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Aggregated commit. Adding support vector machine for outlier detection, improve user interface to dry/live, better standardization, fix various other bugs

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This commit is contained in:
robcaulk
2022-05-22 17:51:49 +02:00
parent c5ecf94177
commit 42d95af829
7 changed files with 404 additions and 300 deletions
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56
freqtrade/freqai/freqai_interface.py
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@@ -62,6 +62,7 @@ class IFreqaiModel(ABC):
self.predictions = None
self.training_on_separate_thread = False
self.retrain = False
self.first = True
def start(self, dataframe: DataFrame, metadata: dict, strategy: IStrategy) -> DataFrame:
"""
@@ -80,12 +81,12 @@ class IFreqaiModel(ABC):
:metadata: pair metadata coming from strategy.
"""
live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE)
self.live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE)
self.pair = metadata["pair"]
self.dh = FreqaiDataKitchen(self.config, dataframe, live)
self.dh = FreqaiDataKitchen(self.config, dataframe, self.live)
if live:
if self.live:
# logger.info('testing live')
self.start_live(dataframe, metadata, strategy)
@@ -115,11 +116,12 @@ class IFreqaiModel(ABC):
self.dh.save_data(self.model)
else:
self.model = self.dh.load_data()
strategy_provided_features = self.dh.find_features(dataframe_train)
if strategy_provided_features != self.dh.training_features_list:
logger.info("User changed input features, retraining model.")
self.model = self.train(dataframe_train, metadata)
self.dh.save_data(self.model)
# strategy_provided_features = self.dh.find_features(dataframe_train)
# # TOFIX doesnt work with PCA
# if strategy_provided_features != self.dh.training_features_list:
# logger.info("User changed input features, retraining model.")
# self.model = self.train(dataframe_train, metadata)
# self.dh.save_data(self.model)
preds, do_preds = self.predict(dataframe_backtest, metadata)
@@ -148,7 +150,7 @@ class IFreqaiModel(ABC):
if not self.training_on_separate_thread:
# this will also prevent other pairs from trying to train simultaneously.
(self.retrain,
new_trained_timerange) = self.dh.check_if_new_training_required(self.freqai_info[
self.new_trained_timerange) = self.dh.check_if_new_training_required(self.freqai_info[
'live_trained_timerange'],
metadata)
else:
@@ -156,14 +158,19 @@ class IFreqaiModel(ABC):
self.retrain = False
if self.retrain or not file_exists:
self.training_on_separate_thread = True # acts like a lock
self.retrain_model_on_separate_thread(new_trained_timerange, metadata, strategy)
if self.first:
self.train_model_in_series(self.new_trained_timerange, metadata, strategy)
self.first = False
else:
self.training_on_separate_thread = True # acts like a lock
self.retrain_model_on_separate_thread(self.new_trained_timerange,
metadata, strategy)
self.model = self.dh.load_data()
strategy_provided_features = self.dh.find_features(dataframe)
if strategy_provided_features != self.dh.training_features_list:
self.train_model_in_series(new_trained_timerange, metadata, strategy)
self.train_model_in_series(self.new_trained_timerange, metadata, strategy)
preds, do_preds = self.predict(dataframe, metadata)
self.dh.append_predictions(preds, do_preds, len(dataframe))
@@ -215,12 +222,36 @@ class IFreqaiModel(ABC):
data (NaNs) or felt uncertain about data (PCA and DI index)
"""
@abstractmethod
def data_cleaning_train(self) -> None:
"""
User can add data analysis and cleaning here.
Any function inside this method should drop training data points from the filtered_dataframe
based on user decided logic. See FreqaiDataKitchen::remove_outliers() for an example
of how outlier data points are dropped from the dataframe used for training.
"""
@abstractmethod
def data_cleaning_predict(self) -> None:
"""
User can add data analysis and cleaning here.
These functions each modify self.dh.do_predict, which is a dataframe with equal length
to the number of candles coming from and returning to the strategy. Inside do_predict,
1 allows prediction and < 0 signals to the strategy that the model is not confident in
the prediction.
See FreqaiDataKitchen::remove_outliers() for an example
of how the do_predict vector is modified. do_predict is ultimately passed back to strategy
for buy signals.
"""
def model_exists(self, pair: str, training_timerange: 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 is None:
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"))
@@ -265,3 +296,4 @@ class IFreqaiModel(ABC):
self.model = self.train(unfiltered_dataframe, metadata)
self.dh.save_data(self.model)
self.retrain = False