CoreWeave’s borrowing costs just fell from 10% to 7%. The AI data centre debt market is repricing risk in real time.

Learn how to analyze bond investments and data center capacity using Python, simulating the financial scenario of CoreWeave's $1.59 billion bond issuance.

Introduction

In this tutorial, you'll learn how to work with financial data related to high-yield bonds and data center investments using Python. We'll simulate the process of analyzing bond yields and calculating investment returns, similar to what financial analysts might do when evaluating companies like CoreWeave that raise capital through bonds. This tutorial will teach you how to:

Work with financial data using Python
Calculate bond yields and returns
Simulate investment scenarios

This is a beginner-friendly tutorial that doesn't require any prior financial knowledge, just basic Python skills.

Prerequisites

To follow this tutorial, you'll need:

A computer with Python installed (version 3.6 or higher)
Basic understanding of Python variables and functions
Optional: A code editor like VS Code or Jupyter Notebook

Step-by-Step Instructions

Step 1: Set Up Your Python Environment

First, we'll create a simple Python script to work with financial calculations. Open your code editor and create a new file called financial_analysis.py.

Step 2: Import Required Libraries

We'll need the math library for some calculations:

import math

print("Financial Analysis Tool for Bond Investments")

This imports the math library and prints a welcome message. The math library will help us with some financial calculations later.

Step 3: Create a Function to Calculate Bond Yield

Let's create a function that calculates the yield of a bond:

def calculate_yield(face_value, coupon_rate, market_price):
    """Calculate the yield of a bond"""
    annual_coupon = face_value * coupon_rate
    yield_percentage = (annual_coupon / market_price) * 100
    return yield_percentage

# Test the function
face_value = 1000  # $1,000 face value
coupon_rate = 0.07  # 7% coupon rate
market_price = 950  # Market price is $950

yield_result = calculate_yield(face_value, coupon_rate, market_price)
print(f"Bond yield: {yield_result:.2f}%")

This function calculates the yield based on the face value, coupon rate, and market price. It's similar to what investors like those analyzing CoreWeave's bonds would do.

Step 4: Simulate the CoreWeave Scenario

Now, let's simulate the scenario from the news article where CoreWeave's borrowing costs fell from 10% to 7%:

def compare_bond_costs(old_rate, new_rate, investment_amount):
    """Compare old and new bond costs"""
    old_cost = investment_amount * old_rate
    new_cost = investment_amount * new_rate
    savings = old_cost - new_cost
    
    print(f"\nInvestment Amount: ${investment_amount:,}")
    print(f"Old borrowing cost: {old_rate*100}% = ${old_cost:,}")
    print(f"New borrowing cost: {new_rate*100}% = ${new_cost:,}")
    print(f"Savings: ${savings:,}")
    
    return savings

# Simulate CoreWeave's scenario
investment = 1590000000  # $1.59 billion investment
old_rate = 0.10  # 10% old rate
new_rate = 0.07  # 7% new rate

savings = compare_bond_costs(old_rate, new_rate, investment)

This simulates the financial benefit that CoreWeave received when their borrowing costs decreased from 10% to 7% on their $1.59 billion bond issuance.

Step 5: Create a Data Center Capacity Calculator

Let's also calculate the computing capacity mentioned in the article:

def calculate_capacity_info(data_center_size_mw, years, contracts):
    """Calculate capacity and contract details"""
    total_capacity = data_center_size_mw * years
    
    print(f"\nData Center Analysis:")
    print(f"Capacity: {data_center_size_mw} MW per year")
    print(f"Contract Duration: {years} years")
    print(f"Total Capacity: {total_capacity} MW-years")
    print(f"Number of Contracts: {contracts}")
    
    return total_capacity

# CoreWeave's scenario
mw_capacity = 150  # 150 megawatts
contract_years = 15  # 15-year contract
number_of_contracts = 1  # Single contract

capacity = calculate_capacity_info(mw_capacity, contract_years, number_of_contracts)

This function calculates the total computing capacity that CoreWeave would provide over the 15-year contract period.

Step 6: Run the Complete Analysis

Now, let's put everything together in one complete program:

import math

print("=== Financial Analysis for CoreWeave's Bond Issuance ===")

# Function to calculate bond yield
def calculate_yield(face_value, coupon_rate, market_price):
    annual_coupon = face_value * coupon_rate
    yield_percentage = (annual_coupon / market_price) * 100
    return yield_percentage

# Function to compare bond costs
def compare_bond_costs(old_rate, new_rate, investment_amount):
    old_cost = investment_amount * old_rate
    new_cost = investment_amount * new_rate
    savings = old_cost - new_cost
    
    print(f"\nInvestment Amount: ${investment_amount:,}")
    print(f"Old borrowing cost: {old_rate*100}% = ${old_cost:,}")
    print(f"New borrowing cost: {new_rate*100}% = ${new_cost:,}")
    print(f"Savings: ${savings:,}")
    
    return savings

# Function to calculate capacity
def calculate_capacity_info(data_center_size_mw, years, contracts):
    total_capacity = data_center_size_mw * years
    
    print(f"\nData Center Analysis:")
    print(f"Capacity: {data_center_size_mw} MW per year")
    print(f"Contract Duration: {years} years")
    print(f"Total Capacity: {total_capacity} MW-years")
    print(f"Number of Contracts: {contracts}")
    
    return total_capacity

# Run the analysis
print("Analyzing CoreWeave's financial scenario:")

# Bond analysis
investment = 1590000000  # $1.59 billion
old_rate = 0.10  # 10% old rate
new_rate = 0.07  # 7% new rate

savings = compare_bond_costs(old_rate, new_rate, investment)

# Capacity analysis
mw_capacity = 150  # 150 megawatts
contract_years = 15  # 15-year contract

capacity = calculate_capacity_info(mw_capacity, contract_years, 1)

print(f"\n=== Summary ===")
print(f"CoreWeave saved ${savings:,} by reducing borrowing costs from 10% to 7%")
print(f"They will provide {capacity} MW-years of computing capacity over 15 years")

This complete program simulates the financial analysis that would be performed when evaluating CoreWeave's bond issuance and data center investment.

Step 7: Test Your Program

Save your file and run it in your Python environment. You should see output similar to:

=== Financial Analysis for CoreWeave's Bond Issuance ===

Analyzing CoreWeave's financial scenario:

Investment Amount: $1,590,000,000
Old borrowing cost: 10.0% = $159,000,000
New borrowing cost: 7.0% = $111,300,000
Savings: $47,700,000

Data Center Analysis:
Capacity: 150 MW per year
Contract Duration: 15 years
Total Capacity: 2250 MW-years
Number of Contracts: 1

=== Summary ===
CoreWeave saved $47,700,000 by reducing borrowing costs from 10% to 7%
They will provide 2250 MW-years of computing capacity over 15 years

Notice how the program shows that CoreWeave saved $47.7 million by reducing their borrowing costs, which is similar to what happened in the real scenario described in the news article.

Summary

In this tutorial, you've learned how to:

Set up a Python environment for financial analysis
Create functions to calculate bond yields and compare borrowing costs
Simulate real-world financial scenarios like CoreWeave's bond issuance
Calculate computing capacity for data centers

These skills help you understand how financial analysts might approach analyzing companies that raise capital through bonds, like CoreWeave did with their $1.59 billion bond issuance. The tutorial demonstrates how a 3% reduction in borrowing costs can result in millions of dollars in savings for large companies.

Remember that this is a simplified simulation. Real financial analysis involves more complex calculations and considerations, but this gives you a foundation for understanding how financial data can be analyzed using Python.