Merge pull request #7737 from freqtrade/backtest_fitlivepredictions

FreqAI - Backtesting enhancements and bug fix

docs/freqai-running.md

@@ -79,16 +79,11 @@ To change your **features**, you **must** set a new `identifier` in the config t
 
 To save the models generated during a particular backtest so that you can start a live deployment from one of them instead of training a new model, you must set `save_backtest_models` to `True` in the config.
 
-### Backtest live models
+### Backtest live collected predictions
 
-FreqAI allow you to reuse ready models through the backtest parameter `--freqai-backtest-live-models`. This can be useful when you want to reuse models generated in dry/run for comparison or other study. For that, you must set `"purge_old_models"` to `True` in the config.
+FreqAI allow you to reuse live historic predictions through the backtest parameter `--freqai-backtest-live-models`. This can be useful when you want to reuse predictions generated in dry/run for comparison or other study.
 
-The `--timerange` parameter must not be informed, as it will be automatically calculated through the training end dates of the models.
+The `--timerange` parameter must not be informed, as it will be automatically calculated through the data in the historic predictions file.
-
-Each model has an identifier derived from the training end date. If you have only 1 model trained, FreqAI will backtest from the training end date until the current date. If you have more than 1 model, each model will perform the backtesting according to the training end date until the training end date of the next model and so on. For the last model, the period of the previous model will be used for the execution.
-
-!!! Note
-    Currently, there is no checking for expired models, even if the `expired_hours` parameter is set.
 
 
 ### Downloading data to cover the full backtest period

freqtrade/freqai/data_drawer.py

@@ -4,7 +4,7 @@ import logging
 import re
 import shutil
 import threading
-from datetime import datetime, timezone
+from datetime import datetime, timedelta, timezone
 from pathlib import Path
 from typing import Any, Dict, Tuple, TypedDict
 
@@ -82,6 +82,7 @@ class FreqaiDataDrawer:
         self.historic_predictions_bkp_path = Path(
             self.full_path / "historic_predictions.backup.pkl")
         self.pair_dictionary_path = Path(self.full_path / "pair_dictionary.json")
+        self.global_metadata_path = Path(self.full_path / "global_metadata.json")
         self.metric_tracker_path = Path(self.full_path / "metric_tracker.json")
         self.follow_mode = follow_mode
         if follow_mode:
@@ -127,6 +128,17 @@ class FreqaiDataDrawer:
         self.update_metric_tracker('cpu_load5min', load5 / cpus, pair)
         self.update_metric_tracker('cpu_load15min', load15 / cpus, pair)
 
+    def load_global_metadata_from_disk(self):
+        """
+        Locate and load a previously saved global metadata in present model folder.
+        """
+        exists = self.global_metadata_path.is_file()
+        if exists:
+            with open(self.global_metadata_path, "r") as fp:
+                metatada_dict = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
+                return metatada_dict
+        return {}
+
     def load_drawer_from_disk(self):
         """
         Locate and load a previously saved data drawer full of all pair model metadata in
@@ -227,6 +239,15 @@ class FreqaiDataDrawer:
             rapidjson.dump(self.follower_dict, fp, default=self.np_encoder,
                            number_mode=rapidjson.NM_NATIVE)
 
+    def save_global_metadata_to_disk(self, metadata: Dict[str, Any]):
+        """
+        Save global metadata json to disk
+        """
+        with self.save_lock:
+            with open(self.global_metadata_path, 'w') as fp:
+                rapidjson.dump(metadata, fp, default=self.np_encoder,
+                               number_mode=rapidjson.NM_NATIVE)
+
     def create_follower_dict(self):
         """
         Create or dictionary for each follower to maintain unique persistent prediction targets
@@ -696,3 +717,31 @@ class FreqaiDataDrawer:
                         ).reset_index(drop=True)
 
         return corr_dataframes, base_dataframes
+
+    def get_timerange_from_live_historic_predictions(self) -> TimeRange:
+        """
+        Returns timerange information based on historic predictions file
+        :return: timerange calculated from saved live data
+        """
+        if not self.historic_predictions_path.is_file():
+            raise OperationalException(
+                'Historic predictions not found. Historic predictions data is required '
+                'to run backtest with the freqai-backtest-live-models option '
+            )
+
+        self.load_historic_predictions_from_disk()
+
+        all_pairs_end_dates = []
+        for pair in self.historic_predictions:
+            pair_historic_data = self.historic_predictions[pair]
+            all_pairs_end_dates.append(pair_historic_data.date_pred.max())
+
+        global_metadata = self.load_global_metadata_from_disk()
+        start_date = datetime.fromtimestamp(int(global_metadata["start_dry_live_date"]))
+        end_date = max(all_pairs_end_dates)
+        # add 1 day to string timerange to ensure BT module will load all dataframe data
+        end_date = end_date + timedelta(days=1)
+        backtesting_timerange = TimeRange(
+            'date', 'date', int(start_date.timestamp()), int(end_date.timestamp())
+        )
+        return backtesting_timerange

freqtrade/freqai/data_kitchen.py

@@ -1,7 +1,7 @@
 import copy
 import logging
 import shutil
-from datetime import datetime, timedelta, timezone
+from datetime import datetime, timezone
 from math import cos, sin
 from pathlib import Path
 from typing import Any, Dict, List, Tuple
@@ -87,12 +87,7 @@ class FreqaiDataKitchen:
         if not self.live:
             self.full_path = self.get_full_models_path(self.config)
 
-            if self.backtest_live_models:
+            if not self.backtest_live_models:
-                if self.pair:
-                    self.set_timerange_from_ready_models()
-                    (self.training_timeranges,
-                     self.backtesting_timeranges) = self.split_timerange_live_models()
-            else:
                 self.full_timerange = self.create_fulltimerange(
                     self.config["timerange"], self.freqai_config.get("train_period_days", 0)
                 )
@@ -460,29 +455,6 @@ class FreqaiDataKitchen:
         # print(tr_training_list, tr_backtesting_list)
         return tr_training_list_timerange, tr_backtesting_list_timerange
 
-    def split_timerange_live_models(
-        self
-    ) -> Tuple[list, list]:
-
-        tr_backtesting_list_timerange = []
-        asset = self.pair.split("/")[0]
-        if asset not in self.backtest_live_models_data["assets_end_dates"]:
-            raise OperationalException(
-                f"Model not available for pair {self.pair}. "
-                "Please, try again after removing this pair from the configuration file."
-            )
-        asset_data = self.backtest_live_models_data["assets_end_dates"][asset]
-        backtesting_timerange = self.backtest_live_models_data["backtesting_timerange"]
-        model_end_dates = [x for x in asset_data]
-        model_end_dates.append(backtesting_timerange.stopts)
-        model_end_dates.sort()
-        for index, item in enumerate(model_end_dates):
-            if len(model_end_dates) > (index + 1):
-                tr_to_add = TimeRange("date", "date", item, model_end_dates[index + 1])
-                tr_backtesting_list_timerange.append(tr_to_add)
-
-        return tr_backtesting_list_timerange, tr_backtesting_list_timerange
-
     def slice_dataframe(self, timerange: TimeRange, df: DataFrame) -> DataFrame:
         """
         Given a full dataframe, extract the user desired window
@@ -978,7 +950,8 @@ class FreqaiDataKitchen:
         return weights
 
     def get_predictions_to_append(self, predictions: DataFrame,
-                                  do_predict: npt.ArrayLike) -> DataFrame:
+                                  do_predict: npt.ArrayLike,
+                                  dataframe_backtest: DataFrame) -> DataFrame:
         """
         Get backtest prediction from current backtest period
         """
@@ -1000,7 +973,9 @@ class FreqaiDataKitchen:
         if self.freqai_config["feature_parameters"].get("DI_threshold", 0) > 0:
             append_df["DI_values"] = self.DI_values
 
-        return append_df
+        dataframe_backtest.reset_index(drop=True, inplace=True)
+        merged_df = pd.concat([dataframe_backtest["date"], append_df], axis=1)
+        return merged_df
 
     def append_predictions(self, append_df: DataFrame) -> None:
         """
@@ -1010,23 +985,18 @@ class FreqaiDataKitchen:
         if self.full_df.empty:
             self.full_df = append_df
         else:
-            self.full_df = pd.concat([self.full_df, append_df], axis=0)
+            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.
         """
-
-        len_filler = len(dataframe) - len(self.full_df.index)  # startup_candle_count
-        filler_df = pd.DataFrame(
-            np.zeros((len_filler, len(self.full_df.columns))), columns=self.full_df.columns
-        )
-
-        self.full_df = pd.concat([filler_df, self.full_df], axis=0, ignore_index=True)
-
         to_keep = [col for col in dataframe.columns if not col.startswith("&")]
-        self.return_dataframe = pd.concat([dataframe[to_keep], self.full_df], axis=1)
+        self.return_dataframe = 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
@@ -1323,22 +1293,22 @@ class FreqaiDataKitchen:
         self, append_df: DataFrame
     ) -> None:
         """
-        Save prediction dataframe from backtesting to h5 file format
+        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_hdf(self.backtesting_results_path, key='append_df', mode='w')
+        append_df.to_feather(self.backtesting_results_path)
 
     def get_backtesting_prediction(
         self
     ) -> DataFrame:
         """
-        Get prediction dataframe from h5 file format
+        Get prediction dataframe from feather file format
         """
-        append_df = pd.read_hdf(self.backtesting_results_path)
+        append_df = pd.read_feather(self.backtesting_results_path)
         return append_df
 
     def check_if_backtest_prediction_is_valid(
@@ -1354,19 +1324,20 @@ class FreqaiDataKitchen:
         """
         path_to_predictionfile = Path(self.full_path /
                                       self.backtest_predictions_folder /
-                                      f"{self.model_filename}_prediction.h5")
+                                      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:
+            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).")
+                            "(Number of predictions is different from dataframe length or "
+                            "old prediction file version).")
                 return False
         else:
             logger.info(
@@ -1374,17 +1345,6 @@ class FreqaiDataKitchen:
             )
             return False
 
-    def set_timerange_from_ready_models(self):
-        backtesting_timerange, \
-            assets_end_dates = (
-                self.get_timerange_and_assets_end_dates_from_ready_models(self.full_path))
-
-        self.backtest_live_models_data = {
-            "backtesting_timerange": backtesting_timerange,
-            "assets_end_dates": assets_end_dates
-            }
-        return
-
     def get_full_models_path(self, config: Config) -> Path:
         """
         Returns default FreqAI model path
@@ -1395,88 +1355,6 @@ class FreqaiDataKitchen:
             config["user_data_dir"] / "models" / str(freqai_config.get("identifier"))
         )
 
-    def get_timerange_and_assets_end_dates_from_ready_models(
-            self, models_path: Path) -> Tuple[TimeRange, Dict[str, Any]]:
-        """
-        Returns timerange information based on a FreqAI model directory
-        :param models_path: FreqAI model path
-
-        :return: a Tuple with (Timerange calculated from directory and
-        a Dict with pair and model end training dates info)
-        """
-        all_models_end_dates = []
-        assets_end_dates: Dict[str, Any] = self.get_assets_timestamps_training_from_ready_models(
-                                           models_path)
-        for key in assets_end_dates:
-            for model_end_date in assets_end_dates[key]:
-                if model_end_date not in all_models_end_dates:
-                    all_models_end_dates.append(model_end_date)
-
-        if len(all_models_end_dates) == 0:
-            raise OperationalException(
-                'At least 1 saved model is required to '
-                'run backtest with the freqai-backtest-live-models option'
-            )
-
-        if len(all_models_end_dates) == 1:
-            logger.warning(
-                "Only 1 model was found. Backtesting will run with the "
-                "timerange from the end of the training date to the current date"
-            )
-
-        finish_timestamp = int(datetime.now(tz=timezone.utc).timestamp())
-        if len(all_models_end_dates) > 1:
-            # After last model end date, use the same period from previous model
-            # to finish the backtest
-            all_models_end_dates.sort(reverse=True)
-            finish_timestamp = all_models_end_dates[0] + \
-                (all_models_end_dates[0] - all_models_end_dates[1])
-
-        all_models_end_dates.append(finish_timestamp)
-        all_models_end_dates.sort()
-        start_date = (datetime(*datetime.fromtimestamp(min(all_models_end_dates),
-                      timezone.utc).timetuple()[:3], tzinfo=timezone.utc))
-        end_date = (datetime(*datetime.fromtimestamp(max(all_models_end_dates),
-                    timezone.utc).timetuple()[:3], tzinfo=timezone.utc))
-
-        # add 1 day to string timerange to ensure BT module will load all dataframe data
-        end_date = end_date + timedelta(days=1)
-        backtesting_timerange = TimeRange(
-            'date', 'date', int(start_date.timestamp()), int(end_date.timestamp())
-        )
-        return backtesting_timerange, assets_end_dates
-
-    def get_assets_timestamps_training_from_ready_models(
-            self, models_path: Path) -> Dict[str, Any]:
-        """
-        Scan the models path and returns all assets end training dates (timestamp)
-        :param models_path: FreqAI model path
-
-        :return: a Dict with asset and model end training dates info
-        """
-        assets_end_dates: Dict[str, Any] = {}
-        if not models_path.is_dir():
-            raise OperationalException(
-                'Model folders not found. Saved models are required '
-                'to run backtest with the freqai-backtest-live-models option'
-            )
-        for model_dir in models_path.iterdir():
-            if str(model_dir.name).startswith("sub-train"):
-                model_end_date = int(model_dir.name.split("_")[1])
-                asset = model_dir.name.split("_")[0].replace("sub-train-", "")
-                model_file_name = (
-                    f"cb_{str(model_dir.name).replace('sub-train-', '').lower()}"
-                    "_model.joblib"
-                )
-
-                model_path_file = Path(model_dir / model_file_name)
-                if model_path_file.is_file():
-                    if asset not in assets_end_dates:
-                        assets_end_dates[asset] = []
-                    assets_end_dates[asset].append(model_end_date)
-
-        return assets_end_dates
-
     def remove_special_chars_from_feature_names(self, dataframe: pd.DataFrame) -> pd.DataFrame:
         """
         Remove all special characters from feature strings (:)

freqtrade/freqai/freqai_interface.py

@@ -69,6 +69,7 @@ class IFreqaiModel(ABC):
         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)
         # set current candle to arbitrary historical date
         self.current_candle: datetime = datetime.fromtimestamp(637887600, tz=timezone.utc)
@@ -100,6 +101,7 @@ class IFreqaiModel(ABC):
         self.get_corr_dataframes: bool = True
         self._threads: List[threading.Thread] = []
         self._stop_event = threading.Event()
+        self.metadata: Dict[str, Any] = self.dd.load_global_metadata_from_disk()
         self.data_provider: Optional[DataProvider] = None
         self.max_system_threads = max(int(psutil.cpu_count() * 2 - 2), 1)
 
@@ -136,6 +138,7 @@ class IFreqaiModel(ABC):
             self.inference_timer('start')
             self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"])
             dk = self.start_live(dataframe, metadata, strategy, self.dk)
+            dataframe = dk.remove_features_from_df(dk.return_dataframe)
 
         # 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"
@@ -144,20 +147,24 @@ class IFreqaiModel(ABC):
         # 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)
+            if not self.config.get("freqai_backtest_live_models", False):
+                logger.info(f"Training {len(self.dk.training_timeranges)} timeranges")
+                dk = self.start_backtesting(dataframe, metadata, self.dk)
+                dataframe = dk.remove_features_from_df(dk.return_dataframe)
+            else:
+                logger.info(
+                    "Backtesting using historic predictions (live models)")
+                dk = self.start_backtesting_from_historic_predictions(
+                    dataframe, metadata, self.dk)
+                dataframe = dk.return_dataframe
 
-        dataframe = dk.remove_features_from_df(dk.return_dataframe)
         self.clean_up()
         if self.live:
             self.inference_timer('stop', metadata["pair"])
+
         return dataframe
 
     def clean_up(self):
@@ -316,10 +323,11 @@ class IFreqaiModel(ABC):
                     self.model = self.dd.load_data(pair, dk)
 
                 pred_df, do_preds = self.predict(dataframe_backtest, dk)
-                append_df = dk.get_predictions_to_append(pred_df, do_preds)
+                append_df = dk.get_predictions_to_append(pred_df, do_preds, dataframe_backtest)
                 dk.append_predictions(append_df)
                 dk.save_backtesting_prediction(append_df)
 
+        self.backtesting_fit_live_predictions(dk)
         dk.fill_predictions(dataframe)
 
         return dk
@@ -632,6 +640,8 @@ class IFreqaiModel(ABC):
         self.dd.historic_predictions[pair] = pred_df
         hist_preds_df = self.dd.historic_predictions[pair]
 
+        self.set_start_dry_live_date(strat_df)
+
         for label in hist_preds_df.columns:
             if hist_preds_df[label].dtype == object:
                 continue
@@ -672,7 +682,8 @@ class IFreqaiModel(ABC):
         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))
+            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
@@ -826,6 +837,81 @@ class IFreqaiModel(ABC):
                 f"to {tr_train.stop_fmt}, {train_it}/{total_trains} "
                 "trains"
             )
+
+    def backtesting_fit_live_predictions(self, dk: FreqaiDataKitchen):
+        """
+        Apply fit_live_predictions function in backtesting with a dummy historic_predictions
+        The loop is required to simulate dry/live operation, as it is not possible to predict
+        the type of logic implemented by the user.
+        :param dk: datakitchen object
+        """
+        fit_live_predictions_candles = self.freqai_info.get("fit_live_predictions_candles", 0)
+        if fit_live_predictions_candles:
+            logger.info("Applying fit_live_predictions in backtesting")
+            label_columns = [col for col in dk.full_df.columns if (
+                col.startswith("&") and
+                not (col.startswith("&") and col.endswith("_mean")) and
+                not (col.startswith("&") and col.endswith("_std")) and
+                col not in self.dk.data["extra_returns_per_train"])
+            ]
+
+            for index in range(len(dk.full_df)):
+                if index >= fit_live_predictions_candles:
+                    self.dd.historic_predictions[self.dk.pair] = (
+                        dk.full_df.iloc[index - fit_live_predictions_candles:index])
+                    self.fit_live_predictions(self.dk, self.dk.pair)
+                    for label in label_columns:
+                        if dk.full_df[label].dtype == object:
+                            continue
+                        if "labels_mean" in self.dk.data:
+                            dk.full_df.at[index, f"{label}_mean"] = (
+                                self.dk.data["labels_mean"][label])
+                        if "labels_std" in self.dk.data:
+                            dk.full_df.at[index, f"{label}_std"] = self.dk.data["labels_std"][label]
+
+                    for extra_col in self.dk.data["extra_returns_per_train"]:
+                        dk.full_df.at[index, f"{extra_col}"] = (
+                            self.dk.data["extra_returns_per_train"][extra_col])
+
+        return
+
+    def update_metadata(self, metadata: Dict[str, Any]):
+        """
+        Update global metadata and save the updated json file
+        :param metadata: new global metadata dict
+        """
+        self.dd.save_global_metadata_to_disk(metadata)
+        self.metadata = metadata
+
+    def set_start_dry_live_date(self, live_dataframe: DataFrame):
+        key_name = "start_dry_live_date"
+        if key_name not in self.metadata:
+            metadata = self.metadata
+            metadata[key_name] = int(
+                pd.to_datetime(live_dataframe.tail(1)["date"].values[0]).timestamp())
+            self.update_metadata(metadata)
+
+    def start_backtesting_from_historic_predictions(
+        self, dataframe: DataFrame, metadata: dict, dk: FreqaiDataKitchen
+    ) -> FreqaiDataKitchen:
+        """
+        :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
+        """
+        pair = metadata["pair"]
+        dk.return_dataframe = dataframe
+        saved_dataframe = self.dd.historic_predictions[pair]
+        columns_to_drop = list(set(saved_dataframe.columns).intersection(
+            dk.return_dataframe.columns))
+        dk.return_dataframe = dk.return_dataframe.drop(columns=list(columns_to_drop))
+        dk.return_dataframe = pd.merge(
+            dk.return_dataframe, saved_dataframe, how='left', left_on='date', right_on="date_pred")
+        # dk.return_dataframe = dk.return_dataframe[saved_dataframe.columns].fillna(0)
+        return dk
+
     # Following methods which are overridden by user made prediction models.
     # See freqai/prediction_models/CatboostPredictionModel.py for an example.
 

freqtrade/freqai/utils.py

@@ -14,6 +14,7 @@ from freqtrade.data.history.history_utils import refresh_backtest_ohlcv_data
 from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import timeframe_to_seconds
 from freqtrade.exchange.exchange import market_is_active
+from freqtrade.freqai.data_drawer import FreqaiDataDrawer
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 from freqtrade.plugins.pairlist.pairlist_helpers import dynamic_expand_pairlist
 
@@ -229,5 +230,6 @@ def get_timerange_backtest_live_models(config: Config) -> str:
     """
     dk = FreqaiDataKitchen(config)
     models_path = dk.get_full_models_path(config)
-    timerange, _ = dk.get_timerange_and_assets_end_dates_from_ready_models(models_path)
+    dd = FreqaiDataDrawer(models_path, config)
+    timerange = dd.get_timerange_from_live_historic_predictions()
     return timerange.timerange_str

tests/freqai/test_freqai_backtesting.py

@@ -65,6 +65,8 @@ def test_freqai_backtest_live_models_model_not_found(freqai_conf, mocker, testda
     mocker.patch('freqtrade.optimize.backtesting.history.load_data')
     mocker.patch('freqtrade.optimize.backtesting.history.get_timerange', return_value=(now, now))
     freqai_conf["timerange"] = ""
+    freqai_conf.get("freqai", {}).update({"backtest_using_historic_predictions": False})
+
     patched_configuration_load_config_file(mocker, freqai_conf)
 
     args = [
@@ -79,7 +81,7 @@ def test_freqai_backtest_live_models_model_not_found(freqai_conf, mocker, testda
     bt_config = setup_optimize_configuration(args, RunMode.BACKTEST)
 
     with pytest.raises(OperationalException,
-                       match=r".* Saved models are required to run backtest .*"):
+                       match=r".* Historic predictions data is required to run backtest .*"):
         Backtesting(bt_config)
 
     Backtesting.cleanup()

tests/freqai/test_freqai_datadrawer.py

@@ -2,8 +2,11 @@
 import shutil
 from pathlib import Path
 
+import pytest
+
 from freqtrade.configuration import TimeRange
 from freqtrade.data.dataprovider import DataProvider
+from freqtrade.exceptions import OperationalException
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 from tests.conftest import get_patched_exchange
 from tests.freqai.conftest import get_patched_freqai_strategy
@@ -93,3 +96,37 @@ def test_use_strategy_to_populate_indicators(mocker, freqai_conf):
 
     assert len(df.columns) == 33
     shutil.rmtree(Path(freqai.dk.full_path))
+
+
+def test_get_timerange_from_live_historic_predictions(mocker, freqai_conf):
+    strategy = get_patched_freqai_strategy(mocker, freqai_conf)
+    exchange = get_patched_exchange(mocker, freqai_conf)
+    strategy.dp = DataProvider(freqai_conf, exchange)
+    freqai = strategy.freqai
+    freqai.live = True
+    freqai.dk = FreqaiDataKitchen(freqai_conf)
+    timerange = TimeRange.parse_timerange("20180126-20180130")
+    freqai.dd.load_all_pair_histories(timerange, freqai.dk)
+    sub_timerange = TimeRange.parse_timerange("20180128-20180130")
+    _, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "ADA/BTC", freqai.dk)
+    base_df["5m"]["date_pred"] = base_df["5m"]["date"]
+    freqai.dd.historic_predictions = {}
+    freqai.dd.historic_predictions["ADA/USDT"] = base_df["5m"]
+    freqai.dd.save_historic_predictions_to_disk()
+    freqai.dd.save_global_metadata_to_disk({"start_dry_live_date": 1516406400})
+
+    timerange = freqai.dd.get_timerange_from_live_historic_predictions()
+    assert timerange.startts == 1516406400
+    assert timerange.stopts == 1517356500
+
+
+def test_get_timerange_from_backtesting_live_df_pred_not_found(mocker, freqai_conf):
+    strategy = get_patched_freqai_strategy(mocker, freqai_conf)
+    exchange = get_patched_exchange(mocker, freqai_conf)
+    strategy.dp = DataProvider(freqai_conf, exchange)
+    freqai = strategy.freqai
+    with pytest.raises(
+            OperationalException,
+            match=r'Historic predictions not found.*'
+            ):
+        freqai.dd.get_timerange_from_live_historic_predictions()

tests/freqai/test_freqai_datakitchen.py

@@ -9,7 +9,6 @@ from freqtrade.configuration import TimeRange
 from freqtrade.data.dataprovider import DataProvider
 from freqtrade.exceptions import OperationalException
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
-from freqtrade.freqai.utils import get_timerange_backtest_live_models
 from tests.conftest import get_patched_exchange, log_has_re
 from tests.freqai.conftest import (get_patched_data_kitchen, get_patched_freqai_strategy,
                                    make_data_dictionary, make_unfiltered_dataframe)
@@ -166,71 +165,6 @@ def test_make_train_test_datasets(mocker, freqai_conf):
     assert len(data_dictionary['train_features'].index) == 1916
 
 
-def test_get_pairs_timestamp_validation(mocker, freqai_conf):
-    exchange = get_patched_exchange(mocker, freqai_conf)
-    strategy = get_patched_freqai_strategy(mocker, freqai_conf)
-    strategy.dp = DataProvider(freqai_conf, exchange)
-    strategy.freqai_info = freqai_conf.get("freqai", {})
-    freqai = strategy.freqai
-    freqai.live = True
-    freqai.dk = FreqaiDataKitchen(freqai_conf)
-    freqai_conf['freqai'].update({"identifier": "invalid_id"})
-    model_path = freqai.dk.get_full_models_path(freqai_conf)
-    with pytest.raises(
-            OperationalException,
-            match=r'.*required to run backtest with the freqai-backtest-live-models.*'
-            ):
-        freqai.dk.get_assets_timestamps_training_from_ready_models(model_path)
-
-
-@pytest.mark.parametrize('model', [
-    'LightGBMRegressor'
-    ])
-def test_get_timerange_from_ready_models(mocker, freqai_conf, model):
-    freqai_conf.update({"freqaimodel": model})
-    freqai_conf.update({"timerange": "20180110-20180130"})
-    freqai_conf.update({"strategy": "freqai_test_strat"})
-
-    strategy = get_patched_freqai_strategy(mocker, freqai_conf)
-    exchange = get_patched_exchange(mocker, freqai_conf)
-    strategy.dp = DataProvider(freqai_conf, exchange)
-    strategy.freqai_info = freqai_conf.get("freqai", {})
-    freqai = strategy.freqai
-    freqai.live = True
-    freqai.dk = FreqaiDataKitchen(freqai_conf)
-    timerange = TimeRange.parse_timerange("20180101-20180130")
-    freqai.dd.load_all_pair_histories(timerange, freqai.dk)
-
-    freqai.dd.pair_dict = MagicMock()
-
-    data_load_timerange = TimeRange.parse_timerange("20180101-20180130")
-
-    # 1516233600 (2018-01-18 00:00) - Start Training 1
-    # 1516406400 (2018-01-20 00:00) - End Training 1 (Backtest slice 1)
-    # 1516579200 (2018-01-22 00:00) - End Training 2 (Backtest slice 2)
-    # 1516838400 (2018-01-25 00:00) - End Timerange
-
-    new_timerange = TimeRange("date", "date", 1516233600, 1516406400)
-    freqai.extract_data_and_train_model(
-        new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
-
-    new_timerange = TimeRange("date", "date", 1516406400, 1516579200)
-    freqai.extract_data_and_train_model(
-        new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
-
-    model_path = freqai.dk.get_full_models_path(freqai_conf)
-    (backtesting_timerange,
-     pairs_end_dates) = freqai.dk.get_timerange_and_assets_end_dates_from_ready_models(
-                        models_path=model_path)
-
-    assert len(pairs_end_dates["ADA"]) == 2
-    assert backtesting_timerange.startts == 1516406400
-    assert backtesting_timerange.stopts == 1516838400
-
-    backtesting_string_timerange = get_timerange_backtest_live_models(freqai_conf)
-    assert backtesting_string_timerange == '20180120-20180125'
-
-
 @pytest.mark.parametrize('model', [
     'LightGBMRegressor'
     ])

tests/freqai/test_freqai_interface.py

@@ -301,7 +301,9 @@ def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
 
     df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
 
-    metadata = {"pair": "ADA/BTC"}
+    pair = "ADA/BTC"
+    metadata = {"pair": pair}
+    freqai.dk.pair = pair
     freqai.start_backtesting(df, metadata, freqai.dk)
     model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
 
@@ -324,6 +326,9 @@ def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
 
     df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
 
+    pair = "ADA/BTC"
+    metadata = {"pair": pair}
+    freqai.dk.pair = pair
     freqai.start_backtesting(df, metadata, freqai.dk)
 
     assert log_has_re(
@@ -331,13 +336,43 @@ def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
         caplog,
     )
 
+    pair = "ETH/BTC"
+    metadata = {"pair": pair}
+    freqai.dk.pair = pair
+    freqai.start_backtesting(df, metadata, freqai.dk)
+
     path = (freqai.dd.full_path / freqai.dk.backtest_predictions_folder)
     prediction_files = [x for x in path.iterdir() if x.is_file()]
-    assert len(prediction_files) == 1
+    assert len(prediction_files) == 2
 
     shutil.rmtree(Path(freqai.dk.full_path))
 
 
+def test_backtesting_fit_live_predictions(mocker, freqai_conf, caplog):
+    freqai_conf.get("freqai", {}).update({"fit_live_predictions_candles": 10})
+    strategy = get_patched_freqai_strategy(mocker, freqai_conf)
+    exchange = get_patched_exchange(mocker, freqai_conf)
+    strategy.dp = DataProvider(freqai_conf, exchange)
+    strategy.freqai_info = freqai_conf.get("freqai", {})
+    freqai = strategy.freqai
+    freqai.live = False
+    freqai.dk = FreqaiDataKitchen(freqai_conf)
+    timerange = TimeRange.parse_timerange("20180128-20180130")
+    freqai.dd.load_all_pair_histories(timerange, freqai.dk)
+    sub_timerange = TimeRange.parse_timerange("20180129-20180130")
+    corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
+    df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
+    freqai.dk.pair = "ADA/BTC"
+    freqai.dk.full_df = df.fillna(0)
+    freqai.dk.full_df
+    assert "&-s_close_mean" not in freqai.dk.full_df.columns
+    assert "&-s_close_std" not in freqai.dk.full_df.columns
+    freqai.backtesting_fit_live_predictions(freqai.dk)
+    assert "&-s_close_mean" in freqai.dk.full_df.columns
+    assert "&-s_close_std" in freqai.dk.full_df.columns
+    shutil.rmtree(Path(freqai.dk.full_path))
+
+
 def test_follow_mode(mocker, freqai_conf):
     freqai_conf.update({"timerange": "20180110-20180130"})