Decoupled custom hyperopts from hyperopt.py

freqtrade/arguments.py

@@ -104,6 +104,14 @@ class Arguments(object):
             type=str,
             metavar='PATH',
         )
+        self.parser.add_argument(
+            '--hyperopt',
+            help='specify hyperopt file (default: %(default)s)',
+            dest='hyperopt',
+            default=Constants.DEFAULT_HYPEROPT,
+            type=str,
+            metavar='PATH',
+        )
         self.parser.add_argument(
             '--dynamic-whitelist',
             help='dynamically generate and update whitelist'

freqtrade/configuration.py

@@ -52,6 +52,9 @@ class Configuration(object):
         if self.args.strategy_path:
             config.update({'strategy_path': self.args.strategy_path})
 
+        # Add the hyperopt file to use
+        config.update({'hyperopt': self.args.hyperopt})
+
         # Load Common configuration
         config = self._load_common_config(config)
 

freqtrade/constants.py

@@ -9,6 +9,7 @@ TICKER_INTERVAL = 5  # min
 HYPEROPT_EPOCH = 100  # epochs
 RETRY_TIMEOUT = 30  # sec
 DEFAULT_STRATEGY = 'DefaultStrategy'
+DEFAULT_HYPEROPT = 'default_hyperopt'
 DEFAULT_DB_PROD_URL = 'sqlite:///tradesv3.sqlite'
 DEFAULT_DB_DRYRUN_URL = 'sqlite://'
 UNLIMITED_STAKE_AMOUNT = 'unlimited'

freqtrade/optimize/custom_hyperopt.py

@@ -0,0 +1,152 @@
+# pragma pylint: disable=attribute-defined-outside-init
+
+"""
+This module load custom hyperopts
+"""
+import importlib
+import os
+import sys
+from typing import Dict, Any, Callable
+
+from pandas import DataFrame
+
+from freqtrade.constants import Constants
+from freqtrade.logger import Logger
+from freqtrade.optimize.interface import IHyperOpt
+
+sys.path.insert(0, r'../../user_data/hyperopts')
+
+
+class CustomHyperOpt(object):
+    """
+    This class contains all the logic to load custom hyperopt class
+    """
+    def __init__(self, config: dict = {}) -> None:
+        """
+        Load the custom class from config parameter
+        :param config:
+        :return:
+        """
+        self.logger = Logger(name=__name__).get_logger()
+
+        # Verify the hyperopt is in the configuration, otherwise fallback to the default hyperopt
+        if 'hyperopt' in config:
+            hyperopt = config['hyperopt']
+        else:
+            hyperopt = Constants.DEFAULT_HYPEROPT
+
+        # Load the hyperopt
+        self._load_hyperopt(hyperopt)
+
+    def _load_hyperopt(self, hyperopt_name: str) -> None:
+        """
+        Search and load the custom hyperopt. If no hyperopt found, fallback on the default hyperopt
+        Set the object into self.custom_hyperopt
+        :param hyperopt_name: name of the module to import
+        :return: None
+        """
+
+        try:
+            # Start by sanitizing the file name (remove any extensions)
+            hyperopt_name = self._sanitize_module_name(filename=hyperopt_name)
+
+            # Search where can be the hyperopt file
+            path = self._search_hyperopt(filename=hyperopt_name)
+
+            # Load the hyperopt
+            self.custom_hyperopt = self._load_class(path + hyperopt_name)
+
+        # Fallback to the default hyperopt
+        except (ImportError, TypeError) as error:
+            self.logger.error(
+                "Impossible to load Hyperopt 'user_data/hyperopts/%s.py'. This file does not exist"
+                " or contains Python code errors",
+                hyperopt_name
+            )
+            self.logger.error(
+                "The error is:\n%s.",
+                error
+            )
+
+    def _load_class(self, filename: str) -> IHyperOpt:
+        """
+        Import a hyperopt as a module
+        :param filename: path to the hyperopt (path from freqtrade/optimize/)
+        :return: return the hyperopt class
+        """
+        module = importlib.import_module(filename, __package__)
+        custom_hyperopt = getattr(module, module.class_name)
+
+        self.logger.info("Load hyperopt class: %s (%s.py)", module.class_name, filename)
+        return custom_hyperopt()
+
+    @staticmethod
+    def _sanitize_module_name(filename: str) -> str:
+        """
+        Remove any extension from filename
+        :param filename: filename to sanatize
+        :return: return the filename without extensions
+        """
+        filename = os.path.basename(filename)
+        filename = os.path.splitext(filename)[0]
+        return filename
+
+    @staticmethod
+    def _search_hyperopt(filename: str) -> str:
+        """
+        Search for the hyperopt file in different folder
+        1. search into the user_data/hyperopts folder
+        2. search into the freqtrade/optimize folder
+        3. if nothing found, return None
+        :param hyperopt_name: module name to search
+        :return: module path where is the hyperopt
+        """
+        pwd = os.path.dirname(os.path.realpath(__file__)) + '/'
+        user_data = os.path.join(pwd, '..', '..', 'user_data', 'hyperopts', filename + '.py')
+        hyperopt_folder = os.path.join(pwd, filename + '.py')
+
+        path = None
+        if os.path.isfile(user_data):
+            path = 'user_data.hyperopts.'
+        elif os.path.isfile(hyperopt_folder):
+            path = '.'
+
+        return path
+
+    def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
+        """
+        Populate indicators that will be used in the Buy and Sell hyperopt
+        :param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
+        :return: a Dataframe with all mandatory indicators for the strategies
+        """
+        return self.custom_hyperopt.populate_indicators(dataframe)
+
+    def buy_strategy_generator(self, params: Dict[str, Any]) -> Callable:
+        """
+        Create a buy strategy generator
+        """
+        return self.custom_hyperopt.buy_strategy_generator(params)
+
+    def indicator_space(self) -> Dict[str, Any]:
+        """
+        Create an indicator space
+        """
+        return self.custom_hyperopt.indicator_space()
+
+    def generate_roi_table(self, params: Dict) -> Dict[int, float]:
+        """
+        Create an roi table
+        """
+        return self.custom_hyperopt.generate_roi_table(params)
+
+    def stoploss_space(self) -> Dict[str, Any]:
+        """
+        Create a stoploss space
+        """
+        return self.custom_hyperopt.stoploss_space()
+
+    def roi_space(self) -> Dict[str, Any]:
+        """
+        Create a roi space
+        """
+        return self.custom_hyperopt.roi_space()

freqtrade/optimize/default_hyperopt.py

@@ -0,0 +1,314 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+
+import talib.abstract as ta
+from pandas import DataFrame
+from typing import Dict, Any, Callable
+from functools import reduce
+
+import numpy
+from hyperopt import hp
+
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+from freqtrade.optimize.interface import IHyperOpt
+
+class_name = 'DefaultHyperOpts'
+
+
+class DefaultHyperOpts(IHyperOpt):
+    """
+    Default hyperopt provided by freqtrade bot.
+    You can override it with your own hyperopt
+    """
+
+    @staticmethod
+    def populate_indicators(dataframe: DataFrame) -> DataFrame:
+        """
+        Adds several different TA indicators to the given DataFrame
+        """
+        dataframe['adx'] = ta.ADX(dataframe)
+        dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
+        dataframe['cci'] = ta.CCI(dataframe)
+        macd = ta.MACD(dataframe)
+        dataframe['macd'] = macd['macd']
+        dataframe['macdsignal'] = macd['macdsignal']
+        dataframe['macdhist'] = macd['macdhist']
+        dataframe['mfi'] = ta.MFI(dataframe)
+        dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
+        dataframe['minus_di'] = ta.MINUS_DI(dataframe)
+        dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
+        dataframe['plus_di'] = ta.PLUS_DI(dataframe)
+        dataframe['roc'] = ta.ROC(dataframe)
+        dataframe['rsi'] = ta.RSI(dataframe)
+        # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
+        rsi = 0.1 * (dataframe['rsi'] - 50)
+        dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)
+        # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
+        dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
+        # Stoch
+        stoch = ta.STOCH(dataframe)
+        dataframe['slowd'] = stoch['slowd']
+        dataframe['slowk'] = stoch['slowk']
+        # Stoch fast
+        stoch_fast = ta.STOCHF(dataframe)
+        dataframe['fastd'] = stoch_fast['fastd']
+        dataframe['fastk'] = stoch_fast['fastk']
+        # Stoch RSI
+        stoch_rsi = ta.STOCHRSI(dataframe)
+        dataframe['fastd_rsi'] = stoch_rsi['fastd']
+        dataframe['fastk_rsi'] = stoch_rsi['fastk']
+        # Bollinger bands
+        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
+        dataframe['bb_lowerband'] = bollinger['lower']
+        dataframe['bb_middleband'] = bollinger['mid']
+        dataframe['bb_upperband'] = bollinger['upper']
+        # EMA - Exponential Moving Average
+        dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
+        dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
+        dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
+        dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
+        dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
+        # SAR Parabolic
+        dataframe['sar'] = ta.SAR(dataframe)
+        # SMA - Simple Moving Average
+        dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
+        # TEMA - Triple Exponential Moving Average
+        dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
+        # Hilbert Transform Indicator - SineWave
+        hilbert = ta.HT_SINE(dataframe)
+        dataframe['htsine'] = hilbert['sine']
+        dataframe['htleadsine'] = hilbert['leadsine']
+
+        # Pattern Recognition - Bullish candlestick patterns
+        # ------------------------------------
+        """
+        # Hammer: values [0, 100]
+        dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
+        # Inverted Hammer: values [0, 100]
+        dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
+        # Dragonfly Doji: values [0, 100]
+        dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
+        # Piercing Line: values [0, 100]
+        dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
+        # Morningstar: values [0, 100]
+        dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
+        # Three White Soldiers: values [0, 100]
+        dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
+        """
+
+        # Pattern Recognition - Bearish candlestick patterns
+        # ------------------------------------
+        """
+        # Hanging Man: values [0, 100]
+        dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
+        # Shooting Star: values [0, 100]
+        dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
+        # Gravestone Doji: values [0, 100]
+        dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
+        # Dark Cloud Cover: values [0, 100]
+        dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
+        # Evening Doji Star: values [0, 100]
+        dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
+        # Evening Star: values [0, 100]
+        dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
+        """
+
+        # Pattern Recognition - Bullish/Bearish candlestick patterns
+        # ------------------------------------
+        """
+        # Three Line Strike: values [0, -100, 100]
+        dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
+        # Spinning Top: values [0, -100, 100]
+        dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
+        # Engulfing: values [0, -100, 100]
+        dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
+        # Harami: values [0, -100, 100]
+        dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
+        # Three Outside Up/Down: values [0, -100, 100]
+        dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
+        # Three Inside Up/Down: values [0, -100, 100]
+        dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
+        """
+
+        # Chart type
+        # ------------------------------------
+        # Heikinashi stategy
+        heikinashi = qtpylib.heikinashi(dataframe)
+        dataframe['ha_open'] = heikinashi['open']
+        dataframe['ha_close'] = heikinashi['close']
+        dataframe['ha_high'] = heikinashi['high']
+        dataframe['ha_low'] = heikinashi['low']
+
+        return dataframe
+
+    @staticmethod
+    def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
+        """
+        Define the buy strategy parameters to be used by hyperopt
+        """
+        def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
+            """
+            Buy strategy Hyperopt will build and use
+            """
+            conditions = []
+            # GUARDS AND TRENDS
+            if 'uptrend_long_ema' in params and params['uptrend_long_ema']['enabled']:
+                conditions.append(dataframe['ema50'] > dataframe['ema100'])
+            if 'macd_below_zero' in params and params['macd_below_zero']['enabled']:
+                conditions.append(dataframe['macd'] < 0)
+            if 'uptrend_short_ema' in params and params['uptrend_short_ema']['enabled']:
+                conditions.append(dataframe['ema5'] > dataframe['ema10'])
+            if 'mfi' in params and params['mfi']['enabled']:
+                conditions.append(dataframe['mfi'] < params['mfi']['value'])
+            if 'fastd' in params and params['fastd']['enabled']:
+                conditions.append(dataframe['fastd'] < params['fastd']['value'])
+            if 'adx' in params and params['adx']['enabled']:
+                conditions.append(dataframe['adx'] > params['adx']['value'])
+            if 'rsi' in params and params['rsi']['enabled']:
+                conditions.append(dataframe['rsi'] < params['rsi']['value'])
+            if 'over_sar' in params and params['over_sar']['enabled']:
+                conditions.append(dataframe['close'] > dataframe['sar'])
+            if 'green_candle' in params and params['green_candle']['enabled']:
+                conditions.append(dataframe['close'] > dataframe['open'])
+            if 'uptrend_sma' in params and params['uptrend_sma']['enabled']:
+                prevsma = dataframe['sma'].shift(1)
+                conditions.append(dataframe['sma'] > prevsma)
+
+            # TRIGGERS
+            triggers = {
+                'lower_bb': (
+                    dataframe['close'] < dataframe['bb_lowerband']
+                ),
+                'lower_bb_tema': (
+                    dataframe['tema'] < dataframe['bb_lowerband']
+                ),
+                'faststoch10': (qtpylib.crossed_above(
+                    dataframe['fastd'], 10.0
+                )),
+                'ao_cross_zero': (qtpylib.crossed_above(
+                    dataframe['ao'], 0.0
+                )),
+                'ema3_cross_ema10': (qtpylib.crossed_above(
+                    dataframe['ema3'], dataframe['ema10']
+                )),
+                'macd_cross_signal': (qtpylib.crossed_above(
+                    dataframe['macd'], dataframe['macdsignal']
+                )),
+                'sar_reversal': (qtpylib.crossed_above(
+                    dataframe['close'], dataframe['sar']
+                )),
+                'ht_sine': (qtpylib.crossed_above(
+                    dataframe['htleadsine'], dataframe['htsine']
+                )),
+                'heiken_reversal_bull': (
+                    (qtpylib.crossed_above(dataframe['ha_close'], dataframe['ha_open'])) &
+                    (dataframe['ha_low'] == dataframe['ha_open'])
+                ),
+                'di_cross': (qtpylib.crossed_above(
+                    dataframe['plus_di'], dataframe['minus_di']
+                )),
+            }
+            conditions.append(triggers.get(params['trigger']['type']))
+
+            dataframe.loc[
+                reduce(lambda x, y: x & y, conditions),
+                'buy'] = 1
+
+            return dataframe
+
+        return populate_buy_trend
+
+    @staticmethod
+    def indicator_space() -> Dict[str, Any]:
+        """
+        Define your Hyperopt space for searching strategy parameters
+        """
+        return {
+            'macd_below_zero': hp.choice('macd_below_zero', [
+                {'enabled': False},
+                {'enabled': True}
+            ]),
+            'mfi': hp.choice('mfi', [
+                {'enabled': False},
+                {'enabled': True, 'value': hp.quniform('mfi-value', 10, 25, 5)}
+            ]),
+            'fastd': hp.choice('fastd', [
+                {'enabled': False},
+                {'enabled': True, 'value': hp.quniform('fastd-value', 15, 45, 5)}
+            ]),
+            'adx': hp.choice('adx', [
+                {'enabled': False},
+                {'enabled': True, 'value': hp.quniform('adx-value', 20, 50, 5)}
+            ]),
+            'rsi': hp.choice('rsi', [
+                {'enabled': False},
+                {'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 5)}
+            ]),
+            'uptrend_long_ema': hp.choice('uptrend_long_ema', [
+                {'enabled': False},
+                {'enabled': True}
+            ]),
+            'uptrend_short_ema': hp.choice('uptrend_short_ema', [
+                {'enabled': False},
+                {'enabled': True}
+            ]),
+            'over_sar': hp.choice('over_sar', [
+                {'enabled': False},
+                {'enabled': True}
+            ]),
+            'green_candle': hp.choice('green_candle', [
+                {'enabled': False},
+                {'enabled': True}
+            ]),
+            'uptrend_sma': hp.choice('uptrend_sma', [
+                {'enabled': False},
+                {'enabled': True}
+            ]),
+            'trigger': hp.choice('trigger', [
+                {'type': 'lower_bb'},
+                {'type': 'lower_bb_tema'},
+                {'type': 'faststoch10'},
+                {'type': 'ao_cross_zero'},
+                {'type': 'ema3_cross_ema10'},
+                {'type': 'macd_cross_signal'},
+                {'type': 'sar_reversal'},
+                {'type': 'ht_sine'},
+                {'type': 'heiken_reversal_bull'},
+                {'type': 'di_cross'},
+            ]),
+        }
+
+    @staticmethod
+    def generate_roi_table(params: Dict) -> Dict[int, float]:
+        """
+        Generate the ROI table that will be used by Hyperopt
+        """
+        roi_table = {}
+        roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
+        roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']
+        roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']
+        roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0
+
+        return roi_table
+
+    @staticmethod
+    def stoploss_space() -> Dict[str, Any]:
+        """
+        Stoploss Value to search
+        """
+        return {
+            'stoploss': hp.quniform('stoploss', -0.5, -0.02, 0.02),
+        }
+
+    @staticmethod
+    def roi_space() -> Dict[str, Any]:
+        """
+        Values to search for each ROI steps
+        """
+        return {
+            'roi_t1': hp.quniform('roi_t1', 10, 120, 20),
+            'roi_t2': hp.quniform('roi_t2', 10, 60, 15),
+            'roi_t3': hp.quniform('roi_t3', 10, 40, 10),
+            'roi_p1': hp.quniform('roi_p1', 0.01, 0.04, 0.01),
+            'roi_p2': hp.quniform('roi_p2', 0.01, 0.07, 0.01),
+            'roi_p3': hp.quniform('roi_p3', 0.01, 0.20, 0.01),
+        }

freqtrade/optimize/hyperopt.py

@@ -9,22 +9,20 @@ import multiprocessing
 import os
 import sys
 from argparse import Namespace
-from functools import reduce
 from math import exp
 from operator import itemgetter
-from typing import Any, Callable, Dict, List
+from typing import Any, Dict, List
 
-import talib.abstract as ta
 from pandas import DataFrame
 from sklearn.externals.joblib import Parallel, delayed, dump, load
 from skopt import Optimizer
-from skopt.space import Categorical, Dimension, Integer, Real
+from skopt.space import Dimension
 
-import freqtrade.vendor.qtpylib.indicators as qtpylib
 from freqtrade.arguments import Arguments
 from freqtrade.configuration import Configuration
 from freqtrade.optimize import load_data
 from freqtrade.optimize.backtesting import Backtesting
+from freqtrade.optimize.custom_hyperopt import CustomHyperOpt
 
 logger = logging.getLogger(__name__)
 
@@ -42,6 +40,9 @@ class Hyperopt(Backtesting):
     """
     def __init__(self, config: Dict[str, Any]) -> None:
         super().__init__(config)
+
+        self.custom_hyperopt = CustomHyperOpt(self.config)
+
         # set TARGET_TRADES to suit your number concurrent trades so its realistic
         # to the number of days
         self.target_trades = 600
@@ -74,24 +75,6 @@ class Hyperopt(Backtesting):
         arg_dict = {dim.name: value for dim, value in zip(dimensions, params)}
         return arg_dict
 
-    @staticmethod
-    def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
-        dataframe['adx'] = ta.ADX(dataframe)
-        macd = ta.MACD(dataframe)
-        dataframe['macd'] = macd['macd']
-        dataframe['macdsignal'] = macd['macdsignal']
-        dataframe['mfi'] = ta.MFI(dataframe)
-        dataframe['rsi'] = ta.RSI(dataframe)
-        stoch_fast = ta.STOCHF(dataframe)
-        dataframe['fastd'] = stoch_fast['fastd']
-        dataframe['minus_di'] = ta.MINUS_DI(dataframe)
-        # Bollinger bands
-        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
-        dataframe['bb_lowerband'] = bollinger['lower']
-        dataframe['sar'] = ta.SAR(dataframe)
-
-        return dataframe
-
     def save_trials(self) -> None:
         """
         Save hyperopt trials to file
@@ -149,59 +132,6 @@ class Hyperopt(Backtesting):
         result = trade_loss + profit_loss + duration_loss
         return result
 
-    @staticmethod
-    def generate_roi_table(params: Dict) -> Dict[int, float]:
-        """
-        Generate the ROI table that will be used by Hyperopt
-        """
-        roi_table = {}
-        roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
-        roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']
-        roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']
-        roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0
-
-        return roi_table
-
-    @staticmethod
-    def roi_space() -> List[Dimension]:
-        """
-        Values to search for each ROI steps
-        """
-        return [
-            Integer(10, 120, name='roi_t1'),
-            Integer(10, 60, name='roi_t2'),
-            Integer(10, 40, name='roi_t3'),
-            Real(0.01, 0.04, name='roi_p1'),
-            Real(0.01, 0.07, name='roi_p2'),
-            Real(0.01, 0.20, name='roi_p3'),
-        ]
-
-    @staticmethod
-    def stoploss_space() -> List[Dimension]:
-        """
-        Stoploss search space
-        """
-        return [
-            Real(-0.5, -0.02, name='stoploss'),
-        ]
-
-    @staticmethod
-    def indicator_space() -> List[Dimension]:
-        """
-        Define your Hyperopt space for searching strategy parameters
-        """
-        return [
-            Integer(10, 25, name='mfi-value'),
-            Integer(15, 45, name='fastd-value'),
-            Integer(20, 50, name='adx-value'),
-            Integer(20, 40, name='rsi-value'),
-            Categorical([True, False], name='mfi-enabled'),
-            Categorical([True, False], name='fastd-enabled'),
-            Categorical([True, False], name='adx-enabled'),
-            Categorical([True, False], name='rsi-enabled'),
-            Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger')
-        ]
-
     def has_space(self, space: str) -> bool:
         """
         Tell if a space value is contained in the configuration
@@ -216,61 +146,19 @@ class Hyperopt(Backtesting):
         """
         spaces: List[Dimension] = []
         if self.has_space('buy'):
-            spaces += Hyperopt.indicator_space()
+            spaces = {**spaces, **self.custom_hyperopt.indicator_space()}
         if self.has_space('roi'):
-            spaces += Hyperopt.roi_space()
+            spaces = {**spaces, **self.custom_hyperopt.roi_space()}
         if self.has_space('stoploss'):
-            spaces += Hyperopt.stoploss_space()
+            spaces = {**spaces, **self.custom_hyperopt.stoploss_space()}
         return spaces
 
-    @staticmethod
+    def generate_optimizer(self, params: Dict) -> Dict:
-    def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
-        """
-        Define the buy strategy parameters to be used by hyperopt
-        """
-        def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
-            """
-            Buy strategy Hyperopt will build and use
-            """
-            conditions = []
-            # GUARDS AND TRENDS
-            if 'mfi-enabled' in params and params['mfi-enabled']:
-                conditions.append(dataframe['mfi'] < params['mfi-value'])
-            if 'fastd-enabled' in params and params['fastd-enabled']:
-                conditions.append(dataframe['fastd'] < params['fastd-value'])
-            if 'adx-enabled' in params and params['adx-enabled']:
-                conditions.append(dataframe['adx'] > params['adx-value'])
-            if 'rsi-enabled' in params and params['rsi-enabled']:
-                conditions.append(dataframe['rsi'] < params['rsi-value'])
-
-            # TRIGGERS
-            if params['trigger'] == 'bb_lower':
-                conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
-            if params['trigger'] == 'macd_cross_signal':
-                conditions.append(qtpylib.crossed_above(
-                    dataframe['macd'], dataframe['macdsignal']
-                ))
-            if params['trigger'] == 'sar_reversal':
-                conditions.append(qtpylib.crossed_above(
-                    dataframe['close'], dataframe['sar']
-                ))
-
-            dataframe.loc[
-                reduce(lambda x, y: x & y, conditions),
-                'buy'] = 1
-
-            return dataframe
-
-        return populate_buy_trend
-
-    def generate_optimizer(self, _params) -> Dict:
-        params = self.get_args(_params)
-
         if self.has_space('roi'):
-            self.strategy.minimal_roi = self.generate_roi_table(params)
+            self.analyze.strategy.minimal_roi = self.custom_hyperopt.generate_roi_table(params)
 
         if self.has_space('buy'):
-            self.advise_buy = self.buy_strategy_generator(params)
+            self.populate_buy_trend = self.custom_hyperopt.buy_strategy_generator(params)
 
         if self.has_space('stoploss'):
             self.strategy.stoploss = params['stoploss']
@@ -351,7 +239,7 @@ class Hyperopt(Backtesting):
         )
 
         if self.has_space('buy'):
-            self.strategy.advise_indicators = Hyperopt.populate_indicators  # type: ignore
+            self.strategy.advise_indicators = self.custom_hyperopt.populate_indicators  # type: ignore
         dump(self.strategy.tickerdata_to_dataframe(data), TICKERDATA_PICKLE)
         self.exchange = None  # type: ignore
         self.load_previous_results()

freqtrade/optimize/interface.py

@@ -0,0 +1,59 @@
+"""
+IHyperOpt interface
+This module defines the interface to apply for hyperopts
+"""
+
+from abc import ABC, abstractmethod
+from typing import Dict, Any, Callable
+
+from pandas import DataFrame
+
+
+class IHyperOpt(ABC):
+    """
+    Interface for freqtrade hyperopts
+    Defines the mandatory structure must follow any custom strategies
+
+    Attributes you can use:
+        minimal_roi -> Dict: Minimal ROI designed for the strategy
+        stoploss -> float: optimal stoploss designed for the strategy
+        ticker_interval -> int: value of the ticker interval to use for the strategy
+    """
+
+    @abstractmethod
+    def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
+        """
+        Populate indicators that will be used in the Buy and Sell strategy
+        :param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
+        :return: a Dataframe with all mandatory indicators for the strategies
+        """
+
+    @abstractmethod
+    def buy_strategy_generator(self, params: Dict[str, Any]) -> Callable:
+        """
+        Create a buy strategy generator
+        """
+
+    @abstractmethod
+    def indicator_space(self) -> Dict[str, Any]:
+        """
+        Create an indicator space
+        """
+
+    @abstractmethod
+    def generate_roi_table(self, params: Dict) -> Dict[int, float]:
+        """
+        Create an roi table
+        """
+
+    @abstractmethod
+    def stoploss_space(self) -> Dict[str, Any]:
+        """
+        Create a stoploss space
+        """
+
+    @abstractmethod
+    def roi_space(self) -> Dict[str, Any]:
+        """
+        Create a roi space
+        """

freqtrade/tests/optimize/test_hyperopt.py

@@ -175,7 +175,7 @@ def test_roi_table_generation(hyperopt) -> None:
         'roi_p3': 3,
     }
 
-    assert hyperopt.generate_roi_table(params) == {0: 6, 15: 3, 25: 1, 30: 0}
+    assert hyperopt.custom_hyperopt.generate_roi_table(params) == {0: 6, 15: 3, 25: 1, 30: 0}
 
 
 def test_start_calls_optimizer(mocker, default_conf, caplog) -> None:
@@ -243,7 +243,8 @@ def test_populate_indicators(hyperopt) -> None:
     tick = load_tickerdata_file(None, 'UNITTEST/BTC', '1m')
     tickerlist = {'UNITTEST/BTC': tick}
     dataframes = hyperopt.strategy.tickerdata_to_dataframe(tickerlist)
-    dataframe = hyperopt.populate_indicators(dataframes['UNITTEST/BTC'], {'pair': 'UNITTEST/BTC'})
+    dataframe = hyperopt.custom_hyperopt.populate_indicators(dataframes['UNITTEST/BTC'],
+                                                             {'pair': 'UNITTEST/BTC'})
 
     # Check if some indicators are generated. We will not test all of them
     assert 'adx' in dataframe
@@ -255,9 +256,10 @@ def test_buy_strategy_generator(hyperopt) -> None:
     tick = load_tickerdata_file(None, 'UNITTEST/BTC', '1m')
     tickerlist = {'UNITTEST/BTC': tick}
     dataframes = hyperopt.strategy.tickerdata_to_dataframe(tickerlist)
-    dataframe = hyperopt.populate_indicators(dataframes['UNITTEST/BTC'], {'pair': 'UNITTEST/BTC'})
+    dataframe = hyperopt.custom_hyperopt.populate_indicators(dataframes['UNITTEST/BTC'],
+                                                             {'pair': 'UNITTEST/BTC'})
 
-    populate_buy_trend = hyperopt.buy_strategy_generator(
+    populate_buy_trend = hyperopt.custom_hyperopt.buy_strategy_generator(
         {
             'adx-value': 20,
             'fastd-value': 20,

user_data/hyperopts/test_hyperopt.py

@@ -0,0 +1,283 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+
+import talib.abstract as ta
+from pandas import DataFrame
+from typing import Dict, Any, Callable
+from functools import reduce
+from math import exp
+
+import numpy
+import talib.abstract as ta
+from hyperopt import STATUS_FAIL, STATUS_OK, Trials, fmin, hp, space_eval, tpe
+
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+from freqtrade.indicator_helpers import fishers_inverse
+from freqtrade.optimize.interface import IHyperOpt
+
+
+# Update this variable if you change the class name
+class_name = 'TestHyperOpt'
+
+
+# This class is a sample. Feel free to customize it.
+class TestHyperOpt(IHyperOpt):
+    """
+    This is a test hyperopt to inspire you.
+    More information in https://github.com/gcarq/freqtrade/blob/develop/docs/hyperopt.md
+
+    You can:
+    - Rename the class name (Do not forget to update class_name)
+    - Add any methods you want to build your hyperopt
+    - Add any lib you need to build your hyperopt
+
+    You must keep:
+    - the prototype for the methods: populate_indicators, indicator_space, buy_strategy_generator,
+    roi_space, generate_roi_table, stoploss_space
+    """
+
+    @staticmethod
+    def populate_indicators(dataframe: DataFrame) -> 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.
+        """
+
+        # Momentum Indicator
+        # ------------------------------------
+
+        # ADX
+        dataframe['adx'] = ta.ADX(dataframe)
+
+        """
+        # Awesome oscillator
+        dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
+
+        # Commodity Channel Index: values Oversold:<-100, Overbought:>100
+        dataframe['cci'] = ta.CCI(dataframe)
+
+        # MACD
+        macd = ta.MACD(dataframe)
+        dataframe['macd'] = macd['macd']
+        dataframe['macdsignal'] = macd['macdsignal']
+        dataframe['macdhist'] = macd['macdhist']
+
+        # MFI
+        dataframe['mfi'] = ta.MFI(dataframe)
+
+        # Minus Directional Indicator / Movement
+        dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
+        dataframe['minus_di'] = ta.MINUS_DI(dataframe)
+
+        # Plus Directional Indicator / Movement
+        dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
+        dataframe['plus_di'] = ta.PLUS_DI(dataframe)
+        dataframe['minus_di'] = ta.MINUS_DI(dataframe)
+
+        # ROC
+        dataframe['roc'] = ta.ROC(dataframe)
+
+        # RSI
+        dataframe['rsi'] = ta.RSI(dataframe)
+
+        # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
+        rsi = 0.1 * (dataframe['rsi'] - 50)
+        dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)
+
+        # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
+        dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
+
+        # Stoch
+        stoch = ta.STOCH(dataframe)
+        dataframe['slowd'] = stoch['slowd']
+        dataframe['slowk'] = stoch['slowk']
+
+        # Stoch fast
+        stoch_fast = ta.STOCHF(dataframe)
+        dataframe['fastd'] = stoch_fast['fastd']
+        dataframe['fastk'] = stoch_fast['fastk']
+
+        # Stoch RSI
+        stoch_rsi = ta.STOCHRSI(dataframe)
+        dataframe['fastd_rsi'] = stoch_rsi['fastd']
+        dataframe['fastk_rsi'] = stoch_rsi['fastk']
+        """
+
+        # Overlap Studies
+        # ------------------------------------
+
+        # Bollinger bands
+        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
+        dataframe['bb_lowerband'] = bollinger['lower']
+        dataframe['bb_middleband'] = bollinger['mid']
+        dataframe['bb_upperband'] = bollinger['upper']
+
+        """
+        # EMA - Exponential Moving Average
+        dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
+        dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
+        dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
+        dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
+        dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
+
+        # SAR Parabol
+        dataframe['sar'] = ta.SAR(dataframe)
+
+        # SMA - Simple Moving Average
+        dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
+        """
+
+        # TEMA - Triple Exponential Moving Average
+        dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
+
+        # Cycle Indicator
+        # ------------------------------------
+        # Hilbert Transform Indicator - SineWave
+        hilbert = ta.HT_SINE(dataframe)
+        dataframe['htsine'] = hilbert['sine']
+        dataframe['htleadsine'] = hilbert['leadsine']
+
+        # Pattern Recognition - Bullish candlestick patterns
+        # ------------------------------------
+        """
+        # Hammer: values [0, 100]
+        dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
+        # Inverted Hammer: values [0, 100]
+        dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
+        # Dragonfly Doji: values [0, 100]
+        dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
+        # Piercing Line: values [0, 100]
+        dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
+        # Morningstar: values [0, 100]
+        dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
+        # Three White Soldiers: values [0, 100]
+        dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
+        """
+
+        # Pattern Recognition - Bearish candlestick patterns
+        # ------------------------------------
+        """
+        # Hanging Man: values [0, 100]
+        dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
+        # Shooting Star: values [0, 100]
+        dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
+        # Gravestone Doji: values [0, 100]
+        dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
+        # Dark Cloud Cover: values [0, 100]
+        dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
+        # Evening Doji Star: values [0, 100]
+        dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
+        # Evening Star: values [0, 100]
+        dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
+        """
+
+        # Pattern Recognition - Bullish/Bearish candlestick patterns
+        # ------------------------------------
+        """
+        # Three Line Strike: values [0, -100, 100]
+        dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
+        # Spinning Top: values [0, -100, 100]
+        dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
+        # Engulfing: values [0, -100, 100]
+        dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
+        # Harami: values [0, -100, 100]
+        dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
+        # Three Outside Up/Down: values [0, -100, 100]
+        dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
+        # Three Inside Up/Down: values [0, -100, 100]
+        dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
+        """
+
+        # Chart type
+        # ------------------------------------
+        """
+        # Heikinashi stategy
+        heikinashi = qtpylib.heikinashi(dataframe)
+        dataframe['ha_open'] = heikinashi['open']
+        dataframe['ha_close'] = heikinashi['close']
+        dataframe['ha_high'] = heikinashi['high']
+        dataframe['ha_low'] = heikinashi['low']
+        """
+
+        return dataframe
+
+    @staticmethod
+    def indicator_space() -> Dict[str, Any]:
+        """
+        Define your Hyperopt space for searching strategy parameters
+        """
+        return {
+            'adx': hp.choice('adx', [
+                {'enabled': False},
+                {'enabled': True, 'value': hp.quniform('adx-value', 50, 80, 5)}
+            ]),
+            'uptrend_tema': hp.choice('uptrend_tema', [
+                {'enabled': False},
+                {'enabled': True}
+            ]),
+            'trigger': hp.choice('trigger', [
+                {'type': 'middle_bb_tema'},
+            ]),
+        }
+    
+    @staticmethod
+    def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
+        """
+        Define the buy strategy parameters to be used by hyperopt
+        """
+        def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
+            conditions = []
+            # GUARDS AND TRENDS
+            if 'adx' in params and params['adx']['enabled']:
+                conditions.append(dataframe['adx'] > params['adx']['value'])
+            if 'uptrend_tema' in params and params['uptrend_tema']['enabled']:
+                prevtema = dataframe['tema'].shift(1)
+                conditions.append(dataframe['tema'] > prevtema)
+
+            # TRIGGERS
+            triggers = {
+                'middle_bb_tema': (
+                    dataframe['tema'] > dataframe['bb_middleband']
+                ),
+            }
+            conditions.append(triggers.get(params['trigger']['type']))
+
+            dataframe.loc[
+                reduce(lambda x, y: x & y, conditions),
+                'buy'] = 1
+
+            return dataframe
+
+        return populate_buy_trend
+        
+    @staticmethod
+    def roi_space() -> Dict[str, Any]:
+        return {
+            'roi_t1': hp.quniform('roi_t1', 10, 120, 20),
+            'roi_t2': hp.quniform('roi_t2', 10, 60, 15),
+            'roi_t3': hp.quniform('roi_t3', 10, 40, 10),
+            'roi_p1': hp.quniform('roi_p1', 0.01, 0.04, 0.01),
+            'roi_p2': hp.quniform('roi_p2', 0.01, 0.07, 0.01),
+            'roi_p3': hp.quniform('roi_p3', 0.01, 0.20, 0.01),
+        }
+
+    @staticmethod
+    def generate_roi_table(params: Dict) -> Dict[int, float]:
+        """
+        Generate the ROI table that will be used by Hyperopt
+        """
+        roi_table = {}
+        roi_table[0] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
+        roi_table[params['roi_t3']] = params['roi_p1'] + params['roi_p2']
+        roi_table[params['roi_t3'] + params['roi_t2']] = params['roi_p1']
+        roi_table[params['roi_t3'] + params['roi_t2'] + params['roi_t1']] = 0
+
+        return roi_table
+
+    @staticmethod
+    def stoploss_space() -> Dict[str, Any]:
+        return {
+            'stoploss': hp.quniform('stoploss', -0.5, -0.02, 0.02),
+        }