Bulding a Powerful Stock Screener with Python: Boost Your Strategy with Deep Learning and Technical Indicators (Part 2).

In the first part of this article series, we introduced a stock screener in Python that allows investors to analyze stocks based on fundamental metrics such as market cap, revenue or debt to equity ratio.

In this second part, we will enhance the stock screener with technical indicators and deep learning, giving investors a more holistic view of a stock’s potential. We will see in detail the code of the new features so it will be necessary to include the code of the previous article.

The technical indicators used as example includes moving averages, relative strength index (RSI), moving average convergence divergence (MACD) and Bollinger Bands. By adding technical indicators to our stock screener, investors can identify trends and patterns in a stock’s price history that may not be apparent through fundamental analysis alone.

To improve our stock screener, we will incorporate deep learning technology alongside our usual technical indicators. By using predictions, we can filter stocks that are expected to increase in price over the next 10 days.

By training a neural network on historical stock data, we can make predictions about a stock’s future performance, giving investors an edge in the market.

From Nicholas Cappello in Unsplash