lychiyu/freqtrade_origin
1
0
Fork 0
mirror of https://github.com/freqtrade/freqtrade.git synced 2024-11-10 10:21:59 +00:00
Code Issues Actions 2 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:
* 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()

3
freqtrade/optimize/default_hyperopt_loss.py
View File

@ -37,10 +37,11 @@ class DefaultHyperOptLoss(IHyperOptLoss):
*args, **kwargs) -> float:
"""
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:
* 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()

15
user_data/hyperopts/sample_hyperopt.py
View File

@ -42,21 +42,6 @@ class SampleHyperOpts(IHyperOpt):
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
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['adx'] = ta.ADX(dataframe)