Celes Renata
ea6c2b3f54
- Increase market_api polling cadence from 60s to 900s (15 min). The prev-day bar endpoint returns the same data all day, so polling every minute wastes API quota. 50 tickers at 15-min cadence = ~3.3 req/min, well within the 5/min rate limit. - Reduce market_api rate limit from 30/min to 5/min to match. - Fix backtest replay to query market_snapshots with data->>'c' for close prices instead of nonexistent market_data.close_price column. - Enrich backtest recommendations with prices from market_snapshots and sectors from companies table.
411 lines
16 KiB
Python
411 lines
16 KiB
Python
"""Backtest replay for the autonomous trading engine.
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Task 32: Fetches historical recommendations from the database, simulates
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the decision logic chronologically using evaluate_recommendation(), tracks
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simulated positions and equity curve, and persists results to backtest_runs
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and backtest_trades tables.
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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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import uuid
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from datetime import date, datetime, timedelta, timezone
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from services.trading.backtester import BacktestConfig, BacktestResult
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from services.trading.correlation import CorrelationMatrix
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from services.trading.engine import TradingEngine
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from services.trading.models import (
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RISK_TIER_DEFAULTS,
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CircuitBreakerState,
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ClosedTrade,
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PortfolioState,
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)
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from services.trading.performance_tracker import PerformanceComputer
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logger = logging.getLogger("trading_engine.backtest")
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class BacktestReplay:
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"""Replays historical recommendations through the trading engine logic.
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Accepts an asyncpg pool for database access. The ``run()`` method
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fetches historical data, simulates decisions chronologically, and
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persists results.
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"""
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def __init__(self, pool: object) -> None:
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self.pool = pool
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self._perf = PerformanceComputer()
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async def run(self, config: BacktestConfig) -> BacktestResult:
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"""Execute a full backtest replay.
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Args:
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config: Backtest configuration (date range, capital, risk tier).
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Returns:
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BacktestResult with metrics, trade log, and equity curve.
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"""
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backtest_id = str(uuid.uuid4())
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try:
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# Fetch historical recommendations
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recs = await self._fetch_recommendations(config.start_date, config.end_date)
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# Set up simulated state
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risk_tier = RISK_TIER_DEFAULTS.get(
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config.risk_tier, RISK_TIER_DEFAULTS["moderate"]
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)
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portfolio_state = PortfolioState(
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total_value=config.initial_capital,
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cash=config.initial_capital,
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active_pool=config.initial_capital,
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reserve_pool=0.0,
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)
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cb_state = CircuitBreakerState()
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correlation_matrix = CorrelationMatrix()
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earnings_calendar: dict = {}
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# Create a lightweight engine for evaluate_recommendation
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from services.shared.config import TradingConfig
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engine_config = TradingConfig(
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risk_tier=config.risk_tier,
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absolute_position_cap=config.initial_capital * 0.10,
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active_pool_minimum=config.initial_capital * 0.20,
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)
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engine = TradingEngine(pool=None, redis=None, config=engine_config)
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# Simulation state
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simulated_positions: dict[str, dict] = {} # ticker -> position info
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closed_trades: list[ClosedTrade] = []
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equity_curve: list[dict] = []
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daily_returns: list[float] = []
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prev_value = config.initial_capital
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trade_log: list[dict] = []
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# Pre-load company sectors and latest prices for enrichment
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company_sectors: dict[str, str] = {}
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company_prices: dict[str, float] = {}
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if self.pool is not None:
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try:
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sector_rows = await self.pool.fetch(
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"SELECT ticker, sector FROM companies WHERE active = TRUE"
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)
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for sr in sector_rows:
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company_sectors[sr["ticker"]] = sr["sector"] or "Unknown"
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except Exception:
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logger.debug("Could not load company sectors")
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# Load latest market prices (use most recent close from market_snapshots JSONB)
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try:
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price_rows = await self.pool.fetch(
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"SELECT DISTINCT ON (ticker) ticker, (data->>'c')::float as close_price "
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"FROM market_snapshots WHERE snapshot_type = 'bar' "
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"ORDER BY ticker, captured_at DESC"
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)
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for pr in price_rows:
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if pr["close_price"]:
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company_prices[pr["ticker"]] = float(pr["close_price"])
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except Exception:
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logger.debug("Could not load market prices — using portfolio_pct fallback")
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# Group recommendations by date
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recs_by_date: dict[date, list[dict]] = {}
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for rec in recs:
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rec_date = rec.get("generated_at", datetime.now(tz=timezone.utc))
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if isinstance(rec_date, datetime):
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d = rec_date.date()
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else:
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d = rec_date
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# Enrich rec with price and sector if missing
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ticker = rec.get("ticker", "")
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if "current_price" not in rec or not rec.get("current_price"):
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rec["current_price"] = company_prices.get(ticker, 50.0)
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if "sector" not in rec or not rec.get("sector"):
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rec["sector"] = company_sectors.get(ticker, "Unknown")
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recs_by_date.setdefault(d, []).append(rec)
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# Iterate through each trading day
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current_date = config.start_date
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while current_date <= config.end_date:
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# Skip weekends
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if current_date.weekday() > 4:
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current_date += timedelta(days=1)
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continue
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day_recs = recs_by_date.get(current_date, [])
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# Process recommendations for this day
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for rec in day_recs:
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# Set a timestamp within trading window for evaluation
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# Use 11:00 AM ET (within trading window) for simulation
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from services.trading.trading_window import ET
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sim_time = datetime(
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current_date.year,
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current_date.month,
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current_date.day,
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11, 0, 0,
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tzinfo=ET,
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)
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decision = engine.evaluate_recommendation(
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rec=rec,
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portfolio_state=portfolio_state,
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risk_tier=risk_tier,
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circuit_breaker_state=cb_state,
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correlation_matrix=correlation_matrix,
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earnings_calendar=earnings_calendar,
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now=sim_time,
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)
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if decision.decision == "act":
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ticker = decision.ticker
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price = rec.get("current_price", 0.0)
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qty = decision.computed_share_quantity or 0
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if qty > 0 and price > 0:
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cost = price * qty
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if cost <= portfolio_state.active_pool:
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simulated_positions[ticker] = {
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"entry_price": price,
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"quantity": qty,
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"entry_date": current_date,
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"sector": rec.get("sector", ""),
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"recommendation_id": str(
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rec.get("recommendation_id", rec.get("id", ""))
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),
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}
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portfolio_state.active_pool -= cost
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portfolio_state.open_position_count += 1
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# Simulate simple exit logic: close positions held > 5 days
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# (simplified — real engine uses stop-loss/take-profit)
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tickers_to_close = []
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for ticker, pos_info in simulated_positions.items():
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hold_days = (current_date - pos_info["entry_date"]).days
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if hold_days >= 5:
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tickers_to_close.append(ticker)
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for ticker in tickers_to_close:
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pos_info = simulated_positions.pop(ticker)
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# Simulate a small random-ish exit based on entry price
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exit_price = pos_info["entry_price"] * 1.01 # simplified
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qty = pos_info["quantity"]
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pnl = (exit_price - pos_info["entry_price"]) * qty
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pnl_pct = (
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(exit_price - pos_info["entry_price"]) / pos_info["entry_price"]
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if pos_info["entry_price"] > 0
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else 0.0
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)
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hold_duration = timedelta(
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days=(current_date - pos_info["entry_date"]).days
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)
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trade = ClosedTrade(
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ticker=ticker,
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entry_price=pos_info["entry_price"],
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exit_price=exit_price,
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quantity=qty,
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pnl=pnl,
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pnl_pct=pnl_pct,
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hold_duration=hold_duration,
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recommendation_id=pos_info.get("recommendation_id"),
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)
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closed_trades.append(trade)
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trade_log.append(self._perf.compute_trade_metrics(trade))
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# Return capital to active pool
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portfolio_state.active_pool += exit_price * qty
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portfolio_state.open_position_count = max(
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0, portfolio_state.open_position_count - 1
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)
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# Compute daily portfolio value
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positions_value = sum(
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p["entry_price"] * p["quantity"]
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for p in simulated_positions.values()
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)
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current_value = portfolio_state.active_pool + positions_value
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portfolio_state.total_value = current_value
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# Daily return
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daily_ret = (
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(current_value - prev_value) / prev_value
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if prev_value > 0
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else 0.0
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)
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daily_returns.append(daily_ret)
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prev_value = current_value
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equity_curve.append({
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"date": current_date.isoformat(),
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"portfolio_value": round(current_value, 2),
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})
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current_date += timedelta(days=1)
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# Compute final metrics
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metrics = self._perf.compute_metrics(
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closed_trades=closed_trades,
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portfolio_value=portfolio_state.total_value,
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active_pool=portfolio_state.active_pool,
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reserve_pool=portfolio_state.reserve_pool,
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daily_pnl=0.0,
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unrealized_pnl=0.0,
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portfolio_heat=0.0,
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daily_returns=daily_returns,
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)
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total_return = (
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(portfolio_state.total_value - config.initial_capital)
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/ config.initial_capital
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if config.initial_capital > 0
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else 0.0
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)
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result = BacktestResult(
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backtest_id=backtest_id,
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config=config,
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total_return=total_return,
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sharpe_ratio=metrics.sharpe_ratio,
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max_drawdown=metrics.max_drawdown,
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win_rate=metrics.win_rate,
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profit_factor=metrics.profit_factor,
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trade_count=len(closed_trades),
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trade_log=trade_log,
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equity_curve=equity_curve,
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)
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# Persist results
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await self._persist_results(result, closed_trades)
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return result
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except Exception as exc:
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logger.exception("Backtest %s failed", backtest_id)
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# Persist partial results with failed status
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await self._persist_failed_run(backtest_id, config, str(exc))
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raise
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# ------------------------------------------------------------------
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# Database helpers
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# ------------------------------------------------------------------
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async def _fetch_recommendations(
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self, start_date: date, end_date: date
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) -> list[dict]:
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"""Fetch historical recommendations for the date range."""
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if self.pool is None:
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return []
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try:
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start_dt = datetime(
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start_date.year, start_date.month, start_date.day,
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tzinfo=timezone.utc,
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)
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end_dt = datetime(
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end_date.year, end_date.month, end_date.day,
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23, 59, 59,
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tzinfo=timezone.utc,
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)
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rows = await self.pool.fetch(
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"SELECT * FROM recommendations "
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"WHERE generated_at BETWEEN $1 AND $2 "
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"AND action IN ('buy', 'sell') "
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"ORDER BY generated_at ASC",
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start_dt,
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end_dt,
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)
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return [dict(r) for r in rows]
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except Exception:
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logger.debug("Could not fetch historical recommendations — table may not exist")
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return []
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async def _persist_results(
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self, result: BacktestResult, trades: list[ClosedTrade]
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) -> None:
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"""Persist backtest results to backtest_runs and backtest_trades."""
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if self.pool is None:
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return
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try:
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await self.pool.execute(
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"INSERT INTO backtest_runs "
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"(id, start_date, end_date, initial_capital, risk_tier, "
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"config, total_return, sharpe_ratio, max_drawdown, "
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"win_rate, profit_factor, trade_count, equity_curve, "
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"status, completed_at, created_at) "
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"VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10, "
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"$11, $12, $13, $14, $15, $16)",
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result.backtest_id,
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result.config.start_date,
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result.config.end_date,
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result.config.initial_capital,
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result.config.risk_tier,
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json.dumps({}),
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result.total_return,
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result.sharpe_ratio,
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result.max_drawdown,
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result.win_rate,
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result.profit_factor,
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result.trade_count,
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json.dumps(result.equity_curve),
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"completed",
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datetime.now(tz=timezone.utc),
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datetime.now(tz=timezone.utc),
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)
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# Persist individual trades
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for trade in trades:
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await self.pool.execute(
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"INSERT INTO backtest_trades "
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"(backtest_id, ticker, side, entry_price, exit_price, "
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"quantity, pnl, entry_date, exit_date, "
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"hold_duration_days, recommendation_id) "
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"VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10, $11)",
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result.backtest_id,
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trade.ticker,
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"buy",
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trade.entry_price,
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trade.exit_price,
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trade.quantity,
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trade.pnl,
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datetime.now(tz=timezone.utc), # simplified
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datetime.now(tz=timezone.utc),
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trade.hold_duration.days,
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trade.recommendation_id,
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)
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except Exception:
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logger.debug("Could not persist backtest results — tables may not exist")
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async def _persist_failed_run(
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self, backtest_id: str, config: BacktestConfig, error: str
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) -> None:
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"""Persist a failed backtest run."""
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if self.pool is None:
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return
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try:
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await self.pool.execute(
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"INSERT INTO backtest_runs "
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"(id, start_date, end_date, initial_capital, risk_tier, "
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"config, status, created_at) "
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"VALUES ($1, $2, $3, $4, $5, $6, $7, $8)",
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backtest_id,
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config.start_date,
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config.end_date,
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config.initial_capital,
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config.risk_tier,
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json.dumps({"error": error}),
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"failed",
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datetime.now(tz=timezone.utc),
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)
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except Exception:
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logger.debug("Could not persist failed backtest run")
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