312 lines
12 KiB
Python
312 lines
12 KiB
Python
import logging
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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": "unique-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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# "120": 0.0, # exit after 120 minutes at break even
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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 = False
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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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def feature_engineering_expand_all(
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self, dataframe: DataFrame, period: int, metadata: dict, **kwargs
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) -> DataFrame:
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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"] - dataframe["bb_lowerband-period"]
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) / dataframe["bb_middleband-period"]
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dataframe["%-close-bb_lower-period"] = dataframe["close"] / dataframe["bb_lowerband-period"]
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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(
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self, dataframe: DataFrame, metadata: dict, **kwargs
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) -> DataFrame:
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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(
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self, dataframe: DataFrame, metadata: dict, **kwargs
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) -> DataFrame:
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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) -> DataFrame:
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logger.info(f"Setting FreqAI targets for pair: {metadata['pair']}")
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logger.info(f"DataFrame shape: {dataframe.shape}")
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logger.info(f"Available columns: {list(dataframe.columns)}")
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logger.info(f"First few rows:\n{dataframe[['date', 'close']].head().to_string()}")
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if "close" not in dataframe.columns:
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logger.error("Required 'close' column missing in dataframe")
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raise ValueError("Required 'close' column missing in dataframe")
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if len(dataframe) < 50:
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logger.error(f"Insufficient data: {len(dataframe)} rows, need at least 50 for shift(-50)")
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raise ValueError("Insufficient data for target calculation")
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try:
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# 生成数值型标签：1 表示上涨，0 表示下跌
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dataframe["&-up_or_down"] = np.where(
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dataframe["close"].shift(-50) > dataframe["close"],
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1.0, # 数值型标签
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0.0
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)
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except Exception as e:
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logger.error(f"Failed to create &-up_or_down column: {str(e)}")
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raise
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logger.info(f"Target column head:\n{dataframe[['&-up_or_down']].head().to_string()}")
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if "&-up_or_down" not in dataframe.columns:
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logger.error("FreqAI failed to generate the &-up_or_down column")
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raise KeyError("FreqAI failed to generate the &-up_or_down column")
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logger.info("FreqAI targets set successfully")
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return dataframe
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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logger.info(f"Processing pair: {metadata['pair']}")
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logger.info(f"Input DataFrame shape: {dataframe.shape}")
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logger.info(f"Input DataFrame columns: {list(dataframe.columns)}")
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logger.info(f"Input DataFrame head:\n{dataframe[['date', 'close', 'volume']].head().to_string()}")
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# Ensure FreqAI processing
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logger.info("Calling self.freqai.start")
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try:
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dataframe = self.freqai.start(dataframe, metadata, self)
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except Exception as e:
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logger.error(f"self.freqai.start failed: {str(e)}")
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raise
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logger.info("self.freqai.start completed")
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logger.info(f"Output DataFrame shape: {dataframe.shape}")
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logger.info(f"Output DataFrame columns: {list(dataframe.columns)}")
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# Safely log columns that exist
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available_columns = [col for col in ['date', 'close', '&-up_or_down'] if col in dataframe.columns]
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logger.info(f"Output DataFrame head:\n{dataframe[available_columns].head().to_string()}")
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if "&-up_or_down" not in dataframe.columns:
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logger.error("FreqAI did not generate the required &-up_or_down column")
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raise KeyError("FreqAI did not generate the required &-up_or_down column")
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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"] = (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"] = (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe[
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"bb_middleband"
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]
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# TEMA
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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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(qtpylib.crossed_above(df["rsi"], self.buy_rsi.value))
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& (df["tema"] <= df["bb_middleband"])
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& (df["tema"] > df["tema"].shift(1))
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& (df["volume"] > 0)
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),
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"enter_long",
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] = 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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(qtpylib.crossed_above(df["rsi"], self.sell_rsi.value))
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& (df["tema"] > df["bb_middleband"])
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& (df["tema"] < df["tema"].shift(1))
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& (df["volume"] > 0)
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),
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"exit_long",
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] = 1
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return df
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