86 lines
2.9 KiB
Python
86 lines
2.9 KiB
Python
"""
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SharpeHyperOptLossTrades
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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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The MINIMUM_TRADES and SLIPPAGE_PER_TRADE_RATIO can be altered to whatever you like.
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The values that make up the maximum trade_grade can be altered as well.
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"""
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import math
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from datetime import datetime
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from pandas import DataFrame, date_range
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from freqtrade.optimize.hyperopt import IHyperOptLoss
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class SharpeHyperOptLossTrades20(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 Daily calculation and the Trade Grade 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 the Sharpe Ratio Daily calculation and the Trade Grade calculation.
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"""
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# CONSTANTS
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MINIMUM_TRADES = 20
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SLIPPAGE_PER_TRADE_RATIO = 0.001
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NUMERATOR_MAX_TRADEGRADE = 80
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DENOMINATOR_MAX_TRADEGRADE = 8
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RESAMPLE_FREQ = '1D'
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DAYS_IN_YEAR = 365
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ANNUAL_RISK_FREE_RATE = 0.0
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risk_free_rate = ANNUAL_RISK_FREE_RATE / DAYS_IN_YEAR
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"""
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Sharpe Ratio Calculation
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"""
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# apply slippage per trade to profit_percent
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results.loc[:, 'profit_percent_after_slippage'] = \
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results['profit_percent'] - SLIPPAGE_PER_TRADE_RATIO
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# create the index within the min_date and end max_date
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t_index = date_range(start=min_date, end=max_date, freq=RESAMPLE_FREQ,
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normalize=True)
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sum_daily = (
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results.resample(RESAMPLE_FREQ, on='close_time').agg(
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{"profit_percent_after_slippage": sum}).reindex(t_index).fillna(0)
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)
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total_profit = sum_daily["profit_percent_after_slippage"] - risk_free_rate
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expected_returns_mean = total_profit.mean()
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up_stdev = total_profit.std()
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if up_stdev != 0:
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sharp_ratio = expected_returns_mean / up_stdev * math.sqrt(DAYS_IN_YEAR)
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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 = -30.
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"""
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Trade Grade Calculation
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This function has a maximum grade of 80/DENOMINATOR_MAX_TRADEGRADE.
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A minimum of 25 trades.
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"""
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if trade_count <= (MINIMUM_TRADES + 5):
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# Define high (negative) trade grade tp be clear that this is NOT optimal
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trade_grade = -30
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else:
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trade_grade = ((1 / (-0.001 * (trade_count - MINIMUM_TRADES))) +
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NUMERATOR_MAX_TRADEGRADE) / DENOMINATOR_MAX_TRADEGRADE
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return -(sharp_ratio + trade_grade)
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