Live Online training

# Quantitative trading with Python

## What you'll learn-and 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:

## 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?
• Hands-on 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
• Hands-on 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 p-value, t-tests, and the Sharpe ratio
• Group discussion: What is p-value? What is hypothetical testing? What is the Sharpe ratio?
• Hands-on exercise: Backtest your strategy in IPython
• Wrap-up and Q&A (10 minutes)

Take-home 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.