Celes Renata
f2d8744a4f
- ID mismatch: API generated a throwaway UUID while BacktestReplay generated its own internally. Frontend polled with wrong ID and never found the DB row. Now pre-generate ID in endpoint and pass it to BacktestReplay. - Field name: API returned 'backtest_id' but frontend read 'data.id'. Unified to 'id' everywhere. - No polling: useBacktestResult fired once and never refreshed. Added refetchInterval that polls every 2s while status is running. - Response shape: GET endpoint nested results under 'result' object but frontend expected flat fields. Flattened response to match BacktestResult type. - Added running/failed/completed status indicators in BacktestPanel.
439 lines
18 KiB
Python
439 lines
18 KiB
Python
"""Backtest replay for the autonomous trading engine.
|
|
|
|
Task 32: Fetches historical recommendations from the database, simulates
|
|
the decision logic chronologically using evaluate_recommendation(), tracks
|
|
simulated positions and equity curve, and persists results to backtest_runs
|
|
and backtest_trades tables.
|
|
"""
|
|
|
|
from __future__ import annotations
|
|
|
|
import json
|
|
import logging
|
|
import uuid
|
|
from datetime import date, datetime, timedelta, timezone
|
|
|
|
from services.trading.backtester import BacktestConfig, BacktestResult
|
|
from services.trading.correlation import CorrelationMatrix
|
|
from services.trading.engine import TradingEngine
|
|
from services.trading.models import (
|
|
RISK_TIER_DEFAULTS,
|
|
CircuitBreakerState,
|
|
ClosedTrade,
|
|
PortfolioState,
|
|
)
|
|
from services.trading.performance_tracker import PerformanceComputer
|
|
|
|
logger = logging.getLogger("trading_engine.backtest")
|
|
|
|
|
|
class BacktestReplay:
|
|
"""Replays historical recommendations through the trading engine logic.
|
|
|
|
Accepts an asyncpg pool for database access. The ``run()`` method
|
|
fetches historical data, simulates decisions chronologically, and
|
|
persists results.
|
|
"""
|
|
|
|
def __init__(self, pool: object) -> None:
|
|
self.pool = pool
|
|
self._perf = PerformanceComputer()
|
|
|
|
async def run(self, config: BacktestConfig, backtest_id: str | None = None) -> BacktestResult:
|
|
"""Execute a full backtest replay.
|
|
|
|
Args:
|
|
config: Backtest configuration (date range, capital, risk tier).
|
|
backtest_id: Optional pre-generated ID. If not provided, one is generated.
|
|
|
|
Returns:
|
|
BacktestResult with metrics, trade log, and equity curve.
|
|
"""
|
|
if backtest_id is None:
|
|
backtest_id = str(uuid.uuid4())
|
|
|
|
try:
|
|
# Fetch historical recommendations
|
|
recs = await self._fetch_recommendations(config.start_date, config.end_date)
|
|
|
|
# Set up simulated state
|
|
risk_tier = RISK_TIER_DEFAULTS.get(
|
|
config.risk_tier, RISK_TIER_DEFAULTS["moderate"]
|
|
)
|
|
portfolio_state = PortfolioState(
|
|
total_value=config.initial_capital,
|
|
cash=config.initial_capital,
|
|
active_pool=config.initial_capital,
|
|
reserve_pool=0.0,
|
|
)
|
|
cb_state = CircuitBreakerState()
|
|
correlation_matrix = CorrelationMatrix()
|
|
earnings_calendar: dict = {}
|
|
|
|
# Create a lightweight engine for evaluate_recommendation
|
|
from services.shared.config import TradingConfig
|
|
|
|
engine_config = TradingConfig(
|
|
risk_tier=config.risk_tier,
|
|
absolute_position_cap=config.initial_capital * 0.10,
|
|
active_pool_minimum=config.initial_capital * 0.20,
|
|
)
|
|
engine = TradingEngine(pool=None, redis=None, config=engine_config)
|
|
|
|
# Simulation state
|
|
simulated_positions: dict[str, dict] = {} # ticker -> position info
|
|
closed_trades: list[ClosedTrade] = []
|
|
equity_curve: list[dict] = []
|
|
daily_returns: list[float] = []
|
|
prev_value = config.initial_capital
|
|
trade_log: list[dict] = []
|
|
|
|
# Pre-load company sectors and latest prices for enrichment
|
|
company_sectors: dict[str, str] = {}
|
|
company_prices: dict[str, float] = {}
|
|
if self.pool is not None:
|
|
try:
|
|
sector_rows = await self.pool.fetch(
|
|
"SELECT ticker, sector FROM companies WHERE active = TRUE"
|
|
)
|
|
for sr in sector_rows:
|
|
company_sectors[sr["ticker"]] = sr["sector"] or "Unknown"
|
|
except Exception:
|
|
logger.debug("Could not load company sectors")
|
|
|
|
# Load latest market prices (use most recent close from market_snapshots JSONB)
|
|
try:
|
|
price_rows = await self.pool.fetch(
|
|
"SELECT DISTINCT ON (ticker) ticker, (data->>'c')::float as close_price "
|
|
"FROM market_snapshots WHERE snapshot_type = 'bar' "
|
|
"ORDER BY ticker, captured_at DESC"
|
|
)
|
|
for pr in price_rows:
|
|
if pr["close_price"]:
|
|
company_prices[pr["ticker"]] = float(pr["close_price"])
|
|
except Exception:
|
|
logger.debug("Could not load market prices — using portfolio_pct fallback")
|
|
|
|
# Group recommendations by date
|
|
recs_by_date: dict[date, list[dict]] = {}
|
|
for rec in recs:
|
|
rec_date = rec.get("generated_at", datetime.now(tz=timezone.utc))
|
|
if isinstance(rec_date, datetime):
|
|
d = rec_date.date()
|
|
else:
|
|
d = rec_date
|
|
|
|
# Enrich rec with price and sector if missing
|
|
ticker = rec.get("ticker", "")
|
|
if "current_price" not in rec or not rec.get("current_price"):
|
|
rec["current_price"] = company_prices.get(ticker, 50.0)
|
|
if "sector" not in rec or not rec.get("sector"):
|
|
rec["sector"] = company_sectors.get(ticker, "Unknown")
|
|
# Map 'id' to 'recommendation_id' for evaluate_recommendation()
|
|
if "recommendation_id" not in rec and "id" in rec:
|
|
rec["recommendation_id"] = str(rec["id"])
|
|
# Ensure confidence is a float
|
|
if rec.get("confidence") is not None:
|
|
rec["confidence"] = float(rec["confidence"])
|
|
|
|
recs_by_date.setdefault(d, []).append(rec)
|
|
|
|
# Iterate through each trading day
|
|
current_date = config.start_date
|
|
while current_date <= config.end_date:
|
|
# Skip weekends
|
|
if current_date.weekday() > 4:
|
|
current_date += timedelta(days=1)
|
|
continue
|
|
|
|
day_recs = recs_by_date.get(current_date, [])
|
|
act_count = 0
|
|
|
|
# Process recommendations for this day
|
|
for rec in day_recs:
|
|
# Set a timestamp within trading window for evaluation
|
|
# Use 11:00 AM ET (within trading window) for simulation
|
|
from services.trading.trading_window import ET
|
|
sim_time = datetime(
|
|
current_date.year,
|
|
current_date.month,
|
|
current_date.day,
|
|
11, 0, 0,
|
|
tzinfo=ET,
|
|
)
|
|
|
|
decision = engine.evaluate_recommendation(
|
|
rec=rec,
|
|
portfolio_state=portfolio_state,
|
|
risk_tier=risk_tier,
|
|
circuit_breaker_state=cb_state,
|
|
correlation_matrix=correlation_matrix,
|
|
earnings_calendar=earnings_calendar,
|
|
now=sim_time,
|
|
)
|
|
|
|
if decision.decision == "act":
|
|
act_count += 1
|
|
ticker = decision.ticker
|
|
price = rec.get("current_price", 0.0)
|
|
qty = decision.computed_share_quantity or 0
|
|
|
|
if qty > 0 and price > 0 and ticker not in simulated_positions:
|
|
cost = price * qty
|
|
if cost <= portfolio_state.active_pool:
|
|
simulated_positions[ticker] = {
|
|
"entry_price": price,
|
|
"quantity": qty,
|
|
"entry_date": current_date,
|
|
"sector": rec.get("sector", ""),
|
|
"recommendation_id": str(
|
|
rec.get("recommendation_id", rec.get("id", ""))
|
|
),
|
|
}
|
|
portfolio_state.active_pool -= cost
|
|
portfolio_state.open_position_count += 1
|
|
elif act_count == 0 and not hasattr(self, '_first_skip_logged'):
|
|
# Log the first skip reason for debugging
|
|
logger.warning(
|
|
"Backtest first skip: ticker=%s reason=%s conf=%.2f price=%.2f pool=%.2f",
|
|
decision.ticker,
|
|
decision.skip_reason,
|
|
rec.get("confidence", 0.0),
|
|
rec.get("current_price", 0.0),
|
|
portfolio_state.active_pool,
|
|
)
|
|
self._first_skip_logged = True
|
|
|
|
if day_recs:
|
|
logger.warning(
|
|
"Backtest day %s: %d recs, %d act, positions=%d, pool=$%.2f",
|
|
current_date, len(day_recs), act_count,
|
|
len(simulated_positions), portfolio_state.active_pool,
|
|
)
|
|
|
|
# Simulate simple exit logic: close positions held > 5 days
|
|
# (simplified — real engine uses stop-loss/take-profit)
|
|
tickers_to_close = []
|
|
for ticker, pos_info in simulated_positions.items():
|
|
hold_days = (current_date - pos_info["entry_date"]).days
|
|
if hold_days >= 5:
|
|
tickers_to_close.append(ticker)
|
|
|
|
for ticker in tickers_to_close:
|
|
pos_info = simulated_positions.pop(ticker)
|
|
# Simulate a small random-ish exit based on entry price
|
|
exit_price = pos_info["entry_price"] * 1.01 # simplified
|
|
qty = pos_info["quantity"]
|
|
pnl = (exit_price - pos_info["entry_price"]) * qty
|
|
pnl_pct = (
|
|
(exit_price - pos_info["entry_price"]) / pos_info["entry_price"]
|
|
if pos_info["entry_price"] > 0
|
|
else 0.0
|
|
)
|
|
hold_duration = timedelta(
|
|
days=(current_date - pos_info["entry_date"]).days
|
|
)
|
|
|
|
trade = ClosedTrade(
|
|
ticker=ticker,
|
|
entry_price=pos_info["entry_price"],
|
|
exit_price=exit_price,
|
|
quantity=qty,
|
|
pnl=pnl,
|
|
pnl_pct=pnl_pct,
|
|
hold_duration=hold_duration,
|
|
recommendation_id=pos_info.get("recommendation_id"),
|
|
)
|
|
closed_trades.append(trade)
|
|
trade_log.append(self._perf.compute_trade_metrics(trade))
|
|
|
|
# Return capital to active pool
|
|
portfolio_state.active_pool += exit_price * qty
|
|
portfolio_state.open_position_count = max(
|
|
0, portfolio_state.open_position_count - 1
|
|
)
|
|
|
|
# Compute daily portfolio value
|
|
positions_value = sum(
|
|
p["entry_price"] * p["quantity"]
|
|
for p in simulated_positions.values()
|
|
)
|
|
current_value = portfolio_state.active_pool + positions_value
|
|
portfolio_state.total_value = current_value
|
|
|
|
# Daily return
|
|
daily_ret = (
|
|
(current_value - prev_value) / prev_value
|
|
if prev_value > 0
|
|
else 0.0
|
|
)
|
|
daily_returns.append(daily_ret)
|
|
prev_value = current_value
|
|
|
|
equity_curve.append({
|
|
"date": current_date.isoformat(),
|
|
"portfolio_value": round(current_value, 2),
|
|
})
|
|
|
|
current_date += timedelta(days=1)
|
|
|
|
# Compute final metrics
|
|
metrics = self._perf.compute_metrics(
|
|
closed_trades=closed_trades,
|
|
portfolio_value=portfolio_state.total_value,
|
|
active_pool=portfolio_state.active_pool,
|
|
reserve_pool=portfolio_state.reserve_pool,
|
|
daily_pnl=0.0,
|
|
unrealized_pnl=0.0,
|
|
portfolio_heat=0.0,
|
|
daily_returns=daily_returns,
|
|
)
|
|
|
|
total_return = (
|
|
(portfolio_state.total_value - config.initial_capital)
|
|
/ config.initial_capital
|
|
if config.initial_capital > 0
|
|
else 0.0
|
|
)
|
|
|
|
result = BacktestResult(
|
|
backtest_id=backtest_id,
|
|
config=config,
|
|
total_return=total_return,
|
|
sharpe_ratio=metrics.sharpe_ratio,
|
|
max_drawdown=metrics.max_drawdown,
|
|
win_rate=metrics.win_rate,
|
|
profit_factor=metrics.profit_factor,
|
|
trade_count=len(closed_trades),
|
|
trade_log=trade_log,
|
|
equity_curve=equity_curve,
|
|
)
|
|
|
|
# Persist results
|
|
await self._persist_results(result, closed_trades)
|
|
|
|
return result
|
|
|
|
except Exception as exc:
|
|
logger.exception("Backtest %s failed", backtest_id)
|
|
# Persist partial results with failed status
|
|
await self._persist_failed_run(backtest_id, config, str(exc))
|
|
raise
|
|
|
|
# ------------------------------------------------------------------
|
|
# Database helpers
|
|
# ------------------------------------------------------------------
|
|
|
|
async def _fetch_recommendations(
|
|
self, start_date: date, end_date: date
|
|
) -> list[dict]:
|
|
"""Fetch historical recommendations for the date range."""
|
|
if self.pool is None:
|
|
return []
|
|
|
|
try:
|
|
start_dt = datetime(
|
|
start_date.year, start_date.month, start_date.day,
|
|
tzinfo=timezone.utc,
|
|
)
|
|
end_dt = datetime(
|
|
end_date.year, end_date.month, end_date.day,
|
|
23, 59, 59,
|
|
tzinfo=timezone.utc,
|
|
)
|
|
|
|
rows = await self.pool.fetch(
|
|
"SELECT * FROM recommendations "
|
|
"WHERE generated_at BETWEEN $1 AND $2 "
|
|
"AND action IN ('buy', 'sell') "
|
|
"ORDER BY generated_at ASC",
|
|
start_dt,
|
|
end_dt,
|
|
)
|
|
return [dict(r) for r in rows]
|
|
except Exception:
|
|
logger.debug("Could not fetch historical recommendations — table may not exist")
|
|
return []
|
|
|
|
async def _persist_results(
|
|
self, result: BacktestResult, trades: list[ClosedTrade]
|
|
) -> None:
|
|
"""Persist backtest results to backtest_runs and backtest_trades."""
|
|
if self.pool is None:
|
|
return
|
|
|
|
try:
|
|
await self.pool.execute(
|
|
"INSERT INTO backtest_runs "
|
|
"(id, start_date, end_date, initial_capital, risk_tier, "
|
|
"config, total_return, sharpe_ratio, max_drawdown, "
|
|
"win_rate, profit_factor, trade_count, equity_curve, "
|
|
"status, completed_at, created_at) "
|
|
"VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10, "
|
|
"$11, $12, $13, $14, $15, $16)",
|
|
result.backtest_id,
|
|
result.config.start_date,
|
|
result.config.end_date,
|
|
result.config.initial_capital,
|
|
result.config.risk_tier,
|
|
json.dumps({}),
|
|
result.total_return,
|
|
result.sharpe_ratio,
|
|
result.max_drawdown,
|
|
result.win_rate,
|
|
result.profit_factor,
|
|
result.trade_count,
|
|
json.dumps(result.equity_curve),
|
|
"completed",
|
|
datetime.now(tz=timezone.utc),
|
|
datetime.now(tz=timezone.utc),
|
|
)
|
|
|
|
# Persist individual trades
|
|
for trade in trades:
|
|
await self.pool.execute(
|
|
"INSERT INTO backtest_trades "
|
|
"(backtest_id, ticker, side, entry_price, exit_price, "
|
|
"quantity, pnl, entry_date, exit_date, "
|
|
"hold_duration_days, recommendation_id) "
|
|
"VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10, $11)",
|
|
result.backtest_id,
|
|
trade.ticker,
|
|
"buy",
|
|
trade.entry_price,
|
|
trade.exit_price,
|
|
trade.quantity,
|
|
trade.pnl,
|
|
datetime.now(tz=timezone.utc), # simplified
|
|
datetime.now(tz=timezone.utc),
|
|
trade.hold_duration.days,
|
|
trade.recommendation_id,
|
|
)
|
|
except Exception:
|
|
logger.debug("Could not persist backtest results — tables may not exist")
|
|
|
|
async def _persist_failed_run(
|
|
self, backtest_id: str, config: BacktestConfig, error: str
|
|
) -> None:
|
|
"""Persist a failed backtest run."""
|
|
if self.pool is None:
|
|
return
|
|
|
|
try:
|
|
await self.pool.execute(
|
|
"INSERT INTO backtest_runs "
|
|
"(id, start_date, end_date, initial_capital, risk_tier, "
|
|
"config, status, created_at) "
|
|
"VALUES ($1, $2, $3, $4, $5, $6, $7, $8)",
|
|
backtest_id,
|
|
config.start_date,
|
|
config.end_date,
|
|
config.initial_capital,
|
|
config.risk_tier,
|
|
json.dumps({"error": error}),
|
|
"failed",
|
|
datetime.now(tz=timezone.utc),
|
|
)
|
|
except Exception:
|
|
logger.debug("Could not persist failed backtest run")
|