Update daily.py

daily.py

@@ -3,13 +3,97 @@ import pandas as pd
 from ta_indi_pat import talib_df  # use the combined talib_df function
 from common import html_card, wrap_html
 
-def fetch_daily(symbol, max_rows=200):
-    """
-    Fetch daily OHLCV data, calculate TA-Lib indicators + patterns,
-    return a single scrollable HTML table.
-    """
+# -----------------------------
+# Global Variables
+# -----------------------------
+nse_del_key_map = {
+    'Symbol': "Symbol", 'Series': "Series",
+    'Date': 'Date', 'Prev Close': 'Preclose',
+    'Open Price': 'Open', 'High Price': 'High',
+    'Low Price': 'Low', 'Last Price': 'Last',
+    'Close Price': 'Close', 'Average Price': 'AvgPrice',
+    'Total Traded Quantity': 'Volume',
+    'Turnover ₹': 'Turnover', 'No. of Trades': "Trades",
+    'Deliverable Qty': "Delivery", '% Dly Qt to Traded Qty': "Del%"
+}
+
+# -----------------------------
+# Data Fetching Functions (NSE)
+# -----------------------------
+def url_nse_del(symbol, start_date, end_date):
+    base_url = "https://www.nseindia.com/api/historicalOR/generateSecurityWiseHistoricalData"
+    start_date_str = start_date.strftime("%d-%m-%Y")
+    end_date_str = end_date.strftime("%d-%m-%Y")
+    url = f"{base_url}?from={start_date_str}&to={end_date_str}&symbol={symbol.split('.')[0]}&type=priceVolumeDeliverable&series=ALL&csv=true"
+    return url
+
+def to_numeric_safe(series):
+    series = series.replace('-', 0)
+    series = series.fillna(0)
+    series = series.astype(str).str.replace(',', '')
+    return pd.to_numeric(series, errors='coerce').fillna(0)
+
+
+def nse_del(symbol, start_date_str=None, end_date_str=None):
+    # Default end date is today
+    end_date = datetime.now()
+    if end_date_str:
+        try:
+            end_date = datetime.strptime(end_date_str, "%Y-%m-%d")
+        except ValueError:
+            print(f"Warning: Invalid end date format '{end_date_str}'. Using today's date.")
+            end_date = datetime.now()
+
+    # Default start date is one year prior to end_date
+    start_date = end_date - timedelta(days=365)
+    if start_date_str:
+        try:
+            start_date = datetime.strptime(start_date_str, "%Y-%m-%d")
+        except ValueError:
+            print(f"Warning: Invalid start date format '{start_date_str}'. Using default start date.")
+            start_date = end_date - timedelta(days=365)
+
+    # Ensure start_date is not after end_date
+    if start_date > end_date:
+        print("Warning: Start date is after end date. Swapping dates.")
+        start_date, end_date = end_date, start_date
+
+    url = url_nse_del(symbol, start_date, end_date)
+    headers = {
+        'User-Agent': 'Mozilla/5.0'
+    }
     try:
-        # --- Fetch daily data ---
+        response = requests.get(url, headers=headers)
+        response.raise_for_status()
+        if response.content:
+            df = pd.read_csv(io.StringIO(response.content.decode('utf-8'))).round(2)
+            df.columns = df.columns.str.strip()
+            df.rename(columns=nse_del_key_map, inplace=True)
+
+            # Capitalize the first letter of ALL column names after renaming
+            df.columns = [col.capitalize() for col in df.columns]
+
+            # Remove 'Symbol', 'Series', 'Avgprice', and 'Last' columns (now capitalized)
+            df.drop(columns=['Symbol','Series','Avgprice','Last'], errors='ignore', inplace=True)
+
+            # Convert 'Date' column to datetime objects
+            df['Date'] = pd.to_datetime(df['Date'], format='%d-%b-%Y').dt.strftime('%Y-%m-%d')
+
+            numeric_cols = ['Close', 'Preclose', 'Open', 'High', 'Low', 'Volume', 'Delivery', 'Turnover', 'Trades']
+            # Ensure numeric_cols are capitalized before checking and conversion
+            numeric_cols_capitalized = [col.capitalize() for col in numeric_cols]
+            for col in numeric_cols_capitalized:
+                if col in df.columns:
+                    df[col] = to_numeric_safe(df[col])
+                else:
+                    df[col] = 0
+            return df
+    except Exception as e:
+        print(f"Error fetching data from NSE for {symbol}: {e}")
+    return None
+
+def daily(symbol,source="yfinace"):
+    if source=="yfinance"
         df = yf.download(symbol + ".NS", period="1y", interval="1d").round(2)
         if df.empty:
             return html_card("Error", f"No daily data found for {symbol}")
@@ -17,6 +101,18 @@ def fetch_daily(symbol, max_rows=200):
         # --- Standardize columns ---
         df.columns = ["Close", "High", "Low", "Open", "Volume"]
         df.reset_index(inplace=True)  # make Date a column
+    return df
+    if source=="NSE":
+        df=nse_del(symbol)
+
+def fetch_daily(symbol, source,max_rows=200):
+    """
+    Fetch daily OHLCV data, calculate TA-Lib indicators + patterns,
+    return a single scrollable HTML table.
+    """
+    try:
+        # --- Fetch daily data ---
+        df=daily(symbol,source)
 
         # --- Limit rows for display ---
         df_display = df.head(max_rows)