Update app.py

app.py

@@ -3,55 +3,33 @@ import yfinance as yf
 import pandas as pd
 import plotly.graph_objects as go
 
-# Function to fetch data from Yahoo Finance
 def fetch_data(ticker, start_date, end_date):
     data = yf.download(ticker, start=start_date, end=end_date)
     return data
 
-# Calculate indicators based on user-defined window sizes
 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()
-    data['High Long'] = data['High'].rolling(window=window_long).max()
-    data['Low Long'] = data['Low'].rolling(window=window_long).min()
     return data
 
-# Identify buy and sell signals based on breakout strategy
 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
 
-# Collect and display signals
 def collect_signals(data):
     signals = pd.DataFrame()
-    signals['Date'] = data[data['Buy Signal'] | data['Sell Signal']].index
-    signals['Price'] = data[data['Buy Signal'] | data['Sell Signal']]['Close']
-    signals['Signal'] = 'Buy'
+    signals['Date'] = data.index
+    signals['Price'] = data['Close']
+    signals['Signal'] = None  # Initialize the column with None
+    signals.loc[data['Buy Signal'], 'Signal'] = 'Buy'
     signals.loc[data['Sell Signal'], 'Signal'] = 'Sell'
+    signals = signals.dropna(subset=['Signal'])
     return signals
 
-# Calculate returns and metrics for backtesting
-def backtest_signals(data):
-    data['Position'] = 0
-    data['Position'] = data['Buy Signal'].replace(True, 1).cumsum()
-    data['Position'] = data['Position'] - data['Sell Signal'].replace(True, 1).cumsum()
-    data['Position'] = data['Position'].clip(lower=0, upper=1)
-
-    data['Market Returns'] = data['Close'].pct_change()
-    data['Strategy Returns'] = data['Market Returns'] * data['Position'].shift(1)
-
-    data['Cumulative Market Returns'] = (1 + data['Market Returns']).cumprod() - 1
-    data['Cumulative Strategy Returns'] = (1 + data['Strategy Returns']).cumprod() - 1
-    
-    return data, data['Cumulative Market Returns'].iloc[-1], data['Cumulative Strategy Returns'].iloc[-1]
-
-# Plotting function using Plotly for interactive charts
 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')))
-    fig.add_trace(go.Scatter(x=data.index, y=data['High Short'], name='High Short', line=dict(dash='dot')))
-    fig.add_trace(go.Scatter(x=data.index, y=data['Low Short'], name='Low Short', line=dict(dash='dot')))
     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))
@@ -59,32 +37,24 @@ def plot_data(data):
     fig.update_layout(title='Stock Price and Trading Signals', xaxis_title='Date', yaxis_title='Price', template='plotly_dark')
     return fig
 
-# Main application function
 def main():
     st.title("Enhanced Turtle Trading Strategy with Backtesting and Signal Table")
+    ticker = st.sidebar.text_input("Enter the ticker symbol, e.g., 'AAPL'")
+    start_date = st.sidebar.date_input("Select the start date")
+    end_date = st.sidebar.date_input("Select the end date")
+    window_short = st.sidebar.number_input("Short term window", min_value=5, max_value=60, value=20)
+    window_long = st.sidebar.number_input("Long term window", min_value=5, max_value=120, value=55)
 
-    # Sidebar for user inputs
-    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)
-
-    if st.button("Analyze"):
+    if st.sidebar.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)
-            data, market_return, strategy_return = backtest_signals(data)
             fig = plot_data(data)
             st.plotly_chart(fig, use_container_width=True)
-            st.subheader("Trading Signals")
-            st.dataframe(signals)
-            st.subheader("Backtesting Results")
-            st.write(f"Market Return: {market_return * 100:.2f}%")
-            st.write(f"Strategy Return: {strategy_return * 100:.2f}%")
+            st.write("Trading Signals:")
+            st.dataframe(signals.style.hide_index())
         else:
             st.error("No data found for the selected ticker and date range.")