Trade One: A Prediction System - Stock Market Price Prediction using Deep Learning

Because of the market's intrinsic volatility, non-stationarity, and non-linear dynamics, accurately and promptly predicting stock prices is a major challenge in financial research. This study offers a comprehensive web-based solution for realtime stock trend forecasting using deep learning. The design combines a dynamic frontend created with HTML, Bootstrap, and JavaScript, a secure MySQL database for user authentication, and a Python Flask backend with a pre-trained Keras-based Long Short-Term Memory (LSTM) time-series model. The system uses a strong data pipeline that comprises sequential data structure using a sliding window technique, normalization using MinMaxScaler to scale features between 0 and 1, and real-time data collecting from Yahoo Finance to guarantee high-quality input for the model. The main features of the tool are presented to users via an interactive dashboard that offers descriptive statistics, technical indicators such as Exponential Moving Averages (EMA), and anticipated price trajectories, indicating the improved perfect angle of the deep learning approach when compared to more conventional forecasting models like ARIMA and Support Vector Regression. The system shows great promise as a decision-support tool for financial market participants, offering clear data visualizations and a historical log of prediction performance to increase interpretability and foster user trust.