38 lines
1.4 KiB
Python
38 lines
1.4 KiB
Python
from math import exp
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from pandas import DataFrame
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# Define some constants:
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# set TARGET_TRADES to suit your number concurrent trades so its realistic
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# to the number of days
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TARGET_TRADES = 600
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# This is assumed to be expected avg profit * expected trade count.
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# For example, for 0.35% avg per trade (or 0.0035 as ratio) and 1100 trades,
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# self.expected_max_profit = 3.85
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# Check that the reported Σ% values do not exceed this!
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# Note, this is ratio. 3.85 stated above means 385Σ%.
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EXPECTED_MAX_PROFIT = 3.0
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# max average trade duration in minutes
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# if eval ends with higher value, we consider it a failed eval
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MAX_ACCEPTED_TRADE_DURATION = 300
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def hyperopt_loss_legacy(results: DataFrame, trade_count: int,
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*args, **kwargs) -> float:
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"""
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Objective function, returns smaller number for better results
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This is the legacy algorithm (used until now in freqtrade).
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Weights are distributed as follows:
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* 0.4 to trade duration
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* 0.25: Avoiding trade loss
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"""
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total_profit = results.profit_percent.sum()
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trade_duration = results.trade_duration.mean()
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trade_loss = 1 - 0.25 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.8)
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profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT)
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duration_loss = 0.4 * min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)
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result = trade_loss + profit_loss + duration_loss
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return result
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