Merge branch 'freqtrade:feat/freqai' into feat/freqai
This commit is contained in:
lolong
GitHub
commit
cb1c3315da
@ -8,10 +8,18 @@ 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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@ -55,7 +63,7 @@ class FreqaiDataDrawer:
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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_name: str = 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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@ -151,14 +159,13 @@ class FreqaiDataDrawer:
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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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self.save_follower_dict_to_disk()
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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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def get_pair_dict_info(self, pair: str) -> Tuple[str, int, 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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@ -167,23 +174,20 @@ class FreqaiDataDrawer:
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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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pair_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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if pair_dict:
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model_filename = pair_dict["model_filename"]
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trained_timestamp = pair_dict["trained_timestamp"]
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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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pair_dict = self.pair_dict[pair] = {}
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model_filename = pair_dict["model_filename"] = ""
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trained_timestamp = pair_dict["trained_timestamp"] = 0
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pair_dict["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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@ -191,9 +195,11 @@ class FreqaiDataDrawer:
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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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trained_timestamp = 0
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model_filename = ''
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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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return model_filename, trained_timestamp, 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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@ -214,7 +220,8 @@ class FreqaiDataDrawer:
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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, pred_df, do_preds) -> None:
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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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@ -351,6 +358,217 @@ class FreqaiDataDrawer:
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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(
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open(dk.data_path / str(dk.model_filename + "_pca_object.pkl"), "rb")
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)
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return model
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def update_historic_data(self, strategy: IStrategy, dk: FreqaiDataKitchen) -> None:
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"""
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Append new candles to our stores historic data (in memory) so that
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we do not need to load candle history from disk and we dont need to
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pinging exchange multiple times for the same candle.
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:params:
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dataframe: DataFrame = strategy provided dataframe
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"""
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feat_params = self.freqai_info.get("feature_parameters", {})
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with self.history_lock:
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history_data = self.historic_data
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for pair in dk.all_pairs:
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for tf in feat_params.get("include_timeframes"):
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# check if newest candle is already appended
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df_dp = strategy.dp.get_pair_dataframe(pair, tf)
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if len(df_dp.index) == 0:
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continue
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if str(history_data[pair][tf].iloc[-1]["date"]) == str(
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df_dp.iloc[-1:]["date"].iloc[-1]
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):
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continue
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try:
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index = (
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df_dp.loc[
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df_dp["date"] == history_data[pair][tf].iloc[-1]["date"]
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].index[0]
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+ 1
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)
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except IndexError:
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logger.warning(
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f"Unable to update pair history for {pair}. "
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"If this does not resolve itself after 1 additional candle, "
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"please report the error to #freqai discord channel"
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)
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return
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history_data[pair][tf] = pd.concat(
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[
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history_data[pair][tf],
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strategy.dp.get_pair_dataframe(pair, tf).iloc[index:],
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],
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ignore_index=True,
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axis=0,
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)
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def load_all_pair_histories(self, timerange: TimeRange, dk: FreqaiDataKitchen) -> None:
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"""
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Load pair histories for all whitelist and corr_pairlist pairs.
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Only called once upon startup of bot.
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:params:
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timerange: TimeRange = full timerange required to populate all indicators
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for training according to user defined train_period_days
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"""
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history_data = self.historic_data
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for pair in dk.all_pairs:
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if pair not in history_data:
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history_data[pair] = {}
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for tf in self.freqai_info.get("feature_parameters", {}).get("include_timeframes"):
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history_data[pair][tf] = load_pair_history(
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datadir=self.config["datadir"],
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timeframe=tf,
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pair=pair,
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timerange=timerange,
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data_format=self.config.get("dataformat_ohlcv", "json"),
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candle_type=self.config.get("trading_mode", "spot"),
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)
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def get_base_and_corr_dataframes(
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self, timerange: TimeRange, pair: str, dk: FreqaiDataKitchen
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) -> Tuple[Dict[Any, Any], Dict[Any, Any]]:
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"""
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Searches through our historic_data in memory and returns the dataframes relevant
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to the present pair.
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:params:
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timerange: TimeRange = full timerange required to populate all indicators
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for training according to user defined train_period_days
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metadata: dict = strategy furnished pair metadata
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"""
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with self.history_lock:
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corr_dataframes: Dict[Any, Any] = {}
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base_dataframes: Dict[Any, Any] = {}
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historic_data = self.historic_data
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pairs = self.freqai_info.get("feature_parameters", {}).get(
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"include_corr_pairlist", []
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)
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for tf in self.freqai_info.get("feature_parameters", {}).get("include_timeframes"):
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base_dataframes[tf] = dk.slice_dataframe(timerange, historic_data[pair][tf])
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if pairs:
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for p in pairs:
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if pair in p:
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continue # dont repeat anything from whitelist
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if p not in corr_dataframes:
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corr_dataframes[p] = {}
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corr_dataframes[p][tf] = dk.slice_dataframe(
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timerange, historic_data[p][tf]
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)
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return corr_dataframes, base_dataframes
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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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@ -1,6 +1,5 @@
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import copy
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import datetime
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import json
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import logging
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import shutil
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from pathlib import Path
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@ -9,18 +8,14 @@ from typing import Any, Dict, List, 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 # , Parallel, delayed # used for auto distribution assignment
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from joblib.externals import cloudpickle
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from pandas import DataFrame
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from sklearn import linear_model
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from sklearn.metrics.pairwise import pairwise_distances
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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.exceptions import OperationalException
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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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@ -57,7 +52,6 @@ class FreqaiDataKitchen:
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def __init__(
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self,
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config: Dict[str, Any],
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data_drawer: FreqaiDataDrawer,
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live: bool = False,
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pair: str = "",
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):
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@ -69,6 +63,7 @@ class FreqaiDataKitchen:
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self.append_df: DataFrame = DataFrame()
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self.data_path = Path()
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self.label_list: List = []
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self.training_features_list: List = []
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self.model_filename: str = ""
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self.live = live
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self.pair = pair
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@ -89,8 +84,6 @@ class FreqaiDataKitchen:
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config["freqai"]["backtest_period_days"],
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)
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self.dd = data_drawer
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|
||||
def set_paths(
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self,
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||||
pair: str,
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||||
@ -113,110 +106,6 @@ class FreqaiDataKitchen:
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||||
|
||||
return
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||||
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||||
def save_data(self, model: Any, coin: str = "", label=None) -> None:
|
||||
"""
|
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Saves all data associated with a model for a single sub-train time range
|
||||
:params:
|
||||
:model: User trained model which can be reused for inferencing to generate
|
||||
predictions
|
||||
"""
|
||||
|
||||
if not self.data_path.is_dir():
|
||||
self.data_path.mkdir(parents=True, exist_ok=True)
|
||||
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||||
save_path = Path(self.data_path)
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||||
|
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# Save the trained model
|
||||
if not self.keras:
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||||
dump(model, save_path / f"{self.model_filename}_model.joblib")
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||||
else:
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model.save(save_path / f"{self.model_filename}_model.h5")
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|
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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["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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self.data["label_list"] = self.label_list
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# store the metadata
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||||
with open(save_path / str(self.model_filename + "_metadata.json"), "w") as fp:
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json.dump(self.data, fp, default=self.np_encoder)
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# save the train data to file so we can check preds for area of applicability later
|
||||
self.data_dictionary["train_features"].to_pickle(
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save_path / str(self.model_filename + "_trained_df.pkl")
|
||||
)
|
||||
|
||||
if self.freqai_config.get("feature_parameters", {}).get("principal_component_analysis"):
|
||||
cloudpickle.dump(
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||||
self.pca, open(self.data_path / str(self.model_filename + "_pca_object.pkl"), "wb")
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||||
)
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||||
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||||
# if self.live:
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self.dd.model_dictionary[self.model_filename] = model
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self.dd.pair_dict[coin]["model_filename"] = self.model_filename
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||||
self.dd.pair_dict[coin]["data_path"] = str(self.data_path)
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||||
self.dd.save_drawer_to_disk()
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||||
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||||
return
|
||||
|
||||
def load_data(self, coin: str = "") -> Any:
|
||||
"""
|
||||
loads all data required to make a prediction on a sub-train time range
|
||||
:returns:
|
||||
:model: User trained model which can be inferenced for new predictions
|
||||
"""
|
||||
|
||||
if not self.dd.pair_dict[coin]["model_filename"]:
|
||||
return None
|
||||
|
||||
if self.live:
|
||||
self.model_filename = self.dd.pair_dict[coin]["model_filename"]
|
||||
self.data_path = Path(self.dd.pair_dict[coin]["data_path"])
|
||||
if self.freqai_config.get("follow_mode", False):
|
||||
# follower can be on a different system which is rsynced to the leader:
|
||||
self.data_path = Path(
|
||||
self.config["user_data_dir"]
|
||||
/ "models"
|
||||
/ self.data_path.parts[-2]
|
||||
/ self.data_path.parts[-1]
|
||||
)
|
||||
|
||||
with open(self.data_path / str(self.model_filename + "_metadata.json"), "r") as fp:
|
||||
self.data = json.load(fp)
|
||||
self.training_features_list = self.data["training_features_list"]
|
||||
self.label_list = self.data["label_list"]
|
||||
|
||||
self.data_dictionary["train_features"] = pd.read_pickle(
|
||||
self.data_path / str(self.model_filename + "_trained_df.pkl")
|
||||
)
|
||||
|
||||
# try to access model in memory instead of loading object from disk to save time
|
||||
if self.live and self.model_filename in self.dd.model_dictionary:
|
||||
model = self.dd.model_dictionary[self.model_filename]
|
||||
elif not self.keras:
|
||||
model = load(self.data_path / str(self.model_filename + "_model.joblib"))
|
||||
else:
|
||||
from tensorflow import keras
|
||||
|
||||
model = keras.models.load_model(self.data_path / str(self.model_filename + "_model.h5"))
|
||||
|
||||
if Path(self.data_path / str(self.model_filename + "_svm_model.joblib")).resolve().exists():
|
||||
self.svm_model = load(self.data_path / str(self.model_filename + "_svm_model.joblib"))
|
||||
|
||||
if not model:
|
||||
raise OperationalException(
|
||||
f"Unable to load model, ensure model exists at " f"{self.data_path} "
|
||||
)
|
||||
|
||||
if self.config["freqai"]["feature_parameters"]["principal_component_analysis"]:
|
||||
self.pca = cloudpickle.load(
|
||||
open(self.data_path / str(self.model_filename + "_pca_object.pkl"), "rb")
|
||||
)
|
||||
|
||||
return model
|
||||
|
||||
def make_train_test_datasets(
|
||||
self, filtered_dataframe: DataFrame, labels: DataFrame
|
||||
) -> Dict[Any, Any]:
|
||||
@ -243,20 +132,28 @@ class FreqaiDataKitchen:
|
||||
else:
|
||||
stratification = None
|
||||
|
||||
(
|
||||
train_features,
|
||||
test_features,
|
||||
train_labels,
|
||||
test_labels,
|
||||
train_weights,
|
||||
test_weights,
|
||||
) = train_test_split(
|
||||
filtered_dataframe[: filtered_dataframe.shape[0]],
|
||||
labels,
|
||||
weights,
|
||||
stratify=stratification,
|
||||
**self.config["freqai"]["data_split_parameters"],
|
||||
)
|
||||
if self.freqai_config.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
|
||||
(
|
||||
train_features,
|
||||
test_features,
|
||||
train_labels,
|
||||
test_labels,
|
||||
train_weights,
|
||||
test_weights,
|
||||
) = train_test_split(
|
||||
filtered_dataframe[: filtered_dataframe.shape[0]],
|
||||
labels,
|
||||
weights,
|
||||
stratify=stratification,
|
||||
**self.config["freqai"]["data_split_parameters"],
|
||||
)
|
||||
else:
|
||||
test_labels = np.zeros(2)
|
||||
test_features = pd.DataFrame()
|
||||
test_weights = np.zeros(2)
|
||||
train_features = filtered_dataframe
|
||||
train_labels = labels
|
||||
train_weights = weights
|
||||
|
||||
return self.build_data_dictionary(
|
||||
train_features, test_features, train_labels, test_labels, train_weights, test_weights
|
||||
@ -392,12 +289,13 @@ class FreqaiDataKitchen:
|
||||
/ (train_labels_max - train_labels_min)
|
||||
- 1
|
||||
)
|
||||
data_dictionary["test_labels"][item] = (
|
||||
2
|
||||
* (data_dictionary["test_labels"][item] - train_labels_min)
|
||||
/ (train_labels_max - train_labels_min)
|
||||
- 1
|
||||
)
|
||||
if self.freqai_config.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
|
||||
data_dictionary["test_labels"][item] = (
|
||||
2
|
||||
* (data_dictionary["test_labels"][item] - train_labels_min)
|
||||
/ (train_labels_max - train_labels_min)
|
||||
- 1
|
||||
)
|
||||
|
||||
self.data[f"{item}_max"] = train_labels_max # .to_dict()
|
||||
self.data[f"{item}_min"] = train_labels_min # .to_dict()
|
||||
@ -555,11 +453,12 @@ class FreqaiDataKitchen:
|
||||
self.data["training_features_list_raw"] = copy.deepcopy(self.training_features_list)
|
||||
self.training_features_list = self.data_dictionary["train_features"].columns
|
||||
|
||||
self.data_dictionary["test_features"] = pd.DataFrame(
|
||||
data=test_components,
|
||||
columns=["PC" + str(i) for i in range(0, n_keep_components)],
|
||||
index=self.data_dictionary["test_features"].index,
|
||||
)
|
||||
if self.freqai_config.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
|
||||
self.data_dictionary["test_features"] = pd.DataFrame(
|
||||
data=test_components,
|
||||
columns=["PC" + str(i) for i in range(0, n_keep_components)],
|
||||
index=self.data_dictionary["test_features"].index,
|
||||
)
|
||||
|
||||
self.data["n_kept_components"] = n_keep_components
|
||||
self.pca = pca2
|
||||
@ -652,15 +551,17 @@ class FreqaiDataKitchen:
|
||||
)
|
||||
|
||||
# same for test data
|
||||
y_pred = self.svm_model.predict(self.data_dictionary["test_features"])
|
||||
dropped_points = np.where(y_pred == -1, 0, y_pred)
|
||||
self.data_dictionary["test_features"] = self.data_dictionary["test_features"][
|
||||
(y_pred == 1)
|
||||
]
|
||||
self.data_dictionary["test_labels"] = self.data_dictionary["test_labels"][(y_pred == 1)]
|
||||
self.data_dictionary["test_weights"] = self.data_dictionary["test_weights"][
|
||||
(y_pred == 1)
|
||||
]
|
||||
if self.freqai_config.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
|
||||
y_pred = self.svm_model.predict(self.data_dictionary["test_features"])
|
||||
dropped_points = np.where(y_pred == -1, 0, y_pred)
|
||||
self.data_dictionary["test_features"] = self.data_dictionary["test_features"][
|
||||
(y_pred == 1)
|
||||
]
|
||||
self.data_dictionary["test_labels"] = self.data_dictionary["test_labels"][(
|
||||
y_pred == 1)]
|
||||
self.data_dictionary["test_weights"] = self.data_dictionary["test_weights"][
|
||||
(y_pred == 1)
|
||||
]
|
||||
|
||||
logger.info(
|
||||
f"svm_remove_outliers() tossed {len(y_pred) - dropped_points.sum()}"
|
||||
@ -909,10 +810,10 @@ class FreqaiDataKitchen:
|
||||
coin, _ = pair.split("/")
|
||||
self.data_path = Path(
|
||||
self.full_path
|
||||
/ str("sub-train" + "-" + pair.split("/")[0] + "_" + str(int(trained_timerange.stopts)))
|
||||
/ f"sub-train-{pair.split('/')[0]}_{int(trained_timerange.stopts)}"
|
||||
)
|
||||
|
||||
self.model_filename = "cb_" + coin.lower() + "_" + str(int(trained_timerange.stopts))
|
||||
self.model_filename = f"cb_{coin.lower()}_{int(trained_timerange.stopts)}"
|
||||
|
||||
def download_all_data_for_training(self, timerange: TimeRange) -> None:
|
||||
"""
|
||||
@ -941,56 +842,6 @@ class FreqaiDataKitchen:
|
||||
prepend=self.config.get("prepend_data", False),
|
||||
)
|
||||
|
||||
def update_historic_data(self, strategy: IStrategy) -> 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_config.get("feature_parameters", {})
|
||||
with self.dd.history_lock:
|
||||
history_data = self.dd.historic_data
|
||||
|
||||
for pair in self.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,
|
||||
)
|
||||
|
||||
# logger.info(f'Length of history data {len(history_data[pair][tf])}')
|
||||
|
||||
def set_all_pairs(self) -> None:
|
||||
|
||||
self.all_pairs = copy.deepcopy(
|
||||
@ -1000,63 +851,6 @@ class FreqaiDataKitchen:
|
||||
if pair not in self.all_pairs:
|
||||
self.all_pairs.append(pair)
|
||||
|
||||
def load_all_pair_histories(self, timerange: TimeRange) -> 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.dd.historic_data
|
||||
|
||||
for pair in self.all_pairs:
|
||||
if pair not in history_data:
|
||||
history_data[pair] = {}
|
||||
for tf in self.freqai_config.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
|
||||
) -> 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.dd.history_lock:
|
||||
corr_dataframes: Dict[Any, Any] = {}
|
||||
base_dataframes: Dict[Any, Any] = {}
|
||||
historic_data = self.dd.historic_data
|
||||
pairs = self.freqai_config.get("feature_parameters", {}).get(
|
||||
"include_corr_pairlist", []
|
||||
)
|
||||
|
||||
for tf in self.freqai_config.get("feature_parameters", {}).get("include_timeframes"):
|
||||
base_dataframes[tf] = self.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] = self.slice_dataframe(
|
||||
timerange, historic_data[p][tf]
|
||||
)
|
||||
|
||||
return corr_dataframes, base_dataframes
|
||||
|
||||
def use_strategy_to_populate_indicators(
|
||||
self,
|
||||
strategy: IStrategy,
|
||||
@ -1122,20 +916,6 @@ class FreqaiDataKitchen:
|
||||
|
||||
return dataframe
|
||||
|
||||
def fit_live_predictions(self) -> None:
|
||||
"""
|
||||
Fit the labels with a gaussian distribution
|
||||
"""
|
||||
import scipy as spy
|
||||
|
||||
num_candles = self.freqai_config.get("fit_live_predictions_candles", 100)
|
||||
self.data["labels_mean"], self.data["labels_std"] = {}, {}
|
||||
for label in self.label_list:
|
||||
f = spy.stats.norm.fit(self.dd.historic_predictions[self.pair][label].tail(num_candles))
|
||||
self.data["labels_mean"][label], self.data["labels_std"][label] = f[0], f[1]
|
||||
|
||||
return
|
||||
|
||||
def fit_labels(self) -> None:
|
||||
"""
|
||||
Fit the labels with a gaussian distribution
|
||||
|
||||
@ -102,7 +102,7 @@ class IFreqaiModel(ABC):
|
||||
self.dd.set_pair_dict_info(metadata)
|
||||
|
||||
if self.live:
|
||||
self.dk = FreqaiDataKitchen(self.config, self.dd, self.live, metadata["pair"])
|
||||
self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"])
|
||||
dk = self.start_live(dataframe, metadata, strategy, self.dk)
|
||||
|
||||
# For backtesting, each pair enters and then gets trained for each window along the
|
||||
@ -111,7 +111,7 @@ class IFreqaiModel(ABC):
|
||||
# FreqAI slides the window and sequentially builds the backtesting results before returning
|
||||
# the concatenated results for the full backtesting period back to the strategy.
|
||||
elif not self.follow_mode:
|
||||
self.dk = FreqaiDataKitchen(self.config, self.dd, self.live, metadata["pair"])
|
||||
self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"])
|
||||
logger.info(f"Training {len(self.dk.training_timeranges)} timeranges")
|
||||
|
||||
dataframe = self.dk.use_strategy_to_populate_indicators(
|
||||
@ -120,7 +120,8 @@ class IFreqaiModel(ABC):
|
||||
dk = self.start_backtesting(dataframe, metadata, self.dk)
|
||||
|
||||
dataframe = dk.remove_features_from_df(dk.return_dataframe)
|
||||
return self.return_values(dataframe, dk)
|
||||
del dk
|
||||
return self.return_values(dataframe)
|
||||
|
||||
@threaded
|
||||
def start_scanning(self, strategy: IStrategy) -> None:
|
||||
@ -134,11 +135,11 @@ class IFreqaiModel(ABC):
|
||||
time.sleep(1)
|
||||
for pair in self.config.get("exchange", {}).get("pair_whitelist"):
|
||||
|
||||
(_, trained_timestamp, _, _) = self.dd.get_pair_dict_info(pair)
|
||||
(_, trained_timestamp, _) = self.dd.get_pair_dict_info(pair)
|
||||
|
||||
if self.dd.pair_dict[pair]["priority"] != 1:
|
||||
continue
|
||||
dk = FreqaiDataKitchen(self.config, self.dd, self.live, pair)
|
||||
dk = FreqaiDataKitchen(self.config, self.live, pair)
|
||||
dk.set_paths(pair, trained_timestamp)
|
||||
(
|
||||
retrain,
|
||||
@ -177,7 +178,7 @@ class IFreqaiModel(ABC):
|
||||
# following tr_train. Both of these windows slide through the
|
||||
# entire backtest
|
||||
for tr_train, tr_backtest in zip(dk.training_timeranges, dk.backtesting_timeranges):
|
||||
(_, _, _, _) = self.dd.get_pair_dict_info(metadata["pair"])
|
||||
(_, _, _) = self.dd.get_pair_dict_info(metadata["pair"])
|
||||
train_it += 1
|
||||
total_trains = len(dk.backtesting_timeranges)
|
||||
gc.collect()
|
||||
@ -210,15 +211,16 @@ class IFreqaiModel(ABC):
|
||||
)
|
||||
)
|
||||
if not self.model_exists(
|
||||
metadata["pair"], dk, trained_timestamp=trained_timestamp.stopts
|
||||
metadata["pair"], dk, trained_timestamp=int(trained_timestamp.stopts)
|
||||
):
|
||||
dk.find_features(dataframe_train)
|
||||
self.model = self.train(dataframe_train, metadata["pair"], dk)
|
||||
self.dd.pair_dict[metadata["pair"]]["trained_timestamp"] = trained_timestamp.stopts
|
||||
self.dd.pair_dict[metadata["pair"]]["trained_timestamp"] = int(
|
||||
trained_timestamp.stopts)
|
||||
dk.set_new_model_names(metadata["pair"], trained_timestamp)
|
||||
dk.save_data(self.model, metadata["pair"])
|
||||
self.dd.save_data(self.model, metadata["pair"], dk)
|
||||
else:
|
||||
self.model = dk.load_data(metadata["pair"])
|
||||
self.model = self.dd.load_data(metadata["pair"], dk)
|
||||
|
||||
self.check_if_feature_list_matches_strategy(dataframe_train, dk)
|
||||
|
||||
@ -249,7 +251,7 @@ class IFreqaiModel(ABC):
|
||||
self.dd.update_follower_metadata()
|
||||
|
||||
# get the model metadata associated with the current pair
|
||||
(_, trained_timestamp, _, return_null_array) = self.dd.get_pair_dict_info(metadata["pair"])
|
||||
(_, trained_timestamp, return_null_array) = self.dd.get_pair_dict_info(metadata["pair"])
|
||||
|
||||
# if the metadata doesnt exist, the follower returns null arrays to strategy
|
||||
if self.follow_mode and return_null_array:
|
||||
@ -259,7 +261,7 @@ class IFreqaiModel(ABC):
|
||||
|
||||
# append the historic data once per round
|
||||
if self.dd.historic_data:
|
||||
dk.update_historic_data(strategy)
|
||||
self.dd.update_historic_data(strategy, dk)
|
||||
logger.debug(f'Updating historic data on pair {metadata["pair"]}')
|
||||
|
||||
if not self.follow_mode:
|
||||
@ -277,7 +279,7 @@ class IFreqaiModel(ABC):
|
||||
"data saved"
|
||||
)
|
||||
dk.download_all_data_for_training(data_load_timerange)
|
||||
dk.load_all_pair_histories(data_load_timerange)
|
||||
self.dd.load_all_pair_histories(data_load_timerange, dk)
|
||||
|
||||
if not self.scanning:
|
||||
self.scanning = True
|
||||
@ -291,7 +293,7 @@ class IFreqaiModel(ABC):
|
||||
)
|
||||
|
||||
# load the model and associated data into the data kitchen
|
||||
self.model = dk.load_data(coin=metadata["pair"])
|
||||
self.model = self.dd.load_data(metadata["pair"], dk)
|
||||
|
||||
dataframe = self.dk.use_strategy_to_populate_indicators(
|
||||
strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
|
||||
@ -467,8 +469,8 @@ class IFreqaiModel(ABC):
|
||||
new_trained_timerange does not contain any NaNs)
|
||||
"""
|
||||
|
||||
corr_dataframes, base_dataframes = dk.get_base_and_corr_dataframes(
|
||||
data_load_timerange, pair
|
||||
corr_dataframes, base_dataframes = self.dd.get_base_and_corr_dataframes(
|
||||
data_load_timerange, pair, dk
|
||||
)
|
||||
|
||||
unfiltered_dataframe = dk.use_strategy_to_populate_indicators(
|
||||
@ -488,7 +490,7 @@ class IFreqaiModel(ABC):
|
||||
if self.dd.pair_dict[pair]["priority"] == 1 and self.scanning:
|
||||
with self.lock:
|
||||
self.dd.pair_to_end_of_training_queue(pair)
|
||||
dk.save_data(model, coin=pair)
|
||||
self.dd.save_data(model, pair, dk)
|
||||
|
||||
if self.freqai_info.get("purge_old_models", False):
|
||||
self.dd.purge_old_models()
|
||||
@ -504,6 +506,20 @@ class IFreqaiModel(ABC):
|
||||
self.dd.historic_predictions[pair] = pd.DataFrame()
|
||||
self.dd.historic_predictions[pair] = copy.deepcopy(pred_df)
|
||||
|
||||
def fit_live_predictions(self, dk: FreqaiDataKitchen) -> None:
|
||||
"""
|
||||
Fit the labels with a gaussian distribution
|
||||
"""
|
||||
import scipy as spy
|
||||
|
||||
num_candles = self.freqai_info.get("fit_live_predictions_candles", 100)
|
||||
dk.data["labels_mean"], dk.data["labels_std"] = {}, {}
|
||||
for label in dk.label_list:
|
||||
f = spy.stats.norm.fit(self.dd.historic_predictions[dk.pair][label].tail(num_candles))
|
||||
dk.data["labels_mean"][label], dk.data["labels_std"][label] = f[0], f[1]
|
||||
|
||||
return
|
||||
|
||||
# Following methods which are overridden by user made prediction models.
|
||||
# See freqai/prediction_models/CatboostPredictionModlel.py for an example.
|
||||
|
||||
@ -545,12 +561,11 @@ class IFreqaiModel(ABC):
|
||||
"""
|
||||
|
||||
@abstractmethod
|
||||
def return_values(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
|
||||
def return_values(self, dataframe: DataFrame) -> DataFrame:
|
||||
"""
|
||||
User defines the dataframe to be returned to strategy here.
|
||||
:param dataframe: DataFrame = the full dataframe for the current prediction (live)
|
||||
or --timerange (backtesting)
|
||||
:param dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
|
||||
:return: dataframe: DataFrame = dataframe filled with user defined data
|
||||
"""
|
||||
|
||||
|
||||
@ -18,7 +18,7 @@ class BaseRegressionModel(IFreqaiModel):
|
||||
such as prediction_models/CatboostPredictionModel.py for guidance.
|
||||
"""
|
||||
|
||||
def return_values(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
|
||||
def return_values(self, dataframe: DataFrame) -> DataFrame:
|
||||
"""
|
||||
User uses this function to add any additional return values to the dataframe.
|
||||
e.g.
|
||||
@ -55,8 +55,6 @@ class BaseRegressionModel(IFreqaiModel):
|
||||
f"{end_date}--------------------")
|
||||
# split data into train/test data.
|
||||
data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
|
||||
if not self.freqai_info.get('fit_live_predictions', 0):
|
||||
dk.fit_labels()
|
||||
# normalize all data based on train_dataset only
|
||||
data_dictionary = dk.normalize_data(data_dictionary)
|
||||
|
||||
@ -73,8 +71,11 @@ class BaseRegressionModel(IFreqaiModel):
|
||||
if pair not in self.dd.historic_predictions:
|
||||
self.set_initial_historic_predictions(
|
||||
data_dictionary['train_features'], model, dk, pair)
|
||||
elif self.freqai_info.get('fit_live_predictions_candles', 0):
|
||||
dk.fit_live_predictions()
|
||||
|
||||
if self.freqai_info.get('fit_live_predictions_candles', 0) and self.live:
|
||||
self.fit_live_predictions(dk)
|
||||
else:
|
||||
dk.fit_labels()
|
||||
|
||||
self.dd.save_historic_predictions_to_disk()
|
||||
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
import logging
|
||||
from typing import Tuple
|
||||
from typing import Any
|
||||
|
||||
from pandas import DataFrame
|
||||
|
||||
@ -16,7 +16,7 @@ class BaseTensorFlowModel(IFreqaiModel):
|
||||
User *must* inherit from this class and set fit() and predict().
|
||||
"""
|
||||
|
||||
def return_values(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
|
||||
def return_values(self, dataframe: DataFrame) -> DataFrame:
|
||||
"""
|
||||
User uses this function to add any additional return values to the dataframe.
|
||||
e.g.
|
||||
@ -27,7 +27,7 @@ class BaseTensorFlowModel(IFreqaiModel):
|
||||
|
||||
def train(
|
||||
self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen
|
||||
) -> Tuple[DataFrame, DataFrame]:
|
||||
) -> Any:
|
||||
"""
|
||||
Filter the training data and train a model to it. Train makes heavy use of the datakitchen
|
||||
for storing, saving, loading, and analyzing the data.
|
||||
@ -49,8 +49,7 @@ class BaseTensorFlowModel(IFreqaiModel):
|
||||
|
||||
# split data into train/test data.
|
||||
data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
|
||||
if not self.freqai_info.get('fit_live_predictions', 0):
|
||||
dk.fit_labels()
|
||||
|
||||
# normalize all data based on train_dataset only
|
||||
data_dictionary = dk.normalize_data(data_dictionary)
|
||||
|
||||
@ -67,8 +66,11 @@ class BaseTensorFlowModel(IFreqaiModel):
|
||||
if pair not in self.dd.historic_predictions:
|
||||
self.set_initial_historic_predictions(
|
||||
data_dictionary['train_features'], model, dk, pair)
|
||||
elif self.freqai_info.get('fit_live_predictions_candles', 0):
|
||||
dk.fit_live_predictions()
|
||||
|
||||
if self.freqai_info.get('fit_live_predictions_candles', 0) and self.live:
|
||||
self.fit_live_predictions(dk)
|
||||
else:
|
||||
dk.fit_labels()
|
||||
|
||||
self.dd.save_historic_predictions_to_disk()
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
import logging
|
||||
from typing import Any, Dict
|
||||
|
||||
import gc
|
||||
from catboost import CatBoostRegressor, Pool
|
||||
|
||||
from freqtrade.freqai.prediction_models.BaseRegressionModel import BaseRegressionModel
|
||||
@ -28,17 +28,25 @@ class CatboostPredictionModel(BaseRegressionModel):
|
||||
label=data_dictionary["train_labels"],
|
||||
weight=data_dictionary["train_weights"],
|
||||
)
|
||||
|
||||
test_data = Pool(
|
||||
data=data_dictionary["test_features"],
|
||||
label=data_dictionary["test_labels"],
|
||||
weight=data_dictionary["test_weights"],
|
||||
)
|
||||
if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0:
|
||||
test_data = None
|
||||
else:
|
||||
test_data = Pool(
|
||||
data=data_dictionary["test_features"],
|
||||
label=data_dictionary["test_labels"],
|
||||
weight=data_dictionary["test_weights"],
|
||||
)
|
||||
|
||||
model = CatBoostRegressor(
|
||||
allow_writing_files=False,
|
||||
**self.model_training_parameters,
|
||||
)
|
||||
|
||||
model.fit(X=train_data, eval_set=test_data)
|
||||
|
||||
# some evidence that catboost pools have memory leaks:
|
||||
# https://github.com/catboost/catboost/issues/1835
|
||||
del train_data, test_data
|
||||
gc.collect()
|
||||
|
||||
return model
|
||||
|
||||
@ -36,7 +36,9 @@ class CatboostPredictionMultiModel(BaseRegressionModel):
|
||||
|
||||
model = MultiOutputRegressor(estimator=cbr)
|
||||
model.fit(X=X, y=y, sample_weight=sample_weight) # , eval_set=eval_set)
|
||||
train_score = model.score(X, y)
|
||||
test_score = model.score(*eval_set)
|
||||
logger.info(f"Train score {train_score}, Test score {test_score}")
|
||||
|
||||
if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
|
||||
train_score = model.score(X, y)
|
||||
test_score = model.score(*eval_set)
|
||||
logger.info(f"Train score {train_score}, Test score {test_score}")
|
||||
return model
|
||||
|
||||
@ -25,11 +25,15 @@ class LightGBMPredictionModel(BaseRegressionModel):
|
||||
all the training and test data/labels.
|
||||
"""
|
||||
|
||||
eval_set = (data_dictionary["test_features"], data_dictionary["test_labels"])
|
||||
if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0:
|
||||
eval_set = None
|
||||
else:
|
||||
eval_set = (data_dictionary["test_features"], data_dictionary["test_labels"])
|
||||
X = data_dictionary["train_features"]
|
||||
y = data_dictionary["train_labels"]
|
||||
|
||||
model = LGBMRegressor(**self.model_training_parameters)
|
||||
|
||||
model.fit(X=X, y=y, eval_set=eval_set)
|
||||
|
||||
return model
|
||||
|
||||
@ -1,11 +1,11 @@
|
||||
from copy import deepcopy
|
||||
from pathlib import Path
|
||||
from unittest.mock import MagicMock
|
||||
|
||||
import pytest
|
||||
|
||||
from freqtrade.configuration import TimeRange
|
||||
from freqtrade.data.dataprovider import DataProvider
|
||||
from freqtrade.freqai.data_drawer import FreqaiDataDrawer
|
||||
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
|
||||
from freqtrade.resolvers import StrategyResolver
|
||||
from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver
|
||||
@ -57,11 +57,17 @@ def freqai_conf(default_conf, tmpdir):
|
||||
|
||||
|
||||
def get_patched_data_kitchen(mocker, freqaiconf):
|
||||
dd = mocker.patch('freqtrade.freqai.data_drawer', MagicMock())
|
||||
dk = FreqaiDataKitchen(freqaiconf, dd)
|
||||
# dd = mocker.patch('freqtrade.freqai.data_drawer', MagicMock())
|
||||
dk = FreqaiDataKitchen(freqaiconf)
|
||||
return dk
|
||||
|
||||
|
||||
def get_patched_data_drawer(mocker, freqaiconf):
|
||||
# dd = mocker.patch('freqtrade.freqai.data_drawer', MagicMock())
|
||||
dd = FreqaiDataDrawer(freqaiconf)
|
||||
return dd
|
||||
|
||||
|
||||
def get_patched_freqai_strategy(mocker, freqaiconf):
|
||||
strategy = StrategyResolver.load_strategy(freqaiconf)
|
||||
strategy.ft_bot_start()
|
||||
|
||||
94
tests/freqai/test_freqai_datadrawer.py
Normal file
94
tests/freqai/test_freqai_datadrawer.py
Normal file
@ -0,0 +1,94 @@
|
||||
|
||||
import shutil
|
||||
from pathlib import Path
|
||||
|
||||
from freqtrade.configuration import TimeRange
|
||||
from freqtrade.data.dataprovider import DataProvider
|
||||
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
|
||||
from tests.conftest import get_patched_exchange
|
||||
from tests.freqai.conftest import get_patched_freqai_strategy
|
||||
|
||||
|
||||
def test_update_historic_data(mocker, freqai_conf):
|
||||
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
|
||||
exchange = get_patched_exchange(mocker, freqai_conf)
|
||||
strategy.dp = DataProvider(freqai_conf, exchange)
|
||||
freqai = strategy.freqai
|
||||
freqai.live = True
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180114")
|
||||
|
||||
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
|
||||
historic_candles = len(freqai.dd.historic_data["ADA/BTC"]["5m"])
|
||||
dp_candles = len(strategy.dp.get_pair_dataframe("ADA/BTC", "5m"))
|
||||
candle_difference = dp_candles - historic_candles
|
||||
freqai.dd.update_historic_data(strategy, freqai.dk)
|
||||
|
||||
updated_historic_candles = len(freqai.dd.historic_data["ADA/BTC"]["5m"])
|
||||
|
||||
assert updated_historic_candles - historic_candles == candle_difference
|
||||
shutil.rmtree(Path(freqai.dk.full_path))
|
||||
|
||||
|
||||
def test_load_all_pairs_histories(mocker, freqai_conf):
|
||||
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
|
||||
exchange = get_patched_exchange(mocker, freqai_conf)
|
||||
strategy.dp = DataProvider(freqai_conf, exchange)
|
||||
freqai = strategy.freqai
|
||||
freqai.live = True
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180114")
|
||||
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
|
||||
|
||||
assert len(freqai.dd.historic_data.keys()) == len(
|
||||
freqai_conf.get("exchange", {}).get("pair_whitelist")
|
||||
)
|
||||
assert len(freqai.dd.historic_data["ADA/BTC"]) == len(
|
||||
freqai_conf.get("freqai", {}).get("feature_parameters", {}).get("include_timeframes")
|
||||
)
|
||||
shutil.rmtree(Path(freqai.dk.full_path))
|
||||
|
||||
|
||||
def test_get_base_and_corr_dataframes(mocker, freqai_conf):
|
||||
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
|
||||
exchange = get_patched_exchange(mocker, freqai_conf)
|
||||
strategy.dp = DataProvider(freqai_conf, exchange)
|
||||
freqai = strategy.freqai
|
||||
freqai.live = True
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180114")
|
||||
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
|
||||
sub_timerange = TimeRange.parse_timerange("20180111-20180114")
|
||||
corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
|
||||
|
||||
num_tfs = len(
|
||||
freqai_conf.get("freqai", {}).get("feature_parameters", {}).get("include_timeframes")
|
||||
)
|
||||
|
||||
assert len(base_df.keys()) == num_tfs
|
||||
|
||||
assert len(corr_df.keys()) == len(
|
||||
freqai_conf.get("freqai", {}).get("feature_parameters", {}).get("include_corr_pairlist")
|
||||
)
|
||||
|
||||
assert len(corr_df["ADA/BTC"].keys()) == num_tfs
|
||||
shutil.rmtree(Path(freqai.dk.full_path))
|
||||
|
||||
|
||||
def test_use_strategy_to_populate_indicators(mocker, freqai_conf):
|
||||
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
|
||||
exchange = get_patched_exchange(mocker, freqai_conf)
|
||||
strategy.dp = DataProvider(freqai_conf, exchange)
|
||||
strategy.freqai_info = freqai_conf.get("freqai", {})
|
||||
freqai = strategy.freqai
|
||||
freqai.live = True
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180114")
|
||||
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
|
||||
sub_timerange = TimeRange.parse_timerange("20180111-20180114")
|
||||
corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
|
||||
|
||||
df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, 'LTC/BTC')
|
||||
|
||||
assert len(df.columns) == 45
|
||||
shutil.rmtree(Path(freqai.dk.full_path))
|
||||
@ -4,13 +4,8 @@ from pathlib import Path
|
||||
|
||||
import pytest
|
||||
|
||||
from freqtrade.configuration import TimeRange
|
||||
from freqtrade.data.dataprovider import DataProvider
|
||||
# from freqtrade.freqai.data_drawer import FreqaiDataDrawer
|
||||
from freqtrade.exceptions import OperationalException
|
||||
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
|
||||
from tests.conftest import get_patched_exchange
|
||||
from tests.freqai.conftest import get_patched_data_kitchen, get_patched_freqai_strategy
|
||||
from tests.freqai.conftest import get_patched_data_kitchen
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
@ -60,27 +55,6 @@ def test_split_timerange(
|
||||
shutil.rmtree(Path(dk.full_path))
|
||||
|
||||
|
||||
def test_update_historic_data(mocker, freqai_conf):
|
||||
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
|
||||
exchange = get_patched_exchange(mocker, freqai_conf)
|
||||
strategy.dp = DataProvider(freqai_conf, exchange)
|
||||
freqai = strategy.freqai
|
||||
freqai.live = True
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180114")
|
||||
|
||||
freqai.dk.load_all_pair_histories(timerange)
|
||||
historic_candles = len(freqai.dd.historic_data["ADA/BTC"]["5m"])
|
||||
dp_candles = len(strategy.dp.get_pair_dataframe("ADA/BTC", "5m"))
|
||||
candle_difference = dp_candles - historic_candles
|
||||
freqai.dk.update_historic_data(strategy)
|
||||
|
||||
updated_historic_candles = len(freqai.dd.historic_data["ADA/BTC"]["5m"])
|
||||
|
||||
assert updated_historic_candles - historic_candles == candle_difference
|
||||
shutil.rmtree(Path(freqai.dk.full_path))
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"timestamp, expected",
|
||||
[
|
||||
@ -92,67 +66,3 @@ def test_check_if_model_expired(mocker, freqai_conf, timestamp, expected):
|
||||
dk = get_patched_data_kitchen(mocker, freqai_conf)
|
||||
assert dk.check_if_model_expired(timestamp) == expected
|
||||
shutil.rmtree(Path(dk.full_path))
|
||||
|
||||
|
||||
def test_load_all_pairs_histories(mocker, freqai_conf):
|
||||
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
|
||||
exchange = get_patched_exchange(mocker, freqai_conf)
|
||||
strategy.dp = DataProvider(freqai_conf, exchange)
|
||||
freqai = strategy.freqai
|
||||
freqai.live = True
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180114")
|
||||
freqai.dk.load_all_pair_histories(timerange)
|
||||
|
||||
assert len(freqai.dd.historic_data.keys()) == len(
|
||||
freqai_conf.get("exchange", {}).get("pair_whitelist")
|
||||
)
|
||||
assert len(freqai.dd.historic_data["ADA/BTC"]) == len(
|
||||
freqai_conf.get("freqai", {}).get("feature_parameters", {}).get("include_timeframes")
|
||||
)
|
||||
shutil.rmtree(Path(freqai.dk.full_path))
|
||||
|
||||
|
||||
def test_get_base_and_corr_dataframes(mocker, freqai_conf):
|
||||
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
|
||||
exchange = get_patched_exchange(mocker, freqai_conf)
|
||||
strategy.dp = DataProvider(freqai_conf, exchange)
|
||||
freqai = strategy.freqai
|
||||
freqai.live = True
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180114")
|
||||
freqai.dk.load_all_pair_histories(timerange)
|
||||
sub_timerange = TimeRange.parse_timerange("20180111-20180114")
|
||||
corr_df, base_df = freqai.dk.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC")
|
||||
|
||||
num_tfs = len(
|
||||
freqai_conf.get("freqai", {}).get("feature_parameters", {}).get("include_timeframes")
|
||||
)
|
||||
|
||||
assert len(base_df.keys()) == num_tfs
|
||||
|
||||
assert len(corr_df.keys()) == len(
|
||||
freqai_conf.get("freqai", {}).get("feature_parameters", {}).get("include_corr_pairlist")
|
||||
)
|
||||
|
||||
assert len(corr_df["ADA/BTC"].keys()) == num_tfs
|
||||
shutil.rmtree(Path(freqai.dk.full_path))
|
||||
|
||||
|
||||
def test_use_strategy_to_populate_indicators(mocker, freqai_conf):
|
||||
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
|
||||
exchange = get_patched_exchange(mocker, freqai_conf)
|
||||
strategy.dp = DataProvider(freqai_conf, exchange)
|
||||
strategy.freqai_info = freqai_conf.get("freqai", {})
|
||||
freqai = strategy.freqai
|
||||
freqai.live = True
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180114")
|
||||
freqai.dk.load_all_pair_histories(timerange)
|
||||
sub_timerange = TimeRange.parse_timerange("20180111-20180114")
|
||||
corr_df, base_df = freqai.dk.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC")
|
||||
|
||||
df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, 'LTC/BTC')
|
||||
|
||||
assert len(df.columns) == 45
|
||||
shutil.rmtree(Path(freqai.dk.full_path))
|
||||
|
||||
@ -1,5 +1,3 @@
|
||||
# from unittest.mock import MagicMock
|
||||
# from freqtrade.commands.optimize_commands import setup_optimize_configuration, start_edge
|
||||
import platform
|
||||
import shutil
|
||||
from pathlib import Path
|
||||
@ -23,9 +21,9 @@ def test_train_model_in_series_LightGBM(mocker, freqai_conf):
|
||||
strategy.freqai_info = freqai_conf.get("freqai", {})
|
||||
freqai = strategy.freqai
|
||||
freqai.live = True
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd)
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
freqai.dk.load_all_pair_histories(timerange)
|
||||
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
|
||||
|
||||
freqai.dd.pair_dict = MagicMock()
|
||||
|
||||
@ -42,6 +40,36 @@ def test_train_model_in_series_LightGBM(mocker, freqai_conf):
|
||||
shutil.rmtree(Path(freqai.dk.full_path))
|
||||
|
||||
|
||||
def test_train_model_in_series_LightGBMMultiModel(mocker, freqai_conf):
|
||||
freqai_conf.update({"timerange": "20180110-20180130"})
|
||||
freqai_conf.update({"strategy": "freqai_test_multimodel_strat"})
|
||||
freqai_conf.update({"freqaimodel": "LightGBMPredictionMultiModel"})
|
||||
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
|
||||
exchange = get_patched_exchange(mocker, freqai_conf)
|
||||
strategy.dp = DataProvider(freqai_conf, exchange)
|
||||
strategy.freqai_info = freqai_conf.get("freqai", {})
|
||||
freqai = strategy.freqai
|
||||
freqai.live = True
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
|
||||
|
||||
freqai.dd.pair_dict = MagicMock()
|
||||
|
||||
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
new_timerange = TimeRange.parse_timerange("20180120-20180130")
|
||||
|
||||
freqai.train_model_in_series(new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
|
||||
|
||||
assert len(freqai.dk.label_list) == 2
|
||||
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").is_file()
|
||||
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").is_file()
|
||||
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").is_file()
|
||||
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_svm_model.joblib").is_file()
|
||||
|
||||
shutil.rmtree(Path(freqai.dk.full_path))
|
||||
|
||||
|
||||
@pytest.mark.skipif("arm" in platform.uname()[-1], reason="no ARM for Catboost ...")
|
||||
def test_train_model_in_series_Catboost(mocker, freqai_conf):
|
||||
freqai_conf.update({"timerange": "20180110-20180130"})
|
||||
@ -54,9 +82,9 @@ def test_train_model_in_series_Catboost(mocker, freqai_conf):
|
||||
strategy.freqai_info = freqai_conf.get("freqai", {})
|
||||
freqai = strategy.freqai
|
||||
freqai.live = True
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd)
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
freqai.dk.load_all_pair_histories(timerange)
|
||||
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
|
||||
|
||||
freqai.dd.pair_dict = MagicMock()
|
||||
|
||||
@ -82,15 +110,15 @@ def test_start_backtesting(mocker, freqai_conf):
|
||||
strategy.freqai_info = freqai_conf.get("freqai", {})
|
||||
freqai = strategy.freqai
|
||||
freqai.live = False
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd)
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
freqai.dk.load_all_pair_histories(timerange)
|
||||
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
|
||||
sub_timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
corr_df, base_df = freqai.dk.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC")
|
||||
corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
|
||||
|
||||
df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
|
||||
|
||||
metadata = {"pair": "ADA/BTC"}
|
||||
metadata = {"pair": "LTC/BTC"}
|
||||
freqai.start_backtesting(df, metadata, freqai.dk)
|
||||
model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
|
||||
|
||||
@ -99,6 +127,31 @@ def test_start_backtesting(mocker, freqai_conf):
|
||||
shutil.rmtree(Path(freqai.dk.full_path))
|
||||
|
||||
|
||||
def test_start_backtesting_subdaily_backtest_period(mocker, freqai_conf):
|
||||
freqai_conf.update({"timerange": "20180120-20180124"})
|
||||
freqai_conf.get("freqai", {}).update({"backtest_period_days": 0.5})
|
||||
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
|
||||
exchange = get_patched_exchange(mocker, freqai_conf)
|
||||
strategy.dp = DataProvider(freqai_conf, exchange)
|
||||
strategy.freqai_info = freqai_conf.get("freqai", {})
|
||||
freqai = strategy.freqai
|
||||
freqai.live = False
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
|
||||
sub_timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
|
||||
|
||||
df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
|
||||
|
||||
metadata = {"pair": "LTC/BTC"}
|
||||
freqai.start_backtesting(df, metadata, freqai.dk)
|
||||
model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
|
||||
assert len(model_folders) == 8
|
||||
|
||||
shutil.rmtree(Path(freqai.dk.full_path))
|
||||
|
||||
|
||||
def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
|
||||
freqai_conf.update({"timerange": "20180120-20180130"})
|
||||
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
|
||||
@ -107,11 +160,11 @@ def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
|
||||
strategy.freqai_info = freqai_conf.get("freqai", {})
|
||||
freqai = strategy.freqai
|
||||
freqai.live = False
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd)
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
freqai.dk.load_all_pair_histories(timerange)
|
||||
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
|
||||
sub_timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
corr_df, base_df = freqai.dk.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC")
|
||||
corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
|
||||
|
||||
df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
|
||||
|
||||
@ -130,11 +183,11 @@ def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
|
||||
strategy.freqai_info = freqai_conf.get("freqai", {})
|
||||
freqai = strategy.freqai
|
||||
freqai.live = False
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd)
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
freqai.dk.load_all_pair_histories(timerange)
|
||||
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
|
||||
sub_timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
corr_df, base_df = freqai.dk.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC")
|
||||
corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
|
||||
|
||||
df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
|
||||
freqai.start_backtesting(df, metadata, freqai.dk)
|
||||
@ -156,13 +209,12 @@ def test_follow_mode(mocker, freqai_conf):
|
||||
strategy.freqai_info = freqai_conf.get("freqai", {})
|
||||
freqai = strategy.freqai
|
||||
freqai.live = True
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd)
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
freqai.dk.load_all_pair_histories(timerange)
|
||||
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
|
||||
|
||||
metadata = {"pair": "ADA/BTC"}
|
||||
freqai.dd.set_pair_dict_info(metadata)
|
||||
# freqai.dd.pair_dict = MagicMock()
|
||||
|
||||
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
new_timerange = TimeRange.parse_timerange("20180120-20180130")
|
||||
@ -184,9 +236,9 @@ def test_follow_mode(mocker, freqai_conf):
|
||||
strategy.freqai_info = freqai_conf.get("freqai", {})
|
||||
freqai = strategy.freqai
|
||||
freqai.live = True
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd, freqai.live)
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.live)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
freqai.dk.load_all_pair_histories(timerange)
|
||||
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
|
||||
|
||||
df = strategy.dp.get_pair_dataframe('ADA/BTC', '5m')
|
||||
freqai.start_live(df, metadata, strategy, freqai.dk)
|
||||
@ -207,9 +259,9 @@ def test_principal_component_analysis(mocker, freqai_conf):
|
||||
strategy.freqai_info = freqai_conf.get("freqai", {})
|
||||
freqai = strategy.freqai
|
||||
freqai.live = True
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.dd)
|
||||
freqai.dk = FreqaiDataKitchen(freqai_conf)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
freqai.dk.load_all_pair_histories(timerange)
|
||||
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
|
||||
|
||||
freqai.dd.pair_dict = MagicMock()
|
||||
|
||||
|
||||
@ -1404,6 +1404,7 @@ def test_api_strategies(botclient):
|
||||
'StrategyTestV3',
|
||||
'StrategyTestV3Analysis',
|
||||
'StrategyTestV3Futures',
|
||||
'freqai_test_multimodel_strat',
|
||||
'freqai_test_strat'
|
||||
]}
|
||||
|
||||
|
||||
188
tests/strategy/strats/freqai_test_multimodel_strat.py
Normal file
188
tests/strategy/strats/freqai_test_multimodel_strat.py
Normal file
@ -0,0 +1,188 @@
|
||||
import logging
|
||||
from functools import reduce
|
||||
|
||||
import pandas as pd
|
||||
import talib.abstract as ta
|
||||
from pandas import DataFrame
|
||||
|
||||
from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy, merge_informative_pair
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class freqai_test_multimodel_strat(IStrategy):
|
||||
"""
|
||||
Example strategy showing how the user connects their own
|
||||
IFreqaiModel to the strategy. Namely, the user uses:
|
||||
self.freqai.start(dataframe, metadata)
|
||||
|
||||
to make predictions on their data. populate_any_indicators() automatically
|
||||
generates the variety of features indicated by the user in the
|
||||
canonical freqtrade configuration file under config['freqai'].
|
||||
"""
|
||||
|
||||
minimal_roi = {"0": 0.1, "240": -1}
|
||||
|
||||
plot_config = {
|
||||
"main_plot": {},
|
||||
"subplots": {
|
||||
"prediction": {"prediction": {"color": "blue"}},
|
||||
"target_roi": {
|
||||
"target_roi": {"color": "brown"},
|
||||
},
|
||||
"do_predict": {
|
||||
"do_predict": {"color": "brown"},
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
process_only_new_candles = True
|
||||
stoploss = -0.05
|
||||
use_exit_signal = True
|
||||
startup_candle_count: int = 300
|
||||
can_short = False
|
||||
|
||||
linear_roi_offset = DecimalParameter(
|
||||
0.00, 0.02, default=0.005, space="sell", optimize=False, load=True
|
||||
)
|
||||
max_roi_time_long = IntParameter(0, 800, default=400, space="sell", optimize=False, load=True)
|
||||
|
||||
def informative_pairs(self):
|
||||
whitelist_pairs = self.dp.current_whitelist()
|
||||
corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
|
||||
informative_pairs = []
|
||||
for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]:
|
||||
for pair in whitelist_pairs:
|
||||
informative_pairs.append((pair, tf))
|
||||
for pair in corr_pairs:
|
||||
if pair in whitelist_pairs:
|
||||
continue # avoid duplication
|
||||
informative_pairs.append((pair, tf))
|
||||
return informative_pairs
|
||||
|
||||
def populate_any_indicators(
|
||||
self, metadata, pair, df, tf, informative=None, coin="", set_generalized_indicators=False
|
||||
):
|
||||
"""
|
||||
Function designed to automatically generate, name and merge features
|
||||
from user indicated timeframes in the configuration file. User controls the indicators
|
||||
passed to the training/prediction by prepending indicators with `'%-' + coin `
|
||||
(see convention below). I.e. user should not prepend any supporting metrics
|
||||
(e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the
|
||||
model.
|
||||
:params:
|
||||
:pair: pair to be used as informative
|
||||
:df: strategy dataframe which will receive merges from informatives
|
||||
:tf: timeframe of the dataframe which will modify the feature names
|
||||
:informative: the dataframe associated with the informative pair
|
||||
:coin: the name of the coin which will modify the feature names.
|
||||
"""
|
||||
|
||||
with self.freqai.lock:
|
||||
if informative is None:
|
||||
informative = self.dp.get_pair_dataframe(pair, tf)
|
||||
|
||||
# first loop is automatically duplicating indicators for time periods
|
||||
for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
|
||||
|
||||
t = int(t)
|
||||
informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
|
||||
informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
|
||||
informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
|
||||
|
||||
informative[f"%-{coin}pct-change"] = informative["close"].pct_change()
|
||||
informative[f"%-{coin}raw_volume"] = informative["volume"]
|
||||
informative[f"%-{coin}raw_price"] = informative["close"]
|
||||
|
||||
indicators = [col for col in informative if col.startswith("%")]
|
||||
# This loop duplicates and shifts all indicators to add a sense of recency to data
|
||||
for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
|
||||
if n == 0:
|
||||
continue
|
||||
informative_shift = informative[indicators].shift(n)
|
||||
informative_shift = informative_shift.add_suffix("_shift-" + str(n))
|
||||
informative = pd.concat((informative, informative_shift), axis=1)
|
||||
|
||||
df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
|
||||
skip_columns = [
|
||||
(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
|
||||
]
|
||||
df = df.drop(columns=skip_columns)
|
||||
|
||||
# Add generalized indicators here (because in live, it will call this
|
||||
# function to populate indicators during training). Notice how we ensure not to
|
||||
# add them multiple times
|
||||
if set_generalized_indicators:
|
||||
df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
|
||||
df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
|
||||
|
||||
# user adds targets here by prepending them with &- (see convention below)
|
||||
# If user wishes to use multiple targets, a multioutput prediction model
|
||||
# needs to be used such as templates/CatboostPredictionMultiModel.py
|
||||
df["&-s_close"] = (
|
||||
df["close"]
|
||||
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
.mean()
|
||||
/ df["close"]
|
||||
- 1
|
||||
)
|
||||
|
||||
df["&-s_range"] = (
|
||||
df["close"]
|
||||
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
.max()
|
||||
-
|
||||
df["close"]
|
||||
.shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
.rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
|
||||
.min()
|
||||
)
|
||||
|
||||
return df
|
||||
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
|
||||
self.freqai_info = self.config["freqai"]
|
||||
|
||||
# All indicators must be populated by populate_any_indicators() for live functionality
|
||||
# to work correctly.
|
||||
# the model will return 4 values, its prediction, an indication of whether or not the
|
||||
# prediction should be accepted, the target mean/std values from the labels used during
|
||||
# each training period.
|
||||
dataframe = self.freqai.start(dataframe, metadata, self)
|
||||
|
||||
dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25
|
||||
dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25
|
||||
return dataframe
|
||||
|
||||
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
|
||||
|
||||
enter_long_conditions = [df["do_predict"] == 1, df["&-s_close"] > df["target_roi"]]
|
||||
|
||||
if enter_long_conditions:
|
||||
df.loc[
|
||||
reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]
|
||||
] = (1, "long")
|
||||
|
||||
enter_short_conditions = [df["do_predict"] == 1, df["&-s_close"] < df["sell_roi"]]
|
||||
|
||||
if enter_short_conditions:
|
||||
df.loc[
|
||||
reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"]
|
||||
] = (1, "short")
|
||||
|
||||
return df
|
||||
|
||||
def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
|
||||
exit_long_conditions = [df["do_predict"] == 1, df["&-s_close"] < df["sell_roi"] * 0.25]
|
||||
if exit_long_conditions:
|
||||
df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1
|
||||
|
||||
exit_short_conditions = [df["do_predict"] == 1, df["&-s_close"] > df["target_roi"] * 0.25]
|
||||
if exit_short_conditions:
|
||||
df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1
|
||||
|
||||
return df
|
||||
@ -34,7 +34,7 @@ def test_search_all_strategies_no_failed():
|
||||
directory = Path(__file__).parent / "strats"
|
||||
strategies = StrategyResolver.search_all_objects(directory, enum_failed=False)
|
||||
assert isinstance(strategies, list)
|
||||
assert len(strategies) == 8
|
||||
assert len(strategies) == 9
|
||||
assert isinstance(strategies[0], dict)
|
||||
|
||||
|
||||
@ -42,10 +42,10 @@ def test_search_all_strategies_with_failed():
|
||||
directory = Path(__file__).parent / "strats"
|
||||
strategies = StrategyResolver.search_all_objects(directory, enum_failed=True)
|
||||
assert isinstance(strategies, list)
|
||||
assert len(strategies) == 9
|
||||
assert len(strategies) == 10
|
||||
# with enum_failed=True search_all_objects() shall find 2 good strategies
|
||||
# and 1 which fails to load
|
||||
assert len([x for x in strategies if x['class'] is not None]) == 8
|
||||
assert len([x for x in strategies if x['class'] is not None]) == 9
|
||||
assert len([x for x in strategies if x['class'] is None]) == 1
|
||||
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user