315 lines
12 KiB
Python
315 lines
12 KiB
Python
import logging
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from typing import Dict
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import numpy as np # noqa
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import pandas as pd # noqa
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import talib.abstract as ta
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from pandas import DataFrame
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from technical import qtpylib
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from freqtrade.strategy import IntParameter, IStrategy, merge_informative_pair # noqa
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logger = logging.getLogger(__name__)
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class FreqaiExampleHybridStrategy(IStrategy):
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"""
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Example of a hybrid FreqAI strat, designed to illustrate how a user may employ
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FreqAI to bolster a typical Freqtrade strategy.
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Launching this strategy would be:
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freqtrade trade --strategy FreqaiExampleHybridStrategy --strategy-path freqtrade/templates
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--freqaimodel CatboostClassifier --config config_examples/config_freqai.example.json
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or the user simply adds this to their config:
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"freqai": {
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"enabled": true,
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"purge_old_models": 2,
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"train_period_days": 15,
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"identifier": "uniqe-id",
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"feature_parameters": {
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"include_timeframes": [
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"3m",
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"15m",
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"1h"
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],
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"include_corr_pairlist": [
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"BTC/USDT",
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"ETH/USDT"
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],
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"label_period_candles": 20,
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"include_shifted_candles": 2,
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"DI_threshold": 0.9,
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"weight_factor": 0.9,
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"principal_component_analysis": false,
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"use_SVM_to_remove_outliers": true,
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"indicator_periods_candles": [10, 20]
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},
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"data_split_parameters": {
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"test_size": 0,
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"random_state": 1
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},
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"model_training_parameters": {
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"n_estimators": 800
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}
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},
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Thanks to @smarmau and @johanvulgt for developing and sharing the strategy.
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"""
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minimal_roi = {
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"60": 0.01,
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"30": 0.02,
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"0": 0.04
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}
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plot_config = {
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'main_plot': {
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'tema': {},
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},
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'subplots': {
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"MACD": {
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'macd': {'color': 'blue'},
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'macdsignal': {'color': 'orange'},
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},
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"RSI": {
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'rsi': {'color': 'red'},
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},
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"Up_or_down": {
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'&s-up_or_down': {'color': 'green'},
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}
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}
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}
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process_only_new_candles = True
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stoploss = -0.05
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use_exit_signal = True
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startup_candle_count: int = 30
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can_short = True
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# Hyperoptable parameters
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buy_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)
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sell_rsi = IntParameter(low=50, high=100, default=70, space='sell', optimize=True, load=True)
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short_rsi = IntParameter(low=51, high=100, default=70, space='sell', optimize=True, load=True)
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exit_short_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)
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def feature_engineering_expand_all(self, dataframe: DataFrame, period: int,
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metadata: Dict, **kwargs):
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"""
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*Only functional with FreqAI enabled strategies*
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This function will automatically expand the defined features on the config defined
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`indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
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`include_corr_pairs`. In other words, a single feature defined in this function
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will automatically expand to a total of
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`indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
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`include_corr_pairs` numbers of features added to the model.
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All features must be prepended with `%` to be recognized by FreqAI internals.
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More details on how these config defined parameters accelerate feature engineering
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in the documentation at:
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https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
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https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
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:param dataframe: strategy dataframe which will receive the features
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:param period: period of the indicator - usage example:
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:param metadata: metadata of current pair
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dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
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"""
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dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
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dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
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dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
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dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period)
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dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
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bollinger = qtpylib.bollinger_bands(
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qtpylib.typical_price(dataframe), window=period, stds=2.2
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)
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dataframe["bb_lowerband-period"] = bollinger["lower"]
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dataframe["bb_middleband-period"] = bollinger["mid"]
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dataframe["bb_upperband-period"] = bollinger["upper"]
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dataframe["%-bb_width-period"] = (
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dataframe["bb_upperband-period"]
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- dataframe["bb_lowerband-period"]
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) / dataframe["bb_middleband-period"]
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dataframe["%-close-bb_lower-period"] = (
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dataframe["close"] / dataframe["bb_lowerband-period"]
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)
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dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
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dataframe["%-relative_volume-period"] = (
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dataframe["volume"] / dataframe["volume"].rolling(period).mean()
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)
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return dataframe
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def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: Dict, **kwargs):
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"""
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*Only functional with FreqAI enabled strategies*
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This function will automatically expand the defined features on the config defined
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`include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
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In other words, a single feature defined in this function
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will automatically expand to a total of
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`include_timeframes` * `include_shifted_candles` * `include_corr_pairs`
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numbers of features added to the model.
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Features defined here will *not* be automatically duplicated on user defined
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`indicator_periods_candles`
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All features must be prepended with `%` to be recognized by FreqAI internals.
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More details on how these config defined parameters accelerate feature engineering
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in the documentation at:
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https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
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https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
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:param dataframe: strategy dataframe which will receive the features
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:param metadata: metadata of current pair
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dataframe["%-pct-change"] = dataframe["close"].pct_change()
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dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
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"""
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dataframe["%-pct-change"] = dataframe["close"].pct_change()
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dataframe["%-raw_volume"] = dataframe["volume"]
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dataframe["%-raw_price"] = dataframe["close"]
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return dataframe
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def feature_engineering_standard(self, dataframe: DataFrame, metadata: Dict, **kwargs):
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"""
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*Only functional with FreqAI enabled strategies*
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This optional function will be called once with the dataframe of the base timeframe.
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This is the final function to be called, which means that the dataframe entering this
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function will contain all the features and columns created by all other
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freqai_feature_engineering_* functions.
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This function is a good place to do custom exotic feature extractions (e.g. tsfresh).
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This function is a good place for any feature that should not be auto-expanded upon
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(e.g. day of the week).
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All features must be prepended with `%` to be recognized by FreqAI internals.
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More details about feature engineering available:
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https://www.freqtrade.io/en/latest/freqai-feature-engineering
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:param dataframe: strategy dataframe which will receive the features
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:param metadata: metadata of current pair
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usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
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"""
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dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
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dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
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return dataframe
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def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs):
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"""
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*Only functional with FreqAI enabled strategies*
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Required function to set the targets for the model.
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All targets must be prepended with `&` to be recognized by the FreqAI internals.
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More details about feature engineering available:
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https://www.freqtrade.io/en/latest/freqai-feature-engineering
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:param dataframe: strategy dataframe which will receive the targets
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:param metadata: metadata of current pair
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usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
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"""
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dataframe['&s-up_or_down'] = np.where(dataframe["close"].shift(-50) >
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dataframe["close"], 'up', 'down')
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return dataframe
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # noqa: C901
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# User creates their own custom strat here. Present example is a supertrend
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# based strategy.
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dataframe = self.freqai.start(dataframe, metadata, self)
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# TA indicators to combine with the Freqai targets
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# RSI
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dataframe['rsi'] = ta.RSI(dataframe)
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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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dataframe["bb_percent"] = (
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(dataframe["close"] - dataframe["bb_lowerband"]) /
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(dataframe["bb_upperband"] - dataframe["bb_lowerband"])
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)
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dataframe["bb_width"] = (
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(dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
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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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return dataframe
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def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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df.loc[
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(
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# Signal: RSI crosses above 30
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(qtpylib.crossed_above(df['rsi'], self.buy_rsi.value)) &
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(df['tema'] <= df['bb_middleband']) & # Guard: tema below BB middle
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(df['tema'] > df['tema'].shift(1)) & # Guard: tema is raising
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(df['volume'] > 0) & # Make sure Volume is not 0
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(df['do_predict'] == 1) & # Make sure Freqai is confident in the prediction
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# Only enter trade if Freqai thinks the trend is in this direction
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(df['&s-up_or_down'] == 'up')
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),
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'enter_long'] = 1
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df.loc[
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(
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# Signal: RSI crosses above 70
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(qtpylib.crossed_above(df['rsi'], self.short_rsi.value)) &
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(df['tema'] > df['bb_middleband']) & # Guard: tema above BB middle
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(df['tema'] < df['tema'].shift(1)) & # Guard: tema is falling
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(df['volume'] > 0) & # Make sure Volume is not 0
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(df['do_predict'] == 1) & # Make sure Freqai is confident in the prediction
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# Only enter trade if Freqai thinks the trend is in this direction
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(df['&s-up_or_down'] == 'down')
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),
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'enter_short'] = 1
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return df
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def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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df.loc[
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(
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# Signal: RSI crosses above 70
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(qtpylib.crossed_above(df['rsi'], self.sell_rsi.value)) &
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(df['tema'] > df['bb_middleband']) & # Guard: tema above BB middle
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(df['tema'] < df['tema'].shift(1)) & # Guard: tema is falling
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(df['volume'] > 0) # Make sure Volume is not 0
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),
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'exit_long'] = 1
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df.loc[
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(
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# Signal: RSI crosses above 30
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(qtpylib.crossed_above(df['rsi'], self.exit_short_rsi.value)) &
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# Guard: tema below BB middle
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(df['tema'] <= df['bb_middleband']) &
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(df['tema'] > df['tema'].shift(1)) & # Guard: tema is raising
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(df['volume'] > 0) # Make sure Volume is not 0
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),
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'exit_short'] = 1
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return df
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