9394559170

Committed 26 Apr 2024 06:36AM UTC coverage: 94.656% (-0.02%) from 94.674%

Build # 9394559170

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push

github

Committed by

xmatthias

Commit Message

Loader should be passed as kwarg for clarity

Run Details

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/freqtrade/optimize/hyperopt_loss/hyperopt_loss_sortino_daily.py

"""
SortinoHyperOptLossDaily

This module defines the alternative HyperOptLoss class which can be used for
Hyperoptimization.
"""
import math
from datetime import datetime

from pandas import DataFrame, date_range

from freqtrade.optimize.hyperopt import IHyperOptLoss


class SortinoHyperOptLossDaily(IHyperOptLoss):
    """
    Defines the loss function for hyperopt.

    This implementation uses the Sortino Ratio calculation.
    """

    @staticmethod
    def hyperopt_loss_function(results: DataFrame, trade_count: int,
                               min_date: datetime, max_date: datetime,
                               *args, **kwargs) -> float:
        """
        Objective function, returns smaller number for more optimal results.

        Uses Sortino Ratio calculation.

        Sortino Ratio calculated as described in
        http://www.redrockcapital.com/Sortino__A__Sharper__Ratio_Red_Rock_Capital.pdf
        """
        resample_freq = '1D'
        slippage_per_trade_ratio = 0.0005
        days_in_year = 365
        minimum_acceptable_return = 0.0

        # apply slippage per trade to profit_ratio
        results.loc[:, 'profit_ratio_after_slippage'] = \
            results['profit_ratio'] - slippage_per_trade_ratio

        # create the index within the min_date and end max_date
        t_index = date_range(start=min_date, end=max_date, freq=resample_freq,
                             normalize=True)

        sum_daily = (
            results.resample(resample_freq, on='close_date').agg(
                {"profit_ratio_after_slippage": 'sum'}).reindex(t_index).fillna(0)
        )

        total_profit = sum_daily["profit_ratio_after_slippage"] - minimum_acceptable_return
        expected_returns_mean = total_profit.mean()

        sum_daily['downside_returns'] = 0.0
        sum_daily.loc[total_profit < 0, 'downside_returns'] = total_profit
        total_downside = sum_daily['downside_returns']
        # Here total_downside contains min(0, P - MAR) values,
        # where P = sum_daily["profit_ratio_after_slippage"]
        down_stdev = math.sqrt((total_downside**2).sum() / len(total_downside))

        if down_stdev != 0:
            sortino_ratio = expected_returns_mean / down_stdev * math.sqrt(days_in_year)
        else:
            # Define high (negative) sortino ratio to be clear that this is NOT optimal.
            sortino_ratio = -20.

        # print(t_index, sum_daily, total_profit)
        # print(minimum_acceptable_return, expected_returns_mean, down_stdev, sortino_ratio)
        return -sortino_ratio

1	"""
2	SortinoHyperOptLossDaily
3
4	This module defines the alternative HyperOptLoss class which can be used for
5	Hyperoptimization.
6	"""
7	import math	1✔
8	from datetime import datetime	1✔
9
10	from pandas import DataFrame, date_range	1✔
11
12	from freqtrade.optimize.hyperopt import IHyperOptLoss	1✔
13
14
15	class SortinoHyperOptLossDaily(IHyperOptLoss):	1✔
16	"""
17	Defines the loss function for hyperopt.
18
19	This implementation uses the Sortino Ratio calculation.
20	"""
21
22	@staticmethod	1✔
23	def hyperopt_loss_function(results: DataFrame, trade_count: int,	1✔
24	min_date: datetime, max_date: datetime,
25	args, *kwargs) -> float:
26	"""
27	Objective function, returns smaller number for more optimal results.
28
29	Uses Sortino Ratio calculation.
30
31	Sortino Ratio calculated as described in
32	http://www.redrockcapital.com/Sortino__A__Sharper__Ratio_Red_Rock_Capital.pdf
33	"""
34	resample_freq = '1D'	1✔
35	slippage_per_trade_ratio = 0.0005	1✔
36	days_in_year = 365	1✔
37	minimum_acceptable_return = 0.0	1✔
38
39	# apply slippage per trade to profit_ratio
40	results.loc[:, 'profit_ratio_after_slippage'] = \	1✔
41	results['profit_ratio'] - slippage_per_trade_ratio
42
43	# create the index within the min_date and end max_date
44	t_index = date_range(start=min_date, end=max_date, freq=resample_freq,	1✔
45	normalize=True)
46
47	sum_daily = (	1✔
48	results.resample(resample_freq, on='close_date').agg(
49	{"profit_ratio_after_slippage": 'sum'}).reindex(t_index).fillna(0)
50	)
51
52	total_profit = sum_daily["profit_ratio_after_slippage"] - minimum_acceptable_return	1✔
53	expected_returns_mean = total_profit.mean()	1✔
54
55	sum_daily['downside_returns'] = 0.0	1✔
56	sum_daily.loc[total_profit < 0, 'downside_returns'] = total_profit	1✔
57	total_downside = sum_daily['downside_returns']	1✔
58	# Here total_downside contains min(0, P - MAR) values,
59	# where P = sum_daily["profit_ratio_after_slippage"]
60	down_stdev = math.sqrt((total_downside**2).sum() / len(total_downside))	1✔
61
62	if down_stdev != 0:	1✔
63	sortino_ratio = expected_returns_mean / down_stdev * math.sqrt(days_in_year)	1✔
64	else:
65	# Define high (negative) sortino ratio to be clear that this is NOT optimal.
66	sortino_ratio = -20.	×
67
68	# print(t_index, sum_daily, total_profit)
69	# print(minimum_acceptable_return, expected_returns_mean, down_stdev, sortino_ratio)
70	return -sortino_ratio	1✔

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