612 lines
26 KiB
Python
612 lines
26 KiB
Python
# import contextlib
|
|
import datetime
|
|
import logging
|
|
import shutil
|
|
import threading
|
|
import time
|
|
from abc import ABC, abstractmethod
|
|
from pathlib import Path
|
|
from typing import Any, Dict, Tuple
|
|
|
|
import numpy as np
|
|
import pandas as pd
|
|
from numpy.typing import NDArray
|
|
from pandas import DataFrame
|
|
|
|
from freqtrade.configuration import TimeRange
|
|
from freqtrade.enums import RunMode
|
|
from freqtrade.exceptions import OperationalException
|
|
from freqtrade.freqai.data_drawer import FreqaiDataDrawer
|
|
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
|
|
from freqtrade.strategy.interface import IStrategy
|
|
|
|
|
|
pd.options.mode.chained_assignment = None
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
def threaded(fn):
|
|
def wrapper(*args, **kwargs):
|
|
threading.Thread(target=fn, args=args, kwargs=kwargs).start()
|
|
|
|
return wrapper
|
|
|
|
|
|
class IFreqaiModel(ABC):
|
|
"""
|
|
Class containing all tools for training and prediction in the strategy.
|
|
Base*PredictionModels inherit from this class.
|
|
|
|
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
|
|
"""
|
|
|
|
def __init__(self, config: Dict[str, Any]) -> None:
|
|
|
|
self.config = config
|
|
self.assert_config(self.config)
|
|
self.freqai_info: Dict[str, Any] = config["freqai"]
|
|
self.data_split_parameters: Dict[str, Any] = config.get("freqai", {}).get(
|
|
"data_split_parameters", {})
|
|
self.model_training_parameters: Dict[str, Any] = config.get("freqai", {}).get(
|
|
"model_training_parameters", {})
|
|
self.feature_parameters = config.get("freqai", {}).get("feature_parameters")
|
|
self.retrain = False
|
|
self.first = True
|
|
self.set_full_path()
|
|
self.follow_mode: bool = self.freqai_info.get("follow_mode", False)
|
|
self.dd = FreqaiDataDrawer(Path(self.full_path), self.config, self.follow_mode)
|
|
self.lock = threading.Lock()
|
|
self.identifier: str = self.freqai_info.get("identifier", "no_id_provided")
|
|
self.scanning = False
|
|
self.keras: bool = self.freqai_info.get("keras", False)
|
|
if self.keras and self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0):
|
|
self.freqai_info["feature_parameters"]["DI_threshold"] = 0
|
|
logger.warning("DI threshold is not configured for Keras models yet. Deactivating.")
|
|
self.CONV_WIDTH = self.freqai_info.get("conv_width", 2)
|
|
self.pair_it = 0
|
|
self.total_pairs = len(self.config.get("exchange", {}).get("pair_whitelist"))
|
|
self.last_trade_database_summary: DataFrame = {}
|
|
self.current_trade_database_summary: DataFrame = {}
|
|
|
|
def assert_config(self, config: Dict[str, Any]) -> None:
|
|
|
|
if not config.get("freqai", {}):
|
|
raise OperationalException("No freqai parameters found in configuration file.")
|
|
|
|
def start(self, dataframe: DataFrame, metadata: dict, strategy: IStrategy) -> DataFrame:
|
|
"""
|
|
Entry point to the FreqaiModel from a specific pair, it will train a new model if
|
|
necessary before making the prediction.
|
|
|
|
:param dataframe: Full dataframe coming from strategy - it contains entire
|
|
backtesting timerange + additional historical data necessary to train
|
|
the model.
|
|
:param metadata: pair metadata coming from strategy.
|
|
:param strategy: Strategy to train on
|
|
"""
|
|
|
|
self.live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE)
|
|
self.dd.set_pair_dict_info(metadata)
|
|
|
|
if self.live:
|
|
self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"])
|
|
dk = self.start_live(dataframe, metadata, strategy, self.dk)
|
|
|
|
# For backtesting, each pair enters and then gets trained for each window along the
|
|
# sliding window defined by "train_period_days" (training window) and "live_retrain_hours"
|
|
# (backtest window, i.e. window immediately following the training window).
|
|
# FreqAI slides the window and sequentially builds the backtesting results before returning
|
|
# the concatenated results for the full backtesting period back to the strategy.
|
|
elif not self.follow_mode:
|
|
self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"])
|
|
logger.info(f"Training {len(self.dk.training_timeranges)} timeranges")
|
|
|
|
dataframe = self.dk.use_strategy_to_populate_indicators(
|
|
strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
|
|
)
|
|
dk = self.start_backtesting(dataframe, metadata, self.dk)
|
|
|
|
dataframe = dk.remove_features_from_df(dk.return_dataframe)
|
|
del dk
|
|
return dataframe
|
|
|
|
@threaded
|
|
def start_scanning(self, strategy: IStrategy) -> None:
|
|
"""
|
|
Function designed to constantly scan pairs for retraining on a separate thread (intracandle)
|
|
to improve model youth. This function is agnostic to data preparation/collection/storage,
|
|
it simply trains on what ever data is available in the self.dd.
|
|
:param strategy: IStrategy = The user defined strategy class
|
|
"""
|
|
while 1:
|
|
time.sleep(1)
|
|
for pair in self.config.get("exchange", {}).get("pair_whitelist"):
|
|
|
|
(_, trained_timestamp, _) = self.dd.get_pair_dict_info(pair)
|
|
|
|
if self.dd.pair_dict[pair]["priority"] != 1:
|
|
continue
|
|
dk = FreqaiDataKitchen(self.config, self.live, pair)
|
|
dk.set_paths(pair, trained_timestamp)
|
|
(
|
|
retrain,
|
|
new_trained_timerange,
|
|
data_load_timerange,
|
|
) = dk.check_if_new_training_required(trained_timestamp)
|
|
dk.set_paths(pair, new_trained_timerange.stopts)
|
|
|
|
if retrain:
|
|
self.train_model_in_series(
|
|
new_trained_timerange, pair, strategy, dk, data_load_timerange
|
|
)
|
|
|
|
def start_backtesting(
|
|
self, dataframe: DataFrame, metadata: dict, dk: FreqaiDataKitchen
|
|
) -> FreqaiDataKitchen:
|
|
"""
|
|
The main broad execution for backtesting. For backtesting, each pair enters and then gets
|
|
trained for each window along the sliding window defined by "train_period_days"
|
|
(training window) and "backtest_period_days" (backtest window, i.e. window immediately
|
|
following the training window). FreqAI slides the window and sequentially builds
|
|
the backtesting results before returning the concatenated results for the full
|
|
backtesting period back to the strategy.
|
|
:param dataframe: DataFrame = strategy passed dataframe
|
|
:param metadata: Dict = pair metadata
|
|
:param dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
|
|
:return:
|
|
FreqaiDataKitchen = Data management/analysis tool associated to present pair only
|
|
"""
|
|
|
|
self.pair_it += 1
|
|
train_it = 0
|
|
# Loop enforcing the sliding window training/backtesting paradigm
|
|
# tr_train is the training time range e.g. 1 historical month
|
|
# tr_backtest is the backtesting time range e.g. the week directly
|
|
# following tr_train. Both of these windows slide through the
|
|
# entire backtest
|
|
for tr_train, tr_backtest in zip(dk.training_timeranges, dk.backtesting_timeranges):
|
|
(_, _, _) = self.dd.get_pair_dict_info(metadata["pair"])
|
|
train_it += 1
|
|
total_trains = len(dk.backtesting_timeranges)
|
|
self.training_timerange = tr_train
|
|
dataframe_train = dk.slice_dataframe(tr_train, dataframe)
|
|
dataframe_backtest = dk.slice_dataframe(tr_backtest, dataframe)
|
|
|
|
trained_timestamp = tr_train
|
|
tr_train_startts_str = datetime.datetime.utcfromtimestamp(tr_train.startts).strftime(
|
|
"%Y-%m-%d %H:%M:%S"
|
|
)
|
|
tr_train_stopts_str = datetime.datetime.utcfromtimestamp(tr_train.stopts).strftime(
|
|
"%Y-%m-%d %H:%M:%S"
|
|
)
|
|
logger.info(
|
|
f"Training {metadata['pair']}, {self.pair_it}/{self.total_pairs} pairs"
|
|
f" from {tr_train_startts_str} to {tr_train_stopts_str}, {train_it}/{total_trains} "
|
|
"trains"
|
|
)
|
|
|
|
dk.data_path = Path(
|
|
dk.full_path
|
|
/
|
|
f"sub-train-{metadata['pair'].split('/')[0]}_{int(trained_timestamp.stopts)}"
|
|
)
|
|
if not self.model_exists(
|
|
metadata["pair"], dk, trained_timestamp=int(trained_timestamp.stopts)
|
|
):
|
|
dk.find_features(dataframe_train)
|
|
self.model = self.train(dataframe_train, metadata["pair"], dk)
|
|
self.dd.pair_dict[metadata["pair"]]["trained_timestamp"] = int(
|
|
trained_timestamp.stopts)
|
|
dk.set_new_model_names(metadata["pair"], trained_timestamp)
|
|
self.dd.save_data(self.model, metadata["pair"], dk)
|
|
else:
|
|
self.model = self.dd.load_data(metadata["pair"], dk)
|
|
|
|
self.check_if_feature_list_matches_strategy(dataframe_train, dk)
|
|
|
|
pred_df, do_preds = self.predict(dataframe_backtest, dk)
|
|
|
|
dk.append_predictions(pred_df, do_preds)
|
|
|
|
dk.fill_predictions(dataframe)
|
|
|
|
return dk
|
|
|
|
def start_live(
|
|
self, dataframe: DataFrame, metadata: dict, strategy: IStrategy, dk: FreqaiDataKitchen
|
|
) -> FreqaiDataKitchen:
|
|
"""
|
|
The main broad execution for dry/live. This function will check if a retraining should be
|
|
performed, and if so, retrain and reset the model.
|
|
:param dataframe: DataFrame = strategy passed dataframe
|
|
:param metadata: Dict = pair metadata
|
|
:param strategy: IStrategy = currently employed strategy
|
|
dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
|
|
:returns:
|
|
dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
|
|
"""
|
|
|
|
# update follower
|
|
if self.follow_mode:
|
|
self.dd.update_follower_metadata()
|
|
|
|
# get the model metadata associated with the current pair
|
|
(_, trained_timestamp, return_null_array) = self.dd.get_pair_dict_info(metadata["pair"])
|
|
|
|
# if the metadata doesn't exist, the follower returns null arrays to strategy
|
|
if self.follow_mode and return_null_array:
|
|
logger.info("Returning null array from follower to strategy")
|
|
self.dd.return_null_values_to_strategy(dataframe, dk)
|
|
return dk
|
|
|
|
# append the historic data once per round
|
|
if self.dd.historic_data:
|
|
self.dd.update_historic_data(strategy, dk)
|
|
logger.debug(f'Updating historic data on pair {metadata["pair"]}')
|
|
|
|
if not self.follow_mode:
|
|
|
|
(_, new_trained_timerange, data_load_timerange) = dk.check_if_new_training_required(
|
|
trained_timestamp
|
|
)
|
|
dk.set_paths(metadata["pair"], new_trained_timerange.stopts)
|
|
|
|
# download candle history if it is not already in memory
|
|
if not self.dd.historic_data:
|
|
logger.info(
|
|
"Downloading all training data for all pairs in whitelist and "
|
|
"corr_pairlist, this may take a while if you do not have the "
|
|
"data saved"
|
|
)
|
|
dk.download_all_data_for_training(data_load_timerange, strategy.dp)
|
|
self.dd.load_all_pair_histories(data_load_timerange, dk)
|
|
|
|
if not self.scanning:
|
|
self.scanning = True
|
|
self.start_scanning(strategy)
|
|
|
|
elif self.follow_mode:
|
|
dk.set_paths(metadata["pair"], trained_timestamp)
|
|
logger.info(
|
|
"FreqAI instance set to follow_mode, finding existing pair "
|
|
f"using { self.identifier }"
|
|
)
|
|
|
|
# load the model and associated data into the data kitchen
|
|
self.model = self.dd.load_data(metadata["pair"], dk)
|
|
|
|
dataframe = self.dk.use_strategy_to_populate_indicators(
|
|
strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
|
|
)
|
|
|
|
if not self.model:
|
|
logger.warning(
|
|
f"No model ready for {metadata['pair']}, returning null values to strategy."
|
|
)
|
|
self.dd.return_null_values_to_strategy(dataframe, dk)
|
|
return dk
|
|
|
|
# ensure user is feeding the correct indicators to the model
|
|
self.check_if_feature_list_matches_strategy(dataframe, dk)
|
|
|
|
self.build_strategy_return_arrays(dataframe, dk, metadata["pair"], trained_timestamp)
|
|
|
|
return dk
|
|
|
|
def build_strategy_return_arrays(
|
|
self, dataframe: DataFrame, dk: FreqaiDataKitchen, pair: str, trained_timestamp: int
|
|
) -> None:
|
|
|
|
# hold the historical predictions in memory so we are sending back
|
|
# correct array to strategy
|
|
|
|
if pair not in self.dd.model_return_values:
|
|
# first predictions are made on entire historical candle set coming from strategy. This
|
|
# allows FreqUI to show full return values.
|
|
pred_df, do_preds = self.predict(dataframe, dk)
|
|
self.dd.set_initial_return_values(pair, dk, pred_df, do_preds)
|
|
dk.return_dataframe = self.dd.attach_return_values_to_return_dataframe(pair, dataframe)
|
|
return
|
|
elif self.dk.check_if_model_expired(trained_timestamp):
|
|
pred_df = DataFrame(np.zeros((2, len(dk.label_list))), columns=dk.label_list)
|
|
do_preds = np.ones(2, dtype=np.int_) * 2
|
|
dk.DI_values = np.zeros(2)
|
|
logger.warning(
|
|
f"Model expired for {pair}, returning null values to strategy. Strategy "
|
|
"construction should take care to consider this event with "
|
|
"prediction == 0 and do_predict == 2"
|
|
)
|
|
else:
|
|
# remaining predictions are made only on the most recent candles for performance and
|
|
# historical accuracy reasons.
|
|
pred_df, do_preds = self.predict(dataframe.iloc[-self.CONV_WIDTH:], dk, first=False)
|
|
|
|
self.dd.append_model_predictions(pair, pred_df, do_preds, dk, len(dataframe))
|
|
dk.return_dataframe = self.dd.attach_return_values_to_return_dataframe(pair, dataframe)
|
|
|
|
return
|
|
|
|
def check_if_feature_list_matches_strategy(
|
|
self, dataframe: DataFrame, dk: FreqaiDataKitchen
|
|
) -> None:
|
|
"""
|
|
Ensure user is passing the proper feature set if they are reusing an `identifier` pointing
|
|
to a folder holding existing models.
|
|
:param dataframe: DataFrame = strategy provided dataframe
|
|
:param dk: FreqaiDataKitchen = non-persistent data container/analyzer for
|
|
current coin/bot loop
|
|
"""
|
|
dk.find_features(dataframe)
|
|
if "training_features_list_raw" in dk.data:
|
|
feature_list = dk.data["training_features_list_raw"]
|
|
else:
|
|
feature_list = dk.training_features_list
|
|
if dk.training_features_list != feature_list:
|
|
raise OperationalException(
|
|
"Trying to access pretrained model with `identifier` "
|
|
"but found different features furnished by current strategy."
|
|
"Change `identifier` to train from scratch, or ensure the"
|
|
"strategy is furnishing the same features as the pretrained"
|
|
"model"
|
|
)
|
|
|
|
def data_cleaning_train(self, dk: FreqaiDataKitchen) -> None:
|
|
"""
|
|
Base data cleaning method for train
|
|
Any function inside this method should drop training data points from the filtered_dataframe
|
|
based on user decided logic. See FreqaiDataKitchen::use_SVM_to_remove_outliers() for an
|
|
example of how outlier data points are dropped from the dataframe used for training.
|
|
"""
|
|
|
|
if self.freqai_info["feature_parameters"].get(
|
|
"principal_component_analysis", False
|
|
):
|
|
dk.principal_component_analysis()
|
|
|
|
if self.freqai_info["feature_parameters"].get("use_SVM_to_remove_outliers", False):
|
|
dk.use_SVM_to_remove_outliers(predict=False)
|
|
|
|
if self.freqai_info["feature_parameters"].get("DI_threshold", 0):
|
|
dk.data["avg_mean_dist"] = dk.compute_distances()
|
|
|
|
if self.freqai_info["feature_parameters"].get("use_DBSCAN_to_remove_outliers", False):
|
|
if dk.pair in self.dd.old_DBSCAN_eps:
|
|
eps = self.dd.old_DBSCAN_eps[dk.pair]
|
|
else:
|
|
eps = None
|
|
dk.use_DBSCAN_to_remove_outliers(predict=False, eps=eps)
|
|
self.dd.old_DBSCAN_eps[dk.pair] = dk.data['DBSCAN_eps']
|
|
|
|
def data_cleaning_predict(self, dk: FreqaiDataKitchen, dataframe: DataFrame) -> None:
|
|
"""
|
|
Base data cleaning method for predict.
|
|
These functions each modify dk.do_predict, which is a dataframe with equal length
|
|
to the number of candles coming from and returning to the strategy. Inside do_predict,
|
|
1 allows prediction and < 0 signals to the strategy that the model is not confident in
|
|
the prediction.
|
|
See FreqaiDataKitchen::remove_outliers() for an example
|
|
of how the do_predict vector is modified. do_predict is ultimately passed back to strategy
|
|
for buy signals.
|
|
"""
|
|
if self.freqai_info["feature_parameters"].get(
|
|
"principal_component_analysis", False
|
|
):
|
|
dk.pca_transform(dataframe)
|
|
|
|
if self.freqai_info["feature_parameters"].get("use_SVM_to_remove_outliers", False):
|
|
dk.use_SVM_to_remove_outliers(predict=True)
|
|
|
|
if self.freqai_info["feature_parameters"].get("DI_threshold", 0):
|
|
dk.check_if_pred_in_training_spaces()
|
|
|
|
if self.freqai_info["feature_parameters"].get("use_DBSCAN_to_remove_outliers", False):
|
|
dk.use_DBSCAN_to_remove_outliers(predict=True)
|
|
|
|
def model_exists(
|
|
self,
|
|
pair: str,
|
|
dk: FreqaiDataKitchen,
|
|
trained_timestamp: int = None,
|
|
model_filename: str = "",
|
|
scanning: bool = False,
|
|
) -> bool:
|
|
"""
|
|
Given a pair and path, check if a model already exists
|
|
:param pair: pair e.g. BTC/USD
|
|
:param path: path to model
|
|
:return:
|
|
:boolean: whether the model file exists or not.
|
|
"""
|
|
coin, _ = pair.split("/")
|
|
|
|
if not self.live:
|
|
dk.model_filename = model_filename = f"cb_{coin.lower()}_{trained_timestamp}"
|
|
|
|
path_to_modelfile = Path(dk.data_path / f"{model_filename}_model.joblib")
|
|
file_exists = path_to_modelfile.is_file()
|
|
if file_exists and not scanning:
|
|
logger.info("Found model at %s", dk.data_path / dk.model_filename)
|
|
elif not scanning:
|
|
logger.info("Could not find model at %s", dk.data_path / dk.model_filename)
|
|
return file_exists
|
|
|
|
def set_full_path(self) -> None:
|
|
self.full_path = Path(
|
|
self.config["user_data_dir"] / "models" / f"{self.freqai_info['identifier']}"
|
|
)
|
|
self.full_path.mkdir(parents=True, exist_ok=True)
|
|
shutil.copy(
|
|
self.config["config_files"][0],
|
|
Path(self.full_path, Path(self.config["config_files"][0]).name),
|
|
)
|
|
|
|
def train_model_in_series(
|
|
self,
|
|
new_trained_timerange: TimeRange,
|
|
pair: str,
|
|
strategy: IStrategy,
|
|
dk: FreqaiDataKitchen,
|
|
data_load_timerange: TimeRange,
|
|
):
|
|
"""
|
|
Retrieve data and train model in single threaded mode (only used if model directory is empty
|
|
upon startup for dry/live )
|
|
:param new_trained_timerange: TimeRange = the timerange to train the model on
|
|
:param metadata: dict = strategy provided metadata
|
|
:param strategy: IStrategy = user defined strategy object
|
|
:param dk: FreqaiDataKitchen = non-persistent data container for current coin/loop
|
|
:param data_load_timerange: TimeRange = the amount of data to be loaded
|
|
for populate_any_indicators
|
|
(larger than new_trained_timerange so that
|
|
new_trained_timerange does not contain any NaNs)
|
|
"""
|
|
|
|
corr_dataframes, base_dataframes = self.dd.get_base_and_corr_dataframes(
|
|
data_load_timerange, pair, dk
|
|
)
|
|
|
|
unfiltered_dataframe = dk.use_strategy_to_populate_indicators(
|
|
strategy, corr_dataframes, base_dataframes, pair
|
|
)
|
|
|
|
unfiltered_dataframe = dk.slice_dataframe(new_trained_timerange, unfiltered_dataframe)
|
|
|
|
# find the features indicated by strategy and store in datakitchen
|
|
dk.find_features(unfiltered_dataframe)
|
|
|
|
model = self.train(unfiltered_dataframe, pair, dk)
|
|
|
|
self.dd.pair_dict[pair]["trained_timestamp"] = new_trained_timerange.stopts
|
|
dk.set_new_model_names(pair, new_trained_timerange)
|
|
self.dd.pair_dict[pair]["first"] = False
|
|
if self.dd.pair_dict[pair]["priority"] == 1 and self.scanning:
|
|
with self.lock:
|
|
self.dd.pair_to_end_of_training_queue(pair)
|
|
self.dd.save_data(model, pair, dk)
|
|
|
|
if self.freqai_info.get("purge_old_models", False):
|
|
self.dd.purge_old_models()
|
|
|
|
def set_initial_historic_predictions(
|
|
self, df: DataFrame, model: Any, dk: FreqaiDataKitchen, pair: str
|
|
) -> None:
|
|
"""
|
|
This function is called only if the datadrawer failed to load an
|
|
existing set of historic predictions. In this case, it builds
|
|
the structure and sets fake predictions off the first training
|
|
data. After that, FreqAI will append new real predictions to the
|
|
set of historic predictions.
|
|
|
|
These values are used to generate live statistics which can be used
|
|
in the strategy for adaptive values. E.g. &*_mean/std are quantities
|
|
that can computed based on live predictions from the set of historical
|
|
predictions. Those values can be used in the user strategy to better
|
|
assess prediction rarity, and thus wait for probabilistically favorable
|
|
entries relative to the live historical predictions.
|
|
|
|
If the user reuses an identifier on a subsequent instance,
|
|
this function will not be called. In that case, "real" predictions
|
|
will be appended to the loaded set of historic predictions.
|
|
:param: df: DataFrame = the dataframe containing the training feature data
|
|
:param: model: Any = A model which was `fit` using a common library such as
|
|
catboost or lightgbm
|
|
:param: dk: FreqaiDataKitchen = object containing methods for data analysis
|
|
:param: pair: str = current pair
|
|
"""
|
|
num_candles = self.freqai_info.get('fit_live_predictions_candles', 600)
|
|
if not num_candles:
|
|
num_candles = 600
|
|
df_tail = df.tail(num_candles)
|
|
trained_predictions = model.predict(df_tail)
|
|
pred_df = DataFrame(trained_predictions, columns=dk.label_list)
|
|
|
|
pred_df = dk.denormalize_labels_from_metadata(pred_df)
|
|
|
|
self.dd.historic_predictions[pair] = pred_df
|
|
hist_preds_df = self.dd.historic_predictions[pair]
|
|
|
|
for label in hist_preds_df.columns:
|
|
if hist_preds_df[label].dtype == object:
|
|
continue
|
|
hist_preds_df[f'{label}_mean'] = 0
|
|
hist_preds_df[f'{label}_std'] = 0
|
|
|
|
hist_preds_df['do_predict'] = 0
|
|
|
|
if self.freqai_info['feature_parameters'].get('DI_threshold', 0) > 0:
|
|
hist_preds_df['DI_values'] = 0
|
|
|
|
for return_str in dk.data['extra_returns_per_train']:
|
|
hist_preds_df[return_str] = 0
|
|
|
|
def fit_live_predictions(self, dk: FreqaiDataKitchen) -> None:
|
|
"""
|
|
Fit the labels with a gaussian distribution
|
|
"""
|
|
import scipy as spy
|
|
|
|
num_candles = self.freqai_info.get("fit_live_predictions_candles", 100)
|
|
dk.data["labels_mean"], dk.data["labels_std"] = {}, {}
|
|
for label in dk.label_list:
|
|
if self.dd.historic_predictions[dk.pair][label].dtype == object:
|
|
continue
|
|
f = spy.stats.norm.fit(self.dd.historic_predictions[dk.pair][label].tail(num_candles))
|
|
dk.data["labels_mean"][label], dk.data["labels_std"][label] = f[0], f[1]
|
|
|
|
return
|
|
|
|
# Following methods which are overridden by user made prediction models.
|
|
# See freqai/prediction_models/CatboostPredictionModel.py for an example.
|
|
|
|
@abstractmethod
|
|
def train(self, unfiltered_dataframe: DataFrame, pair: str, dk: FreqaiDataKitchen) -> Any:
|
|
"""
|
|
Filter the training data and train a model to it. Train makes heavy use of the datahandler
|
|
for storing, saving, loading, and analyzing the data.
|
|
:param unfiltered_dataframe: Full dataframe for the current training period
|
|
:param metadata: pair metadata from strategy.
|
|
:return: Trained model which can be used to inference (self.predict)
|
|
"""
|
|
|
|
@abstractmethod
|
|
def fit(self, data_dictionary: Dict[str, Any]) -> 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.
|
|
:param data_dictionary: Dict = the dictionary constructed by DataHandler to hold
|
|
all the training and test data/labels.
|
|
"""
|
|
|
|
return
|
|
|
|
@abstractmethod
|
|
def predict(
|
|
self, dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = True
|
|
) -> Tuple[DataFrame, NDArray[np.int_]]:
|
|
"""
|
|
Filter the prediction features data and predict with it.
|
|
:param unfiltered_dataframe: Full dataframe for the current backtest period.
|
|
:param dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
|
|
:param first: boolean = whether this is the first prediction or not.
|
|
: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 (i.e. SVM and/or DI index)
|
|
"""
|