feat: autonomous trading engine — full implementation

- 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

services/trading/backtester.py

@@ -0,0 +1,115 @@
+"""Backtester for the autonomous trading engine.
+
+Pure computation module that assembles backtest results from
+pre-computed trade data and daily returns.  The actual replay logic
+(fetching historical data, simulating decisions) requires DB access
+and will be wired in the integration layer.  This module provides
+the pure computation for result assembly.
+"""
+
+from __future__ import annotations
+
+import uuid
+from dataclasses import dataclass, field
+from datetime import date
+
+from services.trading.models import ClosedTrade
+from services.trading.performance_tracker import PerformanceComputer
+
+# ---------------------------------------------------------------------------
+# Data classes
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class BacktestConfig:
+    """Configuration for a backtest run."""
+
+    start_date: date
+    end_date: date
+    initial_capital: float
+    risk_tier: str  # conservative | moderate | aggressive
+
+
+@dataclass
+class BacktestResult:
+    """Output of a completed backtest run."""
+
+    backtest_id: str
+    config: BacktestConfig
+    total_return: float
+    sharpe_ratio: float
+    max_drawdown: float
+    win_rate: float
+    profit_factor: float
+    trade_count: int
+    trade_log: list[dict] = field(default_factory=list)
+    equity_curve: list[dict] = field(default_factory=list)
+
+
+# ---------------------------------------------------------------------------
+# Engine
+# ---------------------------------------------------------------------------
+
+
+class BacktestEngine:
+    """Assembles a BacktestResult from pre-computed trade data.
+
+    Uses :class:`PerformanceComputer` to derive metrics from closed
+    trades and daily returns, then packages everything into a
+    :class:`BacktestResult`.
+    """
+
+    def __init__(self) -> None:
+        self._perf = PerformanceComputer()
+
+    def compute_result(
+        self,
+        config: BacktestConfig,
+        trades: list[ClosedTrade],
+        daily_returns: list[float],
+        equity_curve: list[dict],
+    ) -> BacktestResult:
+        """Build a :class:`BacktestResult` from raw simulation outputs.
+
+        Args:
+            config: The backtest configuration that was used.
+            trades: Closed trades produced by the simulation.
+            daily_returns: Daily return percentages for the simulated period.
+            equity_curve: List of ``{"date": ..., "portfolio_value": ...}``
+                dicts representing the equity curve.
+
+        Returns:
+            A fully populated :class:`BacktestResult`.
+        """
+        metrics = self._perf.compute_metrics(
+            closed_trades=trades,
+            portfolio_value=config.initial_capital,
+            active_pool=config.initial_capital,
+            reserve_pool=0.0,
+            daily_pnl=0.0,
+            unrealized_pnl=0.0,
+            portfolio_heat=0.0,
+            daily_returns=daily_returns,
+        )
+
+        trade_log = [self._perf.compute_trade_metrics(t) for t in trades]
+
+        total_return = (
+            sum(t.pnl for t in trades) / config.initial_capital
+            if config.initial_capital > 0
+            else 0.0
+        )
+
+        return BacktestResult(
+            backtest_id=str(uuid.uuid4()),
+            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(trades),
+            trade_log=trade_log,
+            equity_curve=equity_curve,
+        )