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Update some areas to use default docstring formatting

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This commit is contained in:
Matthias 2022-07-24 16:51:48 +02:00
parent 61c41fd919
commit 70b7a254af
5 changed files with 59 additions and 71 deletions
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11
docs/freqai.md
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@ -183,12 +183,11 @@ various configuration parameters which multiply the feature set such as `include
(see convention below). I.e. user should not prepend any supporting metrics
(e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the
model.
:params:
:pair: pair to be used as informative
:df: strategy dataframe which will receive merges from informatives
:tf: timeframe of the dataframe which will modify the feature names
:informative: the dataframe associated with the informative pair
:coin: the name of the coin which will modify the feature names.
:param pair: pair to be used as informative
:param df: strategy dataframe which will receive merges from informatives
:param tf: timeframe of the dataframe which will modify the feature names
:param informative: the dataframe associated with the informative pair
:param coin: the name of the coin which will modify the feature names.
"""
with self.freqai.lock:

22
freqtrade/freqai/data_drawer.py
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@ -163,13 +163,12 @@ class FreqaiDataDrawer:
Locate and load existing model metadata from persistent storage. If not located,
create a new one and append the current pair to it and prepare it for its first
training
:params:
metadata: dict = strategy furnished pair metadata
:returns:
model_filename: str = unique filename used for loading persistent objects from disk
trained_timestamp: int = the last time the coin was trained
coin_first: bool = If the coin is fresh without metadata
return_null_array: bool = Follower could not find pair metadata
:param pair: str: pair to lookup
:return:
model_filename: str = unique filename used for loading persistent objects from disk
trained_timestamp: int = the last time the coin was trained
coin_first: bool = If the coin is fresh without metadata
return_null_array: bool = Follower could not find pair metadata
"""
pair_in_dict = self.pair_dict.get(pair)
data_path_set = self.pair_dict.get(pair, {}).get("data_path", None)
@ -277,13 +276,12 @@ class FreqaiDataDrawer:
)
df = pd.concat([prepend_df, df], axis=0)
def attach_return_values_to_return_dataframe(self, pair: str, dataframe) -> DataFrame:
def attach_return_values_to_return_dataframe(
self, pair: str, dataframe: DataFrame) -> DataFrame:
"""
Attach the return values to the strat dataframe
:params:
dataframe: DataFrame = strat dataframe
:returns:
dataframe: DataFrame = strat dataframe with return values attached
:param dataframe: DataFrame = strategy dataframe
:return: DataFrame = strat dataframe with return values attached
"""
df = self.model_return_values[pair]
to_keep = [col for col in dataframe.columns if not col.startswith("&")]

75
freqtrade/freqai/freqai_interface.py
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@ -129,8 +129,7 @@ class IFreqaiModel(ABC):
Function designed to constantly scan pairs for retraining on a separate thread (intracandle)
to improve model youth. This function is agnostic to data preparation/collection/storage,
it simply trains on what ever data is available in the self.dd.
:params:
strategy: IStrategy = The user defined strategy class
:param strategy: IStrategy = The user defined strategy class
"""
while 1:
time.sleep(1)
@ -164,12 +163,11 @@ class IFreqaiModel(ABC):
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.
:params:
dataframe: DataFrame = strategy passed dataframe
metadata: Dict = pair metadata
dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
:returns:
dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
:param dataframe: DataFrame = strategy passed dataframe
:param metadata: Dict = pair metadata
:param dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
:return:
FreqaiDataKitchen = Data management/analysis tool associated to present pair only
"""
self.pair_it += 1
@ -239,13 +237,12 @@ class IFreqaiModel(ABC):
"""
The main broad execution for dry/live. This function will check if a retraining should be
performed, and if so, retrain and reset the model.
:params:
dataframe: DataFrame = strategy passed dataframe
metadata: Dict = pair metadata
strategy: IStrategy = currently employed strategy
dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
:param dataframe: DataFrame = strategy passed dataframe
:param metadata: Dict = pair metadata
:param strategy: IStrategy = currently employed strategy
dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
:returns:
dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
"""
# update follower
@ -353,9 +350,9 @@ class IFreqaiModel(ABC):
"""
Ensure user is passing the proper feature set if they are reusing an `identifier` pointing
to a folder holding existing models.
:params:
dataframe: DataFrame = strategy provided dataframe
dk: FreqaiDataKitchen = non-persistent data container/analyzer for current coin/bot loop
:param dataframe: DataFrame = strategy provided dataframe
:param dk: FreqaiDataKitchen = non-persistent data container/analyzer for
current coin/bot loop
"""
dk.find_features(dataframe)
if "training_features_list_raw" in dk.data:
@ -461,13 +458,14 @@ class IFreqaiModel(ABC):
"""
Retreive data and train model in single threaded mode (only used if model directory is empty
upon startup for dry/live )
:params:
new_trained_timerange: TimeRange = the timerange to train the model on
metadata: dict = strategy provided metadata
strategy: IStrategy = user defined strategy object
dk: FreqaiDataKitchen = non-persistent data container for current coin/loop
data_load_timerange: TimeRange = the amount of data to be loaded for populate_any_indicators
(larger than new_trained_timerange so that new_trained_timerange does not contain any NaNs)
:param new_trained_timerange: TimeRange = the timerange to train the model on
:param metadata: dict = strategy provided metadata
:param strategy: IStrategy = user defined strategy object
:param dk: FreqaiDataKitchen = non-persistent data container for current coin/loop
:param data_load_timerange: TimeRange = the amount of data to be loaded
for populate_any_indicators
(larger than new_trained_timerange so that
new_trained_timerange does not contain any NaNs)
"""
corr_dataframes, base_dataframes = dk.get_base_and_corr_dataframes(
@ -515,11 +513,9 @@ class IFreqaiModel(ABC):
"""
Filter the training data and train a model to it. Train makes heavy use of the datahandler
for storing, saving, loading, and analyzing the data.
:params:
:unfiltered_dataframe: Full dataframe for the current training period
:metadata: pair metadata from strategy.
:returns:
:model: Trained model which can be used to inference (self.predict)
:param unfiltered_dataframe: Full dataframe for the current training period
:param metadata: pair metadata from strategy.
:return: Trained model which can be used to inference (self.predict)
"""
@abstractmethod
@ -528,9 +524,8 @@ class IFreqaiModel(ABC):
Most regressors use the same function names and arguments e.g. user
can drop in LGBMRegressor in place of CatBoostRegressor and all data
management will be properly handled by Freqai.
:params:
data_dictionary: Dict = the dictionary constructed by DataHandler to hold
all the training and test data/labels.
:param data_dictionary: Dict = the dictionary constructed by DataHandler to hold
all the training and test data/labels.
"""
return
@ -541,9 +536,9 @@ class IFreqaiModel(ABC):
) -> Tuple[DataFrame, npt.ArrayLike]:
"""
Filter the prediction features data and predict with it.
:param:
unfiltered_dataframe: Full dataframe for the current backtest period.
dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
:param unfiltered_dataframe: Full dataframe for the current backtest period.
:param dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
:param first: boolean = whether this is the first prediction or not.
:return:
:predictions: np.array of predictions
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
@ -554,12 +549,10 @@ class IFreqaiModel(ABC):
def return_values(self, dataframe: DataFrame, dk: FreqaiDataKitchen) -> DataFrame:
"""
User defines the dataframe to be returned to strategy here.
:params:
dataframe: DataFrame = the full dataframe for the current prediction (live)
or --timerange (backtesting)
dk: FreqaiDataKitchen = Data management/analysis tool assoicated to present pair only
:returns:
dataframe: DataFrame = dataframe filled with user defined data
:param dataframe: DataFrame = the full dataframe for the current prediction (live)
or --timerange (backtesting)
:param dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
:return: dataframe: DataFrame = dataframe filled with user defined data
"""
return

11
freqtrade/strategy/interface.py
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@ -566,12 +566,11 @@ class IStrategy(ABC, HyperStrategyMixin):
additional features here, but must follow the naming convention.
This method is *only* used in FreqaiDataKitchen class and therefore
it is only called if FreqAI is active.
:params:
:pair: pair to be used as informative
:df: strategy dataframe which will receive merges from informatives
:tf: timeframe of the dataframe which will modify the feature names
:informative: the dataframe associated with the informative pair
:coin: the name of the coin which will modify the feature names.
:param pair: pair to be used as informative
:param df: strategy dataframe which will receive merges from informatives
:param tf: timeframe of the dataframe which will modify the feature names
:param informative: the dataframe associated with the informative pair
:param coin: the name of the coin which will modify the feature names.
"""
return df

11
freqtrade/templates/FreqaiExampleStrategy.py
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@ -74,12 +74,11 @@ class FreqaiExampleStrategy(IStrategy):
(see convention below). I.e. user should not prepend any supporting metrics
(e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the
model.
:params:
:pair: pair to be used as informative
:df: strategy dataframe which will receive merges from informatives
:tf: timeframe of the dataframe which will modify the feature names
:informative: the dataframe associated with the informative pair
:coin: the name of the coin which will modify the feature names.
:param pair: pair to be used as informative
:param df: strategy dataframe which will receive merges from informatives
:param tf: timeframe of the dataframe which will modify the feature names
:param informative: the dataframe associated with the informative pair
:param coin: the name of the coin which will modify the feature names.
"""
with self.freqai.lock: