# 📊 Quantitative Trading for Beginners: Build and Backtest a Strategy

By Anh Le · Generated by Everwise

A fast, hands-on beginner path into quantitative trading that follows one thread: take a simple trading idea from raw market data to a cleaned dataset, a coded backtest with realistic costs, and a risk check. Built for learners with basic Python who want a working strategy rather than a theory survey.

- **Level:** Beginner
- **Language:** English
- **Focus:** [strategy design and backtesting](/courses?q=strategy%20design%20and%20backtesting), [statistics and time series](/courses?q=statistics%20and%20time%20series), [market microstructure and data](/courses?q=market%20microstructure%20and%20data), [risk and portfolio management](/courses?q=risk%20and%20portfolio%20management)
- **Practice:** Capstone project
- **Time:** ~4.6h
- **Voice:** A patient tutor walking an absolute beginner through each step

## What you will achieve

By the end of this course you will be able to do a set of concrete things, not just talk about them:

- Pull real price data, clean it, and explain why the cleaning steps matter.
- Compute returns, volatility, and basic time series statistics, and read what they tell you about a market.
- Turn a rough trading idea into precise, testable rules with no hidden ambiguity.
- Build a backtest that accounts for fills, slippage, and fees, so the numbers reflect something closer to reality.
- Read backtest results critically and spot the common ways a strategy fools you (overfitting being the big one).
- Size positions and check core risk metrics before trusting any strategy.
- Build, backtest, and risk-check one complete trading strategy of your own, and explain its results honestly.

That last point is the real target. Everything else supports it.

## Who this course is for

This is a beginner course. You do not need prior trading experience, a statistics background, or a finance coding history.

You should be comfortable with the very basics of Python (variables, functions, loops, importing a library). We keep the coding light and focus on the finance-specific patterns rather than language fundamentals. If you can read a short Python script and follow what it does, you are ready.

No prior knowledge is assumed beyond that. If a concept from statistics or time series shows up, we introduce it as we go.

## Course roadmap

The course runs in three modules, and each one builds on the last.

**Module 1: Market Data and Return Statistics**
We start by clarifying what a quant strategy actually needs to exist, then get and clean price data. You learn returns, volatility, and the time series basics that every later step depends on. The module ends with a checkpoint quiz.

**Module 2: Strategy Design and Backtesting**
This is the spine of the course. You turn an idea into testable trading rules, build the backtest with realistic fills, slippage, and fees, and then learn to read the results without fooling yourself. A checkpoint quiz closes the module.

**Module 3: Risk Control and Capstone Build**
You learn position sizing and the core risk metrics that keep a strategy from blowing up, then assemble everything into your capstone build. A final checkpoint quiz confirms you have the whole picture.

## How the course works

The course is about 275 minutes across 11 lessons. That is a comfortable pace spread over a few sessions, though you can move faster if a topic clicks.

Lessons come in a few formats:

- **Theory lessons** introduce a concept with plain explanations and worked examples.
- **Practice lessons** have you write and run code, so the ideas become habits.
- **The capstone project** is the through-line: you build one strategy end to end across the course.
- **Checkpoint quizzes** at the end of each module confirm you are ready to move on.

You can follow the course linearly, and for a first pass we recommend it. The modules are sequenced so each lesson uses what came before. If you already know a topic (say, cleaning price data), you can skim or skip that lesson, but do not skip the checkpoints: they are quick and they catch gaps before they compound.

## The course project

The project is called the **Capstone Strategy Build**, and it is one continuous thread, not a set of disconnected exercises.

You will pick a trading idea, turn it into exact rules, gather and clean the data it needs, backtest it with realistic costs, read the results honestly, and then apply position sizing and risk checks. By the end you have a single strategy you built yourself, with a backtest and a risk review you can explain to someone else.

Lessons that feed the project:

- **1.2 Getting and Cleaning Price Data** gives you the raw material.
- **1.3 Returns, Volatility, and Time Series Basics** gives you the measurements.
- **2.1 From Idea to Testable Trading Rules** turns your idea into rules.
- **2.2 Building the Backtest: Fills, Slippage, and Fees** produces the results.
- **2.3 Reading Results and Avoiding Overfitting** keeps you honest about them.
- **3.1 Position Sizing and Core Risk Metrics** adds the risk layer.
- **3.2 Capstone: Build and Backtest Your Own Strategy** is where it all comes together.

Each of those lessons contributes a piece you carry forward. By the final lesson, the pieces fit into one finished build.

If you are ready to stop reading about strategies and actually build one, start with lesson 1.1.

## Contents

- **📈 Market Data and Return Statistics**
  - [🧭 What a Quant Strategy Actually Needs](https://everwise.study/courses/quantitative-trading-for-beginners-build-and-backtest-a-strategy/what-a-quant-strategy-needs.md)
  - [🗂️ Getting and Cleaning Price Data](https://everwise.study/courses/quantitative-trading-for-beginners-build-and-backtest-a-strategy/getting-and-cleaning-price-data.md) (sign-in required)
  - [📉 Returns, Volatility, and Time Series Basics](https://everwise.study/courses/quantitative-trading-for-beginners-build-and-backtest-a-strategy/returns-volatility-and-time-series-basics.md)
  - [✅ Checkpoint: Market Data and Statistics](https://everwise.study/courses/quantitative-trading-for-beginners-build-and-backtest-a-strategy/checkpoint-market-data-and-statistics.md)
- **🧪 Strategy Design and Backtesting**
  - [📝 From Idea to Testable Trading Rules](https://everwise.study/courses/quantitative-trading-for-beginners-build-and-backtest-a-strategy/from-idea-to-testable-trading-rules.md)
  - [🧮 Building the Backtest: Fills, Slippage, and Fees](https://everwise.study/courses/quantitative-trading-for-beginners-build-and-backtest-a-strategy/building-backtest-fills-slippage-and-fees.md)
  - [🔍 Reading Results and Avoiding Overfitting](https://everwise.study/courses/quantitative-trading-for-beginners-build-and-backtest-a-strategy/reading-results-and-avoiding-overfitting.md)
  - [✅ Checkpoint: Strategy Design and Backtesting](https://everwise.study/courses/quantitative-trading-for-beginners-build-and-backtest-a-strategy/checkpoint-strategy-design-and-backtesting.md)
- **🛡️ Risk Control and Capstone Build**
  - [⚖️ Position Sizing and Core Risk Metrics](https://everwise.study/courses/quantitative-trading-for-beginners-build-and-backtest-a-strategy/position-sizing-and-core-risk-metrics.md)
  - [🏗️ Capstone: Build and Backtest Your Own Strategy](https://everwise.study/courses/quantitative-trading-for-beginners-build-and-backtest-a-strategy/capstone-build-and-backtest-your-strategy.md) (premium content)
  - [✅ Checkpoint: Risk and Capstone](https://everwise.study/courses/quantitative-trading-for-beginners-build-and-backtest-a-strategy/checkpoint-risk-and-capstone.md) (premium content)
- [Course cheatsheet](https://everwise.study/courses/quantitative-trading-for-beginners-build-and-backtest-a-strategy/cheatsheet.md) (premium content)