A New Algorithmic Trading Approach Based on Ensemble Learning and Candlestick Pattern Recognition in Financial Assets

Financial assets considered as time series are chaotic in nature. The main goal of investors is to take a position at the right and direction by making predictions about future this series. These consist opening, low, high, closing prices certain period. approaches used make trend strength using moving averages indicators based on them have noise lag problems they obtained statistically. Candlestick charts, other hand, reflect price-based psychology bear bull investors, facilitate interpretation price movements consolidating said closing, lowest highest single image. It known that it was applied Japanese rice markets for first history there more than 100 candle patterns. In study, an extensible architecture software framework factory patterns object-oriented approach proposed defining candlestick developing intelligent learning algorithms them. studies carried out financial assets, profit factor, which shows portfolio gain strategy, used. desirable number wins be greater 1. When tested 5 major value 1 all assets. can also development new robotic terms being applicable kinds every