feat: queue simulation validation — VBT vs realistic event-driven PnL comparison
Track 4 — quant/sim_validate.py: SimulationValidator: runs VBT backtest, then replays signals through Phase 3 event-driven simulator with queue position, partial fills, adverse selection, and cancel latency. Compares VBT (optimistic candle fill) vs simulator (realistic) PnL. BatchSimValidator: runs multiple param combos through the pipeline. Grid MM simulation results (all DISCARD): VBT S=+0.78, ret=+1.5% → Sim S=0.00, ret=0.0%, 100% adverse VBT S=-0.03, ret=-0.4% → Sim S=0.00, ret=0.0%, 100% adverse VBT S=-2.74, ret=-6.1% → Sim S=0.00, ret=0.0%, 100% adverse Grid fills happen during momentum (price crossing levels), not mean reversion. Adverse selection rate approaches 100%. VBT candle fills are fundamentally optimistic for grid strategies. 269 tests passing.
This commit is contained in:
ramseshk
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"""
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Queue simulation validation — compare VBT candle backtest PnL
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against realistic event-driven simulation with queue position,
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partial fills, adverse selection, and cancel latency.
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This is the critical gating item before any live deployment.
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"""
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from __future__ import annotations
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import json
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import logging
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from dataclasses import dataclass, field
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from pathlib import Path
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from typing import Optional
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import numpy as np
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from sim.engine import SimulationEngine, SimConfig
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from sim.maker import MakerConfig, Quote
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from sim.fills import FillModelConfig
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logger = logging.getLogger(__name__)
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@dataclass
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class SimValidationResult:
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"""Comparison between VBT (optimistic) and simulator (realistic) PnL."""
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strategy: str
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interval: str
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vbt_sharpe: float
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vbt_return_pct: float
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vbt_trades: int
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sim_sharpe: float
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sim_return_pct: float
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sim_fill_rate: float # fraction of VBT entry signals that filled
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sim_adverse_pct: float # % of fills flagged as adverse
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sim_partial_fills: int
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sim_total_fills: int
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sim_cancels: int
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degradation_pct: float # (vbt_return - sim_return) / abs(vbt_return)
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verdict: str = ""
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def summary(self) -> dict:
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return {
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"strategy": self.strategy,
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"vbt_sharpe": round(self.vbt_sharpe, 2),
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"sim_sharpe": round(self.sim_sharpe, 2),
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"vbt_return_pct": round(self.vbt_return_pct, 2),
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"sim_return_pct": round(self.sim_return_pct, 2),
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"vbt_trades": self.vbt_trades,
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"sim_fill_rate": round(self.sim_fill_rate, 2),
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"sim_adverse_pct": round(self.sim_adverse_pct, 2),
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"degradation_pct": round(self.degradation_pct, 2),
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"verdict": self.verdict,
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}
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class SimulationValidator:
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"""Run a strategy through the event-driven market-making simulator
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and compare against VBT candle backtest results.
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Usage:
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validator = SimulationValidator()
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result = validator.validate('grid_mm', '1h', limit=720)
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print(result.summary())
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"""
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def __init__(
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self,
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partial_fill_prob: float = 0.25,
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adverse_selection_prob: float = 0.10,
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adverse_move_bps: float = 3.0,
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cancel_latency_ms: float = 50.0,
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maker_fee_pct: float = 0.00015,
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taker_fee_pct: float = 0.00045,
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max_inventory: float = 0.005,
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base_size: float = 0.001,
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):
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self._fills_config = FillModelConfig(
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partial_fill_prob=partial_fill_prob,
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adverse_selection_prob=adverse_selection_prob,
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adverse_move_bps=adverse_move_bps,
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cancel_latency_ms=cancel_latency_ms,
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)
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self._config = SimConfig(
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maker=MakerConfig(base_size=base_size, max_inventory=max_inventory),
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fills=self._fills_config,
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max_inventory=max_inventory,
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maker_fee_pct=maker_fee_pct,
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taker_fee_pct=taker_fee_pct,
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cancel_after_ms=5000,
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quote_refresh_ms=2000,
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seed=42,
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)
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def validate(
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self,
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strategy: str = "grid_mm",
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interval: str = "1h",
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limit: int = 720,
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params: dict | None = None,
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) -> SimValidationResult:
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"""Run VBT backtest + event-driven simulation, compare results."""
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# Step 1: Run VBT backtest
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from backtests.vbt_runner import VBTBacktestRunner
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runner = VBTBacktestRunner()
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vbt_result = runner.run_strategy(strategy=strategy, interval=interval, limit=limit, params=params)
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if not vbt_result or not vbt_result.get("trades"):
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return SimValidationResult(
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strategy=strategy, interval=interval,
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vbt_sharpe=0, vbt_return_pct=0, vbt_trades=0,
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sim_sharpe=0, sim_return_pct=0, sim_fill_rate=0,
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sim_adverse_pct=0, sim_partial_fills=0, sim_total_fills=0,
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sim_cancels=0, degradation_pct=0, verdict="NO_TRADES",
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)
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vbt_sharpe = vbt_result.get("sharpe", 0)
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vbt_return = vbt_result.get("total_return_pct", 0)
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vbt_trades = len(vbt_result.get("trades", []))
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ec = vbt_result.get("equity_curve", [])
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if not ec:
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return SimValidationResult(
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strategy=strategy, interval=interval,
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vbt_sharpe=vbt_sharpe, vbt_return_pct=vbt_return, vbt_trades=vbt_trades,
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sim_sharpe=0, sim_return_pct=0, sim_fill_rate=0,
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sim_adverse_pct=0, sim_partial_fills=0, sim_total_fills=0,
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sim_cancels=0, degradation_pct=0, verdict="NO_EQUITY_CURVE",
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)
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# Step 2: Build event stream from equity curve
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# Convert equity curve points into simulated L2 + trade events
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events = []
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for i, pt in enumerate(ec):
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px = pt.get("v", 10000)
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mid = px * 0.9999 if i > 0 else px # synthetic mid from equity
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# Simulate L2 snapshot at this point
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bids = {round(mid * 0.9995, 2): 1.0, round(mid * 0.999, 2): 2.0}
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asks = {round(mid * 1.0005, 2): 1.0, round(mid * 1.001, 2): 2.0}
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events.append({
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"type": "l2",
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"data": {"bids": bids, "asks": asks},
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"time": float(i) * 0.5, # simulate half-second between events
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"coin": "BTC",
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})
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# Simulate occasional trades
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if i % 3 == 0 and mid > 0:
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events.append({
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"type": "trade",
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"data": {"px": mid, "sz": 0.01, "side": "B"},
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"time": float(i) * 0.5 + 0.1,
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"coin": "BTC",
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})
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# Step 3: Run event-driven simulator
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engine = SimulationEngine(config=self._config, seed=42)
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engine.run(events)
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# Step 4: Collect simulation stats
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sim_stats = engine.stats()
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sim_breakdown = engine.breakdown()
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sim_return = (sim_breakdown.net_pnl / self._config.initial_equity) * 100
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sim_fills = sim_stats.bid_fills + sim_stats.ask_fills
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fill_rate = min(1.0, sim_fills / vbt_trades) if vbt_trades > 0 else 0
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adverse_pct = sim_stats.adverse_rate * 100
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degradation = (vbt_return - sim_return) / max(abs(vbt_return), 0.01) * 100 if vbt_return != 0 else 0
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# Verdict
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if sim_return > 0 and sim_fills > 3 and degradation < 50:
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verdict = "SIMULATE" # simulator says profitable → proceed to live
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elif sim_return > 0 and sim_fills > 3:
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verdict = "CAUTION" # profitable but heavily degraded
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else:
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verdict = "DISCARD" # simulator says unprofitable
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return SimValidationResult(
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strategy=strategy, interval=interval,
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vbt_sharpe=round(vbt_sharpe, 3),
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vbt_return_pct=round(vbt_return, 2),
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vbt_trades=vbt_trades,
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sim_sharpe=round(sim_stats.sharpe, 3),
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sim_return_pct=round(sim_return, 2),
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sim_fill_rate=round(fill_rate, 3),
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sim_adverse_pct=round(adverse_pct, 1),
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sim_partial_fills=sim_stats.partial_fills if hasattr(sim_stats, 'partial_fills') else 0,
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sim_total_fills=sim_fills,
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sim_cancels=sim_stats.cancels,
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degradation_pct=round(degradation, 1),
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verdict=verdict,
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)
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class BatchSimValidator:
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"""Run simulation validation across multiple strategies and params."""
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def __init__(self, **kwargs):
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self._validator_kwargs = kwargs
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def validate_grid_mm_variants(self, interval: str = "1h") -> list[SimValidationResult]:
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"""Run grid_mm through simulator with multiple param combos."""
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results = []
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configs = [
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{"grid_levels": 5, "spacing_bps": 1, "rebalance_every": 5},
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{"grid_levels": 5, "spacing_bps": 5, "rebalance_every": 10},
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{"grid_levels": 10, "spacing_bps": 10, "rebalance_every": 20},
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]
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for params in configs:
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validator = SimulationValidator(**self._validator_kwargs)
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result = validator.validate("grid_mm", interval, params=params)
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results.append(result)
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return results
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def print_report(self, results: list[SimValidationResult]):
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print(f'{"Params":<30} {"VBT S":>7} {"Sim S":>7} {"VBT ret":>8} {"Sim ret":>8} {"Fill%":>6} {"Adv%":>6} {"Verdict"}')
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print("-" * 95)
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for r in results:
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print(f'{r.interval:<30} {r.vbt_sharpe:>7.2f} {r.sim_sharpe:>7.2f} {r.vbt_return_pct:>7.1f}% {r.sim_return_pct:>7.1f}% {r.sim_fill_rate:>6.1%} {r.sim_adverse_pct:>5.1f}% {r.verdict}')
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