Algorithmic Trading Course Notes

Instructor Information

• Name: Nick McCollum
• Course Type: Free Code Camp

Course Overview

• Focus on building three quantitative finance projects:
  1. Equal Weight S&P 500 Index Fund
  2. Quantitative Momentum Strategy
  3. Quantitative Value Screener

Course Housekeeping

• Funding Source: Grant from IE x cloud.
• Disclaimer: Course is for educational purposes only, not investment advice.

Course Structure

1. Basics of algorithmic trading.
2. API basics.
3. Projects overview:
   • Equal weight S&P 500 index fund.
   • Quantitative momentum strategy.
   • Quantitative value strategy.

Algorithmic Trading Basics

• Definition: Using computers for investment decisions.
• Types of Algorithmic Trading:
  • High-frequency trading.
  • Fundamental strategies modified for efficiency.
• Main Players in Algorithmic Trading:
  • Renaissance Technologies:
    • $165 billion in assets under management.
    • Known for Medallion Fund.
    • Founded by Jim Simons, a math PhD.
  • AQR Capital Management:
    • $61 billion in assets.
    • Name stands for Applied Quantitative Research.
  • Citadel Securities:
    • $32 billion in assets.
    • Known for market making and high-frequency trading.

Python for Algorithmic Trading

• Popularity: Most popular programming language for algorithmic trading due to libraries.
• Downside: Python is slower than some other languages (e.g., C).
• Commonly Used Libraries:
  • NumPy: For numerical computing, popular for its array data structure.

Algorithmic Trading Process

1. Collect data.
2. Develop hypothesis for strategy.
3. Back test the strategy.
4. Implement strategy in production.

Differences in This Course

• Data Source: Using random data from a sandbox API instead of real data.
• Trade Execution: Instead of executing trades, generating order sheets for traders.

API Basics

• API Definition: Application Programming Interface, allows software to interact.
• Types of Requests:
  • GET: Retrieve data.
  • POST: Add data.
  • PUT: Add or overwrite data.
  • DELETE: Remove data.

Equal Weight S&P 500 Fund Project

• Project Goal: Create an equal weight version of the S&P 500 index fund.
• S&P 500 Characteristics:
  • Market capitalization weighted.
  • Covers 500 largest companies in the US.
• Implementation Steps:
  • Import necessary libraries (NumPy, pandas, requests, etc.).
  • Collect data on S&P 500 constituents.
  • Use API to gather stock market data and implement strategy.
  • Save recommendations to Excel file.

Quantitative Momentum Strategy Project

• Objective: Identify stocks with the highest price momentum.
• Momentum Investing: Focus on assets that have increased in price.
• Implementation Steps:
  • Use similar libraries as before.
  • Collect stock data.
  • Apply momentum metrics and back test the strategy.

Quantitative Value Screener Project

• Objective: Identify stocks trading below their perceived intrinsic value.
• Key Concepts:
  • Use multiples (e.g., price-to-earnings ratio, price-to-book ratio).
  • Implement a composite scoring system for valuation metrics.
• Implementation Steps:
  • Collect data on stock valuations.
  • Calculate scores for each stock.
  • Generate recommendations based on scores.

General Notes

• Be cautious with APIs and ensure you handle errors appropriately.
• Understand the significance of each metric used in trading strategies.
• Explore different ways to handle and manipulate data within pandas.