The life is much more digitized now helping facilitate daily life and work and, of course, in banking and investments. As a part of investment activities, trading is probably the most applicable to digitization. Saying a long story short, the basics of trading only requires your ability to buy/sell, with a button in a trading terminal.
If we skip the voodoo magic that lies behind the scene of trading, we only have a buy/sell decision to make based on technical or fundamental assessment of a stock, or sometimes just intuition of a trader. That’s where complexity starts piling up. Every minute or even every second we have to make decisions. This could be quite frustrating and stressful.
Therefore, investment banks and hedge funds employ computers to make that job for them. The secret of success is to teach computer the rules of buying or selling that depend on stock market conditions. That’s it.
Today, trading has become more and more ‘non-human’. With a fast connection to a stock exchange, any ‘digital’ assistant can react rapidly, and make decisions more trusted. Analysts estimate the trading market currently has more than 60–65% of ‘non-human’ trading. Trading robots are popular; and they are evolving very fast.
Adding computation power to your trading experience allows you to process huge amounts of statistical market data, e.g. stock quotes, historical data, trends and patterns. This is hardly achievable by an individual but easily done by a computer. The statistical analysis helps traders make more trusted solution through refining raw data into meaningful instruments.
Even when we apply ordinary statistics, we still generate a lot of information that is not that easy to monitor and follow. You do remember several monitors installed on a typical trader’s desk. The question is if we can redefine trading experience and make it better and easier.
Yes, we definitely can. Higher computation power and larger computer capacities allow us to apply machine learning to trading experience. The difference between machine learning and statistical analysis is that machine learning goes further. It takes statistical methods and adapts them to get better results.
Machine learning incorporates building blocks of regular statistical methods into complex stack of data. The main distinctive feature is that machine learning not only processes huge amounts of stats data but also incorporates self-learning mechanisms they can learn from mistakes, select better algorithms, and fine-tune them, as well as then re-apply to market conditions. Besides, it makes everything automatically and applies in a fraction of a second, and you will see a powerful, flexible, self-adapted tool, especially for trading.
Machine learning penetrates many industries but stock trading is probably one of the most perfectly matched for stock quotes behavior modeling and automation.
Is trading still a vooodoo magic to you?
