ARTICLES
Guides on quant trading, machine learning stock models, and systematic investing.
How to Read the Portfolio Chart
The Portfolio Chart shows confidence levels, daily returns, pick counts, and a live equity curve for your models. A complete visual guide to every element.
What Is a Quant Trading Model?
A quant trading model is a rules-based system that uses data and math to find stocks. Learn how they work and how to build one — no coding required.
How Machine Learning Picks Stocks
Machine learning stock picking uses historical data to find patterns humans miss. Here's exactly how it works — and how to use it without writing a single line of code.
Swing Trading Without Staring at Charts
Swing trading with machine learning means your system finds the setups while you sleep. Learn how to build a data-driven swing trading model without coding.
The QuantConnect Alternative Built for Traders
QuantConnect is powerful but requires Python. QuantBuilder is the alternative — train ML stock models, run backtests, and get daily picks without writing a line of code.
What is Walk-Forward Backtesting? (And Why It Matters)
Walk-forward backtesting tests your model on data it has never seen — rolling forward through time. Here's why it produces reliable results when standard backtesting doesn't.
What is Feature Importance in a Trading Model?
Feature importance tells you which signals your ML trading model actually weighted when it learned. It's the transparency most trading platforms never give you — and it changes how you iterate.
What is Point-in-Time Data? (And Why Most Backtests Ignore It)
Point-in-time data means your backtest only uses information that was actually available on each historical date. Without it, your results are fiction. Here's why it matters.
How to Build a Stock Screening Algorithm Without Writing Code
Traditional stock screeners use rules you set manually. A trained ML model is a screening algorithm that learned the thresholds from historical data. Here's the difference — and how to build one.
Sector Rotation Trading with Machine Learning Models
ML sector models tell you when to rotate by signaling through pick counts. When energy fires 30 picks after days of silence, the model is telling you something changed. Here's how it works.
Two ML Models, Same Universe, Completely Different Personalities
I built two iterations of the same model on QB500. They ended up with completely different strategies — and both worked. Here's what the feature importance charts revealed.
Core and Specialists: How I Run Multiple Quant Models
How to structure a multi-model quant trading portfolio: one core model that runs every day, plus specialist models that sit in the background until conditions align.
Training vs Backtesting: What Every Quant Trader Needs to Know
Most traders confuse training a model with backtesting a rule. They are not the same thing. Here's the difference — and why it changes everything about how you build a trading system.
How the Energy Model Knew to Wait
A real case study: an ML energy trading model sat mostly in cash while the sector sold off, then fired 66 picks in a day when the setup arrived. Here's how it worked.
Building a Short Tech Hedge from Scratch
How I built two ML short tech models, waited for the right entry at IYW $260, deployed in June, and caught a $20 drop in the ETF. The full process, start to finish.
How to Build a Machine Learning Trading Model (Step by Step)
A real end-to-end walkthrough: pick a universe, train the model, backtest it, get daily picks, and set your exits. No code. Built on Quant-Builder.ai.