Merge pull request #2261 from freqtrade/test_speedup
[minor] Test speedup
This commit is contained in:
hroff-1902
GitHub
commit
39f41def54
@ -4,14 +4,16 @@ import talib.abstract as ta
|
||||
from pandas import DataFrame
|
||||
|
||||
import freqtrade.vendor.qtpylib.indicators as qtpylib
|
||||
from freqtrade.indicator_helpers import fishers_inverse
|
||||
from freqtrade.strategy.interface import IStrategy
|
||||
|
||||
|
||||
class DefaultStrategy(IStrategy):
|
||||
"""
|
||||
Default Strategy provided by freqtrade bot.
|
||||
You can override it with your own strategy
|
||||
Please do not modify this strategy, it's intended for internal use only.
|
||||
Please look at the SampleStrategy in the user_data/strategy directory
|
||||
or strategy repository https://github.com/freqtrade/freqtrade-strategies
|
||||
for samples and inspiration.
|
||||
"""
|
||||
INTERFACE_VERSION = 2
|
||||
|
||||
@ -74,67 +76,25 @@ class DefaultStrategy(IStrategy):
|
||||
# ADX
|
||||
dataframe['adx'] = ta.ADX(dataframe)
|
||||
|
||||
# Awesome oscillator
|
||||
dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
|
||||
"""
|
||||
# Commodity Channel Index: values Oversold:<-100, Overbought:>100
|
||||
dataframe['cci'] = ta.CCI(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)
|
||||
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
|
||||
|
||||
"""
|
||||
# ROC
|
||||
dataframe['roc'] = ta.ROC(dataframe)
|
||||
"""
|
||||
# RSI
|
||||
dataframe['rsi'] = ta.RSI(dataframe)
|
||||
|
||||
# Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
|
||||
dataframe['fisher_rsi'] = fishers_inverse(dataframe['rsi'])
|
||||
|
||||
# Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
|
||||
dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
|
||||
|
||||
# Stoch
|
||||
stoch = ta.STOCH(dataframe)
|
||||
dataframe['slowd'] = stoch['slowd']
|
||||
dataframe['slowk'] = stoch['slowk']
|
||||
|
||||
# Stoch fast
|
||||
stoch_fast = ta.STOCHF(dataframe)
|
||||
dataframe['fastd'] = stoch_fast['fastd']
|
||||
dataframe['fastk'] = stoch_fast['fastk']
|
||||
"""
|
||||
# Stoch RSI
|
||||
stoch_rsi = ta.STOCHRSI(dataframe)
|
||||
dataframe['fastd_rsi'] = stoch_rsi['fastd']
|
||||
dataframe['fastk_rsi'] = stoch_rsi['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)
|
||||
@ -143,88 +103,11 @@ class DefaultStrategy(IStrategy):
|
||||
dataframe['bb_upperband'] = bollinger['upper']
|
||||
|
||||
# EMA - Exponential Moving Average
|
||||
dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
|
||||
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']
|
||||
|
||||
# Pattern Recognition - Bullish candlestick patterns
|
||||
# ------------------------------------
|
||||
"""
|
||||
# Hammer: values [0, 100]
|
||||
dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
|
||||
# Inverted Hammer: values [0, 100]
|
||||
dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
|
||||
# Dragonfly Doji: values [0, 100]
|
||||
dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
|
||||
# Piercing Line: values [0, 100]
|
||||
dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
|
||||
# Morningstar: values [0, 100]
|
||||
dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
|
||||
# Three White Soldiers: values [0, 100]
|
||||
dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
|
||||
"""
|
||||
|
||||
# Pattern Recognition - Bearish candlestick patterns
|
||||
# ------------------------------------
|
||||
"""
|
||||
# Hanging Man: values [0, 100]
|
||||
dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
|
||||
# Shooting Star: values [0, 100]
|
||||
dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
|
||||
# Gravestone Doji: values [0, 100]
|
||||
dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
|
||||
# Dark Cloud Cover: values [0, 100]
|
||||
dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
|
||||
# Evening Doji Star: values [0, 100]
|
||||
dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
|
||||
# Evening Star: values [0, 100]
|
||||
dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
|
||||
"""
|
||||
|
||||
# Pattern Recognition - Bullish/Bearish candlestick patterns
|
||||
# ------------------------------------
|
||||
"""
|
||||
# Three Line Strike: values [0, -100, 100]
|
||||
dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
|
||||
# Spinning Top: values [0, -100, 100]
|
||||
dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
|
||||
# Engulfing: values [0, -100, 100]
|
||||
dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
|
||||
# Harami: values [0, -100, 100]
|
||||
dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
|
||||
# Three Outside Up/Down: values [0, -100, 100]
|
||||
dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
|
||||
# Three Inside Up/Down: values [0, -100, 100]
|
||||
dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
|
||||
"""
|
||||
|
||||
# Chart type
|
||||
# ------------------------------------
|
||||
# Heikinashi stategy
|
||||
heikinashi = qtpylib.heikinashi(dataframe)
|
||||
dataframe['ha_open'] = heikinashi['open']
|
||||
dataframe['ha_close'] = heikinashi['close']
|
||||
dataframe['ha_high'] = heikinashi['high']
|
||||
dataframe['ha_low'] = heikinashi['low']
|
||||
|
||||
return dataframe
|
||||
|
||||
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
|
||||
@ -603,7 +603,7 @@ def test_processed(default_conf, mocker, testdatadir) -> None:
|
||||
cols = dataframe.columns
|
||||
# assert the dataframe got some of the indicator columns
|
||||
for col in ['close', 'high', 'low', 'open', 'date',
|
||||
'ema50', 'ao', 'macd', 'plus_dm']:
|
||||
'ema10', 'rsi', 'fastd', 'plus_di']:
|
||||
assert col in cols
|
||||
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user