mirror of
https://github.com/freqtrade/freqtrade.git
synced 2024-11-14 20:23:57 +00:00
53 lines
1.8 KiB
Python
53 lines
1.8 KiB
Python
"""
|
|
DefaultHyperOptLoss
|
|
This module defines the default HyperoptLoss class which is being used for
|
|
Hyperoptimization.
|
|
"""
|
|
from math import exp
|
|
|
|
from pandas import DataFrame
|
|
|
|
from freqtrade.optimize.hyperopt import IHyperOptLoss
|
|
|
|
|
|
# Set TARGET_TRADES to suit your number concurrent trades so its realistic
|
|
# to the number of days
|
|
TARGET_TRADES = 600
|
|
|
|
# This is assumed to be expected avg profit * expected trade count.
|
|
# For example, for 0.35% avg per trade (or 0.0035 as ratio) and 1100 trades,
|
|
# expected max profit = 3.85
|
|
# Check that the reported Σ% values do not exceed this!
|
|
# Note, this is ratio. 3.85 stated above means 385Σ%.
|
|
EXPECTED_MAX_PROFIT = 3.0
|
|
|
|
# Max average trade duration in minutes.
|
|
# If eval ends with higher value, we consider it a failed eval.
|
|
MAX_ACCEPTED_TRADE_DURATION = 300
|
|
|
|
|
|
class DefaultHyperOptLoss(IHyperOptLoss):
|
|
"""
|
|
Defines the default loss function for hyperopt
|
|
"""
|
|
|
|
@staticmethod
|
|
def hyperopt_loss_function(results: DataFrame, trade_count: int,
|
|
*args, **kwargs) -> float:
|
|
"""
|
|
Objective function, returns smaller number for better results
|
|
This is the Default algorithm
|
|
Weights are distributed as follows:
|
|
* 0.4 to trade duration
|
|
* 0.25: Avoiding trade loss
|
|
* 1.0 to total profit, compared to the expected value (`EXPECTED_MAX_PROFIT`) defined above
|
|
"""
|
|
total_profit = results.profit_percent.sum()
|
|
trade_duration = results.trade_duration.mean()
|
|
|
|
trade_loss = 1 - 0.25 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.8)
|
|
profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT)
|
|
duration_loss = 0.4 * min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)
|
|
result = trade_loss + profit_loss + duration_loss
|
|
return result
|