How to Create Your Own Open-Source Video Poker Simulator Using Python

Building a custom video poker simulator has become more accessible than ever with modern open-source libraries. If you’re a developer, educator, or data analyst, Python offers all the flexibility you need to design, simulate, and analyse your own version of a video poker game. In this article, we’ll explain how to use the PokerKit library to create such a simulator with correct logic, no broken scripts, and a clean structure — all while keeping it simple enough to extend in future projects.

Getting Started with PokerKit

The PokerKit library is a reliable and actively maintained toolkit for simulating poker mechanics using Python. It allows for complete control over the game environment — from deck handling to hand evaluation and customisable rules. Its modular structure is particularly useful for building games like Joker Poker or Bonus Poker without rewriting core logic.

Unlike many other libraries that focus only on standard poker types, PokerKit is designed with flexibility in mind. It can be used for research, educational tools, or building real money simulations, as it provides core utilities for betting rounds, card combinations, and even statistical modelling of large hand samples.

Thanks to ≈99% test coverage and consistent updates, PokerKit is widely used in simulations that demand trustworthiness. Whether your goal is game development or mathematical analysis, this tool is an excellent starting point in 2025.

Installing the Library and Setting Up

To begin working with PokerKit, simply install it via pip using your Python environment. You’ll also need to configure basic modules such as `Shoe` for managing the deck, and `Hand` for evaluating card combinations. These are the foundations for simulating a poker deal.

Once installed, start by writing your own functions to handle dealing cards and tracking held cards. This is essential for video poker, where the player gets five cards and chooses which ones to keep before drawing replacements. Use functions to separate logic, such as `deal_hand()`, `replace_cards()`, and `evaluate_hand()`.

This separation allows you to keep your code clean and testable. It also enables you to quickly adapt the logic to different poker variants or integrate the engine into a larger game system or learning tool.

Understanding the Structure of a Video Poker Round

A single round of video poker involves several stages: dealing five cards, choosing which cards to hold, redrawing from the remaining deck, evaluating the final hand, and assigning a payout. Each of these actions can be wrapped into its own function for modular control.

Hand evaluation is handled by PokerKit internally, which provides ranking outcomes like pair, two pair, full house, etc. Based on the evaluation, a payout value is assigned. You can define a payout table as a dictionary, mapping hand ranks to coin returns.

It’s important to test each of these steps carefully before scaling your simulation. Build and test the basic game loop first, then add enhancements like wildcards or bonus hands when you’re confident the core mechanics are solid.

Simulating RTP for Your Custom Poker Variant

Once your core logic is implemented and functional, you can simulate RTP (Return to Player) by repeatedly playing hands and calculating the average return. This is essential if you want to balance your game, compare it with real casino games, or ensure fairness for educational use.

To do this, create a loop that deals hands, evaluates them, and sums up the total winnings. After a fixed number of trials — say 100,000 — divide the total payouts by the number of hands to get the average return per hand. This gives you a simulated RTP value.

You can test different strategies by changing which cards are held or adding more complex rules, such as multipliers or bonus hands. By comparing RTP values across different rule sets, you’ll see how each change affects player returns and game balance.

How to Define Payout Rules Clearly

One of the easiest ways to structure your payout logic is to use a dictionary where each key is a hand name and the value is the payout. For example: `”pair”: 1, “two_pair”: 2, “three_of_a_kind”: 3` and so on. This makes your logic transparent and easy to edit later.

Make sure to include only the hand types you want to reward. In some poker variants, a payout starts from a pair of jacks or better. In others, even a single pair may yield coins. This should be clearly defined and consistently applied.

For more complex games like Joker Poker, you’ll need to implement special rules for wild cards. You can use PokerKit’s card classes to manually define if a Joker is present and adjust evaluation accordingly. This takes extra logic but adds depth to your simulation.

Running the Simulation Efficiently

Use a for-loop to play thousands of hands and track the total payouts. This allows you to gather large-scale data on how often players win and how much they earn back. Efficient code and modular logic will speed up this process significantly.

You can store each result in a list or simply update counters as you go. Once done, print the overall RTP percentage. Comparing multiple simulations helps identify which variants are fair, rewarding, or potentially imbalanced.

Always run your simulations with a fixed random seed if you want repeatable results. This is useful when testing changes or presenting findings in research or product development scenarios.

Visualising and Analysing Results

After collecting RTP and payout statistics, it’s time to analyse your data. This step helps validate whether your game is both entertaining and fair. A payout ratio that is too low makes the game frustrating, while one that’s too high may be unsustainable in real money settings.

To visualise data, export the results to a CSV file and use libraries like `pandas` or `matplotlib`. Plotting hand frequency, win percentage, and payout curves gives you a clearer understanding of player experience and game risk levels.

Visuals are especially useful in presentations, educational contexts, or when fine-tuning a game’s economy. They make it easier to communicate how changes in rules or payout tables affect the game’s long-term outcome.

Building Trust with Transparent Design

If you’re releasing your poker simulator to the public or using it in a classroom, transparency matters. Document your rules, payout logic, and simulation results. Show that your game is based on math, not guesswork.

Explain how you calculate payouts and what assumptions you made during simulation. This is essential in environments like education or product design, where clarity and reliability are key expectations.

Encouraging feedback and comparison with real-world games adds credibility and opens opportunities for refinement. The more transparent your simulator, the more valuable it becomes as a learning and testing tool.

Next Steps for Advanced Developers

Once you’ve mastered basic simulation, you can expand the simulator with user interfaces, strategy bots, or export options. Add a GUI with libraries like tkinter, or connect it to a web server using Flask or Django.

You might also want to implement advanced AI that analyses optimal holding strategies. Using decision trees or reinforcement learning, you can explore how machines play poker — a great application in both data science and game theory.

Finally, document your entire build process and make it available as open-source on platforms like GitHub. This invites collaboration, feedback, and evolution — keeping your poker simulator alive and improving in the long term.