Freqtrade 2021.7 Hyperopt space defining issue

Question

I'm new to freqtrade. I want to optimize some parameters in my strategy. As far as I see, with freqtrade 2021 we can optimize parameters without defining a seperate hyperopt file. I have followed the example on https://www.freqtrade.io/en/stable/hyperopt/ and have written code which is attached through the end of my question. However when I run hyperopt with command below

freqtrade hyperopt --config config.json --hyperopt-loss SharpeHyperOptLoss --strategy MyFirstStrategy -e 500 --spaces all

it outputs error message below:

2021-08-25 00:27:43,590 - freqtrade.configuration.config_validation - INFO - Validating configuration ...
2021-08-25 00:27:43,595 - freqtrade.resolvers.iresolver - INFO - Using resolved pairlist StaticPairList from '/media/Priamos/Projects/AutoTrade/freqtrade/freqtrade/plugins/pairlist/StaticPairList.py'...
2021-08-25 00:27:43,596 - freqtrade.resolvers.iresolver - INFO - Using resolved pairlist AgeFilter from '/media/Priamos/Projects/AutoTrade/freqtrade/freqtrade/plugins/pairlist/AgeFilter.py'...
2021-08-25 00:27:43,598 - freqtrade.resolvers.iresolver - INFO - Using resolved pairlist PrecisionFilter from '/media/Priamos/Projects/AutoTrade/freqtrade/freqtrade/plugins/pairlist/PrecisionFilter.py'...
2021-08-25 00:27:43,603 - freqtrade.resolvers.iresolver - INFO - Using resolved pairlist PriceFilter from '/media/Priamos/Projects/AutoTrade/freqtrade/freqtrade/plugins/pairlist/PriceFilter.py'...
2021-08-25 00:27:43,605 - freqtrade.resolvers.iresolver - INFO - Using resolved pairlist SpreadFilter from '/media/Priamos/Projects/AutoTrade/freqtrade/freqtrade/plugins/pairlist/SpreadFilter.py'...
2021-08-25 00:27:43,611 - freqtrade.resolvers.iresolver - INFO - Using resolved pairlist RangeStabilityFilter from '/media/Priamos/Projects/AutoTrade/freqtrade/freqtrade/plugins/pairlist/rangestabilityfilter.py'...
2021-08-25 00:27:53,516 - freqtrade.resolvers.iresolver - INFO - Using resolved hyperoptloss SharpeHyperOptLoss from '/media/Priamos/Projects/AutoTrade/freqtrade/freqtrade/optimize/hyperopt_loss_sharpe.py'...
2021-08-25 00:27:53,517 - freqtrade.optimize.hyperopt - INFO - Using optimizer random state: 56312
2021-08-25 00:27:53,517 - freqtrade - ERROR - The 'buy' space is included into the hyperoptimization but indicator_space() method is not found in your custom Hyperopt class. You should either implement this method or remove the 'buy' space from hyperoptimization.

I have added the last few lines of output not to create congestion here. Why it says I need to define indicator_space() method I don't understand. Since I'm trying to use default hyperopt to optimize parameters which are defined in my sample strategy. Strategy code is below:

import numpy as np  # noqa
import pandas as pd  # noqa
from freqtrade.optimize.space import SKDecimal, Dimension, Integer
from pandas import DataFrame
from functools import reduce

from typing import Any, Callable, Dict, List

from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
                                IStrategy, IntParameter)

import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib


class MyFirstStrategy(IStrategy):

    # Strategy interface version - allow new iterations of the strategy interface.
    # Check the documentation or the Sample strategy to get the latest version.
    INTERFACE_VERSION = 2

    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi".
    minimal_roi = {
        "0": 0.06337,
        "27": 0.03385,
        "86": 0.01406,
        "120": 0
    }


    # Optimal stoploss designed for the strategy.
    # This attribute will be overridden if the config file contains "stoploss".
    stoploss = -0.49122


    # Trailing stoploss
    trailing_stop = False
    # trailing_only_offset_is_reached = False
    # trailing_stop_positive = 0.01
    # trailing_stop_positive_offset = 0.0  # Disabled / not configured

    # Optimal timeframe for the strategy.
    timeframe = '1h'

    # Run "populate_indicators()" only for new candle.
    process_only_new_candles = False

    # These values can be overridden in the "ask_strategy" section in the config.
    use_sell_signal = True
    sell_profit_only = False
    ignore_roi_if_buy_signal = False

    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 30

    # Optional order type mapping.
    order_types = {
        'buy': 'limit',
        'sell': 'limit',
        'stoploss': 'limit',
        'stoploss_on_exchange': False
    }

    # Optional order time in force.
    order_time_in_force = {
        'buy': 'gtc',
        'sell': 'gtc'
    }
    
    plot_config = {
        # Main plot indicators (Moving averages, ...)
        'main_plot': {
            'tema': {},
            'sar': {'color': 'white'},
        },
        'subplots': {
            # Subplots - each dict defines one additional plot
            "MACD": {
                'macd': {'color': 'blue'},
                'macdsignal': {'color': 'orange'},
            },
            "RSI": {
                'rsi': {'color': 'red'},
            }
        }
    }
    # BUY PARAMS
    buy_rsi = IntParameter(10, 50, default=30, space='buy')
    buy_rsi_enabled = CategoricalParameter([True, False], default=True, space='buy')
    buy_trigger = CategoricalParameter(['buy_bb_lowerband1', 'buy_bb_lowerband2', 'buy_bb_lowerband3', 'buy_bb_lowerband4'],
                                       default='buy_bb_lowerband2', space='buy')

    # SELL PARAMS
    sell_rsi = IntParameter(60, 90, default=30, space='sell')
    sell_rsi_enabled = CategoricalParameter([True, False], default=True, space='sell')
    sell_trigger = CategoricalParameter(['sell_bb_middle1', 'sell_bb_upper1',
                                         'sell_bb_middle2', 'sell_bb_upper2',
                                         'sell_bb_middle3', 'sell_bb_upper3',
                                         'sell_bb_middle4', 'sell_bb_upper4'], default='sell-bb_middle2', space='sell')


    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Adds several different TA indicators to the given DataFrame

        Performance Note: For the best performance be frugal on the number of indicators
        you are using. Let uncomment only the indicator you are using in your strategies
        or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
        :param dataframe: Dataframe with data from the exchange
        :param metadata: Additional information, like the currently traded pair
        :return: a Dataframe with all mandatory indicators for the strategies
        """
        
        dataframe['buy_rsi'] = ta.RSI(dataframe)
        dataframe['sell_rsi'] = ta.RSI(dataframe)

        # Overlap Studies
        # ------------------------------------

        # Bollinger Bands
        bollinger1 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=1)
        dataframe['buy_bb_lowerband1'] = bollinger1['lower']
        dataframe['buy_bb_middleband1'] = bollinger1['mid']
        dataframe['buy_bb_upperband1'] = bollinger1['upper']

        bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['buy_bb_lowerband2'] = bollinger2['lower']
        dataframe['buy_bb_middleband2'] = bollinger2['mid']
        dataframe['buy_bb_upperband2'] = bollinger2['upper']

        bollinger3 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=3)
        dataframe['buy_bb_lowerband3'] = bollinger3['lower']
        dataframe['buy_bb_middleband3'] = bollinger3['mid']
        dataframe['buy_bb_upperband3'] = bollinger3['upper']

        bollinger4 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=4)
        dataframe['buy_bb_lowerband4'] = bollinger4['lower']
        dataframe['buy_bb_middleband4'] = bollinger4['mid']
        dataframe['buy_bb_upperband4'] = bollinger4['upper']

        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Buy strategy Hyperopt will build and use.
        """
        conditions = []

        # GUARDS AND TRENDS
        if self.buy_rsi_enabled.value:
            conditions.append(dataframe['buy_rsi'] < self.buy_rsi.value)

        # TRIGGERS
        if self.buy_trigger.value:
            if self.buy_trigger.value == 'buy_bb_lowerband1':
                conditions.append(dataframe['close'] < dataframe['buy_bb_lowerband1'])
            if self.buy_trigger.value == 'buy_bb_lowerband2':
                conditions.append(dataframe['close'] < dataframe['buy_bb_lowerband2'])
            if self.buy_trigger.value == 'buy_bb_lowerband3':
                conditions.append(dataframe['close'] < dataframe['buy_bb_lowerband3'])
            if self.buy_trigger.value == 'buy_bb_lowerband4':
                conditions.append(dataframe['close'] < dataframe['buy_bb_lowerband4'])

        # Check that the candle had volume
        conditions.append(dataframe['volume'] > 0)

        if conditions:
            dataframe.loc[
                reduce(lambda x, y: x & y, conditions),
                'buy'] = 1

        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the sell signal for the given dataframe
        :param dataframe: DataFrame populated with indicators
        :param metadata: Additional information, like the currently traded pair
        :return: DataFrame with buy column
        """
        conditions = []

        # GUARDS AND TRENDS
        if self.sell_rsi_enabled.value:
            conditions.append(dataframe['rsi'] > self.sell_rsi.value)

        # TRIGGERS
        if self.sell_trigger.value:
            if self.sell_trigger.value == 'sell_bb_middle1':
                conditions.append(dataframe['close'] < dataframe['sell_bb_middle1'])
            if self.sell_trigger.value == 'sell_bb_middle2':
                conditions.append(dataframe['close'] < dataframe['sell_bb_middle2'])
            if self.sell_trigger.value == 'sell_bb_middle3':
                conditions.append(dataframe['close'] < dataframe['sell_bb_middle3'])
            if self.sell_trigger.value == 'sell_bb_middle4':
                conditions.append(dataframe['close'] < dataframe['sell_bb_middle4'])
            if self.sell_trigger.value == 'sell_bb_upper1':
                conditions.append(dataframe['close'] < dataframe['sell_bb_upper1'])
            if self.sell_trigger.value == 'sell_bb_upper2':
                conditions.append(dataframe['close'] < dataframe['sell_bb_upper2'])
            if self.sell_trigger.value == 'sell_bb_upper3':
                conditions.append(dataframe['close'] < dataframe['sell_bb_upper3'])
            if self.sell_trigger.value == 'sell_bb_upper4':
                conditions.append(dataframe['close'] < dataframe['sell_bb_upper4'])

        # Check that the candle had volume
        conditions.append(dataframe['volume'] > 0)

        if conditions:
            dataframe.loc[
                reduce(lambda x, y: x & y, conditions),
                'buy'] = 1

        return dataframe

What is my aim?

I'm trying to optimize parameters in my strategy. Trying to use default hyperopt without defining an explicit hyperopt file. Optimization code is implemented in Strategy.

Define the problem:

I have included error messages above which eventually says "freqtrade - ERROR - The 'buy' space is included into the hyperoptimization but indicator_space() method is not found in your custom Hyperopt class. You should either implement this method or remove the 'buy' space from hyperoptimization."

How to replicate the problem:

Use command: freqtrade hyperopt --config config.json --hyperopt-loss SharpeHyperOptLoss --strategy MyFirstStrategy -e 500 --spaces all

System:

Ubuntu 21.04 (with VMware virtual machine) Python 3.9 Pycharm 2021.2 Community Edition

Thanks in advance for any help or clarification provided.

Answer 1

Freqtrade is expecting a method named indicator_space(), something like the example:

@staticmethod
def indicator_space() -> List[Dimension]:
    """
    Define your Hyperopt space for searching buy strategy parameters.
    """
    return [
        Integer(10, 50, name='buy_rsi'),
        Integer(60, 90, name='sell_rsi'),
        Categorical([True, False], name='buy_rsi_enabled'),
        Categorical([True, False], name='sell_rsi_enabled'),
        Categorical(['buy_bb_lowerband1', 'buy_bb_lowerband2', 
                'buy_bb_lowerband3', 'buy_bb_lowerband4'], name='buy_trigger'),
        Categorical(['sell_bb_middle1', 'sell_bb_upper1',
                     'sell_bb_middle2', 'sell_bb_upper2',
                     'sell_bb_middle3', 'sell_bb_upper3',
                     'sell_bb_middle4', 'sell_bb_upper4'], name='sell_trigger')
    ]