Practical guides to predictive algorithms and stock analysis for Toronto investors just starting out
4 articles to explore
Learn the math behind the simplest supervised learning model. Spoiler: it's just drawing a line through your data points.
A step-by-step walkthrough of how decision trees split data into buy/sell decisions. More intuitive than you'd think.
Hands-on guide to gathering TSX data, cleaning it, and training a model. We'll show you exactly where to find free data sources.
Your model performs perfectly on historical data but tanks on live markets? That's overfitting. We'll explain why it happens and how to prevent it.
Stock markets leave patterns in their wake. Supervised learning finds those patterns in historical data and uses them to forecast future movement. It's not magic — it's pattern recognition at scale. The Toronto market has distinct characteristics: smaller companies with higher volatility, sector concentration, and unique responses to Canadian economic data. When you train your model on TSX-specific data, you're not fighting the algorithm. You're giving it context.
The catch? Markets change. What worked in 2023 might not work in 2026. That's why this guide focuses on robust methods that adapt, not shortcuts that break.
