Making Money in the Stock Market Using Data and Science


Like everything good in this world, your stock market picks should also rely on data and science to be profitable. Data science is becoming more and more popular these days, and everyone is all about data/science. What it can do to help, what it can change, what it does in general, and how it impacts your stock market picks.

Data is usually represented as numbers and those specific numbers can represent many different things. They can represent an inventory, consumers, the number of sales, and even cash. The point is, data readily represents plenty of different things in number form.

Then, we meet financial data, or more specifically, the stock market and your picks. However, this can all keep you guessing. How can data and science help us when it comes to the stock market? One person making a big difference with data and science-driven picks is Eric Ferguson from Mindful Trader.

Eric Ferguson spent years searching for the ‘truth’ about which strategies actually make money in the stock market by relentlessly digging through historical data and relying on science. Another unique part of Eric Ferguson’s success as Mindful Trader is that he heavily relies on mindfulness to trade and make the correct decisions regarding trading.

So, with all that in mind. Let’s get into it!

Data Science

When it comes to data science, people tend to hide back in their shells because it is likely that those words can cause some stress for you. Surrounding those words is a plethora of jargon or fancy words that just don’t make sense for some people. If those words do make sense to you, it is likely that you’re quite fond of numbers, however, numbers are not for everyone and that is understandable.

Luckily, we are here to solve absolutely all of that for you. Data science and investing alike do not have to be a stressful or confusing experience, so we are here to simplify it greatly for you. Essentially, data science requires you to have a love of numbers and knowledge of statistics, math, and programming of some sort.

Using data and science to analyse the stock market and make educated picks is one of the best ways to invest, in fact, there really shouldn’t be another way. With data, science, and investing combines, you can determine which stock is worth the investment and which one is not.


Here is another word that makes you take a sharp breath in, but have no fear, we will simplify this one for you as well! When it comes to data science, analysis, and programming, algorithms are a frequent occurrence and are used fairly extensively. An algorithm is basically a refined set of rules that outline a specific task that needs to be done in order. Perhaps you’ve heard about algorithmic trading before as it has been quite popular in the stock market.

The best thing is, these algorithms remove any possible human error as they can run by themselves without human intervention. People have often even referred to these algorithms as trading bots because of the fact that they trade without emotion and are mechanical in their trading methods.


Before you think of treadmills, weights, and resistance bands, take a minute to keep reading, we aren’t going to make you exercise. This is a whole different type of training. When it comes to investing with data science and machine learning, this training we speak of is actually just using selected data, or a portion of it, to train the machine learning model.

In order for a machine learning model to accurately make good predictions is for it to understand and recognize past data so that they can efficiently work from it. For example, if you were asking for a machine learning model to predict the future prices of a certain stock, you would have to provide it with stock prices from the past year or so for it to be able to predict the next prices.


After you train the model, you would obviously want to know how it is performing. That is where testing comes in. With any product, testing is a highly important part of the process. To validate a model’s performance, it is likely that you would take that model’s predictions and then compare them to that of the testing set.

Features and Target

When it comes to data science, it is usually displayed in a tabular form such as DataFrame or an Excel sheet. These data points can represent absolutely anything, and each column plays an important and dire role. The target is what we want to predict future values for, and the features are used by the machine learning model to make those predictions.

Overfitting and underfitting

Last but not least, we have overfitting and underfitting, an important part of stock market analysis. When it comes to evaluating the model, or more specifically the model’s performance, the errors sometimes reach a point of being either ‘too hot’ or ‘too cold’. When you are searching for ‘just right’, this can cause some stress.

Then we get to overfitting. This usually happens when the model is too complex and is also predicting too complexly to the point that misses the relationship completely. This relationship is between the target variable and the feature.

Underfitting, however, is what happens when the model simply doesn’t fit the data enough and the predictions are far simpler than they should be. When evaluating models, this is something that data scientists need to be very aware of.  Overfitting and underfitting can both cause problems and they can both lead to poor future price predictions and forecasts.

These topics are all very important when it comes to making money in the stock market as well as using data and science to back your stock picks. These things can all help to create a trading edge that will definitely benefit you in the long run. Having a trading edge is the name of the game in the stock market and if you have a trading edge, then it is like you are the house at a casino.


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