feat: per-metric historical exploration with click-to-select context

- Click any metric card to see historical periods where it was at a similar level
- Purple dot highlighting on chart shows matching periods
- Metric overlay line plotted on chart (dashed purple)
- Metric Context panel shows percentile, comparable days, avg forward returns,
  and historical examples from different market cycles
- New /api/metric-context endpoint for per-metric similarity analysis
- Backtest chart_data now includes per-metric raw values
- score_day() returns raw metric values alongside scores
- Fixed JS SyntaxError from broken inline onclick escaping (uses addEventListener)

Co-Authored-By: Claude Opus 4.6 <<EMAIL>>

backtesting/engine.py

@@ -146,9 +146,10 @@ _BT_ML_KEY_MAP = {
 
 
 def score_day(date, index, drawdowns, ml_weights=None):
-    """Score a single day using all available metrics. Returns (composite_score, individual_scores, n_metrics).
+    """Score a single day using all available metrics. Returns (composite_score, details, n_metrics).
 
     If ml_weights is provided, uses ML-optimized weighting instead of equal weights.
+    details includes both "score" and "raw" (the actual metric value before scoring).
     """
     scores = []
     details = {}
@@ -160,7 +161,7 @@ def score_day(date, index, drawdowns, ml_weights=None):
             s = _score_range(val, cfg["ranges"])
             if s is not None:
                 scores.append(s)
-                details[metric_key] = {"value": val, "score": s}
+                details[metric_key] = {"value": val, "score": s, "raw": val}
 
     # Ratio-based metrics (price vs reference)
     for metric_key, cfg in RATIO_SCORERS.items():
@@ -177,7 +178,7 @@ def score_day(date, index, drawdowns, ml_weights=None):
             s = _score_range(pct_above, cfg["ranges"])
             if s is not None:
                 scores.append(s)
-                details[metric_key] = {"value": pct_above, "score": s}
+                details[metric_key] = {"value": pct_above, "score": s, "raw": pct_above}
 
     # Drawdown
     dd = drawdowns.get(date)
@@ -185,7 +186,7 @@ def score_day(date, index, drawdowns, ml_weights=None):
         s = _score_range(dd, DRAWDOWN_RANGES)
         if s is not None:
             scores.append(s)
-            details["drawdown"] = {"value": dd, "score": s}
+            details["drawdown"] = {"value": dd, "score": s, "raw": dd}
 
     if not scores:
         return None, details, 0
@@ -297,12 +298,19 @@ def run_backtest(ml_mode=False):
         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)
+            # Collect raw metric values for per-metric historical exploration
+            metric_values = {}
+            for mk, info in details.items():
+                raw = info.get("raw")
+                if raw is not None:
+                    metric_values[mk] = round(raw, 6) if isinstance(raw, float) else raw
             entry = {
                 "date": d,
                 "score": composite,
                 "n_metrics": n_metrics,
                 "price": price,
                 "forward_returns": fwd_returns.get(d, {}),
+                "metric_values": metric_values,
             }
             daily_scores.append(entry)
 
@@ -462,6 +470,7 @@ def run_backtest(ml_mode=False):
 
     # --- Build time series for charting ---
     # Smart downsampling: daily for last 2 years, weekly before that
+    # Include per-metric values so the frontend can plot any metric.
     chart_data = []
     import datetime as _dt
     try:
@@ -469,14 +478,25 @@ def run_backtest(ml_mode=False):
         cutoff_date = (last_date - _dt.timedelta(days=730)).strftime("%Y-%m-%d")
     except Exception:
         cutoff_date = "2024-01-01"
+
+    # Collect all metric keys that were ever scored (for per-metric series)
+    all_metric_keys = set()
+    for d in daily_scores:
+        all_metric_keys.update(d.get("metric_values", {}).keys())
+
     for i, d in enumerate(daily_scores):
         is_recent = d["date"] >= cutoff_date
         if is_recent or i % 7 == 0 or i == len(daily_scores) - 1:
-            chart_data.append({
+            entry = {
                 "date": d["date"],
                 "score": d["score"],
                 "price": d["price"],
-            })
+            }
+            # Include per-metric values (raw metric value, not score)
+            metric_vals = d.get("metric_values", {})
+            if metric_vals:
+                entry["metrics"] = metric_vals
+            chart_data.append(entry)
 
     result = {
         "date_range": {"start": daily_scores[0]["date"], "end": daily_scores[-1]["date"]},