Resolve issue where backtest results were incorrect when stopping out and perform backtests in parallel using multiprocessing
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@ -4,6 +4,7 @@ import os
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import pandas as pd
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import plotly.express as px
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from concurrent.futures import ProcessPoolExecutor
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from dataclasses import dataclass
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from datetime import datetime
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from dotenv import load_dotenv
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@ -149,13 +150,20 @@ def get_spread_history(historical_option_data: pd.DataFrame, option_strat: Delta
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return spread_history
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def _backtest_iron_condor(
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historical_option_data: pd.DataFrame,
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historical_data_file: str,
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call_spread_strategy: OptionSpreadStrategy,
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put_spread_strategy: OptionSpreadStrategy
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) -> BacktestResult:
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call_spread_history = get_spread_history(historical_option_data, call_spread_strategy)
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put_spread_history = get_spread_history(historical_option_data, put_spread_strategy)
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print('Processing File:', historical_data_file)
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historical_option_data = pd.read_csv(historical_data_file)
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if isinstance(call_spread_strategy, CreditTargetStrategy):
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call_spread_history = get_spread_history_credit(historical_option_data, call_spread_strategy)
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put_spread_history = get_spread_history_credit(historical_option_data, put_spread_strategy)
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else:
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call_spread_history = get_spread_history(historical_option_data, call_spread_strategy)
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put_spread_history = get_spread_history(historical_option_data, put_spread_strategy)
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current_date = call_spread_history.iloc[0].name[:10]
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@ -246,7 +254,6 @@ def _backtest_iron_condor(
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call_spread_stopped_out = True
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exit_time = call_spread.name[-8:]
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logging.info('Call Spread Stopped Out')
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break
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if not put_spread_stopped_out:
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if current_put_spread_price >= ((put_spread_strategy.stop_loss_multiple + 1) * original_put_spread_price):
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@ -256,7 +263,6 @@ def _backtest_iron_condor(
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put_spread_stopped_out = True
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exit_time = call_spread.name[-8:]
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logging.info('Put Spread Stopped Out')
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break
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if not (call_spread_stopped_out and put_spread_stopped_out):
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if current_call_spread_price > current_put_spread_price:
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@ -339,34 +345,36 @@ def backtest_iron_condor(
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total_trades = 0.0
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total_wins = 0.0
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result_dates = []
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result_pnl = []
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backtest_results = []
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start_year = start_date.year
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end_year = end_date.year
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for year in range(start_year, end_year + 1):
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year_directory = os.path.join(OPTION_DATA_DIRECTORY, str(year))
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for file in os.listdir(year_directory):
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historical_data_file = os.path.join(year_directory, file)
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if os.path.isdir(historical_data_file) or not file.endswith('.csv'):
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continue
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futures = []
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with ProcessPoolExecutor(max_workers = 10) as executor:
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for year in range(start_year, end_year + 1):
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year_directory = os.path.join(OPTION_DATA_DIRECTORY, str(year))
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for file in os.listdir(year_directory):
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historical_data_file = os.path.join(year_directory, file)
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if os.path.isdir(historical_data_file) or not file.endswith('.csv'):
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continue
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# Assuming file format 'YYYY-MM-DD.csv'.
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file_date_str = os.path.splitext(file)[0]
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file_date = datetime.strptime(file_date_str, '%Y-%m-%d')
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# Assuming file format 'YYYY-MM-DD.csv'.
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file_date_str = os.path.splitext(file)[0]
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file_date = datetime.strptime(file_date_str, '%Y-%m-%d')
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# TODO: This doesn't work as expected when the start date is not set to midnight.
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if file_date < start_date or file_date > end_date:
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continue
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# TODO: This doesn't work as expected when the start date is not set to midnight.
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if file_date < start_date or file_date > end_date:
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continue
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print('Processing File:', historical_data_file)
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logging.info('Processing File: %s', historical_data_file)
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logging.info('Processing File: %s', historical_data_file)
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historical_option_data = pd.read_csv(historical_data_file)
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backtest_result = _backtest_iron_condor(historical_option_data, call_spread_strategy, put_spread_strategy)
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future = executor.submit(_backtest_iron_condor,
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historical_data_file, call_spread_strategy, put_spread_strategy)
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futures.append(future)
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backtest_results = []
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for future in futures:
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backtest_result = future.result()
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if backtest_result:
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total_premium_received += backtest_result.trade_pnl
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backtest_result.profit = total_premium_received
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backtest_results.append(backtest_result)
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@ -380,10 +388,6 @@ def backtest_iron_condor(
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logging.info('Win Rate: %f', (total_wins / total_trades) if total_trades > 0 else 0.0)
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logging.info('Average Premium Received: %f', (total_premium_received / total_trades) if total_trades > 0 else 0.0)
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current_date = historical_option_data.iloc[0]['quote_datetime'][:10]
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result_dates.append(current_date)
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result_pnl.append(total_premium_received)
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# TODO: Either look up the symbol in the historical option data or have the client provide it.
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backtest_results = pd.DataFrame([{
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'Date': result.date,
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