Merge e4d29e473b into f8e81dde9e
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@ -19,8 +19,8 @@ your strategy file located into [user_data/strategies/](https://github.com/gcarq
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### 1. Configure your Guards and Triggers
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There are two places you need to change in your strategy file to add a
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new buy strategy for testing:
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- Inside [populate_buy_trend()](https://github.com/gcarq/freqtrade/blob/develop/user_data/strategies/test_strategy.py#L278-L294).
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- Inside [hyperopt_space()](https://github.com/gcarq/freqtrade/blob/develop/user_data/strategies/test_strategy.py#L244-L297) known as `SPACE`.
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- Inside [populate_buy_trend()](https://github.com/gcarq/freqtrade/blob/develop/user_data/strategies/test_strategy.py#L273-L294).
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- Inside [indicator_space()](https://github.com/gcarq/freqtrade/blob/develop/user_data/strategies/test_strategy.py#L251-L67).
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There you have two different type of indicators: 1. `guards` and 2.
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`triggers`.
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@ -55,10 +55,14 @@ Your hyperopt file must contain `guards` to find the right value for
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`(dataframe['adx'] > 65)` & and `(dataframe['plus_di'] > 0.5)`. That
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means you will need to enable/disable triggers.
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In our case the `SPACE` and `populate_buy_trend` in your strategy file
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In our case the `indicator_space` and `populate_buy_trend` in your strategy file
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will look like:
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```python
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space = {
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def indicator_space(self) -> Dict[str, Any]:
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"""
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Define your Hyperopt space for searching strategy parameters
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"""
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return {
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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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@ -77,7 +81,7 @@ space = {
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{'type': 'stochf_cross'},
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{'type': 'ht_sine'},
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]),
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}
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}
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...
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@ -100,7 +104,13 @@ def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
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'stochf_cross': (crossed_above(dataframe['fastk'], dataframe['fastd'])),
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'ht_sine': (crossed_above(dataframe['htleadsine'], dataframe['htsine'])),
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}
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...
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conditions.append(triggers.get(params['trigger']['type']))
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dataframe.loc[
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reduce(lambda x, y: x & y, conditions),
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'buy'] = 1
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return dataframe
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```
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@ -116,7 +126,7 @@ The Hyperopt configuration is located in
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## Advanced notions
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### Understand the Guards and Triggers
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When you need to add the new guards and triggers to be hyperopt
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parameters, you do this by adding them into the [hyperopt_space()](https://github.com/gcarq/freqtrade/blob/develop/user_data/strategies/test_strategy.py#L244-L297).
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parameters, you do this by adding them into the [indicator_space()](https://github.com/gcarq/freqtrade/blob/develop/user_data/strategies/test_strategy.py#L251-L267).
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If it's a trigger, you add one line to the 'trigger' choice group and that's it.
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@ -7,18 +7,14 @@ import os
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import pickle
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import signal
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import sys
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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 typing import Dict, Any, Callable
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from typing import Dict, Any
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import numpy
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import talib.abstract as ta
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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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import freqtrade.vendor.qtpylib.indicators as qtpylib
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# Monkey patch config
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from freqtrade import main # noqa; noqa
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from freqtrade import exchange, misc, optimize
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@ -61,126 +57,6 @@ TRIALS = Trials()
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main._CONF = OPTIMIZE_CONFIG
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def populate_indicators(dataframe: DataFrame) -> DataFrame:
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"""
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Adds several different TA indicators to the given DataFrame
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"""
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dataframe['adx'] = ta.ADX(dataframe)
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dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
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dataframe['cci'] = ta.CCI(dataframe)
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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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dataframe['mfi'] = ta.MFI(dataframe)
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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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dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
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dataframe['plus_di'] = ta.PLUS_DI(dataframe)
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dataframe['roc'] = ta.ROC(dataframe)
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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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# 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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# 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 Parabolic
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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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# 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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def save_trials(trials, trials_path=TRIALS_FILE):
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"""Save hyperopt trials to file"""
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logger.info('Saving Trials to \'{}\''.format(trials_path))
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@ -235,188 +111,35 @@ def generate_roi_table(params) -> Dict[int, float]:
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return roi_table
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def roi_space() -> Dict[str, Any]:
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return {
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'roi_t1': hp.quniform('roi_t1', 10, 120, 20),
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'roi_t2': hp.quniform('roi_t2', 10, 60, 15),
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'roi_t3': hp.quniform('roi_t3', 10, 40, 10),
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'roi_p1': hp.quniform('roi_p1', 0.01, 0.04, 0.01),
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'roi_p2': hp.quniform('roi_p2', 0.01, 0.07, 0.01),
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'roi_p3': hp.quniform('roi_p3', 0.01, 0.20, 0.01),
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}
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def stoploss_space() -> Dict[str, Any]:
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return {
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'stoploss': hp.quniform('stoploss', -0.5, -0.02, 0.02),
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}
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def indicator_space() -> Dict[str, Any]:
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"""
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Define your Hyperopt space for searching strategy parameters
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"""
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return {
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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', 10, 25, 5)}
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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', 15, 45, 5)}
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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', 20, 50, 5)}
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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, 5)}
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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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}
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def has_space(spaces, space):
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if space in spaces or 'all' in spaces:
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return True
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return False
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def hyperopt_space(selected_spaces: str) -> Dict[str, Any]:
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def hyperopt_space(selected_spaces: str, strategy) -> Dict[str, Any]:
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spaces = {}
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if has_space(selected_spaces, 'buy'):
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spaces = {**spaces, **indicator_space()}
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spaces = {**spaces, **strategy.indicator_space()}
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if has_space(selected_spaces, 'roi'):
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spaces = {**spaces, **roi_space()}
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spaces = {**spaces, **strategy.roi_space()}
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if has_space(selected_spaces, 'stoploss'):
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spaces = {**spaces, **stoploss_space()}
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spaces = {**spaces, **strategy.stoploss_space()}
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return spaces
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def buy_strategy_generator(params: Dict[str, Any]) -> Callable:
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"""
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Define the buy strategy parameters to be used by hyperopt
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"""
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def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
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conditions = []
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# GUARDS AND TRENDS
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if 'uptrend_long_ema' in params and params['uptrend_long_ema']['enabled']:
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conditions.append(dataframe['ema50'] > dataframe['ema100'])
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if 'macd_below_zero' in params and params['macd_below_zero']['enabled']:
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conditions.append(dataframe['macd'] < 0)
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if 'uptrend_short_ema' in params and params['uptrend_short_ema']['enabled']:
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conditions.append(dataframe['ema5'] > dataframe['ema10'])
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if 'mfi' in params and params['mfi']['enabled']:
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conditions.append(dataframe['mfi'] < params['mfi']['value'])
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if 'fastd' in params and params['fastd']['enabled']:
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conditions.append(dataframe['fastd'] < params['fastd']['value'])
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if 'adx' in params and params['adx']['enabled']:
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conditions.append(dataframe['adx'] > params['adx']['value'])
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if 'rsi' in params and params['rsi']['enabled']:
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conditions.append(dataframe['rsi'] < params['rsi']['value'])
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if 'over_sar' in params and params['over_sar']['enabled']:
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conditions.append(dataframe['close'] > dataframe['sar'])
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if 'green_candle' in params and params['green_candle']['enabled']:
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conditions.append(dataframe['close'] > dataframe['open'])
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if 'uptrend_sma' in params and params['uptrend_sma']['enabled']:
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prevsma = dataframe['sma'].shift(1)
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conditions.append(dataframe['sma'] > prevsma)
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# TRIGGERS
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triggers = {
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'lower_bb': (
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dataframe['close'] < dataframe['bb_lowerband']
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),
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'lower_bb_tema': (
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dataframe['tema'] < dataframe['bb_lowerband']
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),
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'faststoch10': (qtpylib.crossed_above(
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dataframe['fastd'], 10.0
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)),
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'ao_cross_zero': (qtpylib.crossed_above(
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dataframe['ao'], 0.0
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)),
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'ema3_cross_ema10': (qtpylib.crossed_above(
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dataframe['ema3'], dataframe['ema10']
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)),
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'macd_cross_signal': (qtpylib.crossed_above(
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dataframe['macd'], dataframe['macdsignal']
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)),
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'sar_reversal': (qtpylib.crossed_above(
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dataframe['close'], dataframe['sar']
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)),
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'ht_sine': (qtpylib.crossed_above(
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dataframe['htleadsine'], dataframe['htsine']
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)),
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'heiken_reversal_bull': (
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(qtpylib.crossed_above(dataframe['ha_close'], dataframe['ha_open'])) &
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(dataframe['ha_low'] == dataframe['ha_open'])
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),
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'di_cross': (qtpylib.crossed_above(
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dataframe['plus_di'], dataframe['minus_di']
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)),
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}
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conditions.append(triggers.get(params['trigger']['type']))
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dataframe.loc[
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reduce(lambda x, y: x & y, conditions),
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'buy'] = 1
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return dataframe
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return populate_buy_trend
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def generate_optimizer(args):
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def generate_optimizer(args, strategy):
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def optimizer(params):
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global _CURRENT_TRIES
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strategy = Strategy()
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if has_space(args.spaces, 'roi'):
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strategy.minimal_roi = generate_roi_table(params)
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if has_space(args.spaces, 'buy'):
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||||
backtesting.populate_buy_trend = buy_strategy_generator(params)
|
||||
backtesting.populate_buy_trend = strategy.buy_strategy_generator(params)
|
||||
|
||||
if has_space(args.spaces, 'stoploss'):
|
||||
strategy.stoploss = params['stoploss']
|
||||
|
||||
results = backtest({'stake_amount': OPTIMIZE_CONFIG['stake_amount'],
|
||||
'processed': PROCESSED,
|
||||
'realistic': args.realistic_simulation,
|
||||
@ -497,7 +220,7 @@ def start(args):
|
||||
ticker_interval=strategy.ticker_interval,
|
||||
timerange=timerange)
|
||||
if has_space(args.spaces, 'buy'):
|
||||
optimize.populate_indicators = populate_indicators
|
||||
optimize.populate_indicators = strategy.populate_indicators
|
||||
PROCESSED = optimize.tickerdata_to_dataframe(data)
|
||||
|
||||
if args.mongodb:
|
||||
@ -521,8 +244,8 @@ def start(args):
|
||||
|
||||
try:
|
||||
best_parameters = fmin(
|
||||
fn=generate_optimizer(args),
|
||||
space=hyperopt_space(args.spaces),
|
||||
fn=generate_optimizer(args, strategy),
|
||||
space=hyperopt_space(args.spaces, strategy),
|
||||
algo=tpe.suggest,
|
||||
max_evals=TOTAL_TRIES,
|
||||
trials=TRIALS
|
||||
@ -539,7 +262,7 @@ def start(args):
|
||||
# Improve best parameter logging display
|
||||
if best_parameters:
|
||||
best_parameters = space_eval(
|
||||
hyperopt_space(args.spaces),
|
||||
hyperopt_space(args.spaces, strategy),
|
||||
best_parameters
|
||||
)
|
||||
|
||||
|
||||
@ -2,7 +2,10 @@
|
||||
|
||||
import talib.abstract as ta
|
||||
from pandas import DataFrame
|
||||
from typing import Dict, Any, Callable
|
||||
import freqtrade.vendor.qtpylib.indicators as qtpylib
|
||||
from hyperopt import hp
|
||||
from functools import reduce
|
||||
from freqtrade.strategy.interface import IStrategy
|
||||
from freqtrade.indicator_helpers import fishers_inverse
|
||||
|
||||
@ -239,3 +242,150 @@ class DefaultStrategy(IStrategy):
|
||||
),
|
||||
'sell'] = 1
|
||||
return dataframe
|
||||
|
||||
def indicator_space(self) -> Dict[str, Any]:
|
||||
"""
|
||||
Define your Hyperopt space for searching strategy parameters
|
||||
"""
|
||||
return {
|
||||
'macd_below_zero': hp.choice('macd_below_zero', [
|
||||
{'enabled': False},
|
||||
{'enabled': True}
|
||||
]),
|
||||
'mfi': hp.choice('mfi', [
|
||||
{'enabled': False},
|
||||
{'enabled': True, 'value': hp.quniform('mfi-value', 10, 25, 5)}
|
||||
]),
|
||||
'fastd': hp.choice('fastd', [
|
||||
{'enabled': False},
|
||||
{'enabled': True, 'value': hp.quniform('fastd-value', 15, 45, 5)}
|
||||
]),
|
||||
'adx': hp.choice('adx', [
|
||||
{'enabled': False},
|
||||
{'enabled': True, 'value': hp.quniform('adx-value', 20, 50, 5)}
|
||||
]),
|
||||
'rsi': hp.choice('rsi', [
|
||||
{'enabled': False},
|
||||
{'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 5)}
|
||||
]),
|
||||
'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': 'lower_bb_tema'},
|
||||
{'type': 'faststoch10'},
|
||||
{'type': 'ao_cross_zero'},
|
||||
{'type': 'ema3_cross_ema10'},
|
||||
{'type': 'macd_cross_signal'},
|
||||
{'type': 'sar_reversal'},
|
||||
{'type': 'ht_sine'},
|
||||
{'type': 'heiken_reversal_bull'},
|
||||
{'type': 'di_cross'},
|
||||
]),
|
||||
}
|
||||
|
||||
def buy_strategy_generator(self, params: Dict[str, Any]) -> Callable:
|
||||
"""
|
||||
Define the buy strategy parameters to be used by hyperopt
|
||||
"""
|
||||
def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
|
||||
conditions = []
|
||||
# GUARDS AND TRENDS
|
||||
if 'uptrend_long_ema' in params and params['uptrend_long_ema']['enabled']:
|
||||
conditions.append(dataframe['ema50'] > dataframe['ema100'])
|
||||
if 'macd_below_zero' in params and params['macd_below_zero']['enabled']:
|
||||
conditions.append(dataframe['macd'] < 0)
|
||||
if 'uptrend_short_ema' in params and params['uptrend_short_ema']['enabled']:
|
||||
conditions.append(dataframe['ema5'] > dataframe['ema10'])
|
||||
if 'mfi' in params and params['mfi']['enabled']:
|
||||
conditions.append(dataframe['mfi'] < params['mfi']['value'])
|
||||
if 'fastd' in params and params['fastd']['enabled']:
|
||||
conditions.append(dataframe['fastd'] < params['fastd']['value'])
|
||||
if 'adx' in params and params['adx']['enabled']:
|
||||
conditions.append(dataframe['adx'] > params['adx']['value'])
|
||||
if 'rsi' in params and params['rsi']['enabled']:
|
||||
conditions.append(dataframe['rsi'] < params['rsi']['value'])
|
||||
if 'over_sar' in params and params['over_sar']['enabled']:
|
||||
conditions.append(dataframe['close'] > dataframe['sar'])
|
||||
if 'green_candle' in params and params['green_candle']['enabled']:
|
||||
conditions.append(dataframe['close'] > dataframe['open'])
|
||||
if 'uptrend_sma' in params and params['uptrend_sma']['enabled']:
|
||||
prevsma = dataframe['sma'].shift(1)
|
||||
conditions.append(dataframe['sma'] > prevsma)
|
||||
|
||||
# TRIGGERS
|
||||
triggers = {
|
||||
'lower_bb': (
|
||||
dataframe['close'] < dataframe['bb_lowerband']
|
||||
),
|
||||
'lower_bb_tema': (
|
||||
dataframe['tema'] < dataframe['bb_lowerband']
|
||||
),
|
||||
'faststoch10': (qtpylib.crossed_above(
|
||||
dataframe['fastd'], 10.0
|
||||
)),
|
||||
'ao_cross_zero': (qtpylib.crossed_above(
|
||||
dataframe['ao'], 0.0
|
||||
)),
|
||||
'ema3_cross_ema10': (qtpylib.crossed_above(
|
||||
dataframe['ema3'], dataframe['ema10']
|
||||
)),
|
||||
'macd_cross_signal': (qtpylib.crossed_above(
|
||||
dataframe['macd'], dataframe['macdsignal']
|
||||
)),
|
||||
'sar_reversal': (qtpylib.crossed_above(
|
||||
dataframe['close'], dataframe['sar']
|
||||
)),
|
||||
'ht_sine': (qtpylib.crossed_above(
|
||||
dataframe['htleadsine'], dataframe['htsine']
|
||||
)),
|
||||
'heiken_reversal_bull': (
|
||||
(qtpylib.crossed_above(dataframe['ha_close'], dataframe['ha_open'])) &
|
||||
(dataframe['ha_low'] == dataframe['ha_open'])
|
||||
),
|
||||
'di_cross': (qtpylib.crossed_above(
|
||||
dataframe['plus_di'], dataframe['minus_di']
|
||||
)),
|
||||
}
|
||||
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 roi_space(self) -> Dict[str, Any]:
|
||||
return {
|
||||
'roi_t1': hp.quniform('roi_t1', 10, 120, 20),
|
||||
'roi_t2': hp.quniform('roi_t2', 10, 60, 15),
|
||||
'roi_t3': hp.quniform('roi_t3', 10, 40, 10),
|
||||
'roi_p1': hp.quniform('roi_p1', 0.01, 0.04, 0.01),
|
||||
'roi_p2': hp.quniform('roi_p2', 0.01, 0.07, 0.01),
|
||||
'roi_p3': hp.quniform('roi_p3', 0.01, 0.20, 0.01),
|
||||
}
|
||||
|
||||
def stoploss_space(self) -> Dict[str, Any]:
|
||||
return {
|
||||
'stoploss': hp.quniform('stoploss', -0.5, -0.02, 0.02),
|
||||
}
|
||||
|
||||
@ -8,6 +8,7 @@ import sys
|
||||
import logging
|
||||
import importlib
|
||||
from collections import OrderedDict
|
||||
from typing import Dict, Any, Callable
|
||||
|
||||
from pandas import DataFrame
|
||||
from freqtrade.strategy.interface import IStrategy
|
||||
@ -178,3 +179,21 @@ class Strategy(object):
|
||||
:return: DataFrame with buy column
|
||||
"""
|
||||
return self.custom_strategy.populate_sell_trend(dataframe)
|
||||
|
||||
def indicator_space(self) -> Dict[str, Any]:
|
||||
"""
|
||||
Define your Hyperopt space for searching strategy parameters
|
||||
"""
|
||||
return self.custom_strategy.indicator_space()
|
||||
|
||||
def buy_strategy_generator(self, params: Dict[str, Any]) -> Callable:
|
||||
"""
|
||||
Define the buy strategy parameters to be used by hyperopt
|
||||
"""
|
||||
return self.custom_strategy.buy_strategy_generator(params)
|
||||
|
||||
def roi_space(self) -> Dict[str, Any]:
|
||||
return self.custom_strategy.roi_space()
|
||||
|
||||
def stoploss_space(self) -> Dict[str, Any]:
|
||||
return self.custom_strategy.stoploss_space()
|
||||
|
||||
@ -2,6 +2,9 @@
|
||||
# --- Do not remove these libs ---
|
||||
from freqtrade.strategy.interface import IStrategy
|
||||
from pandas import DataFrame
|
||||
from typing import Dict, Any, Callable
|
||||
from hyperopt import STATUS_FAIL, STATUS_OK, Trials, fmin, hp, space_eval, tpe
|
||||
from functools import reduce
|
||||
# --------------------------------
|
||||
|
||||
# Add your lib to import here
|
||||
@ -244,3 +247,66 @@ class TestStrategy(IStrategy):
|
||||
),
|
||||
'sell'] = 1
|
||||
return dataframe
|
||||
|
||||
def indicator_space(self) -> Dict[str, Any]:
|
||||
"""
|
||||
Define your Hyperopt space for searching strategy parameters
|
||||
"""
|
||||
return {
|
||||
'adx': hp.choice('adx', [
|
||||
{'enabled': False},
|
||||
{'enabled': True, 'value': hp.quniform('adx-value', 50, 80, 5)}
|
||||
]),
|
||||
'uptrend_tema': hp.choice('uptrend_tema', [
|
||||
{'enabled': False},
|
||||
{'enabled': True}
|
||||
]),
|
||||
'trigger': hp.choice('trigger', [
|
||||
{'type': 'middle_bb_tema'},
|
||||
]),
|
||||
}
|
||||
|
||||
def buy_strategy_generator(self, params: Dict[str, Any]) -> Callable:
|
||||
"""
|
||||
Define the buy strategy parameters to be used by hyperopt
|
||||
"""
|
||||
def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
|
||||
conditions = []
|
||||
# GUARDS AND TRENDS
|
||||
if 'adx' in params and params['adx']['enabled']:
|
||||
conditions.append(dataframe['adx'] > params['adx']['value'])
|
||||
if 'uptrend_tema' in params and params['uptrend_tema']['enabled']:
|
||||
prevtema = dataframe['tema'].shift(1)
|
||||
conditions.append(dataframe['tema'] > prevtema)
|
||||
|
||||
# TRIGGERS
|
||||
triggers = {
|
||||
'middle_bb_tema': (
|
||||
dataframe['tema'] > dataframe['bb_middleband']
|
||||
),
|
||||
}
|
||||
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 roi_space(self) -> Dict[str, Any]:
|
||||
return {
|
||||
'roi_t1': hp.quniform('roi_t1', 10, 120, 20),
|
||||
'roi_t2': hp.quniform('roi_t2', 10, 60, 15),
|
||||
'roi_t3': hp.quniform('roi_t3', 10, 40, 10),
|
||||
'roi_p1': hp.quniform('roi_p1', 0.01, 0.04, 0.01),
|
||||
'roi_p2': hp.quniform('roi_p2', 0.01, 0.07, 0.01),
|
||||
'roi_p3': hp.quniform('roi_p3', 0.01, 0.20, 0.01),
|
||||
}
|
||||
|
||||
|
||||
def stoploss_space(self) -> Dict[str, Any]:
|
||||
return {
|
||||
'stoploss': hp.quniform('stoploss', -0.5, -0.02, 0.02),
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user