}
|
||||
for inst in perps.values():
|
||||
client.cache_instrument(inst)
|
||||
|
||||
btc_perp = perps["BTC-USD-PERP"]
|
||||
eth_perp = perps["ETH-USD-PERP"]
|
||||
for inst in perps.values(): client.cache_instrument(inst)
|
||||
btc_perp = perps["BTC-USD-PERP"]; eth_perp = perps["ETH-USD-PERP"]
|
||||
|
||||
prices = get_mark_prices()
|
||||
btc_mark = prices.get("BTC", 0)
|
||||
eth_mark = prices.get("ETH", 0)
|
||||
btc_bid, btc_ask, btc_mid = get_orderbook("BTC")
|
||||
eth_bid, eth_ask, eth_mid = get_orderbook("ETH")
|
||||
|
||||
log.info("="*60)
|
||||
log.info(" FTDT Quant Lab — PROFITABLE QUANT NODE")
|
||||
log.info(" FTDT Quant Lab — QUOTING AT BEST BID/ASK")
|
||||
log.info(f" Wallet: {addr}")
|
||||
log.info(f" BTC: ${btc_mark:,.0f} | ETH: ${eth_mark:,.0f}")
|
||||
log.info(f" Mode: POST-ONLY limit orders (maker: 0.02% fee)")
|
||||
log.info(f" 7 strategies x 100 USDC | Reserve: {RESERVE}")
|
||||
log.info(f" BTC: bid=${btc_bid:,.0f} ask=${btc_ask:,.0f} (spread=${btc_ask-btc_bid:.1f})")
|
||||
log.info(f" ETH: bid=${eth_bid:,.0f} ask=${eth_ask:,.0f} (spread=${eth_ask-eth_bid:.1f})")
|
||||
log.info(f" Mode: POST-ONLY at best bid/ask | Maker: 0.02%")
|
||||
log.info(f" 7 strategies | A-S is DUAL-SIDED quoting")
|
||||
log.info(f" Dashboard: https://ftdt.io/cv")
|
||||
log.info("="*60)
|
||||
|
||||
# Cancel stale orders
|
||||
# Cancel stale
|
||||
open_ords = requests.post(TESTNET_API, json={"type":"openOrders","user":addr}, timeout=10).json()
|
||||
for o in open_ords:
|
||||
try:
|
||||
inst_id = InstrumentId.from_str(f"{o['coin']}-USD-PERP.HYPERLIQUID")
|
||||
client.cancel_order(instrument_id=inst_id, client_order_id=ClientOrderId(o["cloid"]))
|
||||
except Exception:
|
||||
pass
|
||||
iid = InstrumentId.from_str(f"{o['coin']}-USD-PERP.HYPERLIQUID")
|
||||
client.cancel_order(instrument_id=iid, client_order_id=ClientOrderId(o["cloid"]))
|
||||
except: pass
|
||||
log.info(f"Cleared {len(open_ords)} stale orders")
|
||||
|
||||
# Track existing fills
|
||||
existing = get_fills(addr)
|
||||
for f in existing:
|
||||
seen_fills.add(f.get("tid", 0))
|
||||
for f in existing: seen_fills.add(f.get("tid",0))
|
||||
log.info(f"Tracking {len(seen_fills)} existing fills")
|
||||
|
||||
for s in STRATEGIES.values():
|
||||
s["status"] = "running"
|
||||
for s in STRATEGIES.values(): s["status"]="running"
|
||||
write_metrics(addr)
|
||||
|
||||
tick = 0
|
||||
strategy_names = list(STRATEGIES.keys())
|
||||
idx = 0
|
||||
tick=0; names=list(STRATEGIES.keys()); idx=0
|
||||
|
||||
try:
|
||||
while True:
|
||||
tick+=1
|
||||
|
||||
# Refresh prices
|
||||
prices = get_mark_prices()
|
||||
btc_mark = prices.get("BTC", 0)
|
||||
eth_mark = prices.get("ETH", 0)
|
||||
if btc_mark > 0:
|
||||
btc_prices.append(btc_mark)
|
||||
if eth_mark > 0:
|
||||
eth_prices.append(eth_mark)
|
||||
btc = prices.get("BTC",0); eth = prices.get("ETH",0)
|
||||
if btc>0: btc_prices.append(btc)
|
||||
if eth>0: eth_prices.append(eth)
|
||||
|
||||
# Process fills
|
||||
fills = get_fills(addr)
|
||||
new_fill_count = 0
|
||||
fills = get_fills(addr); new_fills=0
|
||||
for f in fills:
|
||||
tid=f.get("tid",0)
|
||||
if tid in seen_fills:
|
||||
continue
|
||||
if tid in seen_fills: continue
|
||||
seen_fills.add(tid)
|
||||
side = f.get("side", "")
|
||||
sz = float(f.get("sz", 0))
|
||||
px = float(f.get("px", 0))
|
||||
closed_pnl = float(f.get("closedPnl", 0))
|
||||
fee = float(f.get("fee", "0"))
|
||||
coin = f.get("coin", "")
|
||||
side=f.get("side",""); sz=float(f.get("sz",0)); px=float(f.get("px",0))
|
||||
closed_pnl=float(f.get("closedPnl",0)); fee=float(f.get("fee","0"))
|
||||
|
||||
# Assign to strategy by size
|
||||
strat=None
|
||||
for name, cfg in STRATEGIES.items():
|
||||
if abs(sz - cfg["size"]) < 0.00001:
|
||||
strat = name
|
||||
break
|
||||
if not strat:
|
||||
continue
|
||||
for n,cfg in STRATEGIES.items():
|
||||
if abs(sz-cfg["size"])<0.00001: strat=n; break
|
||||
if not strat: continue
|
||||
|
||||
net=closed_pnl-abs(fee)
|
||||
STRATEGIES[strat]["pnl"] += net
|
||||
STRATEGIES[strat]["trades_today"] += 1
|
||||
STRATEGIES[strat]["pnl"]+=net; STRATEGIES[strat]["trades_today"]+=1
|
||||
STRATEGIES[strat]["fee_paid"]+=abs(fee)
|
||||
if closed_pnl > 0:
|
||||
STRATEGIES[strat]["wins"] += 1
|
||||
STRATEGIES[strat]["pnl_pct"] = (
|
||||
STRATEGIES[strat]["pnl"] / STRATEGIES[strat]["allocation"] * 100
|
||||
)
|
||||
if closed_pnl>0: STRATEGIES[strat]["wins"]+=1
|
||||
STRATEGIES[strat]["pnl_pct"]=STRATEGIES[strat]["pnl"]/STRATEGIES[strat]["allocation"]*100
|
||||
trades_log.append({"time":datetime.now().strftime("%H:%M:%S"),"strategy":strat,"side":"BUY" if side=="B" else "SELL","size":sz,"price":px,"pnl":round(net,4),"fee":round(abs(fee),4)})
|
||||
new_fills+=1
|
||||
|
||||
trades_log.append({
|
||||
"time": datetime.now().strftime("%H:%M:%S"),
|
||||
"strategy": strat,
|
||||
"side": "BUY" if side == "B" else "SELL",
|
||||
"size": sz, "price": px,
|
||||
"pnl": round(net, 4), "fee": round(abs(fee), 4),
|
||||
})
|
||||
new_fill_count += 1
|
||||
# Signals every 5 ticks
|
||||
if tick%5==0: compute_signals()
|
||||
|
||||
# Compute signals every 5 ticks
|
||||
if tick % 5 == 0:
|
||||
compute_signals()
|
||||
# Place/refresh orders every 3-5 ticks
|
||||
if tick>=3 and tick%random.randint(3,5)==0:
|
||||
btc_bid, btc_ask, btc_mid = get_orderbook("BTC")
|
||||
eth_bid, eth_ask, eth_mid = get_orderbook("ETH")
|
||||
|
||||
# Place orders every 3-5 ticks
|
||||
if tick >= 5 and tick % random.randint(3, 5) == 0:
|
||||
name = strategy_names[idx % 7]
|
||||
idx += 1
|
||||
cfg = STRATEGIES[name]
|
||||
name = names[idx%7]; idx+=1; cfg=STRATEGIES[name]
|
||||
coin="BTC" if "BTC" in cfg["instrument"] else "ETH"
|
||||
mark = btc_mark if coin == "BTC" else eth_mark
|
||||
if mark <= 0:
|
||||
continue
|
||||
|
||||
mid = get_orderbook_mid(coin) or mark
|
||||
|
||||
# Determine side from signal
|
||||
signal = None
|
||||
if cfg["signals"]:
|
||||
signal = cfg["signals"][-1]["signal"] if cfg["signals"] else None
|
||||
cfg["signals"] = cfg["signals"][-10:] # Trim
|
||||
|
||||
# Default: market making (Avellaneda-Stoikov style) with post-only
|
||||
if name == "Avellaneda-Stoikov" or signal is None:
|
||||
# Place both sides as maker
|
||||
side = OrderSide.BUY if tick % 2 == 0 else OrderSide.SELL
|
||||
elif "BUY" in str(signal).upper():
|
||||
side = OrderSide.BUY
|
||||
elif "SELL" in str(signal).upper():
|
||||
side = OrderSide.SELL
|
||||
else:
|
||||
continue
|
||||
|
||||
# POST-ONLY at mid ± half spread to capture spread as maker
|
||||
spread_bps = 2 # 0.02% spread — tiny to ensure fill as maker
|
||||
if side == OrderSide.BUY:
|
||||
limit_px = Price.from_str(str(int(mid * (1 - spread_bps / 10000))))
|
||||
else:
|
||||
limit_px = Price.from_str(str(int(mid * (1 + spread_bps / 10000))))
|
||||
|
||||
perp=btc_perp if coin=="BTC" else eth_perp
|
||||
bid=btc_bid if coin=="BTC" else eth_bid
|
||||
ask=btc_ask if coin=="BTC" else eth_ask
|
||||
mid=btc_mid if coin=="BTC" else eth_mid
|
||||
if bid<=0 or ask<=0: continue
|
||||
|
||||
# Cancel previous order for this strategy
|
||||
if name in active_cloids:
|
||||
try:
|
||||
client.submit_order(
|
||||
instrument_id=perp.id,
|
||||
client_order_id=ClientOrderId(str(UUID4())),
|
||||
order_side=side,
|
||||
order_type=OrderType.LIMIT,
|
||||
quantity=Quantity.from_str(str(cfg["size"])),
|
||||
price=limit_px,
|
||||
time_in_force=TimeInForce.GTC,
|
||||
post_only=True, # MAKER ONLY
|
||||
)
|
||||
client.cancel_order(instrument_id=perp.id, client_order_id=ClientOrderId(active_cloids[name]))
|
||||
except: pass
|
||||
|
||||
# Determine side from signal or market-making pattern
|
||||
signal=None
|
||||
if cfg["signals"]: signal=cfg["signals"][-1]["signal"] if cfg["signals"] else None
|
||||
|
||||
if name=="Avellaneda-Stoikov":
|
||||
# DUAL-SIDED: place both bid and ask simultaneously
|
||||
cid_bid=ClientOrderId(str(UUID4())); cid_ask=ClientOrderId(str(UUID4()))
|
||||
try:
|
||||
client.submit_order(instrument_id=perp.id,client_order_id=cid_bid,order_side=OrderSide.BUY,order_type=OrderType.LIMIT,quantity=Quantity.from_str(str(cfg["size"])),price=Price.from_str(str(int(bid))),time_in_force=TimeInForce.GTC,post_only=True)
|
||||
client.submit_order(instrument_id=perp.id,client_order_id=cid_ask,order_side=OrderSide.SELL,order_type=OrderType.LIMIT,quantity=Quantity.from_str(str(cfg["size"])),price=Price.from_str(str(int(ask))),time_in_force=TimeInForce.GTC,post_only=True)
|
||||
log.info(f"[Avel] DUAL: BID {cfg['size']} @ ${int(bid):,} | ASK {cfg['size']} @ ${int(ask):,} | spread=${ask-bid:.1f}")
|
||||
active_cloids[name]=str(cid_bid) # track one
|
||||
except Exception as e: log.warning(f"Avel dual error: {str(e)[:60]}")
|
||||
continue
|
||||
|
||||
# Single-sided for other strategies
|
||||
side=None; px_level=0
|
||||
if signal and "SELL" in str(signal).upper():
|
||||
side=OrderSide.SELL; px_level=ask # at best ask (highest fill probability as maker)
|
||||
elif signal and "BUY" in str(signal).upper():
|
||||
side=OrderSide.BUY; px_level=bid # at best bid
|
||||
else:
|
||||
# No signal: market-making default — alternate sides at best bid/ask
|
||||
side=OrderSide.BUY if tick%2==0 else OrderSide.SELL
|
||||
px_level=bid if side==OrderSide.BUY else ask
|
||||
|
||||
if not side or px_level<=0: continue
|
||||
|
||||
cid=ClientOrderId(str(UUID4()))
|
||||
try:
|
||||
client.submit_order(instrument_id=perp.id,client_order_id=cid,order_side=side,order_type=OrderType.LIMIT,quantity=Quantity.from_str(str(cfg["size"])),price=Price.from_str(str(int(px_level))),time_in_force=TimeInForce.GTC,post_only=True)
|
||||
side_str="BUY " if side==OrderSide.BUY else "SELL"
|
||||
log.info(
|
||||
f"[{name[:4]:4s}] {side_str} {cfg['size']} {coin} "
|
||||
f"MAKER @ ${float(limit_px):,.0f} (mid: ${mid:,.0f})"
|
||||
)
|
||||
log.info(f"[{name[:4]:4s}] {side_str} {cfg['size']} {coin} MAKER @ ${int(px_level):,} (best {'bid' if side==OrderSide.BUY else 'ask'}: ${int(px_level):,})")
|
||||
active_cloids[name]=str(cid)
|
||||
except Exception as e:
|
||||
log.warning(f"Order error [{name[:8]}]: {str(e)[:80]}")
|
||||
err=str(e)
|
||||
if "would have immediately matched" in err or "cross" in err.lower():
|
||||
# Post-only would cross — fall back to regular limit at same level
|
||||
cid2=ClientOrderId(str(UUID4()))
|
||||
try:
|
||||
client.submit_order(instrument_id=perp.id,client_order_id=cid2,order_side=side,order_type=OrderType.LIMIT,quantity=Quantity.from_str(str(cfg["size"])),price=Price.from_str(str(int(px_level))),time_in_force=TimeInForce.IOC)
|
||||
log.info(f"[{name[:4]:4s}] {side_str} {cfg['size']} {coin} FILLED @ ${int(px_level):,} (post-only crossed → IOC)")
|
||||
active_cloids[name]=str(cid2)
|
||||
except Exception as e2: log.debug(f"[{name[:8]}] fallback failed: {str(e2)[:50]}")
|
||||
else: log.warning(f"Order [{name[:8]}]: {err[:60]}")
|
||||
|
||||
# Equity
|
||||
total_pnl = sum(s["pnl"] for s in STRATEGIES.values())
|
||||
if tick % 2 == 0:
|
||||
equity_history.append({"t": time.time(), "v": TOTAL_EQUITY + total_pnl})
|
||||
|
||||
tp=sum(s["pnl"] for s in STRATEGIES.values())
|
||||
if tick%2==0: equity_history.append({"t":time.time(),"v":TOTAL_EQUITY+tp})
|
||||
write_metrics(addr)
|
||||
|
||||
# Log status
|
||||
if tick%20==0:
|
||||
total_pnl = sum(s["pnl"] for s in STRATEGIES.values())
|
||||
total_trades = sum(s["trades_today"] for s in STRATEGIES.values())
|
||||
total_fees = sum(s["fee_paid"] for s in STRATEGIES.values())
|
||||
log.info(
|
||||
f"Tick {tick:4d} | PnL: ${total_pnl:+.2f} | "
|
||||
f"Trades: {total_trades:3d} | Fees: ${total_fees:.4f}"
|
||||
)
|
||||
tp=sum(s["pnl"] for s in STRATEGIES.values())
|
||||
tr=sum(s["trades_today"] for s in STRATEGIES.values())
|
||||
tf=sum(s["fee_paid"] for s in STRATEGIES.values())
|
||||
log.info(f"Tick {tick:4d} | PnL: ${tp:+.2f} | Trades: {tr:3d} | Fees: ${tf:.4f} | New fills: {new_fills}")
|
||||
|
||||
await asyncio.sleep(1)
|
||||
except KeyboardInterrupt: log.info("Stopping...")
|
||||
|
||||
except KeyboardInterrupt:
|
||||
log.info("Stopping...")
|
||||
|
||||
# Cancel orders
|
||||
# Cancel all
|
||||
open_ords = requests.post(TESTNET_API, json={"type":"openOrders","user":addr}, timeout=10).json()
|
||||
for o in open_ords:
|
||||
try:
|
||||
inst_id = InstrumentId.from_str(f"{o['coin']}-USD-PERP.HYPERLIQUID")
|
||||
client.cancel_order(instrument_id=inst_id, client_order_id=ClientOrderId(o["cloid"]))
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
for s in STRATEGIES.values():
|
||||
s["status"] = "idle"
|
||||
iid=InstrumentId.from_str(f"{o['coin']}-USD-PERP.HYPERLIQUID")
|
||||
client.cancel_order(instrument_id=iid,client_order_id=ClientOrderId(o["cloid"]))
|
||||
except: pass
|
||||
for s in STRATEGIES.values(): s["status"]="idle"
|
||||
write_metrics(addr)
|
||||
total_fees = sum(s["fee_paid"] for s in STRATEGIES.values())
|
||||
total_pnl = sum(s["pnl"] for s in STRATEGIES.values())
|
||||
log.info(f"Stopped. PnL: ${total_pnl:+.2f}, Total fees: ${total_fees:.4f}")
|
||||
tf=sum(s["fee_paid"] for s in STRATEGIES.values())
|
||||
tp=sum(s["pnl"] for s in STRATEGIES.values())
|
||||
log.info(f"Stopped. PnL: ${tp:+.2f}, Fees: ${tf:.4f}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
if __name__=="__main__": asyncio.run(main())
|
||||
|
||||
Reference in New Issue
Block a user