Abstract
This research seeks to design, implement, and test a fully automatic high-frequency trading system that operates on the Chilean stock market, so that it is able to generate positive net returns over time. A system that implements high-frequency trading (HFT) is presented through advanced computer tools as an NP-Complete type problem in which it is necessary to optimize the profitability of stock purchase and sale operations. The research performs individual tests of the algorithms implemented, reviewing the theoretical net return (profitability) that can be applied on the last day, month, and semester of real market data. Finally, the research determines which of the variants of the implemented system performs best, using the net returns as a basis for comparison. The use of particle swarm optimization as an optimization algorithm is shown to be an effective solution since it is able to optimize a set of disparate variables but is bounded to a specific domain, resulting in substantial improvement in the final solution.
Highlights
Stock trading is an activity that has been conducted for hundreds of years and is currently performed on stock exchanges around the world
We studied trading technologies that make it possible to operate under an high-frequency trading (HFT) and/or an algorithmic trading (AT) modality
We chose the statistical technique of Moving averages (MA) for its simplicity, its ability to predict price trends based on the history of an instrument, and its applicability in optimization of techniques
Summary
Stock trading is an activity that has been conducted for hundreds of years and is currently performed on stock exchanges around the world. (i) ere is a rapid exchange of capital (ii) A large number of transactions are performed (iii) Generally, a low gain per transaction is obtained (iv) Financial instrument positions are neither accumulated from one trading day to another nor avoided (v) Trading is conducted through a computer system e definition of HFT itself does not indicate whether the system performing it is automatic, semiautomatic or useroperated. Ese investment strategies can be supported by knowledge of economics, statistics, artificial intelligence, metaheuristics, etc It is proposed a sequential process for developing an HFT system that is based on four steps: (i) data analysis; (ii) trading model; (iii) decision-making; and (iv) execution of business [7]. Because rapid assimilation of the large amount of information flowing to and from any stock market by a human operator is becoming an increasingly difficult task; it is desirable to develop systems that are able to detect hidden patterns in price variations and the relationships between financial instruments or other economic indicators and that can incorporate a component of interpretation of “feeling” or “sensing” the market through natural language news processing (e.g., SuperX Plus of Deutsche Bank [11])
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