Goldman Sachs GS Quant SDK Lecture Notes

Overview

• Goldman Sachs GS Quant: An open-source Python package for quantitative finance.
• Key Features: Developer page, GitHub repository, extensive documentation, and example Jupyter notebooks.

GitHub Repository

• Contains: Installation instructions, requirements, and code examples.
• Examples: Variety of tutorials and Jupyter notebooks demonstrating the use of the package.

Example Jupyter Notebook Analysis

• Focus: Analysis of FX markets around the last US election cycle.

GS Data Sets

• Types of Data: FX spot, IRS spot, FX volatility, IRS volatility.
• Security Master: Main database of securities, wide coverage given Goldman Sachs’ market presence.
• DateTime Module, API Access: Modules available to handle time series data and API data extraction.
• Requirements: Need Goldman Sachs account for full access and functionality.

Workflow Breakdown

1. Creating a Session: Necessary for data access via client ID and secret.
2. Data Pulling: Key Data sets (Volatility Data):
   • FX spot
   • IRS spot
   • FX volatility
   • IRS volatility
3. Data Processing: Using pivot tables and plotting data.
4. Time series plotting: Analyze changes in the FX volatility across years.

Principal Component Analysis (PCA)

• Components: Three primary components analyzing risk across assets.
• Focus on 2020: Specific analysis of top risk drivers for the year 2020.
• Tools and Modules:
  • sklearn for PCA.
  • matplotlib for plotting.

Key Points of PCA Analysis

• Data Preprocessing: Organizing instruments, setting real volatility windows, extraction of data via loops.
• Data Organization: Creating dataframes for varying instruments and volatility measures.
• PCA Model Training: Split between full data set and 2020 specific data.
• Graphical Representation: Visualizing risk components and their impact on different financial instruments.
• Prediction vs Actual: Analyze and compare predicted risk components vs actual market data.

Resources

• Further Exploration: Jupyter notebooks and GitHub repository for deeper understanding and practical examples.

Remarks

• Accessibility Issues: Need for Goldman Sachs account for comprehensive usage.
• Overall: GS Quant offers robust tools for quantitative finance analysis, rich with examples and extensive datasets.