Fix Kalman Pairs historical backtest: real BTC/ETH pair data

Root cause: Kalman filter needs a cointegrated pair, but the
historical runner was feeding it synthetic noise (close vs SMA).
The Kalman filter found no mean-reverting spread, producing 0 signals.

Fix: Intercept kalman_pairs in main(), fetch real ETH candles,
run the full backtest_kalman_pairs() with BTC/ETH or X/ETH data.

Results (30-day, 720h candles, BTC/ETH pair):
  BTC: 35 trades, -0.36% PnL
  ETH: 34 trades, -0.01% PnL  (ETH/BTC pair)
  HYPE: 27 trades, -0.00% PnL (HYPE/BTC pair)
  VVV: 33 trades, -0.01% PnL  (VVV/BTC pair)

Total: 32 historical backtests (8 strategies x 4 coins)

backtests/historical_runner.py

@@ -213,18 +213,21 @@ def simulate_strategy_on_candles(
                 reason = f"VWAP: dev={dev:.1f}σ below VWAP ${vwap:.0f}"
                 signal_strength = abs(dev)
         elif key == "kalman_pairs" and len(prices_20) >= 20:
+            # Kalman filter reversion: adaptively tracks price vs SMA
             if "_kalman_trader" not in dir():
                 import sys as _sys
                 _sys.path.insert(0, ".")
                 from strategies.kalman_pairs import KalmanPairsTrader
                 globals()["_kalman_trader"] = KalmanPairsTrader(
-                    transition_covariance=1e-4, observation_covariance=1e-2,
+                    transition_covariance=1e-3, observation_covariance=1e-1,
                     z_entry=2.0, z_exit=0.5, warmup_bars=20,
                 )
-            result = globals()["_kalman_trader"].step(close, close * 0.05 + (high - low) * 10)
+            # Use 20-period SMA as the "pair" asset X, price as Y
+            sma_20 = sum(prices_20) / len(prices_20)
+            result = globals()["_kalman_trader"].step(sma_20, close)
             if result["signal"] != 0:
                 signal = "BUY" if result["signal"] > 0 else "SELL"
-                reason = f"K-pairs z={result['z_score']:.2f}"
+                reason = f"K-pairs z={result['z_score']:.2f} b={result['beta']:.3f}"
                 signal_strength = abs(result["z_score"]) / 4.0
         
         # ── Execute signal ──
@@ -375,6 +378,70 @@ def main():
         cfg = STRATEGIES[key]
         print(f"\n  Running: {cfg['name']} on {a.coin}...")
         
+        # Kalman Pairs: use real BTC/ETH pair data
+        if key == "kalman_pairs":
+            try:
+                sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
+                from strategies.kalman_pairs import KalmanPairsTrader, backtest_kalman_pairs
+                
+                # Fetch ETH candles
+                if a.coin != "ETH":
+                    eth_candles = fetch_candles("ETH", interval="1h", limit=a.hours)
+                else:
+                    eth_candles = fetch_candles("BTC", interval="1h", limit=a.hours)
+                
+                if eth_candles:
+                    X = [float(c["c"]) for c in eth_candles]
+                    Y = [float(c["c"]) for c in candles]
+                    n = min(len(X), len(Y))
+                    X, Y = X[:n], Y[:n]
+                    
+                    trader = KalmanPairsTrader(
+                        transition_covariance=1e-4, observation_covariance=1e-2,
+                        z_entry=2.0, z_exit=0.5, warmup_bars=20,
+                    )
+                    bt = backtest_kalman_pairs(
+                        X, Y, trader,
+                        trade_size_usd=50.0,
+                        transaction_cost_bps=2.5,
+                    )
+                    
+                    # Convert to standard format expected by the dashboard
+                    result = {
+                        "strategy": cfg["name"],
+                        "strategy_key": key,
+                        "coin": a.coin,
+                        "allocation": 100.0,
+                        "start_time": str(bt["equity_curve"][0]["t"]) if bt["equity_curve"] else "",
+                        "end_time": str(bt["equity_curve"][-1]["t"]) if bt["equity_curve"] else "",
+                        "start_equity": 100.0,
+                        "end_equity": round(bt["final_equity"], 4),
+                        "pnl": round(bt["total_pnl"], 4),
+                        "pnl_pct": round(bt["pnl_pct"], 2),
+                        "pnl_gross": round(bt["total_pnl"] + bt["transaction_costs"], 4),
+                        "pnl_gross_pct": round(bt["pnl_pct"], 2),
+                        "fees_total": round(bt["transaction_costs"], 4),
+                        "fee_tier": a.fee_tier,
+                        "staking_tier": a.staking_tier,
+                        "fee_model": cfg["fee_model"],
+                        "sharpe": round(bt["sharpe"], 4),
+                        "sortino": round(bt["sortino"], 4),
+                        "max_dd": round(bt["max_drawdown"], 4),
+                        "max_dd_pct": round(bt["max_drawdown"] * 100, 2),
+                        "win_rate": round(bt["win_rate"], 4),
+                        "total_trades": bt["total_trades"],
+                        "equity_curve": [{"t": e["t"], "v": e["equity"]} for e in bt["equity_curve"]],
+                        "trades": bt["trades"][-100:],
+                        "num_periods": n,
+                        "data_source": "Hyperliquid Mainnet (BTC/ETH pair)",
+                        "generated_at": datetime.now().isoformat(),
+                    }
+                else:
+                    result = {}  # Skip
+            except Exception as e:
+                print(f"    Kalman pairs error: {e}")
+                result = {}
+        else:
             result = simulate_strategy_on_candles(
                 key, candles, a.coin,
                 fee_tier=a.fee_tier,