Flake8 compliance and documentation for hyperopt in strategy file

docs/hyperopt.md

@@ -19,8 +19,8 @@ your strategy file located into [user_data/strategies/](https://github.com/gcarq
 ### 1. Configure your Guards and Triggers
 There are two places you need to change in your strategy file to add a 
 new buy strategy for testing:
-- Inside [populate_buy_trend()](https://github.com/gcarq/freqtrade/blob/develop/user_data/strategies/test_strategy.py#L278-L294).
-- Inside [hyperopt_space()](https://github.com/gcarq/freqtrade/blob/develop/user_data/strategies/test_strategy.py#L244-L297) known as `SPACE`.
+- Inside [populate_buy_trend()](https://github.com/gcarq/freqtrade/blob/develop/user_data/strategies/test_strategy.py#L273-L294).
+- Inside [indicator_space()](https://github.com/gcarq/freqtrade/blob/develop/user_data/strategies/test_strategy.py#L251-L67).
 
 There you have two different type of indicators: 1. `guards` and 2. 
 `triggers`.
@@ -55,10 +55,14 @@ Your hyperopt file must contain `guards` to find the right value for
 `(dataframe['adx'] > 65)` & and `(dataframe['plus_di'] > 0.5)`. That 
 means you will need to enable/disable triggers.
  
-In our case the `SPACE` and `populate_buy_trend` in your strategy file 
+In our case the `indicator_space` and `populate_buy_trend` in your strategy file 
 will look like:
 ```python
-space = {
+    def indicator_space(self) -> Dict[str, Any]:
+        """
+        Define your Hyperopt space for searching strategy parameters
+        """
+        return {
             'rsi': hp.choice('rsi', [
                 {'enabled': False},
                 {'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 1)}
@@ -100,7 +104,13 @@ def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
             'stochf_cross': (crossed_above(dataframe['fastk'], dataframe['fastd'])),
             'ht_sine': (crossed_above(dataframe['htleadsine'], dataframe['htsine'])),
         }
-        ...
+        conditions.append(triggers.get(params['trigger']['type']))
+
+        dataframe.loc[
+            reduce(lambda x, y: x & y, conditions),
+            'buy'] = 1
+
+        return dataframe
 ```
 
 
@@ -116,7 +126,7 @@ The Hyperopt configuration is located in
 ## Advanced notions
 ### Understand the Guards and Triggers
 When you need to add the new guards and triggers to be hyperopt 
-parameters, you do this by adding them into the [hyperopt_space()](https://github.com/gcarq/freqtrade/blob/develop/user_data/strategies/test_strategy.py#L244-L297).
+parameters, you do this by adding them into the [indicator_space()](https://github.com/gcarq/freqtrade/blob/develop/user_data/strategies/test_strategy.py#L251-L267).
 
 If it's a trigger, you add one line to the 'trigger' choice group and that's it.
 

freqtrade/optimize/hyperopt.py

@@ -7,18 +7,14 @@ import os
 import pickle
 import signal
 import sys
-from functools import reduce
 from math import exp
 from operator import itemgetter
-from typing import Dict, Any, Callable
+from typing import Dict, Any
 
-import numpy
-import talib.abstract as ta
-from hyperopt import STATUS_FAIL, STATUS_OK, Trials, fmin, hp, space_eval, tpe
+from hyperopt import STATUS_FAIL, STATUS_OK, Trials, fmin, space_eval, tpe
 from hyperopt.mongoexp import MongoTrials
 from pandas import DataFrame
 
-import freqtrade.vendor.qtpylib.indicators as qtpylib
 # Monkey patch config
 from freqtrade import main  # noqa; noqa
 from freqtrade import exchange, misc, optimize

freqtrade/strategy/default_strategy.py

@@ -4,7 +4,7 @@ import talib.abstract as ta
 from pandas import DataFrame
 from typing import Dict, Any, Callable
 import freqtrade.vendor.qtpylib.indicators as qtpylib
-from hyperopt import STATUS_FAIL, STATUS_OK, Trials, fmin, hp, space_eval, tpe
+from hyperopt import hp
 from functools import reduce
 from freqtrade.strategy.interface import IStrategy
 from freqtrade.indicator_helpers import fishers_inverse
@@ -385,7 +385,6 @@ class DefaultStrategy(IStrategy):
             'roi_p3': hp.quniform('roi_p3', 0.01, 0.20, 0.01),
         }
 
-
     def stoploss_space(self) -> Dict[str, Any]:
         return {
             'stoploss': hp.quniform('stoploss', -0.5, -0.02, 0.02),

freqtrade/strategy/strategy.py

@@ -197,4 +197,3 @@ class Strategy(object):
 
     def stoploss_space(self) -> Dict[str, Any]:
         return self.custom_strategy.stoploss_space()
-