refactor to remove rebundant parameters and use just a config file by default too
This commit is contained in:
Vasily.onl
parent
256ad67742
commit
ebd8ef3d87
@ -3,7 +3,6 @@
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"stop_date": "2025-03-15",
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"initial_usd": 10000,
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"timeframes": ["1min"],
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"stop_loss_pcts": [0.05],
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"strategies": [
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{
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"name": "bbrs",
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@ -17,7 +16,8 @@
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"sideways_rsi_threshold": [40, 60],
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"sideways_bb_multiplier": 1.8,
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"strategy_name": "MarketRegimeStrategy",
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"SqueezeStrategy": true
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"SqueezeStrategy": true,
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"stop_loss_pct": 0.05
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}
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}
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],
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@ -3,12 +3,14 @@
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"stop_date": "2025-03-15",
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"initial_usd": 10000,
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"timeframes": ["15min"],
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"stop_loss_pcts": [0.04],
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"strategies": [
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{
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"name": "default",
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"weight": 0.6,
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"params": {}
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"params": {
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"timeframe": "15min",
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"stop_loss_pct": 0.03
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}
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},
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{
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"name": "bbrs",
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@ -17,7 +19,13 @@
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"bb_width": 0.05,
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"bb_period": 20,
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"rsi_period": 14,
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"strategy_name": "MarketRegimeStrategy"
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"trending_rsi_threshold": [30, 70],
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"trending_bb_multiplier": 2.5,
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"sideways_rsi_threshold": [40, 60],
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"sideways_bb_multiplier": 1.8,
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"strategy_name": "MarketRegimeStrategy",
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"SqueezeStrategy": true,
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"stop_loss_pct": 0.05
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}
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}
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],
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@ -3,12 +3,14 @@
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"stop_date": "2025-03-15",
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"initial_usd": 10000,
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"timeframes": ["15min"],
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"stop_loss_pcts": [0.03, 0.05],
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"strategies": [
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{
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"name": "default",
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"weight": 1.0,
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"params": {}
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"params": {
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"timeframe": "15min",
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"stop_loss_pct": 0.03
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}
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}
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],
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"combination_rules": {
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@ -3,13 +3,13 @@
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"stop_date": "2024-01-31",
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"initial_usd": 10000,
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"timeframes": ["5min"],
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"stop_loss_pcts": [0.03, 0.05],
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"strategies": [
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{
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"name": "default",
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"weight": 1.0,
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"params": {
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"timeframe": "5min"
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"timeframe": "5min",
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"stop_loss_pct": 0.03
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}
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}
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],
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372
main.py
372
main.py
@ -38,183 +38,169 @@ def strategy_manager_exit(backtester: Backtest, df_index: int):
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"""Strategy Manager exit function"""
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return backtester.strategy_manager.get_exit_signal(backtester, df_index)
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def process_timeframe_data(min1_df, df, stop_loss_pcts, rule_name, initial_usd, strategy_config=None, debug=False):
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"""Process the entire timeframe with all stop loss values using Strategy Manager"""
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def process_timeframe_data(data_1min, timeframe, config, debug=False):
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"""Process a timeframe using Strategy Manager with configuration"""
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results_rows = []
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trade_rows = []
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for stop_loss_pct in stop_loss_pcts:
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# Create and initialize strategy manager
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if strategy_config:
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# Use provided strategy configuration
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strategy_manager = create_strategy_manager(strategy_config)
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else:
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# Default to single default strategy for backward compatibility
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default_strategy_config = {
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"strategies": [
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{
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"name": "default",
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"weight": 1.0,
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"params": {"stop_loss_pct": stop_loss_pct}
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}
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],
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"combination_rules": {
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"entry": "any",
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"exit": "any",
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"min_confidence": 0.5
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}
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}
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strategy_manager = create_strategy_manager(default_strategy_config)
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# Inject stop_loss_pct into all strategy params if not present
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for strategy in strategy_manager.strategies:
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if "stop_loss_pct" not in strategy.params:
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strategy.params["stop_loss_pct"] = stop_loss_pct
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# Get the primary timeframe from the first strategy for backtester setup
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primary_strategy = strategy_manager.strategies[0]
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primary_timeframe = primary_strategy.get_timeframes()[0]
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# For BBRS strategy, it works with 1-minute data directly and handles internal resampling
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# For other strategies, use their preferred timeframe
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if primary_strategy.name == "bbrs":
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# BBRS strategy processes 1-minute data and outputs signals on its internal timeframes
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# Use 1-minute data for backtester working dataframe
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working_df = min1_df.copy()
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else:
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# Other strategies specify their preferred timeframe
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# Create backtester working data from the primary strategy's primary timeframe
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temp_backtester = type('temp', (), {})()
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temp_backtester.original_df = min1_df
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# Let the primary strategy resample the data to get the working dataframe
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primary_strategy._resample_data(min1_df)
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working_df = primary_strategy.get_primary_timeframe_data()
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# Prepare working dataframe for backtester (ensure timestamp column)
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working_df_for_backtest = working_df.copy().reset_index()
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if 'index' in working_df_for_backtest.columns:
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working_df_for_backtest = working_df_for_backtest.rename(columns={'index': 'timestamp'})
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# Initialize backtest with strategy manager initialization
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backtester = Backtest(initial_usd, working_df_for_backtest, working_df_for_backtest, strategy_manager_init)
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# Store original min1_df for strategy processing
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backtester.original_df = min1_df
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# Attach strategy manager to backtester and initialize
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backtester.strategy_manager = strategy_manager
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strategy_manager.initialize(backtester)
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# Extract values from config
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initial_usd = config['initial_usd']
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strategy_config = {
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"strategies": config['strategies'],
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"combination_rules": config['combination_rules']
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}
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# Run backtest with strategy manager functions
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results = backtester.run(
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strategy_manager_entry,
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strategy_manager_exit,
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debug
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)
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# Create and initialize strategy manager
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if not strategy_config:
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logging.error("No strategy configuration provided")
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return results_rows, trade_rows
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strategy_manager = create_strategy_manager(strategy_config)
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# Get the primary timeframe from the first strategy for backtester setup
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primary_strategy = strategy_manager.strategies[0]
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primary_timeframe = primary_strategy.get_timeframes()[0]
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# For BBRS strategy, it works with 1-minute data directly and handles internal resampling
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# For other strategies, use their preferred timeframe
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if primary_strategy.name == "bbrs":
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# BBRS strategy processes 1-minute data and outputs signals on its internal timeframes
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# Use 1-minute data for backtester working dataframe
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working_df = data_1min.copy()
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else:
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# Other strategies specify their preferred timeframe
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# Let the primary strategy resample the data to get the working dataframe
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primary_strategy._resample_data(data_1min)
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working_df = primary_strategy.get_primary_timeframe_data()
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# Prepare working dataframe for backtester (ensure timestamp column)
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working_df_for_backtest = working_df.copy().reset_index()
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if 'index' in working_df_for_backtest.columns:
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working_df_for_backtest = working_df_for_backtest.rename(columns={'index': 'timestamp'})
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# Initialize backtest with strategy manager initialization
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backtester = Backtest(initial_usd, working_df_for_backtest, working_df_for_backtest, strategy_manager_init)
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# Store original min1_df for strategy processing
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backtester.original_df = data_1min
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# Attach strategy manager to backtester and initialize
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backtester.strategy_manager = strategy_manager
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strategy_manager.initialize(backtester)
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n_trades = results["n_trades"]
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trades = results.get('trades', [])
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wins = [1 for t in trades if t['exit'] is not None and t['exit'] > t['entry']]
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n_winning_trades = len(wins)
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total_profit = sum(trade['profit_pct'] for trade in trades)
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total_loss = sum(-trade['profit_pct'] for trade in trades if trade['profit_pct'] < 0)
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win_rate = n_winning_trades / n_trades if n_trades > 0 else 0
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avg_trade = total_profit / n_trades if n_trades > 0 else 0
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profit_ratio = total_profit / total_loss if total_loss > 0 else float('inf')
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cumulative_profit = 0
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max_drawdown = 0
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peak = 0
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# Run backtest with strategy manager functions
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results = backtester.run(
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strategy_manager_entry,
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strategy_manager_exit,
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debug
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)
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for trade in trades:
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cumulative_profit += trade['profit_pct']
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n_trades = results["n_trades"]
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trades = results.get('trades', [])
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wins = [1 for t in trades if t['exit'] is not None and t['exit'] > t['entry']]
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n_winning_trades = len(wins)
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total_profit = sum(trade['profit_pct'] for trade in trades)
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total_loss = sum(-trade['profit_pct'] for trade in trades if trade['profit_pct'] < 0)
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win_rate = n_winning_trades / n_trades if n_trades > 0 else 0
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avg_trade = total_profit / n_trades if n_trades > 0 else 0
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profit_ratio = total_profit / total_loss if total_loss > 0 else float('inf')
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cumulative_profit = 0
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max_drawdown = 0
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peak = 0
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if cumulative_profit > peak:
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peak = cumulative_profit
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drawdown = peak - cumulative_profit
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for trade in trades:
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cumulative_profit += trade['profit_pct']
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if drawdown > max_drawdown:
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max_drawdown = drawdown
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if cumulative_profit > peak:
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peak = cumulative_profit
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drawdown = peak - cumulative_profit
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final_usd = initial_usd
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if drawdown > max_drawdown:
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max_drawdown = drawdown
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for trade in trades:
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final_usd *= (1 + trade['profit_pct'])
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final_usd = initial_usd
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total_fees_usd = sum(trade.get('fee_usd', 0.0) for trade in trades)
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for trade in trades:
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final_usd *= (1 + trade['profit_pct'])
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# Update row to include timeframe information
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row = {
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"timeframe": f"{rule_name}({primary_timeframe})", # Show actual timeframe used
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total_fees_usd = sum(trade.get('fee_usd', 0.0) for trade in trades)
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# Get stop_loss_pct from the first strategy for reporting
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# In multi-strategy setups, strategies can have different stop_loss_pct values
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stop_loss_pct = primary_strategy.params.get("stop_loss_pct", "N/A")
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# Update row to include timeframe information
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row = {
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"timeframe": f"{timeframe}({primary_timeframe})", # Show actual timeframe used
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"stop_loss_pct": stop_loss_pct,
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"n_trades": n_trades,
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"n_stop_loss": sum(1 for trade in trades if 'type' in trade and trade['type'] == 'STOP_LOSS'),
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"win_rate": win_rate,
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"max_drawdown": max_drawdown,
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"avg_trade": avg_trade,
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"total_profit": total_profit,
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"total_loss": total_loss,
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"profit_ratio": profit_ratio,
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"initial_usd": initial_usd,
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"final_usd": final_usd,
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"total_fees_usd": total_fees_usd,
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}
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results_rows.append(row)
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for trade in trades:
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trade_rows.append({
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"timeframe": f"{timeframe}({primary_timeframe})",
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"stop_loss_pct": stop_loss_pct,
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"n_trades": n_trades,
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"n_stop_loss": sum(1 for trade in trades if 'type' in trade and trade['type'] == 'STOP_LOSS'),
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"win_rate": win_rate,
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"max_drawdown": max_drawdown,
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"avg_trade": avg_trade,
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"total_profit": total_profit,
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"total_loss": total_loss,
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"profit_ratio": profit_ratio,
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"initial_usd": initial_usd,
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"final_usd": final_usd,
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"total_fees_usd": total_fees_usd,
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}
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results_rows.append(row)
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for trade in trades:
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trade_rows.append({
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"timeframe": f"{rule_name}({primary_timeframe})",
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"stop_loss_pct": stop_loss_pct,
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"entry_time": trade.get("entry_time"),
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"exit_time": trade.get("exit_time"),
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"entry_price": trade.get("entry"),
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"exit_price": trade.get("exit"),
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"profit_pct": trade.get("profit_pct"),
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"type": trade.get("type"),
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"fee_usd": trade.get("fee_usd"),
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})
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# Log strategy summary
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strategy_summary = strategy_manager.get_strategy_summary()
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logging.info(f"Timeframe: {rule_name}({primary_timeframe}), Stop Loss: {stop_loss_pct}, "
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f"Trades: {n_trades}, Strategies: {[s['name'] for s in strategy_summary['strategies']]}")
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if debug:
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# Plot after each backtest run
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try:
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# Check if any strategy has processed_data for universal plotting
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processed_data = None
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for strategy in strategy_manager.strategies:
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if hasattr(backtester, 'processed_data') and backtester.processed_data is not None:
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processed_data = backtester.processed_data
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break
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if processed_data is not None and not processed_data.empty:
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# Format strategy data with actual executed trades for universal plotting
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formatted_data = BacktestCharts.format_strategy_data_with_trades(processed_data, results)
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# Plot using universal function
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BacktestCharts.plot_data(formatted_data)
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"entry_time": trade.get("entry_time"),
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"exit_time": trade.get("exit_time"),
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"entry_price": trade.get("entry"),
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"exit_price": trade.get("exit"),
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"profit_pct": trade.get("profit_pct"),
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"type": trade.get("type"),
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"fee_usd": trade.get("fee_usd"),
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})
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# Log strategy summary
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strategy_summary = strategy_manager.get_strategy_summary()
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logging.info(f"Timeframe: {timeframe}({primary_timeframe}), Stop Loss: {stop_loss_pct}, "
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f"Trades: {n_trades}, Strategies: {[s['name'] for s in strategy_summary['strategies']]}")
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if debug:
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# Plot after each backtest run
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try:
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# Check if any strategy has processed_data for universal plotting
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processed_data = None
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for strategy in strategy_manager.strategies:
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if hasattr(backtester, 'processed_data') and backtester.processed_data is not None:
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processed_data = backtester.processed_data
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break
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if processed_data is not None and not processed_data.empty:
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# Format strategy data with actual executed trades for universal plotting
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formatted_data = BacktestCharts.format_strategy_data_with_trades(processed_data, results)
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# Plot using universal function
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BacktestCharts.plot_data(formatted_data)
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else:
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# Fallback to meta_trend plot if available
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if "meta_trend" in backtester.strategies:
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meta_trend = backtester.strategies["meta_trend"]
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# Use the working dataframe for plotting
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BacktestCharts.plot(working_df, meta_trend)
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else:
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# Fallback to meta_trend plot if available
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if "meta_trend" in backtester.strategies:
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meta_trend = backtester.strategies["meta_trend"]
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# Use the working dataframe for plotting
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BacktestCharts.plot(working_df, meta_trend)
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else:
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print("No plotting data available")
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except Exception as e:
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print(f"Plotting failed: {e}")
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print("No plotting data available")
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except Exception as e:
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print(f"Plotting failed: {e}")
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return results_rows, trade_rows
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def process(timeframe_info, debug=False):
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"""Process a single (timeframe, stop_loss_pct) combination with strategy config"""
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rule, data_1min, stop_loss_pct, initial_usd, strategy_config = timeframe_info
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"""Process a single timeframe with strategy config"""
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timeframe, data_1min, config = timeframe_info
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# Pass the original 1-minute data - strategies will handle their own timeframe resampling
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# Pass the essential data and full config
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results_rows, all_trade_rows = process_timeframe_data(
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data_1min, data_1min, [stop_loss_pct], rule, initial_usd, strategy_config, debug=debug
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data_1min, timeframe, config, debug=debug
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)
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return results_rows, all_trade_rows
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@ -272,68 +258,25 @@ if __name__ == "__main__":
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parser.add_argument("config", type=str, nargs="?", help="Path to config JSON file.")
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args = parser.parse_args()
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# Default values (from config.json)
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default_config = {
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"start_date": "2025-03-01",
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"stop_date": datetime.datetime.today().strftime('%Y-%m-%d'),
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"initial_usd": 10000,
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"timeframes": ["15min"],
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"stop_loss_pcts": [0.03],
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"strategies": [
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{
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"name": "default",
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"weight": 1.0,
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"params": {}
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}
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],
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"combination_rules": {
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"entry": "any",
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"exit": "any",
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"min_confidence": 0.5
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}
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}
|
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|
||||
if args.config:
|
||||
with open(args.config, 'r') as f:
|
||||
# Use config_default.json as fallback if no config provided
|
||||
config_file = args.config or "configs/config_default.json"
|
||||
|
||||
try:
|
||||
with open(config_file, 'r') as f:
|
||||
config = json.load(f)
|
||||
elif not debug:
|
||||
print("No config file provided. Please enter the following values (press Enter to use default):")
|
||||
|
||||
start_date = input(f"Start date [{default_config['start_date']}]: ") or default_config['start_date']
|
||||
stop_date = input(f"Stop date [{default_config['stop_date']}]: ") or default_config['stop_date']
|
||||
|
||||
initial_usd_str = input(f"Initial USD [{default_config['initial_usd']}]: ") or str(default_config['initial_usd'])
|
||||
initial_usd = float(initial_usd_str)
|
||||
|
||||
timeframes_str = input(f"Timeframes (comma separated) [{', '.join(default_config['timeframes'])}]: ") or ','.join(default_config['timeframes'])
|
||||
timeframes = [tf.strip() for tf in timeframes_str.split(',') if tf.strip()]
|
||||
|
||||
stop_loss_pcts_str = input(f"Stop loss pcts (comma separated) [{', '.join(str(x) for x in default_config['stop_loss_pcts'])}]: ") or ','.join(str(x) for x in default_config['stop_loss_pcts'])
|
||||
stop_loss_pcts = [float(x.strip()) for x in stop_loss_pcts_str.split(',') if x.strip()]
|
||||
|
||||
config = {
|
||||
'start_date': start_date,
|
||||
'stop_date': stop_date,
|
||||
'initial_usd': initial_usd,
|
||||
'timeframes': timeframes,
|
||||
'stop_loss_pcts': stop_loss_pcts,
|
||||
'strategies': default_config['strategies'],
|
||||
'combination_rules': default_config['combination_rules']
|
||||
}
|
||||
else:
|
||||
config = default_config
|
||||
print(f"Using config: {config_file}")
|
||||
except FileNotFoundError:
|
||||
print(f"Error: Config file '{config_file}' not found.")
|
||||
print("Available configs: configs/config_default.json, configs/config_bbrs.json, configs/config_combined.json")
|
||||
exit(1)
|
||||
except json.JSONDecodeError as e:
|
||||
print(f"Error: Invalid JSON in config file '{config_file}': {e}")
|
||||
exit(1)
|
||||
|
||||
start_date = config['start_date']
|
||||
stop_date = config['stop_date']
|
||||
initial_usd = config['initial_usd']
|
||||
timeframes = config['timeframes']
|
||||
stop_loss_pcts = config['stop_loss_pcts']
|
||||
|
||||
# Extract strategy configuration
|
||||
strategy_config = {
|
||||
"strategies": config.get('strategies', default_config['strategies']),
|
||||
"combination_rules": config.get('combination_rules', default_config['combination_rules'])
|
||||
}
|
||||
|
||||
timestamp = datetime.datetime.now().strftime("%Y_%m_%d_%H_%M")
|
||||
|
||||
@ -351,11 +294,10 @@ if __name__ == "__main__":
|
||||
f"Initial USD\t{initial_usd}"
|
||||
]
|
||||
|
||||
# Create tasks for each (timeframe, stop_loss_pct) combination
|
||||
# Create tasks for each timeframe
|
||||
tasks = [
|
||||
(name, data_1min, stop_loss_pct, initial_usd, strategy_config)
|
||||
(name, data_1min, config)
|
||||
for name in timeframes
|
||||
for stop_loss_pct in stop_loss_pcts
|
||||
]
|
||||
|
||||
if debug:
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user