Free Code Camp Course: Algorithmic Trading and Python

Instructor: Nick McCollum

• Focus on building three large quantitative finance projects:
  1. Equal weight version of the S&P 500 index fund
  2. Quantitative momentum strategy
  3. Quantitative value screener

Key Points:

• Course sponsored by IEX Cloud (educational purposes only)
• Overview of algorithmic trading and APIs
• Use of Python in algorithmic trading
• Understanding batch API calls and handling data with pandas

Course Overview:

Basics of Algorithmic Trading

• Definition: Using computers to make investment decisions
• Range from high-frequency trading to fundamental strategies
• Importance of speed in trading execution
• Key players: Renaissance Technologies, AQR Capital Management, Citadel Securities
• Python as a popular language for algorithmic trading due to libraries

Algorithmic Trading Process

1. Collect data
2. Develop a hypothesis for a strategy
3. Backtest the strategy
4. Implement real trading with the strategy

Course Projects Overview

• Project 1: Equal Weight S&P 500

  • Focus on equal weighting versus market cap weighting
  • Use of pandas to manage data and XLSX writer for output

• Project 2: Quantitative Momentum Strategy

  • Identify high momentum stocks based on price movement
  • Use of percentile scores and Python loops for data handling

• Project 3: Quantitative Value Screener

  • Focus on identifying value stocks using metrics
  • Use of composite scores to combine various valuation metrics

Detailed Project Notes

Project 1: Equal Weight S&P 500

• Import libraries: NumPy, pandas, requests, XLSX writer, math
• Load stock data and API token
• Create batch API calls to IEX Cloud for stock data
• Calculate equal weight portfolio
• Export results to Excel using XLSX writer

Project 2: Quantitative Momentum Strategy

• Import and configure libraries
• Use of various momentum metrics (1 year, 6 months, 3 months, 1 month)
• Calculate percentile scores using SciPy stats
• Identify top 50 momentum stocks and calculate number of shares
• Export to Excel

Project 3: Quantitative Value Screener

• Import necessary libraries
• Focus on value metrics: P/E, P/B, P/S, EV/EBITDA, EV/Gross Profit
• Handle missing data using pandas fill methods
• Calculate composite value scores and identify top value stocks
• Export to Excel

Additional Notes:

• Importance of using Python functions for repetitive tasks
• Use of try-except blocks for error handling
• Detailed use of pandas for data manipulation
• Hands-on practice with API requests and data analysis

These notes cover the key concepts and processes of the course. Each project builds on the skills learned in previous sections, applying them to different financial strategies in algorithmic trading. The use of Python libraries such as pandas and NumPy, along with API data, provides a practical foundation for developing quantitative finance strategies.