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/correlation.py

@@ -0,0 +1,67 @@
+"""Correlation matrix operations for portfolio diversification.
+
+Feature: autonomous-trading-engine
+
+Pure computation module for price correlation coefficients between
+tracked companies. Used by the Position Sizer to prevent
+over-concentration in correlated positions.
+"""
+
+from __future__ import annotations
+
+from services.trading.models import OpenPosition
+
+
+class CorrelationMatrix:
+    """In-memory correlation matrix for ticker pairs.
+
+    Stores pairwise correlation coefficients and provides lookup
+    methods for individual pairs and weighted portfolio averages.
+    """
+
+    def __init__(self) -> None:
+        self._data: dict[tuple[str, str], float] = {}
+
+    def load(self, data: dict[tuple[str, str], float]) -> None:
+        """Load correlation data from a dict of (ticker_a, ticker_b) -> coefficient."""
+        self._data = dict(data)
+
+    def get_correlation(self, ticker_a: str, ticker_b: str) -> float:
+        """Return the correlation coefficient for a ticker pair.
+
+        Checks both orderings (a, b) and (b, a). Returns 0.0 if the
+        pair is not in the matrix.
+        """
+        if ticker_a == ticker_b:
+            return 1.0
+        return self._data.get(
+            (ticker_a, ticker_b),
+            self._data.get((ticker_b, ticker_a), 0.0),
+        )
+
+    def get_portfolio_correlation(
+        self,
+        candidate: str,
+        positions: list[OpenPosition],
+    ) -> float:
+        """Compute weighted average correlation between candidate and positions.
+
+        Weights are based on each position's market_value. Returns 0.0
+        if there are no positions or total weight is zero.
+        """
+        if not positions:
+            return 0.0
+
+        total_weight = 0.0
+        weighted_corr = 0.0
+
+        for pos in positions:
+            corr = self.get_correlation(candidate, pos.ticker)
+            weight = pos.market_value
+            weighted_corr += corr * weight
+            total_weight += weight
+
+        if total_weight == 0.0:
+            return 0.0
+
+        return weighted_corr / total_weight