From b7f6b8d7baad5b0650686be504d9aa20a96bdcbf Mon Sep 17 00:00:00 2001 From: MoonGem <34537029+MoonGem@users.noreply.github.com> Date: Fri, 23 Mar 2018 12:30:19 -0500 Subject: [PATCH] Blower is broken I think this would be a good edit to serve as a reference on how to set this up. Blower is also broken and pandas throws a bunch of key errors on it. I just drop this file in place of my default strategy. --- user_data/strategies/test_strategy.py | 217 ++++++++++++++++++++++++-- 1 file changed, 208 insertions(+), 9 deletions(-) diff --git a/user_data/strategies/test_strategy.py b/user_data/strategies/test_strategy.py index a164812c4..8d2fac310 100644 --- a/user_data/strategies/test_strategy.py +++ b/user_data/strategies/test_strategy.py @@ -1,6 +1,9 @@ # --- Do not remove these libs --- from freqtrade.strategy.interface import IStrategy +from typing import Dict, List +from hyperopt import hp +from functools import reduce from pandas import DataFrame # -------------------------------- @@ -11,11 +14,10 @@ import numpy # noqa # Update this variable if you change the class name -class_name = 'TestStrategy' +class_name = 'DefaultStrategy' -# This class is a sample. Feel free to customize it. -class TestStrategy(IStrategy): +class DefaultStrategy(IStrategy): """ This is a test strategy to inspire you. More information in https://github.com/gcarq/freqtrade/blob/develop/docs/bot-optimization.md @@ -119,6 +121,16 @@ class TestStrategy(IStrategy): # Overlap Studies # ------------------------------------ + + # Previous Bollinger bands + # Because ta.BBANDS implementation is broken with small numbers, it actually + # returns middle band for all the three bands. Switch to qtpylib.bollinger_bands + # and use middle band instead. + # Is broken + """ + dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband'] + """ + # Bollinger bands bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] @@ -220,12 +232,65 @@ class TestStrategy(IStrategy): :param dataframe: DataFrame :return: DataFrame with buy column """ - dataframe.loc[ - ( - (dataframe['adx'] > 30) & - (dataframe['tema'] <= dataframe['bb_middleband']) & - (dataframe['tema'] > dataframe['tema'].shift(1)) + 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 @@ -239,8 +304,142 @@ class TestStrategy(IStrategy): dataframe.loc[ ( (dataframe['adx'] > 70) & - (dataframe['tema'] > dataframe['bb_middleband']) & (dataframe['tema'] < dataframe['tema'].shift(1)) ), 'sell'] = 1 return dataframe + + def hyperopt_space(self) -> List[Dict]: + """ + Define your Hyperopt space for the strategy + :return: Dict + """ + space = { + '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', 5, 25, 1)} + ]), + 'fastd': hp.choice('fastd', [ + {'enabled': False}, + {'enabled': True, 'value': hp.quniform('fastd-value', 10, 50, 1)} + ]), + 'adx': hp.choice('adx', [ + {'enabled': False}, + {'enabled': True, 'value': hp.quniform('adx-value', 15, 50, 1)} + ]), + 'rsi': hp.choice('rsi', [ + {'enabled': False}, + {'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 1)} + ]), + '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'}, + ]), + 'stoploss': hp.uniform('stoploss', -0.5, -0.02), + } + return space + + def buy_strategy_generator(self, params) -> None: + """ + 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