Finance

Financial economics

Technical analysis

Machine learning

Support vector machine

Trading strategy

Philosophy

Econometrics

Paleontology

Artificial intelligence

Economics

Random forest

Moving average

Stock exchange

Theology

Convergence (economics)

Novelty

Stock market

Horse

Computer vision

Mathematics

Computer science

Economic growth

Artificial neural network

Algorithmic trading

Decision tree

Biology

Knowledge-Based Systems

Within the area of stock market prediction, forecasting price values or movements is one of the most challenging issue. Because of this, the use of machine learning techniques in combination with technical analysis indicators is receiving more and more attention. In order to tackle this problem, in this paper we propose a hybrid approach to generate trading signals. To do so, our proposal consists of applying a technical indicator combined with a machine learning approach in order to produce a trading decision. The novelty of this approach lies in the simplicity and effectiveness of the hybrid rules as well as its possible extension to other technical indicators. In order to select the most suitable machine learning technique, we tested the performances of Linear Model (LM), Artificial Neural Network (ANN), Random Forests (RF) and Support Vector Regression (SVR). As technical strategies for trading, the Triple Exponential Moving Average (TEMA) and Moving Average Convergence/Divergence (MACD) were considered. We tested the resulting technique on daily trading data from three major indices: Ibex35 (IBEX), DAX and Dow Jones Industrial (DJI). Results achieved show that the addition of machine learning techniques to technical analysis strategies improves the trading signals and the competitiveness of the proposed trading rules.

Technical analysis strategy optimization using a machine learning approach in stock market indices