# Roadmap

My roadmap for becoming competent quant and capable of creating automated trading bots.

Current Goal: **Learn Quantitative Foundations**

## Study

Current reading [Introduction to Probability, Statistics, and Random Processes - Hossein Pishro-Nik](./textbooks/reading/20260622012450_Introduction%20to%20Probability,%20Statistics,%20and%20Random%20Processes/Introduction%20to%20Probability,%20Statistics,%20and%20Random%20Processes%20-%20Hossein%20Pishro-Nik.pdf) and [Algorithmic Trading](./textbooks/reading/20260622012451_Algorithmic%20Trading%20Winning%20Strategies%20and%20their%20rationale/Algorithmic%20Trading%20Winning%20Strategies%20and%20their%20rationale.pdf)

## Phases

Recommended path, generated by CLAUDE

### Phase 1 — Math Foundation (*)

1. [ ] Pishro-Nik (*)
2. [x] Quantitative Trading
3. [ ] Elementary Stochastic Calculus
4. [ ] A guide to Brownian motion

### Phase 2 — Get Practical Early

1. [ ] Algorithmic Trading — Chan ← read this soon; short, orients everything else
2. [ ] Analysis of Financial Time Series — Tsay ← essential for price/return modeling

### Phase 3 — Core Quant ML

1. [ ] Data-Driven Science and Engineering — Brunton & Kutz
2. [ ] Advances in Financial Machine Learning — Lopez de Prado ← most important book in your backlog
3. [ ] Stochastic Calculus: An Introduction with Applications ← now the theory lands better
4. [ ] Detecting Regime Change in Computational Finance

### Phase 4 — Portfolio, Risk & Systems

1. [ ] Active Portfolio Management — Grinold & Kahn
2. [ ] The Microstructure of Financial Markets — de Jong & Rindi
3. [ ] Trading Systems and Methods — Kaufman (use as reference)
4. [ ] The Mathematics of Money Management — Vince
5. [ ] The Leverage Space Trading Model — Vince
6. [ ]Testing and Tuning Market Trading Systems

### Phase 5 — Specialized / Optional

1. [ ] Assessing and Improving Prediction and Classification (C++ heavy, niche)
2. [ ] Trading on Sentiment (alt data / NLP, very specialized)
3. [ ] Numerical Recipes (reference only, don't read cover to cover)
4. [ ] Measure Theory (only if you want pure math depth — lowest ROI for bots)

### Missing textbooks

- Systematic Trading: A unique new method for designing trading and investing systems
- Permutation and Randomization Tests for Trading System Development: Algorithms in C++

## Textbooks

- *Introduction to Probability, Statistics, and Random Processes* - Hossein Pishro-Nik
- *Quantitative Trading* - Ernest P. Chan
- *Algorithmic Trading: Winning Strategies and Their Rationale* - Ernest P. Chan
- *Elementary Stochastic Calculus* - Thomas Mikosch
- *A Guide to Brownian Motion and Related Stochastic Processes* - Jim Pitman & Marc Yor
- *Stochastic Calculus: An Introduction with Applications* - Bernt Øksendal
- *Active Portfolio Management* - Richard Grinold & Ronald Kahn
- *Probability and Statistics: The Science of Uncertainty* - Michael J. Evans & Jeffrey S. Rosenthal
- *Analysis of Financial Time Series* - Ruey S. Tsay
- *Data-Driven Science and Engineering* - Steven L. Brunton & J. Nathan Kutz
- *Advances in Financial Machine Learning* - Marcos Lopez de Prado
- *Detecting Regime Change in Computational Finance* - Timothy Masters
- *Trading Systems and Methods* - Perry J. Kaufman
- *The Mathematics of Money Management* - Ralph Vince
- *The Leverage Space Trading Model* - Ralph Vince
- *Testing and Tuning Market Trading Systems* - Timothy Masters
- *Assessing and Improving Prediction and Classification* - Timothy Masters
- *Trading on Sentiment* - Richard L. Peterson
- *Numerical Recipes: The Art of Scientific Computing* - Press, Teukolsky, Vetterling & Flannery
- *An Introduction to Measure Theory* - Terence Tao