Student Kit / Quant Prep
Quant Research Prep
Probability, statistics, puzzles, Python, backtesting hygiene, and research taste for quant-curious students.
Learning track
Build ladder.
Build enough math, coding, and research discipline to discuss quant problems honestly.
Prerequisites
- probability basics
- Python
- comfort being wrong in public notes
- 01 Probability reflexes
Handle conditioning, expectation, variance, and Bayes.
Write solutions to 15 probability puzzles. - 02 Statistics and experiments
Understand estimation, overfitting, and uncertainty.
Run a simulation notebook for false discoveries. - 03 Python research loop
Load data, test ideas, plot diagnostics, and avoid leaks.
Build a toy mean-reversion backtest with caveats. - 04 Market humility
Separate toy signals from tradable systems.
Write a 'why this probably fails' memo. - 05 Interview synthesis
Explain math, code, and judgment under pressure.
Record short written explanations for solved puzzles.
Checkpoints
- Can derive expectation
- Can simulate
- Can identify data leakage
- Can explain a failed idea
- Can write a clean solution
Where This Belongs
This is a student preparation path, not an AI note wearing a probability hat. It belongs with applications and career systems because the reader's next action is study, practice, and interview readiness.
The puzzle lab still matters here. Probability questions often become solvable only after the right representation clicks.
- probability reflexes
- statistics
- clean Python
- puzzle explanations
- backtest hygiene
- career packet
Practice Units
The unit of progress is one solved problem with a written explanation, one simulation notebook with caveats, or one failed idea memo. The track should reward clear thinking over pretending a toy signal is a strategy.
Finance examples stay educational. No public page should drift into investment advice or imply a backtest proves tradability.