Candlestick chart based trading system using ensemble learning for financial assets

Abstract

The candlestick charts, which were developed in the 18th century and were initially used in the Japanese rice market, are widely used in trading strategies in all financial markets after 1991. Candlestick charts can interpret opening, high, low and closing values of an asset in a single visual. In addition to these advantages, the large number of candlestick chart patterns makes their practical use difficult. In th e st udy de veloped fo r th is pu rpose, a so ftware fra mework that uses candlestick charts and predicts the trend direction was created. The study consists of four stages. In the first step, a system that recognizes candle patterns is created. In the second stage, the performance of the model is measured by running training and testing processes on data sets in which candlestick chart types and trend direction are labeled. In the machine learning phase, community methods such as xgboost were used. In the last stage of the study, it was seen that with the strategy based on only recognizing the candlestick pattern and taking position in the direction of the trend based on proposed approach, higher profit was obtained in 11 world indices compared to Buy&Hold strategy.