error fixed for date issue

dashboard.py

@@ -1,221 +1,247 @@
-import yfinance as yf
-import pandas as pd
-import streamlit as st
-from datetime import datetime, timedelta
-
-# Fetch Nifty 50 tickers
-def fetch_nifty50_tickers():
-    return [
-        "TATAMOTORS.NS", "RELIANCE.NS", "INFY.NS", "HDFCBANK.NS", "ICICIBANK.NS",
-        "SBIN.NS", "ITC.NS", "AXISBANK.NS", "MARUTI.NS", "TATASTEEL.NS",
-        "WIPRO.NS", "SUNPHARMA.NS", "HINDALCO.NS", "HCLTECH.NS", "NTPC.NS",
-        "L&T.NS", "M&M.NS", "ONGC.NS", "HDFCLIFE.NS", "ULTRACEMCO.NS",
-        "ADANIGREEN.NS", "BHARTIARTL.NS", "BAJAJFINSV.NS", "JSWSTEEL.NS", "DIVISLAB.NS",
-        "POWERGRID.NS", "KOTAKBANK.NS", "HINDUNILVR.NS", "TCS.NS", "CIPLA.NS",
-        "ASIANPAINT.NS", "GRASIM.NS", "BRITANNIA.NS", "SHREECEM.NS",
-        "TECHM.NS", "INDUSINDBK.NS", "EICHERMOT.NS", "COALINDIA.NS", "GAIL.NS",
-        "BOSCHLTD.NS", "M&MFIN.NS", "IDFCFIRSTB.NS", "HAVELLS.NS"
-    ]
-
-# Fetch large cap tickers
-def fetch_large_cap_tickers():
-    return fetch_nifty50_tickers()  # Assuming large caps are the same as Nifty 50
-
-# Fetch small cap tickers
-def fetch_small_cap_tickers():
-    return [
-        "ALOKINDS.NS", "ADANIENT.NS", "AARTIIND.NS", "AVANTIFEED.NS", "BLS.IN",
-        "BHEL.NS", "BIRLACORP.NS", "CARBORUNIV.NS", "CENTRALBANK.NS", "EMAMILTD.NS",
-        "FDC.NS", "GLAXO.NS", "GODFRYPHLP.NS", "GSKCONS.NS", "HAVELLS.NS",
-        "HEMIPAPER.NS", "HIL.NS", "JINDALSAW.NS", "JUBLFOOD.NS", "KOTAKMAH.NS",
-        "MSTCLAS.NS", "NCC.NS", "PAGEIND.NS", "PIIND.NS", "SBI.CN",
-        "SISL.NS", "SOMANYCERA.NS", "STAR.NS", "SUNDARAM.NS", "TATAINVEST.NS",
-        "VSTIND.NS", "WABCOINDIA.NS", "WELCORP.NS", "ZEELEARN.NS", "ZOMATO.NS"
-    ]
-
-# Get top movers
-def get_top_movers(tickers, days=1):
-    end_date = datetime.now()
-    start_date = end_date - timedelta(days=days)
-    
-    data = {}
-    for ticker in tickers:
-        try:
-            df = yf.download(ticker, start=start_date, end=end_date)
-            if not df.empty and 'Close' in df.columns:
-                df['Ticker'] = ticker
-                data[ticker] = df['Close'].pct_change().iloc[-1]  # Percentage change
-        except Exception as e:
-            st.error(f"Error fetching data for {ticker}: {e}")
-    
-    sorted_data = sorted(data.items(), key=lambda x: x[1], reverse=True)
-    top_gainers = sorted_data[:10]
-    top_losers = sorted_data[-10:]
-    
-    return top_gainers, top_losers
-
-# Format DataFrame with color
-def format_df(df):
-    if not df.empty:
-        df['Percentage Change'] = pd.to_numeric(df['Percentage Change'], errors='coerce')
-        return df.style.applymap(lambda x: 'color: green' if x > 0 else 'color: red', subset=['Percentage Change'])
-    return df
-
-# Display dashboard
-def display_dashboard():
-    st.header("Dashboard")
-    
-    # Fetch tickers
-    nifty50_tickers = fetch_nifty50_tickers()
-    large_cap_tickers = fetch_large_cap_tickers()
-    small_cap_tickers = fetch_small_cap_tickers()
-
-    # Get top gainers and losers
-    top_gainers_nifty50, top_losers_nifty50 = get_top_movers(nifty50_tickers)
-    top_gainers_large_cap, top_losers_large_cap = get_top_movers(large_cap_tickers)
-    top_gainers_small_cap, top_losers_small_cap = get_top_movers(small_cap_tickers)
-    
-    # Create columns for tables
-    col1, col2, col3, col4 = st.columns(4)
-
-    with col1:
-        st.write("### Nifty 50 Top Gainers")
-        if top_gainers_nifty50:
-            df_gainers_nifty50 = pd.DataFrame(top_gainers_nifty50, columns=['Ticker', 'Percentage Change'])
-            st.dataframe(format_df(df_gainers_nifty50))
-
-    with col2:
-        st.write("### Nifty 50 Top Losers")
-        if top_losers_nifty50:
-            df_losers_nifty50 = pd.DataFrame(top_losers_nifty50, columns=['Ticker', 'Percentage Change'])
-            st.dataframe(format_df(df_losers_nifty50))
-
-    with col3:
-        st.write("### Large Cap Top Gainers")
-        if top_gainers_large_cap:
-            df_gainers_large_cap = pd.DataFrame(top_gainers_large_cap, columns=['Ticker', 'Percentage Change'])
-            st.dataframe(format_df(df_gainers_large_cap))
-
-    with col4:
-        st.write("### Large Cap Top Losers")
-        if top_losers_large_cap:
-            df_losers_large_cap = pd.DataFrame(top_losers_large_cap, columns=['Ticker', 'Percentage Change'])
-            st.dataframe(format_df(df_losers_large_cap))
-
-# Fetch and display stock profile
-def fetch_stock_profile(ticker):
-    try:
-        stock = yf.Ticker(ticker)
-        info = stock.info
-
-        profile = {
-            "Name": info.get('shortName', 'N/A'),
-            "Current Price": f"₹ {info.get('currentPrice', 'N/A')}",
-            "Market Cap": f"₹ {info.get('marketCap', 'N/A') / 1e7:.2f} Cr.",
-            "P/E Ratio": info.get('forwardEps', 'N/A'),
-            "Book Value": info.get('bookValue', 'N/A'),
-            "Dividend Yield": info.get('dividendYield', 'N/A'),
-            "ROCE": info.get('returnOnCapitalEmployed', 'N/A'),
-            "ROE": info.get('returnOnEquity', 'N/A'),
-            "Face Value": info.get('faceValue', 'N/A')
-        }
-        return profile
-    except Exception as e:
-        st.error(f"Error fetching profile for {ticker}: {e}")
-        return {}
-
-
-
-
-# Display stock profile as a table
-def display_profile(profile):
-    st.subheader("Stock Profile")
-    profile_df = pd.DataFrame([profile])
-    st.table(profile_df)
-
-# Fetch and display quarterly results
-def display_quarterly_results(ticker):
-    st.subheader("Quarterly Results Summary")
-    try:
-        stock = yf.Ticker(ticker)
-        financials = stock.quarterly_financials.T
-        if not financials.empty:
-            results = {
-                'Sales': financials['Total Revenue'].iloc[-1] if 'Total Revenue' in financials.columns else 'N/A',
-                'Operating Profit Margin': financials['Operating Income'].iloc[-1] if 'Operating Income' in financials.columns else 'N/A',
-                'Net Profit': financials['Net Income'].iloc[-1] if 'Net Income' in financials.columns else 'N/A'
-            }
-            results_df = pd.DataFrame([results])
-            st.table(results_df)
-        else:
-            st.write("No quarterly results available.")
-    except Exception as e:
-        st.write(f"Error fetching quarterly results: {e}")
-
-# Fetch and display shareholding pattern
-def display_shareholding_pattern(ticker):
-    st.subheader("Shareholding Pattern")
-    
-    # Placeholder values; replace with actual data source or API call
-    data = {
-        'Category': ['Promoters', 'FIIs (Foreign Institutional Investors)', 'DIIs (Domestic Institutional Investors)', 'Public'],
-        'Holding (%)': [45.0, 20.0, 15.0, 20.0]
-    }
-    
-    df = pd.DataFrame(data)
-    st.table(df)
-
-
-def display_financial_ratios(ticker):
-    st.subheader("Financial Ratios")
-    stock = yf.Ticker(ticker)
-    
-    try:
-        # Placeholder values, calculate actual values based on your requirements
-        ratios = {
-            'Debtor Days': 73,
-            'Working Capital Days': 194,
-            'Cash Conversion Cycle': 51
-        }
-        ratios_df = pd.DataFrame([ratios])
-        st.table(ratios_df)
-    except Exception as e:
-        st.write("Error fetching financial ratios:", e)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-# Main application
-# def main():
-#     st.title("Stock Analysis Dashboard")
-
-#     # Select ticker input
-#     ticker = st.text_input("Enter Stock Ticker (e.g., TATAMOTORS.NS)")
-
-#     if ticker:
-#         profile = fetch_stock_profile(ticker)
-#         if profile:
-#             display_profile(profile)
-        
-#         display_quarterly_results(ticker)
-#         display_shareholding_pattern(ticker)
-
-#     # Show dashboard
-#     if st.button("Show Dashboard"):
-#         display_dashboard()
-
-# if __name__ == "__main__":
-#     main()
+import yfinance as yf
+import pandas as pd
+import streamlit as st
+from datetime import datetime, timedelta
+
+# Fetch Nifty 50 tickers
+def fetch_nifty50_tickers():
+    return [
+        "TATAMOTORS.NS", "RELIANCE.NS", "INFY.NS", "HDFCBANK.NS", "ICICIBANK.NS",
+        "SBIN.NS", "ITC.NS", "AXISBANK.NS", "MARUTI.NS", "TATASTEEL.NS",
+        "WIPRO.NS", "SUNPHARMA.NS", "HINDALCO.NS", "HCLTECH.NS", "NTPC.NS",
+        "L&T.NS", "M&M.NS", "ONGC.NS", "HDFCLIFE.NS", "ULTRACEMCO.NS",
+        "ADANIGREEN.NS", "BHARTIARTL.NS", "BAJAJFINSV.NS", "JSWSTEEL.NS", "DIVISLAB.NS",
+        "POWERGRID.NS", "KOTAKBANK.NS", "HINDUNILVR.NS", "TCS.NS", "CIPLA.NS",
+        "ASIANPAINT.NS", "GRASIM.NS", "BRITANNIA.NS", "SHREECEM.NS",
+        "TECHM.NS", "INDUSINDBK.NS", "EICHERMOT.NS", "COALINDIA.NS", "GAIL.NS",
+        "BOSCHLTD.NS", "M&MFIN.NS", "IDFCFIRSTB.NS", "HAVELLS.NS"
+    ]
+
+# Fetch large cap tickers
+def fetch_large_cap_tickers():
+    return fetch_nifty50_tickers()  # Assuming large caps are the same as Nifty 50
+
+# Fetch small cap tickers
+def fetch_small_cap_tickers():
+    return [
+        "ALOKINDS.NS", "ADANIENT.NS", "AARTIIND.NS", "AVANTIFEED.NS", "BLS.IN",
+        "BHEL.NS", "BIRLACORP.NS", "CARBORUNIV.NS", "CENTRALBANK.NS", "EMAMILTD.NS",
+        "FDC.NS", "GLAXO.NS", "GODFRYPHLP.NS", "GSKCONS.NS", "HAVELLS.NS",
+        "HEMIPAPER.NS", "HIL.NS", "JINDALSAW.NS", "JUBLFOOD.NS", "KOTAKMAH.NS",
+        "MSTCLAS.NS", "NCC.NS", "PAGEIND.NS", "PIIND.NS", "SBI.CN",
+        "SISL.NS", "SOMANYCERA.NS", "STAR.NS", "SUNDARAM.NS", "TATAINVEST.NS",
+        "VSTIND.NS", "WABCOINDIA.NS", "WELCORP.NS", "ZEELEARN.NS", "ZOMATO.NS"
+    ]
+
+# Get top movers
+# def get_top_movers(tickers, days=1):
+#     end_date = datetime.now()
+#     start_date = end_date - timedelta(days=days)
+    
+#     data = {}
+#     for ticker in tickers:
+#         try:
+#             df = yf.download(ticker, start=start_date, end=end_date)
+#             if not df.empty and 'Close' in df.columns:
+#                 df['Ticker'] = ticker
+#                 data[ticker] = df['Close'].pct_change().iloc[-1]  # Percentage change
+#         except Exception as e:
+#             st.error(f"Error fetching data for {ticker}: {e}")
+    
+#     sorted_data = sorted(data.items(), key=lambda x: x[1], reverse=True)
+#     top_gainers = sorted_data[:10]
+#     top_losers = sorted_data[-10:]
+    
+#     return top_gainers, top_losers
+
+
+def get_top_movers(tickers, days=5):
+    import yfinance as yf
+    data = {}
+
+    for ticker in tickers:
+        try:
+            df = yf.download(ticker, period=f"{days}d")
+            if not df.empty:
+                # compute % change from first to last close
+                pct_change = ((df['Close'].iloc[-1] - df['Close'].iloc[0]) / df['Close'].iloc[0]) * 100
+                data[ticker] = pct_change
+        except Exception as e:
+            print(f"Error fetching {ticker}: {e}")
+
+    # Now each value is a scalar float
+    sorted_data = sorted(data.items(), key=lambda x: x[1], reverse=True)
+
+    top_gainers = sorted_data[:5]
+    top_losers = sorted_data[-5:]
+
+    return top_gainers, top_losers
+
+
+# Format DataFrame with color
+def format_df(df):
+    if not df.empty:
+        df['Percentage Change'] = pd.to_numeric(df['Percentage Change'], errors='coerce')
+        return df.style.applymap(lambda x: 'color: green' if x > 0 else 'color: red', subset=['Percentage Change'])
+    return df
+
+# Display dashboard
+def display_dashboard():
+    st.header("Dashboard")
+    
+    # Fetch tickers
+    nifty50_tickers = fetch_nifty50_tickers()
+    large_cap_tickers = fetch_large_cap_tickers()
+    small_cap_tickers = fetch_small_cap_tickers()
+
+    # Get top gainers and losers
+    top_gainers_nifty50, top_losers_nifty50 = get_top_movers(nifty50_tickers)
+    top_gainers_large_cap, top_losers_large_cap = get_top_movers(large_cap_tickers)
+    top_gainers_small_cap, top_losers_small_cap = get_top_movers(small_cap_tickers)
+    
+    # Create columns for tables
+    col1, col2, col3, col4 = st.columns(4)
+
+    with col1:
+        st.write("### Nifty 50 Top Gainers")
+        if top_gainers_nifty50:
+            df_gainers_nifty50 = pd.DataFrame(top_gainers_nifty50, columns=['Ticker', 'Percentage Change'])
+            st.dataframe(format_df(df_gainers_nifty50))
+
+    with col2:
+        st.write("### Nifty 50 Top Losers")
+        if top_losers_nifty50:
+            df_losers_nifty50 = pd.DataFrame(top_losers_nifty50, columns=['Ticker', 'Percentage Change'])
+            st.dataframe(format_df(df_losers_nifty50))
+
+    with col3:
+        st.write("### Large Cap Top Gainers")
+        if top_gainers_large_cap:
+            df_gainers_large_cap = pd.DataFrame(top_gainers_large_cap, columns=['Ticker', 'Percentage Change'])
+            st.dataframe(format_df(df_gainers_large_cap))
+
+    with col4:
+        st.write("### Large Cap Top Losers")
+        if top_losers_large_cap:
+            df_losers_large_cap = pd.DataFrame(top_losers_large_cap, columns=['Ticker', 'Percentage Change'])
+            st.dataframe(format_df(df_losers_large_cap))
+
+# Fetch and display stock profile
+def fetch_stock_profile(ticker):
+    try:
+        stock = yf.Ticker(ticker)
+        info = stock.info
+
+        profile = {
+            "Name": info.get('shortName', 'N/A'),
+            "Current Price": f"₹ {info.get('currentPrice', 'N/A')}",
+            "Market Cap": f"₹ {info.get('marketCap', 'N/A') / 1e7:.2f} Cr.",
+            "P/E Ratio": info.get('forwardEps', 'N/A'),
+            "Book Value": info.get('bookValue', 'N/A'),
+            "Dividend Yield": info.get('dividendYield', 'N/A'),
+            "ROCE": info.get('returnOnCapitalEmployed', 'N/A'),
+            "ROE": info.get('returnOnEquity', 'N/A'),
+            "Face Value": info.get('faceValue', 'N/A')
+        }
+        return profile
+    except Exception as e:
+        st.error(f"Error fetching profile for {ticker}: {e}")
+        return {}
+
+
+
+
+# Display stock profile as a table
+def display_profile(profile):
+    st.subheader("Stock Profile")
+    profile_df = pd.DataFrame([profile])
+    st.table(profile_df)
+
+# Fetch and display quarterly results
+def display_quarterly_results(ticker):
+    st.subheader("Quarterly Results Summary")
+    try:
+        stock = yf.Ticker(ticker)
+        financials = stock.quarterly_financials.T
+        if not financials.empty:
+            results = {
+                'Sales': financials['Total Revenue'].iloc[-1] if 'Total Revenue' in financials.columns else 'N/A',
+                'Operating Profit Margin': financials['Operating Income'].iloc[-1] if 'Operating Income' in financials.columns else 'N/A',
+                'Net Profit': financials['Net Income'].iloc[-1] if 'Net Income' in financials.columns else 'N/A'
+            }
+            results_df = pd.DataFrame([results])
+            st.table(results_df)
+        else:
+            st.write("No quarterly results available.")
+    except Exception as e:
+        st.write(f"Error fetching quarterly results: {e}")
+
+# Fetch and display shareholding pattern
+def display_shareholding_pattern(ticker):
+    st.subheader("Shareholding Pattern")
+    
+    # Placeholder values; replace with actual data source or API call
+    data = {
+        'Category': ['Promoters', 'FIIs (Foreign Institutional Investors)', 'DIIs (Domestic Institutional Investors)', 'Public'],
+        'Holding (%)': [45.0, 20.0, 15.0, 20.0]
+    }
+    
+    df = pd.DataFrame(data)
+    st.table(df)
+
+
+def display_financial_ratios(ticker):
+    st.subheader("Financial Ratios")
+    stock = yf.Ticker(ticker)
+    
+    try:
+        # Placeholder values, calculate actual values based on your requirements
+        ratios = {
+            'Debtor Days': 73,
+            'Working Capital Days': 194,
+            'Cash Conversion Cycle': 51
+        }
+        ratios_df = pd.DataFrame([ratios])
+        st.table(ratios_df)
+    except Exception as e:
+        st.write("Error fetching financial ratios:", e)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+# Main application
+# def main():
+#     st.title("Stock Analysis Dashboard")
+
+#     # Select ticker input
+#     ticker = st.text_input("Enter Stock Ticker (e.g., TATAMOTORS.NS)")
+
+#     if ticker:
+#         profile = fetch_stock_profile(ticker)
+#         if profile:
+#             display_profile(profile)
+        
+#         display_quarterly_results(ticker)
+#         display_shareholding_pattern(ticker)
+
+#     # Show dashboard
+#     if st.button("Show Dashboard"):
+#         display_dashboard()
+
+# if __name__ == "__main__":
+#     main()
+
+