This is the origin Pytorch implementation of stop-loss adjusted label in the following paper: Stop-loss adjusted labels for machine learning-based trading of risky assets

News(July 31): Accepted to Finance Research Letters, 2023.

1. Install Python 3.6, PyTorch 1.9.0.
2. Download data.
3. Run Stop-loss adjusted labels.

To easily reproduce the results using Docker, conda and Make, you can follow the next steps:

1. Initialize the docker image using: make init.
2. Download the datasets using: make data.
3. Download the datasets using: make coin data.
4. Run each script in runfile/ using make run_module module="bash runfile/btc_runfile.sh" for each script.
5. Alternatively, run all the scripts at once:

for file in `ls scripts`; do make run_module module="bash runfile/runfile"; done

def ST_labels(data, delta):
    """
    Calculate the stop-loss adjusted label.

    Parameters:
    - data: DataFrame containing historical asset prices.
    - delta: Maximum tolerance level for stop-loss trading.

    Returns:
    - Index of rows where the label is 1.
    """

    return data[
        (data["Close"] / data["Close"].shift(1) > 1) & 
        ((data["Low"] / data["Close"].shift(1) - 1) * 100 >= -delta)
    ].index

We will keep adding Predicting movements of asset prices models to expand this repo:

• SVM
• KNN
• MLP
• Catboost
• Random Forest
• Extra tree

If you find this repo useful, please cite our paper.

Hwang, Y., Park, J., Lee, Y., & Lim, D. Y. (2023). Stop-loss adjusted labels for machine learning-based trading of risky assets. Finance Research Letters, 104285.

If you have any questions or want to use the code, please contact [email protected]

SPDX-FileCopyrightText: © 2023 yoontae hwang [email protected]

SPDX-License-Identifier: BSD-3-Clause