Quantitative trading with Python
Build a momentum trading strategy to predict stock prices
Join expert Harshit Tyagi to learn the basics of quantitative analysis, from data processing to trading signal generation with stocks. In this practical, handson training course, you'll use Python to work with historical stock data and develop trading strategies based on the momentum indicator. You'll then discover how to perform a statistical test on the mean of the returns to conclude if there is alpha in the signal.
Whether you want to pursue a new job in finance, get started on the path to a quant trading career, or master the latest AI applications in quantitative finance, this course offers you the opportunity to master valuable data and AI skills that will get you there.
What you'll learnand how you can apply it
By the end of this live online course, you’ll understand:
 How to backtest a trading strategy
 Market mechanics
 How to generate signals with stocks
And you’ll be able to:
 Build a trading strategy based on momentum and momentum crashes
 Implement and test your strategy in Python
 Statistically test the strategies you've built
This training course is for you because...
 You're a programmer or data scientist with a background in fintech who wants to pursue a job in finance or launch yourself on the path to becoming a quant trader.
 You're a data analyst (or you have strong fundamentals in programming and statistics), and you want to work as a quant analyst at an investment bank or a hedge fund.
Prerequisites
 A working knowledge of Python, pandas, and Matplotlib
 A basic understanding of statistics, linear algebra, and calculus
 Familiarity with momentum trading
Recommended preparation:
 Read "Momentum Traders" (article)
 Read "Python Infrastructure" (chapter 2 in Python for Finance)
 Read "ObjectOriented Programming" (chapter 6 in Python for Finance)
 Set up the course environment (see course GitHub repository for instructions)
About your instructor

Harshit Tyagi is a full stack developer and data engineer at Elucidata, a biotech company based in Cambridge, where he develops algorithms for research scientists at some of the world’s best medical schools, including Yale, UCLA, and MIT. Previously, he was a systems development engineer at the investment management firm Tradelogic, where he designed a framework to analyze financial news from prominent sources to produce accurate trading signals. He’s a Python evangelist and loves to contribute to tech communities, including Google Developers Groups and Python Delhi User Groups, as well as other online learning platforms.
Schedule
The timeframes are only estimates and may vary according to how the class is progressing
Introduction to quantitative trading (50 minutes)
 Lecture: Quantitative trading overview; how to access data of any stock or instrument from Oanda
 Group discussion: What is momentum trading? What are log returns? Why should you use Python for trading? How do you get the data for algorithm trading?
 Handson exercise: Read and plot data; correct the format of data extracted
 Q&A
 Break (10 minutes)
Building your strategy on the data collected (50 minutes)
 Lecture: Statistical time series analysis; calculating log returns; using NumPy to calculate log returns and add them to the data frame; building your strategy over those intervals
 Group discussion: Types of log returns, different ways of calculating log returns, deciding intervals/periods over which the log returns are to be calculated
 Handson exercise: Plot the strategies and return
 Q&A
 Break (10 minutes)
Backtest the strategy (50 minutes)
 Lecture: The statistical theory behind backtesting strategies; the significance of pvalue, ttests, and the Sharpe ratio
 Group discussion: What is pvalue? What is hypothetical testing? What is the Sharpe ratio?
 Handson exercise: Backtest your strategy in IPython
 Wrapup and Q&A (10 minutes)
Takehome exercise:
 Working with historical data of a given stock universe, generate a trading signal based on a momentum indicator. Then compute the signal, produce projected returns, and perform a statistical test to conclude if there is alpha in the signal.