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app.py

@@ -7,12 +7,14 @@ from zoneinfo import ZoneInfo
 from data_updater import update_daily_data, is_trading_day
 from forecaster_engine import generate_predictions
 from signal_generator import generate_signals
+from t5_engine import run_t5_pipeline
 
 IST = ZoneInfo("Asia/Kolkata")
 MARKET_CLOSE_BUFFER = time(15, 45) # Update runs after 3:45 PM
 SIGNAL_TIME = time(9, 30)         # Signal generation at 9:30 AM
 PREDICTIONS_FILE = os.path.join(os.path.dirname(__file__), "predictions.json")
 SIGNALS_FILE = os.path.join(os.path.dirname(__file__), "signals.json")
+T5_PREDICTIONS_FILE = os.path.join(os.path.dirname(__file__), "t5_predictions.json")
 
 app = FastAPI(title="HF NIFTY Forecaster Backend")
 
@@ -75,6 +77,41 @@ def cron_trigger(background_tasks: BackgroundTasks):
     
     return {"status": "triggered", "message": "Update and forecast pipeline started in the background."}
 
+# ── NEW: T5 Forecaster Endpoints ─────────────────────────────────────────────
+
+@app.get("/t5/predictions")
+def get_t5_predictions():
+    """Get the latest first 5-minute (T5) predictions for all stocks."""
+    if not os.path.exists(T5_PREDICTIONS_FILE):
+        raise HTTPException(status_code=404, detail="T5 predictions not yet generated")
+    
+    with open(T5_PREDICTIONS_FILE, "r") as f:
+        data = json.load(f)
+        
+    return data
+
+@app.post("/cron/t5_update")
+def t5_update_trigger(background_tasks: BackgroundTasks):
+    """
+    Trigger T5 prediction generation. Should be called at or after 09:20 AM IST.
+    """
+    now = datetime.now(IST)
+    today = now.date()
+    current_time = now.time()
+    
+    # 1. Check if it's a trading day
+    if not is_trading_day(today):
+        return {"status": "skipped", "reason": f"{today} is a holiday or weekend"}
+        
+    # 2. Check if it's past 09:20 AM
+    T5_UPDATE_TIME = time(9, 20)
+    if current_time < T5_UPDATE_TIME:
+        return {"status": "skipped", "reason": "Market first 5 minutes not completed yet. Runs after 09:20 AM IST."}
+        
+    background_tasks.add_task(run_t5_pipeline)
+    
+    return {"status": "triggered", "message": "T5 update pipeline started in the background."}
+
 # ── NEW: Signal Generator Endpoints ──────────────────────────────────────────
 
 @app.get("/signals")

data/t5_rules.json

@@ -0,0 +1,247 @@
+[
+    {
+        "Ticker":"ADANIENT",
+        "Test_Acc":0.6833333333,
+        "Rule":"close_to_high > 0.1216 AND hl_spread > 0.0106"
+    },
+    {
+        "Ticker":"ADANIPORTS",
+        "Test_Acc":0.6416666667,
+        "Rule":"hl_spread > 0.0127"
+    },
+    {
+        "Ticker":"APOLLOHOSP",
+        "Test_Acc":0.6416666667,
+        "Rule":"candle_shape > -0.7032 AND hl_spread > 0.0047"
+    },
+    {
+        "Ticker":"ASIANPAINT",
+        "Test_Acc":0.6333333333,
+        "Rule":"close_to_high < 0.8230 AND hl_spread > 0.0086"
+    },
+    {
+        "Ticker":"AXISBANK",
+        "Test_Acc":0.625,
+        "Rule":"close_to_high < 0.4247 AND prev_ret < -0.0013"
+    },
+    {
+        "Ticker":"BAJAJ-AUTO",
+        "Test_Acc":0.675,
+        "Rule":"ret_5m < -0.0050 AND gap < 0.0044"
+    },
+    {
+        "Ticker":"BAJAJFINSV",
+        "Test_Acc":0.625,
+        "Rule":"ret_5m < -0.0034 AND prev_ret < 0.0039"
+    },
+    {
+        "Ticker":"BAJFINANCE",
+        "Test_Acc":0.6666666667,
+        "Rule":"close_to_high > 0.0602 AND hl_spread > 0.0111"
+    },
+    {
+        "Ticker":"BHARTIARTL",
+        "Test_Acc":0.65,
+        "Rule":"hl_spread > 0.0084 AND prev_ret < 0.0030"
+    },
+    {
+        "Ticker":"BPCL",
+        "Test_Acc":0.6583333333,
+        "Rule":"gap < -0.0015 AND close_to_high < 0.6103"
+    },
+    {
+        "Ticker":"BRITANNIA",
+        "Test_Acc":0.6333333333,
+        "Rule":"ret_5m > -0.0027 AND prev_ret < 0.0002"
+    },
+    {
+        "Ticker":"CIPLA",
+        "Test_Acc":0.6333333333,
+        "Rule":"gap < 0.0010 AND vol_5m_ratio > 0.0126"
+    },
+    {
+        "Ticker":"COALINDIA",
+        "Test_Acc":0.6416666667,
+        "Rule":"close_to_high < 0.2902 AND vol_5m_ratio > 0.0248"
+    },
+    {
+        "Ticker":"DIVISLAB",
+        "Test_Acc":0.6916666667,
+        "Rule":"vol_5m_ratio > 0.0376 AND prev_ret < 0.0064"
+    },
+    {
+        "Ticker":"DRREDDY",
+        "Test_Acc":0.7,
+        "Rule":"close_to_high < 0.3847 AND vol_5m_ratio > 0.0082"
+    },
+    {
+        "Ticker":"EICHERMOT",
+        "Test_Acc":0.6083333333,
+        "Rule":"gap > -0.0095 AND close_to_high > 0.0295"
+    },
+    {
+        "Ticker":"GRASIM",
+        "Test_Acc":0.6416666667,
+        "Rule":"gap < 0.0041 AND prev_ret < -0.0037"
+    },
+    {
+        "Ticker":"HCLTECH",
+        "Test_Acc":0.6333333333,
+        "Rule":"ret_5m > -0.0075 AND gap < -0.0010"
+    },
+    {
+        "Ticker":"HDFCBANK",
+        "Test_Acc":0.6333333333,
+        "Rule":"gap > -0.0046 AND hl_spread > 0.0072"
+    },
+    {
+        "Ticker":"HDFCLIFE",
+        "Test_Acc":0.5916666667,
+        "Rule":"close_to_high < 0.7809 AND vol_5m_ratio < 0.0499"
+    },
+    {
+        "Ticker":"HEROMOTOCO",
+        "Test_Acc":0.6333333333,
+        "Rule":"ret_5m < -0.0066 AND close_to_high > 0.0984"
+    },
+    {
+        "Ticker":"HINDALCO",
+        "Test_Acc":0.6583333333,
+        "Rule":"vol_5m_ratio > 0.0208 AND prev_ret < -0.0007"
+    },
+    {
+        "Ticker":"HINDUNILVR",
+        "Test_Acc":0.6083333333,
+        "Rule":"gap > 0.0004 AND close_to_high < 0.6781"
+    },
+    {
+        "Ticker":"ICICIBANK",
+        "Test_Acc":0.6333333333,
+        "Rule":"gap > -0.0052 AND vol_5m_ratio > 0.0385"
+    },
+    {
+        "Ticker":"INDUSINDBK",
+        "Test_Acc":0.6416666667,
+        "Rule":"vol_5m_ratio > 0.0195 AND hl_spread < 0.0124"
+    },
+    {
+        "Ticker":"INFY",
+        "Test_Acc":0.6583333333,
+        "Rule":"gap < -0.0022 AND prev_ret > -0.0173"
+    },
+    {
+        "Ticker":"ITC",
+        "Test_Acc":0.6416666667,
+        "Rule":"ret_5m < 0.0007 AND close_to_high > 0.3338"
+    },
+    {
+        "Ticker":"JSWSTEEL",
+        "Test_Acc":0.6333333333,
+        "Rule":"gap > -0.0035 AND prev_ret < -0.0008"
+    },
+    {
+        "Ticker":"KOTAKBANK",
+        "Test_Acc":0.6416666667,
+        "Rule":"ret_5m > -0.0076 AND vol_5m_ratio < 0.0171"
+    },
+    {
+        "Ticker":"LTIM",
+        "Test_Acc":0.6333333333,
+        "Rule":"ret_5m > -0.0077 AND close_to_high < 0.3565"
+    },
+    {
+        "Ticker":"LT",
+        "Test_Acc":0.65,
+        "Rule":"vol_5m_ratio < 0.0433 AND hl_spread > 0.0067"
+    },
+    {
+        "Ticker":"MARUTI",
+        "Test_Acc":0.6416666667,
+        "Rule":"gap > -0.0054 AND hl_spread > 0.0059"
+    },
+    {
+        "Ticker":"MM",
+        "Test_Acc":0.6583333333,
+        "Rule":"gap < 0.0041 AND close_to_high > 0.5563"
+    },
+    {
+        "Ticker":"NESTLEIND",
+        "Test_Acc":0.6333333333,
+        "Rule":"vol_5m_ratio > 0.0013 AND prev_ret < 0.0017"
+    },
+    {
+        "Ticker":"NTPC",
+        "Test_Acc":0.6333333333,
+        "Rule":"ret_5m < 0.0049 AND gap < 0.0014"
+    },
+    {
+        "Ticker":"ONGC",
+        "Test_Acc":0.6666666667,
+        "Rule":"vol_5m_ratio > 0.0165 AND hl_spread > 0.0081"
+    },
+    {
+        "Ticker":"POWERGRID",
+        "Test_Acc":0.6416666667,
+        "Rule":"gap < -0.0038 AND hl_spread < 0.0261"
+    },
+    {
+        "Ticker":"RELIANCE",
+        "Test_Acc":0.6666666667,
+        "Rule":"close_to_high > 0.4682 AND prev_ret < 0.0007"
+    },
+    {
+        "Ticker":"SBILIFE",
+        "Test_Acc":0.6,
+        "Rule":"hl_spread > 0.0057 AND prev_ret < 0.0058"
+    },
+    {
+        "Ticker":"SBIN",
+        "Test_Acc":0.6166666667,
+        "Rule":"ret_5m > 0.0001 AND close_to_high < 0.6810"
+    },
+    {
+        "Ticker":"SUNPHARMA",
+        "Test_Acc":0.6083333333,
+        "Rule":"close_to_high > 0.0984 AND vol_5m_ratio < 0.0198"
+    },
+    {
+        "Ticker":"TATACONSUM",
+        "Test_Acc":0.6583333333,
+        "Rule":"candle_shape > 0.5173 AND prev_ret < 0.0138"
+    },
+    {
+        "Ticker":"TATASTEEL",
+        "Test_Acc":0.6,
+        "Rule":"ret_5m > -0.0121 AND hl_spread > 0.0101"
+    },
+    {
+        "Ticker":"TCS",
+        "Test_Acc":0.675,
+        "Rule":"gap < -0.0108 AND candle_shape > -0.6825"
+    },
+    {
+        "Ticker":"TECHM",
+        "Test_Acc":0.6416666667,
+        "Rule":"vol_5m_ratio < 0.0931 AND prev_ret > -0.0072"
+    },
+    {
+        "Ticker":"TITAN",
+        "Test_Acc":0.6416666667,
+        "Rule":"candle_shape < 0.6971 AND hl_spread > 0.0074"
+    },
+    {
+        "Ticker":"ULTRACEMCO",
+        "Test_Acc":0.6666666667,
+        "Rule":"gap > 0.0006 AND prev_ret < 0.0135"
+    },
+    {
+        "Ticker":"UPL",
+        "Test_Acc":0.6666666667,
+        "Rule":"ret_5m > -0.0024 AND gap > 0.0029"
+    },
+    {
+        "Ticker":"WIPRO",
+        "Test_Acc":0.6583333333,
+        "Rule":"candle_shape < 0.7496 AND close_to_high > 0.5824"
+    }
+]

t5_engine.py

@@ -0,0 +1,159 @@
+import os
+import json
+import time
+import requests
+import pandas as pd
+from datetime import datetime, date, timedelta
+from zoneinfo import ZoneInfo
+import pandas_market_calendars as mcal
+import numpy as np
+
+IST = ZoneInfo("Asia/Kolkata")
+BASE_DIR = os.path.dirname(__file__)
+DATA_DIR = os.path.join(BASE_DIR, "data")
+RULES_FILE = os.path.join(DATA_DIR, "t5_rules.json")
+PREDICTIONS_FILE = os.path.join(BASE_DIR, "t5_predictions.json")
+
+TICKERS = [
+    'ADANIENT', 'ADANIPORTS', 'APOLLOHOSP', 'ASIANPAINT', 'AXISBANK', 'BAJAJ-AUTO', 'BAJAJFINSV', 'BAJFINANCE', 
+    'BHARTIARTL', 'BPCL', 'BRITANNIA', 'CIPLA', 'COALINDIA', 'DIVISLAB', 'DRREDDY', 'EICHERMOT', 'GRASIM', 
+    'HCLTECH', 'HDFCBANK', 'HDFCLIFE', 'HEROMOTOCO', 'HINDALCO', 'HINDUNILVR', 'ICICIBANK', 'INDUSINDBK', 
+    'INFY', 'ITC', 'JSWSTEEL', 'KOTAKBANK', 'LT', 'M&M', 'MARUTI', 'NESTLEIND', 'NTPC', 'ONGC', 'POWERGRID', 
+    'RELIANCE', 'SBILIFE', 'SBIN', 'SUNPHARMA', 'TATACONSUM', 'TATAMOTORS', 'TATASTEEL', 'TCS', 'TECHM', 
+    'TITAN', 'ULTRACEMCO', 'UPL', 'WIPRO'
+]
+
+def fetch_groww_history(ticker: str, start_ts: int, end_ts: int):
+    url = f"https://groww.in/v1/api/charting_service/v2/chart/exchange/NSE/segment/CASH/{ticker}?endTimeInMillis={end_ts}&intervalInMinutes=1&startTimeInMillis={start_ts}"
+    headers = {
+        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64)",
+        "Accept": "application/json"
+    }
+    try:
+        res = requests.get(url, headers=headers, timeout=10)
+        if res.status_code == 200:
+            data = res.json()
+            if data and 'candles' in data:
+                return data['candles']
+        return []
+    except Exception as e:
+        print(f"Error fetching {ticker}: {e}")
+        return []
+
+def evaluate_rule(rule_str: str, features: dict) -> int:
+    if not rule_str:
+        return 0
+    # Convert 'AND' to python 'and'
+    py_rule = rule_str.replace("AND", "and")
+    try:
+        # features dict contains e.g. {'ret_5m': -0.01, 'gap': 0.005, ...}
+        # eval evaluates the boolean expression
+        result = eval(py_rule, {"__builtins__": None}, features)
+        return 1 if result else -1
+    except Exception as e:
+        print(f"Rule eval error: {e}")
+        return 0
+
+def run_t5_pipeline():
+    print(f"[{datetime.now(IST)}] Starting T5 Engine Pipeline...")
+    
+    if not os.path.exists(RULES_FILE):
+        print("T5 rules file not found!")
+        return {"status": "error", "reason": "Missing rules file"}
+        
+    with open(RULES_FILE, "r") as f:
+        rules_list = json.load(f)
+        
+    rules_dict = {item['Ticker']: item['Rule'] for item in rules_list if item['Rule']}
+    
+    now = datetime.now(IST)
+    # Fetch data for the last 5 days to ensure we have yesterday and today
+    start_dt = now - timedelta(days=5)
+    
+    start_ts = int(start_dt.timestamp() * 1000)
+    end_ts = int(now.timestamp() * 1000)
+    
+    predictions = {}
+    
+    for ticker in TICKERS:
+        candles = fetch_groww_history(ticker, start_ts, end_ts)
+        if not candles:
+            continue
+            
+        # Format: [timestamp, open, high, low, close, volume]
+        df = pd.DataFrame(candles, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
+        df['date'] = pd.to_datetime(df['timestamp'], unit='s', utc=True).dt.tz_convert(IST)
+        df.set_index('date', inplace=True)
+        df.sort_index(inplace=True)
+        
+        # Group by day
+        df['day'] = df.index.date
+        days = df['day'].unique()
+        
+        if len(days) < 2:
+            print(f"{ticker}: Not enough days of data")
+            continue
+            
+        # Today is the last day in the dataset
+        today_date = days[-1]
+        yesterday_date = days[-2]
+        
+        # Yesterday's aggregation
+        yesterday_df = df[df['day'] == yesterday_date]
+        if yesterday_df.empty:
+            continue
+        prev_open = yesterday_df['open'].iloc[0]
+        prev_close = yesterday_df['close'].iloc[-1]
+        prev_vol = yesterday_df['volume'].sum()
+        
+        # Today's first 5 mins aggregation (09:15 to 09:19 inclusive)
+        today_df = df[df['day'] == today_date]
+        first_5m_df = today_df.between_time('09:15', '09:19')
+        
+        if first_5m_df.empty:
+            print(f"{ticker}: Missing first 5 mins data for today")
+            continue
+            
+        open_5m = first_5m_df['open'].iloc[0]
+        high_5m = first_5m_df['high'].max()
+        low_5m = first_5m_df['low'].min()
+        close_5m = first_5m_df['close'].iloc[-1]
+        vol_5m = first_5m_df['volume'].sum()
+        
+        # Calculate features
+        features = {}
+        features['ret_5m'] = (close_5m - open_5m) / open_5m if open_5m else 0
+        features['gap'] = (open_5m - prev_close) / prev_close if prev_close else 0
+        features['candle_shape'] = (close_5m - open_5m) / (high_5m - low_5m + 1e-9)
+        features['close_to_high'] = (close_5m - low_5m) / (high_5m - low_5m + 1e-9)
+        features['vol_5m_ratio'] = vol_5m / (prev_vol + 1e-9)
+        features['hl_spread'] = (high_5m - low_5m) / open_5m if open_5m else 0
+        features['prev_ret'] = (prev_close - prev_open) / prev_open if prev_open else 0
+        
+        # Evaluate rule
+        rule = rules_dict.get(ticker)
+        if rule:
+            pred = evaluate_rule(rule, features)
+            predictions[ticker] = {
+                "prediction": "UP" if pred == 1 else ("DOWN" if pred == -1 else "FLAT"),
+                "features": features,
+                "rule_used": rule
+            }
+        
+        time.sleep(0.5) # Rate limiting
+        
+    output = {
+        "generated_at": now.isoformat(),
+        "date_target": str(now.date()),
+        "horizon": "Same day close > 09:19 close",
+        "predictions": predictions
+    }
+    
+    with open(PREDICTIONS_FILE, "w") as f:
+        json.dump(output, f, indent=4)
+        
+    print(f"[{datetime.now(IST)}] T5 Pipeline completed. {len(predictions)} predictions generated.")
+    return {"status": "success", "count": len(predictions)}
+
+if __name__ == "__main__":
+    run_t5_pipeline()

t5_predictions.json

@@ -0,0 +1,618 @@
+{
+    "generated_at": "2026-06-19T20:10:28.554750+05:30",
+    "date_target": "2026-06-19",
+    "horizon": "Same day close > 09:19 close",
+    "predictions": {
+        "ADANIENT": {
+            "prediction": "DOWN",
+            "features": {
+                "ret_5m": 0.0069605568445475635,
+                "gap": -0.004947229551451188,
+                "candle_shape": 0.7317073170476803,
+                "close_to_high": 0.8222996515392947,
+                "vol_5m_ratio": 0.0022453547686507947,
+                "hl_spread": 0.00951276102088161,
+                "prev_ret": 0.02710027100271003
+            },
+            "rule_used": "close_to_high > 0.1216 AND hl_spread > 0.0106"
+        },
+        "ADANIPORTS": {
+            "prediction": "DOWN",
+            "features": {
+                "ret_5m": -0.0010857173877639652,
+                "gap": -0.0016259281339764784,
+                "candle_shape": -0.20618556698905208,
+                "close_to_high": 0.7113402061122156,
+                "vol_5m_ratio": 0.001300136238280726,
+                "hl_spread": 0.005265729330655255,
+                "prev_ret": 0.006601200218221445
+            },
+            "rule_used": "hl_spread > 0.0127"
+        },
+        "APOLLOHOSP": {
+            "prediction": "UP",
+            "features": {
+                "ret_5m": 0.004343169919086149,
+                "gap": -0.00130718954248366,
+                "candle_shape": 0.9124999999771876,
+                "close_to_high": 0.9249999999768751,
+                "vol_5m_ratio": 0.00024220589014449094,
+                "hl_spread": 0.00475963826749167,
+                "prev_ret": -0.0027848551282810925
+            },
+            "rule_used": "candle_shape > -0.7032 AND hl_spread > 0.0047"
+        },
+        "ASIANPAINT": {
+            "prediction": "UP",
+            "features": {
+                "ret_5m": -0.007483017908387486,
+                "gap": 0.0,
+                "candle_shape": -0.820717131441405,
+                "close_to_high": 0.17928286851875433,
+                "vol_5m_ratio": 0.0008501888258319456,
+                "hl_spread": 0.009117657742743982,
+                "prev_ret": 0.002877959927140288
+            },
+            "rule_used": "close_to_high < 0.8230 AND hl_spread > 0.0086"
+        },
+        "AXISBANK": {
+            "prediction": "DOWN",
+            "features": {
+                "ret_5m": 0.002284787735849157,
+                "gap": -0.0016188373804268178,
+                "candle_shape": 0.5166666665805782,
+                "close_to_high": 0.5166666665805782,
+                "vol_5m_ratio": 0.0008782219770038296,
+                "hl_spread": 0.004422169811320755,
+                "prev_ret": 0.00845948352626899
+            },
+            "rule_used": "close_to_high < 0.4247 AND prev_ret < -0.0013"
+        },
+        "BAJAJ-AUTO": {
+            "prediction": "UP",
+            "features": {
+                "ret_5m": -0.005920663114268798,
+                "gap": 0.003863298662704309,
+                "candle_shape": -0.7228915662563506,
+                "close_to_high": 0.2771084337316011,
+                "vol_5m_ratio": 0.0056564811145670335,
+                "hl_spread": 0.00819025064140517,
+                "prev_ret": 0.004577569907453478
+            },
+            "rule_used": "ret_5m < -0.0050 AND gap < 0.0044"
+        },
+        "BAJAJFINSV": {
+            "prediction": "UP",
+            "features": {
+                "ret_5m": -0.00496950530833519,
+                "gap": 0.0,
+                "candle_shape": -0.6197183098155076,
+                "close_to_high": 0.38028169011406987,
+                "vol_5m_ratio": 0.0005865222501797871,
+                "hl_spread": 0.00801897447481367,
+                "prev_ret": 0.0005650355972426263
+            },
+            "rule_used": "ret_5m < -0.0034 AND prev_ret < 0.0039"
+        },
+        "BAJFINANCE": {
+            "prediction": "DOWN",
+            "features": {
+                "ret_5m": 0.004297468686127586,
+                "gap": -0.004850318139146856,
+                "candle_shape": 0.7256637166857156,
+                "close_to_high": 0.7256637166857156,
+                "vol_5m_ratio": 0.0009881518046231392,
+                "hl_spread": 0.005922121482102711,
+                "prev_ret": 0.00036521103980802704
+            },
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+                "close_to_high": 0.7784090904668196,
+                "vol_5m_ratio": 0.007039389248394708,
+                "hl_spread": 0.009957003847024163,
+                "prev_ret": -0.00878191575865997
+            },
+            "rule_used": "candle_shape < 0.7496 AND close_to_high > 0.5824"
+        }
+    }
+}