Create app.py

app.py

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+import streamlit as st
+import yfinance as yf
+import pandas as pd
+import plotly.graph_objs as go
+
+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']))
+    return data
+
+def plot_data(data):
+    fig = go.Figure()
+
+    # Adding Close price trace
+    fig.add_trace(go.Scatter(x=data.index, y=data['Close'], name='Close Price', line=dict(color='blue', width=2)))
+
+    # Adding Bollinger Bands traces
+    fig.add_trace(go.Scatter(x=data.index, y=data['Upper Band'], name='Upper Bollinger Band', line=dict(color='red', dash='dash')))
+    fig.add_trace(go.Scatter(x=data.index, y=data['Middle Band'], name='Middle Bollinger Band', line=dict(color='black', dash='dash')))
+    fig.add_trace(go.Scatter(x=data.index, y=data['Lower Band'], name='Lower Bollinger Band', line=dict(color='red', dash='dash')))
+
+    # Adding Moving Averages traces
+    fig.add_trace(go.Scatter(x=data.index, y=data['MA5'], name='5-Day MA', line=dict(color='green', dash='dot')))
+    fig.add_trace(go.Scatter(x=data.index, y=data['MA10'], name='10-Day MA', line=dict(color='orange', dash='dot')))
+
+    # Adding Buy and Sell signals
+    buys = data[data['Buy Signal']]
+    sells = data[data['Sell Signal']]
+    fig.add_trace(go.Scatter(x=buys.index, y=buys['Close'], mode='markers', name='Buy Signal', marker=dict(symbol='triangle-up', size=10, color='green')))
+    fig.add_trace(go.Scatter(x=sells.index, y=sells['Close'], mode='markers', name='Sell Signal', marker=dict(symbol='triangle-down', size=10, color='red')))
+
+    # Layout updates
+    fig.update_layout(title='Stock Price and Trading Signals', xaxis_title='Date', yaxis_title='Price', template='plotly_dark')
+    fig.update_xaxes(rangeslider_visible=True)
+    
+    return fig
+
+def main():
+    st.title("OMA Ally BBMA Trading Strategy Visualization")
+    ticker = st.text_input("Enter the ticker symbol, e.g., 'AAPL'")
+    start_date = st.date_input("Select the start date")
+    end_date = st.date_input("Select the end date")
+    
+    if st.button("Analyze"):
+        data = fetch_data(ticker, start_date, end_date)
+        data = calculate_indicators(data)
+        data = identify_signals(data)
+        fig = plot_data(data)
+        st.plotly_chart(fig, use_container_width=True)
+
+if __name__ == "__main__":
+    main()