Update app.py

app.py

@@ -1,83 +1,69 @@
 import streamlit as st
 import yfinance as yf
 import pandas as pd
-import plotly.graph_objects as go
+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, window_short, window_long):
-    data['High Short'] = data['High'].rolling(window=window_short).max()
-    data['Low Short'] = data['Low'].rolling(window=window_short).min()
+def calculate_indicators(data):
+    # High and low for the breakout signals
+    data['20 Day High'] = data['High'].rolling(window=20).max()
+    data['20 Day Low'] = data['Low'].rolling(window=20).min()
+    data['55 Day High'] = data['High'].rolling(window=55).max()
+    data['55 Day Low'] = data['Low'].rolling(window=55).min()
+    
     return data
 
 def identify_signals(data):
-    data['Buy Signal'] = (data['Close'] > data['High Short'].shift(1))
-    data['Sell Signal'] = (data['Close'] < data['Low Short'].shift(1))
-    return data
-
-def collect_signals(data):
-    signals = pd.DataFrame()
-    signals['Date'] = data.index
-    signals['Price'] = data['Close']  # This will now display the price at which the signal was triggered
-
-    # Initialize all signals to None and update based on the data's indices
-    signals['Signal'] = None
-    buy_indices = data[data['Buy Signal']].index
-    sell_indices = data[data['Sell Signal']].index
-
-    signals.loc[signals['Date'].isin(buy_indices), 'Signal'] = 'Buy'
-    signals.loc[signals['Date'].isin(sell_indices), 'Signal'] = 'Sell'
-
-    signals = signals.dropna(subset=['Signal'])  # Ensure that only rows with signals are kept
-
-    return signals
+    # Buy signals are generated when the price exceeds the 20-day high
+    data['Buy Signal'] = (data['Close'] > data['20 Day High'].shift(1))
+    # Sell signals are generated when the price drops below the 20-day low
+    data['Sell Signal'] = (data['Close'] < data['20 Day Low'].shift(1))
+    
+    signals = []
+    for index, row in data.iterrows():
+        if row['Buy Signal']:
+            signals.append({'Date': index, 'Signal Type': 'Buy', 'Price': row['Close']})
+        if row['Sell Signal']:
+            signals.append({'Date': index, 'Signal Type': 'Sell', 'Price': row['Close']})
+    
+    return data, pd.DataFrame(signals)
 
 def plot_data(data):
-    fig = go.Figure()
-    fig.add_trace(go.Scatter(x=data.index, y=data['Close'], name='Close Price', line=dict(color='blue')))
-    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_symbol='triangle-up', marker_color='green', marker_size=10))
-    fig.add_trace(go.Scatter(x=sells.index, y=sells['Close'], mode='markers', name='Sell Signal', marker_symbol='triangle-down', marker_color='red', marker_size=10))
-    fig.update_layout(title='Stock Price and Trading Signals', xaxis_title='Date', yaxis_title='Price', template='plotly_dark')
-    return fig
+    plt.figure(figsize=(12, 6))
+    plt.plot(data['Close'], label='Close Price')
+    
+    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 Turtle Trading Signals')
+    plt.xlabel('Date')
+    plt.ylabel('Price')
+    plt.legend()
+    plt.grid(True)
+    plt.show()
 
 def main():
     st.title("Turtle Trading Strategy Visualization")
-
-    st.markdown("""
-    ## Description
-    This app visualizes the Turtle Trading Strategy, a well-known systematic trading method that follows trends to decide on buying and selling positions based on historical price breakouts. Users can test different short and long window sizes to see how the strategy performs with different settings.
-
-    ## How to Use
-    1. **Enter the Ticker Symbol:** Input the stock symbol you want to analyze (e.g., 'AAPL', 'GOOGL').
-    2. **Select Date Range:** Choose the start and end dates for the data you wish to analyze.
-    3. **Set Window Sizes:** Adjust the window sizes for the short and long term indicators.
-    4. **Analyze:** Press the analyze button to see the trading signals and performance charts.
-    5. **Review the Outputs:** The chart and the signals table provide visual and data-driven insights respectively.
-    """)
-
-    with st.sidebar:
-        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")
-        window_short = st.number_input("Short term window", min_value=5, max_value=60, value=20)
-        window_long = st.number_input("Long term window", min_value=5, max_value=120, value=55)
-
+    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)
-        if not data.empty:
-            data = calculate_indicators(data, window_short, window_long)
-            data = identify_signals(data)
-            signals = collect_signals(data)
-            fig = plot_data(data)
-            st.plotly_chart(fig, use_container_width=True)
+        data = calculate_indicators(data)
+        data, signals = identify_signals(data)
+        plot_data(data)
+        st.pyplot(plt)
+        if not signals.empty:
             st.write("Trading Signals:")
             st.dataframe(signals)
         else:
-            st.error("No data found for the selected ticker and date range.")
+            st.write("No trading signals found for the selected period.")
 
 if __name__ == "__main__":
     main()