Working thesis
Start here because
Primary danger
Stage 1
Intro to quant now
Learn the map before the calculus. You need a mental model of the machine before learning every bolt.
What quant really is
- Turning uncertainty into repeatable decision rules.
- Testing whether a pattern survives costs, slippage, and regime change.
- Building portfolios and execution logic, not just predictions.
- Using code and statistics to make market judgment scalable.
Bleeding-edge stack
2026 quant stack, from fastest learning path to scalable infra
This is the modern open stack I would learn first unless you are joining a latency-sensitive desk that already lives in C++ or q.
Top-down path
See the frontier
Understand the current desk map, tooling, and AI workflow.
Learn the failure modes
Overfitting, leakage, slippage blindness, and fake Sharpe.
Backfill the foundations
Probability, statistics, optimization, time series, and market structure.
Specialize by desk
Choose equities, HFT, derivatives, or crypto once the map is real.
Stage 2
Beginner
These are the first six modules that matter, ordered for signal per hour.
Stage 3
Practice / simulation
Move from consuming ideas to stressing them. Quant skill compounds once you can falsify yourself.
Quick diagnostic
Can you think like a quant yet?
Edge vs friction lab
Watch expectancy survive or die.
This toy simulator is simplified on purpose. Its job is to teach why hit rate, payoff asymmetry, turnover, and trading costs matter more than a pretty backtest screenshot.
Stage 4
Master level
Mastery means choosing the right game, the right infrastructure, and the right research standard.
Specialization map
Frontier themes
Primary docs + papers
Where this frontier view comes from
The desk map here is an informed synthesis from official docs and recent papers, not a claim that one stack wins every desk.