diff --git a/freqtrade/optimize/hyperopt.py b/freqtrade/optimize/hyperopt.py index 8138f4caa..72b6cbbc4 100644 --- a/freqtrade/optimize/hyperopt.py +++ b/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: - """ - 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 generate_optimizer(args, strategy): 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), - space=hyperopt_space(args.spaces), + fn=generate_optimizer(args, strategy), + 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 ) diff --git a/freqtrade/strategy/default_strategy.py b/freqtrade/strategy/default_strategy.py index 2247ecf27..01fb66298 100644 --- a/freqtrade/strategy/default_strategy.py +++ b/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), + } \ No newline at end of file diff --git a/freqtrade/strategy/strategy.py b/freqtrade/strategy/strategy.py index 27f334d5c..b4f2d87b8 100644 --- a/freqtrade/strategy/strategy.py +++ b/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() + \ No newline at end of file diff --git a/user_data/strategies/test_strategy.py b/user_data/strategies/test_strategy.py index a164812c4..7db711b82 100644 --- a/user_data/strategies/test_strategy.py +++ b/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), + } \ No newline at end of file