Create stock.py

stock.py

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+
+import yfinance as yf
+import pandas as pd
+
+def yfinfo(symbol):
+
+        tk = yf.Ticker(symbol + ".NS")
+        return tk.info
+
+def qresult(symbol):
+
+    ticker = yf.Ticker(symbol + ".NS")
+    df = ticker.quarterly_financials
+    return df
+    
+def result(symbol):
+
+    ticker = yf.Ticker(symbol + ".NS")
+    df = ticker.financials
+    return df
+    
+def balance(symbol):
+
+    ticker = yf.Ticker(symbol + ".NS")
+    df = ticker.balance_sheet
+    return df
+    
+def cashflow(symbol):
+
+    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): 
+
+        df = ticker.download(symbol + ".NS",period="1d",interval="5min").round(2)
+        return df
+    
+def daily(symbol): 
+
+        df = ticker.download(symbol + ".NS",period="1y",interval="1d").round(2)
+        return df
+
+# ============================
+# info.py — Company Info Page
+# EXACT SAME LOOK AS BEFORE
+# ============================
+
+import yfinance as yf
+import pandas as pd
+import traceback
+
+from yf import yfinfo
+
+from common import (format_number,format_large_number,make_table,html_card,html_section,html_error,clean_df,safe_get)
+
+
+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.
+    """
+    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"),
+            "Sector": safe_get(info, "sector"),
+            "Industry": safe_get(info, "industry"),
+            "Website": safe_get(info, "website"),
+            "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")),
+            "Open": format_number(safe_get(info, "open")),
+            "Day High": format_number(safe_get(info, "dayHigh")),
+            "Day Low": format_number(safe_get(info, "dayLow")),
+            "52W High": format_number(safe_get(info, "fiftyTwoWeekHigh")),
+            "52W Low": format_number(safe_get(info, "fiftyTwoWeekLow")),
+            "Volume": format_large_number(safe_get(info, "volume")),
+            "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")),
+            "EPS": format_number(safe_get(info, "trailingEps")),
+            "PB Ratio": format_number(safe_get(info, "priceToBook")),
+            "Dividend Yield": format_number(safe_get(info, "dividendYield")),
+            "ROE": format_number(safe_get(info, "returnOnEquity")),
+            "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")),
+            "Gross Margins": format_number(safe_get(info, "grossMargins")),
+            "Operating Margins": format_number(safe_get(info, "operatingMargins")),
+            "Profit Margins": format_number(safe_get(info, "profitMargins")),
+            "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)
+        )
+
+        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
+# ============================================================
+
+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>")
+
+        # 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
+
+    except Exception as e:
+        return wrap_html(f"<h1>Error fetching intraday data for {symbol}</h1><p>{str(e)}</p>")
+
+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)
+
+        # --- Generate combined TA-Lib DataFrame ---
+        combined_df = talib_df(df_display)
+
+        # --- Convert to HTML table ---
+        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;">
+            {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")
+
+    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
+    """
+
+    try:
+ 
+        df = qresult(symbol)
+
+        if df.empty:
+            return wrap_html(f"<h1>No quarterly results available for {symbol}</h1>")
+
+        # Format numeric columns
+        df_formatted = df.copy()
+        for col in df_formatted.columns:
+            df_formatted[col] = df_formatted[col].apply(
+                lambda x: format_large_number(x) if isinstance(x, (int, float)) else x
+            )
+
+        # Format index (dates)
+        df_formatted.index = [str(i.date()) if hasattr(i, "date") else str(i) for i in df_formatted.index]
+
+        # Convert to pretty HTML table
+        table_html = make_table(df_formatted)
+
+        # 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
+def fetch_result(symbol):
+    
+    try:
+        
+        df = result(symbol)
+
+        if df.empty:
+            return wrap_html(f"<h1>No annual results available for {symbol}</h1>")
+
+        # Format numeric columns
+        df_formatted = df.copy()
+        for col in df_formatted.columns:
+            df_formatted[col] = df_formatted[col].apply(
+                lambda x: format_large_number(x) if isinstance(x, (int, float)) else x
+            )
+
+        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")
+
+    except Exception as e:
+        return wrap_html(html_error(f"Failed to fetch annual results: {e}"))
+
+# 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>")
+
+        df_formatted = df.copy()
+        for col in df_formatted.columns:
+            df_formatted[col] = df_formatted[col].apply(
+                lambda x: format_large_number(x) if isinstance(x, (int, float)) else x
+            )
+
+        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")
+
+    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
+def fetch_cashflow(symbol):
+    
+    try:
+      
+        df = cashflow(symbol)
+
+        if df.empty:
+            return wrap_html(f"<h1>No cash flow data available for {symbol}</h1>")
+
+        df_formatted = df.copy()
+        for col in df_formatted.columns:
+            df_formatted[col] = df_formatted[col].apply(
+                lambda x: format_large_number(x) if isinstance(x, (int, float)) else x
+            )
+
+        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")
+
+    except Exception as e:
+        return wrap_html(html_error(f"Failed to fetch cash flow data: {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:
+
+        df = dividend(symbol)
+
+        if df.empty:
+            return wrap_html(f"<h1>No dividend history available for {symbol}</h1>")
+
+        df_formatted = df.copy()
+        for col in df_formatted.columns:
+            df_formatted[col] = df_formatted[col].apply(
+                lambda x: format_large_number(x) if isinstance(x, (int, float)) else x
+            )
+
+        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")
+
+    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
+def fetch_split(symbol):
+    
+    try:
+        
+        df = split(symbol)
+
+        if df.empty:
+            return wrap_html(f"<h1>No split history available for {symbol}</h1>")
+
+        df_formatted = df.copy()
+        for col in df_formatted.columns:
+            df_formatted[col] = df_formatted[col].apply(
+                lambda x: format_large_number(x) if isinstance(x, (int, float)) else x
+            )
+
+        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")
+
+    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
+
+def fetch_other(symbol):
+    yfsymbol = symbol + ".NS"
+    try:
+        ticker = yf.Ticker(yfsymbol)
+        df = ticker.earnings
+
+        if df.empty:
+            return wrap_html(f"<h1>No earnings data available for {symbol}</h1>")
+
+        df_formatted = df.copy()
+        for col in df_formatted.columns:
+            df_formatted[col] = df_formatted[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")
+
+    except Exception as e:
+        return wrap_html(html_error(f"Failed to fetch earnings data: {e}"))