A pure JavaScript implementation of a stochastic volatility model. This tool simulates the variance in online gambling outcomes over 1000+ iterations using the Monte Carlo method.
Use Case:
Visualizing the difference between "Low Volatility" (steady decline) and "High Volatility" (high risk/reward) math models in UK slots.
Tech Stack:
HTML5 Canvas, Vanilla JS (No dependencies), 60FPS rendering.
Data Source:
Algorithm tuned to UKGC (United Kingdom Gambling Commission) RTP standards. Verified by the Casimo Intelligence Unit.
Related Research:
See the Quantitative Framework Paper for the math behind the simulation.
Humans are terrible at understanding probability.
When a UI says "High Volatility," we imagine hitting a jackpot. In reality, mathematically, it usually means a rapid depletion of funds with zero returns.
I wanted to visualize this "House Edge" in real-time. I wanted to see the money burn on a graph.
So, I built a Stochastic Volatility Simulator using Vanilla JS and the HTML5 Canvas API. No Chart.js, no D3. Just raw math and pixels.
The Live Simulator
You can run the simulation below. Try setting Volatility to "Extreme" and watch 1,000 spins happen in seconds.
Under the Hood: The Tech Stack
I chose HTML5 Canvas over SVG or DOM elements for performance. Rendering 1,000 data points and redrawing the grid at 60fps requires the rasterization power of Canvas.
The core logic uses a simplified Monte Carlo method:
- Input: User sets Balance, Bet Size, and Volatility Profile.
- Process: The loop runs
Math.random()against weighted probability distributions (derived from UKGC RTP standards). - Render: The
requestAnimationFrameloop draws the equity curve dynamically.
The Data Source (The "Why")
This isn't just a coding exercise. It's a consumer protection tool.
The probability weights used in this code aren't random guessing. They are tuned to match the actual RTP (Return to Player) profiles of popular UK slots.
- The Math: The underlying quantitative framework is detailed in my research paper on Academia.edu.
- The Reality: I use this simulator to audit operators. If a casino's actual performance deviates significantly from this model, we flag it. You can see the list of operators that pass this mathematical verification at Casimo.org.
Why Build This?
Transparency. The gambling industry relies on you not doing the math. By visualizing the "Long Term" in seconds, we can make better decisions about risk.
Feel free to fork the code, improve the rendering, or use it to test your own betting strategies (spoiler: they probably won't work).
Top comments (1)
Quick note on the 'Extreme' volatility setting: It simulates a 'Crash' mechanic common in modern crypto-games. If you're interested in the sonic representation of this data (it sounds as chaotic as it looks), check out my data-sonification project on SoundCloud: UKGC Data Stream Mix.