import logging import time from pathlib import Path from typing import Any, Dict, List, Union import pandas as pd from rich.text import Text from freqtrade.constants import Config from freqtrade.exceptions import OperationalException from freqtrade.optimize.analysis.lookahead import LookaheadAnalysis from freqtrade.resolvers import StrategyResolver from freqtrade.util import print_rich_table logger = logging.getLogger(__name__) class LookaheadAnalysisSubFunctions: @staticmethod def text_table_lookahead_analysis_instances( config: Dict[str, Any], lookahead_instances: List[LookaheadAnalysis], caption: Union[str, None] = None, ): headers = [ "filename", "strategy", "has_bias", "total_signals", "biased_entry_signals", "biased_exit_signals", "biased_indicators", ] data = [] for inst in lookahead_instances: if config["minimum_trade_amount"] > inst.current_analysis.total_signals: data.append( [ inst.strategy_obj["location"].parts[-1], inst.strategy_obj["name"], "too few trades caught " f"({inst.current_analysis.total_signals}/{config['minimum_trade_amount']})." f"Test failed.", ] ) elif inst.failed_bias_check: data.append( [ inst.strategy_obj["location"].parts[-1], inst.strategy_obj["name"], "error while checking", ] ) else: data.append( [ inst.strategy_obj["location"].parts[-1], inst.strategy_obj["name"], Text("Yes", style="bold red") if inst.current_analysis.has_bias else Text("No", style="bold green"), inst.current_analysis.total_signals, inst.current_analysis.false_entry_signals, inst.current_analysis.false_exit_signals, ", ".join(inst.current_analysis.false_indicators), ] ) print_rich_table( data, headers, summary="Lookahead Analysis", table_kwargs={"caption": caption} ) return data @staticmethod def export_to_csv(config: Dict[str, Any], lookahead_analysis: List[LookaheadAnalysis]): def add_or_update_row(df, row_data): if ( (df["filename"] == row_data["filename"]) & (df["strategy"] == row_data["strategy"]) ).any(): # Update existing row pd_series = pd.DataFrame([row_data]) df.loc[ (df["filename"] == row_data["filename"]) & (df["strategy"] == row_data["strategy"]) ] = pd_series else: # Add new row df = pd.concat([df, pd.DataFrame([row_data], columns=df.columns)]) return df if Path(config["lookahead_analysis_exportfilename"]).exists(): # Read CSV file into a pandas dataframe csv_df = pd.read_csv(config["lookahead_analysis_exportfilename"]) else: # Create a new empty DataFrame with the desired column names and set the index csv_df = pd.DataFrame( columns=[ "filename", "strategy", "has_bias", "total_signals", "biased_entry_signals", "biased_exit_signals", "biased_indicators", ], index=None, ) for inst in lookahead_analysis: # only update if if ( inst.current_analysis.total_signals > config["minimum_trade_amount"] and inst.failed_bias_check is not True ): new_row_data = { "filename": inst.strategy_obj["location"].parts[-1], "strategy": inst.strategy_obj["name"], "has_bias": inst.current_analysis.has_bias, "total_signals": int(inst.current_analysis.total_signals), "biased_entry_signals": int(inst.current_analysis.false_entry_signals), "biased_exit_signals": int(inst.current_analysis.false_exit_signals), "biased_indicators": ",".join(inst.current_analysis.false_indicators), } csv_df = add_or_update_row(csv_df, new_row_data) # Fill NaN values with a default value (e.g., 0) csv_df["total_signals"] = csv_df["total_signals"].astype(int).fillna(0) csv_df["biased_entry_signals"] = csv_df["biased_entry_signals"].astype(int).fillna(0) csv_df["biased_exit_signals"] = csv_df["biased_exit_signals"].astype(int).fillna(0) # Convert columns to integers csv_df["total_signals"] = csv_df["total_signals"].astype(int) csv_df["biased_entry_signals"] = csv_df["biased_entry_signals"].astype(int) csv_df["biased_exit_signals"] = csv_df["biased_exit_signals"].astype(int) logger.info(f"saving {config['lookahead_analysis_exportfilename']}") csv_df.to_csv(config["lookahead_analysis_exportfilename"], index=False) @staticmethod def calculate_config_overrides(config: Config): if config.get("enable_protections", False): # if protections are used globally, they can produce false positives. config["enable_protections"] = False logger.info( "Protections were enabled. " "Disabling protections now " "since they could otherwise produce false positives." ) if config["targeted_trade_amount"] < config["minimum_trade_amount"]: # this combo doesn't make any sense. raise OperationalException( "Targeted trade amount can't be smaller than minimum trade amount." ) if len(config["pairs"]) > config.get("max_open_trades", 0): logger.info( "Max_open_trades were less than amount of pairs " "or defined in the strategy. " "Set max_open_trades to amount of pairs " "just to avoid false positives." ) config["max_open_trades"] = len(config["pairs"]) min_dry_run_wallet = 1000000000 if config["dry_run_wallet"] < min_dry_run_wallet: logger.info( "Dry run wallet was not set to 1 billion, pushing it up there " "just to avoid false positives" ) config["dry_run_wallet"] = min_dry_run_wallet if "timerange" not in config: # setting a timerange is enforced here raise OperationalException( "Please set a timerange. " "Usually a few months are enough depending on your needs and strategy." ) # fix stake_amount to 10k. # in a combination with a wallet size of 1 billion it should always be able to trade # no matter if they use custom_stake_amount as a small percentage of wallet size # or fixate custom_stake_amount to a certain value. logger.info("fixing stake_amount to 10k") config["stake_amount"] = 10000 # enforce cache to be 'none', shift it to 'none' if not already # (since the default value is 'day') if config.get("backtest_cache") is None: config["backtest_cache"] = "none" elif config["backtest_cache"] != "none": logger.info( f"backtest_cache = " f"{config['backtest_cache']} detected. " f"Inside lookahead-analysis it is enforced to be 'none'. " f"Changed it to 'none'" ) config["backtest_cache"] = "none" return config @staticmethod def initialize_single_lookahead_analysis(config: Config, strategy_obj: Dict[str, Any]): logger.info(f"Bias test of {Path(strategy_obj['location']).name} started.") start = time.perf_counter() current_instance = LookaheadAnalysis(config, strategy_obj) current_instance.start() elapsed = time.perf_counter() - start logger.info( f"Checking look ahead bias via backtests " f"of {Path(strategy_obj['location']).name} " f"took {elapsed:.0f} seconds." ) return current_instance @staticmethod def start(config: Config): config = LookaheadAnalysisSubFunctions.calculate_config_overrides(config) strategy_objs = StrategyResolver.search_all_objects( config, enum_failed=False, recursive=config.get("recursive_strategy_search", False) ) lookaheadAnalysis_instances = [] # unify --strategy and --strategy-list to one list if not (strategy_list := config.get("strategy_list", [])): if config.get("strategy") is None: raise OperationalException( "No Strategy specified. Please specify a strategy via --strategy or " "--strategy-list" ) strategy_list = [config["strategy"]] # check if strategies can be properly loaded, only check them if they can be. for strat in strategy_list: for strategy_obj in strategy_objs: if strategy_obj["name"] == strat and strategy_obj not in strategy_list: lookaheadAnalysis_instances.append( LookaheadAnalysisSubFunctions.initialize_single_lookahead_analysis( config, strategy_obj ) ) break # report the results if lookaheadAnalysis_instances: caption: Union[str, None] = None if any( [ any( [ indicator.startswith("&") for indicator in inst.current_analysis.false_indicators ] ) for inst in lookaheadAnalysis_instances ] ): caption = ( "Any indicators in 'biased_indicators' which are used within " "set_freqai_targets() can be ignored." ) LookaheadAnalysisSubFunctions.text_table_lookahead_analysis_instances( config, lookaheadAnalysis_instances, caption=caption ) if config.get("lookahead_analysis_exportfilename") is not None: LookaheadAnalysisSubFunctions.export_to_csv(config, lookaheadAnalysis_instances) else: logger.error( "There were no strategies specified neither through " "--strategy nor through " "--strategy-list " "or timeframe was not specified." )