AI & ML in Stock Market

In many ways, AI and finance are made for each other. Machine learning and other techniques make it easier to identify patterns that might otherwise not be detected by the human eye, and finance is quantitative to begin with, so that it’s hard not to find traction. Financial firms have also invested heavily in AI in the past, and more are starting to tap into the financial applications of machine learning (ML) and deep learning.
Various institutions are using artificial intelligence in stock trading to build better and more personalized products and services.

One can easily apply machine learning in stock market by understanding and implementing the following steps:
1. Prepare data:
The very first thing we should do is get the data for training our model. Yahoo finance is an amazing place to look for fresh new data about any company.
The dataset should be divided into train/validation/test sections where each of them is independent from the others.
2. Manipulate data:
Our next step is to reform the data in a way so it can be usable for training. We need to normalize it, which basically means to scale each feature to a given range.
3. Display data:
Finally we need to display the normalized data. This isn’t essential for the model performance but is extremely useful when it comes to debugging your code. Thus, one should make it a habit to always visualize the dataset.
Algorithms and computers make decisions and execute trades faster than any human can, and do so free from the influence of emotions.