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An LSTM-based optimization algorithm for enhancing quantitative arbitrage trading.

Guodong Han^1,2, Hecheng Li³

¹College of Computer, Qinghai Normal University, Xining, China.

Peerj. Computer Science

|August 15, 2024

Summary

This summary is machine-generated.

This study introduces Dynamic-LSTM Arb (DLA), an optimized strategy using long-short-term memory (LSTM) to improve cointegration-based arbitrage trading. DLA enhances traditional methods by classifying trends, reducing losses from market fluctuations and achieving significant returns.

Keywords:

Arbitrage trading LSTM model Optimizing algorithm

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Area of Science:

Quantitative Finance
Algorithmic Trading

Background:

Arbitrage trading relies on cointegration between assets, but traditional methods like Engle-Granger are sensitive to price trends.
Disruptions from price fluctuations and trends often invalidate arbitrage strategies, leading to losses.

Purpose of the Study:

To propose an optimized arbitrage strategy, Dynamic-LSTM Arb (DLA), that overcomes limitations of traditional cointegration determination.
To improve the accuracy and profitability of mean-reversion arbitrage trading by dynamically updating trading boundaries.

Main Methods:

Developed Dynamic-LSTM Arb (DLA), employing long-short-term memory (LSTM) networks to classify trend movements in asset linear combinations.
Integrated DLA with the Engle-Granger two-step method to refine cointegration relationship determination, especially during non-stationary trends.
Designed an optimized algorithm for dynamically updating trading boundaries in mean-reversion arbitrage.

Main Results:

The DLA model successfully filtered out unprofitable trades during training.
Backtesting on a futures trading platform yielded a theoretical return of 23% over 10 days at a 1-minute level.
The DLA strategy significantly outperformed the benchmark strategy and the CSI 300 Index returns.

Conclusions:

The proposed Dynamic-LSTM Arb strategy effectively addresses limitations in traditional cointegration-based arbitrage.
DLA enhances strategy robustness by classifying trends and dynamically adjusting trading boundaries, leading to superior performance.