This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle
What I Built
I built Polymarket, an open-source project exploring how prediction-market data and automated trading strategies can be used to make market analysis more systematic.
Prediction markets move quickly, especially when tracking short-term cryptocurrency price movements. Manually monitoring prices, identifying opportunities, and evaluating potential trades can be time-consuming.
This project is my attempt to explore that problem through code: using APIs, automation, and algorithmic logic to make market analysis more efficient.
The intended audience includes developers experimenting with prediction-market APIs, algorithmic trading enthusiasts, and people interested in building their own market-analysis tools.
The potential side-hustle opportunity is a tool that helps users monitor markets, evaluate strategies, and make more informed decisions. This is an experimental project, not a promise of profitable trades.
Demo
GitHub repository: github.com/geekyNads/Polymarket
The repository is the primary place to explore the implementation and follow the project's development.
Code
Explore the source code, review the implementation, and contribute:
View the Polymarket repository on GitHub
How I Built It
I approached this project with a focus on programmatic market analysis and automation.
My development process involved exploring how prediction-market data can be accessed, processed, and used to evaluate trading opportunities. I also wanted to understand how automated decision-making could fit into a system where prices and conditions change rapidly.
The broader technical direction includes:
Market data: Working with prediction-market data and APIs.
Automation: Reducing the need for repetitive manual monitoring.
Strategy development: Exploring rule-based approaches to identifying potential opportunities.
Open-source development: Keeping the code available for inspection, experimentation, and future improvements.
My goal is to keep learning by building something practical rather than treating algorithmic trading as a purely theoretical exercise.
Why Does Open Innovation Matter?
Open innovation matters because financial automation should not be a black box.
When the implementation is open source, developers can inspect the logic, understand how decisions are made, identify weaknesses, and propose improvements. They can also experiment with alternative strategies without depending entirely on a closed platform.
For projects involving AI and automated trading, this transparency is especially important. A model or algorithm can be wrong, market conditions can change, and backtested results do not guarantee future performance.
Open-source development makes it easier to learn collaboratively, reproduce experiments, and build tools that others can adapt to their own research.
That is the value I want this project to explore: making experimentation accessible and encouraging people to understand the systems they use.
My Agent Session
I don't have a public agent-session recording to share yet.
Prize Categories
Side Hustle — exploring the potential for an open-source market-analysis and automation tool.
Built by @geekyNads.
Disclaimer: This project is experimental and is not financial advice. Automated trading involves risk, and no strategy guarantees profits.
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