53 lines
1.5 KiB
Python
53 lines
1.5 KiB
Python
"""
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SharpeHyperOptLossDaily
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This module defines the alternative HyperOptLoss class which can be used for
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Hyperoptimization.
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"""
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from datetime import datetime
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from pandas import DataFrame
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import numpy as np
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from freqtrade.optimize.hyperopt import IHyperOptLoss
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class SharpeHyperOptLossDaily(IHyperOptLoss):
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"""
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Defines the loss function for hyperopt.
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This implementation uses the Sharpe Ratio calculation.
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"""
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@staticmethod
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def hyperopt_loss_function(results: DataFrame, trade_count: int,
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min_date: datetime, max_date: datetime,
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*args, **kwargs) -> float:
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"""
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Objective function, returns smaller number for more optimal results.
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Uses Sharpe Ratio calculation.
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"""
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# get profit_percent and apply slippage of 0.1% per trade
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results.loc[:, 'profit_percent'] = results['profit_percent'] - 0.0005
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sum_daily = (
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results.resample("D", on="close_time").agg(
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{"profit_percent": sum}
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)
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* 100.0
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)
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total_profit = sum_daily["profit_percent"]
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expected_returns_mean = total_profit.mean()
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up_stdev = np.std(total_profit)
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if (np.std(total_profit) != 0.):
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sharp_ratio = expected_returns_mean / up_stdev * np.sqrt(365)
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else:
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# Define high (negative) sharpe ratio to be clear that this is NOT optimal.
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sharp_ratio = -20.
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# print(expected_returns_mean, up_stdev, sharp_ratio)
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return -sharp_ratio
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