Upload 42 files

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
-2026-05-26,2026-05-27,DOWN,0.4892079705003583,0.5107920294996418,0.543,nifty_tomorrow_direction_model,tuned_daily_forest_single,0.5780141843971631,0.6182795698924731
+2026-05-27,2026-05-29,DOWN,0.503279589082978,0.503279589082978,0.543,nifty_tomorrow_direction_model,tuned_daily_forest_single,0.5780141843971631,0.6182795698924731

models/tomorrow_summary.json

@@ -25,13 +25,13 @@
   "valid_end": "2025-08-17",
   "test_start": "2025-08-18",
   "test_end": "2026-05-20",
-  "latest_forecast_date": "2026-05-26",
-  "latest_forecast_for": "next trading session 2026-05-27",
-  "latest_forecast_prob_up": 0.4892079705003583,
+  "latest_forecast_date": "2026-05-27",
+  "latest_forecast_for": "next trading session 2026-05-29",
+  "latest_forecast_prob_up": 0.503279589082978,
   "latest_forecast_signal": "DOWN",
   "feature_count": 301,
   "model_name": "nifty_tomorrow_direction_model",
   "source_model": "tuned_daily_forest_single",
   "target": "next trading session NIFTY 50 direction",
-  "latest_target_date": "2026-05-27"
+  "latest_target_date": "2026-05-29"
 }

nifty_backend/runtime.py

@@ -578,14 +578,34 @@ def load_tomorrow_summary() -> dict[str, Any]:
     }
 
 
-def latest_tomorrow_prediction() -> dict[str, Any]:
-    if TOMORROW_LATEST_PATH.exists():
-        row = pd.read_csv(TOMORROW_LATEST_PATH).iloc[-1].to_dict()
-        return {k: (None if pd.isna(v) else v) for k, v in row.items()}
-    summary = load_tomorrow_summary()
-    return {
-        "input_date": summary.get("latest_forecast_date"),
-        "target_date": None,
+def latest_tomorrow_prediction() -> dict[str, Any]:
+    latest_daily = latest_parquet_date(NIFTY_1D_PATH)
+    if TOMORROW_LATEST_PATH.exists():
+        row = pd.read_csv(TOMORROW_LATEST_PATH).iloc[-1].to_dict()
+        cleaned = {k: (None if pd.isna(v) else v) for k, v in row.items()}
+        try:
+            input_day = date.fromisoformat(str(cleaned.get("input_date"))[:10])
+        except Exception:
+            input_day = None
+        if latest_daily is not None and (input_day is None or input_day < latest_daily):
+            try:
+                return refresh_tomorrow_prediction(session_date=latest_daily)
+            except Exception:
+                return cleaned
+        return cleaned
+    summary = load_tomorrow_summary()
+    try:
+        summary_input_day = date.fromisoformat(str(summary.get("latest_forecast_date"))[:10])
+    except Exception:
+        summary_input_day = None
+    if latest_daily is not None and (summary_input_day is None or summary_input_day < latest_daily):
+        try:
+            return refresh_tomorrow_prediction(session_date=latest_daily)
+        except Exception:
+            pass
+    return {
+        "input_date": summary.get("latest_forecast_date"),
+        "target_date": None,
         "prediction": summary.get("latest_forecast_signal"),
         "prob_up": summary.get("latest_forecast_prob_up"),
         "confidence": None,
@@ -844,17 +864,18 @@ def refresh_tomorrow_prediction(session_date: date | None = None) -> dict[str, A
     }
     pd.DataFrame([row]).to_csv(TOMORROW_LATEST_PATH, index=False)
     summary = dict(summary)
-    summary.update(
-        {
-            "latest_forecast_date": row["input_date"],
+    summary.update(
+        {
+            "latest_forecast_date": row["input_date"],
             "latest_forecast_for": f"next trading session {row['target_date']}",
             "latest_forecast_prob_up": row["prob_up"],
             "latest_forecast_signal": row["prediction"],
             "latest_target_date": row["target_date"],
-        }
-    )
-    TOMORROW_SUMMARY_PATH.write_text(json.dumps(summary, indent=2), encoding="utf-8")
-    return row
+        }
+    )
+    TOMORROW_SUMMARY_PATH.write_text(json.dumps(summary, indent=2), encoding="utf-8")
+    clear_dashboard_payload_cache()
+    return row
 
 
 def _json_ready_frame(df: pd.DataFrame, limit: int | None = None) -> list[dict[str, Any]]:
@@ -1211,6 +1232,15 @@ def latest_prediction_input_date(path: Path) -> date | None:
     return None if pd.isna(value) else value.date()
 
 
+def latest_tomorrow_input_date() -> date | None:
+    try:
+        latest = latest_tomorrow_prediction()
+        raw = latest.get("input_date")
+        return date.fromisoformat(str(raw)[:10]) if raw else None
+    except Exception:
+        return None
+
+
 def expected_completed_daily_date(now: datetime | None = None) -> date:
     now = now or datetime.now(IST)
     if is_trading_day(now.date()) and now.time() < CLOSE_REFRESH_READY:
@@ -1244,6 +1274,7 @@ def stale_data_status(now: datetime | None = None) -> dict[str, Any]:
     latest_daily = latest_parquet_date(NIFTY_1D_PATH)
     latest_minutes = latest_parquet_date(NIFTY_1M_PATH)
     latest_t5 = latest_prediction_input_date(LATEST_PATH)
+    latest_tomorrow = latest_tomorrow_input_date()
     latest_tplus1 = latest_prediction_input_date(TPLUS1_LATEST_PATH)
     return {
         "server_time_ist": now.isoformat(),
@@ -1253,10 +1284,12 @@ def stale_data_status(now: datetime | None = None) -> dict[str, Any]:
         "latest_daily_date": latest_daily.isoformat() if latest_daily else None,
         "latest_minute_date": latest_minutes.isoformat() if latest_minutes else None,
         "latest_t5_date": latest_t5.isoformat() if latest_t5 else None,
+        "latest_tomorrow_date": latest_tomorrow.isoformat() if latest_tomorrow else None,
         "latest_tplus1_date": latest_tplus1.isoformat() if latest_tplus1 else None,
         "daily_stale": is_stale(latest_daily, expected_daily),
         "minutes_stale": is_stale(latest_minutes, expected_minutes),
         "t5_stale": is_stale(latest_t5, expected_minutes),
+        "tomorrow_stale": is_stale(latest_tomorrow, expected_daily),
         "tplus1_stale": is_stale(latest_tplus1, expected_tplus1),
     }
 
@@ -1264,7 +1297,7 @@ def stale_data_status(now: datetime | None = None) -> dict[str, Any]:
 def refresh_stale_data_once(now: datetime | None = None) -> dict[str, Any]:
     now = now or datetime.now(IST)
     status = stale_data_status(now)
-    if not any(status[key] for key in ("daily_stale", "minutes_stale", "t5_stale", "tplus1_stale")):
+    if not any(status[key] for key in ("daily_stale", "minutes_stale", "t5_stale", "tomorrow_stale", "tplus1_stale")):
         return {"status": "fresh", **status, "actions": []}
     if not _stale_refresh_lock.acquire(blocking=False):
         return {"status": "skipped", "reason": "stale refresh already running", **status, "actions": []}
@@ -1287,6 +1320,8 @@ def refresh_stale_data_once(now: datetime | None = None) -> dict[str, Any]:
             outcomes = update_opening_outcomes_from_daily()
             actions.append({"name": "daily", **daily_info})
             actions.append({"name": "opening_outcomes", **outcomes})
+
+        if status["daily_stale"] or status["tomorrow_stale"]:
             try:
                 tomorrow = refresh_tomorrow_prediction(session_date=date.fromisoformat(status["expected_daily_date"]))
                 actions.append({"name": "tomorrow_prediction", "input_date": tomorrow.get("input_date")})