Part 2: Quantitative Fundamentals
Goal for this stage: Build a solid foundation. Understand how markets work, how data is processed, how strategies are validated, and where risks come from. Without these fundamentals, the Agents we build later are castles in the air.
Lessons
| Lesson | Topic | Deliverable |
|---|---|---|
| Lesson 02 | Financial Markets and Trading Basics | Understand market structure and trading costs |
| Lesson 03 | Math and Statistics Fundamentals | Master the mathematical language of returns and risk |
| Lesson 04 | The Real Role of Technical Indicators | Understand that indicators are feature engineering, not holy grails |
| Lesson 05 | Classic Strategy Paradigms | Master trend following, mean reversion, and other foundational strategies |
| Lesson 06 | The Harsh Reality of Data Engineering | Data pipeline scripts |
| Lesson 07 | Backtest System Pitfalls | Backtesting framework template |
| Lesson 08 | Beta, Hedging, and Market Neutrality | Beta decomposition and hedge ratio calculations |
Background Knowledge
| Document | Description | Suggested Reading Time |
|---|---|---|
| Exchanges and Order Book Mechanics | Differences between L1/L2/L3 data | 15 min |
| High-Frequency Trading and Market Microstructure | Understanding HFT strategies, Kyle's Lambda | 20 min |
| HF Market Microstructure | Deep dive into order books, spreads, market impact | 20 min |
| Tick-Level Backtesting Framework | Event-driven backtesting, queue position simulation | 25 min |
| Statistical Traps of Sharpe Ratio | Estimation error, multiple testing, Deflated Sharpe | 20 min |
| Candlestick Patterns and Volume Analysis | Candlestick patterns, volume analysis, and quantitative applications | 20 min |
| Cryptocurrency Trading Characteristics | Unique challenges of 24/7 markets | 10 min |
| Data Sources and API Comparison | Choosing the right data source | 10 min |
After Completing This Stage
You will be able to:
- Understand the characteristics of different markets: stocks, futures, cryptocurrencies
- Describe returns and risk using mathematical language
- Recognize the real value and limitations of technical indicators
- Build data pipelines, handling API rate limits, missing data, and other issues
- Avoid common backtesting pitfalls (overfitting, lookahead bias, etc.)
- Understand the difference between Beta and Alpha, and know where returns come from
- Calculate hedge ratios and understand why market neutrality is difficult
Next Stage
→ Part 3: Machine Learning - From Traditional Models to Agents