All aspiring entrepreneurs and prospective businesses look to those before them for inspiration. For example, Steve Jobs idolised Edwin Land, the inventor of the Polaroid, and Elon Musk credits J.E. Gordon’s book “Structures: Or Why Things Don’t Fall Down” for helping him learn rocket science.
Every business and entrepreneur has their reason behind their inspirations, and if businesses need the motivation to meet their goals and expand their reach, companies within the financial industry like JP Morgan are great examples to look at.
The financial industry is notorious for being risk-averse. However, it’s still one of the first industries to strategically use algorithms to make money. Its long-standing history with algorithms and machine learning makes the financial sector the perfect example for businesses looking to learn why the technology is important and how to use it.
Algorithms In The Workforce
Algorithms are a collection of rules that instruct the computer on how to execute a task. In the financial industry, it’s been estimated that algorithms carry out 90% of equity futures and 80% of cash-equity trades.
In 2019, we saw JP Morgan launch its Deep Neutral Network for Algo Execution, the latest move in bringing the technology to the foreign exchange market. The project is a neural network, which is a sub-field of deep learning that combines its existing foreign exchange algorithms into one bundle. The financial industry’s adoption of the technology has allowed it to make money and for individual players to successfully invest in other areas.
Computer algorithms also play a major role in how social media works (which ads are seen, which posts appear etc.) and how sports betting odds are calculated. For example, the best value horse racing bets are produced by astute algorithms that implement mathematical probability laws and check real-time factors such as horse pedigree and track conditions. Using quantitative analysis to wager outcomes has opened a broad range of prospects for the gambling industry worldwide, especially the UK, where AI-powered prediction tools cover the top horse racing bookmakers in the country.
Why Should You Care?
Algorithms are everywhere today, helping companies make money. For instance, did you know when you make a typo on Google and the automated message “Did you mean…?” appears, that’s one of Google’s machine learning algorithms? It’s meant to give the user suggestions based on previous outcomes.
We use and see algorithms every day, which is why it’s essential to have basic knowledge of the technology, but unless you’re going to university for computer science, it can be difficult to find and understand this information. As such, we’ve compiled some basic tips for learning algorithmic code.
Machine learning is a set of algorithms that make software applications learn from previous outcomes; it allows applications to draw clear results without human intervention. For example, as previously mentioned, Google’s ‘Did you mean?’ search feature is based on a machine-learning algorithm. On the other hand, artificial intelligence is a set of algorithms that includes machine learning. Artificial intelligence is meant to make machines smarter and perform logical tasks that humans can do.
Steps To Learning Algorithmic Coding
By no means is it an easy venture, but algorithmic coding will continue to be a sought-after skill in the coming years, even if you just have a generic foundation of knowledge on the subject. In the beginning, it’s best to spend time researching different programming languages and then selecting the one you feel the most comfortable with and promotes an easy learning approach. Many experts suggest Python, a programming language that helps individuals of all levels write logical, clear code for different project sizes.
Once you select the language, spend time studying the programme’s knowledge and logic. As the learning process progresses and you feel more comfortable, you can gradually start to learn data structures. Start easy with basics like vectors and arrays, and then move on to more advanced concepts. In the last few stages of basic algorithmic coding, you’ll practice simple algorithms before creating your own.
Algorithms aren’t constrained to only those working in computer science: other industries have taken advantage of them to make money and turn their brands into household names, such as the financial industry. Aspiring entrepreneurs should take the time to read up on algorithms, or hire software developers and machine learning because the technologies are likely to play more of a role in future business decisions.