410 lines
18 KiB
Python
410 lines
18 KiB
Python
# import contextlib
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import gc
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import logging
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# import sys
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import threading
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from abc import ABC, abstractmethod
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from pathlib import Path
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from typing import Any, Dict, Tuple
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import numpy.typing as npt
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import pandas as pd
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from pandas import DataFrame
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from freqtrade.configuration import TimeRange
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from freqtrade.enums import RunMode
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from freqtrade.freqai.data_drawer import FreqaiDataDrawer
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from freqtrade.strategy.interface import IStrategy
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pd.options.mode.chained_assignment = None
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logger = logging.getLogger(__name__)
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# FIXME: suppress stdout for background training?
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# class DummyFile(object):
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# def write(self, x): pass
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# @contextlib.contextmanager
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# def nostdout():
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# save_stdout = sys.stdout
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# sys.stdout = DummyFile()
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# yield
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# sys.stdout = save_stdout
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def threaded(fn):
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def wrapper(*args, **kwargs):
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threading.Thread(target=fn, args=args, kwargs=kwargs).start()
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return wrapper
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class IFreqaiModel(ABC):
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"""
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Class containing all tools for training and prediction in the strategy.
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User models should inherit from this class as shown in
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templates/ExamplePredictionModel.py where the user overrides
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train(), predict(), fit(), and make_labels().
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Author: Robert Caulk, rob.caulk@gmail.com
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"""
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def __init__(self, config: Dict[str, Any]) -> None:
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self.config = config
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self.assert_config(self.config)
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self.freqai_info = config["freqai"]
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self.data_split_parameters = config["freqai"]["data_split_parameters"]
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self.model_training_parameters = config["freqai"]["model_training_parameters"]
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self.feature_parameters = config["freqai"]["feature_parameters"]
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# self.backtest_timerange = config["timerange"]
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self.time_last_trained = None
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self.current_time = None
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self.model = None
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self.predictions = None
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self.training_on_separate_thread = False
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self.retrain = False
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self.first = True
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# if self.freqai_info.get('live_trained_timerange'):
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# self.new_trained_timerange = TimeRange.parse_timerange(
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# self.freqai_info['live_trained_timerange'])
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# else:
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# self.new_trained_timerange = TimeRange()
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self.set_full_path()
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self.data_drawer = FreqaiDataDrawer(Path(self.full_path),
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self.config['exchange']['pair_whitelist'])
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def assert_config(self, config: Dict[str, Any]) -> None:
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assert config.get('freqai'), "No Freqai parameters found in config file."
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assert config.get('freqai', {}).get('data_split_parameters'), ("No Freqai"
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"data_split_parameters"
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"in config file.")
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assert config.get('freqai', {}).get('model_training_parameters'), ("No Freqai"
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"modeltrainingparameters"
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"found in config file.")
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assert config.get('freqai', {}).get('feature_parameters'), ("No Freqai"
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"feature_parameters found in"
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"config file.")
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def start(self, dataframe: DataFrame, metadata: dict, strategy: IStrategy) -> DataFrame:
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"""
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Entry point to the FreqaiModel from a specific pair, it will train a new model if
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necessary before making the prediction.
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The backtesting and training paradigm is a sliding training window
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with a following backtest window. Both windows slide according to the
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length of the backtest window. This function is not intended to be
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overridden by children of IFreqaiModel, but technically, it can be
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if the user wishes to make deeper changes to the sliding window
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logic.
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:params:
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:dataframe: Full dataframe coming from strategy - it contains entire
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backtesting timerange + additional historical data necessary to train
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the model.
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:metadata: pair metadata coming from strategy.
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"""
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self.live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE)
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# FreqaiDataKitchen is reinstantiated for each coin
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if self.live:
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self.data_drawer.set_pair_dict_info(metadata)
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print('Current train queue:', self.data_drawer.training_queue)
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if (not self.training_on_separate_thread and
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self.data_drawer.training_queue == 1):
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self.dh = FreqaiDataKitchen(self.config, self.data_drawer,
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self.live, metadata["pair"])
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dh = self.start_live(dataframe, metadata, strategy, self.dh)
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else:
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# we will have at max 2 separate instances of the kitchen at once.
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self.dh_fg = FreqaiDataKitchen(self.config, self.data_drawer,
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self.live, metadata["pair"])
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dh = self.start_live(dataframe, metadata, strategy, self.dh_fg)
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return (dh.full_predictions, dh.full_do_predict,
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dh.full_target_mean, dh.full_target_std)
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# Backtesting only
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self.dh = FreqaiDataKitchen(self.config, self.data_drawer, self.live, metadata["pair"])
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logger.info(f'Training {len(self.dh.training_timeranges)} timeranges')
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# Loop enforcing the sliding window training/backtesting paradigm
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# tr_train is the training time range e.g. 1 historical month
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# tr_backtest is the backtesting time range e.g. the week directly
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# following tr_train. Both of these windows slide through the
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# entire backtest
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for tr_train, tr_backtest in zip(
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self.dh.training_timeranges, self.dh.backtesting_timeranges
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):
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gc.collect()
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# self.config['timerange'] = tr_train
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self.dh.data = {} # clean the pair specific data between models
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self.training_timerange = tr_train
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dataframe_train = self.dh.slice_dataframe(tr_train, dataframe)
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dataframe_backtest = self.dh.slice_dataframe(tr_backtest, dataframe)
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logger.info("training %s for %s", metadata["pair"], tr_train)
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trained_timestamp = TimeRange.parse_timerange(tr_train)
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self.dh.data_path = Path(self.dh.full_path /
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str("sub-train" + "-" + metadata['pair'].split("/")[0] +
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str(int(trained_timestamp.stopts))))
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if not self.model_exists(metadata["pair"], self.dh,
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trained_timestamp=trained_timestamp.stopts):
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self.model = self.train(dataframe_train, metadata, self.dh)
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self.dh.save_data(self.model)
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else:
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self.model = self.dh.load_data()
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# strategy_provided_features = self.dh.find_features(dataframe_train)
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# # TOFIX doesnt work with PCA
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# if strategy_provided_features != self.dh.training_features_list:
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# logger.info("User changed input features, retraining model.")
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# self.model = self.train(dataframe_train, metadata)
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# self.dh.save_data(self.model)
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preds, do_preds = self.predict(dataframe_backtest, self.dh)
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self.dh.append_predictions(preds, do_preds, len(dataframe_backtest))
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print('predictions', len(self.dh.full_predictions),
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'do_predict', len(self.dh.full_do_predict))
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self.dh.fill_predictions(len(dataframe))
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return (self.dh.full_predictions, self.dh.full_do_predict,
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self.dh.full_target_mean, self.dh.full_target_std)
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def start_live(self, dataframe: DataFrame, metadata: dict,
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strategy: IStrategy, dh: FreqaiDataKitchen) -> FreqaiDataKitchen:
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"""
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The main broad execution for dry/live. This function will check if a retraining should be
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performed, and if so, retrain and reset the model.
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"""
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(model_filename,
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trained_timestamp,
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coin_first) = self.data_drawer.get_pair_dict_info(metadata)
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if not self.training_on_separate_thread:
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file_exists = False
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if trained_timestamp != 0:
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dh.set_paths(metadata, trained_timestamp)
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# data_drawer thinks the file eixts, verify here
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file_exists = self.model_exists(metadata['pair'],
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dh,
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trained_timestamp=trained_timestamp,
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model_filename=model_filename)
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# if not self.training_on_separate_thread:
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# this will also prevent other pairs from trying to train simultaneously.
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(self.retrain,
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new_trained_timerange) = dh.check_if_new_training_required(trained_timestamp)
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dh.set_paths(metadata, new_trained_timerange.stopts)
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# if self.training_on_separate_thread:
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# logger.info("FreqAI training a new model on background thread.")
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# self.retrain = False
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if self.retrain or not file_exists:
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if coin_first:
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self.train_model_in_series(new_trained_timerange, metadata, strategy, dh)
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else:
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self.training_on_separate_thread = True # acts like a lock
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self.retrain_model_on_separate_thread(new_trained_timerange,
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metadata, strategy, dh)
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else:
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logger.info("FreqAI training a new model on background thread.")
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self.data_drawer.pair_dict[metadata['pair']]['priority'] = 1
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self.model = dh.load_data(coin=metadata['pair'])
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# strategy_provided_features = dh.find_features(dataframe)
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# if strategy_provided_features != dh.training_features_list:
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# self.train_model_in_series(new_trained_timerange, metadata, strategy)
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preds, do_preds = self.predict(dataframe, dh)
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dh.append_predictions(preds, do_preds, len(dataframe))
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return dh
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def data_cleaning_train(self, dh: FreqaiDataKitchen) -> None:
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"""
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Base data cleaning method for train
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Any function inside this method should drop training data points from the filtered_dataframe
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based on user decided logic. See FreqaiDataKitchen::remove_outliers() for an example
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of how outlier data points are dropped from the dataframe used for training.
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"""
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if self.freqai_info.get('feature_parameters', {}).get('principal_component_analysis'):
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dh.principal_component_analysis()
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# if self.feature_parameters["determine_statistical_distributions"]:
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# dh.determine_statistical_distributions()
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# if self.feature_parameters["remove_outliers"]:
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# dh.remove_outliers(predict=False)
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if self.freqai_info.get('feature_parameters', {}).get('use_SVM_to_remove_outliers'):
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dh.use_SVM_to_remove_outliers(predict=False)
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if self.freqai_info.get('feature_parameters', {}).get('DI_threshold'):
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dh.data["avg_mean_dist"] = dh.compute_distances()
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def data_cleaning_predict(self, dh: FreqaiDataKitchen) -> None:
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"""
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Base data cleaning method for predict.
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These functions each modify dh.do_predict, which is a dataframe with equal length
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to the number of candles coming from and returning to the strategy. Inside do_predict,
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1 allows prediction and < 0 signals to the strategy that the model is not confident in
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the prediction.
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See FreqaiDataKitchen::remove_outliers() for an example
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of how the do_predict vector is modified. do_predict is ultimately passed back to strategy
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for buy signals.
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"""
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if self.freqai_info.get('feature_parameters', {}).get('principal_component_analysis'):
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dh.pca_transform()
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# if self.feature_parameters["determine_statistical_distributions"]:
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# dh.determine_statistical_distributions()
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# if self.feature_parameters["remove_outliers"]:
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# dh.remove_outliers(predict=True) # creates dropped index
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if self.freqai_info.get('feature_parameters', {}).get('use_SVM_to_remove_outliers'):
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dh.use_SVM_to_remove_outliers(predict=True)
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if self.freqai_info.get('feature_parameters', {}).get('DI_threshold'):
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dh.check_if_pred_in_training_spaces() # sets do_predict
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def model_exists(self, pair: str, dh: FreqaiDataKitchen, trained_timestamp: int = None,
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model_filename: str = '') -> bool:
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"""
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Given a pair and path, check if a model already exists
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:param pair: pair e.g. BTC/USD
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:param path: path to model
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"""
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coin, _ = pair.split("/")
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# if self.live and trained_timestamp == 0:
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# dh.model_filename = model_filename
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if not self.live:
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dh.model_filename = model_filename = "cb_" + coin.lower() + "_" + str(trained_timestamp)
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path_to_modelfile = Path(dh.data_path / str(model_filename + "_model.joblib"))
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file_exists = path_to_modelfile.is_file()
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if file_exists:
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logger.info("Found model at %s", dh.data_path / dh.model_filename)
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else:
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logger.info("Could not find model at %s", dh.data_path / dh.model_filename)
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return file_exists
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def set_full_path(self) -> None:
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self.full_path = Path(self.config['user_data_dir'] /
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"models" /
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str(self.freqai_info.get('live_full_backtestrange') +
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self.freqai_info.get('identifier')))
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@threaded
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def retrain_model_on_separate_thread(self, new_trained_timerange: TimeRange, metadata: dict,
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strategy: IStrategy, dh: FreqaiDataKitchen):
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# with nostdout():
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dh.download_new_data_for_retraining(new_trained_timerange, metadata)
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corr_dataframes, base_dataframes = dh.load_pairs_histories(new_trained_timerange,
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metadata)
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unfiltered_dataframe = dh.use_strategy_to_populate_indicators(strategy,
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corr_dataframes,
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base_dataframes,
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metadata)
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self.model = self.train(unfiltered_dataframe, metadata, dh)
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self.data_drawer.pair_dict[metadata['pair']][
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'trained_timestamp'] = new_trained_timerange.stopts
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dh.set_new_model_names(metadata, new_trained_timerange)
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# send the pair to the end of the queue so other coins can take on the background thread
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# retraining
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self.data_drawer.pair_to_end_of_training_queue(metadata['pair'])
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dh.save_data(self.model, coin=metadata['pair'])
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self.training_on_separate_thread = False
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self.retrain = False
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def train_model_in_series(self, new_trained_timerange: TimeRange, metadata: dict,
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strategy: IStrategy, dh: FreqaiDataKitchen):
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dh.download_new_data_for_retraining(new_trained_timerange, metadata)
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corr_dataframes, base_dataframes = dh.load_pairs_histories(new_trained_timerange,
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metadata)
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unfiltered_dataframe = dh.use_strategy_to_populate_indicators(strategy,
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corr_dataframes,
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base_dataframes,
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metadata)
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self.model = self.train(unfiltered_dataframe, metadata, dh)
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self.data_drawer.pair_dict[metadata['pair']][
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'trained_timestamp'] = new_trained_timerange.stopts
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dh.set_new_model_names(metadata, new_trained_timerange)
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self.data_drawer.pair_dict[metadata['pair']]['first'] = False
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dh.save_data(self.model, coin=metadata['pair'])
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self.retrain = False
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# Methods which are overridden by user made prediction models.
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# See freqai/prediction_models/CatboostPredictionModlel.py for an example.
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@abstractmethod
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def train(self, unfiltered_dataframe: DataFrame, metadata: dict, dh: FreqaiDataKitchen) -> Any:
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"""
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Filter the training data and train a model to it. Train makes heavy use of the datahandler
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for storing, saving, loading, and analyzing the data.
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:params:
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:unfiltered_dataframe: Full dataframe for the current training period
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:metadata: pair metadata from strategy.
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:returns:
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:model: Trained model which can be used to inference (self.predict)
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"""
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@abstractmethod
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def fit(self) -> Any:
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"""
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Most regressors use the same function names and arguments e.g. user
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can drop in LGBMRegressor in place of CatBoostRegressor and all data
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management will be properly handled by Freqai.
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:params:
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:data_dictionary: the dictionary constructed by DataHandler to hold
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all the training and test data/labels.
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"""
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return
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@abstractmethod
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def predict(self, dataframe: DataFrame,
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dh: FreqaiDataKitchen) -> Tuple[npt.ArrayLike, npt.ArrayLike]:
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"""
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Filter the prediction features data and predict with it.
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:param: unfiltered_dataframe: Full dataframe for the current backtest period.
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:return:
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:predictions: np.array of predictions
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:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
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data (NaNs) or felt uncertain about data (PCA and DI index)
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"""
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@abstractmethod
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def make_labels(self, dataframe: DataFrame, dh: FreqaiDataKitchen) -> DataFrame:
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"""
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User defines the labels here (target values).
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:params:
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:dataframe: the full dataframe for the present training period
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"""
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return
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