Algorithmic Trading and Python Course Notes

Instructor Introduction

• Instructor: Nick McCollum
• Focus: Building three quantitative finance projects.

Course Projects Overview

1. Equal Weight S&P 500 Index Fund

   • Build an equal weight version of S&P 500 index fund.

2. Quantitative Momentum Strategy

   • Develop a momentum strategy selecting best stocks based on momentum metrics.

3. Quantitative Value Screener

   • Create a screener to select attractive stocks based on value metrics.

Course Housekeeping

• Course is for educational purposes only; not investment advice.
• Made possible by a grant from IEX Cloud.

Algorithmic Trading Basics

• Definition: Using computers to make investment decisions.
• Types of Algorithmic Trading:
  • High Frequency Trading (HFT)
  • Fundamental strategies using software.
• Key Players in Algorithmic Trading:
  • Renaissance Technologies
    • AUM: $165 billion.
    • Famous for Medallion Fund.
  • AQR Capital Management
    • AUM: $61 billion.
    • Focus on applied quantitative research.
  • Citadel Securities
    • AUM: $32 billion.
    • Focus on high-frequency trading.

Using Python for Algorithmic Trading

• Python is popular due to its libraries (e.g., NumPy).
• Python is slower compared to other languages like C.
• NumPy is essential for numerical computing.

Algorithmic Trading Process

1. Collect Data
2. Develop Hypothesis
3. Back Test Strategy
4. Implement Strategy

Course Configuration and API Basics

• API: Application Programming Interface, allows interaction between software.
• We will use IEX Cloud API for stock market data.
• API Request Types:
  • GET: Retrieve data.
  • POST: Send data.
  • PUT: Update data.
  • DELETE: Remove data.

Equal Weight S&P 500 Fund Project

• S&P 500 Index: Market cap weighted index of 500 largest companies in the US.
• Goal: Build an equal weight version where all companies have the same weight.

Quantitative Momentum Strategy Project

• Momentum Investing: Invest in assets that have increased in price the most.
• Example: Choose stock with 35% returns over 20%.

Quantitative Value Screener Project

• Value Investing: Invest in stocks trading below intrinsic value.
• Common metrics:
  • Price to Earnings Ratio (P/E)
  • Price to Book Ratio (P/B)
  • Price to Free Cash Flow Ratio (P/FCF)
• Composite of different metrics to minimize impact of specific metric flaws.

Housekeeping Items for Projects

1. Install Dependencies: NumPy, pandas, XLSX writer.
2. Import Libraries: Import necessary libraries and API keys.

Writing Python Code for Projects

• Use structured approaches: Define functions, utilize loops, etc.
• Handle exceptions and errors gracefully.

Final Thoughts

• Each project builds on skills learned from the previous one.
• Emphasis on real-world applications and data handling.
• Encouragement to explore further in algorithmic trading and finance.