Where markets meet machine learning
Research notes, model explainers and market analysis from the team building TradePredictor. No hype, no signals-for-sale — just how the maths actually works.
AI & Machine Learning
Overfitting in Trading Models: How to Spot It
Overfitting rarely announces itself. It looks exactly like success, right up to the moment you deploy.
AI & Machine Learning
Walk-Forward Validation vs Train-Test Split
The standard machine learning split assumes your rows are interchangeable. Time series rows are not, and that assumption quietly …
AI & Machine Learning
Confidence Scores in AI Predictions Explained
A prediction without a confidence estimate is nearly useless. But most confidence numbers are not probabilities, and treating the…
AI & Machine Learning
Why Machine Learning Models Fail in Live Markets
The gap between backtest and live performance has predictable causes. Most of them are known before deployment and ignored anyway.
AI & Machine Learning
Feature Engineering for Financial Time Series
Model architecture gets the attention, but what you feed a model decides more about its performance than which model you chose.
AI & Machine Learning
Data Leakage in Backtesting: The Silent Killer
The most common reason a strategy looks brilliant in testing and dies in live markets is not bad luck. It is information from the…
AI & Machine Learning
Ensemble Models in Trading: Why Three Models Beat One
Combining models is not about averaging away mistakes. It is about buying information from disagreement.
AI & Machine Learning
What Is XGBoost and Why Traders Use It
Gradient-boosted trees quietly outperform neural networks on a lot of financial problems. The reason has more to do with data siz…
AI & Machine Learning
How LSTM Neural Networks Predict Stock Prices
LSTMs are the most cited architecture in financial deep learning and the most widely misunderstood. Here is what the gates actual…