AUTONOMUS TRADING APP USING MACHINE LEARNING

This paper presented the design of an Autonomous Trading App that applies machine learning to historical stock market data. The methodology collects data through the Angel One SmartAPI, preprocesses it, computes the technical indicators MA, RSI, and MACD, predicts future price trends with an LSTM model, generates Buy, Sell, or Hold recommendations, and displays the results on an interactive dashboard with user authentication and portfolio management. A review of five 2023 studies showed that existing work centers on prediction and provides limited support for recommendations, applications, portfolio management, and user interaction, and the proposed system is designed to close these gaps in one application for beginner investors, individual traders, students, and researchers. Because the source material is a project synopsis, the paper reports expected outcomes and not measured results. The remaining work follows from this status and from the stated limitations: implementing the system as specified, evaluating the prediction and recommendation stages on the collected historical data, and examining how external factors such as news events and global markets, which the system does not currently account for, affect accuracy.

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