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predictions_t5.json

@@ -1,9 +1,14 @@
 {
-    "generated_at": "2026-06-19T11:34:08.524516",
+    "generated_at": "2026-06-19T12:11:53.955651",
     "forecast_date": "2026-06-19",
-    "mean_accuracy": 64.08,
-    "median_accuracy": 62.06,
+    "mean_accuracy": 61.93,
+    "median_accuracy": 61.73,
     "predictions": {
+        "ASIANPAINT": {
+            "prediction": "DOWN",
+            "probability": 53.36,
+            "confidence": "NORMAL"
+        },
         "INFY": {
             "prediction": "UP",
             "probability": 61.73,

t5_engine.py

@@ -131,19 +131,20 @@ def generate_t5_predictions():
             prob_up = clf.predict_proba(X_today)[0][1]
             prob_dn = 1.0 - prob_up
             
-            # Confidence threshold from tuning (0.55)
-            if prob_up > prob_dn and prob_up >= 0.55:
-                predictions[ticker] = {
-                    "prediction": "UP",
-                    "probability": round(prob_up * 100, 2),
-                    "confidence": "HIGH"
-                }
-            elif prob_dn > prob_up and prob_dn >= 0.55:
-                predictions[ticker] = {
-                    "prediction": "DOWN",
-                    "probability": round(prob_dn * 100, 2),
-                    "confidence": "HIGH"
-                }
+            if prob_up > prob_dn:
+                pred_dir = "UP"
+                prob_val = prob_up
+            else:
+                pred_dir = "DOWN"
+                prob_val = prob_dn
+                
+            conf = "HIGH" if prob_val >= 0.55 else "NORMAL"
+            
+            predictions[ticker] = {
+                "prediction": pred_dir,
+                "probability": round(prob_val * 100, 2),
+                "confidence": conf
+            }
 
     probs = [info["probability"] for info in predictions.values()]
     mean_accuracy = round(np.mean(probs), 2) if probs else 0.0