Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import datetime as dt
+import yfinance as yf
+import ta
+
+# List of all futures stocks
+futures_stocks = [
+    "ACC.NS", "ADANIENT.NS", "ADANIPORTS.NS", "AMBUJACEM.NS", "APOLLOHOSP.NS",
+    "ASHOKLEY.NS", "ASIANPAINT.NS", "AXISBANK.NS", "BAJAJ-AUTO.NS", "BAJFINANCE.NS",
+    "BAJAJFINSV.NS", "BALKRISIND.NS", "BANDHANBNK.NS", "BANKBARODA.NS", "BANKINDIA.NS",
+    "BERGEPAINT.NS", "BHARATFORG.NS", "BPCL.NS", "BHARTIARTL.NS", "BIOCON.NS",
+    "BOSCHLTD.NS", "BRITANNIA.NS", "CANBK.NS", "CHOLAFIN.NS", "CIPLA.NS",
+    "COALINDIA.NS", "CONCOR.NS", "CUMMINSIND.NS", "DABUR.NS", "DELTACORP.NS",
+    "DIVISLAB.NS", "DLF.NS", "DRREDDY.NS", "EICHERMOT.NS", "FEDERALBNK.NS",
+    "GAIL.NS", "GLENMARK.NS", "GMRINFRA.NS", "GODREJCP.NS", "GODREJPROP.NS",
+    "GRANULES.NS", "GRASIM.NS", "HAVELLS.NS", "HCLTECH.NS",
+    "HDFCAMC.NS", "HDFCBANK.NS", "HDFCLIFE.NS", "HEROMOTOCO.NS", "HINDALCO.NS",
+    "HINDCOPPER.NS", "HINDPETRO.NS", "HINDUNILVR.NS", "IBULHSGFIN.NS", "ICICIBANK.NS",
+    "ICICIGI.NS", "ICICIPRULI.NS", "IDEA.NS", "IDFCFIRSTB.NS", "INDHOTEL.NS",
+    "INDIACEM.NS", "INDIAMART.NS", "INDIGO.NS", "INDUSINDBK.NS", "INFY.NS",
+    "IRCTC.NS", "ITC.NS", "JINDALSTEL.NS", "JSWSTEEL.NS", "JUBLFOOD.NS",
+    "KOTAKBANK.NS", "L&TFH.NS", "LALPATHLAB.NS", "LAURUSLABS.NS", "LICHSGFIN.NS",
+    "LT.NS", "LUPIN.NS", "M&M.NS", "M&MFIN.NS", "MANAPPURAM.NS",
+    "MARICO.NS", "MARUTI.NS", "MCDOWELL-N.NS", "MCX.NS", "METROPOLIS.NS",
+    "MFSL.NS", "MGL.NS", "MINDTREE.NS", "MOTHERSUMI.NS", "MPHASIS.NS",
+    "MRF.NS", "MUTHOOTFIN.NS", "NAM-INDIA.NS", "NATIONALUM.NS", "NAUKRI.NS",
+    "NAVINFLUOR.NS", "NBCC.NS", "NESTLEIND.NS", "NMDC.NS", "NTPC.NS",
+    "ONGC.NS", "PAGEIND.NS", "PEL.NS", "PETRONET.NS", "PFC.NS",
+    "PFIZER.NS", "PIDILITIND.NS", "PIIND.NS", "PNB.NS", "POLYCAB.NS",
+    "POWERGRID.NS", "PVR.NS", "RAMCOCEM.NS", "RBLBANK.NS", "RECLTD.NS",
+    "RELIANCE.NS", "SAIL.NS", "SBICARD.NS", "SBILIFE.NS", "SBIN.NS",
+    "SHREECEM.NS", "SIEMENS.NS", "SRF.NS", "SRTRANSFIN.NS", "SUNPHARMA.NS",
+    "SUNTV.NS", "TATACHEM.NS", "TATACOMM.NS", "TATACONSUM.NS", "TATAMOTORS.NS",
+    "TATAPOWER.NS", "TATASTEEL.NS", "TCS.NS", "TECHM.NS", "TITAN.NS",
+    "TORNTPHARM.NS", "TORNTPOWER.NS", "TRENT.NS", "TVSMOTOR.NS", "UBL.NS",
+    "ULTRACEMCO.NS", "UPL.NS", "VBL.NS", "VEDL.NS", "VOLTAS.NS",
+    "WHIRLPOOL.NS", "WIPRO.NS", "ZEEL.NS"
+]
+
+
+@st.cache_data
+def fetch_and_calculate_indicators(ticker, start_date, end_date, rsi_length, stoch_length, k_period, d_period):
+    try:
+        df = yf.download(ticker, start=start_date, end=end_date)
+        
+        if df.empty:
+            return None
+
+        # Calculate RSI
+        df['RSI'] = ta.momentum.RSIIndicator(df['Close'], window=rsi_length).rsi()
+
+        # Calculate Stochastic RSI
+        stoch_rsi = ta.momentum.StochRSIIndicator(
+            df['Close'], 
+            window=stoch_length, 
+            smooth1=k_period, 
+            smooth2=d_period
+        )
+        df['StochRSI_K'] = stoch_rsi.stochrsi_k()
+        df['StochRSI_D'] = stoch_rsi.stochrsi_d()
+
+        # Calculate OBV
+        df['OBV'] = ta.volume.OnBalanceVolumeIndicator(df['Close'], df['Volume']).on_balance_volume()
+
+        if df['RSI'].isna().all() or df['StochRSI_K'].isna().all() or df['OBV'].isna().all():
+            return None
+        
+        return df
+    except Exception as e:
+        st.error(f"Error occurred for {ticker}: {str(e)}")
+        return None
+
+def filter_stocks_by_indicators(stocks, start_date, end_date, rsi_threshold, stoch_rsi_threshold, obv_threshold, indicator_choice, rsi_length, stoch_length, k_period, d_period):
+    filtered_stocks = []
+    progress_bar = st.progress(0)
+    for i, ticker in enumerate(stocks):
+        df = fetch_and_calculate_indicators(ticker, start_date, end_date, rsi_length, stoch_length, k_period, d_period)
+        if df is not None and not df['RSI'].empty and not df['StochRSI_K'].empty and not df['OBV'].empty:
+            rsi_value = df['RSI'].iloc[-1]
+            stoch_rsi_value = df['StochRSI_K'].iloc[-1]
+            obv_value = df['OBV'].iloc[-1]
+            obv_change = (df['OBV'].iloc[-1] - df['OBV'].iloc[-2]) / df['OBV'].iloc[-2] * 100  # Percentage change
+            
+            if not pd.isna(rsi_value) and not pd.isna(stoch_rsi_value) and not pd.isna(obv_value):
+                if (indicator_choice == 'RSI' and rsi_value < rsi_threshold) or \
+                   (indicator_choice == 'StochRSI' and stoch_rsi_value < stoch_rsi_threshold) or \
+                   (indicator_choice == 'OBV' and obv_change > obv_threshold) or \
+                   (indicator_choice == 'All' and rsi_value < rsi_threshold and stoch_rsi_value < stoch_rsi_threshold and obv_change > obv_threshold):
+                    filtered_stocks.append(ticker)
+                    st.info(f"{ticker} - RSI: {rsi_value:.2f}, StochRSI: {stoch_rsi_value:.2f}, OBV Change: {obv_change:.2f}%")
+                else:
+                    st.info(f"{ticker} - RSI: {rsi_value:.2f}, StochRSI: {stoch_rsi_value:.2f}, OBV Change: {obv_change:.2f}% (doesn't meet criteria)")
+            else:
+                st.info(f"{ticker} has NaN value for RSI, StochRSI, or OBV.")
+        else:
+            st.info(f"No valid data available for {ticker}.")
+        progress_bar.progress((i + 1) / len(stocks))
+    return filtered_stocks
+
+def main():
+    st.title("Indian Stock Market Scanner: RSI, Stochastic RSI, and OBV")
+
+    start_date = st.date_input("Start Date", dt.date(2020, 1, 1))
+    end_date = st.date_input("End Date", dt.date.today())
+    
+    indicator_choice = st.radio("Choose Indicator", ('RSI', 'StochRSI', 'OBV', 'All'))
+    
+    col1, col2, col3 = st.columns(3)
+    
+    with col1:
+        rsi_threshold = st.slider("RSI Threshold", 0, 100, 30)
+        rsi_length = st.number_input("RSI Length", value=14, min_value=1, max_value=100)
+    
+    with col2:
+        stoch_rsi_threshold = st.slider("Stochastic RSI Threshold", 0.0, 1.0, 0.2)
+        stoch_length = st.number_input("Stochastic Length", value=14, min_value=1, max_value=100)
+    
+    with col3:
+        obv_threshold = st.slider("OBV Change Threshold (%)", -10.0, 10.0, 2.0)
+        k_period = st.number_input("K Period", value=3, min_value=1, max_value=20)
+        d_period = st.number_input("D Period", value=3, min_value=1, max_value=20)
+    
+    output_filename = st.text_input("Enter output filename (with .csv extension):", "filtered_stocks.csv")
+    
+    if st.button("Start Scanning", key="start_scanning_button"):
+        st.write(f"Scanning for stocks based on selected criteria...")
+        filtered_stocks = filter_stocks_by_indicators(
+            futures_stocks, start_date, end_date, rsi_threshold, stoch_rsi_threshold, obv_threshold,
+            indicator_choice, rsi_length, stoch_length, k_period, d_period
+        )
+        
+        if filtered_stocks:
+            st.success(f"Found {len(filtered_stocks)} stocks meeting the criteria:")
+            st.write(filtered_stocks)
+            df = pd.DataFrame(filtered_stocks, columns=["Ticker"])
+            st.download_button(
+                label="Download CSV",
+                data=df.to_csv(index=False).encode('utf-8'),
+                file_name=output_filename,
+                mime='text/csv',
+            )
+            st.success(f"Click the button above to download the filtered stocks.")
+        else:
+            st.warning(f"No stocks found meeting the selected criteria.")
+
+if __name__ == "__main__":
+    main()