mirror of
https://github.com/freqtrade/freqtrade.git
synced 2024-11-15 04:33:57 +00:00
1279 lines
52 KiB
Python
1279 lines
52 KiB
Python
import copy
|
|
import logging
|
|
import shutil
|
|
from datetime import datetime, timezone
|
|
from math import cos, sin
|
|
from pathlib import Path
|
|
from typing import Any, Dict, List, Tuple
|
|
|
|
import numpy as np
|
|
import numpy.typing as npt
|
|
import pandas as pd
|
|
from pandas import DataFrame
|
|
from scipy import stats
|
|
from sklearn import linear_model
|
|
from sklearn.cluster import DBSCAN
|
|
from sklearn.metrics.pairwise import pairwise_distances
|
|
from sklearn.model_selection import train_test_split
|
|
from sklearn.neighbors import NearestNeighbors
|
|
|
|
from freqtrade.configuration import TimeRange
|
|
from freqtrade.exceptions import OperationalException
|
|
from freqtrade.exchange import timeframe_to_seconds
|
|
from freqtrade.strategy.interface import IStrategy
|
|
|
|
|
|
SECONDS_IN_DAY = 86400
|
|
SECONDS_IN_HOUR = 3600
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class FreqaiDataKitchen:
|
|
"""
|
|
Class designed to analyze data for a single pair. Employed by the IFreqaiModel class.
|
|
Functionalities include holding, saving, loading, and analyzing the data.
|
|
|
|
This object is not persistent, it is reinstantiated for each coin, each time the coin
|
|
model needs to be inferenced or trained.
|
|
|
|
Record of contribution:
|
|
FreqAI was developed by a group of individuals who all contributed specific skillsets to the
|
|
project.
|
|
|
|
Conception and software development:
|
|
Robert Caulk @robcaulk
|
|
|
|
Theoretical brainstorming:
|
|
Elin Törnquist @th0rntwig
|
|
|
|
Code review, software architecture brainstorming:
|
|
@xmatthias
|
|
|
|
Beta testing and bug reporting:
|
|
@bloodhunter4rc, Salah Lamkadem @ikonx, @ken11o2, @longyu, @paranoidandy, @smidelis, @smarm
|
|
Juha Nykänen @suikula, Wagner Costa @wagnercosta, Johan Vlugt @Jooopieeert
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
config: Dict[str, Any],
|
|
live: bool = False,
|
|
pair: str = "",
|
|
):
|
|
self.data: Dict[str, Any] = {}
|
|
self.data_dictionary: Dict[str, DataFrame] = {}
|
|
self.config = config
|
|
self.freqai_config: Dict[str, Any] = config["freqai"]
|
|
self.full_df: DataFrame = DataFrame()
|
|
self.append_df: DataFrame = DataFrame()
|
|
self.data_path = Path()
|
|
self.label_list: List = []
|
|
self.training_features_list: List = []
|
|
self.model_filename: str = ""
|
|
self.backtesting_results_path = Path()
|
|
self.backtest_predictions_folder: str = "backtesting_predictions"
|
|
self.live = live
|
|
self.pair = pair
|
|
|
|
self.svm_model: linear_model.SGDOneClassSVM = None
|
|
self.keras: bool = self.freqai_config.get("keras", False)
|
|
self.set_all_pairs()
|
|
if not self.live:
|
|
if not self.config["timerange"]:
|
|
raise OperationalException(
|
|
'Please pass --timerange if you intend to use FreqAI for backtesting.')
|
|
self.full_timerange = self.create_fulltimerange(
|
|
self.config["timerange"], self.freqai_config.get("train_period_days", 0)
|
|
)
|
|
|
|
(self.training_timeranges, self.backtesting_timeranges) = self.split_timerange(
|
|
self.full_timerange,
|
|
config["freqai"]["train_period_days"],
|
|
config["freqai"]["backtest_period_days"],
|
|
)
|
|
|
|
self.data['extra_returns_per_train'] = self.freqai_config.get('extra_returns_per_train', {})
|
|
self.thread_count = self.freqai_config.get("data_kitchen_thread_count", -1)
|
|
self.train_dates: DataFrame = pd.DataFrame()
|
|
self.unique_classes: Dict[str, list] = {}
|
|
self.unique_class_list: list = []
|
|
self.spice_dataframe: DataFrame = None
|
|
|
|
def set_paths(
|
|
self,
|
|
pair: str,
|
|
trained_timestamp: int = None,
|
|
) -> None:
|
|
"""
|
|
Set the paths to the data for the present coin/botloop
|
|
:params:
|
|
metadata: dict = strategy furnished pair metadata
|
|
trained_timestamp: int = timestamp of most recent training
|
|
"""
|
|
self.full_path = Path(
|
|
self.config["user_data_dir"] / "models" / str(self.freqai_config.get("identifier"))
|
|
)
|
|
|
|
self.data_path = Path(
|
|
self.full_path
|
|
/ f"sub-train-{pair.split('/')[0]}_{trained_timestamp}"
|
|
)
|
|
|
|
return
|
|
|
|
def make_train_test_datasets(
|
|
self, filtered_dataframe: DataFrame, labels: DataFrame
|
|
) -> Dict[Any, Any]:
|
|
"""
|
|
Given the dataframe for the full history for training, split the data into
|
|
training and test data according to user specified parameters in configuration
|
|
file.
|
|
:filtered_dataframe: cleaned dataframe ready to be split.
|
|
:labels: cleaned labels ready to be split.
|
|
"""
|
|
feat_dict = self.freqai_config["feature_parameters"]
|
|
|
|
weights: npt.ArrayLike
|
|
if feat_dict.get("weight_factor", 0) > 0:
|
|
weights = self.set_weights_higher_recent(len(filtered_dataframe))
|
|
else:
|
|
weights = np.ones(len(filtered_dataframe))
|
|
|
|
if feat_dict.get("stratify_training_data", 0) > 0:
|
|
stratification = np.zeros(len(filtered_dataframe))
|
|
for i in range(1, len(stratification)):
|
|
if i % feat_dict.get("stratify_training_data", 0) == 0:
|
|
stratification[i] = 1
|
|
else:
|
|
stratification = None
|
|
|
|
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
|
|
|
|
# Simplest way to reverse the order of training and test data:
|
|
if self.freqai_config['feature_parameters'].get('reverse_train_test_order', False):
|
|
return self.build_data_dictionary(
|
|
test_features, train_features, test_labels,
|
|
train_labels, test_weights, train_weights
|
|
)
|
|
else:
|
|
return self.build_data_dictionary(
|
|
train_features, test_features, train_labels,
|
|
test_labels, train_weights, test_weights
|
|
)
|
|
|
|
def filter_features(
|
|
self,
|
|
unfiltered_df: DataFrame,
|
|
training_feature_list: List,
|
|
label_list: List = list(),
|
|
training_filter: bool = True,
|
|
) -> Tuple[DataFrame, DataFrame]:
|
|
"""
|
|
Filter the unfiltered dataframe to extract the user requested features/labels and properly
|
|
remove all NaNs. Any row with a NaN is removed from training dataset or replaced with
|
|
0s in the prediction dataset. However, prediction dataset do_predict will reflect any
|
|
row that had a NaN and will shield user from that prediction.
|
|
:params:
|
|
:unfiltered_df: the full dataframe for the present training period
|
|
:training_feature_list: list, the training feature list constructed by
|
|
self.build_feature_list() according to user specified parameters in the configuration file.
|
|
:labels: the labels for the dataset
|
|
:training_filter: boolean which lets the function know if it is training data or
|
|
prediction data to be filtered.
|
|
:returns:
|
|
:filtered_df: dataframe cleaned of NaNs and only containing the user
|
|
requested feature set.
|
|
:labels: labels cleaned of NaNs.
|
|
"""
|
|
filtered_df = unfiltered_df.filter(training_feature_list, axis=1)
|
|
filtered_df = filtered_df.replace([np.inf, -np.inf], np.nan)
|
|
|
|
drop_index = pd.isnull(filtered_df).any(1) # get the rows that have NaNs,
|
|
drop_index = drop_index.replace(True, 1).replace(False, 0) # pep8 requirement.
|
|
if (training_filter):
|
|
const_cols = list((filtered_df.nunique() == 1).loc[lambda x: x].index)
|
|
if const_cols:
|
|
filtered_df = filtered_df.filter(filtered_df.columns.difference(const_cols))
|
|
logger.warning(f"Removed features {const_cols} with constant values.")
|
|
# we don't care about total row number (total no. datapoints) in training, we only care
|
|
# about removing any row with NaNs
|
|
# if labels has multiple columns (user wants to train multiple modelEs), we detect here
|
|
labels = unfiltered_df.filter(label_list, axis=1)
|
|
drop_index_labels = pd.isnull(labels).any(1)
|
|
drop_index_labels = drop_index_labels.replace(True, 1).replace(False, 0)
|
|
dates = unfiltered_df['date']
|
|
filtered_df = filtered_df[
|
|
(drop_index == 0) & (drop_index_labels == 0)
|
|
] # dropping values
|
|
labels = labels[
|
|
(drop_index == 0) & (drop_index_labels == 0)
|
|
] # assuming the labels depend entirely on the dataframe here.
|
|
self.train_dates = dates[
|
|
(drop_index == 0) & (drop_index_labels == 0)
|
|
]
|
|
logger.info(
|
|
f"dropped {len(unfiltered_df) - len(filtered_df)} training points"
|
|
f" due to NaNs in populated dataset {len(unfiltered_df)}."
|
|
)
|
|
if (1 - len(filtered_df) / len(unfiltered_df)) > 0.1 and self.live:
|
|
worst_indicator = str(unfiltered_df.count().idxmin())
|
|
logger.warning(
|
|
f" {(1 - len(filtered_df)/len(unfiltered_df)) * 100:.0f} percent "
|
|
" of training data dropped due to NaNs, model may perform inconsistent "
|
|
f"with expectations. Verify {worst_indicator}"
|
|
)
|
|
self.data["filter_drop_index_training"] = drop_index
|
|
|
|
else:
|
|
# we are backtesting so we need to preserve row number to send back to strategy,
|
|
# so now we use do_predict to avoid any prediction based on a NaN
|
|
drop_index = pd.isnull(filtered_df).any(1)
|
|
self.data["filter_drop_index_prediction"] = drop_index
|
|
filtered_df.fillna(0, inplace=True)
|
|
# replacing all NaNs with zeros to avoid issues in 'prediction', but any prediction
|
|
# that was based on a single NaN is ultimately protected from buys with do_predict
|
|
drop_index = ~drop_index
|
|
self.do_predict = np.array(drop_index.replace(True, 1).replace(False, 0))
|
|
if (len(self.do_predict) - self.do_predict.sum()) > 0:
|
|
logger.info(
|
|
"dropped %s of %s prediction data points due to NaNs.",
|
|
len(self.do_predict) - self.do_predict.sum(),
|
|
len(filtered_df),
|
|
)
|
|
labels = []
|
|
|
|
return filtered_df, labels
|
|
|
|
def build_data_dictionary(
|
|
self,
|
|
train_df: DataFrame,
|
|
test_df: DataFrame,
|
|
train_labels: DataFrame,
|
|
test_labels: DataFrame,
|
|
train_weights: Any,
|
|
test_weights: Any,
|
|
) -> Dict:
|
|
|
|
self.data_dictionary = {
|
|
"train_features": train_df,
|
|
"test_features": test_df,
|
|
"train_labels": train_labels,
|
|
"test_labels": test_labels,
|
|
"train_weights": train_weights,
|
|
"test_weights": test_weights,
|
|
"train_dates": self.train_dates
|
|
}
|
|
|
|
return self.data_dictionary
|
|
|
|
def normalize_data(self, data_dictionary: Dict) -> Dict[Any, Any]:
|
|
"""
|
|
Normalize all data in the data_dictionary according to the training dataset
|
|
:params:
|
|
:data_dictionary: dictionary containing the cleaned and split training/test data/labels
|
|
:returns:
|
|
:data_dictionary: updated dictionary with standardized values.
|
|
"""
|
|
|
|
# standardize the data by training stats
|
|
train_max = data_dictionary["train_features"].max()
|
|
train_min = data_dictionary["train_features"].min()
|
|
data_dictionary["train_features"] = (
|
|
2 * (data_dictionary["train_features"] - train_min) / (train_max - train_min) - 1
|
|
)
|
|
data_dictionary["test_features"] = (
|
|
2 * (data_dictionary["test_features"] - train_min) / (train_max - train_min) - 1
|
|
)
|
|
|
|
for item in train_max.keys():
|
|
self.data[item + "_max"] = train_max[item]
|
|
self.data[item + "_min"] = train_min[item]
|
|
|
|
for item in data_dictionary["train_labels"].keys():
|
|
if data_dictionary["train_labels"][item].dtype == object:
|
|
continue
|
|
train_labels_max = data_dictionary["train_labels"][item].max()
|
|
train_labels_min = data_dictionary["train_labels"][item].min()
|
|
data_dictionary["train_labels"][item] = (
|
|
2
|
|
* (data_dictionary["train_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
|
|
self.data[f"{item}_min"] = train_labels_min
|
|
return data_dictionary
|
|
|
|
def normalize_single_dataframe(self, df: DataFrame) -> DataFrame:
|
|
|
|
train_max = df.max()
|
|
train_min = df.min()
|
|
df = (
|
|
2 * (df - train_min) / (train_max - train_min) - 1
|
|
)
|
|
|
|
for item in train_max.keys():
|
|
self.data[item + "_max"] = train_max[item]
|
|
self.data[item + "_min"] = train_min[item]
|
|
|
|
return df
|
|
|
|
def normalize_data_from_metadata(self, df: DataFrame) -> DataFrame:
|
|
"""
|
|
Normalize a set of data using the mean and standard deviation from
|
|
the associated training data.
|
|
:param df: Dataframe to be standardized
|
|
"""
|
|
|
|
for item in df.keys():
|
|
df[item] = (
|
|
2
|
|
* (df[item] - self.data[f"{item}_min"])
|
|
/ (self.data[f"{item}_max"] - self.data[f"{item}_min"])
|
|
- 1
|
|
)
|
|
|
|
return df
|
|
|
|
def denormalize_labels_from_metadata(self, df: DataFrame) -> DataFrame:
|
|
"""
|
|
Denormalize a set of data using the mean and standard deviation from
|
|
the associated training data.
|
|
:param df: Dataframe of predictions to be denormalized
|
|
"""
|
|
|
|
for label in df.columns:
|
|
if df[label].dtype == object or label in self.unique_class_list:
|
|
continue
|
|
df[label] = (
|
|
(df[label] + 1)
|
|
* (self.data[f"{label}_max"] - self.data[f"{label}_min"])
|
|
/ 2
|
|
) + self.data[f"{label}_min"]
|
|
|
|
return df
|
|
|
|
def split_timerange(
|
|
self, tr: str, train_split: int = 28, bt_split: float = 7
|
|
) -> Tuple[list, list]:
|
|
"""
|
|
Function which takes a single time range (tr) and splits it
|
|
into sub timeranges to train and backtest on based on user input
|
|
tr: str, full timerange to train on
|
|
train_split: the period length for the each training (days). Specified in user
|
|
configuration file
|
|
bt_split: the backtesting length (days). Specified in user configuration file
|
|
"""
|
|
|
|
if not isinstance(train_split, int) or train_split < 1:
|
|
raise OperationalException(
|
|
f"train_period_days must be an integer greater than 0. Got {train_split}."
|
|
)
|
|
train_period_days = train_split * SECONDS_IN_DAY
|
|
bt_period = bt_split * SECONDS_IN_DAY
|
|
|
|
full_timerange = TimeRange.parse_timerange(tr)
|
|
config_timerange = TimeRange.parse_timerange(self.config["timerange"])
|
|
if config_timerange.stopts == 0:
|
|
config_timerange.stopts = int(
|
|
datetime.now(tz=timezone.utc).timestamp()
|
|
)
|
|
timerange_train = copy.deepcopy(full_timerange)
|
|
timerange_backtest = copy.deepcopy(full_timerange)
|
|
|
|
tr_training_list = []
|
|
tr_backtesting_list = []
|
|
tr_training_list_timerange = []
|
|
tr_backtesting_list_timerange = []
|
|
first = True
|
|
|
|
while True:
|
|
if not first:
|
|
timerange_train.startts = timerange_train.startts + int(bt_period)
|
|
timerange_train.stopts = timerange_train.startts + train_period_days
|
|
|
|
first = False
|
|
start = datetime.fromtimestamp(timerange_train.startts, tz=timezone.utc)
|
|
stop = datetime.fromtimestamp(timerange_train.stopts, tz=timezone.utc)
|
|
tr_training_list.append(start.strftime("%Y%m%d") + "-" + stop.strftime("%Y%m%d"))
|
|
tr_training_list_timerange.append(copy.deepcopy(timerange_train))
|
|
|
|
# associated backtest period
|
|
|
|
timerange_backtest.startts = timerange_train.stopts
|
|
|
|
timerange_backtest.stopts = timerange_backtest.startts + int(bt_period)
|
|
|
|
if timerange_backtest.stopts > config_timerange.stopts:
|
|
timerange_backtest.stopts = config_timerange.stopts
|
|
|
|
start = datetime.fromtimestamp(timerange_backtest.startts, tz=timezone.utc)
|
|
stop = datetime.fromtimestamp(timerange_backtest.stopts, tz=timezone.utc)
|
|
tr_backtesting_list.append(start.strftime("%Y%m%d") + "-" + stop.strftime("%Y%m%d"))
|
|
tr_backtesting_list_timerange.append(copy.deepcopy(timerange_backtest))
|
|
|
|
# ensure we are predicting on exactly same amount of data as requested by user defined
|
|
# --timerange
|
|
if timerange_backtest.stopts == config_timerange.stopts:
|
|
break
|
|
|
|
# print(tr_training_list, tr_backtesting_list)
|
|
return tr_training_list_timerange, tr_backtesting_list_timerange
|
|
|
|
def slice_dataframe(self, timerange: TimeRange, df: DataFrame) -> DataFrame:
|
|
"""
|
|
Given a full dataframe, extract the user desired window
|
|
:param tr: timerange string that we wish to extract from df
|
|
:param df: Dataframe containing all candles to run the entire backtest. Here
|
|
it is sliced down to just the present training period.
|
|
"""
|
|
|
|
start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
|
|
stop = datetime.fromtimestamp(timerange.stopts, tz=timezone.utc)
|
|
df = df.loc[df["date"] >= start, :]
|
|
if not self.live:
|
|
df = df.loc[df["date"] < stop, :]
|
|
|
|
return df
|
|
|
|
def remove_training_from_backtesting(
|
|
self
|
|
) -> DataFrame:
|
|
"""
|
|
Function which takes the backtesting time range and
|
|
remove training data from dataframe, keeping only the
|
|
startup_candle_count candles
|
|
"""
|
|
startup_candle_count = self.config.get('startup_candle_count', 0)
|
|
tf = self.config['timeframe']
|
|
tr = self.config["timerange"]
|
|
|
|
backtesting_timerange = TimeRange.parse_timerange(tr)
|
|
if startup_candle_count > 0 and backtesting_timerange:
|
|
backtesting_timerange.subtract_start(timeframe_to_seconds(tf) * startup_candle_count)
|
|
|
|
start = datetime.fromtimestamp(backtesting_timerange.startts, tz=timezone.utc)
|
|
df = self.return_dataframe
|
|
df = df.loc[df["date"] >= start, :]
|
|
return df
|
|
|
|
def principal_component_analysis(self) -> None:
|
|
"""
|
|
Performs Principal Component Analysis on the data for dimensionality reduction
|
|
and outlier detection (see self.remove_outliers())
|
|
No parameters or returns, it acts on the data_dictionary held by the DataHandler.
|
|
"""
|
|
|
|
from sklearn.decomposition import PCA # avoid importing if we dont need it
|
|
|
|
pca = PCA(0.999)
|
|
pca = pca.fit(self.data_dictionary["train_features"])
|
|
n_keep_components = pca.n_components_
|
|
self.data["n_kept_components"] = n_keep_components
|
|
n_components = self.data_dictionary["train_features"].shape[1]
|
|
logger.info("reduced feature dimension by %s", n_components - n_keep_components)
|
|
logger.info("explained variance %f", np.sum(pca.explained_variance_ratio_))
|
|
|
|
train_components = pca.transform(self.data_dictionary["train_features"])
|
|
self.data_dictionary["train_features"] = pd.DataFrame(
|
|
data=train_components,
|
|
columns=["PC" + str(i) for i in range(0, n_keep_components)],
|
|
index=self.data_dictionary["train_features"].index,
|
|
)
|
|
# normalsing transformed training features
|
|
self.data_dictionary["train_features"] = self.normalize_single_dataframe(
|
|
self.data_dictionary["train_features"])
|
|
|
|
# keeping a copy of the non-transformed features so we can check for errors during
|
|
# model load from disk
|
|
self.data["training_features_list_raw"] = copy.deepcopy(self.training_features_list)
|
|
self.training_features_list = self.data_dictionary["train_features"].columns
|
|
|
|
if self.freqai_config.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
|
|
test_components = pca.transform(self.data_dictionary["test_features"])
|
|
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,
|
|
)
|
|
# normalise transformed test feature to transformed training features
|
|
self.data_dictionary["test_features"] = self.normalize_data_from_metadata(
|
|
self.data_dictionary["test_features"])
|
|
|
|
self.data["n_kept_components"] = n_keep_components
|
|
self.pca = pca
|
|
|
|
logger.info(f"PCA reduced total features from {n_components} to {n_keep_components}")
|
|
|
|
if not self.data_path.is_dir():
|
|
self.data_path.mkdir(parents=True, exist_ok=True)
|
|
|
|
return None
|
|
|
|
def pca_transform(self, filtered_dataframe: DataFrame) -> None:
|
|
"""
|
|
Use an existing pca transform to transform data into components
|
|
:params:
|
|
filtered_dataframe: DataFrame = the cleaned dataframe
|
|
"""
|
|
pca_components = self.pca.transform(filtered_dataframe)
|
|
self.data_dictionary["prediction_features"] = pd.DataFrame(
|
|
data=pca_components,
|
|
columns=["PC" + str(i) for i in range(0, self.data["n_kept_components"])],
|
|
index=filtered_dataframe.index,
|
|
)
|
|
# normalise transformed predictions to transformed training features
|
|
self.data_dictionary["prediction_features"] = self.normalize_data_from_metadata(
|
|
self.data_dictionary["prediction_features"])
|
|
|
|
def compute_distances(self) -> float:
|
|
"""
|
|
Compute distances between each training point and every other training
|
|
point. This metric defines the neighborhood of trained data and is used
|
|
for prediction confidence in the Dissimilarity Index
|
|
"""
|
|
# logger.info("computing average mean distance for all training points")
|
|
pairwise = pairwise_distances(
|
|
self.data_dictionary["train_features"], n_jobs=self.thread_count)
|
|
# remove the diagonal distances which are itself distances ~0
|
|
np.fill_diagonal(pairwise, np.NaN)
|
|
pairwise = pairwise.reshape(-1, 1)
|
|
avg_mean_dist = pairwise[~np.isnan(pairwise)].mean()
|
|
|
|
return avg_mean_dist
|
|
|
|
def get_outlier_percentage(self, dropped_pts: npt.NDArray) -> float:
|
|
"""
|
|
Check if more than X% of points werer dropped during outlier detection.
|
|
"""
|
|
outlier_protection_pct = self.freqai_config["feature_parameters"].get(
|
|
"outlier_protection_percentage", 30)
|
|
outlier_pct = (dropped_pts.sum() / len(dropped_pts)) * 100
|
|
if outlier_pct >= outlier_protection_pct:
|
|
return outlier_pct
|
|
else:
|
|
return 0.0
|
|
|
|
def use_SVM_to_remove_outliers(self, predict: bool) -> None:
|
|
"""
|
|
Build/inference a Support Vector Machine to detect outliers
|
|
in training data and prediction
|
|
:params:
|
|
predict: bool = If true, inference an existing SVM model, else construct one
|
|
"""
|
|
|
|
if self.keras:
|
|
logger.warning(
|
|
"SVM outlier removal not currently supported for Keras based models. "
|
|
"Skipping user requested function."
|
|
)
|
|
if predict:
|
|
self.do_predict = np.ones(len(self.data_dictionary["prediction_features"]))
|
|
return
|
|
|
|
if predict:
|
|
if not self.svm_model:
|
|
logger.warning("No svm model available for outlier removal")
|
|
return
|
|
y_pred = self.svm_model.predict(self.data_dictionary["prediction_features"])
|
|
do_predict = np.where(y_pred == -1, 0, y_pred)
|
|
|
|
if (len(do_predict) - do_predict.sum()) > 0:
|
|
logger.info(f"SVM tossed {len(do_predict) - do_predict.sum()} predictions.")
|
|
self.do_predict += do_predict
|
|
self.do_predict -= 1
|
|
|
|
else:
|
|
# use SGDOneClassSVM to increase speed?
|
|
svm_params = self.freqai_config["feature_parameters"].get(
|
|
"svm_params", {"shuffle": False, "nu": 0.1})
|
|
self.svm_model = linear_model.SGDOneClassSVM(**svm_params).fit(
|
|
self.data_dictionary["train_features"]
|
|
)
|
|
y_pred = self.svm_model.predict(self.data_dictionary["train_features"])
|
|
kept_points = np.where(y_pred == -1, 0, y_pred)
|
|
# keep_index = np.where(y_pred == 1)
|
|
outlier_pct = self.get_outlier_percentage(1 - kept_points)
|
|
if outlier_pct:
|
|
logger.warning(
|
|
f"SVM detected {outlier_pct:.2f}% of the points as outliers. "
|
|
f"Keeping original dataset."
|
|
)
|
|
self.svm_model = None
|
|
return
|
|
|
|
self.data_dictionary["train_features"] = self.data_dictionary["train_features"][
|
|
(y_pred == 1)
|
|
]
|
|
self.data_dictionary["train_labels"] = self.data_dictionary["train_labels"][
|
|
(y_pred == 1)
|
|
]
|
|
self.data_dictionary["train_weights"] = self.data_dictionary["train_weights"][
|
|
(y_pred == 1)
|
|
]
|
|
|
|
logger.info(
|
|
f"SVM tossed {len(y_pred) - kept_points.sum()}"
|
|
f" train points from {len(y_pred)} total points."
|
|
)
|
|
|
|
# same for test data
|
|
# TODO: This (and the part above) could be refactored into a separate function
|
|
# to reduce code duplication
|
|
if self.freqai_config['data_split_parameters'].get('test_size', 0.1) != 0:
|
|
y_pred = self.svm_model.predict(self.data_dictionary["test_features"])
|
|
kept_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 tossed {len(y_pred) - kept_points.sum()}"
|
|
f" test points from {len(y_pred)} total points."
|
|
)
|
|
|
|
return
|
|
|
|
def use_DBSCAN_to_remove_outliers(self, predict: bool, eps=None) -> None:
|
|
"""
|
|
Use DBSCAN to cluster training data and remove "noisy" data (read outliers).
|
|
User controls this via the config param `DBSCAN_outlier_pct` which indicates the
|
|
pct of training data that they want to be considered outliers.
|
|
:params:
|
|
predict: bool = If False (training), iterate to find the best hyper parameters to match
|
|
user requested outlier percent target. If True (prediction), use the parameters
|
|
determined from the previous training to estimate if the current prediction point
|
|
is an outlier.
|
|
"""
|
|
|
|
if predict:
|
|
if not self.data['DBSCAN_eps']:
|
|
return
|
|
train_ft_df = self.data_dictionary['train_features']
|
|
pred_ft_df = self.data_dictionary['prediction_features']
|
|
num_preds = len(pred_ft_df)
|
|
df = pd.concat([train_ft_df, pred_ft_df], axis=0, ignore_index=True)
|
|
clustering = DBSCAN(eps=self.data['DBSCAN_eps'],
|
|
min_samples=self.data['DBSCAN_min_samples'],
|
|
n_jobs=self.thread_count
|
|
).fit(df)
|
|
do_predict = np.where(clustering.labels_[-num_preds:] == -1, 0, 1)
|
|
|
|
if (len(do_predict) - do_predict.sum()) > 0:
|
|
logger.info(f"DBSCAN tossed {len(do_predict) - do_predict.sum()} predictions")
|
|
self.do_predict += do_predict
|
|
self.do_predict -= 1
|
|
|
|
else:
|
|
|
|
def normalise_distances(distances):
|
|
normalised_distances = (distances - distances.min()) / \
|
|
(distances.max() - distances.min())
|
|
return normalised_distances
|
|
|
|
def rotate_point(origin, point, angle):
|
|
# rotate a point counterclockwise by a given angle (in radians)
|
|
# around a given origin
|
|
x = origin[0] + cos(angle) * (point[0] - origin[0]) - \
|
|
sin(angle) * (point[1] - origin[1])
|
|
y = origin[1] + sin(angle) * (point[0] - origin[0]) + \
|
|
cos(angle) * (point[1] - origin[1])
|
|
return (x, y)
|
|
|
|
MinPts = int(len(self.data_dictionary['train_features'].index) * 0.25)
|
|
# measure pairwise distances to nearest neighbours
|
|
neighbors = NearestNeighbors(
|
|
n_neighbors=MinPts, n_jobs=self.thread_count)
|
|
neighbors_fit = neighbors.fit(self.data_dictionary['train_features'])
|
|
distances, _ = neighbors_fit.kneighbors(self.data_dictionary['train_features'])
|
|
distances = np.sort(distances, axis=0).mean(axis=1)
|
|
|
|
normalised_distances = normalise_distances(distances)
|
|
x_range = np.linspace(0, 1, len(distances))
|
|
line = np.linspace(normalised_distances[0],
|
|
normalised_distances[-1], len(normalised_distances))
|
|
deflection = np.abs(normalised_distances - line)
|
|
max_deflection_loc = np.where(deflection == deflection.max())[0][0]
|
|
origin = x_range[max_deflection_loc], line[max_deflection_loc]
|
|
point = x_range[max_deflection_loc], normalised_distances[max_deflection_loc]
|
|
rot_angle = np.pi / 4
|
|
elbow_loc = rotate_point(origin, point, rot_angle)
|
|
|
|
epsilon = elbow_loc[1] * (distances[-1] - distances[0]) + distances[0]
|
|
|
|
clustering = DBSCAN(eps=epsilon, min_samples=MinPts,
|
|
n_jobs=int(self.thread_count)).fit(
|
|
self.data_dictionary['train_features']
|
|
)
|
|
|
|
logger.info(f'DBSCAN found eps of {epsilon:.2f}.')
|
|
|
|
self.data['DBSCAN_eps'] = epsilon
|
|
self.data['DBSCAN_min_samples'] = MinPts
|
|
dropped_points = np.where(clustering.labels_ == -1, 1, 0)
|
|
|
|
outlier_pct = self.get_outlier_percentage(dropped_points)
|
|
if outlier_pct:
|
|
logger.warning(
|
|
f"DBSCAN detected {outlier_pct:.2f}% of the points as outliers. "
|
|
f"Keeping original dataset."
|
|
)
|
|
self.data['DBSCAN_eps'] = 0
|
|
return
|
|
|
|
self.data_dictionary['train_features'] = self.data_dictionary['train_features'][
|
|
(clustering.labels_ != -1)
|
|
]
|
|
self.data_dictionary["train_labels"] = self.data_dictionary["train_labels"][
|
|
(clustering.labels_ != -1)
|
|
]
|
|
self.data_dictionary["train_weights"] = self.data_dictionary["train_weights"][
|
|
(clustering.labels_ != -1)
|
|
]
|
|
|
|
logger.info(
|
|
f"DBSCAN tossed {dropped_points.sum()}"
|
|
f" train points from {len(clustering.labels_)}"
|
|
)
|
|
|
|
return
|
|
|
|
def compute_inlier_metric(self, set_='train') -> None:
|
|
"""
|
|
|
|
Compute inlier metric from backwards distance distributions.
|
|
This metric defines how well features from a timepoint fit
|
|
into previous timepoints.
|
|
"""
|
|
|
|
no_prev_pts = self.freqai_config["feature_parameters"]["inlier_metric_window"]
|
|
|
|
if set_ == 'train':
|
|
compute_df = copy.deepcopy(self.data_dictionary['train_features'])
|
|
elif set_ == 'test':
|
|
compute_df = copy.deepcopy(self.data_dictionary['test_features'])
|
|
else:
|
|
compute_df = copy.deepcopy(self.data_dictionary['prediction_features'])
|
|
|
|
compute_df_reindexed = compute_df.reindex(
|
|
index=np.flip(compute_df.index)
|
|
)
|
|
|
|
pairwise = pd.DataFrame(
|
|
np.triu(
|
|
pairwise_distances(compute_df_reindexed, n_jobs=self.thread_count)
|
|
),
|
|
columns=compute_df_reindexed.index,
|
|
index=compute_df_reindexed.index
|
|
)
|
|
pairwise = pairwise.round(5)
|
|
|
|
column_labels = [
|
|
'{}{}'.format('d', i) for i in range(1, no_prev_pts + 1)
|
|
]
|
|
distances = pd.DataFrame(
|
|
columns=column_labels, index=compute_df.index
|
|
)
|
|
|
|
for index in compute_df.index[no_prev_pts:]:
|
|
current_row = pairwise.loc[[index]]
|
|
current_row_no_zeros = current_row.loc[
|
|
:, (current_row != 0).any(axis=0)
|
|
]
|
|
distances.loc[[index]] = current_row_no_zeros.iloc[
|
|
:, :no_prev_pts
|
|
]
|
|
distances = distances.replace([np.inf, -np.inf], np.nan)
|
|
drop_index = pd.isnull(distances).any(1)
|
|
distances = distances[drop_index == 0]
|
|
|
|
inliers = pd.DataFrame(index=distances.index)
|
|
for key in distances.keys():
|
|
current_distances = distances[key].dropna()
|
|
fit_params = stats.weibull_min.fit(current_distances)
|
|
quantiles = stats.weibull_min.cdf(current_distances, *fit_params)
|
|
|
|
df_inlier = pd.DataFrame(
|
|
{key: quantiles}, index=distances.index
|
|
)
|
|
inliers = pd.concat(
|
|
[inliers, df_inlier], axis=1
|
|
)
|
|
|
|
inlier_metric = pd.DataFrame(
|
|
data=inliers.sum(axis=1) / no_prev_pts,
|
|
columns=['inlier_metric'],
|
|
index=compute_df.index
|
|
)
|
|
|
|
inlier_metric = (2 * (inlier_metric - inlier_metric.min()) /
|
|
(inlier_metric.max() - inlier_metric.min()) - 1)
|
|
|
|
if set_ in ('train', 'test'):
|
|
inlier_metric = inlier_metric.iloc[no_prev_pts:]
|
|
compute_df = compute_df.iloc[no_prev_pts:]
|
|
self.remove_beginning_points_from_data_dict(set_, no_prev_pts)
|
|
self.data_dictionary[f'{set_}_features'] = pd.concat(
|
|
[compute_df, inlier_metric], axis=1)
|
|
else:
|
|
self.data_dictionary['prediction_features'] = pd.concat(
|
|
[compute_df, inlier_metric], axis=1)
|
|
self.data_dictionary['prediction_features'].fillna(0, inplace=True)
|
|
|
|
logger.info('Inlier metric computed and added to features.')
|
|
|
|
return None
|
|
|
|
def remove_beginning_points_from_data_dict(self, set_='train', no_prev_pts: int = 10):
|
|
features = self.data_dictionary[f'{set_}_features']
|
|
weights = self.data_dictionary[f'{set_}_weights']
|
|
labels = self.data_dictionary[f'{set_}_labels']
|
|
self.data_dictionary[f'{set_}_weights'] = weights[no_prev_pts:]
|
|
self.data_dictionary[f'{set_}_features'] = features.iloc[no_prev_pts:]
|
|
self.data_dictionary[f'{set_}_labels'] = labels.iloc[no_prev_pts:]
|
|
|
|
def add_noise_to_training_features(self) -> None:
|
|
"""
|
|
Add noise to train features to reduce the risk of overfitting.
|
|
"""
|
|
mu = 0 # no shift
|
|
sigma = self.freqai_config["feature_parameters"]["noise_standard_deviation"]
|
|
compute_df = self.data_dictionary['train_features']
|
|
noise = np.random.normal(mu, sigma, [compute_df.shape[0], compute_df.shape[1]])
|
|
self.data_dictionary['train_features'] += noise
|
|
return
|
|
|
|
def find_features(self, dataframe: DataFrame) -> None:
|
|
"""
|
|
Find features in the strategy provided dataframe
|
|
:param dataframe: DataFrame = strategy provided dataframe
|
|
:return:
|
|
features: list = the features to be used for training/prediction
|
|
"""
|
|
column_names = dataframe.columns
|
|
features = [c for c in column_names if "%" in c]
|
|
labels = [c for c in column_names if "&" in c]
|
|
if not features:
|
|
raise OperationalException("Could not find any features!")
|
|
|
|
self.training_features_list = features
|
|
self.label_list = labels
|
|
|
|
def check_if_pred_in_training_spaces(self) -> None:
|
|
"""
|
|
Compares the distance from each prediction point to each training data
|
|
point. It uses this information to estimate a Dissimilarity Index (DI)
|
|
and avoid making predictions on any points that are too far away
|
|
from the training data set.
|
|
"""
|
|
|
|
distance = pairwise_distances(
|
|
self.data_dictionary["train_features"],
|
|
self.data_dictionary["prediction_features"],
|
|
n_jobs=self.thread_count,
|
|
)
|
|
|
|
self.DI_values = distance.min(axis=0) / self.data["avg_mean_dist"]
|
|
|
|
do_predict = np.where(
|
|
self.DI_values < self.freqai_config["feature_parameters"]["DI_threshold"],
|
|
1,
|
|
0,
|
|
)
|
|
|
|
if (len(do_predict) - do_predict.sum()) > 0:
|
|
logger.info(
|
|
f"DI tossed {len(do_predict) - do_predict.sum()} predictions for "
|
|
"being too far from training data."
|
|
)
|
|
|
|
self.do_predict += do_predict
|
|
self.do_predict -= 1
|
|
|
|
def set_weights_higher_recent(self, num_weights: int) -> npt.ArrayLike:
|
|
"""
|
|
Set weights so that recent data is more heavily weighted during
|
|
training than older data.
|
|
"""
|
|
wfactor = self.config["freqai"]["feature_parameters"]["weight_factor"]
|
|
weights = np.exp(-np.arange(num_weights) / (wfactor * num_weights))[::-1]
|
|
return weights
|
|
|
|
def get_predictions_to_append(self, predictions: DataFrame,
|
|
do_predict: npt.ArrayLike) -> DataFrame:
|
|
"""
|
|
Get backtest prediction from current backtest period
|
|
"""
|
|
|
|
append_df = DataFrame()
|
|
for label in predictions.columns:
|
|
append_df[label] = predictions[label]
|
|
if append_df[label].dtype == object:
|
|
continue
|
|
append_df[f"{label}_mean"] = self.data["labels_mean"][label]
|
|
append_df[f"{label}_std"] = self.data["labels_std"][label]
|
|
|
|
append_df["do_predict"] = do_predict
|
|
if self.freqai_config["feature_parameters"].get("DI_threshold", 0) > 0:
|
|
append_df["DI_values"] = self.DI_values
|
|
|
|
return append_df
|
|
|
|
def append_predictions(self, append_df: DataFrame) -> None:
|
|
"""
|
|
Append backtest prediction from current backtest period to all previous periods
|
|
"""
|
|
|
|
if self.full_df.empty:
|
|
self.full_df = append_df
|
|
else:
|
|
self.full_df = pd.concat([self.full_df, append_df], axis=0)
|
|
|
|
def fill_predictions(self, dataframe):
|
|
"""
|
|
Back fill values to before the backtesting range so that the dataframe matches size
|
|
when it goes back to the strategy. These rows are not included in the backtest.
|
|
"""
|
|
|
|
len_filler = len(dataframe) - len(self.full_df.index) # startup_candle_count
|
|
filler_df = pd.DataFrame(
|
|
np.zeros((len_filler, len(self.full_df.columns))), columns=self.full_df.columns
|
|
)
|
|
|
|
self.full_df = pd.concat([filler_df, self.full_df], axis=0, ignore_index=True)
|
|
|
|
to_keep = [col for col in dataframe.columns if not col.startswith("&")]
|
|
self.return_dataframe = pd.concat([dataframe[to_keep], self.full_df], axis=1)
|
|
|
|
# self.return_dataframe = self.remove_training_from_backtesting()
|
|
self.full_df = DataFrame()
|
|
|
|
return
|
|
|
|
def create_fulltimerange(self, backtest_tr: str, backtest_period_days: int) -> str:
|
|
|
|
if not isinstance(backtest_period_days, int):
|
|
raise OperationalException("backtest_period_days must be an integer")
|
|
|
|
if backtest_period_days < 0:
|
|
raise OperationalException("backtest_period_days must be positive")
|
|
|
|
backtest_timerange = TimeRange.parse_timerange(backtest_tr)
|
|
|
|
if backtest_timerange.stopts == 0:
|
|
# typically open ended time ranges do work, however, there are some edge cases where
|
|
# it does not. accommodating these kinds of edge cases just to allow open-ended
|
|
# timerange is not high enough priority to warrant the effort. It is safer for now
|
|
# to simply ask user to add their end date
|
|
raise OperationalException("FreqAI backtesting does not allow open ended timeranges. "
|
|
"Please indicate the end date of your desired backtesting. "
|
|
"timerange.")
|
|
# backtest_timerange.stopts = int(
|
|
# datetime.now(tz=timezone.utc).timestamp()
|
|
# )
|
|
|
|
backtest_timerange.startts = (
|
|
backtest_timerange.startts - backtest_period_days * SECONDS_IN_DAY
|
|
)
|
|
start = datetime.fromtimestamp(backtest_timerange.startts, tz=timezone.utc)
|
|
stop = datetime.fromtimestamp(backtest_timerange.stopts, tz=timezone.utc)
|
|
full_timerange = start.strftime("%Y%m%d") + "-" + stop.strftime("%Y%m%d")
|
|
|
|
self.full_path = Path(
|
|
self.config["user_data_dir"] / "models" / f"{self.freqai_config['identifier']}"
|
|
)
|
|
|
|
config_path = Path(self.config["config_files"][0])
|
|
|
|
if not self.full_path.is_dir():
|
|
self.full_path.mkdir(parents=True, exist_ok=True)
|
|
shutil.copy(
|
|
config_path.resolve(),
|
|
Path(self.full_path / config_path.parts[-1]),
|
|
)
|
|
|
|
return full_timerange
|
|
|
|
def check_if_model_expired(self, trained_timestamp: int) -> bool:
|
|
"""
|
|
A model age checker to determine if the model is trustworthy based on user defined
|
|
`expiration_hours` in the configuration file.
|
|
:param trained_timestamp: int = The time of training for the most recent model.
|
|
:return:
|
|
bool = If the model is expired or not.
|
|
"""
|
|
time = datetime.now(tz=timezone.utc).timestamp()
|
|
elapsed_time = (time - trained_timestamp) / 3600 # hours
|
|
max_time = self.freqai_config.get("expiration_hours", 0)
|
|
if max_time > 0:
|
|
return elapsed_time > max_time
|
|
else:
|
|
return False
|
|
|
|
def check_if_new_training_required(
|
|
self, trained_timestamp: int
|
|
) -> Tuple[bool, TimeRange, TimeRange]:
|
|
|
|
time = datetime.now(tz=timezone.utc).timestamp()
|
|
trained_timerange = TimeRange()
|
|
data_load_timerange = TimeRange()
|
|
|
|
timeframes = self.freqai_config["feature_parameters"].get("include_timeframes")
|
|
|
|
max_tf_seconds = 0
|
|
for tf in timeframes:
|
|
secs = timeframe_to_seconds(tf)
|
|
if secs > max_tf_seconds:
|
|
max_tf_seconds = secs
|
|
|
|
# We notice that users like to use exotic indicators where
|
|
# they do not know the required timeperiod. Here we include a factor
|
|
# of safety by multiplying the user considered "max" by 2.
|
|
max_period = self.config.get('startup_candle_count', 20) * 2
|
|
additional_seconds = max_period * max_tf_seconds
|
|
|
|
if trained_timestamp != 0:
|
|
elapsed_time = (time - trained_timestamp) / SECONDS_IN_HOUR
|
|
retrain = elapsed_time > self.freqai_config.get("live_retrain_hours", 0)
|
|
if retrain:
|
|
trained_timerange.startts = int(
|
|
time - self.freqai_config.get("train_period_days", 0) * SECONDS_IN_DAY
|
|
)
|
|
trained_timerange.stopts = int(time)
|
|
# we want to load/populate indicators on more data than we plan to train on so
|
|
# because most of the indicators have a rolling timeperiod, and are thus NaNs
|
|
# unless they have data further back in time before the start of the train period
|
|
data_load_timerange.startts = int(
|
|
time
|
|
- self.freqai_config.get("train_period_days", 0) * SECONDS_IN_DAY
|
|
- additional_seconds
|
|
)
|
|
data_load_timerange.stopts = int(time)
|
|
else: # user passed no live_trained_timerange in config
|
|
trained_timerange.startts = int(
|
|
time - self.freqai_config.get("train_period_days", 0) * SECONDS_IN_DAY
|
|
)
|
|
trained_timerange.stopts = int(time)
|
|
|
|
data_load_timerange.startts = int(
|
|
time
|
|
- self.freqai_config.get("train_period_days", 0) * SECONDS_IN_DAY
|
|
- additional_seconds
|
|
)
|
|
data_load_timerange.stopts = int(time)
|
|
retrain = True
|
|
|
|
return retrain, trained_timerange, data_load_timerange
|
|
|
|
def set_new_model_names(self, pair: str, trained_timerange: TimeRange):
|
|
|
|
coin, _ = pair.split("/")
|
|
self.data_path = Path(
|
|
self.full_path
|
|
/ f"sub-train-{pair.split('/')[0]}_{int(trained_timerange.stopts)}"
|
|
)
|
|
|
|
self.model_filename = f"cb_{coin.lower()}_{int(trained_timerange.stopts)}"
|
|
|
|
def set_all_pairs(self) -> None:
|
|
|
|
self.all_pairs = copy.deepcopy(
|
|
self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
|
|
)
|
|
for pair in self.config.get("exchange", "").get("pair_whitelist"):
|
|
if pair not in self.all_pairs:
|
|
self.all_pairs.append(pair)
|
|
|
|
def use_strategy_to_populate_indicators(
|
|
self,
|
|
strategy: IStrategy,
|
|
corr_dataframes: dict = {},
|
|
base_dataframes: dict = {},
|
|
pair: str = "",
|
|
prediction_dataframe: DataFrame = pd.DataFrame(),
|
|
) -> DataFrame:
|
|
"""
|
|
Use the user defined strategy for populating indicators during
|
|
retrain
|
|
:params:
|
|
strategy: IStrategy = user defined strategy object
|
|
corr_dataframes: dict = dict containing the informative pair dataframes
|
|
(for user defined timeframes)
|
|
base_dataframes: dict = dict containing the current pair dataframes
|
|
(for user defined timeframes)
|
|
metadata: dict = strategy furnished pair metadata
|
|
:returns:
|
|
dataframe: DataFrame = dataframe containing populated indicators
|
|
"""
|
|
|
|
# for prediction dataframe creation, we let dataprovider handle everything in the strategy
|
|
# so we create empty dictionaries, which allows us to pass None to
|
|
# `populate_any_indicators()`. Signaling we want the dp to give us the live dataframe.
|
|
tfs = self.freqai_config["feature_parameters"].get("include_timeframes")
|
|
pairs = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
|
|
if not prediction_dataframe.empty:
|
|
dataframe = prediction_dataframe.copy()
|
|
for tf in tfs:
|
|
base_dataframes[tf] = None
|
|
for p in pairs:
|
|
if p not in corr_dataframes:
|
|
corr_dataframes[p] = {}
|
|
corr_dataframes[p][tf] = None
|
|
else:
|
|
dataframe = base_dataframes[self.config["timeframe"]].copy()
|
|
|
|
sgi = False
|
|
for tf in tfs:
|
|
if tf == tfs[-1]:
|
|
sgi = True # doing this last allows user to use all tf raw prices in labels
|
|
dataframe = strategy.populate_any_indicators(
|
|
pair,
|
|
dataframe.copy(),
|
|
tf,
|
|
informative=base_dataframes[tf],
|
|
set_generalized_indicators=sgi
|
|
)
|
|
if pairs:
|
|
for i in pairs:
|
|
if pair in i:
|
|
continue # dont repeat anything from whitelist
|
|
dataframe = strategy.populate_any_indicators(
|
|
i,
|
|
dataframe.copy(),
|
|
tf,
|
|
informative=corr_dataframes[i][tf]
|
|
)
|
|
|
|
self.get_unique_classes_from_labels(dataframe)
|
|
|
|
return dataframe
|
|
|
|
def fit_labels(self) -> None:
|
|
"""
|
|
Fit the labels with a gaussian distribution
|
|
"""
|
|
import scipy as spy
|
|
|
|
self.data["labels_mean"], self.data["labels_std"] = {}, {}
|
|
for label in self.data_dictionary["train_labels"].columns:
|
|
if self.data_dictionary["train_labels"][label].dtype == object:
|
|
continue
|
|
f = spy.stats.norm.fit(self.data_dictionary["train_labels"][label])
|
|
self.data["labels_mean"][label], self.data["labels_std"][label] = f[0], f[1]
|
|
|
|
# incase targets are classifications
|
|
for label in self.unique_class_list:
|
|
self.data["labels_mean"][label], self.data["labels_std"][label] = 0, 0
|
|
|
|
return
|
|
|
|
def remove_features_from_df(self, dataframe: DataFrame) -> DataFrame:
|
|
"""
|
|
Remove the features from the dataframe before returning it to strategy. This keeps it
|
|
compact for Frequi purposes.
|
|
"""
|
|
to_keep = [
|
|
col for col in dataframe.columns if not col.startswith("%") or col.startswith("%%")
|
|
]
|
|
return dataframe[to_keep]
|
|
|
|
def get_unique_classes_from_labels(self, dataframe: DataFrame) -> None:
|
|
|
|
self.find_features(dataframe)
|
|
|
|
for key in self.label_list:
|
|
if dataframe[key].dtype == object:
|
|
self.unique_classes[key] = dataframe[key].dropna().unique()
|
|
|
|
if self.unique_classes:
|
|
for label in self.unique_classes:
|
|
self.unique_class_list += list(self.unique_classes[label])
|
|
|
|
def save_backtesting_prediction(
|
|
self, append_df: DataFrame
|
|
) -> None:
|
|
"""
|
|
Save prediction dataframe from backtesting to h5 file format
|
|
:param append_df: dataframe for backtesting period
|
|
"""
|
|
full_predictions_folder = Path(self.full_path / self.backtest_predictions_folder)
|
|
if not full_predictions_folder.is_dir():
|
|
full_predictions_folder.mkdir(parents=True, exist_ok=True)
|
|
|
|
append_df.to_hdf(self.backtesting_results_path, key='append_df', mode='w')
|
|
|
|
def get_backtesting_prediction(
|
|
self
|
|
) -> DataFrame:
|
|
"""
|
|
Get prediction dataframe from h5 file format
|
|
"""
|
|
append_df = pd.read_hdf(self.backtesting_results_path)
|
|
return append_df
|
|
|
|
def check_if_backtest_prediction_exists(
|
|
self
|
|
) -> bool:
|
|
"""
|
|
Check if a backtesting prediction already exists
|
|
:param dk: FreqaiDataKitchen
|
|
:return:
|
|
:boolean: whether the prediction file exists or not.
|
|
"""
|
|
path_to_predictionfile = Path(self.full_path /
|
|
self.backtest_predictions_folder /
|
|
f"{self.model_filename}_prediction.h5")
|
|
self.backtesting_results_path = path_to_predictionfile
|
|
|
|
file_exists = path_to_predictionfile.is_file()
|
|
if file_exists:
|
|
logger.info(f"Found backtesting prediction file at {path_to_predictionfile}")
|
|
else:
|
|
logger.info(
|
|
f"Could not find backtesting prediction file at {path_to_predictionfile}"
|
|
)
|
|
return file_exists
|
|
|
|
def spice_extractor(self, indicator: str, dataframe: DataFrame) -> npt.NDArray:
|
|
if indicator in dataframe:
|
|
return np.array(dataframe[indicator])
|
|
else:
|
|
logger.warning(f'User asked spice_rack for {indicator}, '
|
|
f'but it is not available. Returning 0s')
|
|
return np.zeros(len(dataframe.index))
|