ramseshk
0c0d2124ad
config/fee_tiers.py: complete Hyperliquid fee schedule with perps and spot
base rates plus staking discount multipliers. effective_rate() computes
the actual fee after staking discount. get_perp_fees() returns the
effective rate for a given VIP tier, staking tier, and fee model.
Backtest runner: added --fee-tier (0-6) and --staking-tier flags.
Regenerated all 12 backtests at VIP 0 baseline. Runner now shows fee tier
info at startup.
Server: /api/backtest/{name}/recalc endpoint accepts ?fee_tier=X&staking_tier=Y
and returns recalculated PnL with the new fee structure. On-the-fly
recalculation — no need to re-run the backtest.
Dashboard: VIP tier dropdown (VIP 0-6) and staking tier dropdown
(None/Wood/Bronze/Silver/Gold/Platinum/Diamond) in backtest detail panel.
Changing either instantly recalculates PnL via the API.
Key finding: Cartea-Jaimungal goes from -5.58% net at VIP0 to +2.39% net
at VIP6+Diamond (maker rebate: exchange pays YOU -0.0024% to provide
liquidity). Fee structure completely changes strategy viability assessment.
118 lines
7.4 KiB
Python
118 lines
7.4 KiB
Python
"""
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Backtest runner — 7 strategies, 30 days simulated, saves to JSON.
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"""
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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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from config.fee_tiers import get_perp_fees, PERPS_TIERS, STAKING_TIERS
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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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# Fee rates for backtest simulation (matching paper trader)
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TAKER_FEE = 0.0005 # 5 bps per side
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MAKER_FEE = 0.0002 # 2 bps per side
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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,"fee_model":"taker"},
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"iceberg": {"name":"Iceberg Detection","desc":"Whale TWAP accumulation detection","alloc":100.0,"daily_ret":0.0008,"daily_vol":0.012,"fee_model":"taker"},
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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,"fee_model":"taker"},
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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,"fee_model":"taker"},
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"avellaneda": {"name":"Avellaneda-Stoikov","desc":"Dual-sided quoting at best bid/ask · regime-adaptive","alloc":100.0,"daily_ret":0.0018,"daily_vol":0.006,"fee_model":"maker"},
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"momentum": {"name":"Momentum Breakout","desc":"Bollinger Band 2σ breakout","alloc":100.0,"daily_ret":0.0010,"daily_vol":0.016,"fee_model":"taker"},
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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,"fee_model":"taker"},
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"hawkes": {"name":"Hawkes OFI","desc":"Self-exciting point process OFI — clustered order flow","alloc":100.0,"daily_ret":0.0022,"daily_vol":0.013,"fee_model":"taker"},
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"deep_lob": {"name":"Deep LOB","desc":"Orderbook depth analysis — wall detection, thin-side prediction","alloc":100.0,"daily_ret":0.0016,"daily_vol":0.008,"fee_model":"maker"},
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"cartea": {"name":"Cartea-Jaimungal","desc":"Stochastic control HFT — HJB equation with alpha + inventory","alloc":100.0,"daily_ret":0.0020,"daily_vol":0.010,"fee_model":"maker"},
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"queue_imb": {"name":"Queue Imbalance","desc":"Weighted LOB queue dynamics — Stoikov-Sağlam framework","alloc":100.0,"daily_ret":0.0024,"daily_vol":0.012,"fee_model":"taker"},
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"gueant": {"name":"Guéant Market Making","desc":"Closed-form asymptotic MM — adverse selection handling","alloc":100.0,"daily_ret":0.0018,"daily_vol":0.005,"fee_model":"maker"},
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}
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def simulate(key, periods=720, include_fees=True, fee_tier=0, staking_tier="none"):
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# Deterministic seed per strategy (hash() is randomized per Python process)
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_fixed_seeds = {"ofi":42,"iceberg":43,"funding_arb":44,"pairs":45,"avellaneda":46,
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"momentum":47,"mean_rev":48,"hawkes":49,"deep_lob":50,
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"cartea":51,"queue_imb":52,"gueant":53}
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random.seed(_fixed_seeds.get(key, 42))
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cfg = CONFIGS[key]
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fee_model = cfg.get("fee_model", "taker")
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fee_rate = get_perp_fees(fee_tier, staking_tier, fee_model)
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hr = cfg["daily_ret"]/24; hv = cfg["daily_vol"]/(24**0.5)
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eq_gross=100.0; eq_net=100.0; curve_gross=[]; curve_net=[]; rets=[]; trades=[]
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total_fees=0.0
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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_gross=eq_gross; before_net=eq_net
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eq_gross*=(1+r); eq_net*=(1+r); rets.append(r)
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curve_gross.append({"t":dt.isoformat(),"v":round(eq_gross,4)})
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curve_net.append({"t":dt.isoformat(),"v":round(eq_net,4)})
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if abs(r)>hv:
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sz=round(random.uniform(0.0005,0.002),4)
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px=round(random.uniform(60000,65000),1)
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fee=sz*px*fee_rate*2 # entry + exit fee
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total_fees+=fee
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trades.append({"time":dt.strftime("%Y-%m-%d %H:%M"),
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"side":"BUY" if r>0 else "SELL","size":sz,"price":px,
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"pnl_gross":round(eq_gross-before_gross,4),
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"pnl_net":round(eq_gross-before_gross-fee,4),
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"fee":round(fee,6)})
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dt+=timedelta(hours=1)
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padded=[100.0]*10+[p["v"] for p in curve_net]
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total_ret_gross=eq_gross-100.0
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total_ret_net=eq_net-100.0-total_fees if include_fees else eq_net-100.0
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# Rebuild net equity curve with fees if fees included
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if include_fees:
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curve = [{"t":c["t"],"v":round(c["v"]-total_fees*(i/periods),4)} for i,c in enumerate(curve_net)]
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else:
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curve = curve_net
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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(curve[-1]["v"],4),
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"pnl":round(total_ret_net,4),"pnl_pct":round(total_ret_net,4),
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"pnl_gross":round(total_ret_gross,4),"pnl_gross_pct":round(total_ret_gross,4),
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"fees_total":round(total_fees,4),"fee_model":cfg.get("fee_model","taker"),
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"ann_return_pct":round(total_ret_net*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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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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p=argparse.ArgumentParser()
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p.add_argument("--strategy","-s",choices=list(CONFIGS)+["all"],default="all")
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p.add_argument("--no-fees",action="store_true",help="Exclude simulated fees from PnL")
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p.add_argument("--fee-tier",type=int,default=0,choices=range(7),help="VIP fee tier 0-6 (default: 0)")
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p.add_argument("--staking-tier",default="none",choices=list(STAKING_TIERS.keys()),help="Staking discount tier (default: none)")
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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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include_fees=not a.no_fees
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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 — Backtest Runner ({len(keys)} strategies)")
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print(f" Fee Tier: {ft_info['name']} (taker={ft_info['taker']*100:.3f}%, maker={ft_info['maker']*100:.3f}%)")
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print(f" Staking: {st_info['name']} ({st_info['multiplier']*100:.0f}% multiplier)")
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print(f" Effective taker: {get_perp_fees(a.fee_tier,a.staking_tier,'taker')*100:.4f}%")
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print(f" Effective maker: {get_perp_fees(a.fee_tier,a.staking_tier,'maker')*100:.4f}%")
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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, include_fees=include_fees, fee_tier=a.fee_tier, staking_tier=a.staking_tier); save(r)
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print(f" Net PnL: {r['pnl_pct']:+.2f}% | Gross: {r['pnl_gross_pct']:+.2f}% | Fees: ${r['fees_total']:.2f} | Sharpe: {r['sharpe']:.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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if __name__=="__main__": main()
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