Create app.py

app.py

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+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
+
+# Initialize the Streamlit app
+st.title('Momentum Reversal Trading Strategy Analysis')
+
+# 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')
+
+if analyze_button:
+    # Fetch historical data from Yahoo Finance
+    data = yf.download(ticker, start=start_date, end=end_date)
+
+    # 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
+    fig = go.Figure()
+    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