Prerequisites
To get the most out of this blog, it helps to start with an overview of machine learning principles. Begin with Machine Learning Basics: Components, Application, Resources and More, which provides a solid introduction to how ML works, key components of ML workflows, and its growing role in financial markets.
Since the blog uses real-world stock data, familiarity with working in Python and handling market datasets is important. The blog Stock Market Data: Obtaining Data, Visualization & Analysis in Python is a great starting point to understand how to download, visualize, and prepare stock price data for modeling.
For a more structured path, the Python for Trading: Basic course on Quantra will help beginners build essential Python skills in a trading context, while Python for Trading dives deeper into data handling and analytics for financial applications.
Table of Contents
- Introduction
- What is Machine Learning?
- Understanding Classification in Machine Learning
- Types of Classification Problems
- Building a Classification Model in Python: Step-by-Step
- Conclusion
Introduction
Have you ever wondered how Netflix recommends shows you might like, or how Tesla cars can recognise objects on the road? These technologies have something important in common – they both use the "first-principles" approach to solve complex problems.
This approach means breaking down complicated issues into smaller, manageable parts and building solutions from the ground up. Today, we'll use this same approach to understand machine learning classification in Python, starting with the basics.
In this beginner-friendly guide, we'll learn how to build a machine learning model that can predict whether to buy or sell a stock. Don't worry if you're new to this – we'll explain everything step by step!
What is Machine Learning?
In simple terms, machine learning gives computers the ability to learn from experience without someone explicitly programming every possible scenario.
Think about how you learned to recognise animals as a child. Your parents might have pointed to a dog and said, "That's a dog." After seeing many dogs, you learned to identify them by yourself. Machine learning works similarly – we show the computer many examples, and it learns patterns from those examples.
Traditional programming tells a computer exactly what to do in every situation:
IF steering wheel turns right
THEN turn the wheels right
Machine learning, however, shows the computer many examples so it can figure out the patterns by itself:
- Here are 1000 images of roads with obstacles
- Here are 1000 images of clear roads
Now, tell me if this new image shows a clear road or has obstacles
This approach is being used in everything from self-driving cars to stock market trading.
Understanding Classification in Machine Learning
Classification is one of the most common tasks in machine learning. It's about putting things into categories based on their features.
Imagine teaching a child about animals:
- You show them a picture of a cat and say, "This is a cat"
- You show them a picture of a dog and say, "This is a dog"
After showing many examples, you test them by showing a new picture and asking, "What animal is this?"
Machine learning classification works the same way:
- We give the model examples with known categories (training data)
- The model learns patterns from these examples
- We test the model by asking it to classify new examples it hasn't seen before
In trading, we might use classification to predict whether a stock price will go up or down tomorrow based on today's market information.
Types of Classification Problems
Before diving into our Python example, let's quickly understand the main types of classification problems:
Binary Classification: Only two possible categories
- Example: Will the stock price go up or down?
- Example: Is this email spam or not?
Multi-class Classification: More than two categories
- Example: Should we buy, hold, or sell this stock?
- Example: Is this image a cat, dog, or bird?
Imbalanced Classification: When one category appears much more frequently than the others
- Example: Predicting rare events like market crashes
- Example: Detecting fraud in banking transactions (most transactions are legitimate)
Our example below will focus on binary classification (predicting whether the S&P 500 index will go up or down the next day).
Building a Classification Model in Python: Step-by-Step
Let's build a simple classification model to predict whether the S&P 500 price will increase or decrease the next trading day.
Step 1: Import the Required Libraries
First, we need to import the Python libraries that will help us build our model:
These libraries give us the tools we need without having to code everything from scratch.
Step 2: Get Your Data
We'll download S&P 500 data using the yfinance library:
This code downloads five years of S&P 500 ETF (SPY) data and plots the closing price.
Figure: Close Prices Plot for SPY
Step 3: Define What You Want to Predict
This is our "target variable" - what we're asking the model to predict. In this case, we want to predict whether tomorrow's closing price will be higher or lower than today's:
Step 4: Choose Your Prediction Features
These are the clues we give our model to make predictions. While we could use many different indicators, we'll keep it simple with two basic features:
Step 5: Split Data into Training and Testing Sets
We need to divide our data into two parts:
Training data: Used to teach the model
Testing data: Used to evaluate how well the model learned
This is like studying for a test: you learn from your study materials (training data), then test your knowledge with new questions (testing data).
Step 6: Train Your Model
Now we'll create and train our model using the Support Vector Classifier (SVC):
This single line of code does a lot of work behind the scenes! It creates a Support Vector Classifier and trains it on our training data.
Step 7: Check How Well Your Model Performs
We need to check if our model has learned effectively:
Output:
Train Accuracy: 54.98% Test Accuracy: 58.33%
Fig: Accuracy Scores for Train and Test Period
An accuracy above 50% on test data suggests our model is better than random guessing.
Step 8: Make Predictions
Now let's use our model to make predictions and calculate potential returns:
This calculates how much money we would make or lose by following our model's predictions.
Step 9: Visualise Your Results
Finally, let's plot the cumulative returns of our strategy to see how it performs:
This shows the total percentage return of our strategy over time.
Conclusion
Congratulations! You've just built a simple machine learning classification model that predicts stock market movements. While this example used the S&P 500, you could apply the same approach to any tradable asset.
Remember, this is just a starting point. To improve your model, you could:
- Add more features (like technical indicators)
- Try different classification algorithms
- Use more data or different time periods
- Add risk management rules
The key to success in machine learning is experimentation and refinement. Try changing different parts of the code to see how it affects your model's performance.
Happy learning and trading!
Note: All investments and trading in the stock market involve risk. This article is for educational purposes only and should not be considered financial advice. Always do your own research and consider consulting with a financial professional before making investment decisions.
Next Steps
After building your first classification model, you can expand your skills by exploring more advanced ML techniques and integrating them into end-to-end trading workflows.
Start with Machine Learning Classification: Concepts, Models, Algorithms and More, which explores decision trees, logistic regression, k-nearest neighbors (KNN), and other core algorithms that can be applied to classification tasks in trading.
To test your strategies effectively, learning how to backtest is crucial. The blog Backtesting: How to Backtest, Strategy, Analysis, and More introduces key concepts like historical data testing, performance metrics, and risk evaluation—vital for assessing any machine learning-based strategy.
To further integrate ML with trading, the blog Machine Learning for Algorithmic Trading in Python: A Complete Guide offers a full walkthrough of building trading systems powered by machine learning, including feature engineering and model selection.
For a hands-on learning experience, you can explore the Trading with Machine Learning: Classification and SVM course on Quantra, which takes your classification knowledge further and teaches how to apply models in live financial scenarios.
If you're aiming for a comprehensive, career-oriented learning path, the Executive Programme in Algorithmic Trading (EPAT) is highly recommended. EPAT covers Python programming, machine learning, backtesting, and model evaluation, with real-world trading applications and industry mentorship—ideal for professionals serious about algorithmic trading.
File in the download:
ML Clsssification- Python Notebook
Note: The original post has been revamped on 27th May 2025 for recentness, and accuracy.
Disclaimer: All investments and trading in the stock market involve risk. Any decision to place trades in the financial markets, including trading in stock or options or other financial instruments is a personal decision that should only be made after thorough research, including a personal risk and financial assessment and the engagement of professional assistance to the extent you believe necessary. The trading strategies or related information mentioned in this article is for informational purposes only.
