Automatic method for stock trading combining technical analysis and the Artificial Bee Colony Algorithm

Abstract

There are many researches on forecasting time series for building trading systems for financial markets. Some of these studies have shown that it is possible to obtain satisfactory results, thereby contradicting the theory of Efficient Markets Hypothesis (EMH) that suggests that prices are randomly generated over time. This paper proposes an intelligent system based on historical closing prices that uses technical analysis, the Artificial Bee Colony Algorithm (ABC), a selection of past values (lags), nearest neighbor classification (k-NN) and its variation, the Adaptative Classification and Nearest Neighbor (A-k-NN). A very important step for time series prediction is the correct selection of the past observations (lags). Our method uses this strategy since it uses the k-NN and A-k-NN to decide on the buy and seIl points, combined with the ABC algorithm which is used to search for the best parameter settings of system and a good set of lags. This paper compares the results obtained by the proposed method with the buy and hold strategy and with other work that performed similar experiments with the same trading model and the same stocks. The key measure for performance comparison is the profitability in the analyzed period. The proposed method generates much larger profits compared to the other method and to the buy and hold strategy. Our method outperforms the other methods in thirteen out of the fifteen stocks tested, minimizing the risk of market ex pos ure.

Automated trading systems statistical and machine learning methods and hardware implementation: a survey

“…The EC technique has become a popular research subject in quantitative trading since it shows great efficacy in discovering trading rules and optimizing global parameters. Specific algorithms adopted in our surveyed trading systems mainly include Genetic Algorithm (GA), artificial bee colony algorithm (Hsieh, Hsiao, and Yeh 2011;Brasileiro et al 2013), ant colony optimization (Cai et al 2015) and swarm intelligence (Wang, Philip, and Cheung 2014), with GA the most popular. In Kwon and Moon (2007), a 2-D encoding GA was utilized to optimize the weight matrix of a one-layer Recurrent Neural Network (RNN) trading system, which was then tested to ensure that the performance was far superior to that of the 'Buyand-Hold' (BH) strategy.…”

Mid-Price Prediction Based on Machine Learning Methods with Technical and Quantitative Indicators

“…An adaptive neuro fuzzy inference system (ANFIS) is used in [43] for the FOREX market where technical indicators are utilized to benchmark ANFIS performance. Technical indicators are the basis for works in [10], [50], [86], and [74] for passive trading strategies (i.e. buy-and-hold) and stop-loss/stop-gain strategies.…”

A Survey on Impact of Bio-inspired Computation on Stock Market Prediction

“…Identifying this quality Minakhi Rout et al proposed a model using a simple adaptive linear combiner, whose weights are trained by using ABC algorithm. The model is applied on the data S&P 500 and DJIA and found better prediction value compared to ALC with PSO and GA. Another work of stock trends prediction is done by Rodrigo C.Brasileiro et al where a combination of technical analysis, ABC, selection of past values, nearest neighbor classification and its variation [66] and the adaptive classification and nearest neighbor is taken to measure the profitability in the analyzed period.…”