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Using machine learning for medium frequency derivative portfolio trading

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  • Abhijit Sharang
  • Chetan Rao

Abstract

We use machine learning for designing a medium frequency trading strategy for a portfolio of 5 year and 10 year US Treasury note futures. We formulate this as a classification problem where we predict the weekly direction of movement of the portfolio using features extracted from a deep belief network trained on technical indicators of the portfolio constituents. The experimentation shows that the resulting pipeline is effective in making a profitable trade.

Suggested Citation

  • Abhijit Sharang & Chetan Rao, 2015. "Using machine learning for medium frequency derivative portfolio trading," Papers 1512.06228, arXiv.org.
    • Handle: RePEc:arx:papers:1512.06228
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    References listed on IDEAS

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    1. Tay, Francis E. H. & Cao, Lijuan, 2001. "Application of support vector machines in financial time series forecasting," Omega, Elsevier, vol. 29(4), pages 309-317, August.
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    Cited by:

    1. Dat Thanh Tran & Martin Magris & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2017. "Tensor Representation in High-Frequency Financial Data for Price Change Prediction," Papers 1709.01268, arXiv.org, revised Nov 2017.
    2. Dat Thanh Tran & Alexandros Iosifidis & Juho Kanniainen & Moncef Gabbouj, 2017. "Temporal Attention augmented Bilinear Network for Financial Time-Series Data Analysis," Papers 1712.00975, arXiv.org.
    3. Chao Liu & Fengfeng Gao & Mengwan Zhang & Yuanrui Li & Cun Qian, 2024. "Reference Vector-Based Multiobjective Clustering Ensemble Approach for Time Series Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 181-210, July.
    4. Adamantios Ntakaris & Martin Magris & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2017. "Benchmark Dataset for Mid-Price Forecasting of Limit Order Book Data with Machine Learning Methods," Papers 1705.03233, arXiv.org, revised Mar 2020.

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