246 lines
9.3 KiB
Python
246 lines
9.3 KiB
Python
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
|
|
|
|
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
|
|
"""
|
|
|
|
# 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
|
|
|
|
# Optimal ticker interval for the strategy
|
|
ticker_interval = '5m'
|
|
|
|
def populate_indicators(self, dataframe: DataFrame, metadata: dict = {}) -> 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.
|
|
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
|
|
:param metadata: Additional information, like the currently traded pair
|
|
:return: a Dataframe with all mandatory indicators for the strategies
|
|
"""
|
|
|
|
# Momentum Indicator
|
|
# ------------------------------------
|
|
|
|
# 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)
|
|
dataframe['bb_lowerband'] = bollinger['lower']
|
|
dataframe['bb_middleband'] = bollinger['mid']
|
|
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:
|
|
"""
|
|
Based on TA indicators, populates the buy signal for the given dataframe
|
|
:param dataframe: DataFrame
|
|
:param metadata: Additional information, like the currently traded pair
|
|
: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, metadata: dict = {}) -> DataFrame:
|
|
"""
|
|
Based on TA indicators, populates the sell signal for the given dataframe
|
|
:param dataframe: DataFrame
|
|
:param metadata: Additional information, like the currently traded pair
|
|
: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
|