Suitability of using technical indicators as potential strategies within intelligent trading systems

TL;DR

This paper investigates the use of simple technical indicator-based trading strategies within machine learning-driven intelligent trading systems, evaluating their efficacy and potential for portfolio management.

Contribution

It analyzes the eligibility of technical indicator strategies for automation and demonstrates how they can be combined in intelligent systems without relying on underlying assumptions.

Findings

Strategies perform well within their assumptions

Machine learning systems can effectively switch between strategies

Potential for improved portfolio management through strategy combination

Abstract

The potential of machine learning to automate and control nonlinear, complex systems is well established. These same techniques have always presented potential for use in the investment arena, specifically for the managing of equity portfolios. In this paper, the opportunity for such exploitation is investigated through analysis of potential simple trading strategies that can then be meshed together for the machine learning system to switch between. It is the eligibility of these strategies that is being investigated in this paper, rather than application. In order to accomplish this, the underlying assumptions of each trading system are explored, and data is created in order to evaluate the efficacy of these systems when trading on data with the underlying patterns that they expect. The strategies are tested against a buy-and-hold strategy to determine if the act of trading has actually produced any worthwhile results, or are simply facets of the underlying prices. These results are then used to produce targeted returns based upon either a desired return or a desired risk, as both are required within the portfolio-management industry. Results show a very viable opportunity for exploitation within the aforementioned industry, with the Strategies performing well within their narrow assumptions, and the intelligent system combining them to perform without assumptions.