Reinforcement Learning for Automated Financial Trading: Basics and Applications
| Autor: | Marco Corazza, Francesco Bertoluzzo |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Stochastic control
Settore SECS-S/06 - Metodi mat. dell'economia e Scienze Attuariali e Finanziarie Engineering Q-learning algorithm Supervisor Financial trading system High interest business.industry financial time series Technical Analysis Reinforcement Learning stochastic control Kernel-based Reinforcement Learning algorithm Industrial engineering Financial trading Technical analysis Q learning algorithm Reinforcement learning Artificial intelligence business |
| Zdroj: | Recent Advances of Neural Network Models and Applications ISBN: 9783319041285 WIRN |
| DOI: | 10.1007/978-3-319-04129-2_20 |
| Popis: | The construction of automated financial trading systems (FTSs) is a subject of high interest for both the academic environment and the financial one due to the potential promises by self-learning methodologies. In this paper we consider Reinforcement Learning (RL) type algorithms, that is algorithms that real-time optimize their behavior in relation to the responses they get from the environment in which they operate, without the need for a supervisor. In particular, first we introduce the essential aspects of RL which are of interest for our purposes, second we present some original automatic FTSs based on differently configured RL-based algorithms, then we apply such FTSs to artificial and real time series of daily stock prices. Finally, we compare our FTSs with a classical one based on Technical Analysis indicators. All the results we achieve are generally quite satisfactory. |
| Databáze: | OpenAIRE |
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