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@ -74,8 +74,8 @@ class FreqaiDataDrawer:
self.historic_predictions: Dict[str, DataFrame] = {}
self.full_path = full_path
self.historic_predictions_path = Path(self.full_path / "historic_predictions.pkl")
self.historic_predictions_bkp_path = Path(
self.full_path / "historic_predictions.backup.pkl")
self.historic_predictions_folder = Path(self.full_path / "historic_predictions")
self.historic_predictions_bkp_folder = Path(self.full_path / "historic_predictions_backup")
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")
@ -163,11 +163,12 @@ class FreqaiDataDrawer:
Locate and load a previously saved historic predictions.
:return: bool - whether or not the drawer was located
"""
exists = self.historic_predictions_path.is_file()
exists = self.historic_predictions_folder.exists()
convert = self.historic_predictions_path.is_file()
if exists:
try:
with self.historic_predictions_path.open("rb") as fp:
self.historic_predictions = cloudpickle.load(fp)
self.load_historic_predictions_from_folder()
logger.info(
f"Found existing historic predictions at {self.full_path}, but beware "
"that statistics may be inaccurate if the bot has been offline for "
@ -175,25 +176,54 @@ class FreqaiDataDrawer:
)
except EOFError:
logger.warning(
'Historical prediction file was corrupted. Trying to load backup file.')
with self.historic_predictions_bkp_path.open("rb") as fp:
'Historical prediction files were corrupted. Trying to load backup files.')
self.load_historic_predictions_from_folder()
logger.warning('FreqAI successfully loaded the backup '
'historical predictions files.')
elif not exists and convert:
logger.info("Converting your historic predictions pkl to parquet"
"to improve performance.")
with Path.open(self.historic_predictions_path, "rb") as fp:
self.historic_predictions = cloudpickle.load(fp)
logger.warning('FreqAI successfully loaded the backup historical predictions file.')
self.save_historic_predictions_to_disk()
exists = True
else:
logger.info("Could not find existing historic_predictions, starting from scratch")
logger.warning(
f"Follower could not find historic predictions at {self.full_path} "
"sending null values back to strategy"
)
return exists
def load_historic_predictions_from_folder(self):
"""
Try to build the historic_predictions dictionary from parquet
files in the historic_predictions_folder
"""
for file_path in self.historic_predictions_folder.glob("*.parquet"):
key = file_path.stem
key.replace("_", "/")
self.historic_predictions[key] = pd.read_parquet(file_path)
return
def save_historic_predictions_to_disk(self):
"""
Save historic predictions pickle to disk
"""
with self.historic_predictions_path.open("wb") as fp:
cloudpickle.dump(self.historic_predictions, fp, protocol=cloudpickle.DEFAULT_PROTOCOL)
self.historic_predictions_folder.mkdir(parents=True, exist_ok=True)
for key, value in self.historic_predictions.items():
key = key.replace("/", "_")
# pytest.set_trace()
filename = Path(self.historic_predictions_folder / f"{key}.parquet")
value.to_parquet(filename)
# create a backup
shutil.copy(self.historic_predictions_path, self.historic_predictions_bkp_path)
shutil.copytree(self.historic_predictions_folder,
self.historic_predictions_bkp_folder, dirs_exist_ok=True)
def save_metric_tracker_to_disk(self):
"""
@ -675,7 +705,7 @@ class FreqaiDataDrawer:
Returns timerange information based on historic predictions file
:return: timerange calculated from saved live data
"""
if not self.historic_predictions_path.is_file():
if not self.historic_predictions_folder.exists():
raise OperationalException(
'Historic predictions not found. Historic predictions data is required '
'to run backtest with the freqai-backtest-live-models option '