mirror of
https://github.com/freqtrade/freqtrade.git
synced 2024-11-10 18:23:55 +00:00
290 lines
13 KiB
Python
290 lines
13 KiB
Python
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import collections
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import copy
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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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from pathlib import Path
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from typing import Any, Dict, Tuple
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# import pickle as pk
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import numpy as np
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from pandas import DataFrame
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# from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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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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"""
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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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# 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.populated_historic_data: 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.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.training_queue: Dict[str, int] = {}
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# self.create_training_queue(pair_whitelist)
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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 = Path(self.full_path / str('pair_dictionary.json')).resolve().exists()
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if exists:
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with open(self.full_path / str('pair_dictionary.json'), "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(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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return exists
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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.full_path / str('pair_dictionary.json'), "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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follower_name = self.config.get('bot_name', 'follower1')
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with open(self.full_path / str('follower_dictionary-' +
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follower_name + '.json'), "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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follower_name = self.config.get('bot_name', 'follower1')
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whitelist_pairs = self.config.get('exchange', {}).get('pair_whitelist')
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exists = Path(self.full_path / str('follower_dictionary-' +
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follower_name + '.json')).resolve().exists()
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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.full_path / str('follower_dictionary-' +
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follower_name + '.json'), "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, metadata: dict) -> 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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:params:
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metadata: dict = strategy furnished pair metadata
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:returns:
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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(metadata['pair'])
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data_path_set = self.pair_dict.get(metadata['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[metadata['pair']]['model_filename']
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trained_timestamp = self.pair_dict[metadata['pair']]['trained_timestamp']
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coin_first = self.pair_dict[metadata['pair']]['first']
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elif not self.follow_mode:
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self.pair_dict[metadata['pair']] = {}
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model_filename = self.pair_dict[metadata['pair']]['model_filename'] = ''
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coin_first = self.pair_dict[metadata['pair']]['first'] = True
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trained_timestamp = self.pair_dict[metadata['pair']]['trained_timestamp'] = 0
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if not data_path_set and self.follow_mode:
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logger.warning(f'Follower could not find current pair {metadata["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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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, dh):
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self.model_return_values[pair] = {}
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self.model_return_values[pair]['predictions'] = dh.full_predictions
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self.model_return_values[pair]['do_preds'] = dh.full_do_predict
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self.model_return_values[pair]['target_mean'] = dh.full_target_mean
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self.model_return_values[pair]['target_std'] = dh.full_target_std
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self.model_return_values[pair]['DI_values'] = dh.full_DI_values
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# if not self.follow_mode:
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# self.save_model_return_values_to_disk()
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def append_model_predictions(self, pair: str, predictions, do_preds,
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target_mean, target_std, dh, 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]['predictions']) - 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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self.model_return_values[pair]['predictions'] = np.append(
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self.model_return_values[pair]['predictions'][i:], predictions[-1])
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self.model_return_values[pair]['DI_values'] = np.append(
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self.model_return_values[pair]['DI_values'][i:], dh.DI_values[-1])
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self.model_return_values[pair]['do_preds'] = np.append(
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self.model_return_values[pair]['do_preds'][i:], do_preds[-1])
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self.model_return_values[pair]['target_mean'] = np.append(
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self.model_return_values[pair]['target_mean'][i:], target_mean)
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self.model_return_values[pair]['target_std'] = np.append(
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self.model_return_values[pair]['target_std'][i:], target_std)
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if length_difference < 0:
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prepend = np.zeros(abs(length_difference) - 1)
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self.model_return_values[pair]['predictions'] = np.insert(
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self.model_return_values[pair]['predictions'], 0, prepend)
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self.model_return_values[pair]['DI_values'] = np.insert(
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self.model_return_values[pair]['DI_values'], 0, prepend)
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self.model_return_values[pair]['do_preds'] = np.insert(
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self.model_return_values[pair]['do_preds'], 0, prepend)
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self.model_return_values[pair]['target_mean'] = np.insert(
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self.model_return_values[pair]['target_mean'], 0, prepend)
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self.model_return_values[pair]['target_std'] = np.insert(
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self.model_return_values[pair]['target_std'], 0, prepend)
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dh.full_predictions = copy.deepcopy(self.model_return_values[pair]['predictions'])
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dh.full_do_predict = copy.deepcopy(self.model_return_values[pair]['do_preds'])
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dh.full_target_mean = copy.deepcopy(self.model_return_values[pair]['target_mean'])
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dh.full_target_std = copy.deepcopy(self.model_return_values[pair]['target_std'])
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dh.full_DI_values = copy.deepcopy(self.model_return_values[pair]['DI_values'])
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# if not self.follow_mode:
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# self.save_model_return_values_to_disk()
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def return_null_values_to_strategy(self, dataframe: DataFrame, dh) -> None:
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len_df = len(dataframe)
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dh.full_predictions = np.zeros(len_df)
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dh.full_do_predict = np.zeros(len_df)
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dh.full_target_mean = np.zeros(len_df)
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dh.full_target_std = np.zeros(len_df)
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dh.full_DI_values = np.zeros(len_df)
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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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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', {})('purge_old_models', False):
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self.purge_old_models()
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# to be used if we want to send predictions directly to the follower instead of forcing
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# follower to load models and inference
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# def save_model_return_values_to_disk(self) -> None:
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# with open(self.full_path / str('model_return_values.json'), "w") as fp:
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# json.dump(self.model_return_values, fp, default=self.np_encoder)
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# def load_model_return_values_from_disk(self, dh: FreqaiDataKitchen) -> FreqaiDataKitchen:
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# exists = Path(self.full_path / str('model_return_values.json')).resolve().exists()
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# if exists:
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# with open(self.full_path / str('model_return_values.json'), "r") as fp:
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# self.model_return_values = 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(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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# return exists, dh
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