Delete app.py

app.py

@@ -1,80 +0,0 @@
-import streamlit as st
-import yfinance as yf
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-
-def fetch_data(ticker, start_date, end_date):
-    data = yf.download(ticker, start=start_date, end=end_date)
-    return data
-
-def calculate_indicators(data):
-    # Bollinger Bands
-    data['Middle Band'] = data['Close'].rolling(window=20).mean()
-    data['Upper Band'] = data['Middle Band'] + 1.96 * data['Close'].rolling(window=20).std()
-    data['Lower Band'] = data['Middle Band'] - 1.96 * data['Close'].rolling(window=20).std()
-    
-    # Moving Averages
-    data['MA5'] = data['Close'].rolling(window=5).mean()
-    data['MA10'] = data['Close'].rolling(window=10).mean()
-    
-    return data
-
-def identify_signals(data):
-    data['Buy Signal'] = ((data['Close'] < data['Lower Band']) & (data['Close'].shift(1) > data['Lower Band'])) | \
-                         ((data['Close'] > data['MA5']) & (data['Close'].shift(1) < data['MA5']))
-    data['Sell Signal'] = ((data['Close'] > data['Upper Band']) & (data['Close'].shift(1) < data['Upper Band'])) | \
-                          ((data['Close'] < data['MA5']) & (data['Close'].shift(1) > data['MA5']))
-    
-    # To properly track the price at which a signal occurs, we ensure signal markers are associated with the price
-    data['Signal Price'] = data.apply(lambda row: row['Close'] if row['Buy Signal'] or row['Sell Signal'] else np.nan, axis=1)
-    return data
-
-def signal_table(data):
-    # Extracting signal events and necessary information
-    signals = pd.DataFrame()
-    signals['Date'] = data.index
-    signals['Price'] = data['Signal Price']
-    signals['Signal'] = np.where(data['Buy Signal'], 'Buy', np.where(data['Sell Signal'], 'Sell', ''))
-    signals = signals.dropna(subset=['Price'])  # Remove rows where no signal occurred
-    return signals
-
-
-def plot_data(data):
-    plt.figure(figsize=(10, 5))
-    plt.plot(data['Close'], label='Close Price')
-    plt.plot(data['Upper Band'], label='Upper Bollinger Band', linestyle='--')
-    plt.plot(data['Middle Band'], label='Middle Bollinger Band', linestyle='--')
-    plt.plot(data['Lower Band'], label='Lower Bollinger Band', linestyle='--')
-    plt.plot(data['MA5'], label='5-Day MA', color='green', linestyle='-.')
-    plt.plot(data['MA10'], label='10-Day MA', color='red', linestyle='-.')
-    
-    buy_signals = data[data['Buy Signal']]
-    sell_signals = data[data['Sell Signal']]
-    plt.scatter(buy_signals.index, buy_signals['Close'], marker='^', color='green', s=100, label='Buy Signal')
-    plt.scatter(sell_signals.index, sell_signals['Close'], marker='v', color='red', s=100, label='Sell Signal')
-    
-    plt.title('Stock Price and Trading Signals')
-    plt.xlabel('Date')
-    plt.ylabel('Price')
-    plt.legend()
-    plt.grid(True)
-    plt.show()
-
-def main():
-    st.sidebar.title("Settings")
-    ticker = st.sidebar.text_input("Enter the ticker symbol, e.g., 'AAPL'")
-    start_date = st.sidebar.date_input("Select the start date")
-    end_date = st.sidebar.date_input("Select the end date")
-    
-    if st.sidebar.button("Analyze"):
-        data = fetch_data(ticker, start_date, end_date)
-        data = calculate_indicators(data)
-        data = identify_signals(data)
-        signals = signal_table(data)
-        plot_data(data)
-        st.pyplot(plt)
-        st.dataframe(signals)
-
-if __name__ == "__main__":
-    main()