mlfinlab.labeling.matrix_flags
Matrix Flag labeling method.
Module Contents
Classes
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The Matrix Flag labeling method is featured in the paper: Cervelló-Royo, R., Guijarro, F. and Michniuk, K., 2015. |
- class MatrixFlagLabels(prices, window, template_name=None)
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The Matrix Flag labeling method is featured in the paper: Cervelló-Royo, R., Guijarro, F. and Michniuk, K., 2015. Stock market trading rule based on pattern recognition and technical analysis: Forecasting the DJIA index with intraday data.
The method of applying a matrix template was first introduced, and explained in greater detail, in the paper: Leigh, W., Modani, N., Purvis, R. and Roberts, T., 2002. Stock market trading rule discovery using technical charting heuristics.
Cervelló-Royo et al. expand on Leigh et al.’s work by proposing a new bull flag pattern which ameliorates some weaknesses in Leigh’s original template. Additionally, he applies this bull flag labeling method to intraday candlestick data, rather than just closing prices.
To find the total weight for a given day, the current price as well as the preceding window days number of prices is used. The data window is split into 10 buckets each containing a chronological tenth of the data window. Each point in 1 bucket is put into a decile corresponding to a position in a column based on percentile relative to the entire data window. Bottom 10% on lowest row, next 10% on second lowest row etc. The proportion of points in each decile is reported to finalize the column. The first tenth of the data is transformed to the leftmost column, the next tenth to the next column on the right and so on until finally a 10 by 10 matrix is achieved. This matrix is then multiplied element-wise with the 10 by 10 template, and the sum of all columns is the total weight for the day. If desired, the user can specify a threshold to determine positive and negative classes. The value of the threshold depends on how strict of a classifier the user desires, and the allowable values based on the template matrix.
- set_template(template)
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Setting a custom templates to use in the method.
- Parameters:
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template – (pd.DataFrame) Template to override the default template. Must be a 10 by 10 pd.DataFrame. NaN values not allowed, as they will not automatically be treated as zeros.
- apply_labeling_matrix(threshold=None)
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Getting a series of fits to the template.
- Parameters:
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threshold – (float) If None, labels will be returned numerically as the score for the day. If not None, then labels are returned categorically, with the positive category for labels that are equal to or exceed the threshold.
- Returns:
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(pd.Series) Total scores for the data series on each eligible day (meaning for indices self.window and onwards).