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Moving averages and indicators derived from these averages are used to predict the future direction the stocks will move. In manual and algorithmic trading, moving averages play a decisive role in decision making. In this study, a new hybrid approach has been developed that can be used as an alternative to moving averages such as SMA, WMA and EMA used in the literature. In BIST30 stocks in Turkey, the proposed method performs better than widely used indicators such as MACD, Stochastic and RSI, commonly used in the literature.

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

(1) Background: According to the World Health Organization (WHO), 6.3 million intrauterine fetal deaths occur every year. The most common method of diagnosing perinatal death and taking early precautions for maternal and fetal health is a nonstress test (NST). Data on the fetal heart rate and uterus contractions from an NST device are interpreted based on a trace printer’s output, allowing for a diagnosis of fetal health to be made by an expert. (2) Methods: in this study, a predictive method based on ensemble learning is proposed for the classification of fetal health (normal, suspicious, pathology) using a cardiotocography dataset of fetal movements and fetal heart rate acceleration from NST tests. (3) Results: the proposed predictor achieved an accuracy level above 99.5% on the test dataset. (4) Conclusions: from the experimental results, it was observed that a fetal health diagnosis can be made during NST using machine learning.

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