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improve data persistence/mapping for live/dry. This accommodates quick reloads after crash and handles multi-pair cleanly

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This commit is contained in:
robcaulk
2022-05-23 21:05:05 +02:00
parent e1c068ca66
commit b0d2d13eb1
4 changed files with 199 additions and 131 deletions
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102
freqtrade/freqai/freqai_interface.py
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@@ -13,6 +13,7 @@ from pandas import DataFrame
from freqtrade.configuration import TimeRange
from freqtrade.enums import RunMode
from freqtrade.freqai.data_drawer import FreqaiDataDrawer
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.strategy.interface import IStrategy
@@ -65,11 +66,14 @@ class IFreqaiModel(ABC):
self.training_on_separate_thread = False
self.retrain = False
self.first = True
if self.freqai_info.get('live_trained_timerange'):
self.new_trained_timerange = TimeRange.parse_timerange(
self.freqai_info['live_trained_timerange'])
else:
self.new_trained_timerange = TimeRange()
# if self.freqai_info.get('live_trained_timerange'):
# self.new_trained_timerange = TimeRange.parse_timerange(
# self.freqai_info['live_trained_timerange'])
# else:
# self.new_trained_timerange = TimeRange()
self.set_full_path()
self.data_drawer = FreqaiDataDrawer(Path(self.full_path))
def assert_config(self, config: Dict[str, Any]) -> None:
@@ -86,7 +90,7 @@ class IFreqaiModel(ABC):
def start(self, dataframe: DataFrame, metadata: dict, strategy: IStrategy) -> DataFrame:
"""
Entry point to the FreqaiModel, it will train a new model if
Entry point to the FreqaiModel from a specific pair, it will train a new model if
necessary before making the prediction.
The backtesting and training paradigm is a sliding training window
with a following backtest window. Both windows slide according to the
@@ -103,8 +107,8 @@ class IFreqaiModel(ABC):
self.live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE)
self.pair = metadata["pair"]
self.dh = FreqaiDataKitchen(self.config, dataframe, self.live)
# FreqaiDataKitchen is reinstantiated for each coin
self.dh = FreqaiDataKitchen(self.config, self.data_drawer, self.live, metadata["pair"])
if self.live:
# logger.info('testing live')
@@ -113,7 +117,7 @@ class IFreqaiModel(ABC):
return (self.dh.full_predictions, self.dh.full_do_predict,
self.dh.full_target_mean, self.dh.full_target_std)
logger.info("going to train %s timeranges", len(self.dh.training_timeranges))
logger.info(f'Training {len(self.dh.training_timeranges)} timeranges')
# Loop enforcing the sliding window training/backtesting paradigm
# tr_train is the training time range e.g. 1 historical month
@@ -129,9 +133,12 @@ class IFreqaiModel(ABC):
self.training_timerange = tr_train
dataframe_train = self.dh.slice_dataframe(tr_train, dataframe)
dataframe_backtest = self.dh.slice_dataframe(tr_backtest, dataframe)
logger.info("training %s for %s", self.pair, tr_train)
self.dh.model_path = Path(self.dh.full_path / str("sub-train" + "-" + str(tr_train)))
if not self.model_exists(self.pair, training_timerange=tr_train):
logger.info("training %s for %s", metadata["pair"], tr_train)
trained_timestamp = TimeRange.parse_timerange(tr_train)
self.dh.data_path = Path(self.dh.full_path /
str("sub-train" + "-" + metadata['pair'].split("/")[0] +
str(int(trained_timestamp.stopts))))
if not self.model_exists(metadata["pair"], trained_timestamp=trained_timestamp.stopts):
self.model = self.train(dataframe_train, metadata)
self.dh.save_data(self.model)
else:
@@ -161,36 +168,40 @@ class IFreqaiModel(ABC):
"""
self.dh.set_paths()
(model_filename,
trained_timestamp,
coin_first) = self.data_drawer.get_pair_dict_info(metadata)
file_exists = self.model_exists(metadata['pair'],
training_timerange=self.freqai_info[
'live_trained_timerange'])
if trained_timestamp != 0:
self.dh.set_paths(trained_timestamp)
# data_drawer thinks the file eixts, verify here
file_exists = self.model_exists(metadata['pair'],
trained_timestamp=trained_timestamp,
model_filename=model_filename)
if not self.training_on_separate_thread:
# this will also prevent other pairs from trying to train simultaneously.
(self.retrain,
self.new_trained_timerange) = self.dh.check_if_new_training_required(
self.new_trained_timerange,
metadata)
new_trained_timerange) = self.dh.check_if_new_training_required(
trained_timestamp)
self.dh.set_paths(new_trained_timerange.stopts)
else:
logger.info("FreqAI training a new model on background thread.")
self.retrain = False
if self.retrain or not file_exists:
if self.first:
self.train_model_in_series(self.new_trained_timerange, metadata, strategy)
self.first = False
if coin_first:
self.train_model_in_series(new_trained_timerange, metadata, strategy)
else:
self.training_on_separate_thread = True # acts like a lock
self.retrain_model_on_separate_thread(self.new_trained_timerange,
self.retrain_model_on_separate_thread(new_trained_timerange,
metadata, strategy)
self.model = self.dh.load_data()
self.model = self.dh.load_data(coin=metadata['pair'])
strategy_provided_features = self.dh.find_features(dataframe)
if strategy_provided_features != self.dh.training_features_list:
self.train_model_in_series(self.new_trained_timerange, metadata, strategy)
self.train_model_in_series(new_trained_timerange, metadata, strategy)
preds, do_preds = self.predict(dataframe, metadata)
self.dh.append_predictions(preds, do_preds, len(dataframe))
@@ -252,24 +263,34 @@ class IFreqaiModel(ABC):
if self.freqai_info.get('feature_parameters', {}).get('DI_threshold'):
self.dh.check_if_pred_in_training_spaces() # sets do_predict
def model_exists(self, pair: str, training_timerange: str) -> bool:
def model_exists(self, pair: str, trained_timestamp: int = None,
model_filename: str = '') -> bool:
"""
Given a pair and path, check if a model already exists
:param pair: pair e.g. BTC/USD
:param path: path to model
"""
if self.live and training_timerange == "":
return False
coin, _ = pair.split("/")
self.dh.model_filename = "cb_" + coin.lower() + "_" + training_timerange
path_to_modelfile = Path(self.dh.model_path / str(self.dh.model_filename + "_model.joblib"))
if self.live and trained_timestamp is None:
self.dh.model_filename = model_filename
else:
self.dh.model_filename = "cb_" + coin.lower() + "_" + str(trained_timestamp)
path_to_modelfile = Path(self.dh.data_path / str(self.dh.model_filename + "_model.joblib"))
file_exists = path_to_modelfile.is_file()
if file_exists:
logger.info("Found model at %s", self.dh.model_path / self.dh.model_filename)
logger.info("Found model at %s", self.dh.data_path / self.dh.model_filename)
else:
logger.info("Could not find model at %s", self.dh.model_path / self.dh.model_filename)
logger.info("Could not find model at %s", self.dh.data_path / self.dh.model_filename)
return file_exists
def set_full_path(self) -> None:
self.full_path = Path(self.config['user_data_dir'] /
"models" /
str(self.freqai_info.get('live_full_backtestrange') +
self.freqai_info.get('identifier')))
@threaded
def retrain_model_on_separate_thread(self, new_trained_timerange: TimeRange, metadata: dict,
strategy: IStrategy):
@@ -285,7 +306,13 @@ class IFreqaiModel(ABC):
metadata)
self.model = self.train(unfiltered_dataframe, metadata)
self.dh.save_data(self.model)
self.data_drawer.pair_dict[metadata['pair']][
'trained_timestamp'] = new_trained_timerange.stopts
self.dh.set_new_model_names(metadata, new_trained_timerange)
self.dh.save_data(self.model, coin=metadata['pair'])
self.training_on_separate_thread = False
self.retrain = False
@@ -303,7 +330,14 @@ class IFreqaiModel(ABC):
metadata)
self.model = self.train(unfiltered_dataframe, metadata)
self.dh.save_data(self.model)
self.data_drawer.pair_dict[metadata['pair']][
'trained_timestamp'] = new_trained_timerange.stopts
self.dh.set_new_model_names(metadata, new_trained_timerange)
self.data_drawer.pair_dict[metadata['pair']]['first'] = False
self.dh.save_data(self.model, coin=metadata['pair'])
self.retrain = False
# Methods which are overridden by user made prediction models.