feat: Add multi-ticker backtest support with performance filtering
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@ -232,8 +232,26 @@ def backtesting_page():
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with left_col:
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st.subheader("Backtest Settings")
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# Ticker selection
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ticker = st.text_input("Enter Ticker Symbol", value="AAPL").upper()
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# Add radio button for single/multiple ticker mode
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test_mode = st.radio("Testing Mode", ["Single Ticker", "Multiple Tickers"])
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if test_mode == "Single Ticker":
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# Single ticker input
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ticker = st.text_input("Enter Ticker Symbol", value="AAPL").upper()
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tickers = [ticker]
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else:
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# Multiple ticker input
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ticker_input = st.text_area(
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"Enter Ticker Symbols (one per line)",
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value="AAPL\nMSFT\nGOOG"
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)
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tickers = [t.strip().upper() for t in ticker_input.split('\n') if t.strip()]
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# Add minimum performance filters
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st.subheader("Performance Filters")
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min_return = st.number_input("Minimum Return (%)", value=10.0)
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min_sharpe = st.number_input("Minimum Sharpe Ratio", value=1.0)
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max_drawdown = st.number_input("Maximum Drawdown (%)", value=-20.0)
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# Date range selection
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col1, col2 = st.columns(2)
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@ -287,30 +305,79 @@ def backtesting_page():
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if st.button("Run Backtest"):
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with st.spinner('Running backtest...'):
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# Fetch data
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# Convert date to datetime
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start_datetime = datetime.combine(start_date, datetime.min.time())
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end_datetime = datetime.combine(end_date, datetime.min.time())
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df = get_stock_data(ticker, start_datetime, end_datetime, 'daily')
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if df.empty:
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st.error("No data available for the selected period")
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return
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if test_mode == "Single Ticker":
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# Single ticker logic
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df = get_stock_data(ticker, start_datetime, end_datetime, 'daily')
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if df.empty:
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st.error("No data available for the selected period")
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return
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try:
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df = prepare_data_for_backtest(df)
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try:
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df = prepare_data_for_backtest(df)
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if optimize:
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results = run_optimization(df, indicator_settings)
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with right_col:
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display_optimization_results(results)
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else:
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results = run_single_backtest(df, indicator_settings)
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with right_col:
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display_backtest_results(results)
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except Exception as e:
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st.error(f"Error during backtest: {str(e)}")
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else:
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# Multiple ticker logic
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try:
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results_df = run_multi_ticker_backtest(
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tickers, start_datetime, end_datetime, indicator_settings
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)
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# Apply performance filters
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filtered_df = results_df[
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(results_df['Return [%]'] >= min_return) &
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(results_df['Sharpe Ratio'] >= min_sharpe) &
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(results_df['Max Drawdown [%]'] >= max_drawdown)
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]
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if optimize:
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results = run_optimization(df, indicator_settings)
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with right_col:
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display_optimization_results(results)
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else:
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results = run_single_backtest(df, indicator_settings)
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with right_col:
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display_backtest_results(results)
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st.subheader("Multi-Ticker Results")
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except Exception as e:
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st.error(f"Error during backtest: {str(e)}")
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# Display summary statistics
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st.write("### Summary Statistics")
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summary = pd.DataFrame({
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'Metric': ['Average Return', 'Average Sharpe', 'Average Drawdown', 'Success Rate'],
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'Value': [
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f"{results_df['Return [%]'].mean():.2f}%",
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f"{results_df['Sharpe Ratio'].mean():.2f}",
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f"{results_df['Max Drawdown [%]'].mean():.2f}%",
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f"{(len(filtered_df) / len(results_df) * 100):.1f}%"
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]
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})
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st.table(summary)
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# Display full results
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st.write("### All Results")
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st.dataframe(results_df.sort_values('Return [%]', ascending=False))
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# Display filtered results
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st.write("### Filtered Results (Meeting Criteria)")
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st.dataframe(filtered_df.sort_values('Return [%]', ascending=False))
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# Create a downloadable CSV
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csv = results_df.to_csv(index=False)
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st.download_button(
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"Download Results CSV",
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csv,
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"backtest_results.csv",
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"text/csv",
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key='download-csv'
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)
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except Exception as e:
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st.error(f"Error during multi-ticker backtest: {str(e)}")
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def run_optimization(df: pd.DataFrame, indicator_settings: Dict) -> List:
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"""Run optimization with different parameter combinations"""
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@ -390,6 +457,47 @@ def display_optimization_results(results: List):
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st.subheader("Optimization Results")
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st.dataframe(df_results.sort_values('Return [%]', ascending=False))
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def run_multi_ticker_backtest(tickers: list, start_date: datetime, end_date: datetime, indicator_settings: Dict) -> pd.DataFrame:
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"""Run backtest across multiple tickers and aggregate results"""
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all_results = []
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for ticker in tickers:
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try:
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print(f"\nTesting strategy on {ticker}")
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df = get_stock_data(ticker, start_date, end_date, 'daily')
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if df.empty:
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print(f"No data available for {ticker}")
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continue
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df = prepare_data_for_backtest(df)
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# Run backtest
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DynamicStrategy.indicator_configs = indicator_settings
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bt = Backtest(df, DynamicStrategy, cash=100000, commission=.002)
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stats = bt.run()
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# Store results
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result = {
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'Ticker': ticker,
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'Return [%]': stats['Return [%]'],
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'Sharpe Ratio': stats['Sharpe Ratio'],
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'Max Drawdown [%]': stats['Max. Drawdown [%]'],
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'Win Rate [%]': stats['Win Rate [%]'],
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'Number of Trades': stats['# Trades']
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}
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all_results.append(result)
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print(f"{ticker} - Return: {stats['Return [%]']:.2f}%, "
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f"Sharpe: {stats['Sharpe Ratio']:.2f}, "
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f"Drawdown: {stats['Max. Drawdown [%]']:.2f}%")
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except Exception as e:
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print(f"Error processing {ticker}: {str(e)}")
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continue
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return pd.DataFrame(all_results)
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def display_backtest_results(results: Dict):
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"""Display single backtest results with metrics and plots"""
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st.subheader("Backtest Results")
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