Adaptive Hybrid Optimized LSTM Models: A Novel Computational Framework for Algorithmic Financial Trading System

Pei-Chiang Su

doi:10.70393/6a69656173.323338

Authors

Pei-Chiang Su Carnegie Mellon University

DOI:

https://doi.org/10.70393/6a69656173.323338

ARK:

https://n2t.net/ark:/40704/JIEAS.v2n6a06

Disciplines:

Computational Science

Subjects:

Intelligent Computing

References:

53

Keywords:

Algorithmic Trading, Simplified Swarm Optimization, Artificial Intelligence, Deep Learning, Artificial Neural Network, Grid Trading

Abstract

In our modern society, with the development of Internet and information system, pre-programing algorithmic trading strategies for online automatic trading has also flourished, especially in the rapidly fluctuating trading market. Using quantitative trading programs that can automatically trade in response to market conditions has attracted the attention of major financial institutions and governments in the financial market. How to use self-adaptive programs to automatically trade in the ever-changing financial market has become a popular research topic for the development and pursuit of all financial markets in recent years.

This research proposes an online self-adaptive trading algorithm that can be applied to financial markets such as stock market, currency markets, cryptocurrency markets, futures markets, etc. In the first part of the algorithm, the simplified swarm optimization will be used to optimize the parameters of the newly proposed flexible grid in this research. Then the data will be imported into the artificial neural network model for training in the latter part, helping the trading model automatically select the appropriate parameters for construction flexible grid corresponding to the market conditions.

The greatest contribution of the research is to provide a whole new trading algorithm that can adapt to the dynamic trading market, automatically make suitable adjustments to current trading strategy and place real-time orders. The algorithm controlling both profit and risk, which can seem like a balanced trading algorithm that are robust and profitable.

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Author Biography

Pei-Chiang Su, Carnegie Mellon University

Department of Electrical and Computer Engineering, Carnegie Mellon University, USA.

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