Monte Carlo Simulation with Python

Ever wondered how professional investors predict stock market risks? How to simulate 10,000 possible futures for Coca-Cola (KO) with just a few lines of Python? In this video, I am showing automation of a Monte Carlo simulation - turning complex statistics into actionable insights with clean, efficient code! 📈

💡 What you will learn:
• Why log returns beat simple returns for statistical modeling
• How to calculate annualized volatility and returns in Python
• Running 10,000 stock price simulations with numpy
• Visualizing risk with histograms and probability distributions
• Key metrics: Probability of loss, worst-case scenarios, and median outcomes

📂 Resources:
• GitHub Code - https://github.com/Vitosh/Python_pers...
• Article at VitoshAcademy https://www.vitoshacademy.com/python-...

⏰ Timestamps:
00:00 - Introduction
00:40- Downloading KO stock data with yfinance
03:43 - The log return advantage (with live demo)
09:30 - Annualizing key metrics in one line
11:10 - Running 10,000 simulations
17:10 - Visualizing price paths and distributions
26:10 - Calculating your real risk of loss
32:05 - Summary. Trying the code with Pepsi and TSLA (PEP, TSLA)

👇 Questions or Suggestions?
Let me know in the comments! If you found this useful, smash that like button 👍, subscribe 🔔, and share with fellow data-driven investors!

📺 More Python Finance Videos:
•    • Python Simple Stock Market Analysis (AAPL ...  
•    • Python - Reading Data From Internet (Wikip...  
•    • Master Stock Analysis with Python and Yfin...  

Why Watch?
Whether you're a beginner investor or a quant finance pro, this tutorial will help you:
✅ Quantify risk like Wall Street analysts
✅ Make data-backed investment decisions
✅ Master probabilistic thinking in 20 minutes

#Python #Investing #DataScience #MonteCarlo