2018-01-15 08:35:11 +00:00
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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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# Add your lib to import here
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import talib.abstract as ta
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2018-01-18 07:06:37 +00:00
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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import numpy # noqa
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2018-01-15 08:35:11 +00:00
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# Update this variable if you change the class name
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class_name = 'TestStrategy'
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2018-01-18 07:06:37 +00:00
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# This class is a sample. Feel free to customize it.
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2018-01-15 08:35:11 +00:00
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class TestStrategy(IStrategy):
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"""
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This is a test strategy to inspire you.
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2018-01-18 07:06:37 +00:00
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More information in https://github.com/gcarq/freqtrade/blob/develop/docs/bot-optimization.md
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2018-01-15 08:35:11 +00:00
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You can:
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- Rename the class name (Do not forget to update class_name)
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- Add any methods you want to build your strategy
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- Add any lib you need to build your strategy
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You must keep:
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- the lib in the section "Do not remove these libs"
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- the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
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populate_sell_trend, hyperopt_space, buy_strategy_generator
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"""
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# Minimal ROI designed for the strategy.
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# This attribute will be overridden if the config file contains "minimal_roi"
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minimal_roi = {
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"40": 0.0,
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"30": 0.01,
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"20": 0.02,
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"0": 0.04
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}
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# Optimal stoploss designed for the strategy
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# This attribute will be overridden if the config file contains "stoploss"
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stoploss = -0.10
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def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
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"""
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Adds several different TA indicators to the given DataFrame
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Performance Note: For the best performance be frugal on the number of indicators
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you are using. Let uncomment only the indicator you are using in your strategies
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or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
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"""
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2018-01-18 07:06:37 +00:00
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# Momentum Indicator
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# ------------------------------------
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# ADX
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2018-01-15 08:35:11 +00:00
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dataframe['adx'] = ta.ADX(dataframe)
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2018-01-18 07:06:37 +00:00
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"""
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# Awesome oscillator
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dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
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# Commodity Channel Index: values Oversold:<-100, Overbought:>100
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dataframe['cci'] = ta.CCI(dataframe)
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# MACD
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macd = ta.MACD(dataframe)
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dataframe['macd'] = macd['macd']
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dataframe['macdsignal'] = macd['macdsignal']
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dataframe['macdhist'] = macd['macdhist']
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# MFI
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dataframe['mfi'] = ta.MFI(dataframe)
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# Minus Directional Indicator / Movement
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dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
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dataframe['minus_di'] = ta.MINUS_DI(dataframe)
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# Plus Directional Indicator / Movement
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dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
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dataframe['plus_di'] = ta.PLUS_DI(dataframe)
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dataframe['minus_di'] = ta.MINUS_DI(dataframe)
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# ROC
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dataframe['roc'] = ta.ROC(dataframe)
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# RSI
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dataframe['rsi'] = ta.RSI(dataframe)
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# Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
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rsi = 0.1 * (dataframe['rsi'] - 50)
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dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)
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# Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
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dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
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# Stoch
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stoch = ta.STOCH(dataframe)
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dataframe['slowd'] = stoch['slowd']
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dataframe['slowk'] = stoch['slowk']
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# Stoch fast
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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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# Stoch RSI
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stoch_rsi = ta.STOCHRSI(dataframe)
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dataframe['fastd_rsi'] = stoch_rsi['fastd']
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dataframe['fastk_rsi'] = stoch_rsi['fastk']
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"""
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# Overlap Studies
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# ------------------------------------
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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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2018-01-15 08:35:11 +00:00
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dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
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2018-01-18 07:06:37 +00:00
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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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dataframe['bb_middleband'] = bollinger['mid']
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dataframe['bb_upperband'] = bollinger['upper']
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"""
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# EMA - Exponential Moving Average
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dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
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dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
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dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
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dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
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dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
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# SAR Parabol
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dataframe['sar'] = ta.SAR(dataframe)
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# SMA - Simple Moving Average
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dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
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"""
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# TEMA - Triple Exponential Moving Average
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2018-01-15 08:35:11 +00:00
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dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
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2018-01-18 07:06:37 +00:00
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# Cycle Indicator
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# ------------------------------------
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# Hilbert Transform Indicator - SineWave
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hilbert = ta.HT_SINE(dataframe)
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dataframe['htsine'] = hilbert['sine']
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dataframe['htleadsine'] = hilbert['leadsine']
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# Pattern Recognition - Bullish candlestick patterns
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# ------------------------------------
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"""
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# Hammer: values [0, 100]
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dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
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# Inverted Hammer: values [0, 100]
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dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
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# Dragonfly Doji: values [0, 100]
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dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
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# Piercing Line: values [0, 100]
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dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
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# Morningstar: values [0, 100]
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dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
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# Three White Soldiers: values [0, 100]
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dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
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"""
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# Pattern Recognition - Bearish candlestick patterns
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# ------------------------------------
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"""
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# Hanging Man: values [0, 100]
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dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
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# Shooting Star: values [0, 100]
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dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
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# Gravestone Doji: values [0, 100]
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dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
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# Dark Cloud Cover: values [0, 100]
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dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
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# Evening Doji Star: values [0, 100]
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dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
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# Evening Star: values [0, 100]
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dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
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"""
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# Pattern Recognition - Bullish/Bearish candlestick patterns
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# ------------------------------------
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"""
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# Three Line Strike: values [0, -100, 100]
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dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
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# Spinning Top: values [0, -100, 100]
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dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
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# Engulfing: values [0, -100, 100]
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dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
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# Harami: values [0, -100, 100]
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dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
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# Three Outside Up/Down: values [0, -100, 100]
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dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
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# Three Inside Up/Down: values [0, -100, 100]
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dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
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"""
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# Chart type
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# ------------------------------------
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"""
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# Heikinashi stategy
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heikinashi = qtpylib.heikinashi(dataframe)
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dataframe['ha_open'] = heikinashi['open']
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dataframe['ha_close'] = heikinashi['close']
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dataframe['ha_high'] = heikinashi['high']
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dataframe['ha_low'] = heikinashi['low']
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"""
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2018-01-15 08:35:11 +00:00
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return dataframe
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def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
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"""
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Based on TA indicators, populates the buy signal for the given dataframe
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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['blower']) &
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(dataframe['tema'] > dataframe['tema'].shift(1))
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),
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'buy'] = 1
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return dataframe
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def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
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"""
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Based on TA indicators, populates the sell signal for the given dataframe
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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'] > 70) &
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(dataframe['tema'] > dataframe['blower']) &
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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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2018-01-18 07:06:37 +00:00
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:return: Dict
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2018-01-15 08:35:11 +00:00
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"""
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space = {
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2018-01-19 05:27:23 +00:00
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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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|
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|
{'enabled': True}
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|
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|
]),
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2018-01-15 08:35:11 +00:00
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|
'trigger': hp.choice('trigger', [
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|
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|
{'type': 'lower_bb'},
|
2018-01-19 05:27:23 +00:00
|
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|
{'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'},
|
2018-01-15 08:35:11 +00:00
|
|
|
]),
|
|
|
|
|
'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 = []
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|
|
|
|
# GUARDS AND TRENDS
|
2018-01-19 05:27:23 +00:00
|
|
|
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']:
|
2018-01-15 08:35:11 +00:00
|
|
|
conditions.append(dataframe['adx'] > params['adx']['value'])
|
2018-01-19 05:27:23 +00:00
|
|
|
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)
|
2018-01-15 08:35:11 +00:00
|
|
|
|
|
|
|
|
# TRIGGERS
|
|
|
|
|
triggers = {
|
2018-01-19 05:27:23 +00:00
|
|
|
'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']
|
|
|
|
|
)),
|
2018-01-15 08:35:11 +00:00
|
|
|
}
|
|
|
|
|
conditions.append(triggers.get(params['trigger']['type']))
|
|
|
|
|
|
|
|
|
|
dataframe.loc[
|
|
|
|
|
reduce(lambda x, y: x & y, conditions),
|
|
|
|
|
'buy'] = 1
|
|
|
|
|
|
|
|
|
|
return dataframe
|
|
|
|
|
|
|
|
|
|
return populate_buy_trend
|
