Progress · 0/5 phases
📈 📈 Quant Analyst Roadmap — Beginner to Advanced
4 min read · Notion
📈 From statistics fundamentals to building production trading systems. Math, programming, financial markets, derivatives pricing, statistical/ML models, backtesting, and risk management — the complete path to working as a quantitative analyst.
📌 How to use this template
- Work phases in order — each builds on the math/code of the previous one
- Every concept has a corresponding implementation (Python/Go). Quant work is computational — derivations without code are half-finished
- Use the Daily Tracker to log study + code + one market insight per day
- Each phase ends with a project that ties theory to a working artifact (pricer, backtester, model)
💡 The core skill being built: translating a real-world financial question into a mathematical model, implementing it correctly, and knowing exactly where the model breaks down.
🗺️ Roadmap at a glance
| Phase | Focus | Key Outcome |
|---|---|---|
| Phase 1 — Mathematical & Statistical Foundations | Probability, statistics, linear algebra, stochastic calculus, time series | Build the math toolkit every quant model rests on |
| Phase 2 — Programming & Data Infrastructure | Python for quant (NumPy/Pandas), SQL, market data, performance | Clean, fast, production-grade data pipelines |
| Phase 3 — Financial Markets & Instruments | Equities, fixed income, FX, derivatives, market microstructure | Understand what you're actually modelling |
| Phase 4 — Derivatives Pricing & Risk Models | Black-Scholes, binomial trees, Monte Carlo, Greeks, VaR | Price and hedge real instruments |
| Phase 5 — Quant Strategies, ML & Backtesting | Factor models, statistical arbitrage, ML in finance, backtesting engines | Build and rigorously test a trading strategy |
⚡ The quant decision framework
For every model or strategy, ask:
- What is the economic intuition? — if you can't explain it in one sentence to a non-quant, you don't understand it yet
- What are the assumptions, and which ones are false in practice? — every model is wrong; know HOW it's wrong
- What does the data actually look like? — plot it before modelling it. Fat tails, autocorrelation, regime shifts
- How does this fail? — what market conditions break this model (2008, 2020, flash crashes)?
- What's the simplest version that captures 80% of the value? — start simple, add complexity only when justified by out-of-sample performance
- Is this overfit? — in-sample Sharpe of 3.0 with 10 parameters on 2 years of data is a red flag, not a strategy
📊 My progress
- Current phase: Phase 1
- Current day: Day 1
- Models implemented: 0
- Backtests run: 0
- Papers read: 0
🔖 Quick links
- 📐 Phase 1 — Mathematical & Statistical Foundations
- 💻 Phase 2 — Programming & Data Infrastructure
- 🏦 Phase 3 — Financial Markets & Instruments
- ⚖️ Phase 4 — Derivatives Pricing & Risk Models
- 🤖 Phase 5 — Quant Strategies, ML & Backtesting
🛠️ Core tech stack
| Layer | Tools |
|---|---|
| Language | Python (primary), with Go for production systems |
| Numerical computing | NumPy, SciPy, Pandas |
| Statistical modelling | statsmodels, scikit-learn |
| Visualization | Matplotlib, Seaborn, Plotly |
| Backtesting | vectorbt, backtrader, or custom event-driven engine |
| Data sources | yfinance, Quandl, Alpha Vantage, IEX, Bloomberg/Refinitiv (if available) |
| Databases | PostgreSQL/TimescaleDB for tick/OHLCV data |
| Notebooks | Jupyter for research, scripts for production |
| Performance | NumPy vectorization, Numba/Cython for hot loops |
📘 Essential reading list (by phase)
| Book | Use for |
|---|---|
| Introduction to Probability — Blitzstein & Hwang | Phase 1 probability foundation |
| A First Course in Stochastic Processes — Karlin & Taylor | Phase 1 stochastic processes |
| Python for Data Analysis — Wes McKinney | Phase 2 Pandas mastery |
| Options, Futures, and Other Derivatives — John Hull | Phase 3–4 the quant bible |
| Paul Wilmott Introduces Quantitative Finance | Phase 4 derivatives intuition |
| Advances in Financial Machine Learning — Marcos López de Prado | Phase 5 ML pitfalls in finance |
| Quantitative Trading — Ernest Chan | Phase 5 strategy design + backtesting |
| Active Portfolio Management — Grinold & Kahn | Phase 5 factor models |
📅 Quant Daily Tracker