An ML based Stock Prediction and Automated Trading System using ALX

Algorithmic Trading had come into existence by utilizing deep learning methods for better decision-making and automate trading models. The project describes algorithmic trading using machine learning with Arima, LSTM and XGBoost (ALX) for stock prediction and Alpaca API-based trading. It generates historical movements and creates technical indicators like moving average, RSI, MACD and executes the LSTM machine learning algorithms to predict market trends and generate sell or buy signals. The model is programmed to forecast buy or sell orders based on such signal orders totaling more than 0.6 are meant to buy. Selling orders are generated only when a prediction by the model is below 0.4. The system applied is designed with a debugging module that is to enable debugging remotely and configuration files to keep API credentials safe. System deployment is defined and designed by establishing debugging modes. Stocks or cryptocurrencies can be queried and can be fetched after the selection of the symbol for trading or back-testing. Our solution enables algorithmic trading to be done automatically using ALX-based deep learning to predict stock price movements and execute trades through the Alpaca API. This gives the model accuracy to 72.5. This helps traders test the effectiveness of their strategies, to reduce manual interventions and enhance decision-making processes.