Update app.py

app.py

@@ -1,63 +1,70 @@
 import streamlit as st
 import yfinance as yf
 import pandas as pd
-import matplotlib.pyplot as plt
-from matplotlib import dates as mdates
-import plotly.graph_objects as go
+import plotly.graph_objs as go
 
-# Initialize the Streamlit app
-st.title('Momentum Reversal Trading Strategy Analysis')
+def fetch_data(ticker, start_date, end_date):
+    data = yf.download(ticker, start=start_date, end=end_date)
+    return data
 
-# User input for asset ticker, start date, and end date
-ticker = st.text_input('Enter the ticker symbol of the asset:', 'AAPL')
-start_date = st.date_input('Start Date')
-end_date = st.date_input('End Date')
-analyze_button = st.button('Analyze')
+def calculate_indicators(data):
+    # Exponential Moving Averages for trend
+    data['EMA50'] = data['Close'].ewm(span=50, adjust=False).mean()
+    data['EMA200'] = data['Close'].ewm(span=200, adjust=False).mean()
+    
+    # Relative Strength Index for momentum
+    delta = data['Close'].diff()
+    gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
+    loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
+    rs = gain / loss
+    data['RSI'] = 100 - (100 / (1 + rs))
+    
+    return data
 
-if analyze_button:
-    # Fetch historical data from Yahoo Finance
-    data = yf.download(ticker, start=start_date, end=end_date)
+def identify_signals(data):
+    data['Buy Signal'] = (data['EMA50'] > data['EMA200']) & (data['RSI'] < 30)
+    data['Sell Signal'] = (data['EMA50'] < data['EMA200']) & (data['RSI'] > 70)
+    return data
 
-    # Calculate RSI
-    delta = data['Adj Close'].diff()
-    gain = (delta.where(delta > 0, 0)).fillna(0)
-    loss = (-delta.where(delta < 0, 0)).fillna(0)
-    avg_gain = gain.rolling(window=14, min_periods=14).mean()
-    avg_loss = loss.rolling(window=14, min_periods=14).mean()
-    rs = avg_gain / avg_loss
-    rsi = 100 - (100 / (1 + rs))
-
-    # Calculate MACD
-    exp1 = data['Adj Close'].ewm(span=12, adjust=False).mean()
-    exp2 = data['Adj Close'].ewm(span=26, adjust=False).mean()
-    macd = exp1 - exp2
-    signal = macd.ewm(span=9, adjust=False).mean()
-
-    # Identify buy and sell signals
-    buy_signals = (macd > signal) & (macd.shift(1) <= signal.shift(1))
-    sell_signals = (macd < signal) & (macd.shift(1) >= signal.shift(1))
-    data['Buy Signal'] = buy_signals
-    data['Sell Signal'] = sell_signals
-
-    # Plotting
+def plot_data(data):
     fig = go.Figure()
+
+    # Adding price trace
     fig.add_trace(go.Candlestick(x=data.index,
                                  open=data['Open'],
                                  high=data['High'],
                                  low=data['Low'],
-                                 close=data['Close'], name='Market data'))
-    fig.add_trace(go.Scatter(x=data.index, y=rsi, name='RSI'))
-    fig.add_trace(go.Scatter(x=data.index, y=macd, name='MACD', line=dict(color='blue')))
-    fig.add_trace(go.Scatter(x=data.index, y=signal, name='Signal Line', line=dict(color='orange')))
-    fig.update_layout(title='Trading Signals', xaxis_title='Date', yaxis_title='Price')
-    st.plotly_chart(fig)
-
-    # Display Buy and Sell Signals in a table
-    signals = pd.DataFrame(index=data.index)
-    signals['Price'] = data['Adj Close']
-    signals['Buy'] = data['Buy Signal']
-    signals['Sell'] = data['Sell Signal']
-    signals_filtered = signals[(signals['Buy'] | signals['Sell'])]
-    st.table(signals_filtered)
-
-# Streamlit app will run when this script is called
+                                 close=data['Close'], name='Candlestick'))
+
+    # Adding EMAs
+    fig.add_trace(go.Scatter(x=data.index, y=data['EMA50'], name='EMA 50', line=dict(color='blue', width=1.5)))
+    fig.add_trace(go.Scatter(x=data.index, y=data['EMA200'], name='EMA 200', line=dict(color='red', width=1.5)))
+
+    # 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=12, color='green')))
+    fig.add_trace(go.Scatter(x=sells.index, y=sells['Close'], mode='markers', name='Sell Signal', marker=dict(symbol='triangle-down', size=12, color='red')))
+
+    # Layout updates
+    fig.update_layout(title='Momentum Reversal Trading Strategy Visualization', xaxis_title='Date', yaxis_title='Price', template='plotly_dark')
+    fig.update_xaxes(rangeslider_visible=True)
+    
+    return fig
+
+def main():
+    st.title("Momentum Reversal 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()
+