Allow importing interface from hyperopt.py

docs/hyperopt.md

@@ -161,12 +161,14 @@ This class should be in it's own file within the `user_data/hyperopts/` director
 
 ### 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.
 
-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
+from freqtrade.optimize.hyperopt import IHyperOptLoss
+
 TARGET_TRADES = 600
 EXPECTED_MAX_PROFIT = 3.0
 MAX_ACCEPTED_TRADE_DURATION = 300

freqtrade/optimize/default_hyperopt_loss.py

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

freqtrade/optimize/hyperopt.py

@@ -21,6 +21,8 @@ from skopt.space import Dimension
 from freqtrade.configuration import Arguments
 from freqtrade.data.history import load_data, get_timeframe
 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
 
 

freqtrade/optimize/hyperopt_loss_sharpe.py

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

user_data/hyperopts/sample_hyperopt.py

@@ -13,20 +13,6 @@ from skopt.space import Categorical, Dimension, Integer, Real
 import freqtrade.vendor.qtpylib.indicators as qtpylib
 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.
 class SampleHyperOpts(IHyperOpt):