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Merge pull request #8095 from freqtrade/remove-follow-mode

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remove follow mode in favor of producer consumer
This commit is contained in:
Matthias
2023-02-03 07:02:56 +01:00
committed by GitHub
parent 618eb951d3 ef1738fbf6
commit 3c4ff2e037
5 changed files with 17 additions and 146 deletions
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51
freqtrade/freqai/data_drawer.py
View File

@@ -59,7 +59,7 @@ class FreqaiDataDrawer:
Juha Nykänen @suikula, Wagner Costa @wagnercosta, Johan Vlugt @Jooopieeert
"""
def __init__(self, full_path: Path, config: Config, follow_mode: bool = False):
def __init__(self, full_path: Path, config: Config):
self.config = config
self.freqai_info = config.get("freqai", {})
@@ -84,9 +84,6 @@ class FreqaiDataDrawer:
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:
self.create_follower_dict()
self.load_drawer_from_disk()
self.load_historic_predictions_from_disk()
self.metric_tracker: Dict[str, Dict[str, Dict[str, list]]] = {}
@@ -149,13 +146,8 @@ class FreqaiDataDrawer:
if exists:
with open(self.pair_dictionary_path, "r") as fp:
self.pair_dict = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)
elif not self.follow_mode:
logger.info("Could not find existing datadrawer, starting from scratch")
else:
logger.warning(
f"Follower could not find pair_dictionary at {self.full_path} "
"sending null values back to strategy"
)
logger.info("Could not find existing datadrawer, starting from scratch")
def load_metric_tracker_from_disk(self):
"""
@@ -193,13 +185,8 @@ class FreqaiDataDrawer:
self.historic_predictions = cloudpickle.load(fp)
logger.warning('FreqAI successfully loaded the backup historical predictions file.')
elif not self.follow_mode:
logger.info("Could not find existing historic_predictions, starting from scratch")
else:
logger.warning(
f"Follower could not find historic predictions at {self.full_path} "
"sending null values back to strategy"
)
logger.info("Could not find existing historic_predictions, starting from scratch")
return exists
@@ -248,23 +235,6 @@ class FreqaiDataDrawer:
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
"""
whitelist_pairs = self.config.get("exchange", {}).get("pair_whitelist")
exists = self.follower_dict_path.is_file()
if exists:
logger.info("Found an existing follower dictionary")
for pair in whitelist_pairs:
self.follower_dict[pair] = {}
self.save_follower_dict_to_disk()
def np_encoder(self, object):
if isinstance(object, np.generic):
return object.item()
@@ -282,27 +252,17 @@ class FreqaiDataDrawer:
"""
pair_dict = self.pair_dict.get(pair)
data_path_set = self.pair_dict.get(pair, self.empty_pair_dict).get("data_path", "")
# data_path_set = self.pair_dict.get(pair, self.empty_pair_dict).get("data_path", "")
return_null_array = False
if pair_dict:
model_filename = pair_dict["model_filename"]
trained_timestamp = pair_dict["trained_timestamp"]
elif not self.follow_mode:
else:
self.pair_dict[pair] = self.empty_pair_dict.copy()
model_filename = ""
trained_timestamp = 0
if not data_path_set and self.follow_mode:
logger.warning(
f"Follower could not find current pair {pair} in "
f"pair_dictionary at path {self.full_path}, sending null values "
"back to strategy."
)
trained_timestamp = 0
model_filename = ''
return_null_array = True
return model_filename, trained_timestamp, return_null_array
def set_pair_dict_info(self, metadata: dict) -> None:
@@ -311,7 +271,6 @@ class FreqaiDataDrawer:
return
else:
self.pair_dict[metadata["pair"]] = self.empty_pair_dict.copy()
return
def set_initial_return_values(self, pair: str, pred_df: DataFrame) -> None:

43
freqtrade/freqai/freqai_interface.py
View File

@@ -66,12 +66,11 @@ class IFreqaiModel(ABC):
self.retrain = False
self.first = True
self.set_full_path()
self.follow_mode: bool = self.freqai_info.get("follow_mode", False)
self.save_backtest_models: bool = self.freqai_info.get("save_backtest_models", True)
if self.save_backtest_models:
logger.info('Backtesting module configured to save all models.')
self.dd = FreqaiDataDrawer(Path(self.full_path), self.config, self.follow_mode)
self.dd = FreqaiDataDrawer(Path(self.full_path), self.config)
# set current candle to arbitrary historical date
self.current_candle: datetime = datetime.fromtimestamp(637887600, tz=timezone.utc)
self.dd.current_candle = self.current_candle
@@ -153,7 +152,7 @@ class IFreqaiModel(ABC):
# (backtest window, i.e. window immediately following the training window).
# FreqAI slides the window and sequentially builds the backtesting results before returning
# the concatenated results for the full backtesting period back to the strategy.
elif not self.follow_mode:
else:
self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"])
if not self.config.get("freqai_backtest_live_models", False):
logger.info(f"Training {len(self.dk.training_timeranges)} timeranges")
@@ -379,46 +378,28 @@ class IFreqaiModel(ABC):
:returns:
dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
"""
# update follower
if self.follow_mode:
self.dd.update_follower_metadata()
# get the model metadata associated with the current pair
(_, trained_timestamp, return_null_array) = self.dd.get_pair_dict_info(metadata["pair"])
# if the metadata doesn't exist, the follower returns null arrays to strategy
if self.follow_mode and return_null_array:
logger.info("Returning null array from follower to strategy")
self.dd.return_null_values_to_strategy(dataframe, dk)
return dk
# append the historic data once per round
if self.dd.historic_data:
self.dd.update_historic_data(strategy, dk)
logger.debug(f'Updating historic data on pair {metadata["pair"]}')
self.track_current_candle()
if not self.follow_mode:
(_, new_trained_timerange, data_load_timerange) = dk.check_if_new_training_required(
trained_timestamp
)
dk.set_paths(metadata["pair"], new_trained_timerange.stopts)
(_, new_trained_timerange, data_load_timerange) = dk.check_if_new_training_required(
trained_timestamp
)
dk.set_paths(metadata["pair"], new_trained_timerange.stopts)
# load candle history into memory if it is not yet.
if not self.dd.historic_data:
self.dd.load_all_pair_histories(data_load_timerange, dk)
# load candle history into memory if it is not yet.
if not self.dd.historic_data:
self.dd.load_all_pair_histories(data_load_timerange, dk)
if not self.scanning:
self.scanning = True
self.start_scanning(strategy)
elif self.follow_mode:
dk.set_paths(metadata["pair"], trained_timestamp)
logger.info(
"FreqAI instance set to follow_mode, finding existing pair "
f"using { self.identifier }"
)
if not self.scanning:
self.scanning = True
self.start_scanning(strategy)
# load the model and associated data into the data kitchen
self.model = self.dd.load_data(metadata["pair"], dk)