diff --git a/freqtrade/optimize/hyperopt_loss/hyperopt_loss_calmar.py b/freqtrade/optimize/hyperopt_loss/hyperopt_loss_calmar.py index 2b591824f..b8935b08e 100644 --- a/freqtrade/optimize/hyperopt_loss/hyperopt_loss_calmar.py +++ b/freqtrade/optimize/hyperopt_loss/hyperopt_loss_calmar.py @@ -5,13 +5,11 @@ This module defines the alternative HyperOptLoss class which can be used for Hyperoptimization. """ from datetime import datetime -from math import sqrt as msqrt -from typing import Any, Dict from pandas import DataFrame from freqtrade.constants import Config -from freqtrade.data.metrics import calculate_max_drawdown +from freqtrade.data.metrics import calculate_calmar from freqtrade.optimize.hyperopt import IHyperOptLoss @@ -23,42 +21,15 @@ class CalmarHyperOptLoss(IHyperOptLoss): """ @staticmethod - def hyperopt_loss_function( - results: DataFrame, - trade_count: int, - min_date: datetime, - max_date: datetime, - config: Config, - processed: Dict[str, DataFrame], - backtest_stats: Dict[str, Any], - *args, - **kwargs - ) -> float: + def hyperopt_loss_function(results: DataFrame, trade_count: int, + min_date: datetime, max_date: datetime, + config: Config, *args, **kwargs) -> float: """ Objective function, returns smaller number for more optimal results. Uses Calmar Ratio calculation. """ - total_profit = backtest_stats["profit_total"] - days_period = (max_date - min_date).days - - # adding slippage of 0.1% per trade - total_profit = total_profit - 0.0005 - expected_returns_mean = total_profit.sum() / days_period * 100 - - # calculate max drawdown - try: - _, _, _, _, _, max_drawdown = calculate_max_drawdown( - results, value_col="profit_abs" - ) - except ValueError: - max_drawdown = 0 - - if max_drawdown != 0: - calmar_ratio = expected_returns_mean / max_drawdown * msqrt(365) - else: - # Define high (negative) calmar ratio to be clear that this is NOT optimal. - calmar_ratio = -20.0 - + starting_balance = config['dry_run_wallet'] + calmar_ratio = calculate_calmar(results, min_date, max_date, starting_balance) # print(expected_returns_mean, max_drawdown, calmar_ratio) return -calmar_ratio diff --git a/freqtrade/optimize/hyperopt_loss/hyperopt_loss_sharpe.py b/freqtrade/optimize/hyperopt_loss/hyperopt_loss_sharpe.py index 2c8ae552d..8ebb90fc5 100644 --- a/freqtrade/optimize/hyperopt_loss/hyperopt_loss_sharpe.py +++ b/freqtrade/optimize/hyperopt_loss/hyperopt_loss_sharpe.py @@ -6,9 +6,10 @@ Hyperoptimization. """ from datetime import datetime -import numpy as np from pandas import DataFrame +from freqtrade.constants import Config +from freqtrade.data.metrics import calculate_sharpe from freqtrade.optimize.hyperopt import IHyperOptLoss @@ -22,25 +23,13 @@ class SharpeHyperOptLoss(IHyperOptLoss): @staticmethod def hyperopt_loss_function(results: DataFrame, trade_count: int, min_date: datetime, max_date: datetime, - *args, **kwargs) -> float: + config: Config, *args, **kwargs) -> float: """ Objective function, returns smaller number for more optimal results. Uses Sharpe Ratio calculation. """ - total_profit = results["profit_ratio"] - days_period = (max_date - min_date).days - - # adding slippage of 0.1% per trade - total_profit = total_profit - 0.0005 - expected_returns_mean = total_profit.sum() / days_period - up_stdev = np.std(total_profit) - - if up_stdev != 0: - sharp_ratio = expected_returns_mean / up_stdev * np.sqrt(365) - else: - # Define high (negative) sharpe ratio to be clear that this is NOT optimal. - sharp_ratio = -20. - + starting_balance = config['dry_run_wallet'] + sharp_ratio = calculate_sharpe(results, min_date, max_date, starting_balance) # print(expected_returns_mean, up_stdev, sharp_ratio) return -sharp_ratio diff --git a/freqtrade/optimize/hyperopt_loss/hyperopt_loss_sortino.py b/freqtrade/optimize/hyperopt_loss/hyperopt_loss_sortino.py index b231370dd..a0122a0bf 100644 --- a/freqtrade/optimize/hyperopt_loss/hyperopt_loss_sortino.py +++ b/freqtrade/optimize/hyperopt_loss/hyperopt_loss_sortino.py @@ -6,9 +6,10 @@ Hyperoptimization. """ from datetime import datetime -import numpy as np from pandas import DataFrame +from freqtrade.constants import Config +from freqtrade.data.metrics import calculate_sortino from freqtrade.optimize.hyperopt import IHyperOptLoss @@ -22,28 +23,13 @@ class SortinoHyperOptLoss(IHyperOptLoss): @staticmethod def hyperopt_loss_function(results: DataFrame, trade_count: int, min_date: datetime, max_date: datetime, - *args, **kwargs) -> float: + config: Config, *args, **kwargs) -> float: """ Objective function, returns smaller number for more optimal results. Uses Sortino Ratio calculation. """ - total_profit = results["profit_ratio"] - days_period = (max_date - min_date).days - - # adding slippage of 0.1% per trade - total_profit = total_profit - 0.0005 - expected_returns_mean = total_profit.sum() / days_period - - results['downside_returns'] = 0 - results.loc[total_profit < 0, 'downside_returns'] = results['profit_ratio'] - down_stdev = np.std(results['downside_returns']) - - if down_stdev != 0: - sortino_ratio = expected_returns_mean / down_stdev * np.sqrt(365) - else: - # Define high (negative) sortino ratio to be clear that this is NOT optimal. - sortino_ratio = -20. - + starting_balance = config['dry_run_wallet'] + sortino_ratio = calculate_sortino(results, min_date, max_date, starting_balance) # print(expected_returns_mean, down_stdev, sortino_ratio) return -sortino_ratio diff --git a/tests/optimize/conftest.py b/tests/optimize/conftest.py index 3d50f37dd..4d257addc 100644 --- a/tests/optimize/conftest.py +++ b/tests/optimize/conftest.py @@ -48,8 +48,8 @@ def hyperopt_results(): return pd.DataFrame( { 'pair': ['ETH/USDT', 'ETH/USDT', 'ETH/USDT', 'ETH/USDT'], - 'profit_ratio': [-0.1, 0.2, -0.1, 0.3], - 'profit_abs': [-0.2, 0.4, -0.2, 0.6], + 'profit_ratio': [-0.1, 0.2, -0.12, 0.3], + 'profit_abs': [-0.2, 0.4, -0.21, 0.6], 'trade_duration': [10, 30, 10, 10], 'amount': [0.1, 0.1, 0.1, 0.1], 'exit_reason': [ExitType.STOP_LOSS, ExitType.ROI, ExitType.STOP_LOSS, ExitType.ROI],