From d3b1aa7f01247d14f4bb7a571a1f66ddacbddb49 Mon Sep 17 00:00:00 2001 From: Stefano Ariestasia Date: Sat, 7 Jan 2023 09:19:06 +0900 Subject: [PATCH] update sortino calc --- .../hyperopt_loss/hyperopt_loss_sortino.py | 24 ++++--------------- 1 file changed, 5 insertions(+), 19 deletions(-) diff --git a/freqtrade/optimize/hyperopt_loss/hyperopt_loss_sortino.py b/freqtrade/optimize/hyperopt_loss/hyperopt_loss_sortino.py index b231370dd..1d9914f7f 100644 --- a/freqtrade/optimize/hyperopt_loss/hyperopt_loss_sortino.py +++ b/freqtrade/optimize/hyperopt_loss/hyperopt_loss_sortino.py @@ -10,7 +10,8 @@ import numpy as np from pandas import DataFrame from freqtrade.optimize.hyperopt import IHyperOptLoss - +from freqtrade.constants import Config +from freqtrade.data.metrics import calculate_sortino class SortinoHyperOptLoss(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