Big data analysis has transferred a whole new level as a fully-fledged business in its own right. Many fintech organizations rely on data science to generate their own strategies. Trading is the perfect playing field with its big data flows where data science can provide a sizable competitive power.

But, trading is adopting data science on a regular scale only — big data is still primarily the domain of quants, while traders themselves are on the outside looking in. The trade business could profit from the wider adoption of data science.

Traders are the closest to the market, making sense for them to know what patterns drive market behavior. Data science is the trader’s solution to a better knowledge of carrying market processes. Data science can be roughly divided into two main trends: statistical analysis and ML. These two trends are nearly interlinked. Seldom statistical analysis is a separate discipline, or it may be a component of the machine learning model.

How Can Data Analytics Assist with Stock Trading

Trading is an exciting endeavour, giving the best excuse to engage with the markets in a significant way. It is going to be a lot easier to trade productively with new data analytics instruments like automated trading software. 

But, while there are many causes people decide to become traders, the most common reason by far is money. There’s nothing incorrect with trading to boost your earnings, but you’re sorely mistaken if you think it’s a method to get rich quickly. Truly successful traders know it doesn’t work like that at all. Thy have to take advantage of the latest big data technology to have a competitive edge in this convoluted market. 

Many beginners buy into the fake success stories on social media. Yet in the vast bulk of cases, these cases aren’t true. The truth is that successful traders need to make intelligent, informed choices in order to build profitability over time: no fads, no gimmicks — just hard work and thoughtful activities.

What is statistical analysis and how can traders profit?

Statistical analysis is, roughly speaking, similar to traditional statistics, or the science of processing random data. The process is usually as follows: a random data set is analyzed to get a variety of statistical parameters, such as distribution or correlation, then a conclusion is made based on the result.

Distribution analysis is helpful to determine if an event is likely to happen and to what degree. In crypto trading, distribution analysis can show which changes report credible volumes and which exchanges engage in volume inflation. This article describes in more detail how statistics support identifying fraudulent exchanges and describes it in layman’s terms.

Another proper statistical method is correlation analysis. It is used to match each asset in a portfolio against the other or correlate to other assets. Owning a portfolio of highly correlated assets is a risk. If one of the assets drops in value, a trader could lose everything.

Machine Learning techniques in trading

ML is when Artificial Intelligence, neural networks, and other learning techniques are applied to data collections to identify and analyze patterns and predictors. First, an algorithm is created, then it is fed a training data set so it learns to know a given way, and the result of such learning is used to build a mathematical figure.

There are several examples of such mathematical models; two of them are the most fitting for our purposes and are the most famous: decision trees and neural networks.

Decision trees

In ML and statistics, a decision tree is a decision-making instrument that covers of “leaves” and “branches”. “Branches” include the attributes that determine the target role, “leaves” contain the values of such features, while the remaining nodes are comprised of the details that are used to make a difference between cases. Decision trees use a descending or “divide-and-conquer” strategy to analyzing data. The machine splits the data into several yes/no questions, and the goal is to obtain a model that could foretell the value of the target variable based on several input variables.

A more superior model is a decision tree ensemble. It is helpful in positions where there are several big decisions to make, so a tree is built for each of them, and the final decision is made by choosing.

Decision trees are precious for trading; they do precisely what their name suggests — they assist in making informed choices. 

Neural networks

Neural networks are analytical models that loosely model the interlinked system of neurons in the brain. A mathematical neuron is a node following an organic neuron. A neural network follows the methods in the brain and the nervous system, published by experience to avoid repeating past errors.

When well constructed and trained, a neural network can simply replace humans for applications where there are large and rapidly updating data collections. Trading is correctly that. The financial business is overflowing with input data, and it is not humanly likely to handle all this data manually to make right predictions.

Neural networks are able to do just that. Emotions do not sway them, thus, their choices are not based on impulsive or panic thinking. At the same moment, specialists agree that neural networks still have a long distance to go, so their outputs have to be managed by humans to a point before they can efficiently be practiced in trading.

Data science is slowly but relentlessly taking over financial markets. Soon it will be hard to believe how traders from history relied on intuition only, without big data and ML algorithms. Data science stimulates trading, helps make informed choices, reduces risks, and improves profits.

It should be noted that Artificial Intelligence algorithms and traditional statistics cannot yet make 100% true predictions in a market position. Statistical analysis is sensible to incomplete information or a lack of clearly marked patterns.

But, in general, data science gives far more advantages than it does negatives. Neural networks may eventually replace human traders, while people will sit back and reap the fruits of their labor, occurring in the trade only when it is needed.