adelorenzo/stable
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Add sample loss and improve docstring

Browse Source
This commit is contained in:
Matthias 2019-07-17 06:32:24 +02:00
parent c5b244419d
commit 0e500de1a0
3 changed files with 3 additions and 16 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

1
docs/hyperopt.md
View File

@ -186,6 +186,7 @@ class SuperDuperHyperOptLoss(IHyperOptLoss):
Weights are distributed as follows: Weights are distributed as follows:
* 0.4 to trade duration * 0.4 to trade duration
* 0.25: Avoiding trade loss * 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() total_profit = results.profit_percent.sum()
trade_duration = results.trade_duration.mean() trade_duration = results.trade_duration.mean()

3
freqtrade/optimize/default_hyperopt_loss.py
View File

@ -37,10 +37,11 @@ class DefaultHyperOptLoss(IHyperOptLoss):
*args, **kwargs) -> float: *args, **kwargs) -> float:
""" """
Objective function, returns smaller number for better results Objective function, returns smaller number for better results
This is the legacy algorithm (used until now in freqtrade). This is the Default algorithm
Weights are distributed as follows: Weights are distributed as follows:
* 0.4 to trade duration * 0.4 to trade duration
* 0.25: Avoiding trade loss * 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() total_profit = results.profit_percent.sum()
trade_duration = results.trade_duration.mean() trade_duration = results.trade_duration.mean()

15
user_data/hyperopts/sample_hyperopt.py
View File

@ -42,21 +42,6 @@ class SampleHyperOpts(IHyperOpt):
roi_space, generate_roi_table, stoploss_space roi_space, generate_roi_table, stoploss_space
""" """
@staticmethod
def hyperopt_loss_custom(results: DataFrame, trade_count: int,
min_date: datetime, max_date: datetime, *args, **kwargs) -> float:
"""
Objective function, returns smaller number for more optimal results
"""
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
@staticmethod @staticmethod
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame: def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['adx'] = ta.ADX(dataframe) dataframe['adx'] = ta.ADX(dataframe)