feat: ML-optimized accumulation scoring with dashboard toggle

Train GradientBoostedClassifier on 2,601 days of historical data
(2018-2025) to find optimal metric weights for identifying the best
long-term buying opportunities. Uses time-series cross-validation
to prevent look-ahead bias.

Key results:
- pct_above_200w_sma: 50.7% weight (was 11.1% equal)
- drawdown: 14.6%, lth_rp: 10.9%, rhodl: 8.9%
- fear_greed demoted from 11.1% to 5.1%
- nupl/mvrv nearly eliminated (0.7-1.8%)

ML Strong Accumulation bracket: avg +210% 1yr (vs +176% classic)

New files: ml/optimizer.py, config/ml_weights.json
Modified: scoring/engine.py (score_all_ml), backtesting/engine.py
(ml_mode), dashboard/server.py (Classic/ML toggle)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

backtesting/engine.py

@@ -121,8 +121,35 @@ def _compute_ath_series(price_lookup, dates):
     return drawdowns
 
 
-def score_day(date, index, drawdowns):
-    """Score a single day using all available metrics. Returns (composite_score, individual_scores, n_metrics)."""
+def _load_ml_weights():
+    """Load ML weights for ML-optimized scoring mode."""
+    ml_path = _os.path.join(_os.path.dirname(_os.path.dirname(_os.path.abspath(__file__))), "config", "ml_weights.json")
+    try:
+        with open(ml_path) as f:
+            data = _json.load(f)
+        return data.get("weights", {})
+    except Exception:
+        return {}
+
+# ML weight key mapping (backtest metric keys -> ML weight keys)
+_BT_ML_KEY_MAP = {
+    "fear_greed": "fear_greed",
+    "puell_multiple": "puell_multiple",
+    "mvrv_zscore": "mvrv_zscore",
+    "reserve_risk": "reserve_risk",
+    "rhodl_ratio": "rhodl_ratio",
+    "nupl": "nupl",
+    "price_vs_200w_sma": "pct_above_200w_sma",
+    "lth_realized_price": "pct_above_lth_rp",
+    "drawdown": "drawdown",
+}
+
+
+def score_day(date, index, drawdowns, ml_weights=None):
+    """Score a single day using all available metrics. Returns (composite_score, individual_scores, n_metrics).
+
+    If ml_weights is provided, uses ML-optimized weighting instead of equal weights.
+    """
     scores = []
     details = {}
 
@@ -163,7 +190,21 @@ def score_day(date, index, drawdowns):
     if not scores:
         return None, details, 0
 
-    composite = sum(scores) / len(scores) * 10
+    if ml_weights:
+        # ML-weighted composite
+        weighted_sum = 0.0
+        weight_total = 0.0
+        for metric_key, info in details.items():
+            ml_key = _BT_ML_KEY_MAP.get(metric_key, metric_key)
+            w = ml_weights.get(ml_key, 0.0)
+            weighted_sum += info["score"] * w
+            weight_total += w
+        if weight_total > 0:
+            composite = weighted_sum / weight_total * 10
+        else:
+            composite = sum(scores) / len(scores) * 10
+    else:
+        composite = sum(scores) / len(scores) * 10
     return round(composite, 1), details, len(scores)
 
 
@@ -208,9 +249,12 @@ def compute_max_drawdown_forward(price_lookup, date, window=90):
     return round(max_dd, 2) if max_dd > 0 else 0
 
 
-def run_backtest():
-    """Run the full backtest and return comprehensive results."""
-    log.info("Loading historical data...")
+def run_backtest(ml_mode=False):
+    """Run the full backtest and return comprehensive results.
+
+    If ml_mode=True, uses ML-optimized metric weights instead of equal weights.
+    """
+    log.info("Loading historical data... (ml_mode=%s)", ml_mode)
     if not os.path.exists(HISTORY_PATH):
         return {"error": "No historical data found. Run history collector first."}
 
@@ -240,11 +284,17 @@ def run_backtest():
     log.info("Computing forward returns...")
     fwd_returns = compute_forward_returns(price_lookup, all_dates)
 
+    # Load ML weights if in ML mode
+    ml_weights = _load_ml_weights() if ml_mode else None
+    if ml_mode and not ml_weights:
+        log.warning("ML mode requested but no weights found — falling back to equal weights")
+        ml_weights = None
+
     # Score each day
     log.info("Scoring %d days...", len(all_dates))
     daily_scores = []
     for d in all_dates:
-        composite, details, n_metrics = score_day(d, index, drawdowns)
+        composite, details, n_metrics = score_day(d, index, drawdowns, ml_weights=ml_weights)
         if composite is not None and n_metrics >= 3:  # Require at least 3 metrics
             price = price_lookup.get(d)
             entry = {
@@ -435,6 +485,7 @@ def run_backtest():
         "signal_events": signal_events,
         "current_context": current_context,
         "chart_data": chart_data,
+        "ml_mode": ml_mode,
         "computed_at": datetime.utcnow().isoformat() + "Z",
     }