strategy_updater:
removed args_common_optimize for strategy-updater backtest_lookahead_bias_checker: added args and cli-options for minimum and target trade amounts fixed code according to best-practice coding requests of matthias (CamelCase etc)
This commit is contained in:
hippocritical
parent
efefcb240b
commit
7bd55971dc
@ -116,9 +116,10 @@ NO_CONF_REQURIED = ["convert-data", "convert-trade-data", "download-data", "list
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NO_CONF_ALLOWED = ["create-userdir", "list-exchanges", "new-strategy"]
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ARGS_STRATEGY_UPDATER = ARGS_COMMON_OPTIMIZE + ["strategy_list"]
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ARGS_STRATEGY_UPDATER = ["strategy_list"]
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ARGS_BACKTEST_LOOKAHEAD_BIAS_CHECKER = ARGS_BACKTEST
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ARGS_BACKTEST_LOOKAHEAD_BIAS_CHECKER = ARGS_BACKTEST + ["minimum_trade_amount",
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"targeted_trade_amount"]
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# + ["target_trades", "minimum_trades",
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@ -461,7 +462,7 @@ class Arguments:
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# Add backtest lookahead bias checker subcommand
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backtest_lookahead_bias_checker_cmd = \
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subparsers.add_parser('backtest_lookahead_bias_checker',
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subparsers.add_parser('backtest-lookahead-bias-checker',
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help="checks for potential look ahead bias",
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parents=[_common_parser])
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backtest_lookahead_bias_checker_cmd.set_defaults(func=start_backtest_lookahead_bias_checker)
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@ -675,4 +675,18 @@ AVAILABLE_CLI_OPTIONS = {
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help='Run backtest with ready models.',
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action='store_true'
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),
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"minimum_trade_amount": Arg(
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'--minimum-trade-amount',
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help='set INT minimum trade amount',
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type=check_int_positive,
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metavar='INT',
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default=10,
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),
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"targeted_trade_amount": Arg(
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'--targeted-trade-amount',
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help='set INT targeted trade amount',
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type=check_int_positive,
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metavar='INT',
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default=20,
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)
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}
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@ -7,7 +7,7 @@ from typing import Any, Dict
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from freqtrade.configuration import setup_utils_configuration
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from freqtrade.enums import RunMode
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from freqtrade.resolvers import StrategyResolver
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from freqtrade.strategy.backtest_lookahead_bias_checker import backtest_lookahead_bias_checker
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from freqtrade.strategy.backtest_lookahead_bias_checker import BacktestLookaheadBiasChecker
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from freqtrade.strategy.strategyupdater import StrategyUpdater
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@ -67,9 +67,16 @@ def start_backtest_lookahead_bias_checker(args: Dict[str, Any]) -> None:
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"""
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config = setup_utils_configuration(args, RunMode.UTIL_NO_EXCHANGE)
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if args['targeted_trade_amount'] < args['minimum_trade_amount']:
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# add logic that tells the user to check the configuration
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# since this combo doesn't make any sense.
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pass
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strategy_objs = StrategyResolver.search_all_objects(
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config, enum_failed=False, recursive=config.get('recursive_strategy_search', False))
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bias_checker_instances = []
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filtered_strategy_objs = []
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if 'strategy_list' in args and args['strategy_list'] is not None:
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for args_strategy in args['strategy_list']:
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@ -80,21 +87,29 @@ def start_backtest_lookahead_bias_checker(args: Dict[str, Any]) -> None:
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break
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for filtered_strategy_obj in filtered_strategy_objs:
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initialize_single_lookahead_bias_checker(filtered_strategy_obj, config)
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bias_checker_instances = initialize_single_lookahead_bias_checker(
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filtered_strategy_obj, config, args)
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else:
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processed_locations = set()
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for strategy_obj in strategy_objs:
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if strategy_obj['location'] not in processed_locations:
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processed_locations.add(strategy_obj['location'])
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initialize_single_lookahead_bias_checker(strategy_obj, config)
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bias_checker_instances = initialize_single_lookahead_bias_checker(
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strategy_obj, config, args)
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create_result_list(bias_checker_instances)
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def initialize_single_lookahead_bias_checker(strategy_obj, config):
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def create_result_list(bias_checker_instances):
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pass
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def initialize_single_lookahead_bias_checker(strategy_obj, config, args):
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# try:
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print(f"Bias test of {Path(strategy_obj['location']).name} started.")
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instance_backtest_lookahead_bias_checker = backtest_lookahead_bias_checker()
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instance_backtest_lookahead_bias_checker = BacktestLookaheadBiasChecker()
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start = time.perf_counter()
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instance_backtest_lookahead_bias_checker.start(config, strategy_obj)
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current_instance = instance_backtest_lookahead_bias_checker.start(config, strategy_obj, args)
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elapsed = time.perf_counter() - start
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print(f"checking look ahead bias via backtests of {Path(strategy_obj['location']).name} "
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f"took {elapsed:.1f} seconds.")
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return current_instance
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@ -1,4 +1,4 @@
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# pragma pylint: disable=missing-docstring, W0212, line-too-long, C0103, unused-argument
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import copy
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from copy import deepcopy
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from datetime import datetime, timedelta, timezone
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@ -10,8 +10,8 @@ from freqtrade.exchange import timeframe_to_minutes
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from freqtrade.optimize.backtesting import Backtesting
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class backtest_lookahead_bias_checker:
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class varHolder:
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class BacktestLookaheadBiasChecker:
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class VarHolder:
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timerange: TimeRange
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data: pandas.DataFrame
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indicators: pandas.DataFrame
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@ -21,7 +21,7 @@ class backtest_lookahead_bias_checker:
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to_dt: datetime
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compared_dt: datetime
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class analysis:
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class Analysis:
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def __init__(self):
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self.total_signals = 0
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self.false_entry_signals = 0
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@ -37,24 +37,24 @@ class backtest_lookahead_bias_checker:
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has_bias: bool
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def __init__(self):
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self.strategy_obj
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self.current_analysis
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self.config
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self.full_varHolder
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self.entry_varholder
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self.exit_varholder
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self.backtesting
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self.signals_to_check: int = 20
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self.current_analysis
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self.full_varHolder.from_dt
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self.full_varHolder.to_dt
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self.strategy_obj = None
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self.current_analysis = None
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self.local_config = None
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self.full_varHolder = None
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self.entry_varHolder = None
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self.exit_varHolder = None
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self.backtesting = None
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self.current_analysis = None
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self.minimum_trade_amount = None
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self.targeted_trade_amount = None
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@staticmethod
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def dt_to_timestamp(dt):
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timestamp = int(dt.replace(tzinfo=timezone.utc).timestamp())
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return timestamp
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def get_result(self, backtesting, processed):
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@staticmethod
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def get_result(backtesting, processed):
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min_date, max_date = get_timerange(processed)
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result = backtesting.backtest(
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@ -64,6 +64,24 @@ class backtest_lookahead_bias_checker:
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)
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return result
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@staticmethod
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def report_signal(result, column_name, checked_timestamp):
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df = result['results']
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row_count = df[column_name].shape[0]
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if row_count == 0:
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return False
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else:
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df_cut = df[(df[column_name] == checked_timestamp)]
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if df_cut[column_name].shape[0] == 0:
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# print("did NOT find the same signal in column " + column_name +
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# " at timestamp " + str(checked_timestamp))
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return False
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else:
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return True
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return False
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# analyzes two data frames with processed indicators and shows differences between them.
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def analyze_indicators(self, full_vars, cut_vars, current_pair):
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# extract dataframes
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@ -87,12 +105,11 @@ class backtest_lookahead_bias_checker:
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for col_name, values in compare_df.items():
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col_idx = compare_df.columns.get_loc(col_name)
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compare_df_row = compare_df.iloc[0]
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# compare_df now is comprised of tuples with [1] having either 'self' or 'other'
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# compare_df now comprises tuples with [1] having either 'self' or 'other'
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if 'other' in col_name[1]:
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continue
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self_value = compare_df_row[col_idx]
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other_value = compare_df_row[col_idx + 1]
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other_value = compare_df_row[col_idx + 1]
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# output differences
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if self_value != other_value:
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@ -101,90 +118,62 @@ class backtest_lookahead_bias_checker:
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self.current_analysis.false_indicators.append(col_name[0])
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print(f"=> found look ahead bias in indicator {col_name[0]}. " +
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f"{str(self_value)} != {str(other_value)}")
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# return compare_df
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def report_signal(self, result, column_name, checked_timestamp):
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df = result['results']
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row_count = df[column_name].shape[0]
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if row_count == 0:
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return False
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else:
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df_cut = df[(df[column_name] == checked_timestamp)]
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if df_cut[column_name].shape[0] == 0:
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# print("did NOT find the same signal in column " + column_name +
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# " at timestamp " + str(checked_timestamp))
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return False
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else:
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return True
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return False
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def prepare_data(self, varholder, var_pairs):
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self.config['timerange'] = \
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str(int(self.dt_to_timestamp(varholder.from_dt))) + "-" + \
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str(int(self.dt_to_timestamp(varholder.to_dt)))
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self.backtesting = Backtesting(self.config)
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def prepare_data(self, varHolder, pairs_to_load):
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prepare_data_config = copy.deepcopy(self.local_config)
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prepare_data_config['timerange'] = (str(self.dt_to_timestamp(varHolder.from_dt)) + "-" +
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str(self.dt_to_timestamp(varHolder.to_dt)))
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prepare_data_config['pairs'] = pairs_to_load
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self.backtesting = Backtesting(prepare_data_config)
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self.backtesting._set_strategy(self.backtesting.strategylist[0])
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varholder.data, varholder.timerange = self.backtesting.load_bt_data()
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varholder.indicators = self.backtesting.strategy.advise_all_indicators(varholder.data)
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varholder.result = self.get_result(self.backtesting, varholder.indicators)
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varHolder.data, varHolder.timerange = self.backtesting.load_bt_data()
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varHolder.indicators = self.backtesting.strategy.advise_all_indicators(varHolder.data)
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varHolder.result = self.get_result(self.backtesting, varHolder.indicators)
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def start(self, config, strategy_obj: dict) -> None:
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self.strategy_obj = strategy_obj
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self.config = config
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self.current_analysis = backtest_lookahead_bias_checker.analysis()
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def update_output_file(self):
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pass
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max_try_signals: int = 3
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found_signals: int = 0
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continue_with_strategy = True
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def start(self, config, strategy_obj: dict, args) -> None:
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# first we need to get the necessary entry/exit signals
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# so we start by 14 days and increase in 1 month steps
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# until we have the desired trade amount.
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for try_buysignals in range(max_try_signals): # range(3) = 0..2
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# re-initialize backtesting-variable
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self.full_varHolder = backtest_lookahead_bias_checker.varHolder()
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# deepcopy so we can change the pairs for the 2ndary runs
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# and not worry about another strategy to check after.
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self.local_config = deepcopy(config)
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self.local_config['strategy_list'] = [strategy_obj['name']]
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self.current_analysis = BacktestLookaheadBiasChecker.Analysis()
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self.minimum_trade_amount = args['minimum_trade_amount']
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self.targeted_trade_amount = args['targeted_trade_amount']
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# define datetimes in human readable format
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self.full_varHolder.from_dt = datetime(2022, 9, 1)
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self.full_varHolder.to_dt = datetime(2022, 9, 15) + timedelta(days=30 * try_buysignals)
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# first make a single backtest
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self.full_varHolder = BacktestLookaheadBiasChecker.VarHolder()
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self.prepare_data(self.full_varHolder, self.config['pairs'])
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found_signals = self.full_varHolder.result['results'].shape[0] + 1
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if try_buysignals == max_try_signals - 1:
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if found_signals < self.signals_to_check / 2:
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print(f"... only found {str(int(found_signals / 2))} "
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f"buy signals for {self.strategy_obj['name']}. "
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f"Cancelling...")
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continue_with_strategy = False
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else:
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print(
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f"Found {str(found_signals)} buy signals. "
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f"Going with max {str(self.signals_to_check)} "
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f" buy signals in the full timerange from "
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f"{str(self.full_varHolder.from_dt)} to {str(self.full_varHolder.to_dt)}")
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break
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elif found_signals < self.signals_to_check:
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print(
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f"Only found {str(found_signals)} buy signals in the full timerange from "
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f"{str(self.full_varHolder.from_dt)} to "
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f"{str(self.full_varHolder.to_dt)}. "
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f"will increase timerange trying to get at least "
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f"{str(self.signals_to_check)} signals.")
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else:
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print(
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f"Found {str(found_signals)} buy signals, more than necessary. "
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f"Reducing to {str(self.signals_to_check)} "
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f"checked buy signals in the full timerange from "
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f"{str(self.full_varHolder.from_dt)} to {str(self.full_varHolder.to_dt)}")
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break
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if not continue_with_strategy:
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# define datetime in human-readable format
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parsed_timerange = TimeRange.parse_timerange(config['timerange'])
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if (parsed_timerange is not None and
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parsed_timerange.startdt is not None and
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parsed_timerange.stopdt is not None):
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self.full_varHolder.from_dt = parsed_timerange.startdt
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self.full_varHolder.to_dt = parsed_timerange.stopdt
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else:
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print("Parsing of parsed_timerange failed. exiting!")
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return
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self.prepare_data(self.full_varHolder, self.local_config['pairs'])
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found_signals: int = self.full_varHolder.result['results'].shape[0] + 1
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if found_signals >= self.targeted_trade_amount:
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print(f"Found {found_signals} trades, calculating {self.targeted_trade_amount} trades.")
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elif self.targeted_trade_amount >= found_signals >= self.minimum_trade_amount:
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print(f"Only found {found_signals} trades. Calculating all available trades.")
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else:
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print(f"found {found_signals} trades "
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f"which is less than minimum_trade_amount {self.minimum_trade_amount}. "
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f"Cancelling this backtest lookahead bias test.")
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return
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# now we loop through all entry signals
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# starting from the same datetime to avoid miss-reports of bias
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for idx, result_row in self.full_varHolder.result['results'].iterrows():
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if self.current_analysis.total_signals == self.signals_to_check:
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if self.current_analysis.total_signals == self.targeted_trade_amount:
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break
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# if force-sold, ignore this signal since here it will unconditionally exit.
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@ -193,49 +182,45 @@ class backtest_lookahead_bias_checker:
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self.current_analysis.total_signals += 1
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self.entry_varholder = backtest_lookahead_bias_checker.varHolder()
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self.exit_varholder = backtest_lookahead_bias_checker.varHolder()
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self.entry_varholder.from_dt = self.full_varHolder.from_dt # result_row['open_date']
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self.entry_varholder.compared_dt = result_row['open_date']
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self.entry_varHolder = BacktestLookaheadBiasChecker.VarHolder()
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self.exit_varHolder = BacktestLookaheadBiasChecker.VarHolder()
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self.entry_varHolder.from_dt = self.full_varHolder.from_dt
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self.entry_varHolder.compared_dt = result_row['open_date']
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# to_dt needs +1 candle since it won't buy on the last candle
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self.entry_varholder.to_dt = result_row['open_date'] + \
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timedelta(minutes=timeframe_to_minutes(self.config['timeframe']) * 2)
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self.entry_varHolder.to_dt = (result_row['open_date'] +
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timedelta(minutes=timeframe_to_minutes(
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self.local_config['timeframe'])))
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self.prepare_data(self.entry_varholder, [result_row['pair']])
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self.prepare_data(self.entry_varHolder, [result_row['pair']])
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# ---
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# print("analyzing the sell signal")
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# to_dt needs +1 candle since it will always sell all trades on the last candle
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self.exit_varholder.from_dt = self.full_varHolder.from_dt # result_row['open_date']
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self.exit_varholder.to_dt = \
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result_row['close_date'] + \
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timedelta(minutes=timeframe_to_minutes(self.config['timeframe']))
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self.exit_varholder.compared_dt = result_row['close_date']
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# to_dt needs +1 candle since it will always exit/force-exit trades on the last candle
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self.exit_varHolder.from_dt = self.full_varHolder.from_dt
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self.exit_varHolder.to_dt = (result_row['close_date'] +
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timedelta(minutes=timeframe_to_minutes(
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self.local_config['timeframe'])))
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self.exit_varHolder.compared_dt = result_row['close_date']
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self.prepare_data(self.exit_varholder, [result_row['pair']])
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self.prepare_data(self.exit_varHolder, [result_row['pair']])
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# register if buy signal is broken
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if not self.report_signal(
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self.entry_varholder.result,
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"open_date", self.entry_varholder.compared_dt):
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self.entry_varHolder.result, "open_date", self.entry_varHolder.compared_dt):
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self.current_analysis.false_entry_signals += 1
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# register if buy or sell signal is broken
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if not self.report_signal(self.entry_varholder.result,
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"open_date", self.entry_varholder.compared_dt) \
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or not self.report_signal(self.exit_varholder.result,
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"close_date", self.exit_varholder.compared_dt):
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if not self.report_signal(
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self.exit_varHolder.result, "close_date", self.exit_varHolder.compared_dt):
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self.current_analysis.false_exit_signals += 1
|
||||
|
||||
self.analyze_indicators(self.full_varHolder, self.entry_varholder, result_row['pair'])
|
||||
self.analyze_indicators(self.full_varHolder, self.exit_varholder, result_row['pair'])
|
||||
# check if the indicators themselves contain biased data
|
||||
self.analyze_indicators(self.full_varHolder, self.entry_varHolder, result_row['pair'])
|
||||
self.analyze_indicators(self.full_varHolder, self.exit_varHolder, result_row['pair'])
|
||||
|
||||
if self.current_analysis.false_entry_signals > 0 or \
|
||||
self.current_analysis.false_exit_signals > 0 or \
|
||||
len(self.current_analysis.false_indicators) > 0:
|
||||
print(" => " + self.strategy_obj['name'] + ": bias detected!")
|
||||
if (self.current_analysis.false_entry_signals > 0 or
|
||||
self.current_analysis.false_exit_signals > 0 or
|
||||
len(self.current_analysis.false_indicators) > 0):
|
||||
print(" => " + self.local_config['strategy_list'][0] + ": bias detected!")
|
||||
self.current_analysis.has_bias = True
|
||||
else:
|
||||
print(self.strategy_obj['name'] + ": no bias detected")
|
||||
print(self.local_config['strategy_list'][0] + ": no bias detected")
|
||||
|
||||
Loading…
Reference in New Issue
Block a user