consolidate and clean code
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robcaulk
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@ -79,11 +79,11 @@ To change your **features**, you **must** set a new `identifier` in the config t
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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.
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### Backtest live models
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### Backtest live collected predictions
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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.
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The `--timerange` parameter must not be informed, as it will be automatically calculated through the data in historic predictions file.
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The `--timerange` parameter must not be informed, as it will be automatically calculated through the data in the historic predictions file.
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### Downloading data to cover the full backtest period
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@ -75,7 +75,6 @@ class FreqaiDataKitchen:
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self.training_features_list: List = []
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self.model_filename: str = ""
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self.backtesting_results_path = Path()
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self.backtesting_h5_data: HDFStore = {}
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self.backtest_predictions_folder: str = "backtesting_predictions"
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self.live = live
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self.pair = pair
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@ -456,28 +455,6 @@ class FreqaiDataKitchen:
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# print(tr_training_list, tr_backtesting_list)
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return tr_training_list_timerange, tr_backtesting_list_timerange
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# def split_timerange_live_models(
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# self
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# ) -> Tuple[list, list]:
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# tr_backtesting_list_timerange = []
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# asset = self.pair.split("/")[0]
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# if asset not in self.backtest_live_models_data["assets_end_dates"]:
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# raise OperationalException(
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# f"Model not available for pair {self.pair}. "
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# "Please, try again after removing this pair from the configuration file."
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# )
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# asset_data = self.backtest_live_models_data["assets_end_dates"][asset]
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# backtesting_timerange = self.backtest_live_models_data["backtesting_timerange"]
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# model_end_dates = [x for x in asset_data]
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# model_end_dates.append(backtesting_timerange.stopts)
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# model_end_dates.sort()
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# for index, item in enumerate(model_end_dates):
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# if len(model_end_dates) > (index + 1):
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# tr_to_add = TimeRange("date", "date", item, model_end_dates[index + 1])
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# tr_backtesting_list_timerange.append(tr_to_add)
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# return tr_backtesting_list_timerange, tr_backtesting_list_timerange
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def slice_dataframe(self, timerange: TimeRange, df: DataFrame) -> DataFrame:
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"""
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@ -55,7 +55,6 @@ class IFreqaiModel(ABC):
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def __init__(self, config: Config) -> None:
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self.config = config
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self.metadata: Dict[str, Any] = {}
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self.assert_config(self.config)
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self.freqai_info: Dict[str, Any] = config["freqai"]
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self.data_split_parameters: Dict[str, Any] = config.get("freqai", {}).get(
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@ -102,7 +101,7 @@ class IFreqaiModel(ABC):
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self.get_corr_dataframes: bool = True
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self._threads: List[threading.Thread] = []
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self._stop_event = threading.Event()
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self.metadata = self.dd.load_global_metadata_from_disk()
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self.metadata: Dict[str, Any] = self.dd.load_global_metadata_from_disk()
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self.data_provider: Optional[DataProvider] = None
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self.max_system_threads = max(int(psutil.cpu_count() * 2 - 2), 1)
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@ -148,18 +147,13 @@ class IFreqaiModel(ABC):
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# the concatenated results for the full backtesting period back to the strategy.
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elif not self.follow_mode:
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self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"])
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if self.dk.backtest_live_models:
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logger.info(
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"Backtesting using historic predictions (live models)")
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else:
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logger.info(f"Training {len(self.dk.training_timeranges)} timeranges")
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dataframe = self.dk.use_strategy_to_populate_indicators(
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strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
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)
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if not self.config.get("freqai_backtest_live_models", False):
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logger.info(f"Training {len(self.dk.training_timeranges)} timeranges")
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dk = self.start_backtesting(dataframe, metadata, self.dk)
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dataframe = dk.remove_features_from_df(dk.return_dataframe)
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else:
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logger.info(
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"Backtesting using historic predictions (live models)")
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dk = self.start_backtesting_from_historic_predictions(
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dataframe, metadata, self.dk)
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dataframe = dk.return_dataframe
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@ -167,7 +161,6 @@ class IFreqaiModel(ABC):
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self.clean_up()
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if self.live:
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self.inference_timer('stop', metadata["pair"])
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self.set_start_dry_live_date(dataframe)
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return dataframe
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@ -336,27 +329,6 @@ class IFreqaiModel(ABC):
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return dk
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def start_backtesting_from_historic_predictions(
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self, dataframe: DataFrame, metadata: dict, dk: FreqaiDataKitchen
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) -> FreqaiDataKitchen:
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"""
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:param dataframe: DataFrame = strategy passed dataframe
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:param metadata: Dict = pair metadata
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:param dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
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:return:
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FreqaiDataKitchen = Data management/analysis tool associated to present pair only
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"""
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pair = metadata["pair"]
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dk.return_dataframe = dataframe
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saved_dataframe = self.dd.historic_predictions[pair]
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columns_to_drop = list(set(saved_dataframe.columns).intersection(
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dk.return_dataframe.columns))
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dk.return_dataframe = dk.return_dataframe.drop(columns=list(columns_to_drop))
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dk.return_dataframe = pd.merge(
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dk.return_dataframe, saved_dataframe, how='left', left_on='date', right_on="date_pred")
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# dk.return_dataframe = dk.return_dataframe[saved_dataframe.columns].fillna(0)
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return dk
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def start_live(
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self, dataframe: DataFrame, metadata: dict, strategy: IStrategy, dk: FreqaiDataKitchen
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) -> FreqaiDataKitchen:
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@ -665,6 +637,8 @@ class IFreqaiModel(ABC):
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self.dd.historic_predictions[pair] = pred_df
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hist_preds_df = self.dd.historic_predictions[pair]
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self.set_start_dry_live_date(pred_df)
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for label in hist_preds_df.columns:
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if hist_preds_df[label].dtype == object:
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continue
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@ -913,6 +887,27 @@ class IFreqaiModel(ABC):
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pd.to_datetime(live_dataframe.tail(1)["date"].values[0]).timestamp())
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self.update_metadata(metadata)
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def start_backtesting_from_historic_predictions(
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self, dataframe: DataFrame, metadata: dict, dk: FreqaiDataKitchen
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) -> FreqaiDataKitchen:
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"""
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:param dataframe: DataFrame = strategy passed dataframe
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:param metadata: Dict = pair metadata
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:param dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
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:return:
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FreqaiDataKitchen = Data management/analysis tool associated to present pair only
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"""
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pair = metadata["pair"]
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dk.return_dataframe = dataframe
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saved_dataframe = self.dd.historic_predictions[pair]
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columns_to_drop = list(set(saved_dataframe.columns).intersection(
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dk.return_dataframe.columns))
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dk.return_dataframe = dk.return_dataframe.drop(columns=list(columns_to_drop))
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dk.return_dataframe = pd.merge(
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dk.return_dataframe, saved_dataframe, how='left', left_on='date', right_on="date_pred")
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# dk.return_dataframe = dk.return_dataframe[saved_dataframe.columns].fillna(0)
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return dk
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# Following methods which are overridden by user made prediction models.
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# See freqai/prediction_models/CatboostPredictionModel.py for an example.
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