869 lines
37 KiB
Python
869 lines
37 KiB
Python
import logging
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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 collections import deque
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from datetime import datetime, timezone
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from pathlib import Path
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from typing import Any, Dict, List, Literal, Optional, Tuple
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import numpy as np
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import pandas as pd
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import psutil
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from numpy.typing import NDArray
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from pandas import DataFrame
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from freqtrade.configuration import TimeRange
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from freqtrade.constants import Config
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from freqtrade.data.dataprovider import DataProvider
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from freqtrade.enums import RunMode
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from freqtrade.exceptions import OperationalException
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from freqtrade.exchange import timeframe_to_seconds
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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.freqai.utils import plot_feature_importance, record_params
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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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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 @th0rntwig
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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, Johan Vlugt @Jooopieeert
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"""
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def __init__(self, config: Config) -> None:
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self.config = config
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self.assert_config(self.config)
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self.freqai_info: Dict[str, Any] = config["freqai"]
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self.data_split_parameters: Dict[str, Any] = config.get("freqai", {}).get(
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"data_split_parameters", {})
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self.model_training_parameters: Dict[str, Any] = config.get("freqai", {}).get(
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"model_training_parameters", {})
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self.identifier: str = self.freqai_info.get("identifier", "no_id_provided")
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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: bool = self.freqai_info.get("follow_mode", False)
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self.save_backtest_models: bool = self.freqai_info.get("save_backtest_models", True)
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if self.save_backtest_models:
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logger.info('Backtesting module configured to save all models.')
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self.dd = FreqaiDataDrawer(Path(self.full_path), self.config, self.follow_mode)
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# set current candle to arbitrary historical date
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self.current_candle: datetime = datetime.fromtimestamp(637887600, tz=timezone.utc)
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self.dd.current_candle = self.current_candle
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self.scanning = False
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self.ft_params = self.freqai_info["feature_parameters"]
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self.corr_pairlist: List[str] = self.ft_params.get("include_corr_pairlist", [])
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self.keras: bool = self.freqai_info.get("keras", False)
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if self.keras and self.ft_params.get("DI_threshold", 0):
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self.ft_params["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', 1)
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if self.ft_params.get("inlier_metric_window", 0):
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self.CONV_WIDTH = self.ft_params.get("inlier_metric_window", 0) * 2
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self.pair_it = 0
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self.pair_it_train = 0
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self.total_pairs = len(self.config.get("exchange", {}).get("pair_whitelist"))
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self.train_queue = self._set_train_queue()
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self.inference_time: float = 0
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self.train_time: float = 0
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self.begin_time: float = 0
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self.begin_time_train: float = 0
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self.base_tf_seconds = timeframe_to_seconds(self.config['timeframe'])
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self.continual_learning = self.freqai_info.get('continual_learning', False)
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self.plot_features = self.ft_params.get("plot_feature_importances", 0)
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self.corr_dataframes: Dict[str, DataFrame] = {}
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# get_corr_dataframes is controlling the caching of corr_dataframes
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# for improved performance. Careful with this boolean.
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self.get_corr_dataframes: bool = True
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self._threads: List[threading.Thread] = []
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self._stop_event = threading.Event()
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self.data_provider: Optional[DataProvider] = None
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self.max_system_threads = max(int(psutil.cpu_count() * 2 - 2), 1)
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record_params(config, self.full_path)
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def __getstate__(self):
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"""
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Return an empty state to be pickled in hyperopt
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"""
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return ({})
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def assert_config(self, config: Config) -> 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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:param 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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:param metadata: pair metadata coming from strategy.
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:param strategy: Strategy to train on
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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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self.data_provider = strategy.dp
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if self.live:
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self.inference_timer('start')
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self.dk = FreqaiDataKitchen(self.config, 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.live, metadata["pair"])
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if self.dk.backtest_live_models:
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logger.info(
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f"Backtesting {len(self.dk.backtesting_timeranges)} timeranges (live models)")
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else:
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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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self.clean_up()
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if self.live:
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self.inference_timer('stop', metadata["pair"])
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return dataframe
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def clean_up(self):
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"""
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Objects that should be handled by GC already between coins, but
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are explicitly shown here to help demonstrate the non-persistence of these
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objects.
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"""
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self.model = None
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self.dk = None
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self.data_provider = None
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def _on_stop(self):
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"""
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Callback for Subclasses to override to include logic for shutting down resources
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when SIGINT is sent.
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"""
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return
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def shutdown(self):
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"""
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Cleans up threads on Shutdown, set stop event. Join threads to wait
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for current training iteration.
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"""
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logger.info("Stopping FreqAI")
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self._stop_event.set()
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self._on_stop()
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logger.info("Waiting on Training iteration")
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for _thread in self._threads:
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_thread.join()
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def start_scanning(self, *args, **kwargs) -> None:
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"""
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Start `self._start_scanning` in a separate thread
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"""
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_thread = threading.Thread(target=self._start_scanning, args=args, kwargs=kwargs)
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self._threads.append(_thread)
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_thread.start()
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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 not self._stop_event.is_set():
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time.sleep(1)
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pair = self.train_queue[0]
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# ensure pair is avaialble in dp
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if pair not in strategy.dp.current_whitelist():
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self.train_queue.popleft()
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logger.warning(f'{pair} not in current whitelist, removing from train queue.')
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continue
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(_, trained_timestamp, _) = self.dd.get_pair_dict_info(pair)
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dk = FreqaiDataKitchen(self.config, self.live, pair)
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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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if retrain:
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self.train_timer('start')
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dk.set_paths(pair, new_trained_timerange.stopts)
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try:
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self.extract_data_and_train_model(
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new_trained_timerange, pair, strategy, dk, data_load_timerange
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)
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except Exception as msg:
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logger.warning(f"Training {pair} raised exception {msg.__class__.__name__}. "
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f"Message: {msg}, skipping.")
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self.train_timer('stop', pair)
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# only rotate the queue after the first has been trained.
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self.train_queue.rotate(-1)
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self.dd.save_historic_predictions_to_disk()
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if self.freqai_info.get('write_metrics_to_disk', False):
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self.dd.save_metric_tracker_to_disk()
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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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pair = metadata["pair"]
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(_, _, _) = self.dd.get_pair_dict_info(pair)
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train_it += 1
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total_trains = len(dk.backtesting_timeranges)
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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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if not self.ensure_data_exists(dataframe_backtest, tr_backtest, pair):
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continue
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self.log_backtesting_progress(tr_train, pair, train_it, total_trains)
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timestamp_model_id = int(tr_train.stopts)
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if dk.backtest_live_models:
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timestamp_model_id = int(tr_backtest.startts)
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dk.set_paths(pair, timestamp_model_id)
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dk.set_new_model_names(pair, timestamp_model_id)
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if dk.check_if_backtest_prediction_is_valid(len(dataframe_backtest)):
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self.dd.load_metadata(dk)
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dk.find_features(dataframe_train)
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self.check_if_feature_list_matches_strategy(dk)
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append_df = dk.get_backtesting_prediction()
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dk.append_predictions(append_df)
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else:
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if not self.model_exists(dk):
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dk.find_features(dataframe_train)
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dk.find_labels(dataframe_train)
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self.model = self.train(dataframe_train, pair, dk)
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self.dd.pair_dict[pair]["trained_timestamp"] = int(
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tr_train.stopts)
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if self.plot_features:
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plot_feature_importance(self.model, pair, dk, self.plot_features)
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if self.save_backtest_models:
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logger.info('Saving backtest model to disk.')
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self.dd.save_data(self.model, pair, dk)
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else:
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logger.info('Saving metadata to disk.')
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self.dd.save_metadata(dk)
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else:
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self.model = self.dd.load_data(pair, dk)
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pred_df, do_preds = self.predict(dataframe_backtest, dk)
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append_df = dk.get_predictions_to_append(pred_df, do_preds)
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dk.append_predictions(append_df)
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dk.save_backtesting_prediction(append_df)
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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 doesn't 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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self.dd.update_historic_data(strategy, dk)
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logger.debug(f'Updating historic data on pair {metadata["pair"]}')
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self.track_current_candle()
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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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# load candle history into memory if it is not yet.
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if not self.dd.historic_data:
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self.dd.load_all_pair_histories(data_load_timerange, dk)
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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 = self.dd.load_data(metadata["pair"], dk)
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dataframe = dk.use_strategy_to_populate_indicators(
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strategy, prediction_dataframe=dataframe, pair=metadata["pair"],
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do_corr_pairs=self.get_corr_dataframes
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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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if self.corr_pairlist:
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dataframe = self.cache_corr_pairlist_dfs(dataframe, dk)
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dk.find_labels(dataframe)
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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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if pair not in self.dd.historic_predictions:
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self.set_initial_historic_predictions(pred_df, dk, pair, dataframe)
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self.dd.set_initial_return_values(pair, pred_df)
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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 = np.ones(2, dtype=np.int_) * 2
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dk.DI_values = 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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if self.freqai_info.get('fit_live_predictions_candles', 0) and self.live:
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self.fit_live_predictions(dk, pair)
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self.dd.append_model_predictions(pair, pred_df, do_preds, dk, 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, 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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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:
|
|
feature_list = dk.data['training_features_list']
|
|
|
|
if dk.training_features_list != feature_list:
|
|
raise OperationalException(
|
|
"Trying to access pretrained model with `identifier` "
|
|
"but found different features furnished by current strategy."
|
|
"Change `identifier` to train from scratch, or ensure the"
|
|
"strategy is furnishing the same features as the pretrained"
|
|
"model. In case of --strategy-list, please be aware that FreqAI "
|
|
"requires all strategies to maintain identical "
|
|
"populate_any_indicator() functions"
|
|
)
|
|
|
|
def data_cleaning_train(self, dk: FreqaiDataKitchen) -> None:
|
|
"""
|
|
Base data cleaning method for train.
|
|
Functions here improve/modify the input data by identifying outliers,
|
|
computing additional metrics, adding noise, reducing dimensionality etc.
|
|
"""
|
|
|
|
ft_params = self.freqai_info["feature_parameters"]
|
|
|
|
if ft_params.get('inlier_metric_window', 0):
|
|
dk.compute_inlier_metric(set_='train')
|
|
if self.freqai_info["data_split_parameters"]["test_size"] > 0:
|
|
dk.compute_inlier_metric(set_='test')
|
|
|
|
if ft_params.get(
|
|
"principal_component_analysis", False
|
|
):
|
|
dk.principal_component_analysis()
|
|
|
|
if ft_params.get("use_SVM_to_remove_outliers", False):
|
|
dk.use_SVM_to_remove_outliers(predict=False)
|
|
|
|
if ft_params.get("DI_threshold", 0):
|
|
dk.data["avg_mean_dist"] = dk.compute_distances()
|
|
|
|
if ft_params.get("use_DBSCAN_to_remove_outliers", False):
|
|
if dk.pair in self.dd.old_DBSCAN_eps:
|
|
eps = self.dd.old_DBSCAN_eps[dk.pair]
|
|
else:
|
|
eps = None
|
|
dk.use_DBSCAN_to_remove_outliers(predict=False, eps=eps)
|
|
self.dd.old_DBSCAN_eps[dk.pair] = dk.data['DBSCAN_eps']
|
|
|
|
if self.freqai_info["feature_parameters"].get('noise_standard_deviation', 0):
|
|
dk.add_noise_to_training_features()
|
|
|
|
def data_cleaning_predict(self, dk: FreqaiDataKitchen) -> None:
|
|
"""
|
|
Base data cleaning method for predict.
|
|
Functions here are complementary to the functions of data_cleaning_train.
|
|
"""
|
|
ft_params = self.freqai_info["feature_parameters"]
|
|
|
|
# ensure user is feeding the correct indicators to the model
|
|
self.check_if_feature_list_matches_strategy(dk)
|
|
|
|
if ft_params.get('inlier_metric_window', 0):
|
|
dk.compute_inlier_metric(set_='predict')
|
|
|
|
if ft_params.get(
|
|
"principal_component_analysis", False
|
|
):
|
|
dk.pca_transform(dk.data_dictionary['prediction_features'])
|
|
|
|
if ft_params.get("use_SVM_to_remove_outliers", False):
|
|
dk.use_SVM_to_remove_outliers(predict=True)
|
|
|
|
if ft_params.get("DI_threshold", 0):
|
|
dk.check_if_pred_in_training_spaces()
|
|
|
|
if ft_params.get("use_DBSCAN_to_remove_outliers", False):
|
|
dk.use_DBSCAN_to_remove_outliers(predict=True)
|
|
|
|
def model_exists(self, dk: FreqaiDataKitchen) -> bool:
|
|
"""
|
|
Given a pair and path, check if a model already exists
|
|
:param pair: pair e.g. BTC/USD
|
|
:param path: path to model
|
|
:return:
|
|
:boolean: whether the model file exists or not.
|
|
"""
|
|
path_to_modelfile = Path(dk.data_path / f"{dk.model_filename}_model.joblib")
|
|
file_exists = path_to_modelfile.is_file()
|
|
if file_exists:
|
|
logger.info("Found model at %s", dk.data_path / dk.model_filename)
|
|
else:
|
|
logger.info("Could not find model at %s", dk.data_path / dk.model_filename)
|
|
return file_exists
|
|
|
|
def set_full_path(self) -> None:
|
|
"""
|
|
Creates and sets the full path for the identifier
|
|
"""
|
|
self.full_path = Path(
|
|
self.config["user_data_dir"] / "models" / f"{self.identifier}"
|
|
)
|
|
self.full_path.mkdir(parents=True, exist_ok=True)
|
|
|
|
def extract_data_and_train_model(
|
|
self,
|
|
new_trained_timerange: TimeRange,
|
|
pair: str,
|
|
strategy: IStrategy,
|
|
dk: FreqaiDataKitchen,
|
|
data_load_timerange: TimeRange,
|
|
):
|
|
"""
|
|
Retrieve data and train model.
|
|
:param new_trained_timerange: TimeRange = the timerange to train the model on
|
|
:param metadata: dict = strategy provided metadata
|
|
: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 = self.dd.get_base_and_corr_dataframes(
|
|
data_load_timerange, pair, dk
|
|
)
|
|
|
|
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)
|
|
dk.find_labels(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.stopts)
|
|
self.dd.save_data(model, pair, dk)
|
|
|
|
if self.plot_features:
|
|
plot_feature_importance(model, pair, dk, self.plot_features)
|
|
|
|
if self.freqai_info.get("purge_old_models", False):
|
|
self.dd.purge_old_models()
|
|
|
|
def set_initial_historic_predictions(
|
|
self, pred_df: DataFrame, dk: FreqaiDataKitchen, pair: str, strat_df: DataFrame
|
|
) -> None:
|
|
"""
|
|
This function is called only if the datadrawer failed to load an
|
|
existing set of historic predictions. In this case, it builds
|
|
the structure and sets fake predictions off the first training
|
|
data. After that, FreqAI will append new real predictions to the
|
|
set of historic predictions.
|
|
|
|
These values are used to generate live statistics which can be used
|
|
in the strategy for adaptive values. E.g. &*_mean/std are quantities
|
|
that can computed based on live predictions from the set of historical
|
|
predictions. Those values can be used in the user strategy to better
|
|
assess prediction rarity, and thus wait for probabilistically favorable
|
|
entries relative to the live historical predictions.
|
|
|
|
If the user reuses an identifier on a subsequent instance,
|
|
this function will not be called. In that case, "real" predictions
|
|
will be appended to the loaded set of historic predictions.
|
|
:param df: DataFrame = the dataframe containing the training feature data
|
|
:param model: Any = A model which was `fit` using a common library such as
|
|
catboost or lightgbm
|
|
:param dk: FreqaiDataKitchen = object containing methods for data analysis
|
|
:param pair: str = current pair
|
|
"""
|
|
|
|
self.dd.historic_predictions[pair] = pred_df
|
|
hist_preds_df = self.dd.historic_predictions[pair]
|
|
|
|
for label in hist_preds_df.columns:
|
|
if hist_preds_df[label].dtype == object:
|
|
continue
|
|
hist_preds_df[f'{label}_mean'] = 0
|
|
hist_preds_df[f'{label}_std'] = 0
|
|
|
|
hist_preds_df['do_predict'] = 0
|
|
|
|
if self.freqai_info['feature_parameters'].get('DI_threshold', 0) > 0:
|
|
hist_preds_df['DI_values'] = 0
|
|
|
|
for return_str in dk.data['extra_returns_per_train']:
|
|
hist_preds_df[return_str] = 0
|
|
|
|
hist_preds_df['close_price'] = strat_df['close']
|
|
hist_preds_df['date_pred'] = strat_df['date']
|
|
|
|
# # for keras type models, the conv_window needs to be prepended so
|
|
# # viewing is correct in frequi
|
|
if self.freqai_info.get('keras', False) or self.ft_params.get('inlier_metric_window', 0):
|
|
n_lost_points = self.freqai_info.get('conv_width', 2)
|
|
zeros_df = DataFrame(np.zeros((n_lost_points, len(hist_preds_df.columns))),
|
|
columns=hist_preds_df.columns)
|
|
self.dd.historic_predictions[pair] = pd.concat(
|
|
[zeros_df, hist_preds_df], axis=0, ignore_index=True)
|
|
|
|
def fit_live_predictions(self, dk: FreqaiDataKitchen, pair: str) -> None:
|
|
"""
|
|
Fit the labels with a gaussian distribution
|
|
"""
|
|
import scipy as spy
|
|
|
|
# add classes from classifier label types if used
|
|
full_labels = dk.label_list + dk.unique_class_list
|
|
|
|
num_candles = self.freqai_info.get("fit_live_predictions_candles", 100)
|
|
dk.data["labels_mean"], dk.data["labels_std"] = {}, {}
|
|
for label in full_labels:
|
|
if self.dd.historic_predictions[dk.pair][label].dtype == object:
|
|
continue
|
|
f = spy.stats.norm.fit(self.dd.historic_predictions[dk.pair][label].tail(num_candles))
|
|
dk.data["labels_mean"][label], dk.data["labels_std"][label] = f[0], f[1]
|
|
|
|
return
|
|
|
|
def inference_timer(self, do: Literal['start', 'stop'] = 'start', pair: str = ''):
|
|
"""
|
|
Timer designed to track the cumulative time spent in FreqAI for one pass through
|
|
the whitelist. This will check if the time spent is more than 1/4 the time
|
|
of a single candle, and if so, it will warn the user of degraded performance
|
|
"""
|
|
if do == 'start':
|
|
self.pair_it += 1
|
|
self.begin_time = time.time()
|
|
elif do == 'stop':
|
|
end = time.time()
|
|
time_spent = (end - self.begin_time)
|
|
if self.freqai_info.get('write_metrics_to_disk', False):
|
|
self.dd.update_metric_tracker('inference_time', time_spent, pair)
|
|
self.inference_time += time_spent
|
|
if self.pair_it == self.total_pairs:
|
|
logger.info(
|
|
f'Total time spent inferencing pairlist {self.inference_time:.2f} seconds')
|
|
if self.inference_time > 0.25 * self.base_tf_seconds:
|
|
logger.warning("Inference took over 25% of the candle time. Reduce pairlist to"
|
|
" avoid blinding open trades and degrading performance.")
|
|
self.pair_it = 0
|
|
self.inference_time = 0
|
|
return
|
|
|
|
def train_timer(self, do: Literal['start', 'stop'] = 'start', pair: str = ''):
|
|
"""
|
|
Timer designed to track the cumulative time spent training the full pairlist in
|
|
FreqAI.
|
|
"""
|
|
if do == 'start':
|
|
self.pair_it_train += 1
|
|
self.begin_time_train = time.time()
|
|
elif do == 'stop':
|
|
end = time.time()
|
|
time_spent = (end - self.begin_time_train)
|
|
if self.freqai_info.get('write_metrics_to_disk', False):
|
|
self.dd.collect_metrics(time_spent, pair)
|
|
|
|
self.train_time += time_spent
|
|
if self.pair_it_train == self.total_pairs:
|
|
logger.info(
|
|
f'Total time spent training pairlist {self.train_time:.2f} seconds')
|
|
self.pair_it_train = 0
|
|
self.train_time = 0
|
|
return
|
|
|
|
def get_init_model(self, pair: str) -> Any:
|
|
if pair not in self.dd.model_dictionary or not self.continual_learning:
|
|
init_model = None
|
|
else:
|
|
init_model = self.dd.model_dictionary[pair]
|
|
|
|
return init_model
|
|
|
|
def _set_train_queue(self):
|
|
"""
|
|
Sets train queue from existing train timestamps if they exist
|
|
otherwise it sets the train queue based on the provided whitelist.
|
|
"""
|
|
current_pairlist = self.config.get("exchange", {}).get("pair_whitelist")
|
|
if not self.dd.pair_dict:
|
|
logger.info('Set fresh train queue from whitelist. '
|
|
f'Queue: {current_pairlist}')
|
|
return deque(current_pairlist)
|
|
|
|
best_queue = deque()
|
|
|
|
pair_dict_sorted = sorted(self.dd.pair_dict.items(),
|
|
key=lambda k: k[1]['trained_timestamp'])
|
|
for pair in pair_dict_sorted:
|
|
if pair[0] in current_pairlist:
|
|
best_queue.append(pair[0])
|
|
for pair in current_pairlist:
|
|
if pair not in best_queue:
|
|
best_queue.appendleft(pair)
|
|
|
|
logger.info('Set existing queue from trained timestamps. '
|
|
f'Best approximation queue: {best_queue}')
|
|
return best_queue
|
|
|
|
def cache_corr_pairlist_dfs(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
|
|
"""
|
|
Cache the corr_pairlist dfs to speed up performance for subsequent pairs during the
|
|
current candle.
|
|
:param dataframe: strategy fed dataframe
|
|
:param dk: datakitchen object for current asset
|
|
:return: dataframe to attach/extract cached corr_pair dfs to/from.
|
|
"""
|
|
|
|
if self.get_corr_dataframes:
|
|
self.corr_dataframes = dk.extract_corr_pair_columns_from_populated_indicators(dataframe)
|
|
if not self.corr_dataframes:
|
|
logger.warning("Couldn't cache corr_pair dataframes for improved performance. "
|
|
"Consider ensuring that the full coin/stake, e.g. XYZ/USD, "
|
|
"is included in the column names when you are creating features "
|
|
"in `populate_any_indicators()`.")
|
|
self.get_corr_dataframes = not bool(self.corr_dataframes)
|
|
elif self.corr_dataframes:
|
|
dataframe = dk.attach_corr_pair_columns(
|
|
dataframe, self.corr_dataframes, dk.pair)
|
|
|
|
return dataframe
|
|
|
|
def track_current_candle(self):
|
|
"""
|
|
Checks if the latest candle appended by the datadrawer is
|
|
equivalent to the latest candle seen by FreqAI. If not, it
|
|
asks to refresh the cached corr_dfs, and resets the pair
|
|
counter.
|
|
"""
|
|
if self.dd.current_candle > self.current_candle:
|
|
self.get_corr_dataframes = True
|
|
self.pair_it = 1
|
|
self.current_candle = self.dd.current_candle
|
|
|
|
def ensure_data_exists(self, dataframe_backtest: DataFrame,
|
|
tr_backtest: TimeRange, pair: str) -> bool:
|
|
"""
|
|
Check if the dataframe is empty, if not, report useful information to user.
|
|
:param dataframe_backtest: the backtesting dataframe, maybe empty.
|
|
:param tr_backtest: current backtesting timerange.
|
|
:param pair: current pair
|
|
:return: if the data exists or not
|
|
"""
|
|
if self.config.get("freqai_backtest_live_models", False) and len(dataframe_backtest) == 0:
|
|
logger.info(f"No data found for pair {pair} from "
|
|
f"from { tr_backtest.start_fmt} to {tr_backtest.stop_fmt}. "
|
|
"Probably more than one training within the same candle period.")
|
|
return False
|
|
return True
|
|
|
|
def log_backtesting_progress(self, tr_train: TimeRange, pair: str,
|
|
train_it: int, total_trains: int):
|
|
"""
|
|
Log the backtesting progress so user knows how many pairs have been trained and
|
|
how many more pairs/trains remain.
|
|
:param tr_train: the training timerange
|
|
:param train_it: the train iteration for the current pair (the sliding window progress)
|
|
:param pair: the current pair
|
|
:param total_trains: total trains (total number of slides for the sliding window)
|
|
"""
|
|
if not self.config.get("freqai_backtest_live_models", False):
|
|
logger.info(
|
|
f"Training {pair}, {self.pair_it}/{self.total_pairs} pairs"
|
|
f" from {tr_train.start_fmt} "
|
|
f"to {tr_train.stop_fmt}, {train_it}/{total_trains} "
|
|
"trains"
|
|
)
|
|
# Following methods which are overridden by user made prediction models.
|
|
# See freqai/prediction_models/CatboostPredictionModel.py for an example.
|
|
|
|
@abstractmethod
|
|
def train(self, unfiltered_df: DataFrame, pair: str,
|
|
dk: FreqaiDataKitchen, **kwargs) -> 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_df: 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], dk: FreqaiDataKitchen, **kwargs) -> 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, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
|
|
) -> Tuple[DataFrame, NDArray[np.int_]]:
|
|
"""
|
|
Filter the prediction features data and predict with it.
|
|
:param unfiltered_df: 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)
|
|
"""
|