Publications
Research on AI agents, backtesting and audits of market forecasts, and the platform's architecture.
Model pitfalls
Thinkingbox runs 507 stateful business workflows past AI agents 20 times each and reports pass^20, not just pass@1. The strongest model solves 66.50% on one attempt and 47.53% on all twenty; another posts 89.35% pass@20 and 7.50% pass^20. What that gap means for agents running unattended, why the attempts are not coin flips, and how to measure your own.
11 min read9 sections
Model pitfalls
Slide a window across price history and you get thousands of samples. Almost none of them are independent. Here is the arithmetic, measured on real Binance history, and what it means for every confidence interval you have ever published.
7 min read7 sections
Model pitfalls
Regime shift is the most common way machine learning fails in production, and backtests average it away. A worked example with real numbers from our own board, and four habits that catch it before the results do.
5 min read6 sections
Model pitfalls
Survivorship bias is the error of studying only the winners because the losers have quietly disappeared from the data. Here is how it distorts financial results and how to guard against it.
4 min read5 sections
Model pitfalls
Backtesting runs a strategy against historical data to see how it would have performed. Here is what it really measures, why a great backtest is so easy to fake, and what an honest one looks like.
4 min read5 sections
Model pitfalls
A model that looks spectacular on historical data is the easiest thing to build and the hardest to trust. What overfitting is, why it fools you, and the only test that cannot be gamed.
4 min read5 sections
Model pitfalls
Learn how AI models identify unusual patterns in market data — from statistical baselines to deep learning. Education, not trading advice.
7 min read7 sections
Model pitfalls
Learn the key warning signs of overfitted financial models, the tests that expose them, and why transparency in any AI-driven market tool is non-negotiable.
7 min read7 sections
Model pitfalls
Learn what explainable AI means in finance: how XAI works, why transparency in algorithms matters, and what it changes for market participants.
7 min read7 sections
Model pitfalls
Data leakage in ML quietly inflates accuracy and destroys real-world performance. Learn what it is, why it happens, and how to detect and prevent it.
7 min read8 sections
Model pitfalls
Learn what a confusion matrix is, how to read one, and why it matters more than accuracy alone when evaluating machine learning models.
6 min read8 sections
Model pitfalls
Learn what feature importance means in AI trading models, why it matters for transparency, and how to interpret it without being a data scientist.
8 min read8 sections
Model pitfalls
A technical breakdown of how modern AI models handle financial time series — from raw price feeds to pattern recognition, uncertainty, and transparency.
8 min read9 sections
