Celes Renata
4ffde8cc06
- Database migration 018 with 13 tables for trading engine state - Trading engine service (services/trading/) with 12 pure computation modules: position sizer, stop-loss manager, reserve pool, circuit breaker, risk tier controller, correlation matrix, tax lots, trading window, gradual entry, notifications, micro-trading, backtester - Core TradingEngine with pre-trade evaluation pipeline and integration wiring - FastAPI HTTP service with 14 endpoints (health, config, decisions, metrics, backtest) - Performance tracker with Sharpe ratio, drawdown, profit factor computation - 194 Python tests (165 property-based + 29 integration) - Frontend: 13 TanStack Query hooks, 7 dashboard panels, tabbed Trading Engine page - Helm chart entry, network policy, nginx proxy, ingress for trading-engine - Shared infrastructure: enums, Redis keys, TradingConfig in AppConfig
116 lines
3.5 KiB
Python
116 lines
3.5 KiB
Python
"""Backtester for the autonomous trading engine.
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Pure computation module that assembles backtest results from
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pre-computed trade data and daily returns. The actual replay logic
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(fetching historical data, simulating decisions) requires DB access
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and will be wired in the integration layer. This module provides
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the pure computation for result assembly.
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"""
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from __future__ import annotations
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import uuid
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from dataclasses import dataclass, field
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from datetime import date
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from services.trading.models import ClosedTrade
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from services.trading.performance_tracker import PerformanceComputer
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# ---------------------------------------------------------------------------
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# Data classes
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# ---------------------------------------------------------------------------
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@dataclass
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class BacktestConfig:
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"""Configuration for a backtest run."""
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start_date: date
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end_date: date
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initial_capital: float
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risk_tier: str # conservative | moderate | aggressive
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@dataclass
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class BacktestResult:
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"""Output of a completed backtest run."""
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backtest_id: str
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config: BacktestConfig
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total_return: float
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sharpe_ratio: float
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max_drawdown: float
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win_rate: float
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profit_factor: float
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trade_count: int
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trade_log: list[dict] = field(default_factory=list)
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equity_curve: list[dict] = field(default_factory=list)
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# ---------------------------------------------------------------------------
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# Engine
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# ---------------------------------------------------------------------------
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class BacktestEngine:
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"""Assembles a BacktestResult from pre-computed trade data.
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Uses :class:`PerformanceComputer` to derive metrics from closed
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trades and daily returns, then packages everything into a
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:class:`BacktestResult`.
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"""
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def __init__(self) -> None:
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self._perf = PerformanceComputer()
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def compute_result(
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self,
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config: BacktestConfig,
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trades: list[ClosedTrade],
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daily_returns: list[float],
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equity_curve: list[dict],
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) -> BacktestResult:
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"""Build a :class:`BacktestResult` from raw simulation outputs.
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Args:
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config: The backtest configuration that was used.
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trades: Closed trades produced by the simulation.
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daily_returns: Daily return percentages for the simulated period.
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equity_curve: List of ``{"date": ..., "portfolio_value": ...}``
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dicts representing the equity curve.
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Returns:
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A fully populated :class:`BacktestResult`.
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"""
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metrics = self._perf.compute_metrics(
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closed_trades=trades,
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portfolio_value=config.initial_capital,
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active_pool=config.initial_capital,
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reserve_pool=0.0,
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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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trade_log = [self._perf.compute_trade_metrics(t) for t in trades]
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total_return = (
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sum(t.pnl for t in trades) / 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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return BacktestResult(
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backtest_id=str(uuid.uuid4()),
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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(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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