Upload runtime.py

runtime.py

@@ -1144,41 +1144,13 @@ def build_prediction_track_record(
             **(meta or {}),
         }
 
-    # 1. Backtest predictions (lowest priority)
-    for _, row in t5_test.iterrows():
-        pred = row.get("prediction")
-        if pd.isna(pred) and "pred" in row:
-            pred = "UP" if int(row.get("pred")) == 1 else "DOWN"
-        add_prediction(row.get("target_date") or row.get("date"), pred, "T+5 (Backtest)", 10, {"prob_up": row.get("prob_up")})
-
-    for _, row in tplus1_test.iterrows():
-        pred = row.get("prediction")
-        if pd.isna(pred) and "pred" in row:
-            pred = "UP" if int(row.get("pred")) == 1 else "DOWN"
-        add_prediction(row.get("target_date") or row.get("date"), pred, "T+1 (Backtest)", 15, {"prob_up": row.get("prob_up")})
-
     for _, row in tomorrow_test.iterrows():
         pred = row.get("prediction")
         if pd.isna(pred) and "pred" in row:
             pred = "UP" if int(row.get("pred")) == 1 else "DOWN"
-        add_prediction(row.get("target_date") or row.get("date"), pred, "Tomorrow (Backtest)", 20, {"prob_up": row.get("prob_up")})
+        add_prediction(row.get("target_date") or row.get("date"), pred, "Tomorrow", 20, {"prob_up": row.get("prob_up")})
 
-    # 2. Live Ledger predictions (higher priority)
-    try:
-        ledger = load_live_accuracy()
-        for entry in ledger.get("t5", {}).get("entries", []):
-            add_prediction(entry.get("date"), entry.get("prediction"), "T+5 (Live)", 30)
-        for entry in ledger.get("tplus1", {}).get("entries", []):
-            add_prediction(entry.get("date"), entry.get("prediction"), "T+1 (Live)", 35)
-        for entry in ledger.get("tomorrow", {}).get("entries", []):
-            add_prediction(entry.get("date"), entry.get("prediction"), "Tomorrow (Live)", 40)
-    except Exception:
-        pass
-
-    # 3. Latest predictions (highest priority, overwriting if same date)
-    add_prediction(t5_latest.get("target_date") or t5_latest.get("input_date"), t5_latest.get("prediction"), "T+5", 50, {"prob_up": t5_latest.get("prob_up")})
-    add_prediction(tplus1_latest.get("target_date"), tplus1_latest.get("prediction"), "T+1", 55, {"prob_up": tplus1_latest.get("prob_up")})
-    add_prediction(tomorrow_latest.get("target_date"), tomorrow_latest.get("prediction"), "Tomorrow", 60, {"prob_up": tomorrow_latest.get("prob_up")})
+    add_prediction(tomorrow_latest.get("target_date"), tomorrow_latest.get("prediction"), "Tomorrow", 40, {"prob_up": tomorrow_latest.get("prob_up")})
 
     records: list[dict[str, Any]] = []
     for _, row in daily_rows.tail(20).iterrows():
@@ -1527,57 +1499,20 @@ def update_live_accuracy(session_date: date) -> dict[str, Any]:
         except Exception:
             pass
 
-    # Backtest baseline stats
-    t5_summary = load_model_summary()
-    tom_summary = load_tomorrow_summary()
-    t1_summary = load_tplus1_summary()
-    
-    t5_test_total = int(t5_summary.get("test_rows") or len(load_test_predictions()) or 0)
-    t5_test_correct = int(round(t5_test_total * float(t5_summary.get("test_accuracy", 0.0))))
-    
-    tom_test_total = int(tom_summary.get("n_test") or len(load_tomorrow_test_predictions()) or 0)
-    tom_test_correct = int(round(tom_test_total * float(tom_summary.get("test_accuracy", 0.0))))
-    
-    t1_test_total = int(t1_summary.get("test_rows") or len(load_tplus1_test_predictions()) or 0)
-    t1_test_correct = int(round(t1_test_total * float(t1_summary.get("test_accuracy", 0.0))))
-
-    baselines = {
-        "t5": {"total": t5_test_total, "correct": t5_test_correct},
-        "tomorrow": {"total": tom_test_total, "correct": tom_test_correct},
-        "tplus1": {"total": t1_test_total, "correct": t1_test_correct},
-    }
-
     # Recompute summary stats
     for model_id in ("t5", "tomorrow", "tplus1"):
-        if model_id not in ledger:
-            ledger[model_id] = {"entries": []}
-        if "entries" not in ledger[model_id]:
-            ledger[model_id]["entries"] = []
-
         entries = ledger[model_id]["entries"]
-        live_total = len(entries)
-        live_correct = sum(1 for e in entries if e.get("correct"))
-        
-        base_total = baselines[model_id]["total"]
-        base_correct = baselines[model_id]["correct"]
-        
-        combined_total = base_total + live_total
-        combined_correct = base_correct + live_correct
-        
-        ledger[model_id]["live_total"] = live_total
-        ledger[model_id]["live_correct_count"] = live_correct
-        ledger[model_id]["live_accuracy"] = live_correct / live_total if live_total > 0 else None
-        
-        ledger[model_id]["total"] = combined_total
-        ledger[model_id]["correct_count"] = combined_correct
-        ledger[model_id]["accuracy"] = combined_correct / combined_total if combined_total > 0 else None
+        total = len(entries)
+        correct = sum(1 for e in entries if e.get("correct"))
+        ledger[model_id]["total"] = total
+        ledger[model_id]["correct_count"] = correct
+        ledger[model_id]["accuracy"] = correct / total if total > 0 else None
 
     save_live_accuracy(ledger)
     clear_dashboard_payload_cache()
     return ledger
 
 
-
 def refresh_market_close_data(session_date: date | None = None) -> dict[str, Any]:
     now = datetime.now(IST)
     session_date = session_date or now.date()