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 Exercises11,049 Learners
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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.
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.What is financial trading50 xpThe concept of trading50 xpPlot a time series line chart100 xpPlot a candlestick chart100 xpGetting familiar with your trading data50 xpResample the data100 xpPlot a return histogram100 xpCalculate and plot SMAs100 xpFinancial trading with bt50 xpThe bt process100 xpDefine and backtest a simple strategy100 xp
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.Trend indicator MAs50 xpCalculate and plot two EMAs100 xpSMA vs. EMA100 xpStrength indicator: ADX50 xpUnderstand the ADX50 xpCalculate the ADX100 xpVisualize the ADX100 xpMomentum indicator: RSI50 xpUnderstand the RSI50 xpCalculate the RSI100 xpVisualize the RSI100 xpVolatility indicator: Bollinger Bands50 xpUnderstand Bollinger Bands50 xpImplement Bollinger Bands100 xp
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.Trading signals50 xpUnderstand trading signals50 xpBuild an SMA-based signal strategy100 xpBuild an EMA-based signal strategy100 xpTrend-following strategies50 xpConstruct an EMA crossover signal100 xpBuild and backtest a trend-following strategy100 xpMean reversion strategy50 xpMatch the signals with the strategies100 xpConstruct an RSI based signal100 xpBuild and backtest a mean reversion strategy100 xpStrategy optimization and benchmarking50 xpConduct a strategy optimization100 xpPerform a strategy benchmarking100 xp
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.Strategy return analysis50 xpReview return results of a backtest100 xpPlot return histograms of a backtest100 xpCompare return results of multiple strategies100 xpDrawdown50 xpReview performance with drawdowns100 xpCalculate and review the Calmar ratio100 xpSharpe ratio and Sortino ratio50 xpEvaluate strategy performance by Sharpe ratio100 xpEvaluate strategy performance by Sortino ratio100 xpCongratulations!50 xp
PrerequisitesIntermediate Python for Finance
Chelsea YangSee More
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.
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