Data-Driven Investing: Enhancing Trading Strategies with ML and Big Data

Predicting stock prices is a difficult task, but investors must make informed decisions. Using Bigdata and Machine Learning technologies we can accurately predict the stock prices, which analyses past data and quantitative indications. Utilizing big data analytics, this system can analyze and extract useful information from a vast amount of historical stock data. Then a machine-learning model is used to generate trading signals that depend on technical indicators. The RSI, MACD, and Bollinger Bands are only a few of the technical indicators used by the model to forecast how stock prices are going to change over the next few days. A big dataset of past stock prices and technical indicators is used to train the model. The model can produce trading signals when it has been trained. The model outputs a signal for each stock, indicating whether the investor should buy, sell, or hold the stock. The investor can then use these signals to make informed trading decisions. The proposed model is evaluated on a held-out test set. The outcomes prove that the model can generate trading signals that are more profitable than a buy-and-hold strategy. Investors can use the proposed model to improve their trading performance. The model is simple to use and doesn't require any specific machine learning expertise.