app.py

import streamlit as st
import yfinance as yf
import pandas as pd
import numpy as np
import ta
from datetime import datetime

# Streamlit Title
st.title("NIFTY 500 Turtle Strategy Scanner with Support, Resistance, RSI, and Volume Filters")

# Load the NIFTY 500 stock list
stock_list = pd.read_csv('ind_nifty500list.csv')
stock_list['Symbol'] = stock_list['Symbol'] + ".NS"
nifty_500_stocks = stock_list['Symbol'].tolist()

# Sidebar for Parameters
st.sidebar.header("Turtle Strategy, RSI, and Support/Resistance 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 Thresholds
lower_rsi_threshold = st.sidebar.slider("Lower RSI Threshold (Oversold)", min_value=10, max_value=50, value=30, step=5)
upper_rsi_threshold = st.sidebar.slider("Upper RSI Threshold (Overbought)", min_value=50, max_value=90, value=70, step=5)

# Volume Threshold
volume_threshold = st.sidebar.number_input("Volume Threshold", min_value=100000, value=500000)

# Moving Average Parameters
short_term_ma = st.sidebar.slider("Short-Term Moving Average (days)", min_value=5, max_value=50, value=20, step=1)
long_term_ma = st.sidebar.slider("Long-Term Moving Average (days)", min_value=20, max_value=200, value=50, step=1)

# Support and Resistance Lookback Periods
support_period = st.sidebar.slider("Support Lookback Period (days)", min_value=5, max_value=50, value=20, step=1)
resistance_period = st.sidebar.slider("Resistance Lookback Period (days)", min_value=5, max_value=50, value=20, step=1)

# 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=502, step=5)

# Initialize an empty DataFrame to store the filtered stocks
filtered_data = pd.DataFrame(columns=["Stock", "Latest Price", "RSI", "Volume", "Short MA", "Long MA", "Support", "Resistance", "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, Support/Resistance, RSI, and Volume...")
for symbol in nifty_500_stocks[:limit_stocks]:
    try:
        # 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) < long_term_ma:
            st.write(f"Skipping {symbol}: Not enough data available.")
            continue

        # Calculate Turtle Strategy parameters
        data['20D_High'] = data['High'].rolling(window=entry_days).max()
        data['10D_Low'] = data['Low'].rolling(window=exit_days).min()

        # Entry and Exit Signals
        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)

        # Calculate RSI using 'ta' library
        data['RSI'] = ta.momentum.RSIIndicator(data['Close'], window=14).rsi()

        # Calculate Moving Averages for confirmation
        data['Short_MA'] = ta.trend.SMAIndicator(data['Close'], window=short_term_ma).sma_indicator()
        data['Long_MA'] = ta.trend.SMAIndicator(data['Close'], window=long_term_ma).sma_indicator()

        # Calculate Support and Resistance Levels
        data['Support'] = data['Low'].rolling(window=support_period).min()
        data['Resistance'] = data['High'].rolling(window=resistance_period).max()

        # 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, RSI, Volume, and Support/Resistance
        latest_entry = data['Long'].iloc[-1]  # Last Long signal (Entry)
        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
        latest_short_ma = data['Short_MA'].iloc[-1]  # Latest Short-Term MA
        latest_long_ma = data['Long_MA'].iloc[-1]    # Latest Long-Term MA
        latest_support = data['Support'].iloc[-1]  # Latest Support Level
        latest_resistance = data['Resistance'].iloc[-1]  # Latest Resistance Level

        # Filter based on Support/Resistance, RSI, Volume, and Moving Averages
        if (latest_entry == 1 or latest_exit == 1) and ((latest_rsi <= lower_rsi_threshold or latest_rsi >= upper_rsi_threshold)) and latest_volume > volume_threshold and latest_short_ma > latest_long_ma:
            latest_price = data['Close'].iloc[-1]
            filtered_data = pd.concat([filtered_data, pd.DataFrame([[symbol, latest_price, latest_rsi, latest_volume, latest_short_ma, latest_long_ma, latest_support, latest_resistance, 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(f"Filtered Stocks with Support/Resistance, Moving Average, Custom RSI, and Volume Thresholds:")
    st.dataframe(filtered_data)
else:
    st.write("No stocks meeting the criteria for entry/exit signals, Support/Resistance, RSI, MA, or volume thresholds.")