Q: What knowledge/understanding/roadmap to get out of the bookstore?
I guess the question is what next? (after accouning + finance)
- ie what math and science skills will help me?
Misc fun/soft books
- Rona Forooker, Don’t Be Evil
- Gregory Zuckerman, The Case Against Big Tech
- Daniel Markovits, The Meritocracy Trap
Programming (“Advanced”) + SWE Skills
- Martin Fowler, Refactoring
- Kurt Galroth, Optimized C++
- Drew Neil, Practical Vim
- Keith J. Grant, CSS In Depth
- Paul Butcher, Seven Concurrency Models In Seven Weeks
Microsoft Excel books:
- Wayne Winston, Microsoft Excel: Data Analysis and Basic Modeling
- Conrad Carlberg, Statistical Analysis
- Conrad Carlberg, Predictive Analytics
- Albright + Fox, Mathematical Models in Excel
- fancy models + pretty cool! (this is actually a legit math book)
Applied Math / Math-y CS
- Shalev-Schwartz-Ben-David, Understanding ML
- Mohri et al, Foundations of ML
- These two are pretty much the same book!
- (algorithmic/complexity approach to ML – enough to read the real papers!)
- Tu, Differential Geometry
- From the Bott and Tu guy!
- learn Riemannian manifolds properly
- Office Hours with a Geometric Group Theorist
- 1/4 of “grad school nostalgia”
- Akiyama-Matsunaga, Treks Into Intuitive Geometry
- this is “hard recreational math” (like Winning Ways)
Hard math with applications:
- Boissonnant et al, Geometric and Topological Inference
- basically “manifold learning for mathematicians” (from geometric perspective)
- Gautschi, Numerical Analysis
- not that hard, but more mature/advanced than the UG texts I’ve seen
- still assumes no analysis/topology/etc
- no coverage of linear topics
- Audrei + Hare, Derivative-Free and Blackbox Optimization
- aka “RL with math” (lots of it!)
- Lovasz, Graphs and Geometry
- geometrically flavored, optimization algorithms on graphs
- Pablo Pedregal, Optimization and Approximation
- a “quick taste”
- dynamic programming, optimal control, and linear programming / convex opt.
- Beleguck + Chadrapatha, Optimization Concepts and Applications in Engineering
- best overview from hard eng perspective!
- many methods
- complementary with Brunton-Katz which is “modern” (this book is “classical”)
- Brzezido-Zatowniak, Basic Stochastic Processes
- Macros Lopez de Prado, Advances in Financial ML
- BUY IT! RISE to the occasion!
- Cornuejols et al, Optimization Methods in Finance
- classical as well: linear + quadratic programming, stochastics, dynamic programming
- Blyth, An Introduction to Quantitative Finance
- same material as Hull in just 150 pages! Dense!
- easier but similar: Sacari, Mathematics of Finance
- Severini, Introduction to Statistical Methods for Financial Models
- basic portfolio theory with some stats, good “fill in” book
- Bouchard et al, Trades, Quotes, and Prices
- possibly the only book ever about HFT and small scale market dynamics
- “think like RenTech” – maybe?
- Bichler, Market Design
Theory for finance:
- Chen, Stochastic Game Strategies and their Applications
- Oskdal-Salon, Applied Stochastic Control and Jump Diffusions
- Steele, Stochastic Calculus and Financial Applications
- best book I’ve found on “real” (rigorous) QF math
- Ito, stochastic PDE, etc