Skip to main content

Financial Trading in Python

Learn to implement custom trading strategies in Python, backtest them, and evaluate their performance!

Start Course for Free
4 Hours15 Videos50 Exercises8,197 Learners4000 XP

Create Your Free Account



By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA. You confirm you are at least 16 years old (13 if you are an authorized Classrooms user).

Loved by learners at thousands of companies

Course Description

Are you fascinated by the financial markets and interested in financial trading? This course will help you to understand why people trade, what the different trading styles are, and how to use Python to implement and test your trading strategies. Start your trading adventure with an introduction to technical analysis, indicators, and signals. You'll learn to build trading strategies by working with real-world financial data such as stocks, foreign exchange, and cryptocurrencies. By the end of this course, you'll be able to implement custom trading strategies in Python, backtest them, and evaluate their performance.

  1. 1

    Trading Basics


    What is financial trading, why do people trade, and what’s the difference between technical trading and value investing? This chapter answers all these questions and more. You’ll also learn useful tools to explore trading data, generate plots, and how to implement and backtest a simple trading strategy in Python.

    Play Chapter Now
    What is financial trading
    50 xp
    The concept of trading
    50 xp
    Plot a time series line chart
    100 xp
    Plot a candlestick chart
    100 xp
    Getting familiar with your trading data
    50 xp
    Resample the data
    100 xp
    Plot a return histogram
    100 xp
    Calculate and plot SMAs
    100 xp
    Financial trading with bt
    50 xp
    The bt process
    100 xp
    Define and backtest a simple strategy
    100 xp
  2. 2

    Technical Indicators

    Let's dive into the world of technical indicators—a useful tool for constructing trading signals and building strategies. You’ll get familiar with the three main indicator groups, including moving averages, ADX, RSI, and Bollinger Bands. By the end of this chapter, you’ll be able to calculate, plot, and understand the implications of indicators in Python.

    Play Chapter Now
  3. 3

    Trading Strategies

    You’re now ready to construct signals and use them to build trading strategies. You’ll get to know the two main styles of trading strategies: trend following and mean reversion. Working with real-life stock data, you’ll gain hands-on experience in implementing and backtesting these strategies and become more familiar with the concepts of strategy optimization and benchmarking.

    Play Chapter Now
  4. 4

    Performance Evaluation

    How is your trading strategy performing? Now it’s time to take a look at the detailed statistics of the strategy backtest result. You’ll gain knowledge of useful performance metrics, such as returns, drawdowns, Calmar ratio, Sharpe ratio, and Sortino ratio. You’ll then tie it all together by learning how to obtain these ratios from the backtest results and evaluate the strategy performance on a risk-adjusted basis.

    Play Chapter Now


Google Stock DataBitcoin Price DataAmazon Stock DataTesla Stock Data


hadrien-d4e73b49-bc29-46b7-a485-2f598f38e3b9Hadrien Lacroixjustin-saddlemyerJustin Saddlemyerjen-bf588059-33e3-4103-89aa-774fde25261eJen Bricker
Chelsea Yang Headshot

Chelsea Yang

Data Science Instructor

Chelsea is a senior quantitative analyst with over a decade’s experience working for top asset managers and financial institutions. She is a data science enthusiast and passionate about its application in finance. She has expertise in financial modeling, risk management, and machine learning. Chelsea holds a Master's degree in Management Information Systems from Carnegie Mellon University. In her spare time, she enjoys writing Python programs to test her trading ideas.
See More

What do other learners have to say?

I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden
Harvard Business School

DataCamp is by far my favorite website to learn from.

Ronald Bowers
Decision Science Analytics, USAA