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The repository expands the Triple-Barrier Method proposed by Marcos López de Prado. It introduces specific enhancements:

  • Extension to Multiple Barrier Conditions: Dynamically generates multiple barriers through the n_barriers parameter.
  • Centering Capability: Includes a center parameter to center the returns for improved analysis.
  • Improved Readability: Utilizes standard data science toolkit with pandas and numpy instead of for-loops for each time-step t to improve readability and efficiency.
  • Exporting Intermediate Steps: Allows to view intermediate steps as features for model usage (_check_barrier_crossing() method).
  • Convenient Properties: Provides useful properties such as transition_probabilities and signals_pa.

Notebooks:

This repository includes two notebooks:

  1. example.ipynb: Provides an example showcasing the usage of the BarrierMethod class.

  2. study.ipynb: Generates various labels for a range of n and different barriers and tests them for a normal and uniform distribution.

    For some use cases, the goal might be to have (on average) a new signal each week that is approx. normal distributed (neutral most of the time, rare tails) or uniformly distributed.

Examples:

Barrier Method Barrier Frequency Barrier Frequency Centered

Triple-Barrier Method

The traditional approach to labeling data involves a fixed-time horizon. However, Marcos López de Prado introduced the Triple-Barrier Method in his book Advances in Financial Machine Learning (Wiley, 2018). This method constructs three barriers for each observation:

  • Stop-loss Barrier (-1): Indicates a selling opportunity.
  • Neutral Signal (0): Denotes a scenario where the time series doesn't cross any barrier within a maximum holding period of n.
  • Profit-taking Barrier (+1): Signifies a buying opportunity.

A visual example:

Triple-Barrier Method

Installation:

pip install git+https://github.com/nkonts/barrier-method.git