Leveraging Python to Automate Your Discounted Cash Flow (DCF) Model: A Guide for Startups Seeking VC Funding

Over the past quarter, I have immersed myself in studying Python and R programming languages, driven by a passion to enhance my analytical and financial modeling capabilities. This journey has been both challenging and rewarding, as I navigated through complex coding concepts and applied them to real-world financial scenarios. Through dedicated learning and hands-on projects, I discovered how automating Discounted Cash Flow (DCF) models with Python can significantly streamline the process of presenting financial projections to venture capitalists (VCs). Coupled with my proficiency in Excel, I’ve developed robust methods to ensure precision and scalability in financial analyses. I’m eager to share the knowledge and techniques I’ve acquired, demonstrating how these tools not only showcase technical expertise but also provide a competitive edge in the fast-paced startup ecosystem. By integrating Python and R into financial presentations, I aim to empower fellow entrepreneurs to communicate their visions with clarity and confidence, ultimately fostering stronger relationships with VCs.

Why do this!? Because I have done it too many times on XLS!

If you’re a startup founder or financial analyst, this guide will walk you through the process of using Python to build and automate your DCF model. By integrating Python with Excel, you can save time and focus on refining your pitch to potential investors.

Why Automate Your DCF with Python?

The DCF model is a cornerstone of valuation, often used to estimate the present value of future cash flows. It’s essential for startups to showcase realistic yet compelling financial forecasts when pitching to VCs. However, building these models manually in Excel can be time-consuming and error-prone.

Python’s powerful libraries and ease of integration with Excel make it an excellent tool for automating financial models. Here’s why:

1. Accuracy: Python reduces the risk of human error by automating calculations.
2. Efficiency: Once set up, the model can be reused and adjusted with minimal effort.
3. Scalability: Python can handle large datasets and complex calculations that might slow down Excel.
4. Professionalism: Automating your DCF demonstrates technical skills and preparedness to investors.

Setting Up Your DCF Model in Python

Let’s dive into how you can create and automate a DCF model using Python, tailored for a startup seeking VC funding.

Step 1: Prepare Your Excel File

Start by creating an Excel file with the following inputs:

1. Revenue: Place your startup’s projected revenue in cell B2 (e.g., $1,000,000).
2. Growth Rate: Input your expected annual growth rate in cell B3 (e.g., 5% or 0.05).
3. Discount Rate: Specify the discount rate in cell B4 (e.g., 8% or 0.08).
4. Projection Years: Enter the number of years for your projection in cell B5 (e.g., 5).

Reserve rows starting at B10 for the calculated discounted cash flows (DCF).

Save this file as financial_model.xlsx in your Documents folder.

Step 2: Install Python and Required Libraries

Ensure Python is installed on your system. If not, download it from python.org. You’ll also need the openpyxl library for interacting with Excel. Install it using:

pip3 install openpyxl        

Step 3: Write the Python Script

Here’s the Python script to automate your DCF model:

import openpyxl

# Load the Excel file
file_path = "/Users/marcusmagarian/Downloads/financial_model.xlsx"
wb = openpyxl.load_workbook(file_path)
sheet = wb.active  # Use the active sheet or specify by name

# Read input data (example assumes inputs are in specific cells)
revenue_cell = "B2"  # Whatever your current revenue is
growth_rate_cell = "B3"  # choose your growth rate
discount_rate_cell = "B4"  # Growth rate is your expected growth rate
years_cell = "B5"  # could be 3,5 or 10 etc.

# Fetch values from Excel
revenue = float(sheet[revenue_cell].value)
growth_rate = float(sheet[growth_rate_cell].value)
discount_rate = float(sheet[discount_rate_cell].value)
years = int(sheet[years_cell].value)

# Calculate DCF
cash_flows = [] #this signifies that you are looking to make a list ()=tuples
for year in range(1, years + 1):
    cash_flow = revenue * (1 + growth_rate) ** year #standard MBA equations
    discounted_cf = cash_flow / ((1 + discount_rate) ** year)
    cash_flows.append(discounted_cf)

# Write results back to Excel
start_row = 10  # Example row to start writing DCF values
for i, discounted_cf in enumerate(cash_flows, start=1):
    sheet.cell(row=start_row + i, column=2).value = discounted_cf  # Column B

# Save the updated workbook
wb.save(file_path)

print("DCF values have been calculated and written to the Excel file.")
        

Step 4: Run the Script

Save the script as calculate_dcf.py in your Documents folder (the file name is not important and can be amended).

Open Terminal and navigate to the folder containing your script:

cd ~/Documents        

Run the script:

python3 calculate_dcf.py        

If successful, you’ll see a confirmation message, which is the print("DCF values have been calculated and written to the Excel file."), found above in the code:

DCF values have been calculated and written to the Excel file.        

Step 5: Review the Results

Reopen your Excel file and check the calculated discounted cash flows starting from cell B10. These values represent the present value of your startup’s future cash flows.

Enhancing Your Model

1. Scenario Analysis: Add functionality to model best-case, worst-case, and base-case scenarios.
2. Charts: Use Python libraries like matplotlib to generate cash flow and valuation charts.
3. Integration: Connect your model with APIs (e.g., Alpha Vantage) to fetch live market data.
4. Automation: Use a task scheduler to run the script periodically and keep your financial model up to date.

Takeaways:

Reflecting on my own journey over the past quarter, as I delved into Python and R programming, I discovered how integrating these tools into financial modeling unlocks a new level of efficiency and accuracy. By automating DCF models, I’ve transformed a traditionally manual process into one that is streamlined and scalable.

This experience has not only deepened my understanding of financial analysis but has also empowered me to approach challenges with innovative solutions—a skill I’ve come to value immensely. Sharing these techniques is part of my commitment to helping fellow entrepreneurs and startups build their visions with clarity and precision. Whether pitching to venture capitalists or planning long-term strategies, tools like Python bridge the gap between technical expertise and actionable insights.

If you’re seeking to elevate your financial presentations and stand out in the fast-paced startup ecosystem, I encourage you to explore Python for your modeling needs. Together, we can leverage these advancements to drive success and foster meaningful connections with investors.

THIS IS WHAT IT WILL LOOK LIKE IN YOUR TEXT EDITOR, IN THIS CASE VS CODE BY MICROSOFT (PRINT SCREEN):