559 lines
24 KiB
Python
559 lines
24 KiB
Python
# import contextlib
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import copy
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import datetime
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import gc
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import logging
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import shutil
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import threading
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import time
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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 as np
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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.exceptions import OperationalException
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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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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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Base*PredictionModels inherit from this class.
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Record of contribution:
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FreqAI was developed by a group of individuals who all contributed specific skillsets to the
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project.
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Conception and software development:
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Robert Caulk @robcaulk
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Theoretical brainstorming:
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Elin Törnquist @thorntwig
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Code review, software architecture brainstorming:
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@xmatthias
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Beta testing and bug reporting:
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@bloodhunter4rc, Salah Lamkadem @ikonx, @ken11o2, @longyu, @paranoidandy, @smidelis, @smarm
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Juha Nykänen @suikula, Wagner Costa @wagnercosta
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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.get("freqai", {}).get("data_split_parameters")
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self.model_training_parameters = config.get("freqai", {}).get("model_training_parameters")
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self.feature_parameters = config.get("freqai", {}).get("feature_parameters")
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self.model = None
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self.retrain = False
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self.first = True
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self.set_full_path()
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self.follow_mode = self.freqai_info.get("follow_mode", False)
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self.dd = FreqaiDataDrawer(Path(self.full_path), self.config, self.follow_mode)
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self.lock = threading.Lock()
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self.identifier = self.freqai_info.get("identifier", "no_id_provided")
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self.scanning = False
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self.keras = self.freqai_info.get("keras", False)
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if self.keras and self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0):
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self.freqai_info["feature_parameters"]["DI_threshold"] = 0
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logger.warning("DI threshold is not configured for Keras models yet. Deactivating.")
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self.CONV_WIDTH = self.freqai_info.get("conv_width", 2)
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self.pair_it = 0
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self.total_pairs = len(self.config.get("exchange", {}).get("pair_whitelist"))
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def assert_config(self, config: Dict[str, Any]) -> None:
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if not config.get("freqai", {}):
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raise OperationalException("No freqai parameters found in configuration 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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: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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self.dd.set_pair_dict_info(metadata)
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if self.live:
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self.dk = FreqaiDataKitchen(self.config, self.dd, self.live, metadata["pair"])
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dk = self.start_live(dataframe, metadata, strategy, self.dk)
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# For backtesting, each pair enters and then gets trained for each window along the
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# sliding window defined by "train_period_days" (training window) and "live_retrain_hours"
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# (backtest window, i.e. window immediately following the training window).
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# FreqAI slides the window and sequentially builds the backtesting results before returning
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# the concatenated results for the full backtesting period back to the strategy.
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elif not self.follow_mode:
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self.dk = FreqaiDataKitchen(self.config, self.dd, self.live, metadata["pair"])
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logger.info(f"Training {len(self.dk.training_timeranges)} timeranges")
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dataframe = self.dk.use_strategy_to_populate_indicators(
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strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
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)
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dk = self.start_backtesting(dataframe, metadata, self.dk)
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dataframe = dk.remove_features_from_df(dk.return_dataframe)
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return self.return_values(dataframe, dk)
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@threaded
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def start_scanning(self, strategy: IStrategy) -> None:
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"""
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Function designed to constantly scan pairs for retraining on a separate thread (intracandle)
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to improve model youth. This function is agnostic to data preparation/collection/storage,
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it simply trains on what ever data is available in the self.dd.
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:param strategy: IStrategy = The user defined strategy class
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"""
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while 1:
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time.sleep(1)
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for pair in self.config.get("exchange", {}).get("pair_whitelist"):
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(_, trained_timestamp, _, _) = self.dd.get_pair_dict_info(pair)
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if self.dd.pair_dict[pair]["priority"] != 1:
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continue
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dk = FreqaiDataKitchen(self.config, self.dd, self.live, pair)
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dk.set_paths(pair, trained_timestamp)
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(
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retrain,
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new_trained_timerange,
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data_load_timerange,
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) = dk.check_if_new_training_required(trained_timestamp)
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dk.set_paths(pair, new_trained_timerange.stopts)
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if retrain:
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self.train_model_in_series(
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new_trained_timerange, pair, strategy, dk, data_load_timerange
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)
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def start_backtesting(
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self, dataframe: DataFrame, metadata: dict, dk: FreqaiDataKitchen
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) -> FreqaiDataKitchen:
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"""
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The main broad execution for backtesting. For backtesting, each pair enters and then gets
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trained for each window along the sliding window defined by "train_period_days"
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(training window) and "backtest_period_days" (backtest window, i.e. window immediately
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following the training window). FreqAI slides the window and sequentially builds
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the backtesting results before returning the concatenated results for the full
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backtesting period back to the strategy.
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:param dataframe: DataFrame = strategy passed dataframe
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:param metadata: Dict = pair metadata
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:param dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
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:return:
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FreqaiDataKitchen = Data management/analysis tool associated to present pair only
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"""
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self.pair_it += 1
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train_it = 0
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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(dk.training_timeranges, dk.backtesting_timeranges):
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(_, _, _, _) = self.dd.get_pair_dict_info(metadata["pair"])
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train_it += 1
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total_trains = len(dk.backtesting_timeranges)
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gc.collect()
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dk.data = {} # clean the pair specific data between training window sliding
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self.training_timerange = tr_train
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dataframe_train = dk.slice_dataframe(tr_train, dataframe)
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dataframe_backtest = dk.slice_dataframe(tr_backtest, dataframe)
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trained_timestamp = tr_train
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tr_train_startts_str = datetime.datetime.utcfromtimestamp(tr_train.startts).strftime(
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"%Y-%m-%d %H:%M:%S"
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)
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tr_train_stopts_str = datetime.datetime.utcfromtimestamp(tr_train.stopts).strftime(
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"%Y-%m-%d %H:%M:%S"
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)
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logger.info(
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f"Training {metadata['pair']}, {self.pair_it}/{self.total_pairs} pairs"
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f" from {tr_train_startts_str} to {tr_train_stopts_str}, {train_it}/{total_trains} "
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"trains"
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)
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dk.data_path = Path(
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dk.full_path
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/ str(
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"sub-train"
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+ "-"
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+ metadata["pair"].split("/")[0]
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+ "_"
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+ str(int(trained_timestamp.stopts))
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)
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)
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if not self.model_exists(
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metadata["pair"], dk, trained_timestamp=trained_timestamp.stopts
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):
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dk.find_features(dataframe_train)
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self.model = self.train(dataframe_train, metadata["pair"], dk)
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self.dd.pair_dict[metadata["pair"]]["trained_timestamp"] = trained_timestamp.stopts
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dk.set_new_model_names(metadata["pair"], trained_timestamp)
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dk.save_data(self.model, metadata["pair"])
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else:
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self.model = dk.load_data(metadata["pair"])
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self.check_if_feature_list_matches_strategy(dataframe_train, dk)
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pred_df, do_preds = self.predict(dataframe_backtest, dk)
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dk.append_predictions(pred_df, do_preds, len(dataframe_backtest))
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dk.fill_predictions(dataframe)
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return dk
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def start_live(
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self, dataframe: DataFrame, metadata: dict, strategy: IStrategy, dk: FreqaiDataKitchen
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) -> 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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:param dataframe: DataFrame = strategy passed dataframe
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:param metadata: Dict = pair metadata
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:param strategy: IStrategy = currently employed strategy
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dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
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:returns:
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dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
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"""
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# update follower
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if self.follow_mode:
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self.dd.update_follower_metadata()
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# get the model metadata associated with the current pair
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(_, trained_timestamp, _, return_null_array) = self.dd.get_pair_dict_info(metadata["pair"])
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# if the metadata doesnt exist, the follower returns null arrays to strategy
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if self.follow_mode and return_null_array:
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logger.info("Returning null array from follower to strategy")
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self.dd.return_null_values_to_strategy(dataframe, dk)
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return dk
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# append the historic data once per round
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if self.dd.historic_data:
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dk.update_historic_data(strategy)
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logger.debug(f'Updating historic data on pair {metadata["pair"]}')
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if not self.follow_mode:
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(_, new_trained_timerange, data_load_timerange) = dk.check_if_new_training_required(
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trained_timestamp
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)
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dk.set_paths(metadata["pair"], new_trained_timerange.stopts)
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# download candle history if it is not already in memory
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if not self.dd.historic_data:
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logger.info(
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"Downloading all training data for all pairs in whitelist and "
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"corr_pairlist, this may take a while if you do not have the "
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"data saved"
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)
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dk.download_all_data_for_training(data_load_timerange)
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dk.load_all_pair_histories(data_load_timerange)
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if not self.scanning:
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self.scanning = True
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self.start_scanning(strategy)
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elif self.follow_mode:
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dk.set_paths(metadata["pair"], trained_timestamp)
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logger.info(
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"FreqAI instance set to follow_mode, finding existing pair"
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f"using { self.identifier }"
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)
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# load the model and associated data into the data kitchen
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self.model = dk.load_data(coin=metadata["pair"])
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dataframe = self.dk.use_strategy_to_populate_indicators(
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strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
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)
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if not self.model:
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logger.warning(
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f"No model ready for {metadata['pair']}, returning null values to strategy."
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)
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self.dd.return_null_values_to_strategy(dataframe, dk)
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return dk
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# ensure user is feeding the correct indicators to the model
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self.check_if_feature_list_matches_strategy(dataframe, dk)
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self.build_strategy_return_arrays(dataframe, dk, metadata["pair"], trained_timestamp)
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return dk
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def build_strategy_return_arrays(
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self, dataframe: DataFrame, dk: FreqaiDataKitchen, pair: str, trained_timestamp: int
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) -> None:
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# hold the historical predictions in memory so we are sending back
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# correct array to strategy
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if pair not in self.dd.model_return_values:
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# first predictions are made on entire historical candle set coming from strategy. This
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# allows FreqUI to show full return values.
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pred_df, do_preds = self.predict(dataframe, dk)
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self.dd.set_initial_return_values(pair, dk, pred_df, do_preds)
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dk.return_dataframe = self.dd.attach_return_values_to_return_dataframe(pair, dataframe)
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return
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elif self.dk.check_if_model_expired(trained_timestamp):
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pred_df = DataFrame(np.zeros((2, len(dk.label_list))), columns=dk.label_list)
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do_preds, dk.DI_values = np.ones(2) * 2, np.zeros(2)
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logger.warning(
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f"Model expired for {pair}, returning null values to strategy. Strategy "
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"construction should take care to consider this event with "
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"prediction == 0 and do_predict == 2"
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)
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else:
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# remaining predictions are made only on the most recent candles for performance and
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# historical accuracy reasons.
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pred_df, do_preds = self.predict(dataframe.iloc[-self.CONV_WIDTH:], dk, first=False)
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self.dd.append_model_predictions(pair, pred_df, do_preds, dk, len(dataframe))
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dk.return_dataframe = self.dd.attach_return_values_to_return_dataframe(pair, dataframe)
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return
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def check_if_feature_list_matches_strategy(
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self, dataframe: DataFrame, dk: FreqaiDataKitchen
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) -> None:
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"""
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Ensure user is passing the proper feature set if they are reusing an `identifier` pointing
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to a folder holding existing models.
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:param dataframe: DataFrame = strategy provided dataframe
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:param dk: FreqaiDataKitchen = non-persistent data container/analyzer for
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current coin/bot loop
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"""
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dk.find_features(dataframe)
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if "training_features_list_raw" in dk.data:
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feature_list = dk.data["training_features_list_raw"]
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else:
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feature_list = dk.training_features_list
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if dk.training_features_list != feature_list:
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raise OperationalException(
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"Trying to access pretrained model with `identifier` "
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"but found different features furnished by current strategy."
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"Change `identifer` to train from scratch, or ensure the"
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"strategy is furnishing the same features as the pretrained"
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"model"
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)
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def data_cleaning_train(self, dk: 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(
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"principal_component_analysis", False
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):
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dk.principal_component_analysis()
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if self.freqai_info.get("feature_parameters", {}).get("use_SVM_to_remove_outliers", False):
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dk.use_SVM_to_remove_outliers(predict=False)
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if self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0):
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dk.data["avg_mean_dist"] = dk.compute_distances()
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def data_cleaning_predict(self, dk: FreqaiDataKitchen, dataframe: DataFrame) -> None:
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"""
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Base data cleaning method for predict.
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These functions each modify dk.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(
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"principal_component_analysis", False
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):
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dk.pca_transform(dataframe)
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if self.freqai_info.get("feature_parameters", {}).get("use_SVM_to_remove_outliers", False):
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dk.use_SVM_to_remove_outliers(predict=True)
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if self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0):
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dk.check_if_pred_in_training_spaces()
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def model_exists(
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self,
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pair: str,
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dk: FreqaiDataKitchen,
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trained_timestamp: int = None,
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model_filename: str = "",
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scanning: bool = False,
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) -> 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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:return:
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:boolean: whether the model file exists or not.
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"""
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coin, _ = pair.split("/")
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if not self.live:
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dk.model_filename = model_filename = "cb_" + coin.lower() + "_" + str(trained_timestamp)
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path_to_modelfile = Path(dk.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 and not scanning:
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logger.info("Found model at %s", dk.data_path / dk.model_filename)
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elif not scanning:
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logger.info("Could not find model at %s", dk.data_path / dk.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(
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self.config["user_data_dir"] / "models" / str(self.freqai_info.get("identifier"))
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)
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self.full_path.mkdir(parents=True, exist_ok=True)
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shutil.copy(
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self.config["config_files"][0],
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Path(self.full_path, Path(self.config["config_files"][0]).name),
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)
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def train_model_in_series(
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self,
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new_trained_timerange: TimeRange,
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pair: str,
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strategy: IStrategy,
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dk: FreqaiDataKitchen,
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data_load_timerange: TimeRange,
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):
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"""
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Retreive data and train model in single threaded mode (only used if model directory is empty
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upon startup for dry/live )
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:param new_trained_timerange: TimeRange = the timerange to train the model on
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|
:param metadata: dict = strategy provided metadata
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|
:param strategy: IStrategy = user defined strategy object
|
|
:param dk: FreqaiDataKitchen = non-persistent data container for current coin/loop
|
|
:param data_load_timerange: TimeRange = the amount of data to be loaded
|
|
for populate_any_indicators
|
|
(larger than new_trained_timerange so that
|
|
new_trained_timerange does not contain any NaNs)
|
|
"""
|
|
|
|
corr_dataframes, base_dataframes = dk.get_base_and_corr_dataframes(
|
|
data_load_timerange, pair
|
|
)
|
|
|
|
unfiltered_dataframe = dk.use_strategy_to_populate_indicators(
|
|
strategy, corr_dataframes, base_dataframes, pair
|
|
)
|
|
|
|
unfiltered_dataframe = dk.slice_dataframe(new_trained_timerange, unfiltered_dataframe)
|
|
|
|
# find the features indicated by strategy and store in datakitchen
|
|
dk.find_features(unfiltered_dataframe)
|
|
|
|
model = self.train(unfiltered_dataframe, pair, dk)
|
|
|
|
self.dd.pair_dict[pair]["trained_timestamp"] = new_trained_timerange.stopts
|
|
dk.set_new_model_names(pair, new_trained_timerange)
|
|
self.dd.pair_dict[pair]["first"] = False
|
|
if self.dd.pair_dict[pair]["priority"] == 1 and self.scanning:
|
|
with self.lock:
|
|
self.dd.pair_to_end_of_training_queue(pair)
|
|
dk.save_data(model, coin=pair)
|
|
|
|
if self.freqai_info.get("purge_old_models", False):
|
|
self.dd.purge_old_models()
|
|
|
|
def set_initial_historic_predictions(
|
|
self, df: DataFrame, model: Any, dk: FreqaiDataKitchen, pair: str
|
|
) -> None:
|
|
trained_predictions = model.predict(df)
|
|
pred_df = DataFrame(trained_predictions, columns=dk.label_list)
|
|
|
|
pred_df = dk.denormalize_labels_from_metadata(pred_df)
|
|
|
|
self.dd.historic_predictions[pair] = pd.DataFrame()
|
|
self.dd.historic_predictions[pair] = copy.deepcopy(pred_df)
|
|
|
|
# Following methods which are overridden by user made prediction models.
|
|
# See freqai/prediction_models/CatboostPredictionModlel.py for an example.
|
|
|
|
@abstractmethod
|
|
def train(self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen) -> Any:
|
|
"""
|
|
Filter the training data and train a model to it. Train makes heavy use of the datahandler
|
|
for storing, saving, loading, and analyzing the data.
|
|
:param unfiltered_dataframe: Full dataframe for the current training period
|
|
:param metadata: pair metadata from strategy.
|
|
:return: Trained model which can be used to inference (self.predict)
|
|
"""
|
|
|
|
@abstractmethod
|
|
def fit(self, data_dictionary: Dict[str, Any]) -> Any:
|
|
"""
|
|
Most regressors use the same function names and arguments e.g. user
|
|
can drop in LGBMRegressor in place of CatBoostRegressor and all data
|
|
management will be properly handled by Freqai.
|
|
:param data_dictionary: Dict = the dictionary constructed by DataHandler to hold
|
|
all the training and test data/labels.
|
|
"""
|
|
|
|
return
|
|
|
|
@abstractmethod
|
|
def predict(
|
|
self, dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = True
|
|
) -> Tuple[DataFrame, npt.ArrayLike]:
|
|
"""
|
|
Filter the prediction features data and predict with it.
|
|
:param unfiltered_dataframe: Full dataframe for the current backtest period.
|
|
:param dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
|
|
:param first: boolean = whether this is the first prediction or not.
|
|
:return:
|
|
:predictions: np.array of predictions
|
|
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
|
|
data (NaNs) or felt uncertain about data (i.e. SVM and/or DI index)
|
|
"""
|
|
|
|
@abstractmethod
|
|
def return_values(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
|
|
"""
|
|
User defines the dataframe to be returned to strategy here.
|
|
:param dataframe: DataFrame = the full dataframe for the current prediction (live)
|
|
or --timerange (backtesting)
|
|
:param dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
|
|
:return: dataframe: DataFrame = dataframe filled with user defined data
|
|
"""
|
|
|
|
return
|