Aggregated commit. Adding support vector machine for outlier detection, improve user interface to dry/live, better standardization, fix various other bugs
This commit is contained in:
robcaulk
parent
c5ecf94177
commit
42d95af829
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@ -57,8 +57,8 @@
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"train_period": 30,
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"backtest_period": 7,
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"identifier": "example",
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"live_trained_timerange": "20220330-20220429",
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"live_full_backtestrange": "20220302-20220501",
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"live_trained_timerange": "",
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"live_full_backtestrange": "",
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"corr_pairlist": [
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"BTC/USDT",
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"ETH/USDT",
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@ -68,20 +68,19 @@
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"feature_parameters": {
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"period": 12,
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"shift": 1,
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"drop_features": false,
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"DI_threshold": 1,
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"weight_factor": 0,
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"principal_component_analysis": false,
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"remove_outliers": false
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"use_SVM_to_remove_outliers": false
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},
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"data_split_parameters": {
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"test_size": 0.25,
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"random_state": 1
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},
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"model_training_parameters": {
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"n_estimators": 2000,
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"n_estimators": 1000,
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"random_state": 1,
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"learning_rate": 0.02,
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"learning_rate": 0.1,
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"task_type": "CPU"
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}
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},
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@ -331,21 +331,21 @@ Users can reduce the dimensionality of their features by activating the `princip
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Which will perform PCA on the features and reduce the dimensionality of the data so that the explained
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variance of the data set is >= 0.999.
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### Removing outliers based on feature statistical distributions
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### Removing outliers using a Support Vector Machine (SVM)
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The user can tell Freqai to remove outlier data points from the training/test data sets by setting:
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```json
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"freqai": {
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"feature_parameters" : {
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"remove_outliers": true
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"use_SVM_to_remove_outliers: true
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}
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}
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```
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Freqai will check the statistical distributions of each feature (or component if the user activated
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`principal_component_analysis`) and remove any data point that sits more than 3 standard deviations away
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from the mean.
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Freqai will train an SVM on the training data (or components if the user activated
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`principal_component_analysis`) and remove any data point that it deems to be sit beyond the
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feature space.
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## Additional information
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@ -10,8 +10,9 @@ 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
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from joblib import dump, load # , Parallel, delayed # used for auto distribution assignment
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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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@ -22,6 +23,9 @@ from freqtrade.resolvers import ExchangeResolver
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from freqtrade.strategy.interface import IStrategy
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# import scipy as spy # used for auto distribution assignment
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SECONDS_IN_DAY = 86400
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logger = logging.getLogger(__name__)
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@ -52,6 +56,7 @@ class FreqaiDataKitchen:
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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.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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self.freqai_config["train_period"]
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@ -89,6 +94,10 @@ class FreqaiDataKitchen:
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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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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["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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@ -104,6 +113,19 @@ class FreqaiDataKitchen:
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if self.live:
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self.model_dictionary[self.model_filename] = model
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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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# of what we do NOT want.
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# if self.freqai_config['feature_parameters']['determine_statistical_distributions']:
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# self.data_dictionary["upper_quantiles"].to_pickle(
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# save_path / str(self.model_filename + "_upper_quantiles.pkl")
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# )
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# self.data_dictionary["lower_quantiles"].to_pickle(
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# save_path / str(self.model_filename + "_lower_quantiles.pkl")
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# )
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return
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def load_data(self) -> Any:
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@ -121,6 +143,19 @@ class FreqaiDataKitchen:
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self.model_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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# do not want them having to edit the default save/load methods here. Below is an example
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# of what we do NOT want.
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# if self.freqai_config['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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# )
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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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# )
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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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@ -130,6 +165,10 @@ class FreqaiDataKitchen:
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else:
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model = load(self.model_path / str(self.model_filename + "_model.joblib"))
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if Path(self.model_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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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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@ -159,6 +198,12 @@ class FreqaiDataKitchen:
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else:
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weights = np.ones(len(filtered_dataframe))
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if self.config["freqai"]["feature_parameters"]["stratify"] > 0:
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stratification = np.zeros(len(filtered_dataframe))
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for i in range(1, len(stratification)):
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if i % self.config["freqai"]["feature_parameters"]["stratify"] == 0:
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stratification[i] = 1
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(
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train_features,
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test_features,
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@ -170,6 +215,8 @@ class FreqaiDataKitchen:
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filtered_dataframe[: filtered_dataframe.shape[0]],
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labels,
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weights,
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stratify=stratification,
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# shuffle=False,
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**self.config["freqai"]["data_split_parameters"]
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)
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@ -261,9 +308,9 @@ class FreqaiDataKitchen:
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return self.data_dictionary
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def standardize_data(self, data_dictionary: Dict) -> Dict[Any, Any]:
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def normalize_data(self, data_dictionary: Dict) -> Dict[Any, Any]:
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"""
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Standardize all data in the data_dictionary according to the training dataset
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Normalize all data in the data_dictionary according to the training dataset
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:params:
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:data_dictionary: dictionary containing the cleaned and split training/test data/labels
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:returns:
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@ -297,6 +344,42 @@ class FreqaiDataKitchen:
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return data_dictionary
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def standardize_data(self, data_dictionary: Dict) -> Dict[Any, Any]:
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"""
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Standardize all data in the data_dictionary according to the training dataset
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:params:
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:data_dictionary: dictionary containing the cleaned and split training/test data/labels
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:returns:
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:data_dictionary: updated dictionary with standardized values.
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"""
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# standardize the data by training stats
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train_max = data_dictionary["train_features"].max()
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train_min = data_dictionary["train_features"].min()
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data_dictionary["train_features"] = 2 * (
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data_dictionary["train_features"] - train_min
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) / (train_max - train_min) - 1
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data_dictionary["test_features"] = 2 * (
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data_dictionary["test_features"] - train_min
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) / (train_max - train_min) - 1
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train_labels_max = data_dictionary["train_labels"].max()
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train_labels_min = data_dictionary["train_labels"].min()
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data_dictionary["train_labels"] = 2 * (
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data_dictionary["train_labels"] - train_labels_min
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) / (train_labels_max - train_labels_min) - 1
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data_dictionary["test_labels"] = 2 * (
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data_dictionary["test_labels"] - train_labels_min
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) / (train_labels_max - train_labels_min) - 1
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for item in train_max.keys():
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self.data[item + "_max"] = train_max[item]
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self.data[item + "_min"] = train_min[item]
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self.data["labels_max"] = train_labels_max
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self.data["labels_min"] = train_labels_min
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return data_dictionary
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def standardize_data_from_metadata(self, df: DataFrame) -> DataFrame:
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"""
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Standardizes a set of data using the mean and standard deviation from
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@ -305,6 +388,20 @@ class FreqaiDataKitchen:
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:df: Dataframe to be standardized
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"""
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for item in df.keys():
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df[item] = 2 * (df[item] - self.data[item + "_min"]) / (self.data[item + "_max"] -
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self.data[item + '_min']) - 1
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return df
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def normalize_data_from_metadata(self, df: DataFrame) -> DataFrame:
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"""
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Normalizes a set of data using the mean and standard deviation from
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the associated training data.
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:params:
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:df: Dataframe to be standardized
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"""
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for item in df.keys():
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df[item] = (df[item] - self.data[item + "_mean"]) / self.data[item + "_std"]
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@ -420,6 +517,8 @@ class FreqaiDataKitchen:
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self.data["n_kept_components"] = n_keep_components
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self.pca = pca2
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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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@ -434,70 +533,53 @@ class FreqaiDataKitchen:
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return avg_mean_dist
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def remove_outliers(self, predict: bool) -> None:
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"""
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Remove data that looks like an outlier based on the distribution of each
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variable.
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:params:
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:predict: boolean which tells the function if this is prediction data or
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training data coming in.
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"""
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lower_quantile = self.data_dictionary["train_features"].quantile(0.001)
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upper_quantile = self.data_dictionary["train_features"].quantile(0.999)
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def use_SVM_to_remove_outliers(self, predict: bool) -> None:
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if predict:
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df = self.data_dictionary["prediction_features"][
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(self.data_dictionary["prediction_features"] < upper_quantile)
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& (self.data_dictionary["prediction_features"] > lower_quantile)
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]
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drop_index = pd.isnull(df).any(1)
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self.data_dictionary["prediction_features"].fillna(0, inplace=True)
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drop_index = ~drop_index
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do_predict = np.array(drop_index.replace(True, 1).replace(False, 0))
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assert self.svm_model, "No svm model available for outlier removal"
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y_pred = self.svm_model.predict(self.data_dictionary["prediction_features"])
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do_predict = np.where(y_pred == -1, 0, y_pred)
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logger.info(
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"remove_outliers() tossed %s predictions",
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len(do_predict) - do_predict.sum(),
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f'svm_remove_outliers() tossed {len(do_predict) - do_predict.sum()} predictions'
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)
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self.do_predict += do_predict
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self.do_predict -= 1
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else:
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# use SGDOneClassSVM to increase speed?
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self.svm_model = linear_model.SGDOneClassSVM(nu=0.1).fit(
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self.data_dictionary["train_features"]
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)
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y_pred = self.svm_model.predict(self.data_dictionary["train_features"])
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dropped_points = np.where(y_pred == -1, 0, y_pred)
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# keep_index = np.where(y_pred == 1)
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self.data_dictionary["train_features"] = self.data_dictionary[
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"train_features"][(y_pred == 1)]
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self.data_dictionary["train_labels"] = self.data_dictionary[
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"train_labels"][(y_pred == 1)]
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self.data_dictionary["train_weights"] = self.data_dictionary[
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"train_weights"][(y_pred == 1)]
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filter_train_df = self.data_dictionary["train_features"][
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(self.data_dictionary["train_features"] < upper_quantile)
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& (self.data_dictionary["train_features"] > lower_quantile)
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]
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drop_index = pd.isnull(filter_train_df).any(1)
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drop_index = drop_index.replace(True, 1).replace(False, 0)
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self.data_dictionary["train_features"] = self.data_dictionary["train_features"][
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(drop_index == 0)
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]
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self.data_dictionary["train_labels"] = self.data_dictionary["train_labels"][
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(drop_index == 0)
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]
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self.data_dictionary["train_weights"] = self.data_dictionary["train_weights"][
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(drop_index == 0)
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]
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logger.info(
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f'svm_remove_outliers() tossed {len(y_pred) - dropped_points.sum()}'
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f' train points from {len(y_pred)}'
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)
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# do the same for the test data
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filter_test_df = self.data_dictionary["test_features"][
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(self.data_dictionary["test_features"] < upper_quantile)
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& (self.data_dictionary["test_features"] > lower_quantile)
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]
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drop_index = pd.isnull(filter_test_df).any(1)
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drop_index = drop_index.replace(True, 1).replace(False, 0)
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self.data_dictionary["test_labels"] = self.data_dictionary["test_labels"][
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(drop_index == 0)
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]
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self.data_dictionary["test_features"] = self.data_dictionary["test_features"][
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(drop_index == 0)
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]
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self.data_dictionary["test_weights"] = self.data_dictionary["test_weights"][
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(drop_index == 0)
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]
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# same for test data
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y_pred = self.svm_model.predict(self.data_dictionary["test_features"])
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dropped_points = np.where(y_pred == -1, 0, y_pred)
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self.data_dictionary["test_features"] = self.data_dictionary[
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"test_features"][(y_pred == 1)]
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self.data_dictionary["test_labels"] = self.data_dictionary[
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"test_labels"][(y_pred == 1)]
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self.data_dictionary["test_weights"] = self.data_dictionary[
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"test_weights"][(y_pred == 1)]
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logger.info(
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f'svm_remove_outliers() tossed {len(y_pred) - dropped_points.sum()}'
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f' test points from {len(y_pred)}'
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)
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return
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@ -507,32 +589,6 @@ class FreqaiDataKitchen:
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assert features, ("Could not find any features!")
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return features
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# def build_feature_list(self, config: dict, metadata: dict) -> list:
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# """
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# SUPERCEDED BY self.find_features()
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# Build the list of features that will be used to filter
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# the full dataframe. Feature list is construced from the
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# user configuration file.
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# :params:
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# :config: Canonical freqtrade config file containing all
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# user defined input in config['freqai] dictionary.
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# """
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# features = []
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# for tf in config["freqai"]["timeframes"]:
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# for ft in config["freqai"]["base_features"]:
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# for n in range(config["freqai"]["feature_parameters"]["shift"] + 1):
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# shift = ""
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# if n > 0:
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# shift = "_shift-" + str(n)
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# features.append(metadata['pair'].split("/")[0] + "-" + ft + shift + "_" + tf)
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# for p in config["freqai"]["corr_pairlist"]:
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# if metadata['pair'] in p:
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# continue # avoid duplicate features
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# features.append(p.split("/")[0] + "-" + ft + shift + "_" + tf)
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# # logger.info("number of features %s", len(features))
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# return features
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def check_if_pred_in_training_spaces(self) -> None:
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"""
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Compares the distance from each prediction point to each training data
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@ -568,7 +624,7 @@ class FreqaiDataKitchen:
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training than older data.
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"""
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weights = np.zeros_like(num_weights)
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weights = np.zeros(num_weights)
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for i in range(1, len(weights)):
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weights[len(weights) - i] = np.exp(
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-i / (self.config["freqai"]["feature_parameters"]["weight_factor"] * num_weights)
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@ -638,19 +694,23 @@ class FreqaiDataKitchen:
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time = datetime.datetime.now(tz=datetime.timezone.utc).timestamp()
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trained_timerange = TimeRange.parse_timerange(training_timerange)
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if training_timerange: # user passed no live_trained_timerange in config
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trained_timerange = TimeRange.parse_timerange(training_timerange)
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elapsed_time = (time - trained_timerange.stopts) / SECONDS_IN_DAY
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trained_timerange.startts += self.freqai_config['backtest_period'] * SECONDS_IN_DAY
|
||||
trained_timerange.stopts += self.freqai_config['backtest_period'] * SECONDS_IN_DAY
|
||||
retrain = elapsed_time > self.freqai_config['backtest_period']
|
||||
else:
|
||||
trained_timerange = TimeRange.parse_timerange("20000101-20000201")
|
||||
trained_timerange.startts = int(time - self.freqai_config['train_period'] *
|
||||
SECONDS_IN_DAY)
|
||||
trained_timerange.stopts = int(time)
|
||||
retrain = True
|
||||
|
||||
elapsed_time = (time - trained_timerange.stopts) / SECONDS_IN_DAY
|
||||
|
||||
trained_timerange.startts += self.freqai_config['backtest_period'] * SECONDS_IN_DAY
|
||||
trained_timerange.stopts += self.freqai_config['backtest_period'] * SECONDS_IN_DAY
|
||||
start = datetime.datetime.utcfromtimestamp(trained_timerange.startts)
|
||||
stop = datetime.datetime.utcfromtimestamp(trained_timerange.stopts)
|
||||
|
||||
new_trained_timerange = start.strftime("%Y%m%d") + "-" + stop.strftime("%Y%m%d")
|
||||
|
||||
retrain = elapsed_time > self.freqai_config['backtest_period']
|
||||
|
||||
if retrain:
|
||||
coin, _ = metadata['pair'].split("/")
|
||||
# set the new model_path
|
||||
|
|
@ -738,3 +798,141 @@ class FreqaiDataKitchen:
|
|||
def np_encoder(self, object):
|
||||
if isinstance(object, np.generic):
|
||||
return object.item()
|
||||
|
||||
# Functions containing useful data manpulation examples. but not actively in use.
|
||||
|
||||
# def build_feature_list(self, config: dict, metadata: dict) -> list:
|
||||
# """
|
||||
# SUPERCEDED BY self.find_features()
|
||||
# Build the list of features that will be used to filter
|
||||
# the full dataframe. Feature list is construced from the
|
||||
# user configuration file.
|
||||
# :params:
|
||||
# :config: Canonical freqtrade config file containing all
|
||||
# user defined input in config['freqai] dictionary.
|
||||
# """
|
||||
# features = []
|
||||
# for tf in config["freqai"]["timeframes"]:
|
||||
# for ft in config["freqai"]["base_features"]:
|
||||
# for n in range(config["freqai"]["feature_parameters"]["shift"] + 1):
|
||||
# shift = ""
|
||||
# if n > 0:
|
||||
# shift = "_shift-" + str(n)
|
||||
# features.append(metadata['pair'].split("/")[0] + "-" + ft + shift + "_" + tf)
|
||||
# for p in config["freqai"]["corr_pairlist"]:
|
||||
# if metadata['pair'] in p:
|
||||
# continue # avoid duplicate features
|
||||
# features.append(p.split("/")[0] + "-" + ft + shift + "_" + tf)
|
||||
|
||||
# # logger.info("number of features %s", len(features))
|
||||
# return features
|
||||
|
||||
# Possibly phasing these outlier removal methods below out in favor of
|
||||
# use_SVM_to_remove_outliers (computationally more efficient and apparently higher performance).
|
||||
# But these have good data manipulation examples, so keep them commented here for now.
|
||||
|
||||
# def determine_statistical_distributions(self) -> None:
|
||||
# from fitter import Fitter
|
||||
|
||||
# logger.info('Determining best model for all features, may take some time')
|
||||
|
||||
# def compute_quantiles(ft):
|
||||
# f = Fitter(self.data_dictionary["train_features"][ft],
|
||||
# distributions=['gamma', 'cauchy', 'laplace',
|
||||
# 'beta', 'uniform', 'lognorm'])
|
||||
# f.fit()
|
||||
# # f.summary()
|
||||
# dist = list(f.get_best().items())[0][0]
|
||||
# params = f.get_best()[dist]
|
||||
# upper_q = getattr(spy.stats, list(f.get_best().items())[0][0]).ppf(0.999, **params)
|
||||
# lower_q = getattr(spy.stats, list(f.get_best().items())[0][0]).ppf(0.001, **params)
|
||||
|
||||
# return ft, upper_q, lower_q, dist
|
||||
|
||||
# quantiles_tuple = Parallel(n_jobs=-1)(
|
||||
# delayed(compute_quantiles)(ft) for ft in self.data_dictionary[
|
||||
# 'train_features'].columns)
|
||||
|
||||
# df = pd.DataFrame(quantiles_tuple, columns=['features', 'upper_quantiles',
|
||||
# 'lower_quantiles', 'dist'])
|
||||
# self.data_dictionary['upper_quantiles'] = df['upper_quantiles']
|
||||
# self.data_dictionary['lower_quantiles'] = df['lower_quantiles']
|
||||
|
||||
# return
|
||||
|
||||
# def remove_outliers(self, predict: bool) -> None:
|
||||
# """
|
||||
# Remove data that looks like an outlier based on the distribution of each
|
||||
# variable.
|
||||
# :params:
|
||||
# :predict: boolean which tells the function if this is prediction data or
|
||||
# training data coming in.
|
||||
# """
|
||||
|
||||
# lower_quantile = self.data_dictionary["lower_quantiles"].to_numpy()
|
||||
# upper_quantile = self.data_dictionary["upper_quantiles"].to_numpy()
|
||||
|
||||
# if predict:
|
||||
|
||||
# df = self.data_dictionary["prediction_features"][
|
||||
# (self.data_dictionary["prediction_features"] < upper_quantile)
|
||||
# & (self.data_dictionary["prediction_features"] > lower_quantile)
|
||||
# ]
|
||||
# drop_index = pd.isnull(df).any(1)
|
||||
# self.data_dictionary["prediction_features"].fillna(0, inplace=True)
|
||||
# drop_index = ~drop_index
|
||||
# do_predict = np.array(drop_index.replace(True, 1).replace(False, 0))
|
||||
|
||||
# logger.info(
|
||||
# "remove_outliers() tossed %s predictions",
|
||||
# len(do_predict) - do_predict.sum(),
|
||||
# )
|
||||
# self.do_predict += do_predict
|
||||
# self.do_predict -= 1
|
||||
|
||||
# else:
|
||||
|
||||
# filter_train_df = self.data_dictionary["train_features"][
|
||||
# (self.data_dictionary["train_features"] < upper_quantile)
|
||||
# & (self.data_dictionary["train_features"] > lower_quantile)
|
||||
# ]
|
||||
# drop_index = pd.isnull(filter_train_df).any(1)
|
||||
# drop_index = drop_index.replace(True, 1).replace(False, 0)
|
||||
# self.data_dictionary["train_features"] = self.data_dictionary["train_features"][
|
||||
# (drop_index == 0)
|
||||
# ]
|
||||
# self.data_dictionary["train_labels"] = self.data_dictionary["train_labels"][
|
||||
# (drop_index == 0)
|
||||
# ]
|
||||
# self.data_dictionary["train_weights"] = self.data_dictionary["train_weights"][
|
||||
# (drop_index == 0)
|
||||
# ]
|
||||
|
||||
# logger.info(
|
||||
# f'remove_outliers() tossed {drop_index.sum()}'
|
||||
# f' training points from {len(filter_train_df)}'
|
||||
# )
|
||||
|
||||
# # do the same for the test data
|
||||
# filter_test_df = self.data_dictionary["test_features"][
|
||||
# (self.data_dictionary["test_features"] < upper_quantile)
|
||||
# & (self.data_dictionary["test_features"] > lower_quantile)
|
||||
# ]
|
||||
# drop_index = pd.isnull(filter_test_df).any(1)
|
||||
# drop_index = drop_index.replace(True, 1).replace(False, 0)
|
||||
# self.data_dictionary["test_labels"] = self.data_dictionary["test_labels"][
|
||||
# (drop_index == 0)
|
||||
# ]
|
||||
# self.data_dictionary["test_features"] = self.data_dictionary["test_features"][
|
||||
# (drop_index == 0)
|
||||
# ]
|
||||
# self.data_dictionary["test_weights"] = self.data_dictionary["test_weights"][
|
||||
# (drop_index == 0)
|
||||
# ]
|
||||
|
||||
# logger.info(
|
||||
# f'remove_outliers() tossed {drop_index.sum()}'
|
||||
# f' test points from {len(filter_test_df)}'
|
||||
# )
|
||||
|
||||
# return
|
||||
|
|
|
|||
|
|
@ -62,6 +62,7 @@ class IFreqaiModel(ABC):
|
|||
self.predictions = None
|
||||
self.training_on_separate_thread = False
|
||||
self.retrain = False
|
||||
self.first = True
|
||||
|
||||
def start(self, dataframe: DataFrame, metadata: dict, strategy: IStrategy) -> DataFrame:
|
||||
"""
|
||||
|
|
@ -80,12 +81,12 @@ class IFreqaiModel(ABC):
|
|||
:metadata: pair metadata coming from strategy.
|
||||
"""
|
||||
|
||||
live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE)
|
||||
self.live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE)
|
||||
|
||||
self.pair = metadata["pair"]
|
||||
self.dh = FreqaiDataKitchen(self.config, dataframe, live)
|
||||
self.dh = FreqaiDataKitchen(self.config, dataframe, self.live)
|
||||
|
||||
if live:
|
||||
if self.live:
|
||||
# logger.info('testing live')
|
||||
self.start_live(dataframe, metadata, strategy)
|
||||
|
||||
|
|
@ -115,11 +116,12 @@ class IFreqaiModel(ABC):
|
|||
self.dh.save_data(self.model)
|
||||
else:
|
||||
self.model = self.dh.load_data()
|
||||
strategy_provided_features = self.dh.find_features(dataframe_train)
|
||||
if strategy_provided_features != self.dh.training_features_list:
|
||||
logger.info("User changed input features, retraining model.")
|
||||
self.model = self.train(dataframe_train, metadata)
|
||||
self.dh.save_data(self.model)
|
||||
# strategy_provided_features = self.dh.find_features(dataframe_train)
|
||||
# # TOFIX doesnt work with PCA
|
||||
# if strategy_provided_features != self.dh.training_features_list:
|
||||
# logger.info("User changed input features, retraining model.")
|
||||
# self.model = self.train(dataframe_train, metadata)
|
||||
# self.dh.save_data(self.model)
|
||||
|
||||
preds, do_preds = self.predict(dataframe_backtest, metadata)
|
||||
|
||||
|
|
@ -148,7 +150,7 @@ class IFreqaiModel(ABC):
|
|||
if not self.training_on_separate_thread:
|
||||
# this will also prevent other pairs from trying to train simultaneously.
|
||||
(self.retrain,
|
||||
new_trained_timerange) = self.dh.check_if_new_training_required(self.freqai_info[
|
||||
self.new_trained_timerange) = self.dh.check_if_new_training_required(self.freqai_info[
|
||||
'live_trained_timerange'],
|
||||
metadata)
|
||||
else:
|
||||
|
|
@ -156,14 +158,19 @@ class IFreqaiModel(ABC):
|
|||
self.retrain = False
|
||||
|
||||
if self.retrain or not file_exists:
|
||||
self.training_on_separate_thread = True # acts like a lock
|
||||
self.retrain_model_on_separate_thread(new_trained_timerange, metadata, strategy)
|
||||
if self.first:
|
||||
self.train_model_in_series(self.new_trained_timerange, metadata, strategy)
|
||||
self.first = False
|
||||
else:
|
||||
self.training_on_separate_thread = True # acts like a lock
|
||||
self.retrain_model_on_separate_thread(self.new_trained_timerange,
|
||||
metadata, strategy)
|
||||
|
||||
self.model = self.dh.load_data()
|
||||
|
||||
strategy_provided_features = self.dh.find_features(dataframe)
|
||||
if strategy_provided_features != self.dh.training_features_list:
|
||||
self.train_model_in_series(new_trained_timerange, metadata, strategy)
|
||||
self.train_model_in_series(self.new_trained_timerange, metadata, strategy)
|
||||
|
||||
preds, do_preds = self.predict(dataframe, metadata)
|
||||
self.dh.append_predictions(preds, do_preds, len(dataframe))
|
||||
|
|
@ -215,12 +222,36 @@ class IFreqaiModel(ABC):
|
|||
data (NaNs) or felt uncertain about data (PCA and DI index)
|
||||
"""
|
||||
|
||||
@abstractmethod
|
||||
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.
|
||||
"""
|
||||
|
||||
@abstractmethod
|
||||
def data_cleaning_predict(self) -> 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.
|
||||
"""
|
||||
|
||||
def model_exists(self, pair: str, training_timerange: 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 is None:
|
||||
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"))
|
||||
|
|
@ -265,3 +296,4 @@ class IFreqaiModel(ABC):
|
|||
|
||||
self.model = self.train(unfiltered_dataframe, metadata)
|
||||
self.dh.save_data(self.model)
|
||||
self.retrain = False
|
||||
|
|
|
|||
|
|
@ -29,7 +29,7 @@ class CatboostPredictionModel(IFreqaiModel):
|
|||
dataframe["close"]
|
||||
.shift(-self.feature_parameters["period"])
|
||||
.rolling(self.feature_parameters["period"])
|
||||
.max()
|
||||
.mean()
|
||||
/ dataframe["close"]
|
||||
- 1
|
||||
)
|
||||
|
|
@ -68,15 +68,11 @@ class CatboostPredictionModel(IFreqaiModel):
|
|||
# standardize all data based on train_dataset only
|
||||
data_dictionary = self.dh.standardize_data(data_dictionary)
|
||||
|
||||
# optional additional data cleaning
|
||||
if self.feature_parameters["principal_component_analysis"]:
|
||||
self.dh.principal_component_analysis()
|
||||
if self.feature_parameters["remove_outliers"]:
|
||||
self.dh.remove_outliers(predict=False)
|
||||
if self.feature_parameters["DI_threshold"]:
|
||||
self.dh.data["avg_mean_dist"] = self.dh.compute_distances()
|
||||
# optional additional data cleaning/analysis
|
||||
self.data_cleaning_train()
|
||||
|
||||
logger.info("length of train data %s", len(data_dictionary["train_features"]))
|
||||
logger.info(f'Training model on {len(self.dh.training_features_list)} features')
|
||||
logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
|
||||
|
||||
model = self.fit(data_dictionary)
|
||||
|
||||
|
|
@ -86,9 +82,7 @@ class CatboostPredictionModel(IFreqaiModel):
|
|||
|
||||
def fit(self, data_dictionary: Dict) -> Any:
|
||||
"""
|
||||
Most regressors use the same function names and arguments e.g. user
|
||||
can drop in LGBMRegressor in place of CatBoostRegressor and all data
|
||||
management will be properly handled by Freqai.
|
||||
User sets up the training and test data to fit their desired model here
|
||||
:params:
|
||||
:data_dictionary: the dictionary constructed by DataHandler to hold
|
||||
all the training and test data/labels.
|
||||
|
|
@ -133,7 +127,51 @@ class CatboostPredictionModel(IFreqaiModel):
|
|||
filtered_dataframe = self.dh.standardize_data_from_metadata(filtered_dataframe)
|
||||
self.dh.data_dictionary["prediction_features"] = filtered_dataframe
|
||||
|
||||
# optional additional data cleaning
|
||||
# optional additional data cleaning/analysis
|
||||
self.data_cleaning_predict(filtered_dataframe)
|
||||
|
||||
predictions = self.model.predict(self.dh.data_dictionary["prediction_features"])
|
||||
|
||||
# compute the non-standardized predictions
|
||||
self.dh.predictions = (predictions + 1) * (self.dh.data["labels_max"] -
|
||||
self.dh.data["labels_min"]) / 2 + self.dh.data[
|
||||
"labels_min"]
|
||||
|
||||
# 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.feature_parameters["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.feature_parameters["use_SVM_to_remove_outliers"]:
|
||||
self.dh.use_SVM_to_remove_outliers(predict=False)
|
||||
if self.feature_parameters["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.feature_parameters["principal_component_analysis"]:
|
||||
pca_components = self.dh.pca.transform(filtered_dataframe)
|
||||
self.dh.data_dictionary["prediction_features"] = pd.DataFrame(
|
||||
|
|
@ -142,17 +180,13 @@ class CatboostPredictionModel(IFreqaiModel):
|
|||
index=filtered_dataframe.index,
|
||||
)
|
||||
|
||||
if self.feature_parameters["remove_outliers"]:
|
||||
self.dh.remove_outliers(predict=True) # creates dropped index
|
||||
# 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.feature_parameters["use_SVM_to_remove_outliers"]:
|
||||
self.dh.use_SVM_to_remove_outliers(predict=True)
|
||||
|
||||
if self.feature_parameters["DI_threshold"]:
|
||||
self.dh.check_if_pred_in_training_spaces() # sets do_predict
|
||||
|
||||
predictions = self.model.predict(self.dh.data_dictionary["prediction_features"])
|
||||
|
||||
# compute the non-standardized predictions
|
||||
self.dh.predictions = predictions * self.dh.data["labels_std"] + self.dh.data["labels_mean"]
|
||||
|
||||
# logger.info("--------------------Finished prediction--------------------")
|
||||
|
||||
return (self.dh.predictions, self.dh.do_predict)
|
||||
|
|
|
|||
|
|
@ -1,159 +0,0 @@
|
|||
import logging
|
||||
from typing import Any, Dict, Tuple
|
||||
|
||||
import pandas as pd
|
||||
from catboost import CatBoostRegressor, Pool
|
||||
from pandas import DataFrame
|
||||
|
||||
from freqtrade.freqai.freqai_interface import IFreqaiModel
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class ExamplePredictionModel(IFreqaiModel):
|
||||
"""
|
||||
User created prediction model. The class needs to override three necessary
|
||||
functions, predict(), train(), fit(). The class inherits ModelHandler which
|
||||
has its own DataHandler where data is held, saved, loaded, and managed.
|
||||
"""
|
||||
|
||||
def make_labels(self, dataframe: DataFrame) -> DataFrame:
|
||||
"""
|
||||
User defines the labels here (target values).
|
||||
:params:
|
||||
:dataframe: the full dataframe for the present training period
|
||||
"""
|
||||
|
||||
dataframe["s"] = (
|
||||
dataframe["close"]
|
||||
.shift(-self.feature_parameters["period"])
|
||||
.rolling(self.feature_parameters["period"])
|
||||
.max()
|
||||
/ dataframe["close"]
|
||||
- 1
|
||||
)
|
||||
self.dh.data["s_mean"] = dataframe["s"].mean()
|
||||
self.dh.data["s_std"] = dataframe["s"].std()
|
||||
|
||||
# logger.info("label mean", self.dh.data["s_mean"], "label std", self.dh.data["s_std"])
|
||||
|
||||
return dataframe["s"]
|
||||
|
||||
def train(self, unfiltered_dataframe: DataFrame, metadata: dict) -> Tuple[DataFrame, DataFrame]:
|
||||
"""
|
||||
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.
|
||||
:params:
|
||||
:unfiltered_dataframe: Full dataframe for the current training period
|
||||
:metadata: pair metadata from strategy.
|
||||
:returns:
|
||||
:model: Trained model which can be used to inference (self.predict)
|
||||
"""
|
||||
logger.info("--------------------Starting training--------------------")
|
||||
|
||||
# create the full feature list based on user config info
|
||||
self.dh.training_features_list = self.dh.build_feature_list(self.config, metadata)
|
||||
unfiltered_labels = self.make_labels(unfiltered_dataframe)
|
||||
|
||||
# filter the features requested by user in the configuration file and elegantly handle NaNs
|
||||
features_filtered, labels_filtered = self.dh.filter_features(
|
||||
unfiltered_dataframe,
|
||||
self.dh.training_features_list,
|
||||
unfiltered_labels,
|
||||
training_filter=True,
|
||||
)
|
||||
|
||||
# split data into train/test data.
|
||||
data_dictionary = self.dh.make_train_test_datasets(features_filtered, labels_filtered)
|
||||
# standardize all data based on train_dataset only
|
||||
data_dictionary = self.dh.standardize_data(data_dictionary)
|
||||
|
||||
# optional additional data cleaning
|
||||
if self.feature_parameters["principal_component_analysis"]:
|
||||
self.dh.principal_component_analysis()
|
||||
if self.feature_parameters["remove_outliers"]:
|
||||
self.dh.remove_outliers(predict=False)
|
||||
if self.feature_parameters["DI_threshold"]:
|
||||
self.dh.data["avg_mean_dist"] = self.dh.compute_distances()
|
||||
|
||||
logger.info("length of train data %s", len(data_dictionary["train_features"]))
|
||||
|
||||
model = self.fit(data_dictionary)
|
||||
|
||||
logger.info(f'--------------------done training {metadata["pair"]}--------------------')
|
||||
|
||||
return model
|
||||
|
||||
def fit(self, data_dictionary: Dict) -> Any:
|
||||
"""
|
||||
Most regressors use the same function names and arguments e.g. user
|
||||
can drop in LGBMRegressor in place of CatBoostRegressor and all data
|
||||
management will be properly handled by Freqai.
|
||||
:params:
|
||||
:data_dictionary: the dictionary constructed by DataHandler to hold
|
||||
all the training and test data/labels.
|
||||
"""
|
||||
|
||||
train_data = Pool(
|
||||
data=data_dictionary["train_features"],
|
||||
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"],
|
||||
)
|
||||
|
||||
model = CatBoostRegressor(
|
||||
verbose=100, early_stopping_rounds=400, **self.model_training_parameters
|
||||
)
|
||||
model.fit(X=train_data, eval_set=test_data)
|
||||
|
||||
return model
|
||||
|
||||
def predict(self, unfiltered_dataframe: DataFrame, metadata: dict) -> Tuple[DataFrame,
|
||||
DataFrame]:
|
||||
"""
|
||||
Filter the prediction features data and predict with it.
|
||||
:param: unfiltered_dataframe: Full dataframe for the current backtest period.
|
||||
:return:
|
||||
:predictions: np.array of predictions
|
||||
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
|
||||
data (NaNs) or felt uncertain about data (PCA and DI index)
|
||||
"""
|
||||
|
||||
# logger.info("--------------------Starting prediction--------------------")
|
||||
|
||||
original_feature_list = self.dh.build_feature_list(self.config, metadata)
|
||||
filtered_dataframe, _ = self.dh.filter_features(
|
||||
unfiltered_dataframe, original_feature_list, training_filter=False
|
||||
)
|
||||
filtered_dataframe = self.dh.standardize_data_from_metadata(filtered_dataframe)
|
||||
self.dh.data_dictionary["prediction_features"] = filtered_dataframe
|
||||
|
||||
# optional additional data cleaning
|
||||
if self.feature_parameters["principal_component_analysis"]:
|
||||
pca_components = self.dh.pca.transform(filtered_dataframe)
|
||||
self.dh.data_dictionary["prediction_features"] = pd.DataFrame(
|
||||
data=pca_components,
|
||||
columns=["PC" + str(i) for i in range(0, self.dh.data["n_kept_components"])],
|
||||
index=filtered_dataframe.index,
|
||||
)
|
||||
|
||||
if self.feature_parameters["remove_outliers"]:
|
||||
self.dh.remove_outliers(predict=True) # creates dropped index
|
||||
|
||||
if self.feature_parameters["DI_threshold"]:
|
||||
self.dh.check_if_pred_in_training_spaces() # sets do_predict
|
||||
|
||||
predictions = self.model.predict(self.dh.data_dictionary["prediction_features"])
|
||||
|
||||
# compute the non-standardized predictions
|
||||
self.dh.predictions = predictions * self.dh.data["labels_std"] + self.dh.data["labels_mean"]
|
||||
|
||||
# logger.info("--------------------Finished prediction--------------------")
|
||||
|
||||
return (self.dh.predictions, self.dh.do_predict)
|
||||
|
|
@ -166,8 +166,8 @@ class FreqaiExampleStrategy(IStrategy):
|
|||
dataframe["target_std"],
|
||||
) = self.model.bridge.start(dataframe, metadata, self)
|
||||
|
||||
dataframe["target_roi"] = dataframe["target_mean"] + dataframe["target_std"] * 0.5
|
||||
dataframe["sell_roi"] = dataframe["target_mean"] - dataframe["target_std"] * 1.5
|
||||
dataframe["target_roi"] = dataframe["target_mean"] + dataframe["target_std"] * 1.5
|
||||
dataframe["sell_roi"] = dataframe["target_mean"] - dataframe["target_std"] * 1
|
||||
return dataframe
|
||||
|
||||
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
|
|
@ -183,7 +183,7 @@ class FreqaiExampleStrategy(IStrategy):
|
|||
|
||||
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
sell_conditions = [
|
||||
(dataframe["prediction"] < dataframe["sell_roi"]) & (dataframe["do_predict"] == 1)
|
||||
(dataframe["do_predict"] <= 0)
|
||||
]
|
||||
if sell_conditions:
|
||||
dataframe.loc[reduce(lambda x, y: x | y, sell_conditions), "sell"] = 1
|
||||
|
|
|
|||
Loading…
Reference in New Issue