improve data persistence/mapping for live/dry. This accommodates quick reloads after crash and handles multi-pair cleanly
This commit is contained in:
robcaulk
parent
e1c068ca66
commit
b0d2d13eb1
59
freqtrade/freqai/data_drawer.py
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59
freqtrade/freqai/data_drawer.py
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@ -0,0 +1,59 @@
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import json
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import logging
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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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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):
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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.full_path = full_path
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self.load_drawer_from_disk()
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def load_drawer_from_disk(self):
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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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else:
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logger.info("Could not find existing datadrawer, starting from scratch")
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return exists
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def save_drawer_to_disk(self):
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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 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]:
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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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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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else:
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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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return model_filename, trained_timestamp, coin_first
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@ -19,6 +19,7 @@ from sklearn.model_selection import train_test_split
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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.data.history.history_utils import refresh_backtest_ohlcv_data
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from freqtrade.freqai.data_drawer import FreqaiDataDrawer
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from freqtrade.resolvers import ExchangeResolver
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from freqtrade.strategy.interface import IStrategy
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@ -33,13 +34,13 @@ logger = logging.getLogger(__name__)
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class FreqaiDataKitchen:
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"""
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Class designed to handle all the data for the IFreqaiModel class model.
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Class designed to analyze data for a single pair. Employed by the IFreqaiModel class.
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Functionalities include holding, saving, loading, and analyzing the data.
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author: Robert Caulk, rob.caulk@gmail.com
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"""
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def __init__(self, config: Dict[str, Any], dataframe: DataFrame, live: bool = False):
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self.full_dataframe = dataframe
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def __init__(self, config: Dict[str, Any], data_drawer: FreqaiDataDrawer, live: bool = False,
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pair: str = ''):
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self.data: Dict[Any, Any] = {}
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self.data_dictionary: Dict[Any, Any] = {}
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self.config = config
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@ -53,10 +54,10 @@ class FreqaiDataKitchen:
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self.full_do_predict: npt.ArrayLike = np.array([])
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self.full_target_mean: npt.ArrayLike = np.array([])
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self.full_target_std: npt.ArrayLike = np.array([])
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self.model_path = Path()
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self.data_path = Path()
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self.model_filename: str = ""
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self.model_dictionary: Dict[Any, Any] = {}
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self.live = live
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self.pair = pair
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self.svm_model: linear_model.SGDOneClassSVM = None
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if not self.live:
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self.full_timerange = self.create_fulltimerange(self.config["timerange"],
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@ -69,6 +70,8 @@ class FreqaiDataKitchen:
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config["freqai"]["backtest_period"],
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)
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self.data_drawer = data_drawer
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def assert_config(self, config: Dict[str, Any], live: bool) -> None:
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assert config.get('freqai'), "No Freqai parameters found in config file."
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assert config.get('freqai', {}).get('train_period'), ("No Freqai train_period found in"
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@ -88,18 +91,18 @@ class FreqaiDataKitchen:
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assert config.get('freqai', {}).get('feature_parameters'), ("No Freqai feature_parameters"
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"found in config file.")
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def set_paths(self) -> None:
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def set_paths(self, trained_timestamp: int = None) -> None:
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self.full_path = Path(self.config['user_data_dir'] /
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"models" /
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str(self.freqai_config.get('live_full_backtestrange') +
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self.freqai_config.get('identifier')))
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self.model_path = Path(self.full_path / str("sub-train" + "-" +
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str(self.freqai_config.get('live_trained_timerange'))))
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self.data_path = Path(self.full_path / str("sub-train" + "-" + self.pair.split("/")[0] +
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str(trained_timestamp)))
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return
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def save_data(self, model: Any) -> None:
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def save_data(self, model: Any, coin: str = '') -> 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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@ -107,10 +110,10 @@ class FreqaiDataKitchen:
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predictions
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"""
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if not self.model_path.is_dir():
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self.model_path.mkdir(parents=True, exist_ok=True)
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if not self.data_path.is_dir():
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self.data_path.mkdir(parents=True, exist_ok=True)
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save_path = Path(self.model_path)
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save_path = Path(self.data_path)
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# Save the trained model
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dump(model, save_path / str(self.model_filename + "_model.joblib"))
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@ -118,7 +121,7 @@ class FreqaiDataKitchen:
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if self.svm_model is not None:
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dump(self.svm_model, save_path / str(self.model_filename + "_svm_model.joblib"))
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self.data["model_path"] = str(self.model_path)
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self.data["data_path"] = str(self.data_path)
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self.data["model_filename"] = str(self.model_filename)
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self.data["training_features_list"] = list(self.data_dictionary["train_features"].columns)
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# store the metadata
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@ -131,7 +134,10 @@ class FreqaiDataKitchen:
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)
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if self.live:
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self.model_dictionary[self.model_filename] = model
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self.data_drawer.model_dictionary[self.model_filename] = model
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self.data_drawer.pair_dict[coin]['model_filename'] = self.model_filename
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self.data_drawer.pair_dict[coin]['data_path'] = str(self.data_path)
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self.data_drawer.save_drawer_to_disk()
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# TODO add a helper function to let user save/load any data they are custom adding. We
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# do not want them having to edit the default save/load methods here. Below is an example
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@ -148,19 +154,23 @@ class FreqaiDataKitchen:
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return
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def load_data(self) -> Any:
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def load_data(self, coin: str = '') -> 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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with open(self.model_path / str(self.model_filename + "_metadata.json"), "r") as fp:
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if self.live:
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self.model_filename = self.data_drawer.pair_dict[coin]['model_filename']
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self.data_path = Path(self.data_drawer.pair_dict[coin]['data_path'])
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with open(self.data_path / str(self.model_filename + "_metadata.json"), "r") as fp:
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self.data = json.load(fp)
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self.training_features_list = self.data["training_features_list"]
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self.data_dictionary["train_features"] = pd.read_pickle(
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self.model_path / str(self.model_filename + "_trained_df.pkl")
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self.data_path / str(self.model_filename + "_trained_df.pkl")
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)
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# TODO add a helper function to let user save/load any data they are custom adding. We
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@ -169,34 +179,34 @@ class FreqaiDataKitchen:
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# if self.freqai_config.get('feature_parameters','determine_statistical_distributions'):
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# self.data_dictionary["upper_quantiles"] = pd.read_pickle(
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# self.model_path / str(self.model_filename + "_upper_quantiles.pkl")
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# self.data_path / str(self.model_filename + "_upper_quantiles.pkl")
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# )
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# self.data_dictionary["lower_quantiles"] = pd.read_pickle(
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# self.model_path / str(self.model_filename + "_lower_quantiles.pkl")
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# self.data_path / str(self.model_filename + "_lower_quantiles.pkl")
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# )
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self.model_path = Path(self.data["model_path"])
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self.model_filename = self.data["model_filename"]
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# self.data_path = Path(self.data["data_path"])
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# self.model_filename = self.data["model_filename"]
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# try to access model in memory instead of loading object from disk to save time
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if self.live and self.model_filename in self.model_dictionary:
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model = self.model_dictionary[self.model_filename]
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if self.live and self.model_filename in self.data_drawer.model_dictionary:
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model = self.data_drawer.model_dictionary[self.model_filename]
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else:
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model = load(self.model_path / str(self.model_filename + "_model.joblib"))
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model = load(self.data_path / str(self.model_filename + "_model.joblib"))
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if Path(self.model_path / str(self.model_filename +
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if Path(self.data_path / str(self.model_filename +
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"_svm_model.joblib")).resolve().exists():
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self.svm_model = load(self.model_path / str(self.model_filename + "_svm_model.joblib"))
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self.svm_model = load(self.data_path / str(self.model_filename + "_svm_model.joblib"))
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assert model, (
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f"Unable to load model, ensure model exists at "
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f"{self.model_path} "
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f"{self.data_path} "
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)
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if self.config["freqai"]["feature_parameters"]["principal_component_analysis"]:
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self.pca = pk.load(
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open(self.model_path / str(self.model_filename + "_pca_object.pkl"), "rb")
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open(self.data_path / str(self.model_filename + "_pca_object.pkl"), "rb")
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)
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return model
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@ -539,9 +549,9 @@ class FreqaiDataKitchen:
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logger.info(f'PCA reduced total features from {n_components} to {n_keep_components}')
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if not self.model_path.is_dir():
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self.model_path.mkdir(parents=True, exist_ok=True)
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pk.dump(pca2, open(self.model_path / str(self.model_filename + "_pca_object.pkl"), "wb"))
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if not self.data_path.is_dir():
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self.data_path.mkdir(parents=True, exist_ok=True)
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pk.dump(pca2, open(self.data_path / str(self.model_filename + "_pca_object.pkl"), "wb"))
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return None
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@ -717,40 +727,51 @@ class FreqaiDataKitchen:
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return full_timerange
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def check_if_new_training_required(self, trained_timerange: TimeRange,
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metadata: dict) -> Tuple[bool, TimeRange]:
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def check_if_new_training_required(self, trained_timestamp: int) -> Tuple[bool, TimeRange]:
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time = datetime.datetime.now(tz=datetime.timezone.utc).timestamp()
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if trained_timerange.startts != 0:
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elapsed_time = (time - trained_timerange.stopts) / SECONDS_IN_DAY
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trained_timerange = TimeRange()
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if trained_timestamp != 0:
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elapsed_time = (time - trained_timestamp) / SECONDS_IN_DAY
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retrain = elapsed_time > self.freqai_config.get('backtest_period')
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if retrain:
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trained_timerange.startts += self.freqai_config.get(
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'backtest_period', 0) * SECONDS_IN_DAY
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trained_timerange.stopts += self.freqai_config.get(
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'backtest_period', 0) * SECONDS_IN_DAY
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trained_timerange.startts = int(time - self.freqai_config.get(
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'backtest_period', 0) * SECONDS_IN_DAY)
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trained_timerange.stopts = int(time)
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else: # user passed no live_trained_timerange in config
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trained_timerange = TimeRange()
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trained_timerange.startts = int(time - self.freqai_config.get('train_period') *
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SECONDS_IN_DAY)
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trained_timerange.stopts = int(time)
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retrain = True
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if retrain:
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coin, _ = metadata['pair'].split("/")
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# set the new model_path
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self.model_path = Path(self.full_path / str("sub-train" + "-" +
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str(int(trained_timerange.stopts))))
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# if retrain:
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# coin, _ = metadata['pair'].split("/")
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# # set the new data_path
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# self.data_path = Path(self.full_path / str("sub-train" + "-" +
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# str(int(trained_timerange.stopts))))
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self.model_filename = "cb_" + coin.lower() + "_" + str(int(trained_timerange.stopts))
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# this is not persistent at the moment TODO
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self.freqai_config['live_trained_timerange'] = str(int(trained_timerange.stopts))
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# enables persistence, but not fully implemented into save/load data yer
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self.data['live_trained_timerange'] = str(int(trained_timerange.stopts))
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# self.model_filename = "cb_" + coin.lower() + "_" + str(int(trained_timerange.stopts))
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# # this is not persistent at the moment TODO
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# self.freqai_config['live_trained_timerange'] = str(int(trained_timerange.stopts))
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# # enables persistence, but not fully implemented into save/load data yer
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# self.data['live_trained_timerange'] = str(int(trained_timerange.stopts))
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return retrain, trained_timerange
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def set_new_model_names(self, metadata: dict, trained_timerange: TimeRange):
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coin, _ = metadata['pair'].split("/")
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# set the new data_path
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self.data_path = Path(self.full_path / str("sub-train" + "-" +
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metadata['pair'].split("/")[0] +
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str(int(trained_timerange.stopts))))
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self.model_filename = "cb_" + coin.lower() + "_" + str(int(trained_timerange.stopts))
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# this is not persistent at the moment TODO
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self.freqai_config['live_trained_timerange'] = str(int(trained_timerange.stopts))
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# enables persistence, but not fully implemented into save/load data yer
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self.data['live_trained_timerange'] = str(int(trained_timerange.stopts))
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def download_new_data_for_retraining(self, timerange: TimeRange, metadata: dict) -> None:
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exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'],
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@ -13,6 +13,7 @@ from pandas import DataFrame
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from freqtrade.configuration import TimeRange
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from freqtrade.enums import RunMode
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from freqtrade.freqai.data_drawer import FreqaiDataDrawer
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from freqtrade.strategy.interface import IStrategy
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@ -65,11 +66,14 @@ class IFreqaiModel(ABC):
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self.training_on_separate_thread = False
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self.retrain = False
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self.first = True
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if self.freqai_info.get('live_trained_timerange'):
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self.new_trained_timerange = TimeRange.parse_timerange(
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self.freqai_info['live_trained_timerange'])
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else:
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self.new_trained_timerange = TimeRange()
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# if self.freqai_info.get('live_trained_timerange'):
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# self.new_trained_timerange = TimeRange.parse_timerange(
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# self.freqai_info['live_trained_timerange'])
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# else:
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# self.new_trained_timerange = TimeRange()
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self.set_full_path()
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self.data_drawer = FreqaiDataDrawer(Path(self.full_path))
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def assert_config(self, config: Dict[str, Any]) -> None:
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@ -86,7 +90,7 @@ class IFreqaiModel(ABC):
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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, it will train a new model if
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Entry point to the FreqaiModel from a specific pair, it will train a new model if
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necessary before making the prediction.
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The backtesting and training paradigm is a sliding training window
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with a following backtest window. Both windows slide according to the
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@ -103,8 +107,8 @@ class IFreqaiModel(ABC):
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self.live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE)
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self.pair = metadata["pair"]
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self.dh = FreqaiDataKitchen(self.config, dataframe, self.live)
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# FreqaiDataKitchen is reinstantiated for each coin
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self.dh = FreqaiDataKitchen(self.config, self.data_drawer, self.live, metadata["pair"])
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if self.live:
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# logger.info('testing live')
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@ -113,7 +117,7 @@ class IFreqaiModel(ABC):
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return (self.dh.full_predictions, self.dh.full_do_predict,
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self.dh.full_target_mean, self.dh.full_target_std)
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logger.info("going to train %s timeranges", len(self.dh.training_timeranges))
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logger.info(f'Training {len(self.dh.training_timeranges)} timeranges')
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# Loop enforcing the sliding window training/backtesting paradigm
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# tr_train is the training time range e.g. 1 historical month
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@ -129,9 +133,12 @@ class IFreqaiModel(ABC):
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self.training_timerange = tr_train
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dataframe_train = self.dh.slice_dataframe(tr_train, dataframe)
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dataframe_backtest = self.dh.slice_dataframe(tr_backtest, dataframe)
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logger.info("training %s for %s", self.pair, tr_train)
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self.dh.model_path = Path(self.dh.full_path / str("sub-train" + "-" + str(tr_train)))
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if not self.model_exists(self.pair, training_timerange=tr_train):
|
||||
logger.info("training %s for %s", metadata["pair"], tr_train)
|
||||
trained_timestamp = TimeRange.parse_timerange(tr_train)
|
||||
self.dh.data_path = Path(self.dh.full_path /
|
||||
str("sub-train" + "-" + metadata['pair'].split("/")[0] +
|
||||
str(int(trained_timestamp.stopts))))
|
||||
if not self.model_exists(metadata["pair"], trained_timestamp=trained_timestamp.stopts):
|
||||
self.model = self.train(dataframe_train, metadata)
|
||||
self.dh.save_data(self.model)
|
||||
else:
|
||||
@ -161,36 +168,40 @@ class IFreqaiModel(ABC):
|
||||
|
||||
"""
|
||||
|
||||
self.dh.set_paths()
|
||||
(model_filename,
|
||||
trained_timestamp,
|
||||
coin_first) = self.data_drawer.get_pair_dict_info(metadata)
|
||||
|
||||
file_exists = self.model_exists(metadata['pair'],
|
||||
training_timerange=self.freqai_info[
|
||||
'live_trained_timerange'])
|
||||
if trained_timestamp != 0:
|
||||
self.dh.set_paths(trained_timestamp)
|
||||
# data_drawer thinks the file eixts, verify here
|
||||
file_exists = self.model_exists(metadata['pair'],
|
||||
trained_timestamp=trained_timestamp,
|
||||
model_filename=model_filename)
|
||||
|
||||
if not self.training_on_separate_thread:
|
||||
# this will also prevent other pairs from trying to train simultaneously.
|
||||
(self.retrain,
|
||||
self.new_trained_timerange) = self.dh.check_if_new_training_required(
|
||||
self.new_trained_timerange,
|
||||
metadata)
|
||||
new_trained_timerange) = self.dh.check_if_new_training_required(
|
||||
trained_timestamp)
|
||||
self.dh.set_paths(new_trained_timerange.stopts)
|
||||
else:
|
||||
logger.info("FreqAI training a new model on background thread.")
|
||||
self.retrain = False
|
||||
|
||||
if self.retrain or not file_exists:
|
||||
if self.first:
|
||||
self.train_model_in_series(self.new_trained_timerange, metadata, strategy)
|
||||
self.first = False
|
||||
if coin_first:
|
||||
self.train_model_in_series(new_trained_timerange, metadata, strategy)
|
||||
else:
|
||||
self.training_on_separate_thread = True # acts like a lock
|
||||
self.retrain_model_on_separate_thread(self.new_trained_timerange,
|
||||
self.retrain_model_on_separate_thread(new_trained_timerange,
|
||||
metadata, strategy)
|
||||
|
||||
self.model = self.dh.load_data()
|
||||
self.model = self.dh.load_data(coin=metadata['pair'])
|
||||
|
||||
strategy_provided_features = self.dh.find_features(dataframe)
|
||||
if strategy_provided_features != self.dh.training_features_list:
|
||||
self.train_model_in_series(self.new_trained_timerange, metadata, strategy)
|
||||
self.train_model_in_series(new_trained_timerange, metadata, strategy)
|
||||
|
||||
preds, do_preds = self.predict(dataframe, metadata)
|
||||
self.dh.append_predictions(preds, do_preds, len(dataframe))
|
||||
@ -252,24 +263,34 @@ class IFreqaiModel(ABC):
|
||||
if self.freqai_info.get('feature_parameters', {}).get('DI_threshold'):
|
||||
self.dh.check_if_pred_in_training_spaces() # sets do_predict
|
||||
|
||||
def model_exists(self, pair: str, training_timerange: str) -> bool:
|
||||
def model_exists(self, pair: str, trained_timestamp: int = None,
|
||||
model_filename: str = '') -> bool:
|
||||
"""
|
||||
Given a pair and path, check if a model already exists
|
||||
:param pair: pair e.g. BTC/USD
|
||||
:param path: path to model
|
||||
"""
|
||||
if self.live and training_timerange == "":
|
||||
return False
|
||||
coin, _ = pair.split("/")
|
||||
self.dh.model_filename = "cb_" + coin.lower() + "_" + training_timerange
|
||||
path_to_modelfile = Path(self.dh.model_path / str(self.dh.model_filename + "_model.joblib"))
|
||||
|
||||
if self.live and trained_timestamp is None:
|
||||
self.dh.model_filename = model_filename
|
||||
else:
|
||||
self.dh.model_filename = "cb_" + coin.lower() + "_" + str(trained_timestamp)
|
||||
|
||||
path_to_modelfile = Path(self.dh.data_path / str(self.dh.model_filename + "_model.joblib"))
|
||||
file_exists = path_to_modelfile.is_file()
|
||||
if file_exists:
|
||||
logger.info("Found model at %s", self.dh.model_path / self.dh.model_filename)
|
||||
logger.info("Found model at %s", self.dh.data_path / self.dh.model_filename)
|
||||
else:
|
||||
logger.info("Could not find model at %s", self.dh.model_path / self.dh.model_filename)
|
||||
logger.info("Could not find model at %s", self.dh.data_path / self.dh.model_filename)
|
||||
return file_exists
|
||||
|
||||
def set_full_path(self) -> None:
|
||||
self.full_path = Path(self.config['user_data_dir'] /
|
||||
"models" /
|
||||
str(self.freqai_info.get('live_full_backtestrange') +
|
||||
self.freqai_info.get('identifier')))
|
||||
|
||||
@threaded
|
||||
def retrain_model_on_separate_thread(self, new_trained_timerange: TimeRange, metadata: dict,
|
||||
strategy: IStrategy):
|
||||
@ -285,7 +306,13 @@ class IFreqaiModel(ABC):
|
||||
metadata)
|
||||
|
||||
self.model = self.train(unfiltered_dataframe, metadata)
|
||||
self.dh.save_data(self.model)
|
||||
|
||||
self.data_drawer.pair_dict[metadata['pair']][
|
||||
'trained_timestamp'] = new_trained_timerange.stopts
|
||||
|
||||
self.dh.set_new_model_names(metadata, new_trained_timerange)
|
||||
|
||||
self.dh.save_data(self.model, coin=metadata['pair'])
|
||||
|
||||
self.training_on_separate_thread = False
|
||||
self.retrain = False
|
||||
@ -303,7 +330,14 @@ class IFreqaiModel(ABC):
|
||||
metadata)
|
||||
|
||||
self.model = self.train(unfiltered_dataframe, metadata)
|
||||
self.dh.save_data(self.model)
|
||||
|
||||
self.data_drawer.pair_dict[metadata['pair']][
|
||||
'trained_timestamp'] = new_trained_timerange.stopts
|
||||
|
||||
self.dh.set_new_model_names(metadata, new_trained_timerange)
|
||||
|
||||
self.data_drawer.pair_dict[metadata['pair']]['first'] = False
|
||||
self.dh.save_data(self.model, coin=metadata['pair'])
|
||||
self.retrain = False
|
||||
|
||||
# Methods which are overridden by user made prediction models.
|
||||
|
||||
@ -140,49 +140,3 @@ class CatboostPredictionModel(IFreqaiModel):
|
||||
# logger.info("--------------------Finished prediction--------------------")
|
||||
|
||||
return (self.dh.predictions, self.dh.do_predict)
|
||||
|
||||
def data_cleaning_train(self) -> None:
|
||||
"""
|
||||
User can add data analysis and cleaning here.
|
||||
Any function inside this method should drop training data points from the filtered_dataframe
|
||||
based on user decided logic. See FreqaiDataKitchen::remove_outliers() for an example
|
||||
of how outlier data points are dropped from the dataframe used for training.
|
||||
"""
|
||||
if self.freqai_info.get('feature_parameters', {}).get('principal_component_analysis'):
|
||||
self.dh.principal_component_analysis()
|
||||
|
||||
# if self.feature_parameters["determine_statistical_distributions"]:
|
||||
# self.dh.determine_statistical_distributions()
|
||||
# if self.feature_parameters["remove_outliers"]:
|
||||
# self.dh.remove_outliers(predict=False)
|
||||
|
||||
if self.freqai_info.get('feature_parameters', {}).get('use_SVM_to_remove_outliers'):
|
||||
self.dh.use_SVM_to_remove_outliers(predict=False)
|
||||
|
||||
if self.freqai_info.get('feature_parameters', {}).get('DI_threshold'):
|
||||
self.dh.data["avg_mean_dist"] = self.dh.compute_distances()
|
||||
|
||||
def data_cleaning_predict(self, filtered_dataframe: DataFrame) -> None:
|
||||
"""
|
||||
User can add data analysis and cleaning here.
|
||||
These functions each modify self.dh.do_predict, which is a dataframe with equal length
|
||||
to the number of candles coming from and returning to the strategy. Inside do_predict,
|
||||
1 allows prediction and < 0 signals to the strategy that the model is not confident in
|
||||
the prediction.
|
||||
See FreqaiDataKitchen::remove_outliers() for an example
|
||||
of how the do_predict vector is modified. do_predict is ultimately passed back to strategy
|
||||
for buy signals.
|
||||
"""
|
||||
if self.freqai_info.get('feature_parameters', {}).get('principal_component_analysis'):
|
||||
self.dh.pca_transform()
|
||||
|
||||
# if self.feature_parameters["determine_statistical_distributions"]:
|
||||
# self.dh.determine_statistical_distributions()
|
||||
# if self.feature_parameters["remove_outliers"]:
|
||||
# self.dh.remove_outliers(predict=True) # creates dropped index
|
||||
|
||||
if self.freqai_info.get('feature_parameters', {}).get('use_SVM_to_remove_outliers'):
|
||||
self.dh.use_SVM_to_remove_outliers(predict=True)
|
||||
|
||||
if self.freqai_info.get('feature_parameters', {}).get('DI_threshold'):
|
||||
self.dh.check_if_pred_in_training_spaces() # sets do_predict
|
||||
|
||||
Loading…
Reference in New Issue
Block a user