Update strategy.py

strategy.py

@@ -24,55 +24,70 @@ def execute_strategy(proba, y_fwd_test, test_dates, target_etfs, fee_bps,
                      stop_loss_pct=-0.12, z_reentry=1.0,
                      sofr=0.045, z_min_entry=0.5,
                      daily_ret_override=None):
+
+    # ── Filter to NYSE trading days ──────────────────────────────────────────
     arrays_to_filter = [proba, y_fwd_test]
     if daily_ret_override is not None:
         arrays_to_filter.append(daily_ret_override)
 
     filtered_dates, filtered_arrays = filter_to_trading_days(test_dates, arrays_to_filter)
+
+    # Safety: if filter dropped too many dates (calendar issue), use original
+    if len(filtered_dates) < len(test_dates) * 0.8:
+        filtered_dates  = test_dates
+        filtered_arrays = arrays_to_filter
+
     test_dates = filtered_dates
     proba      = filtered_arrays[0]
     y_fwd_test = filtered_arrays[1]
     if daily_ret_override is not None:
         daily_ret_override = filtered_arrays[2]
 
-    strat_rets       = []
-    audit_trail      = []
-    daily_rf         = sofr / 252
-    stop_active      = False
-    recent_rets      = []         # rolling 2-day buffer for stop check
-    consec_loss_rets = []         # rolling 5-day buffer for consecutive loss check
-    rotated_etf_idx  = None       # set when we've rotated away from top pick
-    today            = datetime.now().date()
+    strat_rets      = []
+    audit_trail     = []
+    daily_rf        = sofr / 252
+    stop_active     = False
+    recent_rets     = []    # rolling 2-day buffer for stop-loss check
+    top_pick_rets   = []    # rolling 5-day buffer of TOP PICK's actual daily returns
+                            # (always tracks #1 ranked ETF regardless of rotation)
+    rotated_etf_idx = None  # None = use top pick; int = rotated to this index
+    today           = datetime.now().date()
 
     for i in range(len(proba)):
         day_scores     = np.array(proba[i], dtype=float)
-        ranked_indices = np.argsort(day_scores)[::-1]    # best → worst
+        ranked_indices = np.argsort(day_scores)[::-1]   # best → worst
         best_idx       = int(ranked_indices[0])
         second_idx     = int(ranked_indices[1]) if len(ranked_indices) > 1 else best_idx
 
+        # ── Get top pick's actual return for this day (for rotation tracking) ─
+        top_actual = (float(daily_ret_override[i][best_idx])
+                      if daily_ret_override is not None
+                      else float(y_fwd_test[i][best_idx]))
+
         # ── 5-day consecutive loss rotation ──────────────────────────────────
-        # If top ETF has lost money every single day for 5 days → rotate to #2
-        # Once top ETF has a positive day → rotate back
+        # Evaluate BEFORE appending today — buffer contains last 5 completed days
+        # Rule: if top pick lost every day for 5 consecutive days → rotate to #2
+        # Recovery: as soon as top pick has a positive day → rotate back to #1
         if rotated_etf_idx is not None:
-            top_daily = (float(daily_ret_override[i][best_idx])
-                         if daily_ret_override is not None
-                         else float(y_fwd_test[i][best_idx]))
-            if top_daily > 0:
-                rotated_etf_idx = None   # top pick recovering — return to it
-
-        if rotated_etf_idx is None and len(consec_loss_rets) >= 5:
-            if all(r < 0 for r in consec_loss_rets[-5:]):
-                rotated_etf_idx = second_idx   # rotate to model's #2 pick
+            # Already rotated — check if top pick recovered
+            if top_actual > 0:
+                rotated_etf_idx = None   # top pick positive → return to it
+        else:
+            # Not rotated — check if 5 consecutive losses warrant rotation
+            if len(top_pick_rets) >= 5 and all(r < 0 for r in top_pick_rets[-5:]):
+                rotated_etf_idx = second_idx
 
-        active_idx   = rotated_etf_idx if rotated_etf_idx is not None else best_idx
-        _, day_z, _  = compute_signal_conviction(day_scores)
-        signal_etf   = target_etfs[active_idx].replace('_Ret', '')
+        # ── Active ETF for today ──────────────────────────────────────────────
+        active_idx = rotated_etf_idx if rotated_etf_idx is not None else best_idx
+        _, day_z, _ = compute_signal_conviction(day_scores)
+        signal_etf  = target_etfs[active_idx].replace('_Ret', '')
 
         if daily_ret_override is not None:
             realized_ret = float(daily_ret_override[i][active_idx])
         else:
             realized_ret = float(y_fwd_test[i][active_idx])
 
+        # ── Stop-loss + conviction gate ───────────────────────────────────────
         if stop_active:
             if day_z >= z_reentry and day_z >= z_min_entry:
                 stop_active  = False
@@ -100,25 +115,23 @@ def execute_strategy(proba, y_fwd_test, test_dates, target_etfs, fee_bps,
                     trade_signal = signal_etf
 
         strat_rets.append(net_ret)
+
+        # Update rolling buffers AFTER trade decision
         recent_rets.append(net_ret)
         if len(recent_rets) > 2:
             recent_rets.pop(0)
 
-        # Track top-pick's actual daily return for consecutive loss detection
-        top_actual = (float(daily_ret_override[i][best_idx])
-                      if daily_ret_override is not None
-                      else float(y_fwd_test[i][best_idx]))
-        consec_loss_rets.append(top_actual)
-        if len(consec_loss_rets) > 5:
-            consec_loss_rets.pop(0)
+        top_pick_rets.append(top_actual)
+        if len(top_pick_rets) > 5:
+            top_pick_rets.pop(0)
 
+        # ── Audit trail — include all dates up to and including today ────────
         trade_date = test_dates[i]
         if trade_date.date() <= today:
             audit_trail.append({
                 'Date':         trade_date.strftime('%Y-%m-%d'),
                 'Signal':       trade_signal,
                 'Conviction_Z': round(day_z, 2),
-                'Realized':     round(realized_ret, 5),
                 'Net_Return':   round(net_ret, 5),
                 'Stop_Active':  stop_active,
                 'Rotated':      rotated_etf_idx is not None
@@ -126,11 +139,13 @@ def execute_strategy(proba, y_fwd_test, test_dates, target_etfs, fee_bps,
 
     strat_rets = np.array(strat_rets)
 
+    # ── Next trading day signal ───────────────────────────────────────────────
     if len(test_dates) > 0 and len(proba) > 0:
         last_date         = test_dates[-1]
         next_trading_date = get_next_trading_day(last_date)
         last_scores       = np.array(proba[-1], dtype=float)
-        next_best_idx, conviction_zscore, conviction_label = compute_signal_conviction(last_scores)
+        next_best_idx, conviction_zscore, conviction_label = \
+            compute_signal_conviction(last_scores)
         next_signal    = target_etfs[next_best_idx].replace('_Ret', '')
         all_etf_scores = last_scores
     else: