mirror of
https://github.com/freqtrade/freqtrade.git
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ad428aa9b0
In a combination with a wallet size of 1 billion it should never be able to run out of money avoiding false-positives of some users who just wanted to test a strategy without actually checking how the stake_amount-variable should be used in combination with the strategy-function custom_stake_amount reason: some strategies demand a custom_stake_amount of 1$ demanding a very large wallet-size (which already was set previously) Others start with 100% of a slot size and subdivide the base-orders and safety-orders down to finish at 100% of a slot-size and use unlimited stake_amount. Edited docs to reflect that change too
209 lines
9.4 KiB
Python
209 lines
9.4 KiB
Python
import logging
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import time
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from pathlib import Path
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from typing import Any, Dict, List
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import pandas as pd
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from freqtrade.constants import Config
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from freqtrade.exceptions import OperationalException
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from freqtrade.optimize.lookahead_analysis import LookaheadAnalysis
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from freqtrade.resolvers import StrategyResolver
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logger = logging.getLogger(__name__)
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class LookaheadAnalysisSubFunctions:
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@staticmethod
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def text_table_lookahead_analysis_instances(
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config: Dict[str, Any],
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lookahead_instances: List[LookaheadAnalysis]):
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headers = ['filename', 'strategy', 'has_bias', 'total_signals',
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'biased_entry_signals', 'biased_exit_signals', 'biased_indicators']
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data = []
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for inst in lookahead_instances:
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if config['minimum_trade_amount'] > inst.current_analysis.total_signals:
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data.append(
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[
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inst.strategy_obj['location'].parts[-1],
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inst.strategy_obj['name'],
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"too few trades caught "
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f"({inst.current_analysis.total_signals}/{config['minimum_trade_amount']})."
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f"Test failed."
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]
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)
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elif inst.failed_bias_check:
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data.append(
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[
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inst.strategy_obj['location'].parts[-1],
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inst.strategy_obj['name'],
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'error while checking'
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]
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)
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else:
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data.append(
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[
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inst.strategy_obj['location'].parts[-1],
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inst.strategy_obj['name'],
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inst.current_analysis.has_bias,
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inst.current_analysis.total_signals,
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inst.current_analysis.false_entry_signals,
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inst.current_analysis.false_exit_signals,
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", ".join(inst.current_analysis.false_indicators)
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]
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)
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from tabulate import tabulate
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table = tabulate(data, headers=headers, tablefmt="orgtbl")
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print(table)
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return table, headers, data
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@staticmethod
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def export_to_csv(config: Dict[str, Any], lookahead_analysis: List[LookaheadAnalysis]):
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def add_or_update_row(df, row_data):
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if (
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(df['filename'] == row_data['filename']) &
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(df['strategy'] == row_data['strategy'])
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).any():
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# Update existing row
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pd_series = pd.DataFrame([row_data])
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df.loc[
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(df['filename'] == row_data['filename']) &
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(df['strategy'] == row_data['strategy'])
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] = pd_series
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else:
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# Add new row
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df = pd.concat([df, pd.DataFrame([row_data], columns=df.columns)])
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return df
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if Path(config['lookahead_analysis_exportfilename']).exists():
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# Read CSV file into a pandas dataframe
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csv_df = pd.read_csv(config['lookahead_analysis_exportfilename'])
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else:
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# Create a new empty DataFrame with the desired column names and set the index
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csv_df = pd.DataFrame(columns=[
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'filename', 'strategy', 'has_bias', 'total_signals',
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'biased_entry_signals', 'biased_exit_signals', 'biased_indicators'
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],
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index=None)
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for inst in lookahead_analysis:
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# only update if
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if (inst.current_analysis.total_signals > config['minimum_trade_amount']
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and inst.failed_bias_check is not True):
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new_row_data = {'filename': inst.strategy_obj['location'].parts[-1],
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'strategy': inst.strategy_obj['name'],
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'has_bias': inst.current_analysis.has_bias,
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'total_signals':
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int(inst.current_analysis.total_signals),
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'biased_entry_signals':
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int(inst.current_analysis.false_entry_signals),
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'biased_exit_signals':
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int(inst.current_analysis.false_exit_signals),
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'biased_indicators':
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",".join(inst.current_analysis.false_indicators)}
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csv_df = add_or_update_row(csv_df, new_row_data)
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# Fill NaN values with a default value (e.g., 0)
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csv_df['total_signals'] = csv_df['total_signals'].fillna(0)
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csv_df['biased_entry_signals'] = csv_df['biased_entry_signals'].fillna(0)
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csv_df['biased_exit_signals'] = csv_df['biased_exit_signals'].fillna(0)
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# Convert columns to integers
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csv_df['total_signals'] = csv_df['total_signals'].astype(int)
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csv_df['biased_entry_signals'] = csv_df['biased_entry_signals'].astype(int)
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csv_df['biased_exit_signals'] = csv_df['biased_exit_signals'].astype(int)
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logger.info(f"saving {config['lookahead_analysis_exportfilename']}")
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csv_df.to_csv(config['lookahead_analysis_exportfilename'], index=False)
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@staticmethod
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def calculate_config_overrides(config: Config):
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if config['targeted_trade_amount'] < config['minimum_trade_amount']:
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# this combo doesn't make any sense.
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raise OperationalException(
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"Targeted trade amount can't be smaller than minimum trade amount."
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)
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if len(config['pairs']) > config['max_open_trades']:
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logger.info('Max_open_trades were less than amount of pairs. '
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'Set max_open_trades to amount of pairs just to avoid false positives.')
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config['max_open_trades'] = len(config['pairs'])
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min_dry_run_wallet = 1000000000
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if config['dry_run_wallet'] < min_dry_run_wallet:
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logger.info('Dry run wallet was not set to 1 billion, pushing it up there '
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'just to avoid false positives')
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config['dry_run_wallet'] = min_dry_run_wallet
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# fix stake_amount to 10k.
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# in a combination with a wallet size of 1 billion it should always be able to trade
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# no matter if they use custom_stake_amount as a small percentage of wallet size
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# or fixate custom_stake_amount to a certain value.
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logger.info('fixing stake_amount to 10.000')
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config['stake_amount'] = 10000
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# enforce cache to be 'none', shift it to 'none' if not already
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# (since the default value is 'day')
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if config.get('backtest_cache') is None:
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config['backtest_cache'] = 'none'
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elif config['backtest_cache'] != 'none':
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logger.info(f"backtest_cache = "
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f"{config['backtest_cache']} detected. "
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f"Inside lookahead-analysis it is enforced to be 'none'. "
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f"Changed it to 'none'")
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config['backtest_cache'] = 'none'
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return config
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@staticmethod
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def initialize_single_lookahead_analysis(config: Config, strategy_obj: Dict[str, Any]):
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logger.info(f"Bias test of {Path(strategy_obj['location']).name} started.")
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start = time.perf_counter()
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current_instance = LookaheadAnalysis(config, strategy_obj)
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current_instance.start()
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elapsed = time.perf_counter() - start
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logger.info(f"Checking look ahead bias via backtests "
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f"of {Path(strategy_obj['location']).name} "
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f"took {elapsed:.0f} seconds.")
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return current_instance
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@staticmethod
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def start(config: Config):
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config = LookaheadAnalysisSubFunctions.calculate_config_overrides(config)
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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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lookaheadAnalysis_instances = []
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# unify --strategy and --strategy_list to one list
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if not (strategy_list := config.get('strategy_list', [])):
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if config.get('strategy') is None:
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raise OperationalException(
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"No Strategy specified. Please specify a strategy via --strategy or "
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"--strategy_list"
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)
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strategy_list = [config['strategy']]
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# check if strategies can be properly loaded, only check them if they can be.
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for strat in strategy_list:
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for strategy_obj in strategy_objs:
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if strategy_obj['name'] == strat and strategy_obj not in strategy_list:
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lookaheadAnalysis_instances.append(
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LookaheadAnalysisSubFunctions.initialize_single_lookahead_analysis(
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config, strategy_obj))
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break
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# report the results
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if lookaheadAnalysis_instances:
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LookaheadAnalysisSubFunctions.text_table_lookahead_analysis_instances(
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config, lookaheadAnalysis_instances)
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if config.get('lookahead_analysis_exportfilename') is not None:
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LookaheadAnalysisSubFunctions.export_to_csv(config, lookaheadAnalysis_instances)
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else:
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logger.error("There were no strategies specified neither through "
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"--strategy nor through "
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"--strategy_list "
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"or timeframe was not specified.")
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