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Allow importing interface from hyperopt.py

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This commit is contained in:
Matthias 2019-07-17 07:14:27 +02:00
parent 0e500de1a0
commit 639a4d5cf7
5 changed files with 8 additions and 18 deletions
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6
docs/hyperopt.md
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@ -161,12 +161,14 @@ This class should be in it's own file within the `user_data/hyperopts/` director
### Using a custom loss function ### Using a custom loss function
To use a custom loss Class, make sure that the function `hyperopt_loss_function` is defined in your custom hyperopt class. To use a custom loss Class, make sure that the function `hyperopt_loss_function` is defined in your custom hyperopt loss class.
For the sample below, you then need to add the command line parameter `--hyperoptloss SuperDuperHyperOptLoss` to your hyperopt call so this fuction is being used. For the sample below, you then need to add the command line parameter `--hyperoptloss SuperDuperHyperOptLoss` to your hyperopt call so this fuction is being used.
A sample of this can be found below, which is identical to the Default Hyperopt loss implementation. A sample of this can be found below, which is identical to the Default Hyperopt loss implementation. A full sample can be found [user_data/hyperopts/](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt_loss.py)
``` python ``` python
from freqtrade.optimize.hyperopt import IHyperOptLoss
TARGET_TRADES = 600 TARGET_TRADES = 600
EXPECTED_MAX_PROFIT = 3.0 EXPECTED_MAX_PROFIT = 3.0
MAX_ACCEPTED_TRADE_DURATION = 300 MAX_ACCEPTED_TRADE_DURATION = 300

2
freqtrade/optimize/default_hyperopt_loss.py
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@ -8,7 +8,7 @@ from math import exp
from pandas import DataFrame from pandas import DataFrame
from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss from freqtrade.optimize.hyperopt import IHyperOptLoss
# Define some constants: # Define some constants:

2
freqtrade/optimize/hyperopt.py
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@ -21,6 +21,8 @@ from skopt.space import Dimension
from freqtrade.configuration import Arguments from freqtrade.configuration import Arguments
from freqtrade.data.history import load_data, get_timeframe from freqtrade.data.history import load_data, get_timeframe
from freqtrade.optimize.backtesting import Backtesting from freqtrade.optimize.backtesting import Backtesting
# Import IHyperOptLoss to allow users import from this file
from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss # noqa: F4
from freqtrade.resolvers.hyperopt_resolver import HyperOptResolver, HyperOptLossResolver from freqtrade.resolvers.hyperopt_resolver import HyperOptResolver, HyperOptLossResolver

2
freqtrade/optimize/hyperopt_loss_sharpe.py
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@ -8,7 +8,7 @@ from datetime import datetime
from pandas import DataFrame from pandas import DataFrame
import numpy as np import numpy as np
from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss from freqtrade.optimize.hyperopt import IHyperOptLoss
class SharpeHyperOptLoss(IHyperOptLoss): class SharpeHyperOptLoss(IHyperOptLoss):

14
user_data/hyperopts/sample_hyperopt.py
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@ -13,20 +13,6 @@ from skopt.space import Categorical, Dimension, Integer, Real
import freqtrade.vendor.qtpylib.indicators as qtpylib import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.optimize.hyperopt_interface import IHyperOpt from freqtrade.optimize.hyperopt_interface import IHyperOpt
# 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,
# self.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
# This class is a sample. Feel free to customize it. # This class is a sample. Feel free to customize it.
class SampleHyperOpts(IHyperOpt): class SampleHyperOpts(IHyperOpt):