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Merge pull request #4848 from freqtrade/hyperopt_btresults
Hyperopt store backtest-outcome
This commit is contained in:
Matthias
GitHub
commit
4d9dc2a2ff
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@ -7,6 +7,7 @@ from colorama import init as colorama_init
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from freqtrade.configuration import setup_utils_configuration
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from freqtrade.data.btanalysis import get_latest_hyperopt_file
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from freqtrade.exceptions import OperationalException
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from freqtrade.optimize.optimize_reports import show_backtest_result
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from freqtrade.state import RunMode
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@ -125,6 +126,12 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
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if epochs:
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val = epochs[n]
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metrics = val['results_metrics']
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if 'strategy_name' in metrics:
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show_backtest_result(metrics['strategy_name'], metrics,
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metrics['stake_currency'])
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HyperoptTools.print_epoch_details(val, total_epochs, print_json, no_header,
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header_str="Epoch details")
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@ -132,11 +139,13 @@ def start_hyperopt_show(args: Dict[str, Any]) -> None:
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def hyperopt_filter_epochs(epochs: List, filteroptions: dict) -> List:
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"""
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Filter our items from the list of hyperopt results
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TODO: after 2021.5 remove all "legacy" mode queries.
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"""
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if filteroptions['only_best']:
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epochs = [x for x in epochs if x['is_best']]
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if filteroptions['only_profitable']:
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epochs = [x for x in epochs if x['results_metrics']['profit'] > 0]
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epochs = [x for x in epochs if x['results_metrics'].get(
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'profit', x['results_metrics'].get('profit_total', 0)) > 0]
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epochs = _hyperopt_filter_epochs_trade_count(epochs, filteroptions)
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@ -153,34 +162,55 @@ def hyperopt_filter_epochs(epochs: List, filteroptions: dict) -> List:
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return epochs
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def _hyperopt_filter_epochs_trade(epochs: List, trade_count: int):
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"""
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Filter epochs with trade-counts > trades
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"""
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return [
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x for x in epochs
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if x['results_metrics'].get(
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'trade_count', x['results_metrics'].get('total_trades', 0)
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) > trade_count
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]
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def _hyperopt_filter_epochs_trade_count(epochs: List, filteroptions: dict) -> List:
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if filteroptions['filter_min_trades'] > 0:
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epochs = [
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x for x in epochs
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if x['results_metrics']['trade_count'] > filteroptions['filter_min_trades']
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]
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epochs = _hyperopt_filter_epochs_trade(epochs, filteroptions['filter_min_trades'])
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if filteroptions['filter_max_trades'] > 0:
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epochs = [
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x for x in epochs
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if x['results_metrics']['trade_count'] < filteroptions['filter_max_trades']
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if x['results_metrics'].get(
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'trade_count', x['results_metrics'].get('total_trades')
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) < filteroptions['filter_max_trades']
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]
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return epochs
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def _hyperopt_filter_epochs_duration(epochs: List, filteroptions: dict) -> List:
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def get_duration_value(x):
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# Duration in minutes ...
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if 'duration' in x['results_metrics']:
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return x['results_metrics']['duration']
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else:
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# New mode
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avg = x['results_metrics']['holding_avg']
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return avg.total_seconds() // 60
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if filteroptions['filter_min_avg_time'] is not None:
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epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
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epochs = _hyperopt_filter_epochs_trade(epochs, 0)
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epochs = [
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x for x in epochs
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if x['results_metrics']['duration'] > filteroptions['filter_min_avg_time']
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if get_duration_value(x) > filteroptions['filter_min_avg_time']
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]
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if filteroptions['filter_max_avg_time'] is not None:
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epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
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epochs = _hyperopt_filter_epochs_trade(epochs, 0)
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epochs = [
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x for x in epochs
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if x['results_metrics']['duration'] < filteroptions['filter_max_avg_time']
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if get_duration_value(x) < filteroptions['filter_max_avg_time']
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]
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return epochs
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@ -189,28 +219,36 @@ def _hyperopt_filter_epochs_duration(epochs: List, filteroptions: dict) -> List:
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def _hyperopt_filter_epochs_profit(epochs: List, filteroptions: dict) -> List:
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if filteroptions['filter_min_avg_profit'] is not None:
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epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
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epochs = _hyperopt_filter_epochs_trade(epochs, 0)
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epochs = [
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x for x in epochs
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if x['results_metrics']['avg_profit'] > filteroptions['filter_min_avg_profit']
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if x['results_metrics'].get(
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'avg_profit', x['results_metrics'].get('profit_mean', 0) * 100
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) > filteroptions['filter_min_avg_profit']
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]
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if filteroptions['filter_max_avg_profit'] is not None:
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epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
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epochs = _hyperopt_filter_epochs_trade(epochs, 0)
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epochs = [
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x for x in epochs
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if x['results_metrics']['avg_profit'] < filteroptions['filter_max_avg_profit']
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if x['results_metrics'].get(
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'avg_profit', x['results_metrics'].get('profit_mean', 0) * 100
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) < filteroptions['filter_max_avg_profit']
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]
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if filteroptions['filter_min_total_profit'] is not None:
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epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
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epochs = _hyperopt_filter_epochs_trade(epochs, 0)
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epochs = [
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x for x in epochs
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if x['results_metrics']['profit'] > filteroptions['filter_min_total_profit']
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if x['results_metrics'].get(
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'profit', x['results_metrics'].get('profit_total_abs', 0)
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) > filteroptions['filter_min_total_profit']
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]
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if filteroptions['filter_max_total_profit'] is not None:
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epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
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epochs = _hyperopt_filter_epochs_trade(epochs, 0)
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epochs = [
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x for x in epochs
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if x['results_metrics']['profit'] < filteroptions['filter_max_total_profit']
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if x['results_metrics'].get(
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'profit', x['results_metrics'].get('profit_total_abs', 0)
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) < filteroptions['filter_max_total_profit']
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]
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return epochs
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@ -218,11 +256,11 @@ def _hyperopt_filter_epochs_profit(epochs: List, filteroptions: dict) -> List:
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def _hyperopt_filter_epochs_objective(epochs: List, filteroptions: dict) -> List:
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if filteroptions['filter_min_objective'] is not None:
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epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
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epochs = _hyperopt_filter_epochs_trade(epochs, 0)
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epochs = [x for x in epochs if x['loss'] < filteroptions['filter_min_objective']]
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if filteroptions['filter_max_objective'] is not None:
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epochs = [x for x in epochs if x['results_metrics']['trade_count'] > 0]
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epochs = _hyperopt_filter_epochs_trade(epochs, 0)
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epochs = [x for x in epochs if x['loss'] > filteroptions['filter_max_objective']]
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@ -330,7 +330,7 @@ class Backtesting:
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def backtest(self, processed: Dict,
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start_date: datetime, end_date: datetime,
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max_open_trades: int = 0, position_stacking: bool = False,
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enable_protections: bool = False) -> DataFrame:
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enable_protections: bool = False) -> Dict[str, Any]:
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"""
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Implement backtesting functionality
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@ -417,7 +417,13 @@ class Backtesting:
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trades += self.handle_left_open(open_trades, data=data)
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self.wallets.update()
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return trade_list_to_dataframe(trades)
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results = trade_list_to_dataframe(trades)
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return {
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'results': results,
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'config': self.strategy.config,
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'locks': PairLocks.get_all_locks(),
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'final_balance': self.wallets.get_total(self.strategy.config['stake_currency']),
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}
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def backtest_one_strategy(self, strat: IStrategy, data: Dict[str, Any], timerange: TimeRange):
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logger.info("Running backtesting for Strategy %s", strat.get_strategy_name())
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@ -457,14 +463,12 @@ class Backtesting:
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enable_protections=self.config.get('enable_protections', False),
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)
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backtest_end_time = datetime.now(timezone.utc)
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self.all_results[self.strategy.get_strategy_name()] = {
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'results': results,
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'config': self.strategy.config,
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'locks': PairLocks.get_all_locks(),
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'final_balance': self.wallets.get_total(self.strategy.config['stake_currency']),
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results.update({
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'backtest_start_time': int(backtest_start_time.timestamp()),
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'backtest_end_time': int(backtest_end_time.timestamp()),
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}
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})
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self.all_results[self.strategy.get_strategy_name()] = results
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return min_date, max_date
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def start(self) -> None:
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@ -4,11 +4,10 @@
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This module contains the hyperopt logic
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"""
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import locale
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import logging
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import random
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import warnings
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from datetime import datetime
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from datetime import datetime, timezone
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from math import ceil
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from operator import itemgetter
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from pathlib import Path
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@ -30,6 +29,7 @@ from freqtrade.optimize.hyperopt_auto import HyperOptAuto
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from freqtrade.optimize.hyperopt_interface import IHyperOpt # noqa: F401
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from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss # noqa: F401
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from freqtrade.optimize.hyperopt_tools import HyperoptTools
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from freqtrade.optimize.optimize_reports import generate_strategy_stats
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from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver, HyperOptResolver
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from freqtrade.strategy import IStrategy
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@ -65,6 +65,13 @@ class Hyperopt:
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custom_hyperopt: IHyperOpt
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def __init__(self, config: Dict[str, Any]) -> None:
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self.buy_space: List[Dimension] = []
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self.sell_space: List[Dimension] = []
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self.roi_space: List[Dimension] = []
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self.stoploss_space: List[Dimension] = []
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self.trailing_space: List[Dimension] = []
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self.dimensions: List[Dimension] = []
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self.config = config
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self.backtesting = Backtesting(self.config)
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@ -79,8 +86,7 @@ class Hyperopt:
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self.calculate_loss = self.custom_hyperoptloss.hyperopt_loss_function
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time_now = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
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strategy = str(self.config['strategy'])
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self.results_file = (self.config['user_data_dir'] /
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'hyperopt_results' /
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self.results_file: Path = (self.config['user_data_dir'] / 'hyperopt_results' /
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f'strategy_{strategy}_hyperopt_results_{time_now}.pickle')
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self.data_pickle_file = (self.config['user_data_dir'] /
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'hyperopt_results' / 'hyperopt_tickerdata.pkl')
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@ -140,9 +146,7 @@ class Hyperopt:
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logger.info(f"Removing `{p}`.")
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p.unlink()
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def _get_params_dict(self, raw_params: List[Any]) -> Dict:
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dimensions: List[Dimension] = self.dimensions
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def _get_params_dict(self, dimensions: List[Dimension], raw_params: List[Any]) -> Dict:
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# Ensure the number of dimensions match
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# the number of parameters in the list.
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@ -176,16 +180,13 @@ class Hyperopt:
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result: Dict = {}
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if HyperoptTools.has_space(self.config, 'buy'):
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result['buy'] = {p.name: params.get(p.name)
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for p in self.hyperopt_space('buy')}
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result['buy'] = {p.name: params.get(p.name) for p in self.buy_space}
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if HyperoptTools.has_space(self.config, 'sell'):
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result['sell'] = {p.name: params.get(p.name)
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for p in self.hyperopt_space('sell')}
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result['sell'] = {p.name: params.get(p.name) for p in self.sell_space}
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if HyperoptTools.has_space(self.config, 'roi'):
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result['roi'] = self.custom_hyperopt.generate_roi_table(params)
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if HyperoptTools.has_space(self.config, 'stoploss'):
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result['stoploss'] = {p.name: params.get(p.name)
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for p in self.hyperopt_space('stoploss')}
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result['stoploss'] = {p.name: params.get(p.name) for p in self.stoploss_space}
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if HyperoptTools.has_space(self.config, 'trailing'):
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result['trailing'] = self.custom_hyperopt.generate_trailing_params(params)
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@ -208,47 +209,42 @@ class Hyperopt:
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)
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self.hyperopt_table_header = 2
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def hyperopt_space(self, space: Optional[str] = None) -> List[Dimension]:
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def init_spaces(self):
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"""
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Return the dimensions in the hyperoptimization space.
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:param space: Defines hyperspace to return dimensions for.
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If None, then the self.has_space() will be used to return dimensions
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for all hyperspaces used.
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Assign the dimensions in the hyperoptimization space.
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"""
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spaces: List[Dimension] = []
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if space == 'buy' or (space is None and HyperoptTools.has_space(self.config, 'buy')):
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if HyperoptTools.has_space(self.config, 'buy'):
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logger.debug("Hyperopt has 'buy' space")
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spaces += self.custom_hyperopt.indicator_space()
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self.buy_space = self.custom_hyperopt.indicator_space()
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if space == 'sell' or (space is None and HyperoptTools.has_space(self.config, 'sell')):
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if HyperoptTools.has_space(self.config, 'sell'):
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logger.debug("Hyperopt has 'sell' space")
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spaces += self.custom_hyperopt.sell_indicator_space()
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self.sell_space = self.custom_hyperopt.sell_indicator_space()
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if space == 'roi' or (space is None and HyperoptTools.has_space(self.config, 'roi')):
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if HyperoptTools.has_space(self.config, 'roi'):
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logger.debug("Hyperopt has 'roi' space")
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spaces += self.custom_hyperopt.roi_space()
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self.roi_space = self.custom_hyperopt.roi_space()
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if space == 'stoploss' or (space is None
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and HyperoptTools.has_space(self.config, 'stoploss')):
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if HyperoptTools.has_space(self.config, 'stoploss'):
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logger.debug("Hyperopt has 'stoploss' space")
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spaces += self.custom_hyperopt.stoploss_space()
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self.stoploss_space = self.custom_hyperopt.stoploss_space()
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if space == 'trailing' or (space is None
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and HyperoptTools.has_space(self.config, 'trailing')):
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if HyperoptTools.has_space(self.config, 'trailing'):
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logger.debug("Hyperopt has 'trailing' space")
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spaces += self.custom_hyperopt.trailing_space()
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return spaces
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self.trailing_space = self.custom_hyperopt.trailing_space()
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self.dimensions = (self.buy_space + self.sell_space + self.roi_space +
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self.stoploss_space + self.trailing_space)
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def generate_optimizer(self, raw_params: List[Any], iteration=None) -> Dict:
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"""
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Used Optimize function. Called once per epoch to optimize whatever is configured.
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Keep this function as optimized as possible!
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"""
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params_dict = self._get_params_dict(raw_params)
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params_details = self._get_params_details(params_dict)
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backtest_start_time = datetime.now(timezone.utc)
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params_dict = self._get_params_dict(self.dimensions, raw_params)
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# Apply parameters
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if HyperoptTools.has_space(self.config, 'roi'):
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self.backtesting.strategy.minimal_roi = ( # type: ignore
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self.custom_hyperopt.generate_roi_table(params_dict))
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@ -275,28 +271,40 @@ class Hyperopt:
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processed = load(self.data_pickle_file)
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min_date, max_date = get_timerange(processed)
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backtesting_results = self.backtesting.backtest(
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bt_results = self.backtesting.backtest(
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processed=processed,
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start_date=min_date.datetime,
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end_date=max_date.datetime,
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start_date=self.min_date.datetime,
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end_date=self.max_date.datetime,
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max_open_trades=self.max_open_trades,
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position_stacking=self.position_stacking,
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enable_protections=self.config.get('enable_protections', False),
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)
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return self._get_results_dict(backtesting_results, min_date, max_date,
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params_dict, params_details,
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backtest_end_time = datetime.now(timezone.utc)
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bt_results.update({
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'backtest_start_time': int(backtest_start_time.timestamp()),
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'backtest_end_time': int(backtest_end_time.timestamp()),
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})
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return self._get_results_dict(bt_results, self.min_date, self.max_date,
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params_dict,
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processed=processed)
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def _get_results_dict(self, backtesting_results, min_date, max_date,
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params_dict, params_details, processed: Dict[str, DataFrame]):
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results_metrics = self._calculate_results_metrics(backtesting_results)
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results_explanation = self._format_results_explanation_string(results_metrics)
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params_dict, processed: Dict[str, DataFrame]
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) -> Dict[str, Any]:
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params_details = self._get_params_details(params_dict)
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trade_count = results_metrics['trade_count']
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total_profit = results_metrics['total_profit']
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strat_stats = generate_strategy_stats(
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processed, self.backtesting.strategy.get_strategy_name(),
|
||||
backtesting_results, min_date, max_date, market_change=0
|
||||
)
|
||||
results_explanation = HyperoptTools.format_results_explanation_string(
|
||||
strat_stats, self.config['stake_currency'])
|
||||
|
||||
not_optimized = self.backtesting.strategy.get_params_dict()
|
||||
|
||||
trade_count = strat_stats['total_trades']
|
||||
total_profit = strat_stats['profit_total']
|
||||
|
||||
# If this evaluation contains too short amount of trades to be
|
||||
# interesting -- consider it as 'bad' (assigned max. loss value)
|
||||
|
|
@ -304,50 +312,20 @@ class Hyperopt:
|
|||
# path. We do not want to optimize 'hodl' strategies.
|
||||
loss: float = MAX_LOSS
|
||||
if trade_count >= self.config['hyperopt_min_trades']:
|
||||
loss = self.calculate_loss(results=backtesting_results, trade_count=trade_count,
|
||||
loss = self.calculate_loss(results=backtesting_results['results'],
|
||||
trade_count=trade_count,
|
||||
min_date=min_date.datetime, max_date=max_date.datetime,
|
||||
config=self.config, processed=processed)
|
||||
return {
|
||||
'loss': loss,
|
||||
'params_dict': params_dict,
|
||||
'params_details': params_details,
|
||||
'results_metrics': results_metrics,
|
||||
'params_not_optimized': not_optimized,
|
||||
'results_metrics': strat_stats,
|
||||
'results_explanation': results_explanation,
|
||||
'total_profit': total_profit,
|
||||
}
|
||||
|
||||
def _calculate_results_metrics(self, backtesting_results: DataFrame) -> Dict:
|
||||
wins = len(backtesting_results[backtesting_results['profit_ratio'] > 0])
|
||||
draws = len(backtesting_results[backtesting_results['profit_ratio'] == 0])
|
||||
losses = len(backtesting_results[backtesting_results['profit_ratio'] < 0])
|
||||
return {
|
||||
'trade_count': len(backtesting_results.index),
|
||||
'wins': wins,
|
||||
'draws': draws,
|
||||
'losses': losses,
|
||||
'winsdrawslosses': f"{wins:>4} {draws:>4} {losses:>4}",
|
||||
'avg_profit': backtesting_results['profit_ratio'].mean() * 100.0,
|
||||
'median_profit': backtesting_results['profit_ratio'].median() * 100.0,
|
||||
'total_profit': backtesting_results['profit_abs'].sum(),
|
||||
'profit': backtesting_results['profit_ratio'].sum() * 100.0,
|
||||
'duration': backtesting_results['trade_duration'].mean(),
|
||||
}
|
||||
|
||||
def _format_results_explanation_string(self, results_metrics: Dict) -> str:
|
||||
"""
|
||||
Return the formatted results explanation in a string
|
||||
"""
|
||||
stake_cur = self.config['stake_currency']
|
||||
return (f"{results_metrics['trade_count']:6d} trades. "
|
||||
f"{results_metrics['wins']}/{results_metrics['draws']}"
|
||||
f"/{results_metrics['losses']} Wins/Draws/Losses. "
|
||||
f"Avg profit {results_metrics['avg_profit']: 6.2f}%. "
|
||||
f"Median profit {results_metrics['median_profit']: 6.2f}%. "
|
||||
f"Total profit {results_metrics['total_profit']: 11.8f} {stake_cur} "
|
||||
f"({results_metrics['profit']: 7.2f}\N{GREEK CAPITAL LETTER SIGMA}%). "
|
||||
f"Avg duration {results_metrics['duration']:5.1f} min."
|
||||
).encode(locale.getpreferredencoding(), 'replace').decode('utf-8')
|
||||
|
||||
def get_optimizer(self, dimensions: List[Dimension], cpu_count) -> Optimizer:
|
||||
return Optimizer(
|
||||
dimensions,
|
||||
|
|
@ -370,6 +348,8 @@ class Hyperopt:
|
|||
self.random_state = self._set_random_state(self.config.get('hyperopt_random_state', None))
|
||||
logger.info(f"Using optimizer random state: {self.random_state}")
|
||||
self.hyperopt_table_header = -1
|
||||
# Initialize spaces ...
|
||||
self.init_spaces()
|
||||
data, timerange = self.backtesting.load_bt_data()
|
||||
logger.info("Dataload complete. Calculating indicators")
|
||||
preprocessed = self.backtesting.strategy.ohlcvdata_to_dataframe(data)
|
||||
|
|
@ -378,11 +358,11 @@ class Hyperopt:
|
|||
for pair, df in preprocessed.items():
|
||||
preprocessed[pair] = trim_dataframe(df, timerange,
|
||||
startup_candles=self.backtesting.required_startup)
|
||||
min_date, max_date = get_timerange(preprocessed)
|
||||
self.min_date, self.max_date = get_timerange(preprocessed)
|
||||
|
||||
logger.info(f'Hyperopting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
|
||||
f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
|
||||
f'({(max_date - min_date).days} days)..')
|
||||
logger.info(f'Hyperopting with data from {self.min_date.strftime(DATETIME_PRINT_FORMAT)} '
|
||||
f'up to {self.max_date.strftime(DATETIME_PRINT_FORMAT)} '
|
||||
f'({(self.max_date - self.min_date).days} days)..')
|
||||
|
||||
dump(preprocessed, self.data_pickle_file)
|
||||
|
||||
|
|
@ -400,7 +380,6 @@ class Hyperopt:
|
|||
config_jobs = self.config.get('hyperopt_jobs', -1)
|
||||
logger.info(f'Number of parallel jobs set as: {config_jobs}')
|
||||
|
||||
self.dimensions: List[Dimension] = self.hyperopt_space()
|
||||
self.opt = self.get_optimizer(self.dimensions, config_jobs)
|
||||
|
||||
if self.print_colorized:
|
||||
|
|
|
|||
|
|
@ -1,9 +1,9 @@
|
|||
|
||||
import io
|
||||
import locale
|
||||
import logging
|
||||
from collections import OrderedDict
|
||||
from pathlib import Path
|
||||
from pprint import pformat
|
||||
from typing import Any, Dict, List
|
||||
|
||||
import rapidjson
|
||||
|
|
@ -12,7 +12,7 @@ from colorama import Fore, Style
|
|||
from pandas import isna, json_normalize
|
||||
|
||||
from freqtrade.exceptions import OperationalException
|
||||
from freqtrade.misc import round_dict
|
||||
from freqtrade.misc import round_coin_value, round_dict
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
|
@ -65,6 +65,7 @@ class HyperoptTools():
|
|||
Display details of the hyperopt result
|
||||
"""
|
||||
params = results.get('params_details', {})
|
||||
non_optimized = results.get('params_not_optimized', {})
|
||||
|
||||
# Default header string
|
||||
if header_str is None:
|
||||
|
|
@ -81,8 +82,10 @@ class HyperoptTools():
|
|||
print(rapidjson.dumps(result_dict, default=str, number_mode=rapidjson.NM_NATIVE))
|
||||
|
||||
else:
|
||||
HyperoptTools._params_pretty_print(params, 'buy', "Buy hyperspace params:")
|
||||
HyperoptTools._params_pretty_print(params, 'sell', "Sell hyperspace params:")
|
||||
HyperoptTools._params_pretty_print(params, 'buy', "Buy hyperspace params:",
|
||||
non_optimized)
|
||||
HyperoptTools._params_pretty_print(params, 'sell', "Sell hyperspace params:",
|
||||
non_optimized)
|
||||
HyperoptTools._params_pretty_print(params, 'roi', "ROI table:")
|
||||
HyperoptTools._params_pretty_print(params, 'stoploss', "Stoploss:")
|
||||
HyperoptTools._params_pretty_print(params, 'trailing', "Trailing stop:")
|
||||
|
|
@ -108,12 +111,12 @@ class HyperoptTools():
|
|||
result_dict.update(space_params)
|
||||
|
||||
@staticmethod
|
||||
def _params_pretty_print(params, space: str, header: str) -> None:
|
||||
if space in params:
|
||||
def _params_pretty_print(params, space: str, header: str, non_optimized={}) -> None:
|
||||
if space in params or space in non_optimized:
|
||||
space_params = HyperoptTools._space_params(params, space, 5)
|
||||
params_result = f"\n# {header}\n"
|
||||
result = f"\n# {header}\n"
|
||||
if space == 'stoploss':
|
||||
params_result += f"stoploss = {space_params.get('stoploss')}"
|
||||
result += f"stoploss = {space_params.get('stoploss')}"
|
||||
elif space == 'roi':
|
||||
# TODO: get rid of OrderedDict when support for python 3.6 will be
|
||||
# dropped (dicts keep the order as the language feature)
|
||||
|
|
@ -122,29 +125,64 @@ class HyperoptTools():
|
|||
(str(k), v) for k, v in space_params.items()
|
||||
),
|
||||
default=str, indent=4, number_mode=rapidjson.NM_NATIVE)
|
||||
params_result += f"minimal_roi = {minimal_roi_result}"
|
||||
result += f"minimal_roi = {minimal_roi_result}"
|
||||
elif space == 'trailing':
|
||||
|
||||
for k, v in space_params.items():
|
||||
params_result += f'{k} = {v}\n'
|
||||
result += f'{k} = {v}\n'
|
||||
|
||||
else:
|
||||
params_result += f"{space}_params = {pformat(space_params, indent=4)}"
|
||||
params_result = params_result.replace("}", "\n}").replace("{", "{\n ")
|
||||
no_params = HyperoptTools._space_params(non_optimized, space, 5)
|
||||
|
||||
params_result = params_result.replace("\n", "\n ")
|
||||
print(params_result)
|
||||
result += f"{space}_params = {HyperoptTools._pprint(space_params, no_params)}"
|
||||
|
||||
result = result.replace("\n", "\n ")
|
||||
print(result)
|
||||
|
||||
@staticmethod
|
||||
def _space_params(params, space: str, r: int = None) -> Dict:
|
||||
d = params[space]
|
||||
d = params.get(space)
|
||||
if d:
|
||||
# Round floats to `r` digits after the decimal point if requested
|
||||
return round_dict(d, r) if r else d
|
||||
return {}
|
||||
|
||||
@staticmethod
|
||||
def _pprint(params, non_optimized, indent: int = 4):
|
||||
"""
|
||||
Pretty-print hyperopt results (based on 2 dicts - with add. comment)
|
||||
"""
|
||||
p = params.copy()
|
||||
p.update(non_optimized)
|
||||
result = '{\n'
|
||||
|
||||
for k, param in p.items():
|
||||
result += " " * indent + f'"{k}": {param},'
|
||||
if k in non_optimized:
|
||||
result += " # value loaded from strategy"
|
||||
result += "\n"
|
||||
result += '}'
|
||||
return result
|
||||
|
||||
@staticmethod
|
||||
def is_best_loss(results, current_best_loss: float) -> bool:
|
||||
return results['loss'] < current_best_loss
|
||||
|
||||
@staticmethod
|
||||
def format_results_explanation_string(results_metrics: Dict, stake_currency: str) -> str:
|
||||
"""
|
||||
Return the formatted results explanation in a string
|
||||
"""
|
||||
return (f"{results_metrics['total_trades']:6d} trades. "
|
||||
f"{results_metrics['wins']}/{results_metrics['draws']}"
|
||||
f"/{results_metrics['losses']} Wins/Draws/Losses. "
|
||||
f"Avg profit {results_metrics['profit_mean'] * 100: 6.2f}%. "
|
||||
f"Median profit {results_metrics['profit_median'] * 100: 6.2f}%. "
|
||||
f"Total profit {results_metrics['profit_total_abs']: 11.8f} {stake_currency} "
|
||||
f"({results_metrics['profit_total'] * 100: 7.2f}\N{GREEK CAPITAL LETTER SIGMA}%). "
|
||||
f"Avg duration {results_metrics['holding_avg']} min."
|
||||
).encode(locale.getpreferredencoding(), 'replace').decode('utf-8')
|
||||
|
||||
@staticmethod
|
||||
def _format_explanation_string(results, total_epochs) -> str:
|
||||
return (("*" if results['is_initial_point'] else " ") +
|
||||
|
|
@ -168,12 +206,27 @@ class HyperoptTools():
|
|||
if 'results_metrics.winsdrawslosses' not in trials.columns:
|
||||
# Ensure compatibility with older versions of hyperopt results
|
||||
trials['results_metrics.winsdrawslosses'] = 'N/A'
|
||||
legacy_mode = True
|
||||
|
||||
if 'results_metrics.total_trades' in trials:
|
||||
legacy_mode = False
|
||||
# New mode, using backtest result for metrics
|
||||
trials['results_metrics.winsdrawslosses'] = trials.apply(
|
||||
lambda x: f"{x['results_metrics.wins']} {x['results_metrics.draws']:>4} "
|
||||
f"{x['results_metrics.losses']:>4}", axis=1)
|
||||
trials = trials[['Best', 'current_epoch', 'results_metrics.total_trades',
|
||||
'results_metrics.winsdrawslosses',
|
||||
'results_metrics.profit_mean', 'results_metrics.profit_total_abs',
|
||||
'results_metrics.profit_total', 'results_metrics.holding_avg',
|
||||
'loss', 'is_initial_point', 'is_best']]
|
||||
else:
|
||||
# Legacy mode
|
||||
trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
|
||||
'results_metrics.winsdrawslosses',
|
||||
'results_metrics.avg_profit', 'results_metrics.total_profit',
|
||||
'results_metrics.profit', 'results_metrics.duration',
|
||||
'loss', 'is_initial_point', 'is_best']]
|
||||
|
||||
trials.columns = ['Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
|
||||
'Total profit', 'Profit', 'Avg duration', 'Objective',
|
||||
'is_initial_point', 'is_best']
|
||||
|
|
@ -183,26 +236,28 @@ class HyperoptTools():
|
|||
trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
|
||||
trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
|
||||
trials['Trades'] = trials['Trades'].astype(str)
|
||||
|
||||
perc_multi = 1 if legacy_mode else 100
|
||||
trials['Epoch'] = trials['Epoch'].apply(
|
||||
lambda x: '{}/{}'.format(str(x).rjust(len(str(total_epochs)), ' '), total_epochs)
|
||||
)
|
||||
trials['Avg profit'] = trials['Avg profit'].apply(
|
||||
lambda x: '{:,.2f}%'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
|
||||
lambda x: f'{x * perc_multi:,.2f}%'.rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
|
||||
)
|
||||
trials['Avg duration'] = trials['Avg duration'].apply(
|
||||
lambda x: '{:,.1f} m'.format(x).rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
|
||||
lambda x: f'{x:,.1f} m'.rjust(7, ' ') if isinstance(x, float) else f"{x}"
|
||||
if not isna(x) else "--".rjust(7, ' ')
|
||||
)
|
||||
trials['Objective'] = trials['Objective'].apply(
|
||||
lambda x: '{:,.5f}'.format(x).rjust(8, ' ') if x != 100000 else "N/A".rjust(8, ' ')
|
||||
lambda x: f'{x:,.5f}'.rjust(8, ' ') if x != 100000 else "N/A".rjust(8, ' ')
|
||||
)
|
||||
|
||||
stake_currency = config['stake_currency']
|
||||
trials['Profit'] = trials.apply(
|
||||
lambda x: '{:,.8f} {} {}'.format(
|
||||
x['Total profit'], config['stake_currency'],
|
||||
'({:,.2f}%)'.format(x['Profit']).rjust(10, ' ')
|
||||
).rjust(25+len(config['stake_currency']))
|
||||
if x['Total profit'] != 0.0 else '--'.rjust(25+len(config['stake_currency'])),
|
||||
lambda x: '{} {}'.format(
|
||||
round_coin_value(x['Total profit'], stake_currency),
|
||||
'({:,.2f}%)'.format(x['Profit'] * perc_multi).rjust(10, ' ')
|
||||
).rjust(25+len(stake_currency))
|
||||
if x['Total profit'] != 0.0 else '--'.rjust(25+len(stake_currency)),
|
||||
axis=1
|
||||
)
|
||||
trials = trials.drop(columns=['Total profit'])
|
||||
|
|
@ -263,6 +318,16 @@ class HyperoptTools():
|
|||
trials['Best'] = ''
|
||||
trials['Stake currency'] = config['stake_currency']
|
||||
|
||||
if 'results_metrics.total_trades' in trials:
|
||||
base_metrics = ['Best', 'current_epoch', 'results_metrics.total_trades',
|
||||
'results_metrics.profit_mean', 'results_metrics.profit_median',
|
||||
'results_metrics.profit_total',
|
||||
'Stake currency',
|
||||
'results_metrics.profit_total_abs', 'results_metrics.holding_avg',
|
||||
'loss', 'is_initial_point', 'is_best']
|
||||
perc_multi = 100
|
||||
else:
|
||||
perc_multi = 1
|
||||
base_metrics = ['Best', 'current_epoch', 'results_metrics.trade_count',
|
||||
'results_metrics.avg_profit', 'results_metrics.median_profit',
|
||||
'results_metrics.total_profit',
|
||||
|
|
@ -284,21 +349,23 @@ class HyperoptTools():
|
|||
trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
|
||||
trials['Epoch'] = trials['Epoch'].astype(str)
|
||||
trials['Trades'] = trials['Trades'].astype(str)
|
||||
trials['Median profit'] = trials['Median profit'] * perc_multi
|
||||
|
||||
trials['Total profit'] = trials['Total profit'].apply(
|
||||
lambda x: '{:,.8f}'.format(x) if x != 0.0 else ""
|
||||
lambda x: f'{x:,.8f}' if x != 0.0 else ""
|
||||
)
|
||||
trials['Profit'] = trials['Profit'].apply(
|
||||
lambda x: '{:,.2f}'.format(x) if not isna(x) else ""
|
||||
lambda x: f'{x:,.2f}' if not isna(x) else ""
|
||||
)
|
||||
trials['Avg profit'] = trials['Avg profit'].apply(
|
||||
lambda x: '{:,.2f}%'.format(x) if not isna(x) else ""
|
||||
lambda x: f'{x * perc_multi:,.2f}%' if not isna(x) else ""
|
||||
)
|
||||
trials['Avg duration'] = trials['Avg duration'].apply(
|
||||
lambda x: '{:,.1f} m'.format(x) if not isna(x) else ""
|
||||
lambda x: f'{x:,.1f} m' if isinstance(
|
||||
x, float) else f"{x.total_seconds() // 60:,.1f} m" if not isna(x) else ""
|
||||
)
|
||||
trials['Objective'] = trials['Objective'].apply(
|
||||
lambda x: '{:,.5f}'.format(x) if x != 100000 else ""
|
||||
lambda x: f'{x:,.5f}' if x != 100000 else ""
|
||||
)
|
||||
|
||||
trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit'])
|
||||
|
|
|
|||
|
|
@ -153,7 +153,7 @@ def generate_sell_reason_stats(max_open_trades: int, results: DataFrame) -> List
|
|||
return tabular_data
|
||||
|
||||
|
||||
def generate_strategy_metrics(all_results: Dict) -> List[Dict]:
|
||||
def generate_strategy_comparison(all_results: Dict) -> List[Dict]:
|
||||
"""
|
||||
Generate summary per strategy
|
||||
:param all_results: Dict of <Strategyname: DataFrame> containing results for all strategies
|
||||
|
|
@ -194,7 +194,37 @@ def generate_edge_table(results: dict) -> str:
|
|||
floatfmt=floatfmt, tablefmt="orgtbl", stralign="right") # type: ignore
|
||||
|
||||
|
||||
def generate_trading_stats(results: DataFrame) -> Dict[str, Any]:
|
||||
""" Generate overall trade statistics """
|
||||
if len(results) == 0:
|
||||
return {
|
||||
'wins': 0,
|
||||
'losses': 0,
|
||||
'draws': 0,
|
||||
'holding_avg': timedelta(),
|
||||
'winner_holding_avg': timedelta(),
|
||||
'loser_holding_avg': timedelta(),
|
||||
}
|
||||
|
||||
winning_trades = results.loc[results['profit_ratio'] > 0]
|
||||
draw_trades = results.loc[results['profit_ratio'] == 0]
|
||||
losing_trades = results.loc[results['profit_ratio'] < 0]
|
||||
|
||||
return {
|
||||
'wins': len(winning_trades),
|
||||
'losses': len(losing_trades),
|
||||
'draws': len(draw_trades),
|
||||
'holding_avg': (timedelta(minutes=round(results['trade_duration'].mean()))
|
||||
if not results.empty else timedelta()),
|
||||
'winner_holding_avg': (timedelta(minutes=round(winning_trades['trade_duration'].mean()))
|
||||
if not winning_trades.empty else timedelta()),
|
||||
'loser_holding_avg': (timedelta(minutes=round(losing_trades['trade_duration'].mean()))
|
||||
if not losing_trades.empty else timedelta()),
|
||||
}
|
||||
|
||||
|
||||
def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
|
||||
""" Generate daily statistics """
|
||||
if len(results) == 0:
|
||||
return {
|
||||
'backtest_best_day': 0,
|
||||
|
|
@ -204,8 +234,6 @@ def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
|
|||
'winning_days': 0,
|
||||
'draw_days': 0,
|
||||
'losing_days': 0,
|
||||
'winner_holding_avg': timedelta(),
|
||||
'loser_holding_avg': timedelta(),
|
||||
}
|
||||
daily_profit_rel = results.resample('1d', on='close_date')['profit_ratio'].sum()
|
||||
daily_profit = results.resample('1d', on='close_date')['profit_abs'].sum().round(10)
|
||||
|
|
@ -217,9 +245,6 @@ def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
|
|||
draw_days = sum(daily_profit == 0)
|
||||
losing_days = sum(daily_profit < 0)
|
||||
|
||||
winning_trades = results.loc[results['profit_ratio'] > 0]
|
||||
losing_trades = results.loc[results['profit_ratio'] < 0]
|
||||
|
||||
return {
|
||||
'backtest_best_day': best_rel,
|
||||
'backtest_worst_day': worst_rel,
|
||||
|
|
@ -228,33 +253,28 @@ def generate_daily_stats(results: DataFrame) -> Dict[str, Any]:
|
|||
'winning_days': winning_days,
|
||||
'draw_days': draw_days,
|
||||
'losing_days': losing_days,
|
||||
'winner_holding_avg': (timedelta(minutes=round(winning_trades['trade_duration'].mean()))
|
||||
if not winning_trades.empty else timedelta()),
|
||||
'loser_holding_avg': (timedelta(minutes=round(losing_trades['trade_duration'].mean()))
|
||||
if not losing_trades.empty else timedelta()),
|
||||
}
|
||||
|
||||
|
||||
def generate_backtest_stats(btdata: Dict[str, DataFrame],
|
||||
all_results: Dict[str, Dict[str, Union[DataFrame, Dict]]],
|
||||
min_date: Arrow, max_date: Arrow
|
||||
def generate_strategy_stats(btdata: Dict[str, DataFrame],
|
||||
strategy: str,
|
||||
content: Dict[str, Any],
|
||||
min_date: Arrow, max_date: Arrow,
|
||||
market_change: float
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
:param btdata: Backtest data
|
||||
:param all_results: backtest result - dictionary in the form:
|
||||
{ Strategy: {'results: results, 'config: config}}.
|
||||
:param strategy: Strategy name
|
||||
:param content: Backtest result data in the format:
|
||||
{'results: results, 'config: config}}.
|
||||
:param min_date: Backtest start date
|
||||
:param max_date: Backtest end date
|
||||
:return:
|
||||
Dictionary containing results per strategy and a stratgy summary.
|
||||
:param market_change: float indicating the market change
|
||||
:return: Dictionary containing results per strategy and a stratgy summary.
|
||||
"""
|
||||
result: Dict[str, Any] = {'strategy': {}}
|
||||
market_change = calculate_market_change(btdata, 'close')
|
||||
|
||||
for strategy, content in all_results.items():
|
||||
results: Dict[str, DataFrame] = content['results']
|
||||
if not isinstance(results, DataFrame):
|
||||
continue
|
||||
return {}
|
||||
config = content['config']
|
||||
max_open_trades = min(config['max_open_trades'], len(btdata.keys()))
|
||||
starting_balance = config['dry_run_wallet']
|
||||
|
|
@ -270,6 +290,7 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
|
|||
results=results.loc[results['is_open']],
|
||||
skip_nan=True)
|
||||
daily_stats = generate_daily_stats(results)
|
||||
trade_stats = generate_trading_stats(results)
|
||||
best_pair = max([pair for pair in pair_results if pair['key'] != 'TOTAL'],
|
||||
key=lambda x: x['profit_sum']) if len(pair_results) > 1 else None
|
||||
worst_pair = min([pair for pair in pair_results if pair['key'] != 'TOTAL'],
|
||||
|
|
@ -290,6 +311,7 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
|
|||
'total_volume': float(results['stake_amount'].sum()),
|
||||
'avg_stake_amount': results['stake_amount'].mean() if len(results) > 0 else 0,
|
||||
'profit_mean': results['profit_ratio'].mean() if len(results) > 0 else 0,
|
||||
'profit_median': results['profit_ratio'].median() if len(results) > 0 else 0,
|
||||
'profit_total': results['profit_abs'].sum() / starting_balance,
|
||||
'profit_total_abs': results['profit_abs'].sum(),
|
||||
'backtest_start': min_date.datetime,
|
||||
|
|
@ -330,8 +352,8 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
|
|||
'sell_profit_offset': config['ask_strategy']['sell_profit_offset'],
|
||||
'ignore_roi_if_buy_signal': config['ask_strategy']['ignore_roi_if_buy_signal'],
|
||||
**daily_stats,
|
||||
**trade_stats
|
||||
}
|
||||
result['strategy'][strategy] = strat_stats
|
||||
|
||||
try:
|
||||
max_drawdown, _, _, _, _ = calculate_max_drawdown(
|
||||
|
|
@ -370,7 +392,30 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
|
|||
'csum_max': 0
|
||||
})
|
||||
|
||||
strategy_results = generate_strategy_metrics(all_results=all_results)
|
||||
return strat_stats
|
||||
|
||||
|
||||
def generate_backtest_stats(btdata: Dict[str, DataFrame],
|
||||
all_results: Dict[str, Dict[str, Union[DataFrame, Dict]]],
|
||||
min_date: Arrow, max_date: Arrow
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
:param btdata: Backtest data
|
||||
:param all_results: backtest result - dictionary in the form:
|
||||
{ Strategy: {'results: results, 'config: config}}.
|
||||
:param min_date: Backtest start date
|
||||
:param max_date: Backtest end date
|
||||
:return: Dictionary containing results per strategy and a stratgy summary.
|
||||
"""
|
||||
result: Dict[str, Any] = {'strategy': {}}
|
||||
market_change = calculate_market_change(btdata, 'close')
|
||||
|
||||
for strategy, content in all_results.items():
|
||||
strat_stats = generate_strategy_stats(btdata, strategy, content,
|
||||
min_date, max_date, market_change=market_change)
|
||||
result['strategy'][strategy] = strat_stats
|
||||
|
||||
strategy_results = generate_strategy_comparison(all_results=all_results)
|
||||
|
||||
result['strategy_comparison'] = strategy_results
|
||||
|
||||
|
|
@ -522,11 +567,10 @@ def text_table_add_metrics(strat_results: Dict) -> str:
|
|||
return message
|
||||
|
||||
|
||||
def show_backtest_results(config: Dict, backtest_stats: Dict):
|
||||
stake_currency = config['stake_currency']
|
||||
|
||||
for strategy, results in backtest_stats['strategy'].items():
|
||||
|
||||
def show_backtest_result(strategy: str, results: Dict[str, Any], stake_currency: str):
|
||||
"""
|
||||
Print results for one strategy
|
||||
"""
|
||||
# Print results
|
||||
print(f"Result for strategy {strategy}")
|
||||
table = text_table_bt_results(results['results_per_pair'], stake_currency=stake_currency)
|
||||
|
|
@ -554,6 +598,13 @@ def show_backtest_results(config: Dict, backtest_stats: Dict):
|
|||
print('=' * len(table.splitlines()[0]))
|
||||
print()
|
||||
|
||||
|
||||
def show_backtest_results(config: Dict, backtest_stats: Dict):
|
||||
stake_currency = config['stake_currency']
|
||||
|
||||
for strategy, results in backtest_stats['strategy'].items():
|
||||
show_backtest_result(strategy, results, stake_currency)
|
||||
|
||||
if len(backtest_stats['strategy']) > 1:
|
||||
# Print Strategy summary table
|
||||
|
||||
|
|
|
|||
|
|
@ -5,7 +5,7 @@ This module defines a base class for auto-hyperoptable strategies.
|
|||
import logging
|
||||
from abc import ABC, abstractmethod
|
||||
from contextlib import suppress
|
||||
from typing import Any, Dict, Iterator, Optional, Sequence, Tuple, Union
|
||||
from typing import Any, Dict, Iterator, List, Optional, Sequence, Tuple, Union
|
||||
|
||||
from freqtrade.optimize.hyperopt_tools import HyperoptTools
|
||||
|
||||
|
|
@ -29,6 +29,7 @@ class BaseParameter(ABC):
|
|||
default: Any
|
||||
value: Any
|
||||
in_space: bool = False
|
||||
name: str
|
||||
|
||||
def __init__(self, *, default: Any, space: Optional[str] = None,
|
||||
optimize: bool = True, load: bool = True, **kwargs):
|
||||
|
|
@ -250,6 +251,9 @@ class HyperStrategyMixin(object):
|
|||
Initialize hyperoptable strategy mixin.
|
||||
"""
|
||||
self.config = config
|
||||
self.ft_buy_params: List[BaseParameter] = []
|
||||
self.ft_sell_params: List[BaseParameter] = []
|
||||
|
||||
self._load_hyper_params(config.get('runmode') == RunMode.HYPEROPT)
|
||||
|
||||
def enumerate_parameters(self, category: str = None) -> Iterator[Tuple[str, BaseParameter]]:
|
||||
|
|
@ -260,15 +264,26 @@ class HyperStrategyMixin(object):
|
|||
"""
|
||||
if category not in ('buy', 'sell', None):
|
||||
raise OperationalException('Category must be one of: "buy", "sell", None.')
|
||||
|
||||
if category is None:
|
||||
params = self.ft_buy_params + self.ft_sell_params
|
||||
else:
|
||||
params = getattr(self, f"ft_{category}_params")
|
||||
|
||||
for par in params:
|
||||
yield par.name, par
|
||||
|
||||
def _detect_parameters(self, category: str) -> Iterator[Tuple[str, BaseParameter]]:
|
||||
""" Detect all parameters for 'category' """
|
||||
for attr_name in dir(self):
|
||||
if not attr_name.startswith('__'): # Ignore internals, not strictly necessary.
|
||||
attr = getattr(self, attr_name)
|
||||
if issubclass(attr.__class__, BaseParameter):
|
||||
if (category and attr_name.startswith(category + '_')
|
||||
if (attr_name.startswith(category + '_')
|
||||
and attr.category is not None and attr.category != category):
|
||||
raise OperationalException(
|
||||
f'Inconclusive parameter name {attr_name}, category: {attr.category}.')
|
||||
if (category is None or category == attr.category or
|
||||
if (category == attr.category or
|
||||
(attr_name.startswith(category + '_') and attr.category is None)):
|
||||
yield attr_name, attr
|
||||
|
||||
|
|
@ -286,9 +301,16 @@ class HyperStrategyMixin(object):
|
|||
"""
|
||||
if not params:
|
||||
logger.info(f"No params for {space} found, using default values.")
|
||||
param_container: List[BaseParameter] = getattr(self, f"ft_{space}_params")
|
||||
|
||||
for attr_name, attr in self.enumerate_parameters(space):
|
||||
for attr_name, attr in self._detect_parameters(space):
|
||||
attr.name = attr_name
|
||||
attr.in_space = hyperopt and HyperoptTools.has_space(self.config, space)
|
||||
if not attr.category:
|
||||
attr.category = space
|
||||
|
||||
param_container.append(attr)
|
||||
|
||||
if params and attr_name in params:
|
||||
if attr.load:
|
||||
attr.value = params[attr_name]
|
||||
|
|
@ -298,3 +320,16 @@ class HyperStrategyMixin(object):
|
|||
f'Default value "{attr.value}" used.')
|
||||
else:
|
||||
logger.info(f'Strategy Parameter(default): {attr_name} = {attr.value}')
|
||||
|
||||
def get_params_dict(self):
|
||||
"""
|
||||
Returns list of Parameters that are not part of the current optimize job
|
||||
"""
|
||||
params = {
|
||||
'buy': {},
|
||||
'sell': {}
|
||||
}
|
||||
for name, p in self.enumerate_parameters():
|
||||
if not p.optimize or not p.in_space:
|
||||
params[p.category][name] = p.value
|
||||
return params
|
||||
|
|
|
|||
|
|
@ -918,10 +918,12 @@ def test_start_test_pairlist(mocker, caplog, tickers, default_conf, capsys):
|
|||
captured.out)
|
||||
|
||||
|
||||
def test_hyperopt_list(mocker, capsys, caplog, hyperopt_results):
|
||||
def test_hyperopt_list(mocker, capsys, caplog, saved_hyperopt_results,
|
||||
saved_hyperopt_results_legacy):
|
||||
for _ in (saved_hyperopt_results, saved_hyperopt_results_legacy):
|
||||
mocker.patch(
|
||||
'freqtrade.optimize.hyperopt_tools.HyperoptTools.load_previous_results',
|
||||
MagicMock(return_value=hyperopt_results)
|
||||
MagicMock(return_value=saved_hyperopt_results_legacy)
|
||||
)
|
||||
|
||||
args = [
|
||||
|
|
@ -1150,10 +1152,10 @@ def test_hyperopt_list(mocker, capsys, caplog, hyperopt_results):
|
|||
f.unlink()
|
||||
|
||||
|
||||
def test_hyperopt_show(mocker, capsys, hyperopt_results):
|
||||
def test_hyperopt_show(mocker, capsys, saved_hyperopt_results):
|
||||
mocker.patch(
|
||||
'freqtrade.optimize.hyperopt_tools.HyperoptTools.load_previous_results',
|
||||
MagicMock(return_value=hyperopt_results)
|
||||
MagicMock(return_value=saved_hyperopt_results)
|
||||
)
|
||||
|
||||
args = [
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ import json
|
|||
import logging
|
||||
import re
|
||||
from copy import deepcopy
|
||||
from datetime import datetime
|
||||
from datetime import datetime, timedelta
|
||||
from functools import reduce
|
||||
from pathlib import Path
|
||||
from unittest.mock import MagicMock, Mock, PropertyMock
|
||||
|
|
@ -1778,7 +1778,7 @@ def open_trade():
|
|||
|
||||
|
||||
@pytest.fixture
|
||||
def hyperopt_results():
|
||||
def saved_hyperopt_results_legacy():
|
||||
return [
|
||||
{
|
||||
'loss': 0.4366182531161519,
|
||||
|
|
@ -1907,3 +1907,136 @@ def hyperopt_results():
|
|||
'is_best': False
|
||||
}
|
||||
]
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def saved_hyperopt_results():
|
||||
return [
|
||||
{
|
||||
'loss': 0.4366182531161519,
|
||||
'params_dict': {
|
||||
'mfi-value': 15, 'fastd-value': 20, 'adx-value': 25, 'rsi-value': 28, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 88, 'sell-fastd-value': 97, 'sell-adx-value': 51, 'sell-rsi-value': 67, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1190, 'roi_t2': 541, 'roi_t3': 408, 'roi_p1': 0.026035863879169705, 'roi_p2': 0.12508730043628782, 'roi_p3': 0.27766427921605896, 'stoploss': -0.2562930402099556}, # noqa: E501
|
||||
'params_details': {'buy': {'mfi-value': 15, 'fastd-value': 20, 'adx-value': 25, 'rsi-value': 28, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 88, 'sell-fastd-value': 97, 'sell-adx-value': 51, 'sell-rsi-value': 67, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.4287874435315165, 408: 0.15112316431545753, 949: 0.026035863879169705, 2139: 0}, 'stoploss': {'stoploss': -0.2562930402099556}}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 2, 'wins': 0, 'draws': 0, 'losses': 2, 'profit_mean': -0.01254995, 'profit_median': -0.012222, 'profit_total': -0.00125625, 'profit_total_abs': -2.50999, 'holding_avg': timedelta(minutes=3930.0)}, # noqa: E501
|
||||
'results_explanation': ' 2 trades. Avg profit -1.25%. Total profit -0.00125625 BTC ( -2.51Σ%). Avg duration 3930.0 min.', # noqa: E501
|
||||
'total_profit': -0.00125625,
|
||||
'current_epoch': 1,
|
||||
'is_initial_point': True,
|
||||
'is_best': True
|
||||
}, {
|
||||
'loss': 20.0,
|
||||
'params_dict': {
|
||||
'mfi-value': 17, 'fastd-value': 38, 'adx-value': 48, 'rsi-value': 22, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 96, 'sell-fastd-value': 68, 'sell-adx-value': 63, 'sell-rsi-value': 81, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal', 'roi_t1': 334, 'roi_t2': 683, 'roi_t3': 140, 'roi_p1': 0.06403981740598495, 'roi_p2': 0.055519840060645045, 'roi_p3': 0.3253712811342459, 'stoploss': -0.338070047333259}, # noqa: E501
|
||||
'params_details': {
|
||||
'buy': {'mfi-value': 17, 'fastd-value': 38, 'adx-value': 48, 'rsi-value': 22, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'}, # noqa: E501
|
||||
'sell': {'sell-mfi-value': 96, 'sell-fastd-value': 68, 'sell-adx-value': 63, 'sell-rsi-value': 81, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal'}, # noqa: E501
|
||||
'roi': {0: 0.4449309386008759, 140: 0.11955965746663, 823: 0.06403981740598495, 1157: 0}, # noqa: E501
|
||||
'stoploss': {'stoploss': -0.338070047333259}},
|
||||
'results_metrics': {'total_trades': 1, 'wins': 0, 'draws': 0, 'losses': 1, 'profit_mean': 0.012357, 'profit_median': -0.012222, 'profit_total': 6.185e-05, 'profit_total_abs': 0.12357, 'holding_avg': timedelta(minutes=1200.0)}, # noqa: E501
|
||||
'results_explanation': ' 1 trades. Avg profit 0.12%. Total profit 0.00006185 BTC ( 0.12Σ%). Avg duration 1200.0 min.', # noqa: E501
|
||||
'total_profit': 6.185e-05,
|
||||
'current_epoch': 2,
|
||||
'is_initial_point': True,
|
||||
'is_best': False
|
||||
}, {
|
||||
'loss': 14.241196856510731,
|
||||
'params_dict': {'mfi-value': 25, 'fastd-value': 16, 'adx-value': 29, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 98, 'sell-fastd-value': 72, 'sell-adx-value': 51, 'sell-rsi-value': 82, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 889, 'roi_t2': 533, 'roi_t3': 263, 'roi_p1': 0.04759065393663096, 'roi_p2': 0.1488819964638463, 'roi_p3': 0.4102801822104605, 'stoploss': -0.05394588767607611}, # noqa: E501
|
||||
'params_details': {'buy': {'mfi-value': 25, 'fastd-value': 16, 'adx-value': 29, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 98, 'sell-fastd-value': 72, 'sell-adx-value': 51, 'sell-rsi-value': 82, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.6067528326109377, 263: 0.19647265040047726, 796: 0.04759065393663096, 1685: 0}, 'stoploss': {'stoploss': -0.05394588767607611}}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 621, 'wins': 320, 'draws': 0, 'losses': 301, 'profit_mean': -0.043883302093397747, 'profit_median': -0.012222, 'profit_total': -0.13639474, 'profit_total_abs': -272.515306, 'holding_avg': timedelta(minutes=1691.207729468599)}, # noqa: E501
|
||||
'results_explanation': ' 621 trades. Avg profit -0.44%. Total profit -0.13639474 BTC (-272.52Σ%). Avg duration 1691.2 min.', # noqa: E501
|
||||
'total_profit': -0.13639474,
|
||||
'current_epoch': 3,
|
||||
'is_initial_point': True,
|
||||
'is_best': False
|
||||
}, {
|
||||
'loss': 100000,
|
||||
'params_dict': {'mfi-value': 13, 'fastd-value': 35, 'adx-value': 39, 'rsi-value': 29, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 87, 'sell-fastd-value': 54, 'sell-adx-value': 63, 'sell-rsi-value': 93, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1402, 'roi_t2': 676, 'roi_t3': 215, 'roi_p1': 0.06264755784937427, 'roi_p2': 0.14258587851894644, 'roi_p3': 0.20671291201040828, 'stoploss': -0.11818343570194478}, # noqa: E501
|
||||
'params_details': {'buy': {'mfi-value': 13, 'fastd-value': 35, 'adx-value': 39, 'rsi-value': 29, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 87, 'sell-fastd-value': 54, 'sell-adx-value': 63, 'sell-rsi-value': 93, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.411946348378729, 215: 0.2052334363683207, 891: 0.06264755784937427, 2293: 0}, 'stoploss': {'stoploss': -0.11818343570194478}}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 0, 'wins': 0, 'draws': 0, 'losses': 0, 'profit_mean': None, 'profit_median': None, 'profit_total': 0, 'profit': 0.0, 'holding_avg': timedelta()}, # noqa: E501
|
||||
'results_explanation': ' 0 trades. Avg profit nan%. Total profit 0.00000000 BTC ( 0.00Σ%). Avg duration nan min.', # noqa: E501
|
||||
'total_profit': 0, 'current_epoch': 4, 'is_initial_point': True, 'is_best': False
|
||||
}, {
|
||||
'loss': 0.22195522184191518,
|
||||
'params_dict': {'mfi-value': 17, 'fastd-value': 21, 'adx-value': 38, 'rsi-value': 33, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 87, 'sell-fastd-value': 82, 'sell-adx-value': 78, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 1269, 'roi_t2': 601, 'roi_t3': 444, 'roi_p1': 0.07280999507931168, 'roi_p2': 0.08946698095898986, 'roi_p3': 0.1454876733325284, 'stoploss': -0.18181041180901014}, # noqa: E501
|
||||
'params_details': {'buy': {'mfi-value': 17, 'fastd-value': 21, 'adx-value': 38, 'rsi-value': 33, 'mfi-enabled': True, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 87, 'sell-fastd-value': 82, 'sell-adx-value': 78, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.3077646493708299, 444: 0.16227697603830155, 1045: 0.07280999507931168, 2314: 0}, 'stoploss': {'stoploss': -0.18181041180901014}}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 14, 'wins': 6, 'draws': 0, 'losses': 8, 'profit_mean': -0.003539515, 'profit_median': -0.012222, 'profit_total': -0.002480140000000001, 'profit_total_abs': -4.955321, 'holding_avg': timedelta(minutes=3402.8571428571427)}, # noqa: E501
|
||||
'results_explanation': ' 14 trades. Avg profit -0.35%. Total profit -0.00248014 BTC ( -4.96Σ%). Avg duration 3402.9 min.', # noqa: E501
|
||||
'total_profit': -0.002480140000000001,
|
||||
'current_epoch': 5,
|
||||
'is_initial_point': True,
|
||||
'is_best': True
|
||||
}, {
|
||||
'loss': 0.545315889154162,
|
||||
'params_dict': {'mfi-value': 22, 'fastd-value': 43, 'adx-value': 46, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'bb_lower', 'sell-mfi-value': 87, 'sell-fastd-value': 65, 'sell-adx-value': 94, 'sell-rsi-value': 63, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 319, 'roi_t2': 556, 'roi_t3': 216, 'roi_p1': 0.06251955472249589, 'roi_p2': 0.11659519602202795, 'roi_p3': 0.0953744132197762, 'stoploss': -0.024551752215582423}, # noqa: E501
|
||||
'params_details': {'buy': {'mfi-value': 22, 'fastd-value': 43, 'adx-value': 46, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'bb_lower'}, 'sell': {'sell-mfi-value': 87, 'sell-fastd-value': 65, 'sell-adx-value': 94, 'sell-rsi-value': 63, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.2744891639643, 216: 0.17911475074452382, 772: 0.06251955472249589, 1091: 0}, 'stoploss': {'stoploss': -0.024551752215582423}}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 39, 'wins': 20, 'draws': 0, 'losses': 19, 'profit_mean': -0.0021400679487179478, 'profit_median': -0.012222, 'profit_total': -0.0041773, 'profit_total_abs': -8.346264999999997, 'holding_avg': timedelta(minutes=636.9230769230769)}, # noqa: E501
|
||||
'results_explanation': ' 39 trades. Avg profit -0.21%. Total profit -0.00417730 BTC ( -8.35Σ%). Avg duration 636.9 min.', # noqa: E501
|
||||
'total_profit': -0.0041773,
|
||||
'current_epoch': 6,
|
||||
'is_initial_point': True,
|
||||
'is_best': False
|
||||
}, {
|
||||
'loss': 4.713497421432944,
|
||||
'params_dict': {'mfi-value': 13, 'fastd-value': 41, 'adx-value': 21, 'rsi-value': 29, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower', 'sell-mfi-value': 99, 'sell-fastd-value': 60, 'sell-adx-value': 81, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 771, 'roi_t2': 620, 'roi_t3': 145, 'roi_p1': 0.0586919200378493, 'roi_p2': 0.04984118697312542, 'roi_p3': 0.37521058680247044, 'stoploss': -0.14613268022709905}, # noqa: E501
|
||||
'params_details': {
|
||||
'buy': {'mfi-value': 13, 'fastd-value': 41, 'adx-value': 21, 'rsi-value': 29, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower'}, 'sell': {'sell-mfi-value': 99, 'sell-fastd-value': 60, 'sell-adx-value': 81, 'sell-rsi-value': 69, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': False, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.4837436938134452, 145: 0.10853310701097472, 765: 0.0586919200378493, 1536: 0}, # noqa: E501
|
||||
'stoploss': {'stoploss': -0.14613268022709905}}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 318, 'wins': 100, 'draws': 0, 'losses': 218, 'profit_mean': -0.0039833954716981146, 'profit_median': -0.012222, 'profit_total': -0.06339929, 'profit_total_abs': -126.67197600000004, 'holding_avg': timedelta(minutes=3140.377358490566)}, # noqa: E501
|
||||
'results_explanation': ' 318 trades. Avg profit -0.40%. Total profit -0.06339929 BTC (-126.67Σ%). Avg duration 3140.4 min.', # noqa: E501
|
||||
'total_profit': -0.06339929,
|
||||
'current_epoch': 7,
|
||||
'is_initial_point': True,
|
||||
'is_best': False
|
||||
}, {
|
||||
'loss': 20.0, # noqa: E501
|
||||
'params_dict': {'mfi-value': 24, 'fastd-value': 43, 'adx-value': 33, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'sar_reversal', 'sell-mfi-value': 89, 'sell-fastd-value': 74, 'sell-adx-value': 70, 'sell-rsi-value': 70, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': False, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal', 'roi_t1': 1149, 'roi_t2': 375, 'roi_t3': 289, 'roi_p1': 0.05571820757172588, 'roi_p2': 0.0606240398618907, 'roi_p3': 0.1729012220156157, 'stoploss': -0.1588514289110401}, # noqa: E501
|
||||
'params_details': {'buy': {'mfi-value': 24, 'fastd-value': 43, 'adx-value': 33, 'rsi-value': 20, 'mfi-enabled': False, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': True, 'trigger': 'sar_reversal'}, 'sell': {'sell-mfi-value': 89, 'sell-fastd-value': 74, 'sell-adx-value': 70, 'sell-rsi-value': 70, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': False, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal'}, 'roi': {0: 0.2892434694492323, 289: 0.11634224743361658, 664: 0.05571820757172588, 1813: 0}, 'stoploss': {'stoploss': -0.1588514289110401}}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 1, 'wins': 0, 'draws': 1, 'losses': 0, 'profit_mean': 0.0, 'profit_median': 0.0, 'profit_total': 0.0, 'profit_total_abs': 0.0, 'holding_avg': timedelta(minutes=5340.0)}, # noqa: E501
|
||||
'results_explanation': ' 1 trades. Avg profit 0.00%. Total profit 0.00000000 BTC ( 0.00Σ%). Avg duration 5340.0 min.', # noqa: E501
|
||||
'total_profit': 0.0,
|
||||
'current_epoch': 8,
|
||||
'is_initial_point': True,
|
||||
'is_best': False
|
||||
}, {
|
||||
'loss': 2.4731817780991223,
|
||||
'params_dict': {'mfi-value': 22, 'fastd-value': 20, 'adx-value': 29, 'rsi-value': 40, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'sar_reversal', 'sell-mfi-value': 97, 'sell-fastd-value': 65, 'sell-adx-value': 81, 'sell-rsi-value': 64, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1012, 'roi_t2': 584, 'roi_t3': 422, 'roi_p1': 0.036764323603472565, 'roi_p2': 0.10335480573205287, 'roi_p3': 0.10322347377503042, 'stoploss': -0.2780610808108503}, # noqa: E501
|
||||
'params_details': {'buy': {'mfi-value': 22, 'fastd-value': 20, 'adx-value': 29, 'rsi-value': 40, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'sar_reversal'}, 'sell': {'sell-mfi-value': 97, 'sell-fastd-value': 65, 'sell-adx-value': 81, 'sell-rsi-value': 64, 'sell-mfi-enabled': True, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.2433426031105559, 422: 0.14011912933552545, 1006: 0.036764323603472565, 2018: 0}, 'stoploss': {'stoploss': -0.2780610808108503}}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 229, 'wins': 150, 'draws': 0, 'losses': 79, 'profit_mean': -0.0038433433624454144, 'profit_median': -0.012222, 'profit_total': -0.044050070000000004, 'profit_total_abs': -88.01256299999999, 'holding_avg': timedelta(minutes=6505.676855895196)}, # noqa: E501
|
||||
'results_explanation': ' 229 trades. Avg profit -0.38%. Total profit -0.04405007 BTC ( -88.01Σ%). Avg duration 6505.7 min.', # noqa: E501
|
||||
'total_profit': -0.044050070000000004, # noqa: E501
|
||||
'current_epoch': 9,
|
||||
'is_initial_point': True,
|
||||
'is_best': False
|
||||
}, {
|
||||
'loss': -0.2604606005845212, # noqa: E501
|
||||
'params_dict': {'mfi-value': 23, 'fastd-value': 24, 'adx-value': 22, 'rsi-value': 24, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal', 'sell-mfi-value': 97, 'sell-fastd-value': 70, 'sell-adx-value': 64, 'sell-rsi-value': 80, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal', 'roi_t1': 792, 'roi_t2': 464, 'roi_t3': 215, 'roi_p1': 0.04594053535385903, 'roi_p2': 0.09623192684243963, 'roi_p3': 0.04428219070850663, 'stoploss': -0.16992287161634415}, # noqa: E501
|
||||
'params_details': {'buy': {'mfi-value': 23, 'fastd-value': 24, 'adx-value': 22, 'rsi-value': 24, 'mfi-enabled': False, 'fastd-enabled': False, 'adx-enabled': False, 'rsi-enabled': True, 'trigger': 'macd_cross_signal'}, 'sell': {'sell-mfi-value': 97, 'sell-fastd-value': 70, 'sell-adx-value': 64, 'sell-rsi-value': 80, 'sell-mfi-enabled': False, 'sell-fastd-enabled': True, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-sar_reversal'}, 'roi': {0: 0.18645465290480528, 215: 0.14217246219629864, 679: 0.04594053535385903, 1471: 0}, 'stoploss': {'stoploss': -0.16992287161634415}}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 4, 'wins': 0, 'draws': 0, 'losses': 4, 'profit_mean': 0.001080385, 'profit_median': -0.012222, 'profit_total': 0.00021629, 'profit_total_abs': 0.432154, 'holding_avg': timedelta(minutes=2850.0)}, # noqa: E501
|
||||
'results_explanation': ' 4 trades. Avg profit 0.11%. Total profit 0.00021629 BTC ( 0.43Σ%). Avg duration 2850.0 min.', # noqa: E501
|
||||
'total_profit': 0.00021629,
|
||||
'current_epoch': 10,
|
||||
'is_initial_point': True,
|
||||
'is_best': True
|
||||
}, {
|
||||
'loss': 4.876465945994304, # noqa: E501
|
||||
'params_dict': {'mfi-value': 20, 'fastd-value': 32, 'adx-value': 49, 'rsi-value': 23, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower', 'sell-mfi-value': 75, 'sell-fastd-value': 56, 'sell-adx-value': 61, 'sell-rsi-value': 62, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal', 'roi_t1': 579, 'roi_t2': 614, 'roi_t3': 273, 'roi_p1': 0.05307643172744114, 'roi_p2': 0.1352282078262871, 'roi_p3': 0.1913307406325751, 'stoploss': -0.25728526022513887}, # noqa: E501
|
||||
'params_details': {'buy': {'mfi-value': 20, 'fastd-value': 32, 'adx-value': 49, 'rsi-value': 23, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': False, 'rsi-enabled': False, 'trigger': 'bb_lower'}, 'sell': {'sell-mfi-value': 75, 'sell-fastd-value': 56, 'sell-adx-value': 61, 'sell-rsi-value': 62, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-macd_cross_signal'}, 'roi': {0: 0.3796353801863034, 273: 0.18830463955372825, 887: 0.05307643172744114, 1466: 0}, 'stoploss': {'stoploss': -0.25728526022513887}}, # noqa: E501
|
||||
# New Hyperopt mode!
|
||||
'results_metrics': {'total_trades': 117, 'wins': 67, 'draws': 0, 'losses': 50, 'profit_mean': -0.012698609145299145, 'profit_median': -0.012222, 'profit_total': -0.07436117, 'profit_total_abs': -148.573727, 'holding_avg': timedelta(minutes=4282.5641025641025)}, # noqa: E501
|
||||
'results_explanation': ' 117 trades. Avg profit -1.27%. Total profit -0.07436117 BTC (-148.57Σ%). Avg duration 4282.6 min.', # noqa: E501
|
||||
'total_profit': -0.07436117,
|
||||
'current_epoch': 11,
|
||||
'is_initial_point': True,
|
||||
'is_best': False
|
||||
}, {
|
||||
'loss': 100000,
|
||||
'params_dict': {'mfi-value': 10, 'fastd-value': 36, 'adx-value': 31, 'rsi-value': 22, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'sar_reversal', 'sell-mfi-value': 80, 'sell-fastd-value': 71, 'sell-adx-value': 60, 'sell-rsi-value': 85, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper', 'roi_t1': 1156, 'roi_t2': 581, 'roi_t3': 408, 'roi_p1': 0.06860454019988212, 'roi_p2': 0.12473718444931989, 'roi_p3': 0.2896360635226823, 'stoploss': -0.30889015124682806}, # noqa: E501
|
||||
'params_details': {'buy': {'mfi-value': 10, 'fastd-value': 36, 'adx-value': 31, 'rsi-value': 22, 'mfi-enabled': True, 'fastd-enabled': True, 'adx-enabled': True, 'rsi-enabled': False, 'trigger': 'sar_reversal'}, 'sell': {'sell-mfi-value': 80, 'sell-fastd-value': 71, 'sell-adx-value': 60, 'sell-rsi-value': 85, 'sell-mfi-enabled': False, 'sell-fastd-enabled': False, 'sell-adx-enabled': True, 'sell-rsi-enabled': True, 'sell-trigger': 'sell-bb_upper'}, 'roi': {0: 0.4829777881718843, 408: 0.19334172464920202, 989: 0.06860454019988212, 2145: 0}, 'stoploss': {'stoploss': -0.30889015124682806}}, # noqa: E501
|
||||
'results_metrics': {'total_trades': 0, 'wins': 0, 'draws': 0, 'losses': 0, 'profit_mean': None, 'profit_median': None, 'profit_total': 0, 'profit_total_abs': 0.0, 'holding_avg': timedelta()}, # noqa: E501
|
||||
'results_explanation': ' 0 trades. Avg profit nan%. Total profit 0.00000000 BTC ( 0.00Σ%). Avg duration nan min.', # noqa: E501
|
||||
'total_profit': 0,
|
||||
'current_epoch': 12,
|
||||
'is_initial_point': True,
|
||||
'is_best': False
|
||||
}
|
||||
]
|
||||
|
|
|
|||
|
|
@ -501,13 +501,14 @@ def test_backtest_results(default_conf, fee, mocker, caplog, data) -> None:
|
|||
# Dummy data as we mock the analyze functions
|
||||
data_processed = {pair: frame.copy()}
|
||||
min_date, max_date = get_timerange({pair: frame})
|
||||
results = backtesting.backtest(
|
||||
result = backtesting.backtest(
|
||||
processed=data_processed,
|
||||
start_date=min_date,
|
||||
end_date=max_date,
|
||||
max_open_trades=10,
|
||||
)
|
||||
|
||||
results = result['results']
|
||||
assert len(results) == len(data.trades)
|
||||
assert round(results["profit_ratio"].sum(), 3) == round(data.profit_perc, 3)
|
||||
|
||||
|
|
|
|||
|
|
@ -514,13 +514,14 @@ def test_backtest_one(default_conf, fee, mocker, testdatadir) -> None:
|
|||
timerange=timerange)
|
||||
processed = backtesting.strategy.ohlcvdata_to_dataframe(data)
|
||||
min_date, max_date = get_timerange(processed)
|
||||
results = backtesting.backtest(
|
||||
result = backtesting.backtest(
|
||||
processed=processed,
|
||||
start_date=min_date,
|
||||
end_date=max_date,
|
||||
max_open_trades=10,
|
||||
position_stacking=False,
|
||||
)
|
||||
results = result['results']
|
||||
assert not results.empty
|
||||
assert len(results) == 2
|
||||
|
||||
|
|
@ -583,8 +584,8 @@ def test_backtest_1min_timeframe(default_conf, fee, mocker, testdatadir) -> None
|
|||
max_open_trades=1,
|
||||
position_stacking=False,
|
||||
)
|
||||
assert not results.empty
|
||||
assert len(results) == 1
|
||||
assert not results['results'].empty
|
||||
assert len(results['results']) == 1
|
||||
|
||||
|
||||
def test_processed(default_conf, mocker, testdatadir) -> None:
|
||||
|
|
@ -623,7 +624,7 @@ def test_backtest_pricecontours_protections(default_conf, fee, mocker, testdatad
|
|||
# While buy-signals are unrealistic, running backtesting
|
||||
# over and over again should not cause different results
|
||||
for [contour, numres] in tests:
|
||||
assert len(simple_backtest(default_conf, contour, mocker, testdatadir)) == numres
|
||||
assert len(simple_backtest(default_conf, contour, mocker, testdatadir)['results']) == numres
|
||||
|
||||
|
||||
@pytest.mark.parametrize('protections,contour,expected', [
|
||||
|
|
@ -648,7 +649,7 @@ def test_backtest_pricecontours(default_conf, fee, mocker, testdatadir,
|
|||
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
|
||||
# While buy-signals are unrealistic, running backtesting
|
||||
# over and over again should not cause different results
|
||||
assert len(simple_backtest(default_conf, contour, mocker, testdatadir)) == expected
|
||||
assert len(simple_backtest(default_conf, contour, mocker, testdatadir)['results']) == expected
|
||||
|
||||
|
||||
def test_backtest_clash_buy_sell(mocker, default_conf, testdatadir):
|
||||
|
|
@ -662,8 +663,8 @@ def test_backtest_clash_buy_sell(mocker, default_conf, testdatadir):
|
|||
backtesting = Backtesting(default_conf)
|
||||
backtesting.strategy.advise_buy = fun # Override
|
||||
backtesting.strategy.advise_sell = fun # Override
|
||||
results = backtesting.backtest(**backtest_conf)
|
||||
assert results.empty
|
||||
result = backtesting.backtest(**backtest_conf)
|
||||
assert result['results'].empty
|
||||
|
||||
|
||||
def test_backtest_only_sell(mocker, default_conf, testdatadir):
|
||||
|
|
@ -677,8 +678,8 @@ def test_backtest_only_sell(mocker, default_conf, testdatadir):
|
|||
backtesting = Backtesting(default_conf)
|
||||
backtesting.strategy.advise_buy = fun # Override
|
||||
backtesting.strategy.advise_sell = fun # Override
|
||||
results = backtesting.backtest(**backtest_conf)
|
||||
assert results.empty
|
||||
result = backtesting.backtest(**backtest_conf)
|
||||
assert result['results'].empty
|
||||
|
||||
|
||||
def test_backtest_alternate_buy_sell(default_conf, fee, mocker, testdatadir):
|
||||
|
|
@ -690,10 +691,11 @@ def test_backtest_alternate_buy_sell(default_conf, fee, mocker, testdatadir):
|
|||
backtesting = Backtesting(default_conf)
|
||||
backtesting.strategy.advise_buy = _trend_alternate # Override
|
||||
backtesting.strategy.advise_sell = _trend_alternate # Override
|
||||
results = backtesting.backtest(**backtest_conf)
|
||||
result = backtesting.backtest(**backtest_conf)
|
||||
# 200 candles in backtest data
|
||||
# won't buy on first (shifted by 1)
|
||||
# 100 buys signals
|
||||
results = result['results']
|
||||
assert len(results) == 100
|
||||
# One trade was force-closed at the end
|
||||
assert len(results.loc[results['is_open']]) == 0
|
||||
|
|
@ -745,9 +747,9 @@ def test_backtest_multi_pair(default_conf, fee, mocker, tres, pair, testdatadir)
|
|||
results = backtesting.backtest(**backtest_conf)
|
||||
|
||||
# Make sure we have parallel trades
|
||||
assert len(evaluate_result_multi(results, '5m', 2)) > 0
|
||||
assert len(evaluate_result_multi(results['results'], '5m', 2)) > 0
|
||||
# make sure we don't have trades with more than configured max_open_trades
|
||||
assert len(evaluate_result_multi(results, '5m', 3)) == 0
|
||||
assert len(evaluate_result_multi(results['results'], '5m', 3)) == 0
|
||||
|
||||
backtest_conf = {
|
||||
'processed': processed,
|
||||
|
|
@ -757,7 +759,7 @@ def test_backtest_multi_pair(default_conf, fee, mocker, tres, pair, testdatadir)
|
|||
'position_stacking': False,
|
||||
}
|
||||
results = backtesting.backtest(**backtest_conf)
|
||||
assert len(evaluate_result_multi(results, '5m', 1)) == 0
|
||||
assert len(evaluate_result_multi(results['results'], '5m', 1)) == 0
|
||||
|
||||
|
||||
def test_backtest_start_timerange(default_conf, mocker, caplog, testdatadir):
|
||||
|
|
@ -802,8 +804,19 @@ def test_backtest_start_timerange(default_conf, mocker, caplog, testdatadir):
|
|||
@pytest.mark.filterwarnings("ignore:deprecated")
|
||||
def test_backtest_start_multi_strat(default_conf, mocker, caplog, testdatadir):
|
||||
|
||||
default_conf['ask_strategy'].update({
|
||||
"use_sell_signal": True,
|
||||
"sell_profit_only": False,
|
||||
"sell_profit_offset": 0.0,
|
||||
"ignore_roi_if_buy_signal": False,
|
||||
})
|
||||
patch_exchange(mocker)
|
||||
backtestmock = MagicMock(return_value=pd.DataFrame(columns=BT_DATA_COLUMNS))
|
||||
backtestmock = MagicMock(return_value={
|
||||
'results': pd.DataFrame(columns=BT_DATA_COLUMNS),
|
||||
'config': default_conf,
|
||||
'locks': [],
|
||||
'final_balance': 1000,
|
||||
})
|
||||
mocker.patch('freqtrade.plugins.pairlistmanager.PairListManager.whitelist',
|
||||
PropertyMock(return_value=['UNITTEST/BTC']))
|
||||
mocker.patch('freqtrade.optimize.backtesting.Backtesting.backtest', backtestmock)
|
||||
|
|
@ -817,7 +830,7 @@ def test_backtest_start_multi_strat(default_conf, mocker, caplog, testdatadir):
|
|||
text_table_strategy=strattable_mock,
|
||||
generate_pair_metrics=MagicMock(),
|
||||
generate_sell_reason_stats=sell_reason_mock,
|
||||
generate_strategy_metrics=strat_summary,
|
||||
generate_strategy_comparison=strat_summary,
|
||||
generate_daily_stats=MagicMock(),
|
||||
)
|
||||
patched_configuration_load_config_file(mocker, default_conf)
|
||||
|
|
@ -865,10 +878,14 @@ def test_backtest_start_multi_strat(default_conf, mocker, caplog, testdatadir):
|
|||
|
||||
@pytest.mark.filterwarnings("ignore:deprecated")
|
||||
def test_backtest_start_multi_strat_nomock(default_conf, mocker, caplog, testdatadir, capsys):
|
||||
|
||||
default_conf['ask_strategy'].update({
|
||||
"use_sell_signal": True,
|
||||
"sell_profit_only": False,
|
||||
"sell_profit_offset": 0.0,
|
||||
"ignore_roi_if_buy_signal": False,
|
||||
})
|
||||
patch_exchange(mocker)
|
||||
backtestmock = MagicMock(side_effect=[
|
||||
pd.DataFrame({'pair': ['XRP/BTC', 'LTC/BTC'],
|
||||
result1 = pd.DataFrame({'pair': ['XRP/BTC', 'LTC/BTC'],
|
||||
'profit_ratio': [0.0, 0.0],
|
||||
'profit_abs': [0.0, 0.0],
|
||||
'open_date': pd.to_datetime(['2018-01-29 18:40:00',
|
||||
|
|
@ -882,8 +899,8 @@ def test_backtest_start_multi_strat_nomock(default_conf, mocker, caplog, testdat
|
|||
'open_rate': [0.104445, 0.10302485],
|
||||
'close_rate': [0.104969, 0.103541],
|
||||
'sell_reason': [SellType.ROI, SellType.ROI]
|
||||
}),
|
||||
pd.DataFrame({'pair': ['XRP/BTC', 'LTC/BTC', 'ETH/BTC'],
|
||||
})
|
||||
result2 = pd.DataFrame({'pair': ['XRP/BTC', 'LTC/BTC', 'ETH/BTC'],
|
||||
'profit_ratio': [0.03, 0.01, 0.1],
|
||||
'profit_abs': [0.01, 0.02, 0.2],
|
||||
'open_date': pd.to_datetime(['2018-01-29 18:40:00',
|
||||
|
|
@ -899,7 +916,20 @@ def test_backtest_start_multi_strat_nomock(default_conf, mocker, caplog, testdat
|
|||
'open_rate': [0.104445, 0.10302485, 0.122541],
|
||||
'close_rate': [0.104969, 0.103541, 0.123541],
|
||||
'sell_reason': [SellType.ROI, SellType.ROI, SellType.STOP_LOSS]
|
||||
}),
|
||||
})
|
||||
backtestmock = MagicMock(side_effect=[
|
||||
{
|
||||
'results': result1,
|
||||
'config': default_conf,
|
||||
'locks': [],
|
||||
'final_balance': 1000,
|
||||
},
|
||||
{
|
||||
'results': result2,
|
||||
'config': default_conf,
|
||||
'locks': [],
|
||||
'final_balance': 1000,
|
||||
}
|
||||
])
|
||||
mocker.patch('freqtrade.plugins.pairlistmanager.PairListManager.whitelist',
|
||||
PropertyMock(return_value=['UNITTEST/BTC']))
|
||||
|
|
|
|||
|
|
@ -5,7 +5,7 @@ import re
|
|||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
from typing import Dict, List
|
||||
from unittest.mock import MagicMock
|
||||
from unittest.mock import ANY, MagicMock
|
||||
|
||||
import pandas as pd
|
||||
import pytest
|
||||
|
|
@ -18,10 +18,12 @@ from freqtrade.exceptions import OperationalException
|
|||
from freqtrade.optimize.hyperopt import Hyperopt
|
||||
from freqtrade.optimize.hyperopt_auto import HyperOptAuto
|
||||
from freqtrade.optimize.hyperopt_tools import HyperoptTools
|
||||
from freqtrade.optimize.optimize_reports import generate_strategy_stats
|
||||
from freqtrade.optimize.space import SKDecimal
|
||||
from freqtrade.resolvers.hyperopt_resolver import HyperOptResolver
|
||||
from freqtrade.state import RunMode
|
||||
from freqtrade.strategy.hyper import IntParameter
|
||||
from freqtrade.strategy.interface import SellType
|
||||
from tests.conftest import (get_args, log_has, log_has_re, patch_exchange,
|
||||
patched_configuration_load_config_file)
|
||||
|
||||
|
|
@ -433,18 +435,41 @@ def test_start_calls_optimizer(mocker, hyperopt_conf, capsys) -> None:
|
|||
assert hasattr(hyperopt, "position_stacking")
|
||||
|
||||
|
||||
def test_format_results(hyperopt):
|
||||
# Test with BTC as stake_currency
|
||||
trades = [
|
||||
('ETH/BTC', 2, 2, 123),
|
||||
('LTC/BTC', 1, 1, 123),
|
||||
('XPR/BTC', -1, -2, -246)
|
||||
]
|
||||
labels = ['currency', 'profit_ratio', 'profit_abs', 'trade_duration']
|
||||
df = pd.DataFrame.from_records(trades, columns=labels)
|
||||
results_metrics = hyperopt._calculate_results_metrics(df)
|
||||
results_explanation = hyperopt._format_results_explanation_string(results_metrics)
|
||||
total_profit = results_metrics['total_profit']
|
||||
def test_hyperopt_format_results(hyperopt):
|
||||
|
||||
bt_result = {
|
||||
'results': pd.DataFrame({"pair": ["UNITTEST/BTC", "UNITTEST/BTC",
|
||||
"UNITTEST/BTC", "UNITTEST/BTC"],
|
||||
"profit_ratio": [0.003312, 0.010801, 0.013803, 0.002780],
|
||||
"profit_abs": [0.000003, 0.000011, 0.000014, 0.000003],
|
||||
"open_date": [Arrow(2017, 11, 14, 19, 32, 00).datetime,
|
||||
Arrow(2017, 11, 14, 21, 36, 00).datetime,
|
||||
Arrow(2017, 11, 14, 22, 12, 00).datetime,
|
||||
Arrow(2017, 11, 14, 22, 44, 00).datetime],
|
||||
"close_date": [Arrow(2017, 11, 14, 21, 35, 00).datetime,
|
||||
Arrow(2017, 11, 14, 22, 10, 00).datetime,
|
||||
Arrow(2017, 11, 14, 22, 43, 00).datetime,
|
||||
Arrow(2017, 11, 14, 22, 58, 00).datetime],
|
||||
"open_rate": [0.002543, 0.003003, 0.003089, 0.003214],
|
||||
"close_rate": [0.002546, 0.003014, 0.003103, 0.003217],
|
||||
"trade_duration": [123, 34, 31, 14],
|
||||
"is_open": [False, False, False, True],
|
||||
"stake_amount": [0.01, 0.01, 0.01, 0.01],
|
||||
"sell_reason": [SellType.ROI, SellType.STOP_LOSS,
|
||||
SellType.ROI, SellType.FORCE_SELL]
|
||||
}),
|
||||
'config': hyperopt.config,
|
||||
'locks': [],
|
||||
'final_balance': 0.02,
|
||||
'backtest_start_time': 1619718665,
|
||||
'backtest_end_time': 1619718665,
|
||||
}
|
||||
results_metrics = generate_strategy_stats({'XRP/BTC': None}, '', bt_result,
|
||||
Arrow(2017, 11, 14, 19, 32, 00),
|
||||
Arrow(2017, 12, 14, 19, 32, 00), market_change=0)
|
||||
|
||||
results_explanation = HyperoptTools.format_results_explanation_string(results_metrics, 'BTC')
|
||||
total_profit = results_metrics['profit_total_abs']
|
||||
|
||||
results = {
|
||||
'loss': 0.0,
|
||||
|
|
@ -458,21 +483,9 @@ def test_format_results(hyperopt):
|
|||
}
|
||||
|
||||
result = HyperoptTools._format_explanation_string(results, 1)
|
||||
assert result.find(' 66.67%')
|
||||
assert result.find('Total profit 1.00000000 BTC')
|
||||
assert result.find('2.0000Σ %')
|
||||
|
||||
# Test with EUR as stake_currency
|
||||
trades = [
|
||||
('ETH/EUR', 2, 2, 123),
|
||||
('LTC/EUR', 1, 1, 123),
|
||||
('XPR/EUR', -1, -2, -246)
|
||||
]
|
||||
df = pd.DataFrame.from_records(trades, columns=labels)
|
||||
results_metrics = hyperopt._calculate_results_metrics(df)
|
||||
results['total_profit'] = results_metrics['total_profit']
|
||||
result = HyperoptTools._format_explanation_string(results, 1)
|
||||
assert result.find('Total profit 1.00000000 EUR')
|
||||
assert ' 0.71%' in result
|
||||
assert 'Total profit 0.00003100 BTC' in result
|
||||
assert '0:50:00 min' in result
|
||||
|
||||
|
||||
@pytest.mark.parametrize("spaces, expected_results", [
|
||||
|
|
@ -577,22 +590,37 @@ def test_generate_optimizer(mocker, hyperopt_conf) -> None:
|
|||
'hyperopt_min_trades': 1,
|
||||
})
|
||||
|
||||
trades = [
|
||||
('TRX/BTC', 0.023117, 0.000233, 100)
|
||||
]
|
||||
labels = ['currency', 'profit_ratio', 'profit_abs', 'trade_duration']
|
||||
backtest_result = pd.DataFrame.from_records(trades, columns=labels)
|
||||
backtest_result = {
|
||||
'results': pd.DataFrame({"pair": ["UNITTEST/BTC", "UNITTEST/BTC",
|
||||
"UNITTEST/BTC", "UNITTEST/BTC"],
|
||||
"profit_ratio": [0.003312, 0.010801, 0.013803, 0.002780],
|
||||
"profit_abs": [0.000003, 0.000011, 0.000014, 0.000003],
|
||||
"open_date": [Arrow(2017, 11, 14, 19, 32, 00).datetime,
|
||||
Arrow(2017, 11, 14, 21, 36, 00).datetime,
|
||||
Arrow(2017, 11, 14, 22, 12, 00).datetime,
|
||||
Arrow(2017, 11, 14, 22, 44, 00).datetime],
|
||||
"close_date": [Arrow(2017, 11, 14, 21, 35, 00).datetime,
|
||||
Arrow(2017, 11, 14, 22, 10, 00).datetime,
|
||||
Arrow(2017, 11, 14, 22, 43, 00).datetime,
|
||||
Arrow(2017, 11, 14, 22, 58, 00).datetime],
|
||||
"open_rate": [0.002543, 0.003003, 0.003089, 0.003214],
|
||||
"close_rate": [0.002546, 0.003014, 0.003103, 0.003217],
|
||||
"trade_duration": [123, 34, 31, 14],
|
||||
"is_open": [False, False, False, True],
|
||||
"stake_amount": [0.01, 0.01, 0.01, 0.01],
|
||||
"sell_reason": [SellType.ROI, SellType.STOP_LOSS,
|
||||
SellType.ROI, SellType.FORCE_SELL]
|
||||
}),
|
||||
'config': hyperopt_conf,
|
||||
'locks': [],
|
||||
'final_balance': 1000,
|
||||
}
|
||||
|
||||
mocker.patch(
|
||||
'freqtrade.optimize.hyperopt.Backtesting.backtest',
|
||||
MagicMock(return_value=backtest_result)
|
||||
)
|
||||
mocker.patch(
|
||||
'freqtrade.optimize.hyperopt.get_timerange',
|
||||
MagicMock(return_value=(Arrow(2017, 12, 10), Arrow(2017, 12, 13)))
|
||||
)
|
||||
mocker.patch('freqtrade.optimize.hyperopt.Backtesting.backtest', return_value=backtest_result)
|
||||
mocker.patch('freqtrade.optimize.hyperopt.get_timerange',
|
||||
return_value=(Arrow(2017, 12, 10), Arrow(2017, 12, 13)))
|
||||
patch_exchange(mocker)
|
||||
mocker.patch('freqtrade.optimize.hyperopt.load', MagicMock())
|
||||
mocker.patch('freqtrade.optimize.hyperopt.load', return_value={'XRP/BTC': None})
|
||||
|
||||
optimizer_param = {
|
||||
'adx-value': 0,
|
||||
|
|
@ -626,11 +654,11 @@ def test_generate_optimizer(mocker, hyperopt_conf) -> None:
|
|||
'trailing_only_offset_is_reached': False,
|
||||
}
|
||||
response_expected = {
|
||||
'loss': 1.9840569076926293,
|
||||
'results_explanation': (' 1 trades. 1/0/0 Wins/Draws/Losses. '
|
||||
'Avg profit 2.31%. Median profit 2.31%. Total profit '
|
||||
'0.00023300 BTC ( 2.31\N{GREEK CAPITAL LETTER SIGMA}%). '
|
||||
'Avg duration 100.0 min.'
|
||||
'loss': 1.9147239021396234,
|
||||
'results_explanation': (' 4 trades. 4/0/0 Wins/Draws/Losses. '
|
||||
'Avg profit 0.77%. Median profit 0.71%. Total profit '
|
||||
'0.00003100 BTC ( 0.00\N{GREEK CAPITAL LETTER SIGMA}%). '
|
||||
'Avg duration 0:50:00 min.'
|
||||
).encode(locale.getpreferredencoding(), 'replace').decode('utf-8'),
|
||||
'params_details': {'buy': {'adx-enabled': False,
|
||||
'adx-value': 0,
|
||||
|
|
@ -660,21 +688,16 @@ def test_generate_optimizer(mocker, hyperopt_conf) -> None:
|
|||
'trailing_stop_positive': 0.02,
|
||||
'trailing_stop_positive_offset': 0.07}},
|
||||
'params_dict': optimizer_param,
|
||||
'results_metrics': {'avg_profit': 2.3117,
|
||||
'draws': 0,
|
||||
'duration': 100.0,
|
||||
'losses': 0,
|
||||
'winsdrawslosses': ' 1 0 0',
|
||||
'median_profit': 2.3117,
|
||||
'profit': 2.3117,
|
||||
'total_profit': 0.000233,
|
||||
'trade_count': 1,
|
||||
'wins': 1},
|
||||
'total_profit': 0.00023300
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'params_not_optimized': {'buy': {}, 'sell': {}},
|
||||
'results_metrics': ANY,
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||||
'total_profit': 3.1e-08
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}
|
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|
||||
hyperopt = Hyperopt(hyperopt_conf)
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||||
hyperopt.dimensions = hyperopt.hyperopt_space()
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||||
hyperopt.min_date = Arrow(2017, 12, 10)
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hyperopt.max_date = Arrow(2017, 12, 13)
|
||||
hyperopt.init_spaces()
|
||||
hyperopt.dimensions = hyperopt.dimensions
|
||||
generate_optimizer_value = hyperopt.generate_optimizer(list(optimizer_param.values()))
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||||
assert generate_optimizer_value == response_expected
|
||||
|
||||
|
|
|
|||
|
|
@ -14,7 +14,8 @@ from freqtrade.edge import PairInfo
|
|||
from freqtrade.optimize.optimize_reports import (generate_backtest_stats, generate_daily_stats,
|
||||
generate_edge_table, generate_pair_metrics,
|
||||
generate_sell_reason_stats,
|
||||
generate_strategy_metrics, store_backtest_stats,
|
||||
generate_strategy_comparison,
|
||||
generate_trading_stats, store_backtest_stats,
|
||||
text_table_bt_results, text_table_sell_reason,
|
||||
text_table_strategy)
|
||||
from freqtrade.resolvers.strategy_resolver import StrategyResolver
|
||||
|
|
@ -226,8 +227,6 @@ def test_generate_daily_stats(testdatadir):
|
|||
assert res['winning_days'] == 14
|
||||
assert res['draw_days'] == 4
|
||||
assert res['losing_days'] == 3
|
||||
assert res['winner_holding_avg'] == timedelta(seconds=1440)
|
||||
assert res['loser_holding_avg'] == timedelta(days=1, seconds=21420)
|
||||
|
||||
# Select empty dataframe!
|
||||
res = generate_daily_stats(bt_data.loc[bt_data['open_date'] == '2000-01-01', :])
|
||||
|
|
@ -238,6 +237,23 @@ def test_generate_daily_stats(testdatadir):
|
|||
assert res['losing_days'] == 0
|
||||
|
||||
|
||||
def test_generate_trading_stats(testdatadir):
|
||||
filename = testdatadir / "backtest-result_new.json"
|
||||
bt_data = load_backtest_data(filename)
|
||||
res = generate_trading_stats(bt_data)
|
||||
assert isinstance(res, dict)
|
||||
assert res['winner_holding_avg'] == timedelta(seconds=1440)
|
||||
assert res['loser_holding_avg'] == timedelta(days=1, seconds=21420)
|
||||
assert 'wins' in res
|
||||
assert 'losses' in res
|
||||
assert 'draws' in res
|
||||
|
||||
# Select empty dataframe!
|
||||
res = generate_trading_stats(bt_data.loc[bt_data['open_date'] == '2000-01-01', :])
|
||||
assert res['wins'] == 0
|
||||
assert res['losses'] == 0
|
||||
|
||||
|
||||
def test_text_table_sell_reason():
|
||||
|
||||
results = pd.DataFrame(
|
||||
|
|
@ -345,7 +361,7 @@ def test_text_table_strategy(default_conf):
|
|||
' 43.33 | 0:20:00 | 3 | 0 | 0 |'
|
||||
)
|
||||
|
||||
strategy_results = generate_strategy_metrics(all_results=results)
|
||||
strategy_results = generate_strategy_comparison(all_results=results)
|
||||
|
||||
assert text_table_strategy(strategy_results, 'BTC') == result_str
|
||||
|
||||
|
|
|
|||
|
|
@ -671,4 +671,4 @@ def test_auto_hyperopt_interface(default_conf):
|
|||
strategy.sell_rsi = IntParameter([0, 10], default=5, space='buy')
|
||||
|
||||
with pytest.raises(OperationalException, match=r"Inconclusive parameter.*"):
|
||||
[x for x in strategy.enumerate_parameters('sell')]
|
||||
[x for x in strategy._detect_parameters('sell')]
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user