172 lines
6.3 KiB
Python
172 lines
6.3 KiB
Python
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
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import talib.abstract as ta
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import numpy as np
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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from pandas import DataFrame
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from typing import Dict, Any, Callable, List
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from functools import reduce
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from skopt.space import Categorical, Dimension, Integer, Real
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from freqtrade.optimize.hyperopt_interface import IHyperOpt
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__author__ = "Kevin Ossenbrueck"
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__github__ = "github.com/OtenMoten"
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__linkedin__ = "linkedin.com/in/kevin-ossenbrueck/?locale=en_US"
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__twitter__ = "twitter.com/ossenbrueck"
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__instagram__ = "instagram.com/kevin_ossenbrueck"
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__facebook__ = "facebook.com/kevin.ossenbrueck"
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__creator__ = ["github.com/xmatthias", "github.com/mishaker"]
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__credits__ = ["MontrealTradingGroup", "Udemy", "Mohsen Hassan", "Ilyass Tabiai"]
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__version__ = "3.0"
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__copyright__ = "GNU GPL"
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__status__ = "Live"
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"""
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I was inspired by: https://github.com/freqtrade/freqtrade-strategies/blob/master/user_data/strategies/Strategy005.py
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Therefore, I wrote this hyperopt to make it more better. Thank you xmatthias and mishaker!
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"""
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# Rolling volume range
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volumeAvgValueMin = 50
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volumeAvgValueMax = 300
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# RSI range
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rsiValueMin = 1
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rsiValueMax = 100
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# STOCH FAST range
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fastdValueMin = 1
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fastdValueMax = 100
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# MINUS DI range
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minusdiValueMin = 1
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minusdiValueMax = 100
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fishRsiNormaValueMin = 1
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fishRsiNormaValueMax = 100
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class HODobby(IHyperOpt):
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"""
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If you trade on Binance then the API endopoint is "api.binance.com".
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It's based in Tokyo. You can get a VPS in Tokyo on Vultr with 2ms latency.
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I feel free to share my referral link (you get a bonus too):
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> https://www.vultr.com/?ref=8806640
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"""
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############### THIS STRATEGY IS DESIGNED FOR 5m TIMEFRAME ###############
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@staticmethod
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def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
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# MACD
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# tadoc.org/indicator/MACD.htm
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macd = ta.MACD(dataframe)
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dataframe['macd'] = macd['macd']
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# MINUS DI
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# tadoc.org/indicator/MINUS_DI.htm
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dataframe['minus_di'] = ta.MINUS_DI(dataframe)
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# RSI
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# tadoc.org/indicator/RSI.htm
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# tradingview.com/scripts/fishertransform/
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# goo.gl/2JGGoy
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dataframe['rsi'] = ta.RSI(dataframe)
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rsi = 0.1 * (dataframe['rsi'] - 50)
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dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1) # Inverse Fisher transform on RSI, values [-1.0, 1.0]
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dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1) # Inverse Fisher transform on RSI normalized, value [0.0, 100.0]
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# STOCH FAST
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# tadoc.org/indicator/STOCHF.htm
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stoch_fast = ta.STOCHF(dataframe)
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dataframe['fastd'] = stoch_fast['fastd']
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dataframe['fastk'] = stoch_fast['fastk']
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# SAR
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dataframe['sar'] = ta.SAR(dataframe)
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# SMA
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dataframe['sma'] = ta.SMA(dataframe, timeperiod=50)
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return dataframe
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@staticmethod
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def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
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def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
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conditions = []
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# TRIGGER and GUARD
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if 'buy-trigger' in params:
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conditions.append(dataframe['close'] > 0.00000200)
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conditions.append(dataframe['volume'] > dataframe['volume'].rolling(params['volumeAVG-buy-value']).mean())
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conditions.append(dataframe['close'] < dataframe['sma'])
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conditions.append(dataframe['rsi'] > params['rsi-buy-value'])
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conditions.append(dataframe['fastd'] > dataframe['fastk'])
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conditions.append(dataframe['fastd'] > params['fastd-buy-value'])
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conditions.append(dataframe['fisher_rsi_norma'] < params['fishRsiNorma-buy-value'])
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if conditions:
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dataframe.loc[reduce(lambda x, y: x & y, conditions), 'buy'] = 1
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return dataframe
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return populate_buy_trend
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@staticmethod
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def indicator_space() -> List[Dimension]:
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buyTriggerList = ["True"]
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return [
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Integer(volumeAvgValueMin, volumeAvgValueMax, name='volumeAVG-buy-value'),
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Integer(rsiValueMin, rsiValueMax, name='rsi-buy-value'),
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Integer(fastdValueMin, fastdValueMax, name='fastd-buy-value'),
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Integer(fishRsiNormaValueMin, fishRsiNormaValueMax, name='fishRsiNorma-buy-value'),
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Categorical(buyTriggerList, name='buy-trigger')
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]
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@staticmethod
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def sell_strategy_generator(params: Dict[str, Any]) -> Callable:
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def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
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# TRIGGERS and GUARDS
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# Solving a mistery: Which sell trigger is better?
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# The winner of both will be displayed in the output of the hyperopt.
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conditions = []
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if 'sell-trigger' in params:
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if params['sell-trigger'] == 'rsi-macd-minusdi':
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conditions.append(qtpylib.crossed_above(dataframe['rsi'], params['rsi-sell-value']))
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conditions.append(dataframe['macd'] < 0)
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conditions.append(dataframe['minus_di'] > params['minusdi-sell-value'])
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if 'sell-trigger' in params:
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if params['sell-trigger'] == 'sar-fisherRsi':
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conditions.append(dataframe['sar'] > dataframe['close'])
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conditions.append(dataframe['fisher_rsi'] > params['fishRsiNorma-sell-value'])
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if conditions:
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dataframe.loc[reduce(lambda x, y: x & y, conditions), 'sell'] = 1
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return dataframe
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return populate_sell_trend
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@staticmethod
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def sell_indicator_space() -> List[Dimension]:
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sellTriggerList = ["rsi-macd-minusdi", "sar-fisherRsi"]
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return [
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Integer(rsiValueMin, rsiValueMax, name='rsi-sell-value'),
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Integer(minusdiValueMin, minusdiValueMax, name='minusdi-sell-value'),
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Integer(fishRsiNormaValueMin, fishRsiNormaValueMax, name='fishRsiNorma-sell-value'),
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Categorical(sellTriggerList, name='sell-trigger')
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]
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