Merge branch 'develop' into cleaner-tests
This commit is contained in:
Janne Sinivirta
GitHub
235
freqtrade/tests/strategy/legacy_strategy.py
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235
freqtrade/tests/strategy/legacy_strategy.py
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@@ -0,0 +1,235 @@
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# --- Do not remove these libs ---
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from freqtrade.strategy.interface import IStrategy
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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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import freqtrade.vendor.qtpylib.indicators as qtpylib
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import numpy # noqa
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# This class is a sample. Feel free to customize it.
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class TestStrategyLegacy(IStrategy):
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"""
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This is a test strategy using the legacy function headers, which will be
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removed in a future update.
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Please do not use this as a template, but refer to user_data/strategy/TestStrategy.py
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for a uptodate version of this template.
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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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# Optimal ticker interval for the strategy
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ticker_interval = '5m'
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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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# Momentum Indicator
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# ------------------------------------
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# ADX
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dataframe['adx'] = ta.ADX(dataframe)
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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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# 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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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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"""
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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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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['bb_middleband']) &
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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['bb_middleband']) &
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(dataframe['tema'] < dataframe['tema'].shift(1))
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),
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'sell'] = 1
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return dataframe
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@@ -25,10 +25,11 @@ def test_default_strategy_structure():
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def test_default_strategy(result):
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strategy = DefaultStrategy({})
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metadata = {'pair': 'ETH/BTC'}
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assert type(strategy.minimal_roi) is dict
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assert type(strategy.stoploss) is float
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assert type(strategy.ticker_interval) is str
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indicators = strategy.populate_indicators(result)
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indicators = strategy.populate_indicators(result, metadata)
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assert type(indicators) is DataFrame
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assert type(strategy.populate_buy_trend(indicators)) is DataFrame
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assert type(strategy.populate_sell_trend(indicators)) is DataFrame
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assert type(strategy.populate_buy_trend(indicators, metadata)) is DataFrame
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assert type(strategy.populate_sell_trend(indicators, metadata)) is DataFrame
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@@ -1,8 +1,10 @@
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# pragma pylint: disable=missing-docstring, protected-access, C0103
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import logging
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import os
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from os import path
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import warnings
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import pytest
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from pandas import DataFrame
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from freqtrade.strategy import import_strategy
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from freqtrade.strategy.default_strategy import DefaultStrategy
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@@ -37,8 +39,8 @@ def test_import_strategy(caplog):
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def test_search_strategy():
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default_config = {}
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default_location = os.path.join(os.path.dirname(
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os.path.realpath(__file__)), '..', '..', 'strategy'
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default_location = path.join(path.dirname(
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path.realpath(__file__)), '..', '..', 'strategy'
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)
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assert isinstance(
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StrategyResolver._search_strategy(
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@@ -57,12 +59,13 @@ def test_search_strategy():
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def test_load_strategy(result):
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resolver = StrategyResolver({'strategy': 'TestStrategy'})
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assert 'adx' in resolver.strategy.populate_indicators(result)
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metadata = {'pair': 'ETH/BTC'}
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assert 'adx' in resolver.strategy.advise_indicators(result, metadata=metadata)
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def test_load_strategy_invalid_directory(result, caplog):
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resolver = StrategyResolver()
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extra_dir = os.path.join('some', 'path')
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extra_dir = path.join('some', 'path')
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resolver._load_strategy('TestStrategy', config={}, extra_dir=extra_dir)
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assert (
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@@ -70,7 +73,8 @@ def test_load_strategy_invalid_directory(result, caplog):
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logging.WARNING,
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'Path "{}" does not exist'.format(extra_dir),
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) in caplog.record_tuples
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assert 'adx' in resolver.strategy.populate_indicators(result)
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assert 'adx' in resolver.strategy.advise_indicators(result, {'pair': 'ETH/BTC'})
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def test_load_not_found_strategy():
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@@ -85,7 +89,7 @@ def test_strategy(result):
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config = {'strategy': 'DefaultStrategy'}
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resolver = StrategyResolver(config)
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metadata = {'pair': 'ETH/BTC'}
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assert resolver.strategy.minimal_roi[0] == 0.04
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assert config["minimal_roi"]['0'] == 0.04
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@@ -95,12 +99,13 @@ def test_strategy(result):
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assert resolver.strategy.ticker_interval == '5m'
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assert config['ticker_interval'] == '5m'
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assert 'adx' in resolver.strategy.populate_indicators(result)
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df_indicators = resolver.strategy.advise_indicators(result, metadata=metadata)
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assert 'adx' in df_indicators
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dataframe = resolver.strategy.populate_buy_trend(resolver.strategy.populate_indicators(result))
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dataframe = resolver.strategy.advise_buy(df_indicators, metadata=metadata)
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assert 'buy' in dataframe.columns
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dataframe = resolver.strategy.populate_sell_trend(resolver.strategy.populate_indicators(result))
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dataframe = resolver.strategy.advise_sell(df_indicators, metadata=metadata)
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assert 'sell' in dataframe.columns
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@@ -150,3 +155,59 @@ def test_strategy_override_ticker_interval(caplog):
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logging.INFO,
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'Override strategy \'ticker_interval\' with value in config file: 60.'
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) in caplog.record_tuples
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def test_deprecate_populate_indicators(result):
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default_location = path.join(path.dirname(path.realpath(__file__)))
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resolver = StrategyResolver({'strategy': 'TestStrategyLegacy',
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'strategy_path': default_location})
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with warnings.catch_warnings(record=True) as w:
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# Cause all warnings to always be triggered.
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warnings.simplefilter("always")
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indicators = resolver.strategy.advise_indicators(result, 'ETH/BTC')
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assert len(w) == 1
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assert issubclass(w[-1].category, DeprecationWarning)
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assert "deprecated - check out the Sample strategy to see the current function headers!" \
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in str(w[-1].message)
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with warnings.catch_warnings(record=True) as w:
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# Cause all warnings to always be triggered.
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warnings.simplefilter("always")
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resolver.strategy.advise_buy(indicators, 'ETH/BTC')
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assert len(w) == 1
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assert issubclass(w[-1].category, DeprecationWarning)
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assert "deprecated - check out the Sample strategy to see the current function headers!" \
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in str(w[-1].message)
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with warnings.catch_warnings(record=True) as w:
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# Cause all warnings to always be triggered.
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warnings.simplefilter("always")
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resolver.strategy.advise_sell(indicators, 'ETH_BTC')
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assert len(w) == 1
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assert issubclass(w[-1].category, DeprecationWarning)
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assert "deprecated - check out the Sample strategy to see the current function headers!" \
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in str(w[-1].message)
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def test_call_deprecated_function(result, monkeypatch):
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default_location = path.join(path.dirname(path.realpath(__file__)))
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resolver = StrategyResolver({'strategy': 'TestStrategyLegacy',
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'strategy_path': default_location})
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metadata = {'pair': 'ETH/BTC'}
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# Make sure we are using a legacy function
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assert resolver.strategy._populate_fun_len == 2
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assert resolver.strategy._buy_fun_len == 2
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assert resolver.strategy._sell_fun_len == 2
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indicator_df = resolver.strategy.advise_indicators(result, metadata=metadata)
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assert type(indicator_df) is DataFrame
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assert 'adx' in indicator_df.columns
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buydf = resolver.strategy.advise_buy(result, metadata=metadata)
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assert type(buydf) is DataFrame
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assert 'buy' in buydf.columns
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selldf = resolver.strategy.advise_sell(result, metadata=metadata)
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assert type(selldf) is DataFrame
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assert 'sell' in selldf
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