Stock indicator scanner customization tool

Yong, Kai Wen (2025) Stock indicator scanner customization tool. Final Year Project, UTAR.

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Abstract

Stock market prediction is a critical yet challenging task due to the inherent volatility and complexity of the stock market. Traditional stock analysis techniques, such as technical and fundamental analysis, often fail to predict price movement due to a lack of trading experience and time. These methods, although valuable, lack the flexibility to adapt to evolving market conditions and personalised trading strategies. Recent advancements in deep learning have shown promise in improving stock trend prediction accuracy. This project seeks to bridge the gap by developing a customisable stock indicator scanner tool that integrates deep learning, specifically Long Short-Term Memory (LSTM) networks, with technical analysis. The proposed system enables users to customise input parameters, such as Simple Moving Average (SMA), Exponential Moving Average (EMA), Moving Average Convergence Divergence (MACD), Relative Strength Index (RSI), Stochastic Oscillator, and 46 other technical indicators. This allows users to personalise the models trained to the user preferences of technical indicators. This customisation enables traders to adjust the model based on their unique trading strategies. Additionally, the system recommends optimal combinations of technical indicators to improve model performance, making the tool adaptive to different market scenarios. The model will be evaluated using accuracy. By incorporating deep learning, this project offers a novel customisation approach to stock trend prediction, enabling investors to make informed decisions with personalised models that better align with their trading goals. This tool has the potential to significantly enhance stock market analysis by providing more accurate and adaptive predictions, contributing to the broader field of financial market forecasting.

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