9394559170

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

Build # 9394559170

Build Type

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_sharpe_daily.py

"""
SharpeHyperOptLossDaily

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

    This implementation uses the Sharpe 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 Sharpe Ratio calculation.
        """
        resample_freq = '1D'
        slippage_per_trade_ratio = 0.0005
        days_in_year = 365
        annual_risk_free_rate = 0.0
        risk_free_rate = annual_risk_free_rate / days_in_year

        # 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"] - risk_free_rate
        expected_returns_mean = total_profit.mean()
        up_stdev = total_profit.std()

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

        # print(t_index, sum_daily, total_profit)
        # print(risk_free_rate, expected_returns_mean, up_stdev, sharp_ratio)
        return -sharp_ratio

1	"""
2	SharpeHyperOptLossDaily
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 SharpeHyperOptLossDaily(IHyperOptLoss):	1✔
16	"""
17	Defines the loss function for hyperopt.
18
19	This implementation uses the Sharpe 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 Sharpe Ratio calculation.
30	"""
31	resample_freq = '1D'	1✔
32	slippage_per_trade_ratio = 0.0005	1✔
33	days_in_year = 365	1✔
34	annual_risk_free_rate = 0.0	1✔
35	risk_free_rate = annual_risk_free_rate / days_in_year	1✔
36
37	# apply slippage per trade to profit_ratio
38	results.loc[:, 'profit_ratio_after_slippage'] = \	1✔
39	results['profit_ratio'] - slippage_per_trade_ratio
40
41	# create the index within the min_date and end max_date
42	t_index = date_range(start=min_date, end=max_date, freq=resample_freq,	1✔
43	normalize=True)
44
45	sum_daily = (	1✔
46	results.resample(resample_freq, on='close_date').agg(
47	{"profit_ratio_after_slippage": 'sum'}).reindex(t_index).fillna(0)
48	)
49
50	total_profit = sum_daily["profit_ratio_after_slippage"] - risk_free_rate	1✔
51	expected_returns_mean = total_profit.mean()	1✔
52	up_stdev = total_profit.std()	1✔
53
54	if up_stdev != 0:	1✔
55	sharp_ratio = expected_returns_mean / up_stdev * math.sqrt(days_in_year)	1✔
56	else:
57	# Define high (negative) sharpe ratio to be clear that this is NOT optimal.
58	sharp_ratio = -20.	×
59
60	# print(t_index, sum_daily, total_profit)
61	# print(risk_free_rate, expected_returns_mean, up_stdev, sharp_ratio)
62	return -sharp_ratio	1✔

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