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.
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MoonGem
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@ -1,6 +1,9 @@
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# --- Do not remove these libs ---
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from freqtrade.strategy.interface import IStrategy
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from typing import Dict, List
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from hyperopt import hp
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from functools import reduce
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from pandas import DataFrame
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# --------------------------------
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@ -11,11 +14,10 @@ import numpy # noqa
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# Update this variable if you change the class name
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class_name = 'TestStrategy'
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class_name = 'DefaultStrategy'
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# This class is a sample. Feel free to customize it.
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class TestStrategy(IStrategy):
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class DefaultStrategy(IStrategy):
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"""
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This is a test strategy to inspire you.
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More information in https://github.com/gcarq/freqtrade/blob/develop/docs/bot-optimization.md
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@ -119,6 +121,16 @@ class TestStrategy(IStrategy):
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# Overlap Studies
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# ------------------------------------
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# Previous Bollinger bands
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# Because ta.BBANDS implementation is broken with small numbers, it actually
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# returns middle band for all the three bands. Switch to qtpylib.bollinger_bands
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# and use middle band instead.
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# Is broken
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"""
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dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
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"""
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# Bollinger bands
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bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
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dataframe['bb_lowerband'] = bollinger['lower']
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@ -220,12 +232,65 @@ class TestStrategy(IStrategy):
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:param dataframe: DataFrame
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:return: DataFrame with buy column
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"""
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dataframe.loc[
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(
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(dataframe['adx'] > 30) &
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(dataframe['tema'] <= dataframe['bb_middleband']) &
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(dataframe['tema'] > dataframe['tema'].shift(1))
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if 'uptrend_long_ema' in params and params['uptrend_long_ema']['enabled']:
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conditions.append(dataframe['ema50'] > dataframe['ema100'])
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if 'macd_below_zero' in params and params['macd_below_zero']['enabled']:
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conditions.append(dataframe['macd'] < 0)
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if 'uptrend_short_ema' in params and params['uptrend_short_ema']['enabled']:
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conditions.append(dataframe['ema5'] > dataframe['ema10'])
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if 'mfi' in params and params['mfi']['enabled']:
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conditions.append(dataframe['mfi'] < params['mfi']['value'])
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if 'fastd' in params and params['fastd']['enabled']:
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conditions.append(dataframe['fastd'] < params['fastd']['value'])
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if 'adx' in params and params['adx']['enabled']:
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conditions.append(dataframe['adx'] > params['adx']['value'])
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if 'rsi' in params and params['rsi']['enabled']:
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conditions.append(dataframe['rsi'] < params['rsi']['value'])
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if 'over_sar' in params and params['over_sar']['enabled']:
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conditions.append(dataframe['close'] > dataframe['sar'])
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if 'green_candle' in params and params['green_candle']['enabled']:
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conditions.append(dataframe['close'] > dataframe['open'])
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if 'uptrend_sma' in params and params['uptrend_sma']['enabled']:
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prevsma = dataframe['sma'].shift(1)
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conditions.append(dataframe['sma'] > prevsma)
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# TRIGGERS
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triggers = {
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'lower_bb': (
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dataframe['close'] < dataframe['bb_lowerband']
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),
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'lower_bb_tema': (
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dataframe['tema'] < dataframe['bb_lowerband']
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),
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'faststoch10': (qtpylib.crossed_above(
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dataframe['fastd'], 10.0
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)),
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'ao_cross_zero': (qtpylib.crossed_above(
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dataframe['ao'], 0.0
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)),
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'ema3_cross_ema10': (qtpylib.crossed_above(
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dataframe['ema3'], dataframe['ema10']
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)),
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'macd_cross_signal': (qtpylib.crossed_above(
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dataframe['macd'], dataframe['macdsignal']
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)),
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'sar_reversal': (qtpylib.crossed_above(
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dataframe['close'], dataframe['sar']
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)),
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'ht_sine': (qtpylib.crossed_above(
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dataframe['htleadsine'], dataframe['htsine']
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)),
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'heiken_reversal_bull': (
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(qtpylib.crossed_above(dataframe['ha_close'], dataframe['ha_open'])) &
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(dataframe['ha_low'] == dataframe['ha_open'])
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),
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'di_cross': (qtpylib.crossed_above(
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dataframe['plus_di'], dataframe['minus_di']
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)),
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}
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conditions.append(triggers.get(params['trigger']['type']))
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dataframe.loc[
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reduce(lambda x, y: x & y, conditions),
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'buy'] = 1
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return dataframe
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@ -239,8 +304,142 @@ class TestStrategy(IStrategy):
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dataframe.loc[
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(
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(dataframe['adx'] > 70) &
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(dataframe['tema'] > dataframe['bb_middleband']) &
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(dataframe['tema'] < dataframe['tema'].shift(1))
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),
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'sell'] = 1
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return dataframe
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def hyperopt_space(self) -> List[Dict]:
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"""
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Define your Hyperopt space for the strategy
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:return: Dict
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"""
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space = {
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'macd_below_zero': hp.choice('macd_below_zero', [
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{'enabled': False},
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{'enabled': True}
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]),
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'mfi': hp.choice('mfi', [
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{'enabled': False},
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{'enabled': True, 'value': hp.quniform('mfi-value', 5, 25, 1)}
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]),
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'fastd': hp.choice('fastd', [
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{'enabled': False},
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{'enabled': True, 'value': hp.quniform('fastd-value', 10, 50, 1)}
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]),
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'adx': hp.choice('adx', [
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{'enabled': False},
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{'enabled': True, 'value': hp.quniform('adx-value', 15, 50, 1)}
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]),
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'rsi': hp.choice('rsi', [
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{'enabled': False},
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{'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 1)}
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]),
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'uptrend_long_ema': hp.choice('uptrend_long_ema', [
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{'enabled': False},
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{'enabled': True}
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]),
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'uptrend_short_ema': hp.choice('uptrend_short_ema', [
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{'enabled': False},
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{'enabled': True}
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]),
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'over_sar': hp.choice('over_sar', [
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{'enabled': False},
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{'enabled': True}
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]),
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'green_candle': hp.choice('green_candle', [
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{'enabled': False},
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{'enabled': True}
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]),
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'uptrend_sma': hp.choice('uptrend_sma', [
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{'enabled': False},
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{'enabled': True}
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]),
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'trigger': hp.choice('trigger', [
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{'type': 'lower_bb'},
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{'type': 'lower_bb_tema'},
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{'type': 'faststoch10'},
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{'type': 'ao_cross_zero'},
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{'type': 'ema3_cross_ema10'},
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{'type': 'macd_cross_signal'},
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{'type': 'sar_reversal'},
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{'type': 'ht_sine'},
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{'type': 'heiken_reversal_bull'},
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{'type': 'di_cross'},
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]),
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'stoploss': hp.uniform('stoploss', -0.5, -0.02),
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}
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return space
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def buy_strategy_generator(self, params) -> None:
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"""
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Define the buy strategy parameters to be used by hyperopt
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"""
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def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
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conditions = []
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# GUARDS AND TRENDS
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if 'uptrend_long_ema' in params and params['uptrend_long_ema']['enabled']:
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conditions.append(dataframe['ema50'] > dataframe['ema100'])
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if 'macd_below_zero' in params and params['macd_below_zero']['enabled']:
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conditions.append(dataframe['macd'] < 0)
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if 'uptrend_short_ema' in params and params['uptrend_short_ema']['enabled']:
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conditions.append(dataframe['ema5'] > dataframe['ema10'])
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if 'mfi' in params and params['mfi']['enabled']:
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conditions.append(dataframe['mfi'] < params['mfi']['value'])
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if 'fastd' in params and params['fastd']['enabled']:
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conditions.append(dataframe['fastd'] < params['fastd']['value'])
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if 'adx' in params and params['adx']['enabled']:
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conditions.append(dataframe['adx'] > params['adx']['value'])
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if 'rsi' in params and params['rsi']['enabled']:
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conditions.append(dataframe['rsi'] < params['rsi']['value'])
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if 'over_sar' in params and params['over_sar']['enabled']:
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conditions.append(dataframe['close'] > dataframe['sar'])
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if 'green_candle' in params and params['green_candle']['enabled']:
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conditions.append(dataframe['close'] > dataframe['open'])
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if 'uptrend_sma' in params and params['uptrend_sma']['enabled']:
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prevsma = dataframe['sma'].shift(1)
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conditions.append(dataframe['sma'] > prevsma)
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# TRIGGERS
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triggers = {
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'lower_bb': (
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dataframe['close'] < dataframe['bb_lowerband']
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),
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'lower_bb_tema': (
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dataframe['tema'] < dataframe['bb_lowerband']
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),
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'faststoch10': (qtpylib.crossed_above(
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dataframe['fastd'], 10.0
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)),
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'ao_cross_zero': (qtpylib.crossed_above(
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dataframe['ao'], 0.0
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)),
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'ema3_cross_ema10': (qtpylib.crossed_above(
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dataframe['ema3'], dataframe['ema10']
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)),
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'macd_cross_signal': (qtpylib.crossed_above(
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dataframe['macd'], dataframe['macdsignal']
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)),
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'sar_reversal': (qtpylib.crossed_above(
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dataframe['close'], dataframe['sar']
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)),
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'ht_sine': (qtpylib.crossed_above(
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dataframe['htleadsine'], dataframe['htsine']
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)),
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'heiken_reversal_bull': (
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(qtpylib.crossed_above(dataframe['ha_close'], dataframe['ha_open'])) &
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(dataframe['ha_low'] == dataframe['ha_open'])
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),
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'di_cross': (qtpylib.crossed_above(
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dataframe['plus_di'], dataframe['minus_di']
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)),
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}
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conditions.append(triggers.get(params['trigger']['type']))
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dataframe.loc[
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reduce(lambda x, y: x & y, conditions),
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'buy'] = 1
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return dataframe
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return populate_buy_trend
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