Skip to content

README.md

Latest commit

 

History

History
55 lines (30 loc) · 2.43 KB

README.md

File metadata and controls

55 lines (30 loc) · 2.43 KB
Raw

Kalshi Bet Predictor

This repository contains a set of Python scripts designed to find equivalent binary markets across Kalshi and Polymarket, use an LLM via OpenAI and web search via Exa to generate an independent prediction, and then calculate the trading "edge" on both platforms.

Core Components

The project is structured around three main scripts:

find_equiv_markets.py: A utility script to automatically search Kalshi and Polymarket APIs, use a Sentence Transformer and FAISS vector index to find markets with similar titles (i.e., equivalent questions), and save potential matches to a CSV file for manual review.

analyst.py and main.py: These scripts form the core prediction engine.

  • analyst.py handles API interactions with OpenAI (for prediction and question generation) and Exa (for web information retrieval).

  • main.py fetches the current prices from both Kalshi and Polymarket, runs the prediction via BetAnalyst, and calculates the trading edge against the model's prediction. This logic is intended to be hosted on Cerebrium

  • compare.py: This script reads the market pairs from the CSV, asynchronously calls the hosted prediction endpoint for each pair, and compiles statistics on the trading edge and which platform offers a better opportunity more frequently.

Prerequisites

You will need API keys for the following services:

  • OpenAI: For the large language model (BetAnalyst class).

  • Exa: For semantic search/information retrieval (BetAnalyst class).

  • Cerebrium (or similar hosting platform): To deploy the main.py and analyst.py logic as a prediction endpoint.

Create a .env file in your project root to store your keys:

OPENAI_API_KEY="your_openai_key" EXA_API_KEY="your_exa_key"

Setup and Installation

Dependencies

Install the required Python packages:

pip install -r requirements.txt

Workflow

  1. Host the prediction service by deploying main.py and analyst.py on Cerebrium to expose a predict endpoint that runs the BetAnalyst logic.
  2. Run find_equiv_markets.py to identify equivalent Kalshi and Polymarket markets and export the candidate pairs to a CSV file.
  3. Execute compare.py, which loads the CSV pairs, calls the hosted prediction endpoint for each pair, and aggregates the edge statistics to highlight the most favorable markets.