Predicting Future Stock Market Trends with Python & Machine Learning

Predicting Future Stock Market Trends with Python & Machine Learning

Utilizing Ensemble Modelling to accurately predict stock market trends. Note from Towards Data Science’s editors: While we allow independent authors to publish articles in accordance with our rules and guidelines, we do not endorse each author’s contribution. You should not rely on an author’s works without seeking professional advice. See our Reader Terms for details.

Note from Towards Data Science’s editors:_ While we allow independent authors to publish articles in accordance with our [rules and guidelines_](https://towardsdatascience.com/questions-96667b06af5), we do not endorse each author’s contribution. You should not rely on an author’s works without seeking professional advice. See our [Reader Terms_](https://towardsdatascience.com/readers-terms-b5d780a700a4) for details._

With the recent volatility of the stock market due to the COVID-19 pandemic, I thought it was a good idea to try and utilize machine learning to predict the near-future trends of the stock market. I’m fairly new to machine learning, and this is my first Medium article so I thought this would be a good project to start off with and showcase.

This article tackles different topics concerning data science, namely; data collection and cleaning, feature engineering, as well as the creation of machine learning models to make predictions.

Author’s disclaimer: This project is not financial or investment advice. It is not a guarantee that it will provide the correct results most of the time. Therefore you should be very careful and not use this as a primary source of trading insight.

1. Imports and Data Collection

To begin, we include all of the libraries used for this project. I used the yfinance API to gather all of the historical stock market data. It’s taken directly from the yahoo finance website, so it’s very reliable data.

We then define some constants that used in data retrieval and data processing. The list with the indicator symbols is useful to help use produce more features for our model.

Now we pull our historical data from yfinance. We don’t have many features to work with — not particularly useful unless we find a way to normalize them at least or derive more features from them.

2. Data Processing & Feature Engineering

We see that our data above is rough and contains lots of spikes for a time series. It isn’t very smooth and can be difficult for the model to extract trends from. To reduce the appearance of this we want to exponentially smooth our data before we compute the technical indicators.

artificial-intelligence stock-market machine-learning data-science python

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