Merge branch 'develop' into backtest_live_models

freqtrade/commands/cli_options.py

@@ -440,7 +440,7 @@ AVAILABLE_CLI_OPTIONS = {
     "dataformat_trades": Arg(
         '--data-format-trades',
         help='Storage format for downloaded trades data. (default: `jsongz`).',
-        choices=constants.AVAILABLE_DATAHANDLERS,
+        choices=constants.AVAILABLE_DATAHANDLERS_TRADES,
     ),
     "show_timerange": Arg(
         '--show-timerange',

freqtrade/constants.py

@@ -36,7 +36,8 @@ AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList',
                        'PrecisionFilter', 'PriceFilter', 'RangeStabilityFilter',
                        'ShuffleFilter', 'SpreadFilter', 'VolatilityFilter']
 AVAILABLE_PROTECTIONS = ['CooldownPeriod', 'LowProfitPairs', 'MaxDrawdown', 'StoplossGuard']
-AVAILABLE_DATAHANDLERS = ['json', 'jsongz', 'hdf5']
+AVAILABLE_DATAHANDLERS_TRADES = ['json', 'jsongz', 'hdf5']
+AVAILABLE_DATAHANDLERS = AVAILABLE_DATAHANDLERS_TRADES + ['feather', 'parquet']
 BACKTEST_BREAKDOWNS = ['day', 'week', 'month']
 BACKTEST_CACHE_AGE = ['none', 'day', 'week', 'month']
 BACKTEST_CACHE_DEFAULT = 'day'
@@ -434,7 +435,7 @@ CONF_SCHEMA = {
         },
         'dataformat_trades': {
             'type': 'string',
-            'enum': AVAILABLE_DATAHANDLERS,
+            'enum': AVAILABLE_DATAHANDLERS_TRADES,
             'default': 'jsongz'
         },
         'position_adjustment_enable': {'type': 'boolean'},

freqtrade/data/history/featherdatahandler.py

@@ -0,0 +1,130 @@
+import logging
+from typing import Optional
+
+from pandas import DataFrame, read_feather, to_datetime
+
+from freqtrade.configuration import TimeRange
+from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, TradeList
+from freqtrade.enums import CandleType
+
+from .idatahandler import IDataHandler
+
+
+logger = logging.getLogger(__name__)
+
+
+class FeatherDataHandler(IDataHandler):
+
+    _columns = DEFAULT_DATAFRAME_COLUMNS
+
+    def ohlcv_store(
+            self, pair: str, timeframe: str, data: DataFrame, candle_type: CandleType) -> None:
+        """
+        Store data in json format "values".
+            format looks as follows:
+            [[<date>,<open>,<high>,<low>,<close>]]
+        :param pair: Pair - used to generate filename
+        :param timeframe: Timeframe - used to generate filename
+        :param data: Dataframe containing OHLCV data
+        :param candle_type: Any of the enum CandleType (must match trading mode!)
+        :return: None
+        """
+        filename = self._pair_data_filename(self._datadir, pair, timeframe, candle_type)
+        self.create_dir_if_needed(filename)
+
+        data.reset_index(drop=True).loc[:, self._columns].to_feather(
+            filename, compression_level=9, compression='lz4')
+
+    def _ohlcv_load(self, pair: str, timeframe: str,
+                    timerange: Optional[TimeRange], candle_type: CandleType
+                    ) -> DataFrame:
+        """
+        Internal method used to load data for one pair from disk.
+        Implements the loading and conversion to a Pandas dataframe.
+        Timerange trimming and dataframe validation happens outside of this method.
+        :param pair: Pair to load data
+        :param timeframe: Timeframe (e.g. "5m")
+        :param timerange: Limit data to be loaded to this timerange.
+                        Optionally implemented by subclasses to avoid loading
+                        all data where possible.
+        :param candle_type: Any of the enum CandleType (must match trading mode!)
+        :return: DataFrame with ohlcv data, or empty DataFrame
+        """
+        filename = self._pair_data_filename(
+            self._datadir, pair, timeframe, candle_type=candle_type)
+        if not filename.exists():
+            # Fallback mode for 1M files
+            filename = self._pair_data_filename(
+                self._datadir, pair, timeframe, candle_type=candle_type, no_timeframe_modify=True)
+            if not filename.exists():
+                return DataFrame(columns=self._columns)
+
+        pairdata = read_feather(filename)
+        pairdata.columns = self._columns
+        pairdata = pairdata.astype(dtype={'open': 'float', 'high': 'float',
+                                          'low': 'float', 'close': 'float', 'volume': 'float'})
+        pairdata['date'] = to_datetime(pairdata['date'],
+                                       unit='ms',
+                                       utc=True,
+                                       infer_datetime_format=True)
+        return pairdata
+
+    def ohlcv_append(
+        self,
+        pair: str,
+        timeframe: str,
+        data: DataFrame,
+        candle_type: CandleType
+    ) -> None:
+        """
+        Append data to existing data structures
+        :param pair: Pair
+        :param timeframe: Timeframe this ohlcv data is for
+        :param data: Data to append.
+        :param candle_type: Any of the enum CandleType (must match trading mode!)
+        """
+        raise NotImplementedError()
+
+    def trades_store(self, pair: str, data: TradeList) -> None:
+        """
+        Store trades data (list of Dicts) to file
+        :param pair: Pair - used for filename
+        :param data: List of Lists containing trade data,
+                     column sequence as in DEFAULT_TRADES_COLUMNS
+        """
+        # filename = self._pair_trades_filename(self._datadir, pair)
+
+        raise NotImplementedError()
+        # array = pa.array(data)
+        # array
+        # feather.write_feather(data, filename)
+
+    def trades_append(self, pair: str, data: TradeList):
+        """
+        Append data to existing files
+        :param pair: Pair - used for filename
+        :param data: List of Lists containing trade data,
+                     column sequence as in DEFAULT_TRADES_COLUMNS
+        """
+        raise NotImplementedError()
+
+    def _trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> TradeList:
+        """
+        Load a pair from file, either .json.gz or .json
+        # TODO: respect timerange ...
+        :param pair: Load trades for this pair
+        :param timerange: Timerange to load trades for - currently not implemented
+        :return: List of trades
+        """
+        raise NotImplementedError()
+        # filename = self._pair_trades_filename(self._datadir, pair)
+        # tradesdata = misc.file_load_json(filename)
+
+        # if not tradesdata:
+        #     return []
+
+        # return tradesdata
+
+    @classmethod
+    def _get_file_extension(cls):
+        return "feather"

freqtrade/data/history/hdf5datahandler.py

@@ -81,6 +81,7 @@ class HDF5DataHandler(IDataHandler):
             raise ValueError("Wrong dataframe format")
         pairdata = pairdata.astype(dtype={'open': 'float', 'high': 'float',
                                           'low': 'float', 'close': 'float', 'volume': 'float'})
+        pairdata = pairdata.reset_index(drop=True)
         return pairdata
 
     def ohlcv_append(

freqtrade/data/history/idatahandler.py

@@ -375,6 +375,12 @@ def get_datahandlerclass(datatype: str) -> Type[IDataHandler]:
     elif datatype == 'hdf5':
         from .hdf5datahandler import HDF5DataHandler
         return HDF5DataHandler
+    elif datatype == 'feather':
+        from .featherdatahandler import FeatherDataHandler
+        return FeatherDataHandler
+    elif datatype == 'parquet':
+        from .parquetdatahandler import ParquetDataHandler
+        return ParquetDataHandler
     else:
         raise ValueError(f"No datahandler for datatype {datatype} available.")
 

freqtrade/data/history/parquetdatahandler.py

@@ -0,0 +1,129 @@
+import logging
+from typing import Optional
+
+from pandas import DataFrame, read_parquet, to_datetime
+
+from freqtrade.configuration import TimeRange
+from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, TradeList
+from freqtrade.enums import CandleType
+
+from .idatahandler import IDataHandler
+
+
+logger = logging.getLogger(__name__)
+
+
+class ParquetDataHandler(IDataHandler):
+
+    _columns = DEFAULT_DATAFRAME_COLUMNS
+
+    def ohlcv_store(
+            self, pair: str, timeframe: str, data: DataFrame, candle_type: CandleType) -> None:
+        """
+        Store data in json format "values".
+            format looks as follows:
+            [[<date>,<open>,<high>,<low>,<close>]]
+        :param pair: Pair - used to generate filename
+        :param timeframe: Timeframe - used to generate filename
+        :param data: Dataframe containing OHLCV data
+        :param candle_type: Any of the enum CandleType (must match trading mode!)
+        :return: None
+        """
+        filename = self._pair_data_filename(self._datadir, pair, timeframe, candle_type)
+        self.create_dir_if_needed(filename)
+
+        data.reset_index(drop=True).loc[:, self._columns].to_parquet(filename)
+
+    def _ohlcv_load(self, pair: str, timeframe: str,
+                    timerange: Optional[TimeRange], candle_type: CandleType
+                    ) -> DataFrame:
+        """
+        Internal method used to load data for one pair from disk.
+        Implements the loading and conversion to a Pandas dataframe.
+        Timerange trimming and dataframe validation happens outside of this method.
+        :param pair: Pair to load data
+        :param timeframe: Timeframe (e.g. "5m")
+        :param timerange: Limit data to be loaded to this timerange.
+                        Optionally implemented by subclasses to avoid loading
+                        all data where possible.
+        :param candle_type: Any of the enum CandleType (must match trading mode!)
+        :return: DataFrame with ohlcv data, or empty DataFrame
+        """
+        filename = self._pair_data_filename(
+            self._datadir, pair, timeframe, candle_type=candle_type)
+        if not filename.exists():
+            # Fallback mode for 1M files
+            filename = self._pair_data_filename(
+                self._datadir, pair, timeframe, candle_type=candle_type, no_timeframe_modify=True)
+            if not filename.exists():
+                return DataFrame(columns=self._columns)
+
+        pairdata = read_parquet(filename)
+        pairdata.columns = self._columns
+        pairdata = pairdata.astype(dtype={'open': 'float', 'high': 'float',
+                                          'low': 'float', 'close': 'float', 'volume': 'float'})
+        pairdata['date'] = to_datetime(pairdata['date'],
+                                       unit='ms',
+                                       utc=True,
+                                       infer_datetime_format=True)
+        return pairdata
+
+    def ohlcv_append(
+        self,
+        pair: str,
+        timeframe: str,
+        data: DataFrame,
+        candle_type: CandleType
+    ) -> None:
+        """
+        Append data to existing data structures
+        :param pair: Pair
+        :param timeframe: Timeframe this ohlcv data is for
+        :param data: Data to append.
+        :param candle_type: Any of the enum CandleType (must match trading mode!)
+        """
+        raise NotImplementedError()
+
+    def trades_store(self, pair: str, data: TradeList) -> None:
+        """
+        Store trades data (list of Dicts) to file
+        :param pair: Pair - used for filename
+        :param data: List of Lists containing trade data,
+                     column sequence as in DEFAULT_TRADES_COLUMNS
+        """
+        # filename = self._pair_trades_filename(self._datadir, pair)
+
+        raise NotImplementedError()
+        # array = pa.array(data)
+        # array
+        # feather.write_feather(data, filename)
+
+    def trades_append(self, pair: str, data: TradeList):
+        """
+        Append data to existing files
+        :param pair: Pair - used for filename
+        :param data: List of Lists containing trade data,
+                     column sequence as in DEFAULT_TRADES_COLUMNS
+        """
+        raise NotImplementedError()
+
+    def _trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> TradeList:
+        """
+        Load a pair from file, either .json.gz or .json
+        # TODO: respect timerange ...
+        :param pair: Load trades for this pair
+        :param timerange: Timerange to load trades for - currently not implemented
+        :return: List of trades
+        """
+        raise NotImplementedError()
+        # filename = self._pair_trades_filename(self._datadir, pair)
+        # tradesdata = misc.file_load_json(filename)
+
+        # if not tradesdata:
+        #     return []
+
+        # return tradesdata
+
+    @classmethod
+    def _get_file_extension(cls):
+        return "parquet"

freqtrade/freqai/data_drawer.py

@@ -313,6 +313,7 @@ class FreqaiDataDrawer:
         """
 
         dk.find_features(dataframe)
+        dk.find_labels(dataframe)
 
         full_labels = dk.label_list + dk.unique_class_list
 
@@ -376,7 +377,27 @@ class FreqaiDataDrawer:
         if self.config.get("freqai", {}).get("purge_old_models", False):
             self.purge_old_models()
 
-    # Functions pulled back from FreqaiDataKitchen because they relied on DataDrawer
+    def save_metadata(self, dk: FreqaiDataKitchen) -> None:
+        """
+        Saves only metadata for backtesting studies if user prefers
+        not to save model data. This saves tremendous amounts of space
+        for users generating huge studies.
+        This is only active when `save_backtest_models`: false (not default)
+        """
+        if not dk.data_path.is_dir():
+            dk.data_path.mkdir(parents=True, exist_ok=True)
+
+        save_path = Path(dk.data_path)
+
+        dk.data["data_path"] = str(dk.data_path)
+        dk.data["model_filename"] = str(dk.model_filename)
+        dk.data["training_features_list"] = list(dk.data_dictionary["train_features"].columns)
+        dk.data["label_list"] = dk.label_list
+
+        with open(save_path / f"{dk.model_filename}_metadata.json", "w") as fp:
+            rapidjson.dump(dk.data, fp, default=self.np_encoder, number_mode=rapidjson.NM_NATIVE)
+
+        return
 
     def save_data(self, model: Any, coin: str, dk: FreqaiDataKitchen) -> None:
         """

freqtrade/freqai/data_kitchen.py

@@ -858,7 +858,7 @@ class FreqaiDataKitchen:
 
         inlier_metric = pd.DataFrame(
             data=inliers.sum(axis=1) / no_prev_pts,
-            columns=['inlier_metric'],
+            columns=['%-inlier_metric'],
             index=compute_df.index
         )
 
@@ -908,11 +908,14 @@ class FreqaiDataKitchen:
         """
         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
+
+    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 check_if_pred_in_training_spaces(self) -> None:
@@ -1233,7 +1236,8 @@ class FreqaiDataKitchen:
 
     def get_unique_classes_from_labels(self, dataframe: DataFrame) -> None:
 
-        self.find_features(dataframe)
+        # self.find_features(dataframe)
+        self.find_labels(dataframe)
 
         for key in self.label_list:
             if dataframe[key].dtype == object:

freqtrade/freqai/freqai_interface copy.py

@@ -0,0 +1,783 @@
+import logging
+import shutil
+import threading
+import time
+from abc import ABC, abstractmethod
+from collections import deque
+from datetime import datetime, timezone
+from pathlib import Path
+from threading import Lock
+from typing import Any, Dict, List, 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.constants import DATETIME_PRINT_FORMAT, Config
+from freqtrade.enums import RunMode
+from freqtrade.exceptions import OperationalException
+from freqtrade.exchange import timeframe_to_seconds
+from freqtrade.freqai.data_drawer import FreqaiDataDrawer
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from freqtrade.freqai.utils import plot_feature_importance
+from freqtrade.strategy.interface import IStrategy
+
+
+pd.options.mode.chained_assignment = None
+logger = logging.getLogger(__name__)
+
+
+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, Johan Vlugt @Jooopieeert
+    """
+
+    def __init__(self, config: Config) -> 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.retrain = False
+        self.first = True
+        self.set_full_path()
+        self.follow_mode: bool = self.freqai_info.get("follow_mode", False)
+        self.save_backtest_models: bool = self.freqai_info.get("save_backtest_models", True)
+        if self.save_backtest_models:
+            logger.info('Backtesting module configured to save all models.')
+        self.dd = FreqaiDataDrawer(Path(self.full_path), self.config, self.follow_mode)
+        self.identifier: str = self.freqai_info.get("identifier", "no_id_provided")
+        self.scanning = False
+        self.ft_params = self.freqai_info["feature_parameters"]
+        self.keras: bool = self.freqai_info.get("keras", False)
+        if self.keras and self.ft_params.get("DI_threshold", 0):
+            self.ft_params["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)
+        if self.ft_params.get("inlier_metric_window", 0):
+            self.CONV_WIDTH = self.ft_params.get("inlier_metric_window", 0) * 2
+        self.pair_it = 0
+        self.pair_it_train = 0
+        self.total_pairs = len(self.config.get("exchange", {}).get("pair_whitelist"))
+        self.train_queue = self._set_train_queue()
+        self.last_trade_database_summary: DataFrame = {}
+        self.current_trade_database_summary: DataFrame = {}
+        self.analysis_lock = Lock()
+        self.inference_time: float = 0
+        self.train_time: float = 0
+        self.begin_time: float = 0
+        self.begin_time_train: float = 0
+        self.base_tf_seconds = timeframe_to_seconds(self.config['timeframe'])
+        self.continual_learning = self.freqai_info.get('continual_learning', False)
+
+        self._threads: List[threading.Thread] = []
+        self._stop_event = threading.Event()
+
+    def __getstate__(self):
+        """
+        Return an empty state to be pickled in hyperopt
+        """
+        return ({})
+
+    def assert_config(self, config: Config) -> 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.inference_timer('start')
+            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"])
+            if(self.dk.backtest_live_models):
+                logger.info(
+                    f"Backtesting {len(self.dk.backtesting_timeranges)} timeranges (Live Models)")
+            else:
+                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)
+            # else:
+            #     dk = self.start_backtesting_live_models(dataframe, metadata, self.dk)
+
+        dataframe = dk.remove_features_from_df(dk.return_dataframe)
+        self.clean_up()
+        if self.live:
+            self.inference_timer('stop')
+        return dataframe
+
+    def clean_up(self):
+        """
+        Objects that should be handled by GC already between coins, but
+        are explicitly shown here to help demonstrate the non-persistence of these
+        objects.
+        """
+        self.model = None
+        self.dk = None
+
+    def shutdown(self):
+        """
+        Cleans up threads on Shutdown, set stop event. Join threads to wait
+        for current training iteration.
+        """
+        logger.info("Stopping FreqAI")
+        self._stop_event.set()
+
+        logger.info("Waiting on Training iteration")
+        for _thread in self._threads:
+            _thread.join()
+
+    def start_scanning(self, *args, **kwargs) -> None:
+        """
+        Start `self._start_scanning` in a separate thread
+        """
+        _thread = threading.Thread(target=self._start_scanning, args=args, kwargs=kwargs)
+        self._threads.append(_thread)
+        _thread.start()
+
+    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 not self._stop_event.is_set():
+            time.sleep(1)
+            pair = self.train_queue[0]
+
+            # ensure pair is avaialble in dp
+            if pair not in strategy.dp.current_whitelist():
+                self.train_queue.popleft()
+                logger.warning(f'{pair} not in current whitelist, removing from train queue.')
+                continue
+
+            (_, trained_timestamp, _) = self.dd.get_pair_dict_info(pair)
+
+            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_timer('start')
+                try:
+                    self.extract_data_and_train_model(
+                        new_trained_timerange, pair, strategy, dk, data_load_timerange
+                    )
+                except Exception as msg:
+                    logger.warning(f'Training {pair} raised exception {msg}, skipping.')
+
+                self.train_timer('stop')
+
+                # only rotate the queue after the first has been trained.
+                self.train_queue.rotate(-1)
+
+                self.dd.save_historic_predictions_to_disk()
+
+    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):
+            pair = metadata["pair"]
+            (_, _, _) = self.dd.get_pair_dict_info(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.fromtimestamp(
+                                                tr_train.startts,
+                                                tz=timezone.utc).strftime(DATETIME_PRINT_FORMAT)
+            tr_train_stopts_str = datetime.fromtimestamp(
+                                                tr_train.stopts,
+                                                tz=timezone.utc).strftime(DATETIME_PRINT_FORMAT)
+            if not dk.backtest_live_models:
+                logger.info(
+                    f"Training {pair}, {self.pair_it}/{self.total_pairs} pairs"
+                    f" from {tr_train_startts_str}"
+                    f" to {tr_train_stopts_str}, {train_it}/{total_trains} "
+                    "trains"
+                )
+
+            timestamp_model_id = int(trained_timestamp.stopts)
+            if dk.backtest_live_models:
+                timestamp_model_id = int(tr_backtest.startts)
+
+            dk.data_path = Path(
+                dk.full_path / f"sub-train-{pair.split('/')[0]}_{timestamp_model_id}"
+                )
+
+            dk.set_new_model_names(pair, timestamp_model_id)
+
+            if dk.check_if_backtest_prediction_exists():
+                self.dd.load_metadata(dk)
+                if not dk.backtest_live_models:
+                    self.check_if_feature_list_matches_strategy(dataframe_train, dk)
+
+                append_df = dk.get_backtesting_prediction()
+                dk.append_predictions(append_df)
+            else:
+                if not self.model_exists(dk):
+                    if dk.backtest_live_models:
+                        raise OperationalException(
+                            "Training models is not allowed "
+                            "in backtest_live_models backtesting "
+                            "mode"
+                        )
+                    dk.find_features(dataframe_train)
+                    self.model = self.train(dataframe_train, pair, dk)
+                    self.dd.pair_dict[pair]["trained_timestamp"] = int(
+                        trained_timestamp.stopts)
+
+                    if self.save_backtest_models:
+                        logger.info('Saving backtest model to disk.')
+                        self.dd.save_data(self.model, pair, dk)
+                else:
+                    self.model = self.dd.load_data(pair, dk)
+
+                self.check_if_feature_list_matches_strategy(dataframe_train, dk)
+
+                pred_df, do_preds = self.predict(dataframe_backtest, dk)
+                append_df = dk.get_predictions_to_append(pred_df, do_preds)
+                dk.append_predictions(append_df)
+                dk.save_backtesting_prediction(append_df)
+
+        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)
+
+            # load candle history into memory if it is not yet.
+            if not self.dd.historic_data:
+                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)
+
+        with self.analysis_lock:
+            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)
+            if pair not in self.dd.historic_predictions:
+                self.set_initial_historic_predictions(pred_df, dk, pair)
+            self.dd.set_initial_return_values(pair, pred_df)
+
+            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)
+
+        if self.freqai_info.get('fit_live_predictions_candles', 0) and self.live:
+            self.fit_live_predictions(dk, pair)
+        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.data['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. In case of --strategy-list, please be aware that FreqAI "
+                "requires all strategies to maintain identical "
+                "populate_any_indicator() functions"
+            )
+
+    def data_cleaning_train(self, dk: FreqaiDataKitchen) -> None:
+        """
+        Base data cleaning method for train.
+        Functions here improve/modify the input data by identifying outliers,
+        computing additional metrics, adding noise, reducing dimensionality etc.
+        """
+
+        ft_params = self.freqai_info["feature_parameters"]
+
+        if ft_params.get('inlier_metric_window', 0):
+            dk.compute_inlier_metric(set_='train')
+            if self.freqai_info["data_split_parameters"]["test_size"] > 0:
+                dk.compute_inlier_metric(set_='test')
+
+        if ft_params.get(
+            "principal_component_analysis", False
+        ):
+            dk.principal_component_analysis()
+
+        if ft_params.get("use_SVM_to_remove_outliers", False):
+            dk.use_SVM_to_remove_outliers(predict=False)
+
+        if ft_params.get("DI_threshold", 0):
+            dk.data["avg_mean_dist"] = dk.compute_distances()
+
+        if ft_params.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']
+
+        if self.freqai_info["feature_parameters"].get('noise_standard_deviation', 0):
+            dk.add_noise_to_training_features()
+
+    def data_cleaning_predict(self, dk: FreqaiDataKitchen, dataframe: DataFrame) -> None:
+        """
+        Base data cleaning method for predict.
+        Functions here are complementary to the functions of data_cleaning_train.
+        """
+        ft_params = self.freqai_info["feature_parameters"]
+
+        if ft_params.get('inlier_metric_window', 0):
+            dk.compute_inlier_metric(set_='predict')
+
+        if ft_params.get(
+            "principal_component_analysis", False
+        ):
+            dk.pca_transform(self.dk.data_dictionary['prediction_features'])
+
+        if ft_params.get("use_SVM_to_remove_outliers", False):
+            dk.use_SVM_to_remove_outliers(predict=True)
+
+        if ft_params.get("DI_threshold", 0):
+            dk.check_if_pred_in_training_spaces()
+
+        if ft_params.get("use_DBSCAN_to_remove_outliers", False):
+            dk.use_DBSCAN_to_remove_outliers(predict=True)
+
+    def model_exists(
+        self,
+        dk: FreqaiDataKitchen,
+        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.
+        """
+        path_to_modelfile = Path(dk.data_path / f"{dk.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 extract_data_and_train_model(
+        self,
+        new_trained_timerange: TimeRange,
+        pair: str,
+        strategy: IStrategy,
+        dk: FreqaiDataKitchen,
+        data_load_timerange: TimeRange,
+    ):
+        """
+        Retrieve data and train model.
+        :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
+        )
+
+        with self.analysis_lock:
+            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, int(new_trained_timerange.stopts))
+        self.dd.save_data(model, pair, dk)
+
+        if self.freqai_info["feature_parameters"].get("plot_feature_importance", False):
+            plot_feature_importance(model, pair, dk)
+
+        if self.freqai_info.get("purge_old_models", False):
+            self.dd.purge_old_models()
+
+    def set_initial_historic_predictions(
+        self, pred_df: DataFrame, 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
+        """
+
+        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
+
+        # # for keras type models, the conv_window needs to be prepended so
+        # # viewing is correct in frequi
+        if self.freqai_info.get('keras', False) or self.ft_params.get('inlier_metric_window', 0):
+            n_lost_points = self.freqai_info.get('conv_width', 2)
+            zeros_df = DataFrame(np.zeros((n_lost_points, len(hist_preds_df.columns))),
+                                 columns=hist_preds_df.columns)
+            self.dd.historic_predictions[pair] = pd.concat(
+                [zeros_df, hist_preds_df], axis=0, ignore_index=True)
+
+    def fit_live_predictions(self, dk: FreqaiDataKitchen, pair: str) -> None:
+        """
+        Fit the labels with a gaussian distribution
+        """
+        import scipy as spy
+
+        # add classes from classifier label types if used
+        full_labels = dk.label_list + dk.unique_class_list
+
+        num_candles = self.freqai_info.get("fit_live_predictions_candles", 100)
+        dk.data["labels_mean"], dk.data["labels_std"] = {}, {}
+        for label in full_labels:
+            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
+
+    def inference_timer(self, do='start'):
+        """
+        Timer designed to track the cumulative time spent in FreqAI for one pass through
+        the whitelist. This will check if the time spent is more than 1/4 the time
+        of a single candle, and if so, it will warn the user of degraded performance
+        """
+        if do == 'start':
+            self.pair_it += 1
+            self.begin_time = time.time()
+        elif do == 'stop':
+            end = time.time()
+            self.inference_time += (end - self.begin_time)
+            if self.pair_it == self.total_pairs:
+                logger.info(
+                    f'Total time spent inferencing pairlist {self.inference_time:.2f} seconds')
+                if self.inference_time > 0.25 * self.base_tf_seconds:
+                    logger.warning("Inference took over 25% of the candle time. Reduce pairlist to"
+                                   " avoid blinding open trades and degrading performance.")
+                self.pair_it = 0
+                self.inference_time = 0
+        return
+
+    def train_timer(self, do='start'):
+        """
+        Timer designed to track the cumulative time spent training the full pairlist in
+        FreqAI.
+        """
+        if do == 'start':
+            self.pair_it_train += 1
+            self.begin_time_train = time.time()
+        elif do == 'stop':
+            end = time.time()
+            self.train_time += (end - self.begin_time_train)
+            if self.pair_it_train == self.total_pairs:
+                logger.info(
+                    f'Total time spent training pairlist {self.train_time:.2f} seconds')
+                self.pair_it_train = 0
+                self.train_time = 0
+        return
+
+    def get_init_model(self, pair: str) -> Any:
+        if pair not in self.dd.model_dictionary or not self.continual_learning:
+            init_model = None
+        else:
+            init_model = self.dd.model_dictionary[pair]
+
+        return init_model
+
+    def _set_train_queue(self):
+        """
+        Sets train queue from existing train timestamps if they exist
+        otherwise it sets the train queue based on the provided whitelist.
+        """
+        current_pairlist = self.config.get("exchange", {}).get("pair_whitelist")
+        if not self.dd.pair_dict:
+            logger.info('Set fresh train queue from whitelist. '
+                        f'Queue: {current_pairlist}')
+            return deque(current_pairlist)
+
+        best_queue = deque()
+
+        pair_dict_sorted = sorted(self.dd.pair_dict.items(),
+                                  key=lambda k: k[1]['trained_timestamp'])
+        for pair in pair_dict_sorted:
+            if pair[0] in current_pairlist:
+                best_queue.append(pair[0])
+        for pair in current_pairlist:
+            if pair not in best_queue:
+                best_queue.appendleft(pair)
+
+        logger.info('Set existing queue from trained timestamps. '
+                    f'Best approximation queue: {best_queue}')
+        return best_queue
+
+    # Following methods which are overridden by user made prediction models.
+    # See freqai/prediction_models/CatboostPredictionModel.py for an example.
+
+    @abstractmethod
+    def train(self, unfiltered_df: DataFrame, pair: str,
+              dk: FreqaiDataKitchen, **kwargs) -> 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_df: 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], dk: FreqaiDataKitchen, **kwargs) -> 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, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
+    ) -> Tuple[DataFrame, NDArray[np.int_]]:
+        """
+        Filter the prediction features data and predict with it.
+        :param unfiltered_df: 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)
+        """

freqtrade/freqai/freqai_interface.py

@@ -92,6 +92,7 @@ class IFreqaiModel(ABC):
         self.begin_time_train: float = 0
         self.base_tf_seconds = timeframe_to_seconds(self.config['timeframe'])
         self.continual_learning = self.freqai_info.get('continual_learning', False)
+        self.plot_features = self.ft_params.get("plot_feature_importances", 0)
 
         self._threads: List[threading.Thread] = []
         self._stop_event = threading.Event()
@@ -143,8 +144,6 @@ class IFreqaiModel(ABC):
                 strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
             )
             dk = self.start_backtesting(dataframe, metadata, self.dk)
-            # else:
-            #     dk = self.start_backtesting_live_models(dataframe, metadata, self.dk)
 
         dataframe = dk.remove_features_from_df(dk.return_dataframe)
         self.clean_up()
@@ -268,8 +267,8 @@ class IFreqaiModel(ABC):
             if not dk.backtest_live_models:
                 logger.info(
                     f"Training {pair}, {self.pair_it}/{self.total_pairs} pairs"
-                    f" from {tr_train_startts_str}"
-                    f" to {tr_train_stopts_str}, {train_it}/{total_trains} "
+                    f" from {tr_train_startts_str} "
+                    f"to {tr_train_stopts_str}, {train_it}/{total_trains} "
                     "trains"
                 )
 
@@ -285,9 +284,7 @@ class IFreqaiModel(ABC):
 
             if dk.check_if_backtest_prediction_exists():
                 self.dd.load_metadata(dk)
-                if not dk.backtest_live_models:
-                    self.check_if_feature_list_matches_strategy(dataframe_train, dk)
-
+                self.check_if_feature_list_matches_strategy(dataframe_train, dk)
                 append_df = dk.get_backtesting_prediction()
                 dk.append_predictions(append_df)
             else:
@@ -299,24 +296,29 @@ class IFreqaiModel(ABC):
                             "mode"
                         )
                     dk.find_features(dataframe_train)
+                    dk.find_labels(dataframe_train)
                     self.model = self.train(dataframe_train, pair, dk)
                     self.dd.pair_dict[pair]["trained_timestamp"] = int(
                         trained_timestamp.stopts)
-
+                    if self.plot_features:
+                        plot_feature_importance(self.model, pair, dk, self.plot_features)
                     if self.save_backtest_models:
                         logger.info('Saving backtest model to disk.')
                         self.dd.save_data(self.model, pair, dk)
+                    else:
+                        logger.info('Saving metadata to disk.')
+                        self.dd.save_metadata(dk)
                 else:
                     self.model = self.dd.load_data(pair, dk)
 
-                self.check_if_feature_list_matches_strategy(dataframe_train, dk)
-
+                # self.check_if_feature_list_matches_strategy(dataframe_train, dk)
                 pred_df, do_preds = self.predict(dataframe_backtest, dk)
                 append_df = dk.get_predictions_to_append(pred_df, do_preds)
                 dk.append_predictions(append_df)
                 dk.save_backtesting_prediction(append_df)
 
         dk.fill_predictions(dataframe)
+
         return dk
 
     def start_live(
@@ -388,8 +390,7 @@ class IFreqaiModel(ABC):
             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)
+        dk.find_labels(dataframe)
 
         self.build_strategy_return_arrays(dataframe, dk, metadata["pair"], trained_timestamp)
 
@@ -508,7 +509,7 @@ class IFreqaiModel(ABC):
         if ft_params.get(
             "principal_component_analysis", False
         ):
-            dk.pca_transform(self.dk.data_dictionary['prediction_features'])
+            dk.pca_transform(dk.data_dictionary['prediction_features'])
 
         if ft_params.get("use_SVM_to_remove_outliers", False):
             dk.use_SVM_to_remove_outliers(predict=True)
@@ -519,11 +520,10 @@ class IFreqaiModel(ABC):
         if ft_params.get("use_DBSCAN_to_remove_outliers", False):
             dk.use_DBSCAN_to_remove_outliers(predict=True)
 
-    def model_exists(
-        self,
-        dk: FreqaiDataKitchen,
-        scanning: bool = False,
-    ) -> bool:
+        # ensure user is feeding the correct indicators to the model
+        self.check_if_feature_list_matches_strategy(dk.data_dictionary['prediction_features'], dk)
+
+    def model_exists(self, dk: FreqaiDataKitchen) -> bool:
         """
         Given a pair and path, check if a model already exists
         :param pair: pair e.g. BTC/USD
@@ -533,9 +533,9 @@ class IFreqaiModel(ABC):
         """
         path_to_modelfile = Path(dk.data_path / f"{dk.model_filename}_model.joblib")
         file_exists = path_to_modelfile.is_file()
-        if file_exists and not scanning:
+        if file_exists:
             logger.info("Found model at %s", dk.data_path / dk.model_filename)
-        elif not scanning:
+        else:
             logger.info("Could not find model at %s", dk.data_path / dk.model_filename)
         return file_exists
 
@@ -582,6 +582,7 @@ class IFreqaiModel(ABC):
 
         # find the features indicated by strategy and store in datakitchen
         dk.find_features(unfiltered_dataframe)
+        dk.find_labels(unfiltered_dataframe)
 
         model = self.train(unfiltered_dataframe, pair, dk)
 
@@ -589,8 +590,8 @@ class IFreqaiModel(ABC):
         dk.set_new_model_names(pair, int(new_trained_timerange.stopts))
         self.dd.save_data(model, pair, dk)
 
-        if self.freqai_info["feature_parameters"].get("plot_feature_importance", False):
-            plot_feature_importance(model, pair, dk)
+        if self.plot_features:
+            plot_feature_importance(model, pair, dk, self.plot_features)
 
         if self.freqai_info.get("purge_old_models", False):
             self.dd.purge_old_models()

freqtrade/freqai/utils.py

@@ -170,7 +170,7 @@ def plot_feature_importance(model: Any, pair: str, dk: FreqaiDataKitchen,
 
         # Data preparation
         fi_df = pd.DataFrame({
-            "feature_names": np.array(dk.training_features_list),
+            "feature_names": np.array(dk.data_dictionary['train_features'].columns),
             "feature_importance": np.array(feature_importance)
         })
         fi_df_top = fi_df.nlargest(count_max, "feature_importance")[::-1]