Discussing the article: "Multiple Symbol Analysis With Python And MQL5 (Part I): NASDAQ Integrated Circuit Makers"

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Check out the new article: Multiple Symbol Analysis With Python And MQL5 (Part I): NASDAQ Integrated Circuit Makers.

Join us as we discuss how you can use AI to optimize your position sizing and order quantities to maximize the returns of your portfolio. We will showcase how to algorithmically identify an optimal portfolio and tailor your portfolio to your returns expectations or risk tolerance levels. In this discussion, we will use the SciPy library and the MQL5 language to create an optimal and diversified portfolio using all the data we have.

Integrated circuits have become a staple of our everyday life. These electronic chips permeate all aspects of our modern lives, from the proprietary MetaQuotes servers that host this very website that you are reading this article on, down to the device you are using to read this article all these devices rely on technology that is most likely developed by one of these 5 companies. The world’s first integrated circuit was developed by Intel, it was branded the Intel 4004, and was launched in 1971, the same year the NASDAQ exchange was founded. The Intel 4004 had approximately, 2600 transistors, a far-cry from modern chips that easily have billions of transistors.

Since we are motivated by the global demand for integrated circuits, we desire to intelligently gain exposure to the chip market. Given a basket of these 5 stocks, we will demonstrate how to maximize the return of your portfolio by prudently allocating capital between them. A traditional approach of uniform distribution of capital between all 5 stocks will not suffice in modern, volatile markets. We will instead build a model that informs us whether we should buy or sell each stock, and the optimal quantities we should trade. In other words, we are using the data we have at hand to algorithmically learn our position sizing and quantities.

Author: Gamuchirai Zororo Ndawana