Trading Cryptocurrency with Deep Deterministic Policy Gradients

Tummon, Evan; Raja, Muhammad Adil; Ryan, Conor

doi:10.1007/978-3-030-62362-3_22

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…Many studies use TA-Lib as a toolkit for financial time-series data processing. For example, Duvinage et al [25] analyzed the intra-day performance on the Japanese market, while Nelson et al [26] used TA-Lib to generate features and train a CNN to predict stock movements. Even cryptocurrency can be traded by TA-Lib processed features [27] .…”

Section: D Approach Of Financial Time-series Datamentioning

confidence: 99%

HXPY: A High-Performance Data Processing Package for Financial Time-Series Data

Guo

Peng

Yuan

et al. 2023

J. Comput. Sci. Technol.

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A tremendous amount of data has been generated by global financial markets everyday, and such time-series data needs to be analyzed in real time to explore its potential value. In recent years, we have witnessed the successful adoption of machine learning models on financial data, where the importance of accuracy and timeliness demands highly effective computing frameworks. However, traditional financial time-series data processing frameworks have shown performance degradation and adaptation issues, such as the outlier handling with stock suspension in Pandas and TA-Lib. In this paper, we propose HXPY, a high-performance data processing package with a C++/Python interface for financial time-series data. HXPY supports miscellaneous acceleration techniques such as the streaming algorithm, the vectorization instruction set, and memory optimization, together with various functions such as time window functions, group operations, down-sampling operations, cross-section operations, row-wise or column-wise operations, shape transformations, and alignment functions. The results of benchmark and incremental analysis demonstrate the superior performance of HXPY compared with its counterparts. From MiBs to GiBs data, HXPY significantly outperforms other in-memory dataframe computing rivals even up to hundreds of times.

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Section: D Approach Of Financial Time-series Datamentioning

confidence: 99%