Cheatsheet: Quantitative Trading for Beginners: Build and Backtest a Strategy
A lookup card for the whole course: data, stats, strategy rules, backtesting, and risk. Skim the table of contents, then jump to the section you need.
Table of Contents
| Section | Covers |
|---|---|
| 1. What a Strategy Needs | 1.1 |
| 2. Market Data and Cleaning | 1.2 |
| 3. Returns and Volatility | 1.3 |
| 4. From Idea to Rules | 2.1 |
| 5. Backtest Mechanics | 2.2 |
| 6. Reading Results and Overfitting | 2.3 |
| 7. Position Sizing and Risk | 3.1 |
| 8. Capstone Workflow | 3.2 |
| 9. Checkpoint Answers | 1.4, 2.4, 3.3 |
1. What a Quant Strategy Actually Needs (1.1)
A tradable strategy is a complete, testable loop, not just an idea.
| Component | Question it answers | Example |
|---|---|---|
| Universe | What do I trade? | US large-cap equities |
| Signal | When do I want to be long/short? | 20-day momentum > 0 |
| Entry rule | Exact trigger | Close above 20-day MA |
| Exit rule | Exact trigger | Close below 20-day MA |
| Position size | How much? | 2% risk per trade |
| Costs | What does trading cost? | Commission + slippage |
| Risk limit | When do I stop? | Max 10% drawdown |
The rule of thumb: if you cannot write the rule so a computer could follow it with no judgment, it is not yet a strategy.
- In-sample vs out-of-sample: design on one slice of history, confirm on a slice you never looked at.
- A hypothesis before a rule: "stocks that rose over 3 months keep rising short-term" -> then translate to rules.
- Kill criteria up front: decide what result would make you abandon the idea.
Note: A strategy with no costs, no exit rule, or no sizing is a signal study, not a backtest. Add all three before trusting any number.
2. Market Data and Cleaning (1.2)
Data types
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