Жарияланымдар
ЖИ-агенттер, нарық болжамдарын бэктестілеу мен аудиттеу және платформа архитектурасы бойынша зерттеулер.
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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 мин оқу9 бөлім
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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 мин оқу7 бөлім
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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 мин оқу6 бөлім
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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 мин оқу5 бөлім
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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 мин оқу5 бөлім
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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 мин оқу5 бөлім
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Learn how AI models identify unusual patterns in market data — from statistical baselines to deep learning. Education, not trading advice.
7 мин оқу7 бөлім
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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 мин оқу7 бөлім
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Learn what explainable AI means in finance: how XAI works, why transparency in algorithms matters, and what it changes for market participants.
7 мин оқу7 бөлім
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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 мин оқу8 бөлім
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Learn what a confusion matrix is, how to read one, and why it matters more than accuracy alone when evaluating machine learning models.
6 мин оқу8 бөлім
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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 мин оқу8 бөлім
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A technical breakdown of how modern AI models handle financial time series — from raw price feeds to pattern recognition, uncertainty, and transparency.
8 мин оқу9 бөлім
