feat: Add comprehensive multi-ticker testing with batch processing and advanced filtering
This commit is contained in:
parent
1204b18a76
commit
a4e828da8c
@ -2,6 +2,7 @@ import streamlit as st
|
||||
import pandas_ta as ta
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from utils.common_utils import get_qualified_stocks
|
||||
from backtesting import Backtest, Strategy
|
||||
from typing import Dict, List, Union
|
||||
import itertools
|
||||
@ -232,26 +233,58 @@ def backtesting_page():
|
||||
with left_col:
|
||||
st.subheader("Backtest Settings")
|
||||
|
||||
# Add radio button for single/multiple ticker mode
|
||||
test_mode = st.radio("Testing Mode", ["Single Ticker", "Multiple Tickers"])
|
||||
# Add radio button for test mode
|
||||
test_mode = st.radio("Testing Mode", ["Single Ticker", "Multiple Tickers", "All Available Tickers"])
|
||||
|
||||
if test_mode == "Single Ticker":
|
||||
# Single ticker input
|
||||
ticker = st.text_input("Enter Ticker Symbol", value="AAPL").upper()
|
||||
tickers = [ticker]
|
||||
else:
|
||||
elif test_mode == "Multiple Tickers":
|
||||
# Multiple ticker input
|
||||
ticker_input = st.text_area(
|
||||
"Enter Ticker Symbols (one per line)",
|
||||
value="AAPL\nMSFT\nGOOG"
|
||||
)
|
||||
tickers = [t.strip().upper() for t in ticker_input.split('\n') if t.strip()]
|
||||
else: # All Available Tickers
|
||||
st.subheader("Filter Settings")
|
||||
min_price = st.number_input("Minimum Price", value=5.0)
|
||||
max_price = st.number_input("Maximum Price", value=1000.0)
|
||||
min_volume = st.number_input("Minimum Volume", value=100000)
|
||||
|
||||
# Add minimum performance filters
|
||||
# Get all qualified stocks based on filters
|
||||
try:
|
||||
qualified_stocks = get_qualified_stocks(
|
||||
start_date=start_datetime,
|
||||
end_date=end_datetime,
|
||||
min_price=min_price,
|
||||
max_price=max_price,
|
||||
min_volume=min_volume
|
||||
)
|
||||
st.info(f"Found {len(qualified_stocks)} qualified stocks for testing")
|
||||
tickers = qualified_stocks
|
||||
except Exception as e:
|
||||
st.error(f"Error getting qualified stocks: {str(e)}")
|
||||
tickers = []
|
||||
|
||||
# Add performance filters for multiple and all tickers modes
|
||||
if test_mode in ["Multiple Tickers", "All Available Tickers"]:
|
||||
st.subheader("Performance Filters")
|
||||
min_return = st.number_input("Minimum Return (%)", value=10.0)
|
||||
min_sharpe = st.number_input("Minimum Sharpe Ratio", value=1.0)
|
||||
max_drawdown = st.number_input("Maximum Drawdown (%)", value=-20.0)
|
||||
col1, col2, col3 = st.columns(3)
|
||||
with col1:
|
||||
min_return = st.number_input("Minimum Return (%)", value=10.0)
|
||||
with col2:
|
||||
min_sharpe = st.number_input("Minimum Sharpe Ratio", value=1.0)
|
||||
with col3:
|
||||
max_drawdown = st.number_input("Maximum Drawdown (%)", value=-20.0)
|
||||
|
||||
# Add batch size control for processing
|
||||
batch_size = st.number_input("Batch Size (tickers per batch)", value=50, min_value=1)
|
||||
|
||||
# Add progress tracking
|
||||
progress_bar = st.progress(0)
|
||||
status_text = st.empty()
|
||||
|
||||
# Date range selection
|
||||
col1, col2 = st.columns(2)
|
||||
@ -329,55 +362,87 @@ def backtesting_page():
|
||||
st.error(f"Error during backtest: {str(e)}")
|
||||
|
||||
else:
|
||||
# Multiple ticker logic
|
||||
# Multiple ticker or All Available Tickers logic
|
||||
try:
|
||||
results_df = run_multi_ticker_backtest(
|
||||
tickers, start_datetime, end_datetime, indicator_settings
|
||||
)
|
||||
total_tickers = len(tickers)
|
||||
processed_tickers = []
|
||||
all_results = []
|
||||
|
||||
# Apply performance filters
|
||||
filtered_df = results_df[
|
||||
(results_df['Return [%]'] >= min_return) &
|
||||
(results_df['Sharpe Ratio'] >= min_sharpe) &
|
||||
(results_df['Max Drawdown [%]'] >= max_drawdown)
|
||||
]
|
||||
for i in range(0, total_tickers, batch_size):
|
||||
batch = tickers[i:i+batch_size]
|
||||
status_text.text(f"Processing batch {i//batch_size + 1} of {(total_tickers + batch_size - 1)//batch_size}")
|
||||
|
||||
with right_col:
|
||||
st.subheader("Multi-Ticker Results")
|
||||
|
||||
# Display summary statistics
|
||||
st.write("### Summary Statistics")
|
||||
summary = pd.DataFrame({
|
||||
'Metric': ['Average Return', 'Average Sharpe', 'Average Drawdown', 'Success Rate'],
|
||||
'Value': [
|
||||
f"{results_df['Return [%]'].mean():.2f}%",
|
||||
f"{results_df['Sharpe Ratio'].mean():.2f}",
|
||||
f"{results_df['Max Drawdown [%]'].mean():.2f}%",
|
||||
f"{(len(filtered_df) / len(results_df) * 100):.1f}%"
|
||||
]
|
||||
})
|
||||
st.table(summary)
|
||||
|
||||
# Display full results
|
||||
st.write("### All Results")
|
||||
st.dataframe(results_df.sort_values('Return [%]', ascending=False))
|
||||
|
||||
# Display filtered results
|
||||
st.write("### Filtered Results (Meeting Criteria)")
|
||||
st.dataframe(filtered_df.sort_values('Return [%]', ascending=False))
|
||||
|
||||
# Create a downloadable CSV
|
||||
csv = results_df.to_csv(index=False)
|
||||
st.download_button(
|
||||
"Download Results CSV",
|
||||
csv,
|
||||
"backtest_results.csv",
|
||||
"text/csv",
|
||||
key='download-csv'
|
||||
results_df = run_multi_ticker_backtest(
|
||||
batch, start_datetime, end_datetime, indicator_settings
|
||||
)
|
||||
|
||||
if not results_df.empty:
|
||||
all_results.append(results_df)
|
||||
processed_tickers.extend(batch)
|
||||
|
||||
# Update progress
|
||||
progress = min((i + batch_size) / total_tickers, 1.0)
|
||||
progress_bar.progress(progress)
|
||||
|
||||
if all_results:
|
||||
# Combine all results
|
||||
results_df = pd.concat(all_results, ignore_index=True)
|
||||
|
||||
# Apply performance filters
|
||||
filtered_df = results_df[
|
||||
(results_df['Return [%]'] >= min_return) &
|
||||
(results_df['Sharpe Ratio'] >= min_sharpe) &
|
||||
(results_df['Max Drawdown [%]'] >= max_drawdown)
|
||||
]
|
||||
|
||||
with right_col:
|
||||
st.subheader("Multi-Ticker Results")
|
||||
|
||||
# Display summary statistics
|
||||
st.write("### Summary Statistics")
|
||||
summary = pd.DataFrame({
|
||||
'Metric': [
|
||||
'Total Tickers Tested',
|
||||
'Successful Tests',
|
||||
'Average Return',
|
||||
'Average Sharpe',
|
||||
'Average Drawdown',
|
||||
'Success Rate'
|
||||
],
|
||||
'Value': [
|
||||
f"{len(processed_tickers)}",
|
||||
f"{len(results_df)}",
|
||||
f"{results_df['Return [%]'].mean():.2f}%",
|
||||
f"{results_df['Sharpe Ratio'].mean():.2f}",
|
||||
f"{results_df['Max Drawdown [%]'].mean():.2f}%",
|
||||
f"{(len(filtered_df) / len(results_df) * 100):.1f}%"
|
||||
]
|
||||
})
|
||||
st.table(summary)
|
||||
|
||||
# Display full results
|
||||
st.write("### All Results")
|
||||
st.dataframe(results_df.sort_values('Return [%]', ascending=False))
|
||||
|
||||
# Display filtered results
|
||||
st.write("### Filtered Results (Meeting Criteria)")
|
||||
st.dataframe(filtered_df.sort_values('Return [%]', ascending=False))
|
||||
|
||||
# Create a downloadable CSV
|
||||
csv = results_df.to_csv(index=False)
|
||||
st.download_button(
|
||||
"Download Results CSV",
|
||||
csv,
|
||||
"backtest_results.csv",
|
||||
"text/csv",
|
||||
key='download-csv'
|
||||
)
|
||||
else:
|
||||
st.error("No valid results were generated from any ticker")
|
||||
|
||||
except Exception as e:
|
||||
st.error(f"Error during multi-ticker backtest: {str(e)}")
|
||||
st.error("Full error details:", exc_info=True)
|
||||
|
||||
def run_optimization(df: pd.DataFrame, indicator_settings: Dict) -> List:
|
||||
"""Run optimization with different parameter combinations"""
|
||||
|
||||
Loading…
Reference in New Issue
Block a user