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@RitOnchain • 2,140 subscribers

engineering alpha through systematic infrastructure in quant & ai

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Anya Rossi

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Jane Street pays $750K/year for quants who master large deviations and tail bounds. 82-minutes. free. By MIT professor. "Markov gives us a really bad bound, Chebyshev is pretty reasonable, Chernoff gives us exponential." here's what they cover: • Markov vs Chebyshev vs Chernoff bounds • bounding tail risk for extreme random variables • why variance alone fails in high-frequency regimes • exponential 10x tighter risk modeling Bookmark it & watch today. Then read the article below.

Jane Street pays $750K/year for quants who master large deviations and tail bounds. 82-minutes. free. By MIT professor. "Markov gives us a really bad bound, Chebyshev is pretty reasonable, Chernoff gives us exponential." here's what they cover: • Markov vs Chebyshev vs Chernoff bounds • bounding tail risk for extreme random variables • why variance alone fails in high-frequency regimes • exponential 10x tighter risk modeling Bookmark it & watch today. Then read the article below.

61,358 views • 20 days ago

Citadel Senior Quant Developer just dropped the complete math framework for scaling a pairs trading desk to 200+ concurrent market positions. 54-minutes. free. By Quant Trader. here's what they cover: • integration orders (I(0) vs I(1)) for asset pricing • using the Augmented Dickey-Fuller test over standard graphs • calculating dynamic rolling betas on log returns • managing independent bets across 200+ parallel tables Bookmark it & watch today. Then read the article below.

Citadel Senior Quant Developer just dropped the complete math framework for scaling a pairs trading desk to 200+ concurrent market positions. 54-minutes. free. By Quant Trader. here's what they cover: • integration orders (I(0) vs I(1)) for asset pricing • using the Augmented Dickey-Fuller test over standard graphs • calculating dynamic rolling betas on log returns • managing independent bets across 200+ parallel tables Bookmark it & watch today. Then read the article below.

29,449 views • 10 days ago

Citadel & Two Sigma Quant just showed how quants build uncorrelated factor portfolios using PCA. 83-minutes. free. By Harvard PhD at MIT. here's what they cover: • isolating idiosyncratic yield curve factors (Level, Slope, Curvature) • using massive leverage to scale market-neutral portfolios • out-of-sample stability & handling post-COVID regime shifts • explaining 90% of bond market variance with 1 dominant factor watch full video then read article.

Citadel & Two Sigma Quant just showed how quants build uncorrelated factor portfolios using PCA. 83-minutes. free. By Harvard PhD at MIT. here's what they cover: • isolating idiosyncratic yield curve factors (Level, Slope, Curvature) • using massive leverage to scale market-neutral portfolios • out-of-sample stability & handling post-COVID regime shifts • explaining 90% of bond market variance with 1 dominant factor watch full video then read article.

40,185 views • 18 days ago

AI Phd Professor just showed how to build Hidden Markov Models for time-series data. 61-minutes. free. By AI Researcher for Quants. "for stock market prediction is the same idea you want to predict what prices are going to be in the future" here's what they cover: •why the independent data assumption fails •calculating transition and emission distributions •predicting future states step-by-step •extracting hidden sequences with the Viterbi algorithm Bookmark it & watch today. Then read the article below.

AI Phd Professor just showed how to build Hidden Markov Models for time-series data. 61-minutes. free. By AI Researcher for Quants. "for stock market prediction is the same idea you want to predict what prices are going to be in the future" here's what they cover: •why the independent data assumption fails •calculating transition and emission distributions •predicting future states step-by-step •extracting hidden sequences with the Viterbi algorithm Bookmark it & watch today. Then read the article below.

43,465 views • 1 month ago

Stanford CS professor just dropped the ultimate framework for building self-learning ai agents. 65-minutes. free. by stanford ai faculty. "reward hacking - your agent is gonna learn to do exactly what it is that you tell him to do" here's what they cover: •solving the credit assignment problem and delayed rewards •exploration vs exploitation trade-offs in high-stakes domains •model-based vs model-free system evaluation •4 foundational pillars of autonomous decision making Bookmark & watch today. Worth more than any $500 machine learning course.

Stanford CS professor just dropped the ultimate framework for building self-learning ai agents. 65-minutes. free. by stanford ai faculty. "reward hacking - your agent is gonna learn to do exactly what it is that you tell him to do" here's what they cover: •solving the credit assignment problem and delayed rewards •exploration vs exploitation trade-offs in high-stakes domains •model-based vs model-free system evaluation •4 foundational pillars of autonomous decision making Bookmark & watch today. Worth more than any $500 machine learning course.

14,835 views • 1 month ago

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