Update stock.py

stock.py

@@ -1,83 +1,70 @@
+# stock.py  — compact, merged, single-file
 
 import yfinance as yf
 import pandas as pd
+import traceback
 
-def yfinfo(symbol):
+# ================================================================
+#                    BASIC YFINANCE FETCHERS
+# ================================================================
 
-        tk = yf.Ticker(symbol + ".NS")
-        return tk.info
+def yfinfo(symbol):
+    return yf.Ticker(symbol + ".NS").info
 
 def qresult(symbol):
+    return yf.Ticker(symbol + ".NS").quarterly_financials
 
-    ticker = yf.Ticker(symbol + ".NS")
-    df = ticker.quarterly_financials
-    return df
-    
 def result(symbol):
+    return yf.Ticker(symbol + ".NS").financials
 
-    ticker = yf.Ticker(symbol + ".NS")
-    df = ticker.financials
-    return df
-    
 def balance(symbol):
+    return yf.Ticker(symbol + ".NS").balance_sheet
 
-    ticker = yf.Ticker(symbol + ".NS")
-    df = ticker.balance_sheet
-    return df
-    
 def cashflow(symbol):
+    return yf.Ticker(symbol + ".NS").cashflow
 
-    ticker = yf.Ticker(symbol + ".NS")
-    df = ticker.cashflow
-    return df
-    
-def dividend(symbol):        
-    ticker = yf.Ticker(symbol + ".NS")
-    df = ticker.dividends.to_frame('Dividend')
-    return df
-    
-def split(symbol): 
-        ticker = yf.Ticker(symbol + ".NS")
-        df = ticker.splits.to_frame('Split')
-        return df
-    
-def intraday(symbol): 
+def dividend(symbol):
+    return yf.Ticker(symbol + ".NS").dividends.to_frame("Dividend")
 
-        df = ticker.download(symbol + ".NS",period="1d",interval="5min").round(2)
-        return df
-    
-def daily(symbol): 
+def split(symbol):
+    return yf.Ticker(symbol + ".NS").splits.to_frame("Split")
 
-        df = ticker.download(symbol + ".NS",period="1y",interval="1d").round(2)
-        return df
+def intraday(symbol):
+    return yf.download(symbol + ".NS", period="1d", interval="5m").round(2)
 
-# ============================
-# info.py — Company Info Page
-# EXACT SAME LOOK AS BEFORE
-# ============================
+def daily(symbol):
+    return yf.download(symbol + ".NS", period="1y", interval="1d").round(2)
 
-import yfinance as yf
-import pandas as pd
-import traceback
 
-from yf import yfinfo
+# ================================================================
+#              FETCH INFO  (USES COMMON.PY HELPERS)
+# ================================================================
 
-from common import (format_number,format_large_number,make_table,html_card,html_section,html_error,clean_df,safe_get)
+from common import (
+    format_number,
+    format_large_number,
+    make_table,
+    html_card,
+    html_section,
+    html_error,
+    clean_df,
+    safe_get,
+    wrap_html
+)
+
+from chart_builder import build_chart
+from ta_indi_pat import talib_df
+from nse import daily as nse_daily
 
 
-def fetch_info(symbol: str):
-    """
-    Fetch full company info and return the SAME layout you used earlier.
-    Only internal code updated to use common.py helpers.
-    """
+# -------------------------- INFO ------------------------------
+
+def fetch_info(symbol):
     try:
-        
         info = yfinfo(symbol)
-
         if not info:
             return html_error(f"No information found for {symbol}")
 
-        # ===== BASIC DETAILS =====
         basic = {
             "Symbol": symbol,
             "Name": safe_get(info, "longName"),
@@ -87,9 +74,7 @@ def fetch_info(symbol: str):
             "Employee Count": format_large_number(safe_get(info, "fullTimeEmployees")),
         }
         df_basic = pd.DataFrame(basic.items(), columns=["Field", "Value"])
-        basic_html = make_table(df_basic)
 
-        # ===== PRICE DETAILS =====
         price_info = {
             "Current Price": format_number(safe_get(info, "currentPrice")),
             "Previous Close": format_number(safe_get(info, "previousClose")),
@@ -102,9 +87,7 @@ def fetch_info(symbol: str):
             "Avg Volume": format_large_number(safe_get(info, "averageVolume")),
         }
         df_price = pd.DataFrame(price_info.items(), columns=["Field", "Value"])
-        price_html = make_table(df_price)
 
-        # ===== VALUATION METRICS =====
         valuation = {
             "Market Cap": format_large_number(safe_get(info, "marketCap")),
             "PE Ratio": format_number(safe_get(info, "trailingPE")),
@@ -115,9 +98,7 @@ def fetch_info(symbol: str):
             "ROA": format_number(safe_get(info, "returnOnAssets")),
         }
         df_val = pd.DataFrame(valuation.items(), columns=["Field", "Value"])
-        val_html = make_table(df_val)
 
-        # ===== COMPANY EXTRA DETAILS =====
         extra = {
             "Beta": format_number(safe_get(info, "beta")),
             "Revenue": format_large_number(safe_get(info, "totalRevenue")),
@@ -127,294 +108,215 @@ def fetch_info(symbol: str):
             "Book Value": format_number(safe_get(info, "bookValue")),
         }
         df_extra = pd.DataFrame(extra.items(), columns=["Field", "Value"])
-        extra_html = make_table(df_extra)
 
-        # ========================
-        # Final HTML (Same Layout)
-        # ========================
         final_html = (
-            html_card("Basic Information", basic_html)
-            + html_card("Price Details", price_html)
-            + html_card("Valuation Metrics", val_html)
-            + html_card("Additional Company Data", extra_html)
+            html_card("Basic Information", make_table(df_basic))
+            + html_card("Price Details", make_table(df_price))
+            + html_card("Valuation Metrics", make_table(df_val))
+            + html_card("Additional Company Data", make_table(df_extra))
         )
-
         return final_html
 
     except Exception as e:
-        return html_error(f"INFO MODULE ERROR: {e}<br><pre>{traceback.format_exc()}</pre>")
-# intraday.py
-import yfinance as yf
-import pandas as pd
-from common import format_large_number, wrap_html, make_table
-from chart_builder import build_chart
-from yf import intraday
-# ============================================================
-#               INTRADAY DATA PROCESSING
-# ============================================================
+        return html_error(f"INFO ERROR: {e}<br><pre>{traceback.format_exc()}</pre>")
 
-def fetch_intraday(symbol, indicators=None):
 
+# -------------------------- INTRADAY ------------------------------
+
+def fetch_intraday(symbol, indicators=None):
     try:
-        # Fetch 1-day intraday 5-min interval
         df = intraday(symbol)
         if df.empty:
-            return wrap_html(f"<h1>No intraday data available for {symbol}</h1>")
+            return wrap_html(f"<h1>No intraday data for {symbol}</h1>")
 
-        # Reset MultiIndex if exists
         if isinstance(df.columns, pd.MultiIndex):
             df.columns = df.columns.get_level_values(0)
 
-        # Build chart with indicators
         chart_html = build_chart(df, indicators=indicators, volume=True)
-
-        # Format last 50 rows for table
         table_html = make_table(df.tail(50))
 
-        # Wrap in full HTML
-        full_html = wrap_html(f"{chart_html}<h2>Recent Intraday Data (last 50 rows)</h2>{table_html}",
-                              title=f"Intraday Data for {symbol}")
-        return full_html
+        return wrap_html(f"{chart_html}<h2>Last 50 Rows</h2>{table_html}",
+                         title=f"{symbol} Intraday")
 
     except Exception as e:
-        return wrap_html(f"<h1>Error fetching intraday data for {symbol}</h1><p>{str(e)}</p>")
+        return wrap_html(f"<h1>Error:{e}</h1>")
 
-import yfinance as yf
-import pandas as pd
-import io
-import requests
-from  nse import daily
-from datetime import datetime, timedelta
-from ta_indi_pat import talib_df  # use the combined talib_df function
-from common import html_card, wrap_html
-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)
+# -------------------------- DAILY ------------------------------
 
-        # --- Generate combined TA-Lib DataFrame ---
-        combined_df = talib_df(df_display)
+def fetch_daily(symbol, source="yfinance", max_rows=200):
+    try:
+        df = nse_daily(symbol, source) if source == "nse" else daily(symbol)
+        df_disp = df.head(max_rows)
 
-        # --- Convert to HTML table ---
+        combined_df = talib_df(df_disp)
         table_html = combined_df.to_html(
             classes="table table-striped table-bordered",
             index=False
         )
 
-        # --- Wrap in scrollable div ---
-        scrollable_html = f"""
-        <div style="overflow-x:auto; overflow-y:auto; max-height:600px; border:1px solid #ccc; padding:5px;">
+        scroll = f"""
+        <div style="overflow:auto; max-height:600px; border:1px solid #ccc;">
             {table_html}
         </div>
         """
 
-        # --- Wrap in card and full HTML ---
-        content = f"""
-        <h2>{symbol} - Daily Data (OHLCV + Indicators + Patterns)</h2>
-        {html_card("TA-Lib Data", scrollable_html)}
-        """
-
-        return wrap_html(content, title=f"{symbol} Daily Data")
+        return wrap_html(f"<h2>{symbol} Daily</h2>" + html_card("TA-Lib", scroll))
 
     except Exception as e:
         return html_card("Error", str(e))
-# qresult.py
-import yfinance as yf
-import pandas as pd
-from common import make_table, wrap_html, format_large_number, html_error
-from yf import qresult
-def fetch_qresult(symbol):
-    """
-    Fetch quarterly financials for a stock symbol and return HTML
-    """
 
+
+# -------------------------- QUARTERLY ------------------------------
+
+def fetch_qresult(symbol):
     try:
- 
         df = qresult(symbol)
-
         if df.empty:
-            return wrap_html(f"<h1>No quarterly results available for {symbol}</h1>")
+            return wrap_html(f"<h1>No quarterly results for {symbol}</h1>")
 
-        # Format numeric columns
-        df_formatted = df.copy()
-        for col in df_formatted.columns:
-            df_formatted[col] = df_formatted[col].apply(
+        df_fmt = df.copy()
+        for col in df_fmt.columns:
+            df_fmt[col] = df_fmt[col].apply(
                 lambda x: format_large_number(x) if isinstance(x, (int, float)) else x
             )
+        df_fmt.index = [str(i.date()) if hasattr(i, "date") else str(i) for i in df_fmt.index]
 
-        # Format index (dates)
-        df_formatted.index = [str(i.date()) if hasattr(i, "date") else str(i) for i in df_formatted.index]
+        return wrap_html(make_table(df_fmt),
+                         title=f"{symbol} Quarterly Results")
 
-        # Convert to pretty HTML table
-        table_html = make_table(df_formatted)
+    except Exception as e:
+        return wrap_html(html_error(f"Quarterly Error: {e}"))
 
-        # Wrap into full HTML page
-        return wrap_html(table_html, title=f"{symbol} - Quarterly Results")
 
-    except Exception as e:
-        return wrap_html(html_error(f"Failed to fetch quarterly results: {e}"))
-# result.py
-import yfinance as yf
-from common import make_table, wrap_html, format_large_number, html_error
-from yf import result
+# -------------------------- ANNUAL ------------------------------
+
 def fetch_result(symbol):
-    
     try:
-        
         df = result(symbol)
-
         if df.empty:
-            return wrap_html(f"<h1>No annual results available for {symbol}</h1>")
+            return wrap_html(f"<h1>No annual results for {symbol}</h1>")
 
-        # Format numeric columns
-        df_formatted = df.copy()
-        for col in df_formatted.columns:
-            df_formatted[col] = df_formatted[col].apply(
+        df_fmt = df.copy()
+        for col in df_fmt.columns:
+            df_fmt[col] = df_fmt[col].apply(
                 lambda x: format_large_number(x) if isinstance(x, (int, float)) else x
             )
+        df_fmt.index = [str(i.date()) if hasattr(i, "date") else str(i) for i in df_fmt.index]
 
-        df_formatted.index = [str(i.date()) if hasattr(i, "date") else str(i) for i in df_formatted.index]
-        table_html = make_table(df_formatted)
-
-        return wrap_html(table_html, title=f"{symbol} - Annual Results")
+        return wrap_html(make_table(df_fmt),
+                         title=f"{symbol} Annual Results")
 
     except Exception as e:
-        return wrap_html(html_error(f"Failed to fetch annual results: {e}"))
+        return wrap_html(html_error(f"Annual Error: {e}"))
+
+
+# -------------------------- BALANCE ------------------------------
 
-# balance.py
-import yfinance as yf
-from common import make_table, wrap_html, format_large_number, html_error
-from yf import balance
 def fetch_balance(symbol):
-    
     try:
         df = balance(symbol)
-
         if df.empty:
-            return wrap_html(f"<h1>No balance sheet available for {symbol}</h1>")
+            return wrap_html(f"<h1>No balance sheet for {symbol}</h1>")
 
-        df_formatted = df.copy()
-        for col in df_formatted.columns:
-            df_formatted[col] = df_formatted[col].apply(
+        df_fmt = df.copy()
+        for col in df_fmt.columns:
+            df_fmt[col] = df_fmt[col].apply(
                 lambda x: format_large_number(x) if isinstance(x, (int, float)) else x
             )
+        df_fmt.index = [str(i.date()) if hasattr(i, "date") else str(i) for i in df_fmt.index]
 
-        df_formatted.index = [str(i.date()) if hasattr(i, "date") else str(i) for i in df_formatted.index]
-        table_html = make_table(df_formatted)
-
-        return wrap_html(table_html, title=f"{symbol} - Balance Sheet")
+        return wrap_html(make_table(df_fmt),
+                         title=f"{symbol} Balance Sheet")
 
     except Exception as e:
-        return wrap_html(html_error(f"Failed to fetch balance sheet: {e}"))
-# cashflow.py
-import yfinance as yf
-from common import make_table, wrap_html, format_large_number, html_error
-from yf import cashflow
+        return wrap_html(html_error(f"Balance Error: {e}"))
+
+
+# -------------------------- CASHFLOW ------------------------------
+
 def fetch_cashflow(symbol):
-    
     try:
-      
         df = cashflow(symbol)
-
         if df.empty:
-            return wrap_html(f"<h1>No cash flow data available for {symbol}</h1>")
+            return wrap_html(f"<h1>No cashflow for {symbol}</h1>")
 
-        df_formatted = df.copy()
-        for col in df_formatted.columns:
-            df_formatted[col] = df_formatted[col].apply(
+        df_fmt = df.copy()
+        for col in df_fmt.columns:
+            df_fmt[col] = df_fmt[col].apply(
                 lambda x: format_large_number(x) if isinstance(x, (int, float)) else x
             )
+        df_fmt.index = [str(i.date()) if hasattr(i, "date") else str(i) for i in df_fmt.index]
 
-        df_formatted.index = [str(i.date()) if hasattr(i, "date") else str(i) for i in df_formatted.index]
-        table_html = make_table(df_formatted)
-
-        return wrap_html(table_html, title=f"{symbol} - Cash Flow")
+        return wrap_html(make_table(df_fmt),
+                         title=f"{symbol} Cash Flow")
 
     except Exception as e:
-        return wrap_html(html_error(f"Failed to fetch cash flow data: {e}"))
+        return wrap_html(html_error(f"Cash Flow Error: {e}"))
 
-# dividend.py
-import yfinance as yf
-from common import make_table, wrap_html, format_large_number, html_error
-from yf import dividend
-def fetch_dividend(symbol):
 
-    try:
+# -------------------------- DIVIDEND ------------------------------
 
+def fetch_dividend(symbol):
+    try:
         df = dividend(symbol)
-
         if df.empty:
-            return wrap_html(f"<h1>No dividend history available for {symbol}</h1>")
+            return wrap_html(f"<h1>No dividend history for {symbol}</h1>")
 
-        df_formatted = df.copy()
-        for col in df_formatted.columns:
-            df_formatted[col] = df_formatted[col].apply(
+        df_fmt = df.copy()
+        for col in df_fmt.columns:
+            df_fmt[col] = df_fmt[col].apply(
                 lambda x: format_large_number(x) if isinstance(x, (int, float)) else x
             )
+        df_fmt.index = [str(i.date()) if hasattr(i, "date") else str(i) for i in df_fmt.index]
 
-        df_formatted.index = [str(i.date()) if hasattr(i, "date") else str(i) for i in df_formatted.index]
-        table_html = make_table(df_formatted)
-
-        return wrap_html(table_html, title=f"{symbol} - Dividend History")
+        return wrap_html(make_table(df_fmt),
+                         title=f"{symbol} Dividends")
 
     except Exception as e:
-        return wrap_html(html_error(f"Failed to fetch dividend history: {e}"))
-# split.py
-import yfinance as yf
-from common import make_table, wrap_html, format_large_number, html_error
-from yf import split
+        return wrap_html(html_error(f"Dividend Error: {e}"))
+
+
+# -------------------------- SPLIT ------------------------------
+
 def fetch_split(symbol):
-    
     try:
-        
         df = split(symbol)
-
         if df.empty:
-            return wrap_html(f"<h1>No split history available for {symbol}</h1>")
+            return wrap_html(f"<h1>No splits for {symbol}</h1>")
 
-        df_formatted = df.copy()
-        for col in df_formatted.columns:
-            df_formatted[col] = df_formatted[col].apply(
+        df_fmt = df.copy()
+        for col in df_fmt.columns:
+            df_fmt[col] = df_fmt[col].apply(
                 lambda x: format_large_number(x) if isinstance(x, (int, float)) else x
             )
+        df_fmt.index = [str(i.date()) if hasattr(i, "date") else str(i) for i in df_fmt.index]
 
-        df_formatted.index = [str(i.date()) if hasattr(i, "date") else str(i) for i in df_formatted.index]
-        table_html = make_table(df_formatted)
-
-        return wrap_html(table_html, title=f"{symbol} - Split History")
+        return wrap_html(make_table(df_fmt),
+                         title=f"{symbol} Splits")
 
     except Exception as e:
-        return wrap_html(html_error(f"Failed to fetch split history: {e}"))
-# other.py
-import yfinance as yf
-from common import make_table, wrap_html, format_large_number, html_error
+        return wrap_html(html_error(f"Split Error: {e}"))
+
+
+# -------------------------- OTHER (EARNINGS) ------------------------------
 
 def fetch_other(symbol):
-    yfsymbol = symbol + ".NS"
     try:
-        ticker = yf.Ticker(yfsymbol)
+        ticker = yf.Ticker(symbol + ".NS")
         df = ticker.earnings
 
         if df.empty:
-            return wrap_html(f"<h1>No earnings data available for {symbol}</h1>")
+            return wrap_html(f"<h1>No earnings data for {symbol}</h1>")
 
-        df_formatted = df.copy()
-        for col in df_formatted.columns:
-            df_formatted[col] = df_formatted[col].apply(
+        df_fmt = df.copy()
+        for col in df_fmt.columns:
+            df_fmt[col] = df_fmt[col].apply(
                 lambda x: format_large_number(x) if isinstance(x, (int, float)) else x
             )
 
-        df_formatted.index = [str(i) for i in df_formatted.index]
-        table_html = make_table(df_formatted)
-
-        return wrap_html(table_html, title=f"{symbol} - Earnings")
+        return wrap_html(make_table(df_fmt),
+                         title=f"{symbol} Earnings")
 
     except Exception as e:
-        return wrap_html(html_error(f"Failed to fetch earnings data: {e}"))
+        return wrap_html(html_error(f"Earnings Error: {e}"))