The Science Behind AI Stockbots

The Science Behind AI Stockbots

Share this:

The Future Is Now

As the age of machine learning and AI begins to make its mark on present society, many data scientists have taken the step to utilize AI in the stock market. These AI tools are trained to heavily analyze stock data to optimize trading for maximum profit margins; some models mimic data from human traders while others develop their own pattern recognition.

Terabytes of data are fed into these systems to determine the best trend predictions for a particular stock as well as when the best buy time will occur. Intelligences in the stock market are employed by novice and experienced traders alike as a further aid to their own trades as a hands-free system to manage their money. Even in today’s bear market, on average, these programs have a 2% return daily. Expanding beyond the standard stock market, these bots are able to predict the fluctuating trends of cryptocurrencies across the board.

The questions arise: How bulletproof are these algorithms? How do these AI tools work? What do we really know about AI stock bots? Is the hefty investment really worth your money?

Crypto Algorithms

Using complex algorithms, trading bots use compiled data from stock market APIs to determine the optimal entry points and exit points for a particular stock. They use information regarding past stock paths as well as measures such as PE ratios, free cash flow, and various factors in public company financial reports to most accurately determine the upcoming path of a stock.

These algorithms are perfected using machine learning – once given an algorithm to follow, the computer will analyze the success of that particular algorithm by testing on several stocks that the computer operator manages. This technique is especially useful in cryptocurrency trading because of the volatility of that particular market. Stocks in the American market are much easier to analyze by understanding the industry they come from as well as past performances on long durations. Cryptocurrency is harder to analyze because of its recent introduction and lack of uniqueness per currency. Each currency is similar in the sense that there isn’t a concrete basis or determining factor to make one particular currency more reliable than another. A Forbes article explains how there are a “reported 21,910 unique cryptocurrencies” in a period of just over 14 years after its initial 2009 introduction. Utilizing the emotionless analysis of a trading bot gives insight on particular coins that humans simply cannot determine. These AIs are able to place trades in the market autonomously when they are able to determine the most optimal time to make a trade. Most bots follow the Bitcoin Waves Model, which measures the true value of crypto coins as a whole based on crypto inflation rates.

As we can see in the annual coinmetrics inflation rate (above), the sudden drops in inflation occur around the times bitcoin grew rapidly in value; it followed the modern stunts of popularization. Here, the y-axis represents inflation rate and the x-axis represents the year. The Bitcoin Wave Model represents five different trends, each following a similar rise and fall pattern but reaching differing maximum and minimum price per coin in USD. As we can see in the model below, we can visualize the downtrend of the crypto market as a whole.

The model expects a steep rise around early 2023, with an expected increase from around $30,000 to $60,000. In an article by Software Testing Help, modern cryptocurrency bots are able to make “predictions at an accuracy of up to 55% are common when using machine learning models. Machine learning models perform better than statistical methods in the long term. Sometimes these prediction accuracies can go up to 70 and 80%.” Considering the extreme volatility of the crypto market, this is a remarkable metric. AI was able to design a model that has proven to be reliable over the last few years and have positive return on extreme volatility.

Algorithm vs ETFs

Alongside heavy cryptocurrency analysis, these bots are used on general stocks that we might use in our own trades. A research project at Stanford University highlighted an LSTM-based (long term short memory) prediction model to trade stock at a higher efficiency than most index funds and ETFs on the market. An article by MathWorks best describes their use: “LSTM stands for Long short-term memory. LSTM cells are used in recurrent neural networks that learn to predict the future from sequences of variable lengths. Note that recurrent neural networks work with any kind of sequential data and, unlike ARIMA and Prophet, are not restricted to time series.”

In the context of the stock market, the Stanford LSTM model will track stock price indefinitely to develop indicators of ideal entry/leave points, and their own estimates of intrinsic stock value. The Phd students have developed and compiled algorithms to determine whether a particular day on the stock market is a “good” or “bad” day to enter a stock. “The prediction step can be done for all the tickers or just for a single one which is very useful for performing a more robust prediction for stocks with insufficient historical data. In addition, a bot is used to perform the buy or sell operations at the time of closing every day in order to maximize gains.”

Their algorithm follows a very robust and direct approach: “Calculate the δi changes given by δi = sign(ci+1 − ci), where ci is the stock price on the ith day … check the evolutions of the δi, by following ∆i = δi+1 − δi. The decision is made regarding the value of ∆. When ∆ = −2, the bot decides to buy since it indicates the end of a trough. While ∆i = 2 indicates the beginning of a dip, the bot decides to sell.” 


Even machine learning models on the simpler side can be utilized to compete with investing decisions by international firms and the “most aggressive” ETFs of the market as a whole. It is uncertain what the market will bring in the future, but it is imperative that we learn about the technology that might allow AI and machine learning to completely take over the investing scene.

About the author

+ posts

Leave a Reply