Investors and financial analysts continually seek cutting-edge tools to predict stock market trends with high accuracy. This endeavor not only boosts investment returns but also mitigates financial…

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This post explores the use of deep learning models, specifically LSTM, in stock price prediction. It highlights the advantages of using the EODHD API for high-quality real-time data. The post provides step-by-step instructions on how to fetch stock and news data using Python. It also discusses feature engineering techniques such as moving averages, daily percentage change, MACD, RSI, and VWAP. The importance of data normalization and splitting is emphasized. The post explains how to build and train an LSTM model and demonstrates how to use rolling prices for continuous forecasting. The conclusion highlights the potential of combining LSTM models and the EODHD API for more accurate financial analyses and trading strategies.

Stock Price Prediction Using News Data and Deep Learning

Advantages of EODHD API in Financial Data Analysis