Moved hyperopt params to strategy file

freqtrade/optimize/hyperopt.py

@@ -61,126 +61,6 @@ TRIALS = Trials()
 main._CONF = OPTIMIZE_CONFIG
 
 
-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
-
-
 def save_trials(trials, trials_path=TRIALS_FILE):
     """Save hyperopt trials to file"""
     logger.info('Saving Trials to \'{}\''.format(trials_path))
@@ -235,188 +115,35 @@ def generate_roi_table(params) -> Dict[int, float]:
     return roi_table
 
 
-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),
-    }
-
-
-def stoploss_space() -> Dict[str, Any]:
-    return {
-        'stoploss': hp.quniform('stoploss', -0.5, -0.02, 0.02),
-    }
-
-
-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'},
-        ]),
-    }
-
-
 def has_space(spaces, space):
     if space in spaces or 'all' in spaces:
         return True
     return False
 
 
-def hyperopt_space(selected_spaces: str) -> Dict[str, Any]:
+def hyperopt_space(selected_spaces: str, strategy) -> Dict[str, Any]:
     spaces = {}
     if has_space(selected_spaces, 'buy'):
-        spaces = {**spaces, **indicator_space()}
+        spaces = {**spaces, **strategy.indicator_space()}
     if has_space(selected_spaces, 'roi'):
-        spaces = {**spaces, **roi_space()}
+        spaces = {**spaces, **strategy.roi_space()}
     if has_space(selected_spaces, 'stoploss'):
-        spaces = {**spaces, **stoploss_space()}
+        spaces = {**spaces, **strategy.stoploss_space()}
     return spaces
 
 
-def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
+def generate_optimizer(args, strategy):
-    """
-    Define the buy strategy parameters to be used by hyperopt
-    """
-    def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
-        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
-
-
-def generate_optimizer(args):
     def optimizer(params):
         global _CURRENT_TRIES
 
-        strategy = Strategy()
         if has_space(args.spaces, 'roi'):
             strategy.minimal_roi = generate_roi_table(params)
 
         if has_space(args.spaces, 'buy'):
-            backtesting.populate_buy_trend = buy_strategy_generator(params)
+            backtesting.populate_buy_trend = strategy.buy_strategy_generator(params)
 
         if has_space(args.spaces, 'stoploss'):
             strategy.stoploss = params['stoploss']
-
         results = backtest({'stake_amount': OPTIMIZE_CONFIG['stake_amount'],
                             'processed': PROCESSED,
                             'realistic': args.realistic_simulation,
@@ -497,7 +224,7 @@ def start(args):
                               ticker_interval=strategy.ticker_interval,
                               timerange=timerange)
     if has_space(args.spaces, 'buy'):
-        optimize.populate_indicators = populate_indicators
+        optimize.populate_indicators = strategy.populate_indicators
     PROCESSED = optimize.tickerdata_to_dataframe(data)
 
     if args.mongodb:
@@ -521,8 +248,8 @@ def start(args):
 
     try:
         best_parameters = fmin(
-            fn=generate_optimizer(args),
+            fn=generate_optimizer(args, strategy),
-            space=hyperopt_space(args.spaces),
+            space=hyperopt_space(args.spaces, strategy),
             algo=tpe.suggest,
             max_evals=TOTAL_TRIES,
             trials=TRIALS
@@ -539,7 +266,7 @@ def start(args):
     # Improve best parameter logging display
     if best_parameters:
         best_parameters = space_eval(
-            hyperopt_space(args.spaces),
+            hyperopt_space(args.spaces, strategy),
             best_parameters
         )
 

freqtrade/strategy/default_strategy.py

@@ -2,7 +2,10 @@
 
 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 functools import reduce
 from freqtrade.strategy.interface import IStrategy
 from freqtrade.indicator_helpers import fishers_inverse
 
@@ -239,3 +242,151 @@ class DefaultStrategy(IStrategy):
             ),
             'sell'] = 1
         return dataframe
+
+    def indicator_space(self) -> 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'},
+            ]),
+        }
+    
+    def buy_strategy_generator(self, 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 '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
+        
+    def roi_space(self) -> 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),
+        }
+
+
+    def stoploss_space(self) -> Dict[str, Any]:
+        return {
+            'stoploss': hp.quniform('stoploss', -0.5, -0.02, 0.02),
+        }

freqtrade/strategy/strategy.py

@@ -8,6 +8,7 @@ import sys
 import logging
 import importlib
 from collections import OrderedDict
+from typing import Dict, Any, Callable
 
 from pandas import DataFrame
 from freqtrade.strategy.interface import IStrategy
@@ -178,3 +179,22 @@ class Strategy(object):
         :return: DataFrame with buy column
         """
         return self.custom_strategy.populate_sell_trend(dataframe)
+
+    def indicator_space(self) -> Dict[str, Any]:
+        """
+        Define your Hyperopt space for searching strategy parameters
+        """
+        return self.custom_strategy.indicator_space()
+        
+    def buy_strategy_generator(self, params: Dict[str, Any]) -> Callable:
+        """
+        Define the buy strategy parameters to be used by hyperopt
+        """
+        return self.custom_strategy.buy_strategy_generator(params)
+        
+    def roi_space(self) -> Dict[str, Any]:
+        return self.custom_strategy.roi_space()
+        
+    def stoploss_space(self) -> Dict[str, Any]:
+        return self.custom_strategy.stoploss_space()
+    

user_data/strategies/test_strategy.py

@@ -2,6 +2,9 @@
 # --- Do not remove these libs ---
 from freqtrade.strategy.interface import IStrategy
 from pandas import DataFrame
+from typing import Dict, Any, Callable
+from hyperopt import STATUS_FAIL, STATUS_OK, Trials, fmin, hp, space_eval, tpe
+from functools import reduce
 # --------------------------------
 
 # Add your lib to import here
@@ -244,3 +247,66 @@ class TestStrategy(IStrategy):
             ),
             'sell'] = 1
         return dataframe
+
+    def indicator_space(self) -> 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'},
+            ]),
+        }
+        
+    def buy_strategy_generator(self, 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
+        
+    def roi_space(self) -> 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),
+        }
+
+
+    def stoploss_space(self) -> Dict[str, Any]:
+        return {
+            'stoploss': hp.quniform('stoploss', -0.5, -0.02, 0.02),
+        }