PurgeEmbargoKFoldCV

A Python implementation of K-fold cross-validation tailored for time-series data, integrating purging and embargo techniques to effectively mitigate data leakage. This approach is particularly suitable for financial modeling and other scenarios where preserving the temporal order is crucial.

Installation

To use PurgeEmbargoKFoldCV, first ensure you have Python and pandas installed in your environment. Then, follow these steps:

1. Clone this repository to your local machine or download the purge_embargo_kfold.py file directly.

   git clone https://github.com/Furkan-rgb/PurgeEmbargoKFoldCV.git
   

2. Install pandas, if you haven't already:

   pip install pandas
   

Usage

Here's a simple example on how to use PurgeEmbargoKFoldCV in your project:

import pandas as pd
from purge_embargo_kfold import PurgedKFoldCVWithEmbargos

# Example DataFrame
date_range = pd.date_range(start="2020-01-01", end="2020-12-31", freq="D")
data = {'feature': range(len(date_range))}
df = pd.DataFrame(data, index=date_range)

# Initialize the class
purge_cv = PurgedKFoldCVWithEmbargos(df)

# Generate splits
n_splits = 5
train_size = 0.7
embargo_period_pct = 0.1
splits = purge_cv.purged_k_fold_cv_with_embargos(n_splits, train_size, embargo_period_pct)

for train_idx, test_idx in splits:
    print(f"Train indices: {train_idx}, Test indices: {test_idx}")