Decouple strategy from analyse.py
This commit is contained in:
Gerald Lonlas
parent
f7e979f3ba
commit
c46d78b4b9
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@ -5,6 +5,8 @@ config.json
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*.sqlite
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.hyperopt
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logfile.txt
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hyperopt_trials.pickle
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user_data/
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# Byte-compiled / optimized / DLL files
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__pycache__/
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@ -85,5 +87,3 @@ target/
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.venv
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.idea
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.vscode
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hyperopt_trials.pickle
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@ -7,11 +7,10 @@ from enum import Enum
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from typing import Dict, List
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import arrow
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import talib.abstract as ta
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from pandas import DataFrame, to_datetime
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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from freqtrade.exchange import get_ticker_history
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from freqtrade.strategy.strategy import Strategy
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logger = logging.getLogger(__name__)
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@ -46,182 +45,8 @@ def populate_indicators(dataframe: DataFrame) -> DataFrame:
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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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# Momentum Indicator
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# ------------------------------------
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# ADX
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dataframe['adx'] = ta.ADX(dataframe)
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# Awesome oscillator
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dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
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"""
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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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"""
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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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"""
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# ROC
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dataframe['roc'] = ta.ROC(dataframe)
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"""
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# RSI
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dataframe['rsi'] = ta.RSI(dataframe)
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"""
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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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"""
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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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"""
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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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# 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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dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
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"""
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# Bollinger bands
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"""
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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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# 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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# TEMA - Triple Exponential Moving Average
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dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
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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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# 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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return dataframe
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strategy = Strategy()
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return strategy.populate_indicators(dataframe=dataframe)
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def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
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@ -230,20 +55,8 @@ def populate_buy_trend(dataframe: DataFrame) -> 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['rsi'] < 35) &
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(dataframe['fastd'] < 35) &
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(dataframe['adx'] > 30) &
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(dataframe['plus_di'] > 0.5)
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) |
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(
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(dataframe['adx'] > 65) &
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(dataframe['plus_di'] > 0.5)
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),
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'buy'] = 1
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return dataframe
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strategy = Strategy()
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return strategy.populate_buy_trend(dataframe=dataframe)
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def populate_sell_trend(dataframe: DataFrame) -> DataFrame:
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@ -252,21 +65,8 @@ def populate_sell_trend(dataframe: DataFrame) -> 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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(
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(qtpylib.crossed_above(dataframe['rsi'], 70)) |
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(qtpylib.crossed_above(dataframe['fastd'], 70))
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) &
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(dataframe['adx'] > 10) &
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(dataframe['minus_di'] > 0)
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) |
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(
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(dataframe['adx'] > 70) &
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(dataframe['minus_di'] > 0.5)
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),
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'sell'] = 1
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return dataframe
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strategy = Strategy()
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return strategy.populate_sell_trend(dataframe=dataframe)
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def analyze_ticker(ticker_history: List[Dict]) -> DataFrame:
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@ -19,6 +19,7 @@ from freqtrade.fiat_convert import CryptoToFiatConverter
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from freqtrade.misc import (State, get_state, load_config, parse_args,
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throttle, update_state)
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from freqtrade.persistence import Trade
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from freqtrade.strategy.strategy import Strategy
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logger = logging.getLogger('freqtrade')
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@ -235,14 +236,16 @@ def min_roi_reached(trade: Trade, current_rate: float, current_time: datetime) -
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Based an earlier trade and current price and ROI configuration, decides whether bot should sell
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:return True if bot should sell at current rate
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"""
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strategy = Strategy()
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current_profit = trade.calc_profit_percent(current_rate)
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if 'stoploss' in _CONF and current_profit < float(_CONF['stoploss']):
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if strategy.stoploss is not None and current_profit < float(strategy.stoploss):
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logger.debug('Stop loss hit.')
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return True
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# Check if time matches and current rate is above threshold
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time_diff = (current_time - trade.open_date).total_seconds() / 60
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for duration, threshold in sorted(_CONF['minimal_roi'].items()):
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for duration, threshold in sorted(strategy.minimal_roi.items()):
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if time_diff > float(duration) and current_profit > threshold:
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return True
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@ -378,6 +381,9 @@ def init(config: dict, db_url: Optional[str] = None) -> None:
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persistence.init(config, db_url)
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exchange.init(config)
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strategy = Strategy()
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strategy.init(config)
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# Set initial application state
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initial_state = config.get('initial_state')
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if initial_state:
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@ -445,6 +451,9 @@ def main(sysargv=sys.argv[1:]) -> None:
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# Load and validate configuration
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_CONF = load_config(args.config)
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# Add the strategy file to use
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_CONF.update({'strategy': args.strategy})
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# Initialize all modules and start main loop
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if args.dynamic_whitelist:
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logger.info('Using dynamically generated whitelist. (--dynamic-whitelist detected)')
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@ -462,6 +471,7 @@ def main(sysargv=sys.argv[1:]) -> None:
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try:
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init(_CONF)
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old_state = None
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while True:
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new_state = get_state()
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# Log state transition
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@ -124,6 +124,14 @@ def common_args_parser(description: str):
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type=str,
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metavar='PATH',
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)
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parser.add_argument(
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'-s', '--strategy',
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help='specify strategy file (default: freqtrade/strategy/default_strategy.py)',
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dest='strategy',
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default='.default_strategy',
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type=str,
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metavar='PATH',
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)
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return parser
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@ -380,7 +388,6 @@ CONF_SCHEMA = {
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'stake_amount',
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'fiat_display_currency',
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'dry_run',
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'minimal_roi',
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'bid_strategy',
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'telegram'
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]
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@ -14,6 +14,7 @@ from freqtrade.analyze import populate_buy_trend, populate_sell_trend
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from freqtrade.exchange import Bittrex
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from freqtrade.main import min_roi_reached
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from freqtrade.persistence import Trade
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from freqtrade.strategy.strategy import Strategy
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logger = logging.getLogger(__name__)
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@ -199,6 +200,11 @@ def start(args):
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logger.info('Using max_open_trades: %s ...', config['max_open_trades'])
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max_open_trades = config['max_open_trades']
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# init the strategy to use
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config.update({'strategy': args.strategy})
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strategy = Strategy()
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strategy.init(config)
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# Monkey patch config
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from freqtrade import main
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main._CONF = config
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@ -7,11 +7,10 @@ import sys
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import pickle
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import signal
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import os
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from functools import reduce
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from math import exp
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from operator import itemgetter
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from hyperopt import STATUS_FAIL, STATUS_OK, Trials, fmin, hp, space_eval, tpe
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from hyperopt import STATUS_FAIL, STATUS_OK, Trials, fmin, space_eval, tpe
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from hyperopt.mongoexp import MongoTrials
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from pandas import DataFrame
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@ -21,7 +20,7 @@ from freqtrade.exchange import Bittrex
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from freqtrade.misc import load_config
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from freqtrade.optimize.backtesting import backtest
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from freqtrade.optimize.hyperopt_conf import hyperopt_optimize_conf
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from freqtrade.vendor.qtpylib.indicators import crossed_above
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from freqtrade.strategy.strategy import Strategy
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# Remove noisy log messages
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logging.getLogger('hyperopt.mongoexp').setLevel(logging.WARNING)
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@ -57,63 +56,6 @@ from freqtrade import main # noqa
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main._CONF = OPTIMIZE_CONFIG
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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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}
|
||||
|
||||
|
||||
def save_trials(trials, trials_path=TRIALS_FILE):
|
||||
"""Save hyperopt trials to file"""
|
||||
logger.info('Saving Trials to \'{}\''.format(trials_path))
|
||||
|
|
@ -162,7 +104,9 @@ def optimizer(params):
|
|||
global _CURRENT_TRIES
|
||||
|
||||
from freqtrade.optimize import backtesting
|
||||
backtesting.populate_buy_trend = buy_strategy_generator(params)
|
||||
|
||||
strategy = Strategy()
|
||||
backtesting.populate_buy_trend = strategy.buy_strategy_generator(params)
|
||||
|
||||
results = backtest({'stake_amount': OPTIMIZE_CONFIG['stake_amount'],
|
||||
'processed': PROCESSED,
|
||||
|
|
@ -208,59 +152,8 @@ def format_results(results: DataFrame):
|
|||
results.duration.mean() * 5,
|
||||
)
|
||||
|
||||
|
||||
def buy_strategy_generator(params):
|
||||
def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
|
||||
conditions = []
|
||||
# GUARDS AND TRENDS
|
||||
if params['uptrend_long_ema']['enabled']:
|
||||
conditions.append(dataframe['ema50'] > dataframe['ema100'])
|
||||
if params['macd_below_zero']['enabled']:
|
||||
conditions.append(dataframe['macd'] < 0)
|
||||
if params['uptrend_short_ema']['enabled']:
|
||||
conditions.append(dataframe['ema5'] > dataframe['ema10'])
|
||||
if params['mfi']['enabled']:
|
||||
conditions.append(dataframe['mfi'] < params['mfi']['value'])
|
||||
if params['fastd']['enabled']:
|
||||
conditions.append(dataframe['fastd'] < params['fastd']['value'])
|
||||
if params['adx']['enabled']:
|
||||
conditions.append(dataframe['adx'] > params['adx']['value'])
|
||||
if params['rsi']['enabled']:
|
||||
conditions.append(dataframe['rsi'] < params['rsi']['value'])
|
||||
if params['over_sar']['enabled']:
|
||||
conditions.append(dataframe['close'] > dataframe['sar'])
|
||||
if params['green_candle']['enabled']:
|
||||
conditions.append(dataframe['close'] > dataframe['open'])
|
||||
if 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': (crossed_above(dataframe['fastd'], 10.0)),
|
||||
'ao_cross_zero': (crossed_above(dataframe['ao'], 0.0)),
|
||||
'ema3_cross_ema10': (crossed_above(dataframe['ema3'], dataframe['ema10'])),
|
||||
'macd_cross_signal': (crossed_above(dataframe['macd'], dataframe['macdsignal'])),
|
||||
'sar_reversal': (crossed_above(dataframe['close'], dataframe['sar'])),
|
||||
'ht_sine': (crossed_above(dataframe['htleadsine'], dataframe['htsine'])),
|
||||
'heiken_reversal_bull': (crossed_above(dataframe['ha_close'], dataframe['ha_open'])) &
|
||||
(dataframe['ha_low'] == dataframe['ha_open']),
|
||||
'di_cross': (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 start(args):
|
||||
global TOTAL_TRIES, PROCESSED, SPACE, TRIALS, _CURRENT_TRIES
|
||||
global TOTAL_TRIES, PROCESSED, TRIALS, _CURRENT_TRIES
|
||||
|
||||
TOTAL_TRIES = args.epochs
|
||||
|
||||
|
|
@ -275,6 +168,12 @@ def start(args):
|
|||
logger.info('Using config: %s ...', args.config)
|
||||
config = load_config(args.config)
|
||||
pairs = config['exchange']['pair_whitelist']
|
||||
|
||||
# init the strategy to use
|
||||
config.update({'strategy': args.strategy})
|
||||
strategy = Strategy()
|
||||
strategy.init(config)
|
||||
|
||||
timerange = misc.parse_timerange(args.timerange)
|
||||
data = optimize.load_data(args.datadir, pairs=pairs,
|
||||
ticker_interval=args.ticker_interval,
|
||||
|
|
@ -303,7 +202,7 @@ def start(args):
|
|||
try:
|
||||
best_parameters = fmin(
|
||||
fn=optimizer,
|
||||
space=SPACE,
|
||||
space=strategy.hyperopt_space(),
|
||||
algo=tpe.suggest,
|
||||
max_evals=TOTAL_TRIES,
|
||||
trials=TRIALS
|
||||
|
|
@ -319,7 +218,10 @@ def start(args):
|
|||
|
||||
# Improve best parameter logging display
|
||||
if best_parameters:
|
||||
best_parameters = space_eval(SPACE, best_parameters)
|
||||
best_parameters = space_eval(
|
||||
strategy.hyperopt_space(),
|
||||
best_parameters
|
||||
)
|
||||
|
||||
logger.info('Best parameters:\n%s', json.dumps(best_parameters, indent=4))
|
||||
logger.info('Best Result:\n%s', best_result)
|
||||
|
|
|
|||
|
|
@ -0,0 +1,262 @@
|
|||
import talib.abstract as ta
|
||||
import freqtrade.vendor.qtpylib.indicators as qtpylib
|
||||
from freqtrade.strategy.interface import IStrategy
|
||||
from pandas import DataFrame
|
||||
from hyperopt import hp
|
||||
from functools import reduce
|
||||
from typing import Dict, List
|
||||
|
||||
|
||||
class_name = 'DefaultStrategy'
|
||||
|
||||
|
||||
class DefaultStrategy(IStrategy):
|
||||
"""
|
||||
Default Strategy provided by freqtrade bot.
|
||||
You can override it with your own strategy
|
||||
"""
|
||||
|
||||
# Minimal ROI designed for the strategy
|
||||
minimal_roi = {
|
||||
"40": 0.0,
|
||||
"30": 0.01,
|
||||
"20": 0.02,
|
||||
"0": 0.04
|
||||
}
|
||||
|
||||
# Optimal stoploss designed for the strategy
|
||||
stoploss = -0.10
|
||||
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
|
||||
"""
|
||||
Adds several different TA indicators to the given DataFrame
|
||||
|
||||
Performance Note: For the best performance be frugal on the number of indicators
|
||||
you are using. Let uncomment only the indicator you are using in your strategies
|
||||
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
|
||||
"""
|
||||
|
||||
# Momentum Indicator
|
||||
# ------------------------------------
|
||||
|
||||
# ADX
|
||||
dataframe['adx'] = ta.ADX(dataframe)
|
||||
|
||||
# Awesome oscillator
|
||||
dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
|
||||
|
||||
# MACD
|
||||
macd = ta.MACD(dataframe)
|
||||
dataframe['macd'] = macd['macd']
|
||||
dataframe['macdsignal'] = macd['macdsignal']
|
||||
dataframe['macdhist'] = macd['macdhist']
|
||||
|
||||
# MFI
|
||||
dataframe['mfi'] = ta.MFI(dataframe)
|
||||
|
||||
# Minus Directional Indicator / Movement
|
||||
dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
|
||||
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
|
||||
|
||||
# Plus Directional Indicator / Movement
|
||||
dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
|
||||
dataframe['plus_di'] = ta.PLUS_DI(dataframe)
|
||||
|
||||
# RSI
|
||||
dataframe['rsi'] = ta.RSI(dataframe)
|
||||
|
||||
# Stoch fast
|
||||
stoch_fast = ta.STOCHF(dataframe)
|
||||
dataframe['fastd'] = stoch_fast['fastd']
|
||||
dataframe['fastk'] = stoch_fast['fastk']
|
||||
|
||||
# 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.
|
||||
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']
|
||||
dataframe['bb_middleband'] = bollinger['mid']
|
||||
dataframe['bb_upperband'] = bollinger['upper']
|
||||
"""
|
||||
|
||||
# EMA - Exponential Moving Average
|
||||
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 Parabol
|
||||
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)
|
||||
|
||||
# Cycle Indicator
|
||||
# ------------------------------------
|
||||
# Hilbert Transform Indicator - SineWave
|
||||
hilbert = ta.HT_SINE(dataframe)
|
||||
dataframe['htsine'] = hilbert['sine']
|
||||
dataframe['htleadsine'] = hilbert['leadsine']
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
|
||||
"""
|
||||
Based on TA indicators, populates the buy signal for the given dataframe
|
||||
:param dataframe: DataFrame
|
||||
:return: DataFrame with buy column
|
||||
"""
|
||||
dataframe.loc[
|
||||
(
|
||||
(dataframe['rsi'] < 35) &
|
||||
(dataframe['fastd'] < 35) &
|
||||
(dataframe['adx'] > 30) &
|
||||
(dataframe['plus_di'] > 0.5)
|
||||
) |
|
||||
(
|
||||
(dataframe['adx'] > 65) &
|
||||
(dataframe['plus_di'] > 0.5)
|
||||
),
|
||||
'buy'] = 1
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
|
||||
"""
|
||||
Based on TA indicators, populates the sell signal for the given dataframe
|
||||
:param dataframe: DataFrame
|
||||
:return: DataFrame with buy column
|
||||
"""
|
||||
dataframe.loc[
|
||||
(
|
||||
(
|
||||
(qtpylib.crossed_above(dataframe['rsi'], 70)) |
|
||||
(qtpylib.crossed_above(dataframe['fastd'], 70))
|
||||
) &
|
||||
(dataframe['adx'] > 10) &
|
||||
(dataframe['minus_di'] > 0)
|
||||
) |
|
||||
(
|
||||
(dataframe['adx'] > 70) &
|
||||
(dataframe['minus_di'] > 0.5)
|
||||
),
|
||||
'sell'] = 1
|
||||
return dataframe
|
||||
|
||||
def hyperopt_space(self) -> List[Dict]:
|
||||
"""
|
||||
Define your Hyperopt space for the strategy
|
||||
"""
|
||||
space = {
|
||||
'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': 'faststoch10'},
|
||||
{'type': 'ao_cross_zero'},
|
||||
{'type': 'ema5_cross_ema10'},
|
||||
{'type': 'macd_cross_signal'},
|
||||
{'type': 'sar_reversal'},
|
||||
{'type': 'stochf_cross'},
|
||||
{'type': 'ht_sine'},
|
||||
]),
|
||||
'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 params['uptrend_long_ema']['enabled']:
|
||||
conditions.append(dataframe['ema50'] > dataframe['ema100'])
|
||||
if params['uptrend_short_ema']['enabled']:
|
||||
conditions.append(dataframe['ema5'] > dataframe['ema10'])
|
||||
if params['mfi']['enabled']:
|
||||
conditions.append(dataframe['mfi'] < params['mfi']['value'])
|
||||
if params['fastd']['enabled']:
|
||||
conditions.append(dataframe['fastd'] < params['fastd']['value'])
|
||||
if params['adx']['enabled']:
|
||||
conditions.append(dataframe['adx'] > params['adx']['value'])
|
||||
if params['rsi']['enabled']:
|
||||
conditions.append(dataframe['rsi'] < params['rsi']['value'])
|
||||
if params['over_sar']['enabled']:
|
||||
conditions.append(dataframe['close'] > dataframe['sar'])
|
||||
if params['green_candle']['enabled']:
|
||||
conditions.append(dataframe['close'] > dataframe['open'])
|
||||
if params['uptrend_sma']['enabled']:
|
||||
prevsma = dataframe['sma'].shift(1)
|
||||
conditions.append(dataframe['sma'] > prevsma)
|
||||
|
||||
# TRIGGERS
|
||||
triggers = {
|
||||
'lower_bb': dataframe['tema'] <= dataframe['blower'],
|
||||
'faststoch10': (qtpylib.crossed_above(dataframe['fastd'], 10.0)),
|
||||
'ao_cross_zero': (qtpylib.crossed_above(dataframe['ao'], 0.0)),
|
||||
'ema5_cross_ema10': (
|
||||
qtpylib.crossed_above(dataframe['ema5'], dataframe['ema10'])
|
||||
),
|
||||
'macd_cross_signal': (
|
||||
qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal'])
|
||||
),
|
||||
'sar_reversal': (qtpylib.crossed_above(dataframe['close'], dataframe['sar'])),
|
||||
'stochf_cross': (qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd'])),
|
||||
'ht_sine': (qtpylib.crossed_above(dataframe['htleadsine'], dataframe['htsine'])),
|
||||
}
|
||||
conditions.append(triggers.get(params['trigger']['type']))
|
||||
|
||||
dataframe.loc[
|
||||
reduce(lambda x, y: x & y, conditions),
|
||||
'buy'] = 1
|
||||
|
||||
return dataframe
|
||||
|
||||
return populate_buy_trend
|
||||
|
|
@ -0,0 +1,56 @@
|
|||
from abc import ABC, abstractmethod
|
||||
from pandas import DataFrame
|
||||
from typing import Dict
|
||||
|
||||
|
||||
class IStrategy(ABC):
|
||||
@property
|
||||
def name(self) -> str:
|
||||
"""
|
||||
Name of the strategy.
|
||||
:return: str representation of the class name
|
||||
"""
|
||||
return self.__class__.__name__
|
||||
|
||||
"""
|
||||
Attributes you can use:
|
||||
minimal_roi -> Dict: Minimal ROI designed for the strategy
|
||||
stoploss -> float: ptimal stoploss designed for the strategy
|
||||
"""
|
||||
|
||||
@abstractmethod
|
||||
def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
|
||||
"""
|
||||
Populate indicators that will be used in the Buy and Sell strategy
|
||||
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
|
||||
:return: a Dataframe with all mandatory indicators for the strategies
|
||||
"""
|
||||
|
||||
@abstractmethod
|
||||
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
|
||||
"""
|
||||
Based on TA indicators, populates the buy signal for the given dataframe
|
||||
:param dataframe: DataFrame
|
||||
:return: DataFrame with buy column
|
||||
:return:
|
||||
"""
|
||||
|
||||
@abstractmethod
|
||||
def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
|
||||
"""
|
||||
Based on TA indicators, populates the sell signal for the given dataframe
|
||||
:param dataframe: DataFrame
|
||||
:return: DataFrame with buy column
|
||||
"""
|
||||
|
||||
@abstractmethod
|
||||
def hyperopt_space(self) -> Dict:
|
||||
"""
|
||||
Define your Hyperopt space for the strategy
|
||||
"""
|
||||
|
||||
@abstractmethod
|
||||
def buy_strategy_generator(self, params) -> None:
|
||||
"""
|
||||
Define the buy strategy parameters to be used by hyperopt
|
||||
"""
|
||||
|
|
@ -0,0 +1,165 @@
|
|||
import os
|
||||
import sys
|
||||
import logging
|
||||
import importlib
|
||||
|
||||
from pandas import DataFrame
|
||||
from typing import Dict
|
||||
from freqtrade.strategy.interface import IStrategy
|
||||
|
||||
|
||||
sys.path.insert(0, r'../../user_data/strategies')
|
||||
|
||||
|
||||
class Strategy(object):
|
||||
__instance = None
|
||||
|
||||
DEFAULT_STRATEGY = 'default_strategy'
|
||||
|
||||
def __new__(cls):
|
||||
if Strategy.__instance is None:
|
||||
Strategy.__instance = object.__new__(cls)
|
||||
return Strategy.__instance
|
||||
|
||||
def init(self, config):
|
||||
self.logger = logging.getLogger(__name__)
|
||||
|
||||
# Verify the strategy is in the configuration, otherwise fallback to the default strategy
|
||||
if 'strategy' in config:
|
||||
strategy = config['strategy']
|
||||
else:
|
||||
strategy = self.DEFAULT_STRATEGY
|
||||
|
||||
# Load the strategy
|
||||
self._load_strategy(strategy)
|
||||
|
||||
# Set attributes
|
||||
# Check if we need to override configuration
|
||||
if 'minimal_roi' in config:
|
||||
self.custom_strategy.minimal_roi = config['minimal_roi']
|
||||
self.logger.info("Override strategy \'minimal_roi\' with value in config file.")
|
||||
|
||||
if 'stoploss' in config:
|
||||
self.custom_strategy.stoploss = config['stoploss']
|
||||
self.logger.info("Override strategy \'stoploss\' with value in config file.")
|
||||
|
||||
self.minimal_roi = self.custom_strategy.minimal_roi
|
||||
self.stoploss = self.custom_strategy.stoploss
|
||||
|
||||
def _load_strategy(self, strategy_name: str) -> None:
|
||||
"""
|
||||
Search and load the custom strategy. If no strategy found, fallback on the default strategy
|
||||
Set the object into self.custom_strategy
|
||||
:param strategy_name: name of the module to import
|
||||
:return: None
|
||||
"""
|
||||
|
||||
try:
|
||||
# Start by sanitizing the file name (remove any extensions)
|
||||
strategy_name = self._sanitize_module_name(filename=strategy_name)
|
||||
|
||||
# Search where can be the strategy file
|
||||
path = self._search_strategy(filename=strategy_name)
|
||||
|
||||
# Load the strategy
|
||||
self.custom_strategy = self._load_class(path + strategy_name)
|
||||
|
||||
# Fallback to the default strategy
|
||||
except (ImportError, TypeError):
|
||||
self.custom_strategy = self._load_class('.' + self.DEFAULT_STRATEGY)
|
||||
|
||||
def _load_class(self, filename: str) -> IStrategy:
|
||||
"""
|
||||
Import a strategy as a module
|
||||
:param filename: path to the strategy (path from freqtrade/strategy/)
|
||||
:return: return the strategy class
|
||||
"""
|
||||
module = importlib.import_module(filename, __package__)
|
||||
custom_strategy = getattr(module, module.class_name)
|
||||
|
||||
self.logger.info("Load strategy class: {} ({}.py)".format(module.class_name, filename))
|
||||
return custom_strategy()
|
||||
|
||||
@staticmethod
|
||||
def _sanitize_module_name(filename: str) -> str:
|
||||
"""
|
||||
Remove any extension from filename
|
||||
:param filename: filename to sanatize
|
||||
:return: return the filename without extensions
|
||||
"""
|
||||
filename = os.path.basename(filename)
|
||||
filename = os.path.splitext(filename)[0]
|
||||
return filename
|
||||
|
||||
@staticmethod
|
||||
def _search_strategy(filename: str) -> str:
|
||||
"""
|
||||
Search for the Strategy file in different folder
|
||||
1. search into the user_data/strategies folder
|
||||
2. search into the freqtrade/strategy folder
|
||||
3. if nothing found, return None
|
||||
:param strategy_name: module name to search
|
||||
:return: module path where is the strategy
|
||||
"""
|
||||
pwd = os.path.dirname(os.path.realpath(__file__)) + '/'
|
||||
user_data = os.path.join(pwd, '..', '..', 'user_data', 'strategies', filename + '.py')
|
||||
strategy_folder = os.path.join(pwd, filename + '.py')
|
||||
|
||||
path = None
|
||||
if os.path.isfile(user_data):
|
||||
path = 'user_data.strategies.'
|
||||
elif os.path.isfile(strategy_folder):
|
||||
path = '.'
|
||||
|
||||
return path
|
||||
|
||||
def minimal_roi(self) -> Dict:
|
||||
"""
|
||||
Minimal ROI designed for the strategy
|
||||
:return: Dict: Value for the Minimal ROI
|
||||
"""
|
||||
return
|
||||
|
||||
def stoploss(self) -> float:
|
||||
"""
|
||||
Optimal stoploss designed for the strategy
|
||||
:return: float | return None to disable it
|
||||
"""
|
||||
return self.custom_strategy.stoploss
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
|
||||
"""
|
||||
Populate indicators that will be used in the Buy and Sell strategy
|
||||
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
|
||||
:return: a Dataframe with all mandatory indicators for the strategies
|
||||
"""
|
||||
return self.custom_strategy.populate_indicators(dataframe)
|
||||
|
||||
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
|
||||
"""
|
||||
Based on TA indicators, populates the buy signal for the given dataframe
|
||||
:param dataframe: DataFrame
|
||||
:return: DataFrame with buy column
|
||||
:return:
|
||||
"""
|
||||
return self.custom_strategy.populate_buy_trend(dataframe)
|
||||
|
||||
def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
|
||||
"""
|
||||
Based on TA indicators, populates the sell signal for the given dataframe
|
||||
:param dataframe: DataFrame
|
||||
:return: DataFrame with buy column
|
||||
"""
|
||||
return self.custom_strategy.populate_sell_trend(dataframe)
|
||||
|
||||
def hyperopt_space(self) -> Dict:
|
||||
"""
|
||||
Define your Hyperopt space for the strategy
|
||||
"""
|
||||
return self.custom_strategy.hyperopt_space()
|
||||
|
||||
def buy_strategy_generator(self, params) -> None:
|
||||
"""
|
||||
Define the buy strategy parameters to be used by hyperopt
|
||||
"""
|
||||
return self.custom_strategy.buy_strategy_generator(params)
|
||||
|
|
@ -0,0 +1,36 @@
|
|||
import json
|
||||
import pytest
|
||||
from pandas import DataFrame
|
||||
from freqtrade.strategy.default_strategy import DefaultStrategy, class_name
|
||||
from freqtrade.analyze import parse_ticker_dataframe
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def result():
|
||||
with open('freqtrade/tests/testdata/BTC_ETH-1.json') as data_file:
|
||||
return parse_ticker_dataframe(json.load(data_file))
|
||||
|
||||
|
||||
def test_default_strategy_class_name():
|
||||
assert class_name == DefaultStrategy.__name__
|
||||
|
||||
def test_default_strategy_structure():
|
||||
assert hasattr(DefaultStrategy, 'minimal_roi')
|
||||
assert hasattr(DefaultStrategy, 'stoploss')
|
||||
assert hasattr(DefaultStrategy, 'populate_indicators')
|
||||
assert hasattr(DefaultStrategy, 'populate_buy_trend')
|
||||
assert hasattr(DefaultStrategy, 'populate_sell_trend')
|
||||
assert hasattr(DefaultStrategy, 'hyperopt_space')
|
||||
assert hasattr(DefaultStrategy, 'buy_strategy_generator')
|
||||
|
||||
def test_default_strategy(result):
|
||||
strategy = DefaultStrategy()
|
||||
|
||||
assert type(strategy.minimal_roi) is dict
|
||||
assert type(strategy.stoploss) is float
|
||||
indicators = strategy.populate_indicators(result)
|
||||
assert type(indicators) is DataFrame
|
||||
assert type(strategy.populate_buy_trend(indicators)) is DataFrame
|
||||
assert type(strategy.populate_sell_trend(indicators)) is DataFrame
|
||||
assert type(strategy.hyperopt_space()) is dict
|
||||
assert callable(strategy.buy_strategy_generator({}))
|
||||
|
|
@ -0,0 +1,132 @@
|
|||
import json
|
||||
import logging
|
||||
import pytest
|
||||
from freqtrade.strategy.strategy import Strategy
|
||||
from freqtrade.analyze import parse_ticker_dataframe
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def result():
|
||||
with open('freqtrade/tests/testdata/BTC_ETH-1.json') as data_file:
|
||||
return parse_ticker_dataframe(json.load(data_file))
|
||||
|
||||
|
||||
def test_sanitize_module_name():
|
||||
assert Strategy._sanitize_module_name('default_strategy') == 'default_strategy'
|
||||
assert Strategy._sanitize_module_name('default_strategy.py') == 'default_strategy'
|
||||
assert Strategy._sanitize_module_name('../default_strategy.py') == 'default_strategy'
|
||||
assert Strategy._sanitize_module_name('../default_strategy') == 'default_strategy'
|
||||
assert Strategy._sanitize_module_name('.default_strategy') == '.default_strategy'
|
||||
assert Strategy._sanitize_module_name('foo-bar') == 'foo-bar'
|
||||
assert Strategy._sanitize_module_name('foo/bar') == 'bar'
|
||||
|
||||
|
||||
def test_search_strategy():
|
||||
assert Strategy._search_strategy('default_strategy') == '.'
|
||||
assert Strategy._search_strategy('super_duper') is None
|
||||
|
||||
|
||||
def test_strategy_structure():
|
||||
assert hasattr(Strategy, 'init')
|
||||
assert hasattr(Strategy, 'minimal_roi')
|
||||
assert hasattr(Strategy, 'stoploss')
|
||||
assert hasattr(Strategy, 'populate_indicators')
|
||||
assert hasattr(Strategy, 'populate_buy_trend')
|
||||
assert hasattr(Strategy, 'populate_sell_trend')
|
||||
assert hasattr(Strategy, 'hyperopt_space')
|
||||
assert hasattr(Strategy, 'buy_strategy_generator')
|
||||
|
||||
|
||||
def test_load_strategy(result):
|
||||
strategy = Strategy()
|
||||
strategy.logger = logging.getLogger(__name__)
|
||||
|
||||
assert not hasattr(Strategy, 'custom_strategy')
|
||||
strategy._load_strategy('default_strategy')
|
||||
|
||||
assert not hasattr(Strategy, 'custom_strategy')
|
||||
|
||||
assert hasattr(strategy.custom_strategy, 'populate_indicators')
|
||||
assert 'adx' in strategy.populate_indicators(result)
|
||||
|
||||
|
||||
def test_strategy(result):
|
||||
strategy = Strategy()
|
||||
strategy.init({'strategy': 'default_strategy'})
|
||||
|
||||
assert hasattr(strategy.custom_strategy, 'minimal_roi')
|
||||
assert strategy.minimal_roi['0'] == 0.04
|
||||
|
||||
assert hasattr(strategy.custom_strategy, 'stoploss')
|
||||
assert strategy.stoploss == -0.10
|
||||
|
||||
assert hasattr(strategy.custom_strategy, 'populate_indicators')
|
||||
assert 'adx' in strategy.populate_indicators(result)
|
||||
|
||||
assert hasattr(strategy.custom_strategy, 'populate_buy_trend')
|
||||
dataframe = strategy.populate_buy_trend(strategy.populate_indicators(result))
|
||||
assert 'buy' in dataframe.columns
|
||||
|
||||
assert hasattr(strategy.custom_strategy, 'populate_sell_trend')
|
||||
dataframe = strategy.populate_sell_trend(strategy.populate_indicators(result))
|
||||
assert 'sell' in dataframe.columns
|
||||
|
||||
assert hasattr(strategy.custom_strategy, 'hyperopt_space')
|
||||
assert 'adx' in strategy.hyperopt_space()
|
||||
|
||||
assert hasattr(strategy.custom_strategy, 'buy_strategy_generator')
|
||||
assert callable(strategy.buy_strategy_generator({}))
|
||||
|
||||
|
||||
def test_strategy_override_minimal_roi(caplog):
|
||||
config = {
|
||||
'strategy': 'default_strategy',
|
||||
'minimal_roi': {
|
||||
"0": 0.5
|
||||
}
|
||||
}
|
||||
strategy = Strategy()
|
||||
strategy.init(config)
|
||||
|
||||
assert hasattr(strategy.custom_strategy, 'minimal_roi')
|
||||
assert strategy.minimal_roi['0'] == 0.5
|
||||
assert ('freqtrade.strategy.strategy',
|
||||
logging.INFO,
|
||||
'Override strategy \'minimal_roi\' with value in config file.'
|
||||
) in caplog.record_tuples
|
||||
|
||||
|
||||
def test_strategy_override_stoploss(caplog):
|
||||
config = {
|
||||
'strategy': 'default_strategy',
|
||||
'stoploss': -0.5
|
||||
}
|
||||
strategy = Strategy()
|
||||
strategy.init(config)
|
||||
|
||||
assert hasattr(strategy.custom_strategy, 'stoploss')
|
||||
assert strategy.stoploss == -0.5
|
||||
assert ('freqtrade.strategy.strategy',
|
||||
logging.INFO,
|
||||
'Override strategy \'stoploss\' with value in config file.'
|
||||
) in caplog.record_tuples
|
||||
|
||||
|
||||
def test_strategy_fallback_default_strategy():
|
||||
strategy = Strategy()
|
||||
strategy.logger = logging.getLogger(__name__)
|
||||
|
||||
assert not hasattr(Strategy, 'custom_strategy')
|
||||
strategy._load_strategy('../../super_duper')
|
||||
assert not hasattr(Strategy, 'custom_strategy')
|
||||
|
||||
def test_strategy_singleton():
|
||||
strategy1 = Strategy()
|
||||
strategy1.init({'strategy': 'default_strategy'})
|
||||
|
||||
assert hasattr(strategy1.custom_strategy, 'minimal_roi')
|
||||
assert strategy1.minimal_roi['0'] == 0.04
|
||||
|
||||
strategy2 = Strategy()
|
||||
assert hasattr(strategy2.custom_strategy, 'minimal_roi')
|
||||
assert strategy2.minimal_roi['0'] == 0.04
|
||||
|
|
@ -9,6 +9,7 @@ from pandas import DataFrame
|
|||
from freqtrade.analyze import (get_signal, parse_ticker_dataframe,
|
||||
populate_buy_trend, populate_indicators,
|
||||
populate_sell_trend)
|
||||
from freqtrade.strategy.strategy import Strategy
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
|
|
@ -27,11 +28,17 @@ def test_dataframe_correct_length(result):
|
|||
|
||||
|
||||
def test_populates_buy_trend(result):
|
||||
# Load the default strategy for the unit test, because this logic is done in main.py
|
||||
Strategy().init({'strategy': 'default_strategy'})
|
||||
|
||||
dataframe = populate_buy_trend(populate_indicators(result))
|
||||
assert 'buy' in dataframe.columns
|
||||
|
||||
|
||||
def test_populates_sell_trend(result):
|
||||
# Load the default strategy for the unit test, because this logic is done in main.py
|
||||
Strategy().init({'strategy': 'default_strategy'})
|
||||
|
||||
dataframe = populate_sell_trend(populate_indicators(result))
|
||||
assert 'sell' in dataframe.columns
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,129 @@
|
|||
|
||||
# --- 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
|
||||
# --------------------------------
|
||||
|
||||
# Add your lib to import here
|
||||
import talib.abstract as ta
|
||||
|
||||
|
||||
# Update this variable if you change the class name
|
||||
class_name = 'TestStrategy'
|
||||
|
||||
|
||||
class TestStrategy(IStrategy):
|
||||
"""
|
||||
This is a test strategy to inspire you.
|
||||
You can:
|
||||
- Rename the class name (Do not forget to update class_name)
|
||||
- Add any methods you want to build your strategy
|
||||
- Add any lib you need to build your strategy
|
||||
|
||||
You must keep:
|
||||
- the lib in the section "Do not remove these libs"
|
||||
- the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
|
||||
populate_sell_trend, hyperopt_space, buy_strategy_generator
|
||||
"""
|
||||
|
||||
# Minimal ROI designed for the strategy.
|
||||
# This attribute will be overridden if the config file contains "minimal_roi"
|
||||
minimal_roi = {
|
||||
"40": 0.0,
|
||||
"30": 0.01,
|
||||
"20": 0.02,
|
||||
"0": 0.04
|
||||
}
|
||||
|
||||
# Optimal stoploss designed for the strategy
|
||||
# This attribute will be overridden if the config file contains "stoploss"
|
||||
stoploss = -0.10
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
|
||||
"""
|
||||
Adds several different TA indicators to the given DataFrame
|
||||
|
||||
Performance Note: For the best performance be frugal on the number of indicators
|
||||
you are using. Let uncomment only the indicator you are using in your strategies
|
||||
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
|
||||
"""
|
||||
|
||||
dataframe['adx'] = ta.ADX(dataframe)
|
||||
dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
|
||||
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
|
||||
"""
|
||||
Based on TA indicators, populates the buy signal for the given dataframe
|
||||
:param dataframe: DataFrame
|
||||
:return: DataFrame with buy column
|
||||
"""
|
||||
dataframe.loc[
|
||||
(
|
||||
(dataframe['adx'] > 30) &
|
||||
(dataframe['tema'] <= dataframe['blower']) &
|
||||
(dataframe['tema'] > dataframe['tema'].shift(1))
|
||||
),
|
||||
'buy'] = 1
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
|
||||
"""
|
||||
Based on TA indicators, populates the sell signal for the given dataframe
|
||||
:param dataframe: DataFrame
|
||||
:return: DataFrame with buy column
|
||||
"""
|
||||
dataframe.loc[
|
||||
(
|
||||
(dataframe['adx'] > 70) &
|
||||
(dataframe['tema'] > dataframe['blower']) &
|
||||
(dataframe['tema'] < dataframe['tema'].shift(1))
|
||||
),
|
||||
'sell'] = 1
|
||||
return dataframe
|
||||
|
||||
def hyperopt_space(self) -> List[Dict]:
|
||||
"""
|
||||
Define your Hyperopt space for the strategy
|
||||
"""
|
||||
space = {
|
||||
'adx': hp.choice('adx', [
|
||||
{'enabled': False},
|
||||
{'enabled': True, 'value': hp.quniform('adx-value', 15, 50, 1)}
|
||||
]),
|
||||
'trigger': hp.choice('trigger', [
|
||||
{'type': 'lower_bb'},
|
||||
]),
|
||||
'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 params['adx']['enabled']:
|
||||
conditions.append(dataframe['adx'] > params['adx']['value'])
|
||||
|
||||
# TRIGGERS
|
||||
triggers = {
|
||||
'lower_bb': dataframe['tema'] <= dataframe['blower'],
|
||||
}
|
||||
conditions.append(triggers.get(params['trigger']['type']))
|
||||
|
||||
dataframe.loc[
|
||||
reduce(lambda x, y: x & y, conditions),
|
||||
'buy'] = 1
|
||||
|
||||
return dataframe
|
||||
|
||||
return populate_buy_trend
|
||||
Loading…
Reference in New Issue