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Python

Hands-On Algorithmic Trading With Python

Published by O'Reilly Media, Inc.

Design and automate your trading strategies

July 2, 2019

3:00 p.m. - 7:00 p.m. Coordinated Universal Time

This event has ended.

What you’ll learn and how you can apply it

By the end of this live, hands-on, online course, you’ll understand:

  • The advantages and disadvantages of algorithmic trading
  • The different types of models used to generate trading and investment strategies
  • The process and tools used for researching, designing and developing them
  • Pitfalls of backtesting algorithmic strategies
  • Risk-adjusted metrics for evaluating their performance
  • The paramount importance of risk management and position sizing

And you’ll be able to:

  • Use the Pandas library to import, analyze and visualize data from market, fundamental, and alternative sources available for free on the web.
  • Design and automate your own specific investment and trading strategies in Python
  • Backtest and evaluate the performance of your strategies using the Zipline library
  • Prepare for competitions by crowd-sourced hedge funds such as Quantopian to fund your algorithmic trading strategies.

This live event is for you because…

  • You’re a retail equity investor, financial analyst, or trader who wants to develop algorithmic trading strategies and mitigate the disadvantages of emotional, manual trading.

Prerequisites

  • Basic experience trading and investing in equities
  • Basic knowledge of Python and Pandas data frames
  • Create a Google Colab document: https://colab.research.google.com/

Recommended preparation:

Recommended follow-up:

Schedule

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

Overview of investment/trading process and various models (60 minutes)

  • Presentation: Brief review of financial/economic models including the Capital Asset Pricing Model, Arbitrage Pricing Model, and Multi-Factor Models. The presentation will focus on the concepts and not mathematical formulas
  • Discussion: We will discuss financial concepts and issues
  • Presentation: Brief review of technical analysis and common trading strategies, including trending and mean-reverting strategies.
  • Q&A: On the materials presented in this section
  • Break (5 minutes)

Sourcing and analyzing market, fundamental and alternative data to design strategies (60 minutes)

  • Exercise: Setup Colab notebook. Create pandas dataframes to import data from freely available public sources such as FRED (economic), IEX (equity), Alpha Vantage (various), Quandl(various), EDGAR (fundamental), Stocktwits (alternative)
  • Discussion: We will discuss the pros and cons of various data sources. Other issues of data extraction, cleaning, and storage
  • Exercise: Use pandas dataframes and plotting functions to analyze and visualize data. This generally leads to insights and generating trade ideas
  • Q&A: On materials presented in this section
  • Break (5 minutes)

Developing and backtesting algorithmic trading strategies in Zipline (60 minutes)

  • Exercise: Import Zipline library into the notebook. Create a simple moving average crossover strategy discussed earlier
  • Discussion: Analysis of the implemented algorithmic strategy
  • Exercise: Backtest the trading strategy over 36 months
  • Discussion: Analysis of the backtest results
  • Q&A: On materials presented in this section
  • Break (5 minutes)

Evaluating risk-adjusted performance and preparing to compete in funding contests (60 minutes)

  • Presentation: Review the pitfalls of backtesting, especially overfitting and hindsight bias. Overview of risk-adjusted performance metrics including Sharpe/information and Sortino ratios. Introduction to the Kelly Criterion for money management
  • Discussion: About the paramount importance of risk management and position sizing in trading
  • Discussion: Next steps including forward testing/paper trading. Issues and risks with going live with algorithmic trading. Competing for funds in contests hosted by firms such as Quantopian
  • Q&A: On the entire course

Your Instructor

  • Deepak Kanungo

    Deepak Kanungo is the founder and CEO of Hedged Capital LLC, an AI-powered, proprietary trading and analytics firm. Previously, Deepak was a financial advisor at Morgan Stanley, a Silicon Valley fintech entrepreneur, and a director in the Global Planning Department at Mastercard International. He was educated at Princeton University (astrophysics) and the London School of Economics (finance and information systems).

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