Upload 10 files

app.py

@@ -13,7 +13,16 @@ from fastapi.middleware.cors import CORSMiddleware
 from fastapi import FastAPI
 
 sys.path.insert(0, str(Path(__file__).resolve().parent))
-from nifty_backend.runtime import dashboard_payload, latest_saved_prediction, refresh_daily_data, refresh_first5_prediction, seconds_until_next_ist_run, IST
+from nifty_backend.runtime import (
+    IST,
+    dashboard_payload,
+    is_trading_day,
+    latest_saved_prediction,
+    next_trading_day,
+    refresh_daily_data,
+    refresh_first5_prediction,
+    seconds_until_next_ist_run,
+)
 
 
 app = FastAPI(title="NIFTY 50 Forecaster Backend")
@@ -36,22 +45,6 @@ def configured_pin() -> str:
     return os.getenv("DASHBOARD_PIN", "1979")
 
 
-def is_trading_day(day: date) -> bool:
-    holidays = {
-        d.strip()
-        for d in os.getenv("NSE_HOLIDAYS", "").split(",")
-        if d.strip()
-    }
-    return day.weekday() < 5 and day.isoformat() not in holidays
-
-
-def next_trading_day(start: date) -> date:
-    day = start
-    while not is_trading_day(day):
-        day += timedelta(days=1)
-    return day
-
-
 def latest_prediction_date(payload: dict | None = None) -> date | None:
     try:
         latest = payload if payload is not None else latest_saved_prediction()
@@ -68,12 +61,12 @@ def current_market_state(now: datetime | None = None) -> dict:
     current_time = now.time()
     trading_day = is_trading_day(today)
     latest_date = latest_prediction_date()
-    has_live_first5 = trading_day and latest_date == today and FIRST5_READY <= current_time <= MARKET_CLOSE
+    has_current_first5 = trading_day and latest_date == today and FIRST5_READY <= current_time
+    next_session = today if trading_day and current_time < MARKET_CLOSE else next_trading_day(today + timedelta(days=1))
 
     if not trading_day:
-        next_day = next_trading_day(today + timedelta(days=1))
         status = "Market Closed"
-        detail = f"Next trading session is {next_day.isoformat()}."
+        detail = f"Next trading session is {next_session.isoformat()}."
     elif current_time < time(9, 0):
         status = "Waiting for 9:00 AM"
         detail = "Market has not entered pre-open yet."
@@ -87,7 +80,7 @@ def current_market_state(now: datetime | None = None) -> dict:
         if market_status in {"Fetching T+5 Prediction Data...", "Prediction Failed"}:
             status = market_status
             detail = "The first-five-minute prediction job is still resolving."
-        elif has_live_first5:
+        elif has_current_first5:
             status = "Prediction Ready"
             detail = "Today's first-five-minute prediction is available."
         else:
@@ -102,8 +95,9 @@ def current_market_state(now: datetime | None = None) -> dict:
         "market_detail": detail,
         "is_trading_day": trading_day,
         "session_date": today.isoformat(),
+        "next_session_date": next_session.isoformat(),
         "latest_prediction_date": latest_date.isoformat() if latest_date else None,
-        "t5_available": has_live_first5,
+        "t5_available": has_current_first5,
     }
 
 
@@ -116,11 +110,17 @@ def attach_market_state(payload: dict) -> dict:
     t5_available = bool(state["t5_available"] and latest.get("prediction"))
     market_closed = state["market_status"] == "Market Closed"
     unavailable_reason = "Market Closed" if market_closed else state["market_status"]
+    tomorrow_available = bool(latest.get("prediction"))
     payload["predictions"] = {
         "tomorrow": {
-            "available": False,
-            "status": "Market Closed" if market_closed else "Pending",
-            "reason": "No next-session model is generated by this backend.",
+            "available": tomorrow_available,
+            "status": "Ready" if tomorrow_available else "Pending",
+            "reason": None if tomorrow_available else "No saved next-session signal is available.",
+            "target_date": state["next_session_date"],
+            "input_date": latest.get("input_date"),
+            "prediction": latest.get("prediction") if tomorrow_available else None,
+            "prob_up": latest.get("prob_up") if tomorrow_available else None,
+            "confidence": latest.get("confidence") if tomorrow_available else None,
         },
         "t5": {
             "available": t5_available,
@@ -182,6 +182,28 @@ async def daily_ist_refresh_loop() -> None:
         except Exception as exc:
             print(f"[scheduler] daily refresh failed: {exc}", flush=True)
 
+
+async def refresh_current_session_once() -> None:
+    global market_status
+    now = datetime.now(IST)
+    if not is_trading_day(now.date()) or now.time() < FIRST5_READY:
+        return
+    if latest_prediction_date() == now.date():
+        return
+    market_status = "Fetching T+5 Prediction Data..."
+    print("[startup] Current session needs first-five refresh; fetching now.", flush=True)
+    try:
+        await asyncio.to_thread(refresh_first5_prediction)
+        market_status = "Prediction Ready"
+    except Exception as exc:
+        print(f"[startup] first5 refresh failed: {exc}", flush=True)
+        market_status = "Prediction Failed"
+    try:
+        await asyncio.to_thread(refresh_daily_data)
+    except Exception as exc:
+        print(f"[startup] daily refresh failed: {exc}", flush=True)
+
+
 @app.on_event("startup")
 async def start_scheduler() -> None:
     global market_status
@@ -196,9 +218,12 @@ async def start_scheduler() -> None:
         market_status = "Market Pre-Open"
     elif now < time(9, 20):
         market_status = "Market Officially Opened"
-    else:
+    elif latest_prediction_date() == today:
         market_status = "Prediction Ready"
+    else:
+        market_status = "Prediction Pending"
         
+    asyncio.create_task(refresh_current_session_once())
     asyncio.create_task(daily_ist_refresh_loop())
 
 

nifty_backend/runtime.py

@@ -13,6 +13,11 @@ import numpy as np
 import pandas as pd
 import yfinance as yf
 
+try:
+    import pandas_market_calendars as mcal
+except ImportError:  # pragma: no cover - production dependency, local fallback below.
+    mcal = None
+
 
 IST = ZoneInfo("Asia/Kolkata")
 YAHOO_NIFTY_SYMBOL = "^NSEI"
@@ -42,6 +47,52 @@ DECISION_OVERLAYS = [
 ]
 
 
+def _nse_calendar():
+    if mcal is None:
+        return None
+    for name in ("XNSE", "NSE", "BSE"):
+        try:
+            return mcal.get_calendar(name)
+        except Exception:
+            continue
+    return None
+
+
+def trading_schedule(start: date, end: date) -> pd.DataFrame:
+    calendar = _nse_calendar()
+    if calendar is None:
+        days = pd.date_range(start=start, end=end, freq="B")
+        return pd.DataFrame(index=days)
+    return calendar.schedule(start_date=start, end_date=end)
+
+
+def is_trading_day(day: date) -> bool:
+    schedule = trading_schedule(day, day)
+    return not schedule.empty
+
+
+def next_trading_day(start: date) -> date:
+    end = start + timedelta(days=14)
+    schedule = trading_schedule(start, end)
+    if schedule.empty:
+        day = start
+        while not is_trading_day(day):
+            day += timedelta(days=1)
+        return day
+    return pd.Timestamp(schedule.index[0]).date()
+
+
+def previous_trading_day(start: date) -> date:
+    begin = start - timedelta(days=14)
+    schedule = trading_schedule(begin, start)
+    if schedule.empty:
+        day = start
+        while not is_trading_day(day):
+            day -= timedelta(days=1)
+        return day
+    return pd.Timestamp(schedule.index[-1]).date()
+
+
 class ProbabilityBlend:
     def __init__(self, models: list[Any], weights: np.ndarray):
         self.models = models
@@ -370,6 +421,8 @@ def dashboard_payload() -> dict[str, Any]:
         "test_brier": summary.get("test_brier"),
         "feature_count": summary.get("feature_count"),
         "recent_accuracy": recent_accuracy,
+        "recent_accuracy_days": int(len(recent_predictions)) if not recent_predictions.empty else 0,
+        "total_test_days": int(len(test)) if not test.empty else int(summary.get("test_rows") or 0),
     }
     return {
         "latest": latest,
@@ -394,6 +447,10 @@ def dashboard_payload() -> dict[str, Any]:
 
 
 def refresh_first5_prediction(session_date: date | None = None) -> Prediction:
+    if session_date is None:
+        today = datetime.now(IST).date()
+        if not is_trading_day(today):
+            raise RuntimeError(f"{today.isoformat()} is not an NSE trading session.")
     minutes = fetch_yahoo_minutes(period="5d")
     append_parquet_rows(NIFTY_1M_PATH, minutes, ["date"])
     first5 = first5_features_from_minutes(minutes, session_date=session_date)
@@ -415,9 +472,16 @@ def refresh_daily_data() -> dict[str, Any]:
     }
 
 
+def next_ist_run_at(run_time: time = time(9, 20), now: datetime | None = None) -> datetime:
+    now = now or datetime.now(IST)
+    target_day = now.date()
+    if now >= datetime.combine(target_day, run_time, tzinfo=IST):
+        target_day += timedelta(days=1)
+    target_day = next_trading_day(target_day)
+    return datetime.combine(target_day, run_time, tzinfo=IST)
+
+
 def seconds_until_next_ist_run(run_time: time = time(9, 20)) -> float:
     now = datetime.now(IST)
-    target = datetime.combine(now.date(), run_time, tzinfo=IST)
-    if now >= target:
-        target += timedelta(days=1)
+    target = next_ist_run_at(run_time, now=now)
     return max(1.0, (target - now).total_seconds())

scripts/run_ist_scheduler.py

@@ -1,22 +1,53 @@
 from __future__ import annotations
 
-import time
 import sys
-from datetime import datetime
+import time
+from datetime import date, datetime, time as dt_time
 from pathlib import Path
 from zoneinfo import ZoneInfo
 
 sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
-from nifty_backend.runtime import refresh_daily_data, refresh_first5_prediction, seconds_until_next_ist_run
+from nifty_backend.runtime import (
+    is_trading_day,
+    latest_saved_prediction,
+    refresh_daily_data,
+    refresh_first5_prediction,
+    seconds_until_next_ist_run,
+)
 
 
 IST = ZoneInfo("Asia/Kolkata")
+FIRST5_READY = dt_time(9, 20)
+
+
+def latest_prediction_date() -> date | None:
+    try:
+        raw = latest_saved_prediction().get("input_date")
+        return date.fromisoformat(str(raw)) if raw else None
+    except Exception:
+        return None
+
+
+def refresh_if_current_session_is_ready() -> None:
+    now = datetime.now(IST)
+    if not is_trading_day(now.date()) or now.time() < FIRST5_READY:
+        return
+    if latest_prediction_date() == now.date():
+        return
+    prediction = refresh_first5_prediction()
+    print(f"[scheduler] first5 prediction refreshed: {prediction.to_dict()}")
+    info = refresh_daily_data()
+    print(f"[scheduler] daily data refreshed: {info}")
 
 
 def main() -> None:
     print("[scheduler] NIFTY first-five-minute scheduler started.")
     print("[scheduler] Runs the opening prediction after 09:20 IST so the 09:15-09:19 candles are complete.")
     while True:
+        try:
+            refresh_if_current_session_is_ready()
+        except Exception as exc:
+            print(f"[scheduler] current-session refresh failed: {exc}")
         sleep_for = seconds_until_next_ist_run()
         target = datetime.now(IST).timestamp() + sleep_for
         print(f"[scheduler] sleeping {sleep_for / 60:.1f} minutes; next wake timestamp={target:.0f}")