Merge pull request #7260 from JohanVlugt/develop
Example FreqAI hybrid strategy
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Matthias
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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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"stratify_training_data": 0,
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"indicator_max_period_candles": 20,
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"indicator_periods_candles": [10, 20]
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},
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259
freqtrade/templates/FreqaiHybridExampleStrategy.py
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259
freqtrade/templates/FreqaiHybridExampleStrategy.py
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import logging
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import numpy as np
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import pandas as pd
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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
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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 FreqaiExampleHyridStrategy --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": true,
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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_max_period_candles": 20,
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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 = 300
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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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# FreqAI required function, leave as is or add additional informatives to existing structure.
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def informative_pairs(self):
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whitelist_pairs = self.dp.current_whitelist()
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corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
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informative_pairs = []
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for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]:
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for pair in whitelist_pairs:
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informative_pairs.append((pair, tf))
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for pair in corr_pairs:
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if pair in whitelist_pairs:
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continue # avoid duplication
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informative_pairs.append((pair, tf))
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return informative_pairs
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# FreqAI required function, user can add or remove indicators, but general structure
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# must stay the same.
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def populate_any_indicators(
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self, pair, df, tf, informative=None, set_generalized_indicators=False
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):
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"""
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User feeds these indicators to FreqAI to train a classifier to decide
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if the market will go up or down.
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:param pair: pair to be used as informative
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:param df: strategy dataframe which will receive merges from informatives
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:param tf: timeframe of the dataframe which will modify the feature names
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:param informative: the dataframe associated with the informative pair
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"""
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coin = pair.split('/')[0]
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if informative is None:
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informative = self.dp.get_pair_dataframe(pair, tf)
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# first loop is automatically duplicating indicators for time periods
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for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
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t = int(t)
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informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
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informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
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informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
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informative[f"%-{coin}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
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informative[f"%-{coin}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
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informative[f"%-{coin}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
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informative[f"%-{coin}relative_volume-period_{t}"] = (
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informative["volume"] / informative["volume"].rolling(t).mean()
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)
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# FreqAI needs the following lines in order to detect features and automatically
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# expand upon them.
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indicators = [col for col in informative if col.startswith("%")]
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# This loop duplicates and shifts all indicators to add a sense of recency to data
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for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
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if n == 0:
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continue
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informative_shift = informative[indicators].shift(n)
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informative_shift = informative_shift.add_suffix("_shift-" + str(n))
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informative = pd.concat((informative, informative_shift), axis=1)
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df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
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skip_columns = [
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(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
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]
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df = df.drop(columns=skip_columns)
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# User can set the "target" here (in present case it is the
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# "up" or "down")
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if set_generalized_indicators:
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# User "looks into the future" here to figure out if the future
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# will be "up" or "down". This same column name is available to
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# the user
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df['&s-up_or_down'] = np.where(df["close"].shift(-50) >
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df["close"], 'up', 'down')
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return df
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# flake8: noqa: C901
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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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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