update code to use historic_predictions for freqai_backtest_live_models
This commit is contained in:
Wagner Costa Santos
parent
3d3195847c
commit
80d070e9ee
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@ -15,7 +15,7 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
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| `expiration_hours` | Avoid making predictions if a model is more than `expiration_hours` old. <br> **Datatype:** Positive integer. <br> Default: `0` (models never expire).
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| `purge_old_models` | Delete obsolete models. <br> **Datatype:** Boolean. <br> Default: `False` (all historic models remain on disk).
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| `save_backtest_models` | Save models to disk when running backtesting. Backtesting operates most efficiently by saving the prediction data and reusing them directly for subsequent runs (when you wish to tune entry/exit parameters). Saving backtesting models to disk also allows to use the same model files for starting a dry/live instance with the same model `identifier`. <br> **Datatype:** Boolean. <br> Default: `False` (no models are saved).
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| `save_live_data_backtest` | Save live dataframe during dry/live runs to reuse in backtesting with [Backtest live models](freqai-running.md#backtest_live_models)) option.
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| `backtest_using_historic_predictions` | Reuse `historic_predictions` in backtesting with [Backtest live models](freqai-running.md#backtest_live_models)) option. <br> Default: `True`
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| `fit_live_predictions_candles` | Number of historical candles to use for computing target (label) statistics from prediction data, instead of from the training dataset (more information can be found [here](freqai-configuration.md#creating-a-dynamic-target-threshold)). <br> **Datatype:** Positive integer.
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| `follow_mode` | Use a `follower` that will look for models associated with a specific `identifier` and load those for inferencing. A `follower` will **not** train new models. <br> **Datatype:** Boolean. <br> Default: `False`.
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| `continual_learning` | Use the final state of the most recently trained model as starting point for the new model, allowing for incremental learning (more information can be found [here](freqai-running.md#continual-learning)). <br> **Datatype:** Boolean. <br> Default: `False`.
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@ -83,8 +83,8 @@ To save the models generated during a particular backtest so that you can start
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FreqAI allow you to reuse ready models 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. For that, you have 2 options:
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1. Set `"save_live_data_backtest"` to `True` in the config. With this option, FreqAI will save the live dataframe for reuse in backtesting. This option requires less disk space and backtesting will run faster.
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2. Set `"purge_old_models"` to `False` and `"save_live_data_backtest"` to `False` in the config. In this case, FreqAI will use the saved models to make the predictions in backtesting. This option requires more disk space and the backtest will have a longer execution time.
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1. Set `"backtest_using_historic_predictions"` to `True` in the config. With this option, FreqAI will reuse `historic_predictions` in backtesting. This option requires less disk space and backtesting will run faster.
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2. Set `"purge_old_models"` to `False` and `"backtest_using_historic_predictions"` to `False` in the config. In this case, FreqAI will use the saved models to make the predictions in backtesting. This option requires more disk space and the backtest will have a longer execution time.
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The `--timerange` parameter must not be informed, as it will be automatically calculated through the training end dates of the models.
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@ -81,6 +81,7 @@ class FreqaiDataDrawer:
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self.historic_predictions_bkp_path = Path(
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self.full_path / "historic_predictions.backup.pkl")
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self.pair_dictionary_path = Path(self.full_path / "pair_dictionary.json")
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self.global_metadata_path = Path(self.full_path / "global_metadata.json")
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self.metric_tracker_path = Path(self.full_path / "metric_tracker.json")
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self.follow_mode = follow_mode
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if follow_mode:
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@ -125,6 +126,17 @@ class FreqaiDataDrawer:
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self.update_metric_tracker('cpu_load5min', load5 / cpus, pair)
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self.update_metric_tracker('cpu_load15min', load15 / cpus, pair)
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def load_global_metadata_from_disk(self):
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"""
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Locate and load a previously saved global metadata in present model folder.
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"""
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exists = self.global_metadata_path.is_file()
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if exists:
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with open(self.global_metadata_path, "r") as fp:
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metatada_dict = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
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return metatada_dict
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return {}
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def load_drawer_from_disk(self):
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"""
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Locate and load a previously saved data drawer full of all pair model metadata in
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@ -225,6 +237,15 @@ class FreqaiDataDrawer:
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rapidjson.dump(self.follower_dict, fp, default=self.np_encoder,
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number_mode=rapidjson.NM_NATIVE)
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def save_global_metadata_to_disk(self, metadata: Dict[str, Any]):
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"""
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Save global metadata json to disk
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"""
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with self.save_lock:
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with open(self.global_metadata_path, 'w') as fp:
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rapidjson.dump(metadata, fp, default=self.np_encoder,
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number_mode=rapidjson.NM_NATIVE)
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def create_follower_dict(self):
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"""
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Create or dictionary for each follower to maintain unique persistent prediction targets
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@ -9,7 +9,7 @@ from typing import Any, Dict, List, Tuple
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import numpy as np
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import numpy.typing as npt
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import pandas as pd
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from pandas import DataFrame, read_feather
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from pandas import DataFrame
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from scipy import stats
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from sklearn import linear_model
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from sklearn.cluster import DBSCAN
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@ -74,9 +74,6 @@ 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_live_model_folder_path = Path()
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self.backtesting_live_model_path = Path()
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self.backtesting_live_model_bkp_path = Path()
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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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@ -90,7 +87,9 @@ class FreqaiDataKitchen:
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self.full_path = self.get_full_models_path(self.config)
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if self.backtest_live_models:
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if self.pair:
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if self.pair and not (
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self.freqai_config.get("backtest_using_historic_predictions", True)
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):
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self.set_timerange_from_ready_models()
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(self.training_timeranges,
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self.backtesting_timeranges) = self.split_timerange_live_models()
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@ -1488,101 +1487,30 @@ class FreqaiDataKitchen:
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return dataframe
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def set_backtesting_live_dataframe_folder_path(
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self
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) -> None:
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"""
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Set live backtesting dataframe path
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:param pair: current pair
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"""
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self.backtesting_live_model_folder_path = Path(
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self.full_path / self.backtest_predictions_folder / "live_data")
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def set_backtesting_live_dataframe_path(
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self, pair: str
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) -> None:
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"""
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Set live backtesting dataframe path
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:param pair: current pair
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"""
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self.set_backtesting_live_dataframe_folder_path()
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if not self.backtesting_live_model_folder_path.is_dir():
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self.backtesting_live_model_folder_path.mkdir(parents=True, exist_ok=True)
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pair_path = pair.split(":")[0].replace("/", "_").lower()
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file_name = f"live_backtesting_{pair_path}.feather"
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self.backtesting_live_model_path = Path(
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self.full_path /
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self.backtesting_live_model_folder_path /
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file_name)
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self.backtesting_live_model_bkp_path = Path(
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self.full_path /
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self.backtesting_live_model_folder_path /
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file_name.replace(".feather", ".backup.feather"))
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def save_backtesting_live_dataframe(
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self, dataframe: DataFrame, pair: str
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) -> None:
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"""
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Save live backtesting dataframe to feather file format
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:param dataframe: current live dataframe
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:param pair: current pair
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"""
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self.set_backtesting_live_dataframe_path(pair)
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last_row_df = dataframe.tail(1)
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if self.backtesting_live_model_path.is_file():
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saved_dataframe = self.get_backtesting_live_dataframe()
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concat_dataframe = pd.concat([saved_dataframe, last_row_df])
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self.save_backtesting_live_dataframe_to_feather(concat_dataframe)
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else:
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self.save_backtesting_live_dataframe_to_feather(last_row_df)
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shutil.copy(self.backtesting_live_model_path, self.backtesting_live_model_bkp_path)
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def save_backtesting_live_dataframe_to_feather(self, dataframe: DataFrame):
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dataframe.reset_index(drop=True).to_feather(
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self.backtesting_live_model_path, compression_level=9, compression='lz4')
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def get_backtesting_live_dataframe(
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self
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) -> DataFrame:
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"""
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Get live backtesting dataframe from feather file format
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return: saved dataframe from previous dry/run or live
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"""
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if self.backtesting_live_model_path.is_file():
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saved_dataframe = DataFrame()
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try:
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saved_dataframe = read_feather(self.backtesting_live_model_path)
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except Exception:
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saved_dataframe = read_feather(self.backtesting_live_model_bkp_path)
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return saved_dataframe
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else:
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raise OperationalException(
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"Saved live backtesting dataframe file not found."
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)
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def get_timerange_from_backtesting_live_dataframe(self) -> TimeRange:
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"""
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Returns timerange information based on live backtesting dataframe file
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Returns timerange information based on historic predictions file
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:return: timerange calculated from saved live data
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"""
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all_assets_start_dates = []
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all_assets_end_dates = []
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self.set_backtesting_live_dataframe_folder_path()
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if not self.backtesting_live_model_folder_path.is_dir():
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from freqtrade.freqai.data_drawer import FreqaiDataDrawer
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dd = FreqaiDataDrawer(Path(self.full_path), self.config)
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if not dd.historic_predictions_path.is_file():
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raise OperationalException(
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'Saved live data not found. Saved lived data is required '
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'Historic predictions not found. Historic predictions data is required '
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'to run backtest with the freqai-backtest-live-models option '
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'and save_live_data_backtest config option as true'
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'and backtest_using_historic_predictions config option as true'
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)
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for file_in_dir in self.backtesting_live_model_folder_path.iterdir():
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if file_in_dir.is_file() and "backup" not in file_in_dir.name:
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saved_dataframe = read_feather(file_in_dir)
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all_assets_start_dates.append(saved_dataframe.date.min())
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all_assets_end_dates.append(saved_dataframe.date.max())
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start_date = min(all_assets_start_dates)
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end_date = max(all_assets_end_dates)
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dd.load_historic_predictions_from_disk()
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all_pairs_end_dates = []
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for pair in dd.historic_predictions:
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pair_historic_data = dd.historic_predictions[pair]
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all_pairs_end_dates.append(pair_historic_data.date_pred.max())
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global_metadata = dd.load_global_metadata_from_disk()
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start_date = datetime.fromtimestamp(int(global_metadata["start_dry_live_date"]))
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end_date = max(all_pairs_end_dates)
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# add 1 day to string timerange to ensure BT module will load all dataframe data
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end_date = end_date + timedelta(days=1)
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backtesting_timerange = TimeRange(
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@ -53,6 +53,7 @@ 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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@ -67,10 +68,10 @@ class IFreqaiModel(ABC):
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self.save_backtest_models: bool = self.freqai_info.get("save_backtest_models", True)
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if self.save_backtest_models:
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logger.info('Backtesting module configured to save all models.')
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self.save_live_data_backtest: bool = self.freqai_info.get(
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"save_live_data_backtest", False)
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if self.save_live_data_backtest:
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logger.info('Live configured to save data for backtest.')
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self.backtest_using_historic_predictions: bool = self.freqai_info.get(
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"backtest_using_historic_predictions", True)
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if self.backtest_using_historic_predictions:
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logger.info('Backtesting live models configured to use historic predictions.')
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self.dd = FreqaiDataDrawer(Path(self.full_path), self.config, self.follow_mode)
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# set current candle to arbitrary historical date
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@ -103,6 +104,7 @@ 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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record_params(config, self.full_path)
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@ -136,6 +138,7 @@ class IFreqaiModel(ABC):
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self.inference_timer('start')
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self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"])
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dk = self.start_live(dataframe, metadata, strategy, self.dk)
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dataframe = dk.remove_features_from_df(dk.return_dataframe)
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# For backtesting, each pair enters and then gets trained for each window along the
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# sliding window defined by "train_period_days" (training window) and "live_retrain_hours"
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@ -145,14 +148,19 @@ class IFreqaiModel(ABC):
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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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f"Backtesting {len(self.dk.backtesting_timeranges)} timeranges (live models)")
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if self.backtest_using_historic_predictions:
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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(
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f"Backtesting {len(self.dk.backtesting_timeranges)} "
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"timeranges (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.save_live_data_backtest:
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if not self.backtest_using_historic_predictions:
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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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@ -163,8 +171,7 @@ 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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if self.save_live_data_backtest:
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dk.save_backtesting_live_dataframe(dataframe, metadata["pair"])
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self.set_start_dry_live_date(dataframe)
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return dataframe
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@ -335,14 +342,12 @@ class IFreqaiModel(ABC):
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"""
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pair = metadata["pair"]
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dk.return_dataframe = dataframe
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self.dk.set_backtesting_live_dataframe_path(pair)
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saved_dataframe = self.dk.get_backtesting_live_dataframe()
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columns_to_drop = list(set(dk.return_dataframe.columns).difference(
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["date", "open", "high", "low", "close", "volume"]))
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saved_dataframe = saved_dataframe.drop(
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columns=["open", "high", "low", "close", "volume"])
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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(dk.return_dataframe, saved_dataframe, how='left', on='date')
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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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@ -886,6 +891,22 @@ class IFreqaiModel(ABC):
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return
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def update_metadata(self, metadata: Dict[str, Any]):
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"""
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Update global metadata and save the updated json file
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:param metadata: new global metadata dict
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"""
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self.dd.save_global_metadata_to_disk(metadata)
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self.metadata = metadata
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def set_start_dry_live_date(self, live_dataframe: DataFrame):
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key_name = "start_dry_live_date"
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if key_name not in self.metadata:
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metadata = self.metadata
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metadata[key_name] = int(
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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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# 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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@ -230,7 +230,7 @@ def get_timerange_backtest_live_models(config: Config) -> str:
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dk = FreqaiDataKitchen(config)
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models_path = dk.get_full_models_path(config)
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timerange: TimeRange = TimeRange()
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if not config.get("save_live_data_backtest", False):
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if not config.get("freqai", {}).get("backtest_using_historic_predictions", True):
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timerange, _ = dk.get_timerange_and_assets_end_dates_from_ready_models(models_path)
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else:
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timerange = dk.get_timerange_from_backtesting_live_dataframe()
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@ -261,45 +261,18 @@ def test_get_full_model_path(mocker, freqai_conf, model):
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assert model_path.is_dir() is True
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def test_save_backtesting_live_dataframe(mocker, freqai_conf):
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freqai, dataframe = make_unfiltered_dataframe(mocker, freqai_conf)
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dataframe_without_last_candle = dataframe.copy()
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dataframe_without_last_candle.drop(dataframe.tail(1).index, inplace=True)
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freqai_conf.update({"save_live_data_backtest": True})
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freqai.dk.save_backtesting_live_dataframe(dataframe_without_last_candle, "ADA/BTC")
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saved_dataframe = freqai.dk.get_backtesting_live_dataframe()
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assert len(saved_dataframe) == 1
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assert saved_dataframe.iloc[-1, 0] == dataframe_without_last_candle.iloc[-1, 0]
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freqai.dk.save_backtesting_live_dataframe(dataframe, "ADA/BTC")
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saved_dataframe = freqai.dk.get_backtesting_live_dataframe()
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assert len(saved_dataframe) == 2
|
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assert saved_dataframe.iloc[-1, 0] == dataframe.iloc[-1, 0]
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assert saved_dataframe.iloc[-2, 0] == dataframe.iloc[-2, 0]
|
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|
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|
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def test_get_timerange_from_backtesting_live_dataframe(mocker, freqai_conf):
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freqai, dataframe = make_unfiltered_dataframe(mocker, freqai_conf)
|
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freqai_conf.update({"save_live_data_backtest": True})
|
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freqai.dk.set_backtesting_live_dataframe_path("ADA/BTC")
|
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freqai.dk.save_backtesting_live_dataframe_to_feather(dataframe)
|
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freqai_conf.update({"backtest_using_historic_predictions": True})
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timerange = freqai.dk.get_timerange_from_backtesting_live_dataframe()
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assert timerange.startts == 1516406400
|
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assert timerange.stopts == 1517356500
|
||||
|
||||
|
||||
def test_get_timerange_from_backtesting_live_dataframe_folder_not_found(mocker, freqai_conf):
|
||||
def test_get_timerange_from_backtesting_live_df_pred_not_found(mocker, freqai_conf):
|
||||
freqai, _ = make_unfiltered_dataframe(mocker, freqai_conf)
|
||||
with pytest.raises(
|
||||
OperationalException,
|
||||
match=r'Saved live data not found.*'
|
||||
match=r'Historic predictions not found.*'
|
||||
):
|
||||
freqai.dk.get_timerange_from_backtesting_live_dataframe()
|
||||
|
||||
|
||||
def test_saved_live_bt_file_not_found(mocker, freqai_conf):
|
||||
freqai, _ = make_unfiltered_dataframe(mocker, freqai_conf)
|
||||
with pytest.raises(
|
||||
OperationalException,
|
||||
match=r'.*live backtesting dataframe file not found.*'
|
||||
):
|
||||
freqai.dk.get_backtesting_live_dataframe()
|
||||
|
|
|
|||
|
|
@ -300,37 +300,6 @@ def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
|
|||
shutil.rmtree(Path(freqai.dk.full_path))
|
||||
|
||||
|
||||
def test_start_backtesting_from_saved_live_dataframe(mocker, freqai_conf, caplog):
|
||||
freqai_conf.update({"save_live_data_backtest": True})
|
||||
freqai_conf.update({"freqai_backtest_live_models": True})
|
||||
|
||||
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("20180110-20180130")
|
||||
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
|
||||
sub_timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
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")
|
||||
metadata = {"pair": "ADA/BTC"}
|
||||
|
||||
# create a dummy live dataframe file with 10 rows
|
||||
dataframe_predictions = df.tail(10).copy()
|
||||
dataframe_predictions["&s_close"] = dataframe_predictions["close"] * 1.1
|
||||
freqai.dk.set_backtesting_live_dataframe_path("ADA/BTC")
|
||||
freqai.dk.save_backtesting_live_dataframe_to_feather(dataframe_predictions)
|
||||
|
||||
freqai.start_backtesting_from_live_saved_files(df, metadata, freqai.dk)
|
||||
assert len(freqai.dk.return_dataframe) == len(df)
|
||||
assert len(freqai.dk.return_dataframe[freqai.dk.return_dataframe["&s_close"] > 0]) == (
|
||||
len(dataframe_predictions))
|
||||
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)
|
||||
|
|
|
|||
Loading…
Reference in New Issue