Update app.py

app.py

@@ -2,9 +2,11 @@ import streamlit as st
 import yfinance as yf
 import pandas as pd
 import numpy as np
+import talib
+from datetime import datetime
 
 # Streamlit Title
-st.title("NIFTY 500 Turtle Strategy Scanner with Entry and Exit Signals")
+st.title("NIFTY 500 Turtle Strategy Scanner with Entry, Exit, RSI, and Volume")
 
 # Load the NIFTY 500 stock list
 stock_list = pd.read_csv('ind_nifty500list.csv')
@@ -12,22 +14,32 @@ stock_list['Symbol'] = stock_list['Symbol'] + ".NS"
 nifty_500_stocks = stock_list['Symbol'].tolist()
 
 # Sidebar for Parameters
-st.sidebar.header("Turtle Strategy Parameters")
+st.sidebar.header("Turtle Strategy and RSI Parameters")
 entry_days = st.sidebar.slider("Entry Lookback Period (days)", min_value=10, max_value=60, value=20, step=1)
 exit_days = st.sidebar.slider("Exit Lookback Period (days)", min_value=5, max_value=30, value=10, step=1)
+rsi_threshold = st.sidebar.slider("RSI Threshold", min_value=10, max_value=90, value=30, step=5)
+volume_threshold = st.sidebar.number_input("Volume Threshold", min_value=100000, value=500000)
 
 # Option to limit the number of stocks to scan
 limit_stocks = st.sidebar.slider("Number of Stocks to Scan", min_value=5, max_value=len(nifty_500_stocks), value=50, step=5)
 
 # Initialize an empty DataFrame to store the filtered stocks
-filtered_data = pd.DataFrame(columns=["Stock", "Latest Price", "Entry Signal", "Exit Signal"])
+filtered_data = pd.DataFrame(columns=["Stock", "Latest Price", "RSI", "Volume", "Entry Signal", "Exit Signal"])
+
+# Get today's date for the end date
+end_date = datetime.today().strftime('%Y-%m-%d')
 
 # Loop through the selected stocks to scan
-st.write(f"Scanning the top {limit_stocks} stocks in NIFTY 500 based on Turtle Strategy parameters...")
+st.write(f"Scanning the top {limit_stocks} stocks in NIFTY 500 based on Turtle Strategy, RSI, and Volume...")
 for symbol in nifty_500_stocks[:limit_stocks]:
     try:
-        # Fetch historical data for the stock
-        data = yf.download(symbol, start='2022-01-01', end='2023-01-01', progress=False)
+        # Fetch historical data for the stock up to today's date
+        data = yf.download(symbol, start='2022-01-01', end=end_date, progress=False)
+
+        # Check if the data is sufficient for analysis
+        if len(data) < entry_days:
+            st.write(f"Skipping {symbol}: Not enough data available.")
+            continue
 
         # Calculate Turtle Strategy parameters
         data['20D_High'] = data['High'].rolling(window=entry_days).max()
@@ -37,31 +49,36 @@ for symbol in nifty_500_stocks[:limit_stocks]:
         data['Long'] = np.where(data['Close'] > data['20D_High'].shift(1), 1, 0)
         data['Exit'] = np.where(data['Close'] < data['10D_Low'].shift(1), 1, 0)  # Exit Signal as 1 if true
 
+        # Calculate RSI using TA-Lib
+        data['RSI'] = talib.RSI(data['Close'], timeperiod=14)
+
         # Position Management
         data['Position'] = 0
         data.loc[data['Long'] == 1, 'Position'] = 1
         data.loc[data['Exit'] == 1, 'Position'] = 0
         data['Position'] = data['Position'].ffill().shift(1).fillna(0)
 
-        # Check for the latest entry or exit signal
+        # Check for the latest entry or exit signal, RSI, and Volume
         latest_entry = data['Long'].iloc[-1]  # Last Long signal (Entry)
-        latest_exit = data['Exit'].iloc[-1]    # Last Exit signal
+        latest_exit = data['Exit'].iloc[-1]   # Last Exit signal
+        latest_rsi = data['RSI'].iloc[-1]     # Last RSI value
+        latest_volume = data['Volume'].iloc[-1]  # Last Volume
 
-        # Add to filtered data if an entry or exit signal is active
-        if latest_entry == 1 or latest_exit == 1:
+        # Filter based on RSI and Volume thresholds
+        if (latest_entry == 1 or latest_exit == 1) and (latest_rsi <= rsi_threshold or latest_rsi >= 70) and latest_volume > volume_threshold:
             latest_price = data['Close'].iloc[-1]
-            filtered_data = pd.concat([filtered_data, pd.DataFrame([[symbol, latest_price, latest_entry, latest_exit]], columns=filtered_data.columns)], ignore_index=True)
+            filtered_data = pd.concat([filtered_data, pd.DataFrame([[symbol, latest_price, latest_rsi, latest_volume, latest_entry, latest_exit]], columns=filtered_data.columns)], ignore_index=True)
 
     except Exception as e:
         st.write(f"Error processing {symbol}: {e}")
 
 # Display the filtered stocks that meet the criteria
 if not filtered_data.empty:
-    st.subheader("Filtered Stocks with Entry or Exit Signals:")
+    st.subheader("Filtered Stocks with Entry or Exit Signals, RSI, and Volume:")
     # Display as a table
     st.dataframe(filtered_data)
 else:
-    st.write("No stocks meeting the criteria for entry or exit signals.")
+    st.write("No stocks meeting the criteria for entry/exit signals, RSI, or volume thresholds.")
 
 # Optional: Show performance for a single stock
 selected_stock = st.selectbox("Select a Stock to View Detailed Performance", nifty_500_stocks)
@@ -69,7 +86,7 @@ if selected_stock:
     st.write(f"### Detailed Analysis for {selected_stock}")
 
     # Fetch detailed data for the selected stock
-    detailed_data = yf.download(selected_stock, start='2022-01-01', end='2023-01-01')
+    detailed_data = yf.download(selected_stock, start='2022-01-01', end=end_date)
     
     # Turtle Strategy Calculations
     detailed_data['20D_High'] = detailed_data['High'].rolling(window=entry_days).max()
@@ -81,9 +98,8 @@ if selected_stock:
     detailed_data.loc[detailed_data['Exit'] == 1, 'Position'] = 0
     detailed_data['Position'] = detailed_data['Position'].ffill().shift(1).fillna(0)
 
-    # Strategy Returns Calculation
-    detailed_data['Strategy Returns'] = detailed_data['Position'] * detailed_data['Close'].pct_change()
-    detailed_data['Cumulative Returns'] = (1 + detailed_data['Strategy Returns']).cumprod()
+    # Calculate RSI
+    detailed_data['RSI'] = talib.RSI(detailed_data['Close'], timeperiod=14)
 
     # Show Raw Data
     st.subheader("Strategy Data Preview")