A stock trading algorithm based on trend forecasting and time series classification - Padovani, Matheus Rosisca; Bertini Junior, João Roberto (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2021: Anais do XVIII Encontro Nacional de Inteligência Artificial e ComputacionalDO - 10.5753/eniac.2021.18272, 2021)
Algorithm trading relies on the automatic identification of buying and selling points of a given asset to maximize profit. In this paper, we propose the Trend Classification Trading Algorithm (TCTA) which is based on time series classification and trend forecasting to perform trade. TCTA first employs the K-means to cluster 5-days closing price segments and label them according to its trend. A deep learning classification model is then trained with these label sequences to estimate the next trend. Trading points are given by the alternation on trend estimates. Results considering 20 shares from Ibovespa show TCTA present higher profit than buy-and-hold and trading schemes based on Moving Average Converge Divergence (MACD) or Bollinger bands.