import copy import inspect import logging import random import shutil from datetime import datetime, timezone from pathlib import Path from typing import Any import numpy as np import numpy.typing as npt import pandas as pd import psutil from datasieve.pipeline import Pipeline from pandas import DataFrame from sklearn.model_selection import train_test_split from freqtrade.configuration import TimeRange from freqtrade.constants import DOCS_LINK, Config from freqtrade.data.converter import reduce_dataframe_footprint from freqtrade.exceptions import OperationalException from freqtrade.exchange import timeframe_to_seconds from freqtrade.strategy import merge_informative_pair from freqtrade.strategy.interface import IStrategy pd.set_option("future.no_silent_downcasting", True) 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: Config, 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.keras: bool = self.freqai_config.get("keras", False) self.set_all_pairs() self.backtest_live_models = config.get("freqai_backtest_live_models", False) self.feature_pipeline = Pipeline() self.label_pipeline = Pipeline() self.DI_values: npt.NDArray = np.array([]) if not self.live: self.full_path = self.get_full_models_path(self.config) if not self.backtest_live_models: 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", {}) if not self.freqai_config.get("data_kitchen_thread_count", 0): self.thread_count = max(int(psutil.cpu_count() * 2 - 2), 1) else: self.thread_count = self.freqai_config["data_kitchen_thread_count"] self.train_dates: DataFrame = pd.DataFrame() self.unique_classes: dict[str, list] = {} self.unique_class_list: list = [] self.backtest_live_models_data: dict[str, Any] = {} def set_paths( self, pair: str, trained_timestamp: int | None = None, ) -> None: """ Set the paths to the data for the present coin/botloop :param metadata: dict = strategy furnished pair metadata :param trained_timestamp: int = timestamp of most recent training """ self.full_path = self.get_full_models_path(self.config) 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. :param filtered_dataframe: cleaned dataframe ready to be split. :param labels: cleaned labels ready to be split. """ feat_dict = self.freqai_config["feature_parameters"] if "shuffle" not in self.freqai_config["data_split_parameters"]: self.freqai_config["data_split_parameters"].update({"shuffle": False}) 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 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, **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 if feat_dict["shuffle_after_split"]: rint1 = random.randint(0, 100) rint2 = random.randint(0, 100) train_features = train_features.sample(frac=1, random_state=rint1).reset_index( drop=True ) train_labels = train_labels.sample(frac=1, random_state=rint1).reset_index(drop=True) train_weights = ( pd.DataFrame(train_weights) .sample(frac=1, random_state=rint1) .reset_index(drop=True) .to_numpy()[:, 0] ) test_features = test_features.sample(frac=1, random_state=rint2).reset_index(drop=True) test_labels = test_labels.sample(frac=1, random_state=rint2).reset_index(drop=True) test_weights = ( pd.DataFrame(test_weights) .sample(frac=1, random_state=rint2) .reset_index(drop=True) .to_numpy()[:, 0] ) # 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. :param unfiltered_df: the full dataframe for the present training period :param training_feature_list: list, the training feature list constructed by self.build_feature_list() according to user specified parameters in the configuration file. :param labels: the labels for the dataset :param 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(axis=1) # get the rows that have NaNs, drop_index = drop_index.replace(True, 1).replace(False, 0).infer_objects(copy=False) if training_filter: # 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(axis=1) drop_index_labels = ( drop_index_labels.replace(True, 1).replace(False, 0).infer_objects(copy=False) ) 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"{self.pair}: dropped {len(unfiltered_df) - len(filtered_df)} training points" f" due to NaNs in populated dataset {len(unfiltered_df)}." ) if len(filtered_df) == 0 and not self.live: raise OperationalException( f"{self.pair}: all training data dropped due to NaNs. " "You likely did not download enough training data prior " "to your backtest timerange. Hint:\n" f"{DOCS_LINK}/freqai-running/" "#downloading-data-to-cover-the-full-backtest-period" ) 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(axis=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 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 tr_training_list.append(timerange_train.timerange_str) 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 tr_backtesting_list.append(timerange_backtest.timerange_str) 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. """ if not self.live: df = df.loc[(df["date"] >= timerange.startdt) & (df["date"] < timerange.stopdt), :] else: df = df.loc[df["date"] >= timerange.startdt, :] return df 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] if not features: raise OperationalException("Could not find any features!") self.training_features_list = features def find_labels(self, dataframe: DataFrame) -> None: column_names = dataframe.columns labels = [c for c in column_names if "&" in c] self.label_list = labels 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_backtest: DataFrame ) -> 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 if "labels_mean" in self.data: append_df[f"{label}_mean"] = self.data["labels_mean"][label] if "labels_std" in self.data: append_df[f"{label}_std"] = self.data["labels_std"][label] for extra_col in self.data["extra_returns_per_train"]: append_df[f"{extra_col}"] = self.data["extra_returns_per_train"][extra_col] append_df["do_predict"] = do_predict if self.freqai_config["feature_parameters"].get("DI_threshold", 0) > 0: append_df["DI_values"] = self.DI_values user_cols = [col for col in dataframe_backtest.columns if col.startswith("%%")] cols = ["date"] cols.extend(user_cols) dataframe_backtest.reset_index(drop=True, inplace=True) merged_df = pd.concat([dataframe_backtest[cols], append_df], axis=1) return merged_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, ignore_index=True) 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. """ to_keep = [ col for col in dataframe.columns if not col.startswith("&") and not col.startswith("%%") ] self.return_dataframe = pd.merge(dataframe[to_keep], self.full_df, how="left", on="date") self.return_dataframe[self.full_df.columns] = self.return_dataframe[ self.full_df.columns ].fillna(value=0) 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 ) full_timerange = backtest_timerange.timerange_str 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, timestamp_id: int): coin, _ = pair.split("/") self.data_path = Path(self.full_path / f"sub-train-{pair.split('/')[0]}_{timestamp_id}") self.model_filename = f"cb_{coin.lower()}_{timestamp_id}" 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 extract_corr_pair_columns_from_populated_indicators( self, dataframe: DataFrame ) -> dict[str, DataFrame]: """ Find the columns of the dataframe corresponding to the corr_pairlist, save them in a dictionary to be reused and attached to other pairs. :param dataframe: fully populated dataframe (current pair + corr_pairs) :return: corr_dataframes, dictionary of dataframes to be attached to other pairs in same candle. """ corr_dataframes: dict[str, DataFrame] = {} pairs = self.freqai_config["feature_parameters"].get("include_corr_pairlist", []) for pair in pairs: pair = pair.replace(":", "") # lightgbm does not like colons pair_cols = [ col for col in dataframe.columns if col.startswith("%") and f"{pair}_" in col ] if pair_cols: pair_cols.insert(0, "date") corr_dataframes[pair] = dataframe.filter(pair_cols, axis=1) return corr_dataframes def attach_corr_pair_columns( self, dataframe: DataFrame, corr_dataframes: dict[str, DataFrame], current_pair: str ) -> DataFrame: """ Attach the existing corr_pair dataframes to the current pair dataframe before training :param dataframe: current pair strategy dataframe, indicators populated already :param corr_dataframes: dictionary of saved dataframes from earlier in the same candle :param current_pair: current pair to which we will attach corr pair dataframe :return: :dataframe: current pair dataframe of populated indicators, concatenated with corr_pairs ready for training """ pairs = self.freqai_config["feature_parameters"].get("include_corr_pairlist", []) current_pair = current_pair.replace(":", "") for pair in pairs: pair = pair.replace(":", "") # lightgbm does not work with colons if current_pair != pair: dataframe = dataframe.merge(corr_dataframes[pair], how="left", on="date") return dataframe def get_pair_data_for_features( self, pair: str, tf: str, strategy: IStrategy, corr_dataframes: dict = {}, base_dataframes: dict = {}, is_corr_pairs: bool = False, ) -> DataFrame: """ Get the data for the pair. If it's not in the dictionary, get it from the data provider :param pair: str = pair to get data for :param tf: str = timeframe to get data for :param strategy: IStrategy = user defined strategy object :param corr_dataframes: dict = dict containing the df pair dataframes (for user defined timeframes) :param base_dataframes: dict = dict containing the current pair dataframes (for user defined timeframes) :param is_corr_pairs: bool = whether the pair is a corr pair or not :return: dataframe = dataframe containing the pair data """ if is_corr_pairs: dataframe = corr_dataframes[pair][tf] if not dataframe.empty: return dataframe else: dataframe = strategy.dp.get_pair_dataframe(pair=pair, timeframe=tf) return dataframe else: dataframe = base_dataframes[tf] if not dataframe.empty: return dataframe else: dataframe = strategy.dp.get_pair_dataframe(pair=pair, timeframe=tf) return dataframe def merge_features( self, df_main: DataFrame, df_to_merge: DataFrame, tf: str, timeframe_inf: str, suffix: str ) -> DataFrame: """ Merge the features of the dataframe and remove HLCV and date added columns :param df_main: DataFrame = main dataframe :param df_to_merge: DataFrame = dataframe to merge :param tf: str = timeframe of the main dataframe :param timeframe_inf: str = timeframe of the dataframe to merge :param suffix: str = suffix to add to the columns of the dataframe to merge :return: dataframe = merged dataframe """ dataframe = merge_informative_pair( df_main, df_to_merge, tf, timeframe_inf=timeframe_inf, append_timeframe=False, suffix=suffix, ffill=True, ) skip_columns = [ (f"{s}_{suffix}") for s in ["date", "open", "high", "low", "close", "volume"] ] dataframe = dataframe.drop(columns=skip_columns) return dataframe def populate_features( self, dataframe: DataFrame, pair: str, strategy: IStrategy, corr_dataframes: dict, base_dataframes: dict, is_corr_pairs: bool = False, ) -> DataFrame: """ Use the user defined strategy functions for populating features :param dataframe: DataFrame = dataframe to populate :param pair: str = pair to populate :param strategy: IStrategy = user defined strategy object :param corr_dataframes: dict = dict containing the df pair dataframes :param base_dataframes: dict = dict containing the current pair dataframes :param is_corr_pairs: bool = whether the pair is a corr pair or not :return: dataframe = populated dataframe """ tfs: list[str] = self.freqai_config["feature_parameters"].get("include_timeframes") for tf in tfs: metadata = {"pair": pair, "tf": tf} informative_df = self.get_pair_data_for_features( pair, tf, strategy, corr_dataframes, base_dataframes, is_corr_pairs ) informative_copy = informative_df.copy() logger.debug(f"Populating features for {pair} {tf}") for t in self.freqai_config["feature_parameters"]["indicator_periods_candles"]: df_features = strategy.feature_engineering_expand_all( informative_copy.copy(), t, metadata=metadata ) suffix = f"{t}" informative_df = self.merge_features(informative_df, df_features, tf, tf, suffix) generic_df = strategy.feature_engineering_expand_basic( informative_copy.copy(), metadata=metadata ) suffix = "gen" informative_df = self.merge_features(informative_df, generic_df, tf, tf, suffix) indicators = [col for col in informative_df if col.startswith("%")] for n in range(self.freqai_config["feature_parameters"]["include_shifted_candles"] + 1): if n == 0: continue df_shift = informative_df[indicators].shift(n) df_shift = df_shift.add_suffix("_shift-" + str(n)) informative_df = pd.concat((informative_df, df_shift), axis=1) dataframe = self.merge_features( dataframe.copy(), informative_df, self.config["timeframe"], tf, f"{pair}_{tf}" ) return dataframe def use_strategy_to_populate_indicators( # noqa: C901 self, strategy: IStrategy, corr_dataframes: dict = {}, base_dataframes: dict = {}, pair: str = "", prediction_dataframe: DataFrame = pd.DataFrame(), do_corr_pairs: bool = True, ) -> DataFrame: """ Use the user defined strategy for populating indicators during retrain :param strategy: IStrategy = user defined strategy object :param corr_dataframes: dict = dict containing the df pair dataframes (for user defined timeframes) :param base_dataframes: dict = dict containing the current pair dataframes (for user defined timeframes) :param pair: str = pair to populate :param prediction_dataframe: DataFrame = dataframe containing the pair data used for prediction :param do_corr_pairs: bool = whether to populate corr pairs or not :return: dataframe: DataFrame = dataframe containing populated indicators """ # check if the user is using the deprecated populate_any_indicators function new_version = inspect.getsource(strategy.populate_any_indicators) == ( inspect.getsource(IStrategy.populate_any_indicators) ) if not new_version: raise OperationalException( "You are using the `populate_any_indicators()` function" " which was deprecated on March 1, 2023. Please refer " "to the strategy migration guide to use the new " "feature_engineering_* methods: \n" f"{DOCS_LINK}/strategy_migration/#freqai-strategy \n" "And the feature_engineering_* documentation: \n" f"{DOCS_LINK}/freqai-feature-engineering/" ) tfs: list[str] = self.freqai_config["feature_parameters"].get("include_timeframes") pairs: list[str] = self.freqai_config["feature_parameters"].get("include_corr_pairlist", []) for tf in tfs: if tf not in base_dataframes: base_dataframes[tf] = pd.DataFrame() for p in pairs: if p not in corr_dataframes: corr_dataframes[p] = {} if tf not in corr_dataframes[p]: corr_dataframes[p][tf] = pd.DataFrame() if not prediction_dataframe.empty: dataframe = prediction_dataframe.copy() base_dataframes[self.config["timeframe"]] = dataframe.copy() else: dataframe = base_dataframes[self.config["timeframe"]].copy() corr_pairs: list[str] = self.freqai_config["feature_parameters"].get( "include_corr_pairlist", [] ) dataframe = self.populate_features( dataframe.copy(), pair, strategy, corr_dataframes, base_dataframes ) metadata = {"pair": pair} dataframe = strategy.feature_engineering_standard(dataframe.copy(), metadata=metadata) # ensure corr pairs are always last for corr_pair in corr_pairs: if pair == corr_pair: continue # dont repeat anything from whitelist if corr_pairs and do_corr_pairs: dataframe = self.populate_features( dataframe.copy(), corr_pair, strategy, corr_dataframes, base_dataframes, True ) if self.live: dataframe = strategy.set_freqai_targets(dataframe.copy(), metadata=metadata) dataframe = self.remove_special_chars_from_feature_names(dataframe) self.get_unique_classes_from_labels(dataframe) if self.config.get("reduce_df_footprint", False): dataframe = reduce_dataframe_footprint(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] # in case 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) self.find_labels(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 feather 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_feather(self.backtesting_results_path) def get_backtesting_prediction(self) -> DataFrame: """ Get prediction dataframe from feather file format """ append_df = pd.read_feather(self.backtesting_results_path) return append_df def check_if_backtest_prediction_is_valid(self, len_backtest_df: int) -> bool: """ Check if a backtesting prediction already exists and if the predictions to append have the same size as the backtesting dataframe slice :param length_backtesting_dataframe: Length of backtesting dataframe slice :return: :boolean: whether the prediction file is valid. """ path_to_predictionfile = Path( self.full_path / self.backtest_predictions_folder / f"{self.model_filename}_prediction.feather" ) self.backtesting_results_path = path_to_predictionfile file_exists = path_to_predictionfile.is_file() if file_exists: append_df = self.get_backtesting_prediction() if len(append_df) == len_backtest_df and "date" in append_df: logger.info(f"Found backtesting prediction file at {path_to_predictionfile}") return True else: logger.info( "A new backtesting prediction file is required. " "(Number of predictions is different from dataframe length or " "old prediction file version)." ) return False else: logger.info(f"Could not find backtesting prediction file at {path_to_predictionfile}") return False def get_full_models_path(self, config: Config) -> Path: """ Returns default FreqAI model path :param config: Configuration dictionary """ freqai_config: dict[str, Any] = config["freqai"] return Path(config["user_data_dir"] / "models" / str(freqai_config.get("identifier"))) def remove_special_chars_from_feature_names(self, dataframe: pd.DataFrame) -> pd.DataFrame: """ Remove all special characters from feature strings (:) :param dataframe: the dataframe that just finished indicator population. (unfiltered) :return: dataframe with cleaned feature names """ spec_chars = [":"] for c in spec_chars: dataframe.columns = dataframe.columns.str.replace(c, "") return dataframe def buffer_timerange(self, timerange: TimeRange): """ Buffer the start and end of the timerange. This is used *after* the indicators are populated. The main example use is when predicting maxima and minima, the argrelextrema function cannot know the maxima/minima at the edges of the timerange. To improve model accuracy, it is best to compute argrelextrema on the full timerange and then use this function to cut off the edges (buffer) by the kernel. In another case, if the targets are set to a shifted price movement, this buffer is unnecessary because the shifted candles at the end of the timerange will be NaN and FreqAI will automatically cut those off of the training dataset. """ buffer = self.freqai_config["feature_parameters"]["buffer_train_data_candles"] if buffer: timerange.stopts -= buffer * timeframe_to_seconds(self.config["timeframe"]) timerange.startts += buffer * timeframe_to_seconds(self.config["timeframe"]) return timerange # deprecated functions def normalize_data(self, data_dictionary: dict) -> dict[Any, Any]: """ Deprecation warning, migration assistance """ logger.warning( f"Your custom IFreqaiModel relies on the deprecated" " data pipeline. Please update your model to use the new data pipeline." " This can be achieved by following the migration guide at " f"{DOCS_LINK}/strategy_migration/#freqai-new-data-pipeline " "We added a basic pipeline for you, but this will be removed " "in a future version." ) return data_dictionary def denormalize_labels_from_metadata(self, df: DataFrame) -> DataFrame: """ Deprecation warning, migration assistance """ logger.warning( f"Your custom IFreqaiModel relies on the deprecated" " data pipeline. Please update your model to use the new data pipeline." " This can be achieved by following the migration guide at " f"{DOCS_LINK}/strategy_migration/#freqai-new-data-pipeline " "We added a basic pipeline for you, but this will be removed " "in a future version." ) pred_df, _, _ = self.label_pipeline.inverse_transform(df) return pred_df