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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 fetch_weekly_data(ticker, start_date, end_date):
data = yf.download(ticker, start=start_date, end=end_date, interval='1wk')
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, weekly_data):
# Calculate Buy and Sell signals on daily 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']))
# Filter signals by volume
avg_volume = data['Volume'].rolling(window=20).mean()
data['Buy Signal'] = data['Buy Signal'] & (data['Volume'] > avg_volume)
data['Sell Signal'] = data['Sell Signal'] & (data['Volume'] > avg_volume)
# Confirm signals with weekly data
weekly_data = calculate_indicators(weekly_data)
data['Weekly Buy Signal'] = weekly_data['Buy Signal'].reindex(data.index, method='ffill')
data['Weekly Sell Signal'] = weekly_data['Sell Signal'].reindex(data.index, method='ffill')
data['Buy Signal'] = data['Buy Signal'] & data['Weekly Buy Signal']
data['Sell Signal'] = data['Sell Signal'] & data['Weekly Sell Signal']
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='white', 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)
weekly_data = fetch_weekly_data(ticker, start_date, end_date)
data = calculate_indicators(data)
data = identify_signals(data, weekly_data)
fig = plot_data(data)
st.plotly_chart(fig, use_container_width=True)
if __name__ == "__main__":
main()
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