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Quantitative Trading Next Steps

Using alternative data and machine learning to build trading signals in Python

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Topic: Data
Chakri Cherukuri

Investors have long developed quantitative trading strategies using structured financial datasets like stock price time series and fundamental data. Recently, unstructured datasets (such as text and images) and corresponding machine learning methods to process them have grown in popularity. These unstructured datasets are called “alternative datasets,” or “alt data” for short. With an exponential increase in the amount of data available and advances in machine learning/deep learning, it’s now possible to process these alternative datasets and use them to build trading signals.

Expert Chakri Cherukuri offers a brief overview of quantitative trading and backtesting before taking you through the different flavors of alt data, focusing on one particular dataset in detail: text data based on news stories and tweets. You’ll explore the machine learning models needed to process these datasets, then explore quantitative approaches for constructing portfolios of stocks based on sentiment scores. Along the way, you’ll discover how to construct long-short factor portfolios, backtest them, and compute performance statistics.

What you'll learn-and how you can apply it

By the end of this live online course, you’ll understand: - What alternative data is - Different flavors of alternative datasets - How to apply machine learning algorithms to process alt data - How to build factor scores from these datasets - How to construct factor portfolios and backtest them

And you’ll be able to: - Build machine learning models and NLP techniques in Python to process text datasets - Learn about factor scoring and long-short factor portfolios - Backtest the trading strategies and compute performance statistics

This training course is for you because...

  • You’re a retail equity investor or a trader who wants to build quantitative strategies.
  • You work at a buy-side trading firm and want to know how to use machine learning to build trading signals using alternative data.

Prerequisites

  • Experience in equities trading and investing
  • A working knowledge of Python, including familiarity with NumPy, pandas, and building classifiers with scikit-learn
  • A basic understanding of the principles of of machine learning, text processing, and natural language processing

Recommended preparation: - Explore “Algorithmic Trading Systems” (module 1 in Hands-on Algorithmic Trading with Python) - Read “Supervised Learning” (chapter 2 in Introduction to Machine Learning with Python)

Recommended follow-up: - Take Hands-On Algorithmic Trading with Python (live online training course with Deepak Kanungo) - Explore Hands-On Algorithmic Trading with Python (learning path) - Read Hands-On Machine Learning for Algorithmic Trading (book)

About your instructor

  • Chakri Cherukuri is a senior researcher in the Quantitative Financial Research group at Bloomberg LP. His research interests include quantitative portfolio management, algorithmic trading strategies and applied machine learning/deep learning. Previously, he built analytical tools for the trading desks at Goldman Sachs and Lehman Brothers. Before that he worked in the Silicon Valley for startups building enterprise software applications. He has extensive experience in scientific computing and software development. He is a core contributor to bqplot, a 2D plotting library for the Jupyter notebook. He holds an undergraduate degree in mechanical engineering from Indian Institute of Technology (IIT), Madras, an MS in computer science from Arizona State University and another MS in computational finance from Carnegie Mellon University.

Schedule

The timeframes are only estimates and may vary according to how the class is progressing

Overview of quantitative trading and backtesting (55 minutes) - Group discussion: What’s your skill set (Python, machine learning, etc.)? Presentation: Overview of quantitative trading and backtesting - Demo: Moving average crossover - Jupyter Notebook exercise: Perform ETF performance analysis

Break (5 minutes)

Factor investing and backtesting (55 minutes) - Presentation: Factor investing basics; equity alpha factors - Jupyter Notebook exercise: Factor scoring and portfolio construction - Demos: Factor scoring and q-spreads; Fama-French factors visualization

Break (5 minutes)

Alternative datasets and ML techniques for text processing (55 minutes) - Presentation: Overview of alternative data; its popularity in finance; NLP and sentiment classification; vendors offering machine learning analytics on alt data; building trading strategies using alt data - Q&A

Break (5 minutes)

Twitter sentiment model and sentiment portfolios (60 minutes) - Presentation: News and Twitter sentiment; constructing sentiment portfolios and backtesting - Demo: Twitter sentiment model - Q&A