Utilising Technical Analysis, Commodities Data, and Market Indices to Predict Stock Price Movements with Deep Learning

Osama Aloraini


Supervised by Xianfang Sun; Moderated by Yipeng Qin

The objective of this project is to develop stock price prediction algorithms using two deep learning models: LSTM and CNN. The data features utilised include stock technical analysis, commodities data like energy prices and gold, and key market indices from the U.S. stock market. Additionally, technical indicators are employed to obtain trading strategies, represented as vectors for data features. Moreover, based on features categories, nine experiments were conducted for each model to assess the impact of various feature combinations. Therefore, the primary evaluation metrics for both models are accuracy and simulated trading profit. Lastly, the results from these experiments are compared, and the outputs of the two models are also compared.

Final Report (07/09/2023) [Zip Archive]

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