study/README.md

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 and Algorithmic Trading

Phases

Recommended path, generated by CLAUDE

Phase 1 — Math Foundation (*)

1. Pishro-Nik ← in progress, finish this
2. Elementary Stochastic Calculus
3. 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. Trading Systems and Methods — Kaufman (use as reference)
3. The Mathematics of Money Management — Vince
4. The Leverage Space Trading Model — Vince
5. 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++