Add NSE Dashboard: live Indian market data + Kronos AI prediction + ORB levels

.claude/launch.json

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+{
+  "version": "0.0.1",
+  "configurations": [
+    {
+      "name": "kronos-nse-dashboard",
+      "runtimeExecutable": "python",
+      "runtimeArgs": ["webui/nse_dashboard.py"],
+      "port": 7071
+    }
+  ]
+}

webui/data_fetcher.py

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+import pandas as pd
+import pytz
+from datetime import datetime, time as dt_time, timedelta
+
+IST = pytz.timezone('Asia/Kolkata')
+
+NSE_SYMBOLS = {
+    'NIFTY 50': '^NSEI',
+    'BANK NIFTY': '^NSEBANK',
+    'NIFTY IT': '^CNXIT',
+    'NIFTY MIDCAP 50': '^NSEMDCP50',
+    'RELIANCE': 'RELIANCE.NS',
+    'TCS': 'TCS.NS',
+    'HDFC BANK': 'HDFCBANK.NS',
+    'INFOSYS': 'INFY.NS',
+    'ICICI BANK': 'ICICIBANK.NS',
+    'KOTAK BANK': 'KOTAKBANK.NS',
+    'AXIS BANK': 'AXISBANK.NS',
+    'SBI': 'SBIN.NS',
+}
+
+INTERVAL_PERIOD = {
+    '1m':  '7d',
+    '5m':  '60d',
+    '15m': '60d',
+    '30m': '60d',
+    '60m': '730d',
+    '1d':  'max',
+}
+
+
+def fetch_ohlcv(symbol: str, interval: str = '5m') -> pd.DataFrame:
+    """Fetch OHLCV data from Yahoo Finance for an NSE symbol."""
+    try:
+        import yfinance as yf
+    except ImportError:
+        raise ImportError("yfinance not installed. Run: pip install yfinance")
+
+    period = INTERVAL_PERIOD.get(interval, '60d')
+    ticker = yf.Ticker(symbol)
+    df = ticker.history(period=period, interval=interval)
+
+    if df.empty:
+        raise ValueError(f"No data returned for {symbol} at {interval} interval")
+
+    df = df.rename(columns={
+        'Open': 'open', 'High': 'high',
+        'Low': 'low', 'Close': 'close', 'Volume': 'volume'
+    })
+    df.index.name = 'timestamps'
+    df = df.reset_index()
+
+    # Normalise timezone to IST
+    if df['timestamps'].dt.tz is not None:
+        df['timestamps'] = df['timestamps'].dt.tz_convert(IST).dt.tz_localize(None)
+    else:
+        df['timestamps'] = pd.to_datetime(df['timestamps'])
+
+    df = df[['timestamps', 'open', 'high', 'low', 'close', 'volume']].dropna()
+    df = df.sort_values('timestamps').reset_index(drop=True)
+    return df
+
+
+def calculate_orb(df: pd.DataFrame, orb_minutes: int = 15) -> dict:
+    """
+    Calculate Opening Range Breakout levels per trading day.
+    NSE opens at 09:15 IST; ORB window = first orb_minutes after open.
+    Returns {date_str: {'high': float, 'low': float}}.
+    """
+    df = df.copy()
+    df['_date'] = df['timestamps'].dt.date
+    df['_time'] = df['timestamps'].dt.time
+
+    open_time = dt_time(9, 15)
+    end_time = (datetime.combine(datetime.today(), open_time) + timedelta(minutes=orb_minutes)).time()
+
+    orb_levels = {}
+    for date, day_df in df.groupby('_date'):
+        window = day_df[(day_df['_time'] >= open_time) & (day_df['_time'] < end_time)]
+        if not window.empty:
+            orb_levels[str(date)] = {
+                'high': float(window['high'].max()),
+                'low':  float(window['low'].min()),
+            }
+    return orb_levels
+
+
+def df_to_records(df: pd.DataFrame) -> list:
+    """Convert DataFrame rows to JSON-serialisable dicts."""
+    records = []
+    for _, row in df.iterrows():
+        records.append({
+            'timestamp': row['timestamps'].isoformat(),
+            'open':   float(row['open']),
+            'high':   float(row['high']),
+            'low':    float(row['low']),
+            'close':  float(row['close']),
+            'volume': float(row.get('volume', 0)),
+        })
+    return records