fix-track-record-dates

models/tomorrow_latest_prediction.csv

@@ -1,2 +1,2 @@
-input_date,target_date,prediction,prob_up,confidence,threshold,model_name,source_model,validation_accuracy,test_accuracy,artifact_source
-2026-05-27,2026-05-29,UP,0.536599063991701,0.536599063991701,0.534,nifty_tomorrow_direction_model,locked_multiwindow_nifty50_ensemble_v2,0.5780141843971631,0.6736842105263158,C:\Users\jhaji\Downloads\forecasting project\Code\models\nifty_forecaster\outputs
+input_date,target_date,prediction,prob_up,confidence,threshold,model_name,source_model,validation_accuracy,test_accuracy
+2026-06-01,2026-06-02,DOWN,0.44104410506976766,0.5589558949302323,0.534,nifty_tomorrow_direction_model,locked_multiwindow_nifty50_ensemble_v2,0.5780141843971631,0.6736842105263158

models/tomorrow_summary.json

@@ -25,10 +25,10 @@
   "valid_end": "2025-08-17",
   "test_start": "2025-08-18",
   "test_end": "2026-05-26",
-  "latest_forecast_date": "2026-05-27",
-  "latest_forecast_for": "next trading bar after 2026-05-27",
-  "latest_forecast_prob_up": 0.536599063991701,
-  "latest_forecast_signal": "UP",
+  "latest_forecast_date": "2026-06-01",
+  "latest_forecast_for": "next trading session 2026-06-02",
+  "latest_forecast_prob_up": 0.44104410506976766,
+  "latest_forecast_signal": "DOWN",
   "feature_count": 204,
   "validation_prob_std": 0.06800064531350844,
   "test_prob_std": 0.06311239013827799,
@@ -38,5 +38,6 @@
   "source_model": "locked_multiwindow_nifty50_ensemble_v2",
   "target": "next trading session NIFTY 50 direction",
   "artifact_type": "daily_forecaster_outputs",
-  "artifact_source": "C:\\Users\\jhaji\\Downloads\\forecasting project\\Code\\models\\nifty_forecaster\\outputs"
+  "artifact_source": "C:\\Users\\jhaji\\Downloads\\forecasting project\\Code\\models\\nifty_forecaster\\outputs",
+  "latest_target_date": "2026-06-02"
 }

nifty_backend/__pycache__/runtime.cpython-311.pyc

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6968b7ea4eb2d4595aaafb2e5dbd05ac9ef04691779edcf223dcec996fd657a5
+size 118798

nifty_backend/runtime.py

@@ -978,7 +978,7 @@ def refresh_tomorrow_prediction(session_date: date | None = None) -> dict[str, A
             input_day = date.fromisoformat(str(cleaned.get("input_date"))[:10])
         except Exception:
             input_day = None
-        if input_day is not None:
+        if input_day is not None and (session_date is None or input_day >= session_date):
             clear_dashboard_payload_cache()
             return cleaned
     summary = load_tomorrow_summary()
@@ -1101,12 +1101,86 @@ def dashboard_payload() -> dict[str, Any]:
         return copy.deepcopy(_dashboard_payload_cached(key))
 
 
-def warm_dashboard_payload_cache() -> None:
-    dashboard_payload()
-
-
-@lru_cache(maxsize=4)
-def _dashboard_payload_cached(key: tuple[tuple[str, int | None, int | None], ...]) -> dict[str, Any]:
+def warm_dashboard_payload_cache() -> None:
+    dashboard_payload()
+
+
+def build_prediction_track_record(
+    daily: pd.DataFrame,
+    t5_test: pd.DataFrame,
+    tomorrow_test: pd.DataFrame,
+    tplus1_test: pd.DataFrame,
+    t5_latest: dict[str, Any],
+    tomorrow_latest: dict[str, Any],
+    tplus1_latest: dict[str, Any],
+) -> list[dict[str, Any]]:
+    daily_rows = daily.copy()
+    daily_rows["date"] = pd.to_datetime(daily_rows["date"], errors="coerce").dt.normalize()
+    daily_rows = daily_rows.dropna(subset=["date"]).sort_values("date")
+    daily_rows = daily_rows[
+        daily_rows["open"].map(lambda value: np.isfinite(float(value)) if pd.notna(value) else False)
+        & daily_rows["close"].map(lambda value: np.isfinite(float(value)) if pd.notna(value) else False)
+    ].copy()
+    daily_rows = daily_rows[daily_rows["open"].astype(float) != 0]
+    if daily_rows.empty:
+        return []
+
+    predictions_by_date: dict[str, dict[str, Any]] = {}
+
+    def add_prediction(target_date: Any, prediction: Any, source: str, priority: int, meta: dict[str, Any] | None = None) -> None:
+        day = str(target_date or "")[:10]
+        pred = str(prediction or "").upper()
+        if not day or pred not in {"UP", "DOWN"}:
+            return
+        existing = predictions_by_date.get(day)
+        if existing and existing.get("_priority", 0) >= priority:
+            return
+        predictions_by_date[day] = {
+            "prediction": pred,
+            "source": source,
+            "_priority": priority,
+            **(meta or {}),
+        }
+
+    for _, row in t5_test.iterrows():
+        add_prediction(row.get("date"), row.get("prediction"), "T+5", 30, {"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", 20, {"prob_up": row.get("prob_up")})
+    for _, row in tplus1_test.iterrows():
+        add_prediction(row.get("target_date") or row.get("date"), row.get("prediction"), "T+1", 10, {"prob_up": row.get("prob_up")})
+
+    add_prediction(tomorrow_latest.get("target_date"), tomorrow_latest.get("prediction"), "Tomorrow", 40, {"prob_up": tomorrow_latest.get("prob_up")})
+    add_prediction(tplus1_latest.get("target_date"), tplus1_latest.get("prediction"), "T+1", 35, {"prob_up": tplus1_latest.get("prob_up")})
+    add_prediction(t5_latest.get("input_date"), t5_latest.get("prediction"), "T+5", 45, {"prob_up": t5_latest.get("prob_up")})
+
+    records: list[dict[str, Any]] = []
+    for _, row in daily_rows.tail(20).iterrows():
+        day = row["date"].date().isoformat()
+        day_open = float(row["open"])
+        day_close = float(row["close"])
+        actual_move = (day_close - day_open) / day_open
+        actual_direction = "UP" if actual_move >= 0 else "DOWN"
+        pred = predictions_by_date.get(day)
+        prediction = pred.get("prediction") if pred else None
+        records.append(
+            {
+                "date": day,
+                "prediction": prediction,
+                "prediction_source": pred.get("source") if pred else None,
+                "prob_up": pred.get("prob_up") if pred else None,
+                "actual_move": actual_move,
+                "actual_direction": actual_direction,
+                "correct": None if prediction is None else prediction == actual_direction,
+            }
+        )
+    return records
+
+
+@lru_cache(maxsize=4)
+def _dashboard_payload_cached(key: tuple[tuple[str, int | None, int | None], ...]) -> dict[str, Any]:
     summary = load_model_summary()
     t5_latest = _latest_saved_prediction_uncached()
     tomorrow_summary = load_tomorrow_summary()
@@ -1182,9 +1256,9 @@ def _dashboard_payload_cached(key: tuple[tuple[str, int | None, int | None], ...
             "test_rows": int(len(t5_test)) if not t5_test.empty else int(summary.get("test_rows") or 0),
         },
     ]
-    metrics = {
-        "validation_accuracy": tomorrow_summary.get("validation_accuracy"),
-        "test_accuracy": tomorrow_summary.get("test_accuracy"),
+    metrics = {
+        "validation_accuracy": tomorrow_summary.get("validation_accuracy"),
+        "test_accuracy": tomorrow_summary.get("test_accuracy"),
         "baseline_test_accuracy": tomorrow_summary.get("baseline_accuracy"),
         "validation_auc": summary.get("validation_auc"),
         "test_auc": summary.get("test_auc"),
@@ -1192,10 +1266,19 @@ def _dashboard_payload_cached(key: tuple[tuple[str, int | None, int | None], ...
         "feature_count": tomorrow_summary.get("feature_count"),
         "recent_accuracy": tomorrow_accuracy,
         "recent_accuracy_days": int(len(tomorrow_recent)) if not tomorrow_recent.empty else 0,
-        "total_test_days": int(tomorrow_summary.get("n_test") or len(tomorrow_test) or 0),
-        "models": model_metrics,
-    }
-    return {
+        "total_test_days": int(tomorrow_summary.get("n_test") or len(tomorrow_test) or 0),
+        "models": model_metrics,
+    }
+    track_record = build_prediction_track_record(
+        daily,
+        t5_test,
+        tomorrow_test,
+        tplus1_test,
+        t5_latest,
+        tomorrow_latest,
+        tplus1_latest,
+    )
+    return {
         "latest": t5_latest,
         "tomorrow_latest": tomorrow_latest,
         "tplus1_latest": tplus1_latest,
@@ -1210,11 +1293,12 @@ def _dashboard_payload_cached(key: tuple[tuple[str, int | None, int | None], ...
             "opening_features": _json_ready_frame(opening),
             "monthly_accuracy": _json_ready_frame(monthly),
             "direction_mix": _json_ready_frame(direction_mix),
-            "recent_predictions": _json_ready_frame(recent_predictions),
-            "t5_recent_predictions": _json_ready_frame(recent_predictions),
-            "tomorrow_recent_predictions": _json_ready_frame(tomorrow_recent),
-            "tplus1_recent_predictions": _json_ready_frame(tplus1_test.tail(40)),
-        },
+            "recent_predictions": _json_ready_frame(recent_predictions),
+            "t5_recent_predictions": _json_ready_frame(recent_predictions),
+            "tomorrow_recent_predictions": _json_ready_frame(tomorrow_recent),
+            "tplus1_recent_predictions": _json_ready_frame(tplus1_test.tail(40)),
+            "track_record": track_record,
+        },
         "data_status": {
             "nifty_1m_rows": int(len(pd.read_parquet(NIFTY_1M_PATH, columns=["date"]))),
             "nifty_1d_rows": int(len(pd.read_parquet(NIFTY_1D_PATH, columns=["date"]))),