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@ActurialCapital ActurialCapital released this 11 Jul 14:18
0.1.10
8d9e818

Release 0.1.10

New Transformers Added

This release introduces five new transformers, expanding the blocks package's functionality for data preprocessing and feature engineering.

1. RSTradeEntry

  • Description: This transformer generates trade signals based on the Relative Strength Index (RSI). It identifies overbought and oversold conditions using RSI thresholds.
  • Parameters:
    • window: The window length for calculating RSI. Default is 14.
    • thresh: A tuple for RSI thresholds, default is (30, 70).
  • Usage: 
    rsi_trade_entry = RSTradeEntry(window=14, thresh=(30, 70))
signals = rsi_trade_entry.fit_transform(data_series)

2. RSInterval

  • Description: This transformer generates trade signals based on RSI intervals, differentiating between overbought and oversold conditions and their transitions.
  • Parameters:
    • window: The window length for calculating RSI. Default is 14.
    • thresh: A tuple for RSI thresholds, default is (30, 70).
  • Usage: 
    rsi_interval = RSInterval(window=14, thresh=(30, 70))
signals = rsi_interval.fit_transform(data_series)

3. FilterCollinear

  • Description: This transformer removes collinear features from the dataset based on the Variance Inflation Factor (VIF).
  • Parameters:
    • target: The target variable to exclude from VIF calculation. Default is None.
    • subset: Subset of columns to consider for collinearity. Default is None.
    • threshold: VIF threshold above which features are removed. Default is 5.0.
  • Usage: 
    filter_collinear = FilterCollinear(threshold=5.0)
filtered_data = filter_collinear.fit_transform(dataframe)

4. LinearImputer

  • Description: This transformer performs linear interpolation to impute missing values in the dataset.
  • Parameters:
    • subset: Subset of columns to apply interpolation. Default is None.
    • kwargs: Additional arguments for pandas' interpolate method.
  • Usage: 
    linear_imputer = LinearImputer(subset=['column1', 'column2'])
imputed_data = linear_imputer.fit_transform(dataframe)

5. ForestImputer

  • Description: This transformer uses Random Forests to impute missing values in the dataset.
  • Parameters:
    • Various parameters for customizing the random forest model used for imputation.
  • Usage: 
    forest_imputer = ForestImputer(subset=['column1', 'column2'], n_estimators=100)
imputed_data = forest_imputer.fit_transform(dataframe)

Installation

To install the latest version of the blocks package, use:

pip install blocks==0.1.10
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