592 lines
24 KiB
Python
592 lines
24 KiB
Python
import collections
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import json
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import logging
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import re
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import shutil
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import threading
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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 joblib import dump, load
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from joblib.externals import cloudpickle
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from pandas import DataFrame
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from freqtrade.configuration import TimeRange
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from freqtrade.data.history import load_pair_history
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from freqtrade.exceptions import OperationalException
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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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logger = logging.getLogger(__name__)
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class FreqaiDataDrawer:
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"""
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Class aimed at holding all pair models/info in memory for better inferencing/retrainig/saving
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/loading to/from disk.
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This object remains persistent throughout live/dry, unlike FreqaiDataKitchen, which is
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reinstantiated for each coin.
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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, full_path: Path, config: dict, follow_mode: bool = False):
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self.config = config
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self.freqai_info = config.get("freqai", {})
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# dictionary holding all pair metadata necessary to load in from disk
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self.pair_dict: Dict[str, Any] = {}
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# dictionary holding all actively inferenced models in memory given a model filename
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self.model_dictionary: Dict[str, Any] = {}
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self.model_return_values: Dict[str, Any] = {}
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self.pair_data_dict: Dict[str, Any] = {}
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self.historic_data: Dict[str, Any] = {}
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self.historic_predictions: Dict[str, Any] = {}
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self.follower_dict: Dict[str, Any] = {}
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self.full_path = full_path
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self.follower_name = self.config.get("bot_name", "follower1")
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self.follower_dict_path = Path(
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self.full_path / f"follower_dictionary-{self.follower_name}.json"
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)
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self.historic_predictions_path = Path(self.full_path / "historic_predictions.pkl")
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self.pair_dictionary_path = Path(self.full_path / "pair_dictionary.json")
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self.follow_mode = follow_mode
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if follow_mode:
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self.create_follower_dict()
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self.load_drawer_from_disk()
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self.load_historic_predictions_from_disk()
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self.training_queue: Dict[str, int] = {}
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self.history_lock = threading.Lock()
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def load_drawer_from_disk(self):
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"""
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Locate and load a previously saved data drawer full of all pair model metadata in
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present model folder.
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:returns:
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exists: bool = whether or not the drawer was located
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"""
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exists = self.pair_dictionary_path.is_file()
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if exists:
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with open(self.pair_dictionary_path, "r") as fp:
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self.pair_dict = json.load(fp)
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elif not self.follow_mode:
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logger.info("Could not find existing datadrawer, starting from scratch")
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else:
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logger.warning(
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f"Follower could not find pair_dictionary at {self.full_path} "
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"sending null values back to strategy"
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)
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return exists
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def load_historic_predictions_from_disk(self):
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"""
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Locate and load a previously saved historic predictions.
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:returns:
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exists: bool = whether or not the drawer was located
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"""
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exists = self.historic_predictions_path.is_file()
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if exists:
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with open(self.historic_predictions_path, "rb") as fp:
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self.historic_predictions = cloudpickle.load(fp)
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logger.info(
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f"Found existing historic predictions at {self.full_path}, but beware "
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"that statistics may be inaccurate if the bot has been offline for "
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"an extended period of time."
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)
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elif not self.follow_mode:
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logger.info("Could not find existing historic_predictions, starting from scratch")
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else:
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logger.warning(
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f"Follower could not find historic predictions at {self.full_path} "
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"sending null values back to strategy"
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)
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return exists
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def save_historic_predictions_to_disk(self):
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"""
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Save data drawer full of all pair model metadata in present model folder.
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"""
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with open(self.historic_predictions_path, "wb") as fp:
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cloudpickle.dump(self.historic_predictions, fp, protocol=cloudpickle.DEFAULT_PROTOCOL)
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def save_drawer_to_disk(self):
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"""
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Save data drawer full of all pair model metadata in present model folder.
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"""
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with open(self.pair_dictionary_path, "w") as fp:
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json.dump(self.pair_dict, fp, default=self.np_encoder)
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def save_follower_dict_to_disk(self):
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"""
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Save follower dictionary to disk (used by strategy for persistent prediction targets)
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"""
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with open(self.follower_dict_path, "w") as fp:
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json.dump(self.follower_dict, fp, default=self.np_encoder)
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def create_follower_dict(self):
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"""
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Create or dictionary for each follower to maintain unique persistent prediction targets
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"""
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whitelist_pairs = self.config.get("exchange", {}).get("pair_whitelist")
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exists = self.follower_dict_path.is_file()
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if exists:
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logger.info("Found an existing follower dictionary")
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for pair in whitelist_pairs:
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self.follower_dict[pair] = {}
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with open(self.follower_dict_path, "w") as fp:
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json.dump(self.follower_dict, fp, default=self.np_encoder)
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def np_encoder(self, object):
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if isinstance(object, np.generic):
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return object.item()
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def get_pair_dict_info(self, pair: str) -> Tuple[str, int, bool, bool]:
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"""
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Locate and load existing model metadata from persistent storage. If not located,
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create a new one and append the current pair to it and prepare it for its first
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training
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:param pair: str: pair to lookup
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:return:
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model_filename: str = unique filename used for loading persistent objects from disk
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trained_timestamp: int = the last time the coin was trained
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coin_first: bool = If the coin is fresh without metadata
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return_null_array: bool = Follower could not find pair metadata
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"""
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pair_in_dict = self.pair_dict.get(pair)
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data_path_set = self.pair_dict.get(pair, {}).get("data_path", None)
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return_null_array = False
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if pair_in_dict:
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model_filename = self.pair_dict[pair]["model_filename"]
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trained_timestamp = self.pair_dict[pair]["trained_timestamp"]
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coin_first = self.pair_dict[pair]["first"]
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elif not self.follow_mode:
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self.pair_dict[pair] = {}
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model_filename = self.pair_dict[pair]["model_filename"] = ""
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coin_first = self.pair_dict[pair]["first"] = True
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trained_timestamp = self.pair_dict[pair]["trained_timestamp"] = 0
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self.pair_dict[pair]["priority"] = len(self.pair_dict)
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if not data_path_set and self.follow_mode:
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logger.warning(
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f"Follower could not find current pair {pair} in "
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f"pair_dictionary at path {self.full_path}, sending null values "
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"back to strategy."
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)
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return_null_array = True
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return model_filename, trained_timestamp, coin_first, return_null_array
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def set_pair_dict_info(self, metadata: dict) -> None:
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pair_in_dict = self.pair_dict.get(metadata["pair"])
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if pair_in_dict:
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return
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else:
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self.pair_dict[metadata["pair"]] = {}
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self.pair_dict[metadata["pair"]]["model_filename"] = ""
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self.pair_dict[metadata["pair"]]["first"] = True
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self.pair_dict[metadata["pair"]]["trained_timestamp"] = 0
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self.pair_dict[metadata["pair"]]["priority"] = len(self.pair_dict)
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return
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def pair_to_end_of_training_queue(self, pair: str) -> None:
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# march all pairs up in the queue
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for p in self.pair_dict:
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self.pair_dict[p]["priority"] -= 1
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# send pair to end of queue
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self.pair_dict[pair]["priority"] = len(self.pair_dict)
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def set_initial_return_values(self, pair: str, dk: FreqaiDataKitchen,
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pred_df: DataFrame, do_preds: npt.ArrayLike) -> None:
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"""
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Set the initial return values to a persistent dataframe. This avoids needing to repredict on
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historical candles, and also stores historical predictions despite retrainings (so stored
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predictions are true predictions, not just inferencing on trained data)
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"""
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# dynamic df returned to strategy and plotted in frequi
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mrv_df = self.model_return_values[pair] = pd.DataFrame()
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for label in dk.label_list:
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mrv_df[label] = pred_df[label]
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mrv_df[f"{label}_mean"] = dk.data["labels_mean"][label]
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mrv_df[f"{label}_std"] = dk.data["labels_std"][label]
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if self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0) > 0:
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mrv_df["DI_values"] = dk.DI_values
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mrv_df["do_predict"] = do_preds
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def append_model_predictions(self, pair: str, predictions, do_preds, dk, len_df) -> None:
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# strat seems to feed us variable sized dataframes - and since we are trying to build our
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# own return array in the same shape, we need to figure out how the size has changed
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# and adapt our stored/returned info accordingly.
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length_difference = len(self.model_return_values[pair]) - len_df
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i = 0
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if length_difference == 0:
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i = 1
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elif length_difference > 0:
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i = length_difference + 1
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df = self.model_return_values[pair] = self.model_return_values[pair].shift(-i)
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if pair in self.historic_predictions:
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hp_df = self.historic_predictions[pair]
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# here are some pandas hula hoops to accommodate the possibility of a series
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# or dataframe depending number of labels requested by user
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nan_df = pd.DataFrame(np.nan, index=hp_df.index[-2:] + 2, columns=hp_df.columns)
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hp_df = pd.concat([hp_df, nan_df], ignore_index=True, axis=0)
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self.historic_predictions[pair] = hp_df[:-1]
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for label in dk.label_list:
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df[label].iloc[-1] = predictions[label].iloc[-1]
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df[f"{label}_mean"].iloc[-1] = dk.data["labels_mean"][label]
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df[f"{label}_std"].iloc[-1] = dk.data["labels_std"][label]
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# df['prediction'].iloc[-1] = predictions[-1]
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df["do_predict"].iloc[-1] = do_preds[-1]
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if self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0) > 0:
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df["DI_values"].iloc[-1] = dk.DI_values[-1]
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# append the new predictions to persistent storage
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if pair in self.historic_predictions:
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self.historic_predictions[pair].iloc[-1] = df[label].iloc[-1]
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if length_difference < 0:
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prepend_df = pd.DataFrame(
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np.zeros((abs(length_difference) - 1, len(df.columns))), columns=df.columns
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)
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df = pd.concat([prepend_df, df], axis=0)
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def attach_return_values_to_return_dataframe(
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self, pair: str, dataframe: DataFrame) -> DataFrame:
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"""
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Attach the return values to the strat dataframe
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:param dataframe: DataFrame = strategy dataframe
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:return: DataFrame = strat dataframe with return values attached
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"""
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df = self.model_return_values[pair]
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to_keep = [col for col in dataframe.columns if not col.startswith("&")]
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dataframe = pd.concat([dataframe[to_keep], df], axis=1)
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return dataframe
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def return_null_values_to_strategy(self, dataframe: DataFrame, dk) -> None:
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"""
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Build 0 filled dataframe to return to strategy
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"""
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dk.find_features(dataframe)
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for label in dk.label_list:
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dataframe[label] = 0
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dataframe[f"{label}_mean"] = 0
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dataframe[f"{label}_std"] = 0
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# dataframe['prediction'] = 0
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dataframe["do_predict"] = 0
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if self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0) > 0:
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dataframe["DI_value"] = 0
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dk.return_dataframe = dataframe
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def purge_old_models(self) -> None:
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model_folders = [x for x in self.full_path.iterdir() if x.is_dir()]
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pattern = re.compile(r"sub-train-(\w+)_(\d{10})")
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delete_dict: Dict[str, Any] = {}
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for dir in model_folders:
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result = pattern.match(str(dir.name))
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if result is None:
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break
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coin = result.group(1)
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timestamp = result.group(2)
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if coin not in delete_dict:
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delete_dict[coin] = {}
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delete_dict[coin]["num_folders"] = 1
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delete_dict[coin]["timestamps"] = {int(timestamp): dir}
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else:
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delete_dict[coin]["num_folders"] += 1
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delete_dict[coin]["timestamps"][int(timestamp)] = dir
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for coin in delete_dict:
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if delete_dict[coin]["num_folders"] > 2:
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sorted_dict = collections.OrderedDict(
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sorted(delete_dict[coin]["timestamps"].items())
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)
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num_delete = len(sorted_dict) - 2
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deleted = 0
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for k, v in sorted_dict.items():
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if deleted >= num_delete:
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break
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logger.info(f"Freqai purging old model file {v}")
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shutil.rmtree(v)
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deleted += 1
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def update_follower_metadata(self):
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# follower needs to load from disk to get any changes made by leader to pair_dict
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self.load_drawer_from_disk()
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if self.config.get("freqai", {}).get("purge_old_models", False):
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self.purge_old_models()
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# Functions pulled back from FreqaiDataKitchen because they relied on DataDrawer
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def save_data(self, model: Any, coin: str, dk: FreqaiDataKitchen) -> None:
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"""
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Saves all data associated with a model for a single sub-train time range
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:params:
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:model: User trained model which can be reused for inferencing to generate
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predictions
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"""
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if not dk.data_path.is_dir():
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dk.data_path.mkdir(parents=True, exist_ok=True)
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save_path = Path(dk.data_path)
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# Save the trained model
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if not dk.keras:
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dump(model, save_path / f"{dk.model_filename}_model.joblib")
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else:
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model.save(save_path / f"{dk.model_filename}_model.h5")
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if dk.svm_model is not None:
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dump(dk.svm_model, save_path / str(dk.model_filename + "_svm_model.joblib"))
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dk.data["data_path"] = str(dk.data_path)
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dk.data["model_filename"] = str(dk.model_filename)
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dk.data["training_features_list"] = list(dk.data_dictionary["train_features"].columns)
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dk.data["label_list"] = dk.label_list
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# store the metadata
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with open(save_path / str(dk.model_filename + "_metadata.json"), "w") as fp:
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json.dump(dk.data, fp, default=dk.np_encoder)
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# save the train data to file so we can check preds for area of applicability later
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dk.data_dictionary["train_features"].to_pickle(
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save_path / str(dk.model_filename + "_trained_df.pkl")
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)
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if self.freqai_info.get("feature_parameters", {}).get("principal_component_analysis"):
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cloudpickle.dump(
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dk.pca, open(dk.data_path / str(dk.model_filename + "_pca_object.pkl"), "wb")
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)
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# if self.live:
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self.model_dictionary[dk.model_filename] = model
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self.pair_dict[coin]["model_filename"] = dk.model_filename
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self.pair_dict[coin]["data_path"] = str(dk.data_path)
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self.save_drawer_to_disk()
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return
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def load_data(self, coin: str, dk: FreqaiDataKitchen) -> Any:
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"""
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loads all data required to make a prediction on a sub-train time range
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:returns:
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:model: User trained model which can be inferenced for new predictions
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"""
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if not self.pair_dict[coin]["model_filename"]:
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return None
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if dk.live:
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dk.model_filename = self.pair_dict[coin]["model_filename"]
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dk.data_path = Path(self.pair_dict[coin]["data_path"])
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if self.freqai_info.get("follow_mode", False):
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# follower can be on a different system which is rsynced to the leader:
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dk.data_path = Path(
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self.config["user_data_dir"]
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/ "models"
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/ dk.data_path.parts[-2]
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/ dk.data_path.parts[-1]
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)
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with open(dk.data_path / str(dk.model_filename + "_metadata.json"), "r") as fp:
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dk.data = json.load(fp)
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dk.training_features_list = dk.data["training_features_list"]
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dk.label_list = dk.data["label_list"]
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dk.data_dictionary["train_features"] = pd.read_pickle(
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dk.data_path / str(dk.model_filename + "_trained_df.pkl")
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)
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# try to access model in memory instead of loading object from disk to save time
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if dk.live and dk.model_filename in self.model_dictionary:
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model = self.model_dictionary[dk.model_filename]
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elif not dk.keras:
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model = load(dk.data_path / str(dk.model_filename + "_model.joblib"))
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else:
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from tensorflow import keras
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model = keras.models.load_model(dk.data_path / str(dk.model_filename + "_model.h5"))
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if Path(dk.data_path / str(dk.model_filename + "_svm_model.joblib")).resolve().exists():
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dk.svm_model = load(dk.data_path / str(dk.model_filename + "_svm_model.joblib"))
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if not model:
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raise OperationalException(
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f"Unable to load model, ensure model exists at " f"{dk.data_path} "
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)
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if self.config["freqai"]["feature_parameters"]["principal_component_analysis"]:
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dk.pca = cloudpickle.load(
|
|
open(dk.data_path / str(dk.model_filename + "_pca_object.pkl"), "rb")
|
|
)
|
|
|
|
return model
|
|
|
|
def update_historic_data(self, strategy: IStrategy, dk: FreqaiDataKitchen) -> None:
|
|
"""
|
|
Append new candles to our stores historic data (in memory) so that
|
|
we do not need to load candle history from disk and we dont need to
|
|
pinging exchange multiple times for the same candle.
|
|
:params:
|
|
dataframe: DataFrame = strategy provided dataframe
|
|
"""
|
|
feat_params = self.freqai_info.get("feature_parameters", {})
|
|
with self.history_lock:
|
|
history_data = self.historic_data
|
|
|
|
for pair in dk.all_pairs:
|
|
for tf in feat_params.get("include_timeframes"):
|
|
|
|
# check if newest candle is already appended
|
|
df_dp = strategy.dp.get_pair_dataframe(pair, tf)
|
|
if len(df_dp.index) == 0:
|
|
continue
|
|
if str(history_data[pair][tf].iloc[-1]["date"]) == str(
|
|
df_dp.iloc[-1:]["date"].iloc[-1]
|
|
):
|
|
continue
|
|
|
|
try:
|
|
index = (
|
|
df_dp.loc[
|
|
df_dp["date"] == history_data[pair][tf].iloc[-1]["date"]
|
|
].index[0]
|
|
+ 1
|
|
)
|
|
except IndexError:
|
|
logger.warning(
|
|
f"Unable to update pair history for {pair}. "
|
|
"If this does not resolve itself after 1 additional candle, "
|
|
"please report the error to #freqai discord channel"
|
|
)
|
|
return
|
|
|
|
history_data[pair][tf] = pd.concat(
|
|
[
|
|
history_data[pair][tf],
|
|
strategy.dp.get_pair_dataframe(pair, tf).iloc[index:],
|
|
],
|
|
ignore_index=True,
|
|
axis=0,
|
|
)
|
|
|
|
def load_all_pair_histories(self, timerange: TimeRange, dk: FreqaiDataKitchen) -> None:
|
|
"""
|
|
Load pair histories for all whitelist and corr_pairlist pairs.
|
|
Only called once upon startup of bot.
|
|
:params:
|
|
timerange: TimeRange = full timerange required to populate all indicators
|
|
for training according to user defined train_period_days
|
|
"""
|
|
history_data = self.historic_data
|
|
|
|
for pair in dk.all_pairs:
|
|
if pair not in history_data:
|
|
history_data[pair] = {}
|
|
for tf in self.freqai_info.get("feature_parameters", {}).get("include_timeframes"):
|
|
history_data[pair][tf] = load_pair_history(
|
|
datadir=self.config["datadir"],
|
|
timeframe=tf,
|
|
pair=pair,
|
|
timerange=timerange,
|
|
data_format=self.config.get("dataformat_ohlcv", "json"),
|
|
candle_type=self.config.get("trading_mode", "spot"),
|
|
)
|
|
|
|
def get_base_and_corr_dataframes(
|
|
self, timerange: TimeRange, pair: str, dk: FreqaiDataKitchen
|
|
) -> Tuple[Dict[Any, Any], Dict[Any, Any]]:
|
|
"""
|
|
Searches through our historic_data in memory and returns the dataframes relevant
|
|
to the present pair.
|
|
:params:
|
|
timerange: TimeRange = full timerange required to populate all indicators
|
|
for training according to user defined train_period_days
|
|
metadata: dict = strategy furnished pair metadata
|
|
"""
|
|
|
|
with self.history_lock:
|
|
corr_dataframes: Dict[Any, Any] = {}
|
|
base_dataframes: Dict[Any, Any] = {}
|
|
historic_data = self.historic_data
|
|
pairs = self.freqai_info.get("feature_parameters", {}).get(
|
|
"include_corr_pairlist", []
|
|
)
|
|
|
|
for tf in self.freqai_info.get("feature_parameters", {}).get("include_timeframes"):
|
|
base_dataframes[tf] = dk.slice_dataframe(timerange, historic_data[pair][tf])
|
|
if pairs:
|
|
for p in pairs:
|
|
if pair in p:
|
|
continue # dont repeat anything from whitelist
|
|
if p not in corr_dataframes:
|
|
corr_dataframes[p] = {}
|
|
corr_dataframes[p][tf] = dk.slice_dataframe(
|
|
timerange, historic_data[p][tf]
|
|
)
|
|
|
|
return corr_dataframes, base_dataframes
|
|
|
|
# to be used if we want to send predictions directly to the follower instead of forcing
|
|
# follower to load models and inference
|
|
# def save_model_return_values_to_disk(self) -> None:
|
|
# with open(self.full_path / str('model_return_values.json'), "w") as fp:
|
|
# json.dump(self.model_return_values, fp, default=self.np_encoder)
|
|
|
|
# def load_model_return_values_from_disk(self, dk: FreqaiDataKitchen) -> FreqaiDataKitchen:
|
|
# exists = Path(self.full_path / str('model_return_values.json')).resolve().exists()
|
|
# if exists:
|
|
# with open(self.full_path / str('model_return_values.json'), "r") as fp:
|
|
# self.model_return_values = json.load(fp)
|
|
# elif not self.follow_mode:
|
|
# logger.info("Could not find existing datadrawer, starting from scratch")
|
|
# else:
|
|
# logger.warning(f'Follower could not find pair_dictionary at {self.full_path} '
|
|
# 'sending null values back to strategy')
|
|
|
|
# return exists, dk
|