Imagine waking up to the smell of your favorite coffee, prepared just the way you like it, at a time that is perfectly attuned to your sleeping schedule. All the AI-driven smart home tech was worth the money, you think with satisfaction, walking to grab your mug. During breakfast, you skim through the newsfeed, which features stories that fit your interests and habits. Then, you jump in your car and run through the report from your algorithmic trading bot, which has just earned you big time, as your self-driving vehicle navigates the traffic all by itself.

If you follow the tech news, you may feel, at least every now and then, that this is what life is going to look like in a few years. At the core of everything that we just imagined is the booming artificial intelligence industry, which is re-inventing the world even today. Advanced statistical programming is based on the idea of making the machine figure out the patterns in the data itself, with or without human guidance. With an appropriate set, whether regarding your tastes in coffee or the trends on the stock market, it is possible to train an AI that would pick out your perfect blend or make money for you by beating the markets.

But how exactly does that work, you may ask? What does a trading AI have in common with a self-driving vehicle? Let us delve deeper into this to find out.

How To Train Your AI

So what exactly is artificial intelligence in the first place? After all, this umbrella term has been used so generously these days that it can be hard to wrap your head around it. Furthermore, for this very reason, it would be a bit more professional to speak about machine learning rather than AI.

So what is machine learning, then? Another umbrella term, of course, one bringing together a variety of tools and methods that are centered on the idea that we have already mentioned. Give the machine a dataset and let it figure out a mathematical formula that describes the patterns within it, either to classify the objects that are described by the data or to predict some variables. The formula in question can be super complex, but the important takeaway number one is that we are talking about a formula, at the end of the day, not an actual intelligent lifeform.

As an example, let us imagine training a deep (i.e. having multiple hidden layers, we will get to those in a minute) neural network that will try to find cats in pictures. We will feed it a dataset of 30,000 pictures, each labeled 1 or 0, depending on whether there is a cat in it or not. The algorithm would go through these pictures, breaking them up into pixels and trying to figure out what, for lack of a better word, quantifies catness as such – the shapes, the colors, anything. If it finds these patterns on a new picture we feed to it, it will inform us that a cat is indeed present.

To do so, it would, once again, first break up the image into pixels. These pixels would work as the input for its first layer, comprised of dozens of nodes, each of them being a mathematical formula on their own. These would send their output through the hidden layers, i.e., those between the input and output layers, where the data would go through further transformations. Finally, the output layer would use all the information accumulated during these transformations to make the final judgement as to whether a cat is present or not.

A similar approach lies in the technique known as imitation learning, which Tesla reportedly relies on in developing its self-driving car. Here, an AI is given a huge set of samples of good human driving. It studies the data and figures out the best lines of action for any situation it may find itself facing on the road.

Another way to do this is to use reinforcement learning. Here, the idea is to make the AI aware of its own performance and evolve based on how well it does its job. To do so, the learning agent has to have a way to assess its actions; in other words, the algorithm must include rules and conditions codifying what constitutes good and bad performance. Using these, the machine trains to navigate the learning environment by doing random actions and accessing the outcomes in what is effectively a mathematical approximation of a trial and error method.

Some extensive research has already been done on implementing self-driving AI this way. You can train the AI on a dataset covering good and bad driving, then have it navigate a car around a virtual city, using virtual sensors to keep track of reality. As it does so, it constantly checks its own performance, trying to attune the models to maximize its performance, with maximization defined in terms of the very conditions that we set for it to measure its performance. When the AI is ready, you re-settle it to a real car with all the necessary hardware and hook up the real cameras and sensors.

Now, is there a way to make your trading as advanced and elegant, you may ask. If you do, rest assured: the same techniques can be applied to the financial markets – let us tell you how.

Machine Learning For AI-Driven Algorithmic Trading

In the previous section, we spoke about a virtual car navigating a virtual city and learning from the process. Now, let us change gears a bit and envisage something different. Instead of a city, our new algorithmic trading AI would now be navigating the stock market.

First, we will train it on a historical dataset that would cover years worth of trading. In doing so, it would pick up the patterns in the data, learning the market behavior and training to predict its future swings. But that is not it: we will also use reinforcement learning in our design. How? The idea would be to make the algorithmic trading AI assess its own performance. Precision makes for an intuitively good point of reference here: if we want the AI to be able to predict the market dynamics, the correlation between its predictions and the actual price movements is a sensible measure of its success.

From there, we follow pretty much the same logic. With new data coming in every day and a clear benchmark for measuring the performance, our algorithm will be able to not just predict the market dynamics, but also improve its own accuracy with every new forecast delivered.

Thankfully, everything that we just spoke about does not belong to the realm of science fiction. The tech is already there, and so are the services that are making AI-driven algorithmic trading accessible to everybody. Among them is an Israeli-based company called I Know First, which delivers daily forecasts for over 10,500 financial instruments, including stocks, ETFs and currencies.

Trained on a dataset covering 15 years of trading, its AI delivers its forecasts as a heatmap with two indicators: signal and predictability. Signal shows the machine’s estimate for the gap between the actual and the fair price for the asset. A positive signal means growth, while a negative one predicts a nosedive. Predictability, in its turn, demonstrates how successful the algorithm has been in predicting the asset before.

Most relevantly to our discussion, the algorithm also features elements of reinforcement learning in its design. Keeping track of its past successes and failures, it updates its own predictive models as soon as they seem to be getting out of touch with the market. This allows it to easily adapt to new market conditions and crank up its accuracy with every forecast delivered.

The AI also draws on chaos theory to account for market volatility. It delivers its forecasts for time horizons ranging from 3 to 365 days, covering short, medium and long-term perspectives, which helps the traders to identify opportunities both for a quick profit from short trading and find the stocks that would generate most value over time.