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@@ -8,6 +8,41 @@ Current Goal: **Learn Quantitative Foundations**
Current reading [Introduction to Probability, Statistics, and Random Processes - Hossein Pishro-Nik](./textbooks/000_Introduction%20to%20Probability,%20Statistics,%20and%20Random%20Processes/Introduction%20to%20Probability,%20Statistics,%20and%20Random%20Processes%20-%20Hossein%20Pishro-Nik.pdf)
## Phases
### Phase 1 — Math Foundation (you're here)
1. Pishro-Nik ← in progress, finish this
2. Elementary Stochastic Calculus
3. A guide to Brownian motion
### Phase 2 — Get Practical Early (don't wait on this)
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
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# Algorithmic Trading - Winning Strategies and their rationale
[pdf](./Algorithmic%20Trading%20Winning%20Strategies%20and%20their%20rationale.pdf)
total pages=225
**Currently reading:** chapter 1, page 1