Agent Inspired Trading Using Recurrent Reinforcement Learning and LSTM Neural Networks

2017/07/23 by David W. Lu, Lu, David W. · 1 citation
Decision Sciences · Economics, Econometrics and Finance · #Complex Systems and Time Series Analysis #Computational Finance (q-fin.CP) #FOS: Economics and business #Stock Market Forecasting Methods #q-fin.CP

paper · pdf · doi:10.48550/arxiv.1707.07338

arxiv created 2017/07/23 · openalex publication_date 2017/07/23 · arxiv updated 2017/07/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

With the breakthrough of computational power and deep neural networks, many areas that we haven't explore with various techniques that was researched rigorously in past is feasible. In this paper, we will walk through possible concepts to achieve robo-like trading or advising. In order to accomplish similar level of performance and generality, like a human trader, our agents learn for themselves to create successful strategies that lead to the human-level long-term rewards. The learning model is implemented in Long Short Term Memory (LSTM) recurrent structures with Reinforcement Learning or Evolution Strategies acting as agents The robustness and feasibility of the system is verified on GBPUSD trading.

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