Open-source GitHub repositories democratize access to sophisticated Polymarket trading infrastructure previously available only to well-funded quant firms and professional traders. This comprehensive guide ranks the top 10 free GitHub repos by functionality, ease of use, and profitability potential, provides detailed descriptions of features and capabilities, explains setup requirements and technical prerequisites, compares weather trading bots using different forecast models, reviews AI agent frameworks for autonomous trading, and offers implementation guidance for traders at beginner, intermediate, and advanced skill levels.
Top 10 GitHub Repositories Ranked
The following repositories represent highest-quality open-source Polymarket trading tools available in 2026.
1. alteregoeth-ai/weatherbot - Pure Python Weather Trading
- Category: Weather Trading Bot
- Skill Level: Beginner to Intermediate
- GitHub: https://github.com/alteregoeth-ai/weatherbot
Overview
Pure Python implementation focusing exclusively on weather market profitability through three complementary forecast sources providing ensemble probability estimates.
Key Features
Multi-Model Forecasting: Integrates ECMWF (European Centre for Medium-Range Weather Forecasts) providing global 10-day forecasts with 0.1°C accuracy, HRRR (High Resolution Rapid Refresh) offering hourly 18-hour forecasts for US cities with 0.05°C precision ideal for same-day temperature markets, and METAR observations from airport weather stations confirming current conditions and short-term trends.
Temperature Bucket Analysis: Polymarket temperature markets ask "Will NYC temperature exceed 85°F tomorrow?" or "Will the temperature fall between 70-75°F?" The bot calculates probability distributions from ensemble forecasts identifying which temperature buckets show mispricing relative to model predictions.
Expected Value Calculation: For each market, compare Polymarket price (implied probability) to forecast model probability calculating expected value. Only executes trades when EV exceeds 8 to 12 percent threshold ensuring positive long-term returns after 2 percent Polymarket fees.
Kelly Criterion Position Sizing: Implements full Kelly and fractional Kelly (25%, 50% Kelly) position sizing based on estimated edge and bankroll automatically calculating optimal bet amounts maximizing growth while limiting drawdown risk from forecast errors or unexpected weather events.
Simulation Mode: Backtest strategies against historical weather data and Polymarket market prices without risking capital. Evaluate win rates, ROI, Sharpe ratios, and maximum drawdowns before deploying real money positions..
2. suislanchez/polymarket-kalshi-weather-bot - Cross-Platform Trading
- Category: Weather Trading Bot, Arbitrage
- Skill Level: Intermediate
- GitHub: https://github.com/suislanchez/polymarket-kalshi-weather-bot
Overview
Sophisticated cross-platform implementation trading weather markets simultaneously on Polymarket and Kalshi exploiting pricing discrepancies and ensemble forecast analysis.
Key Features
31-Member GFS Ensemble: Uses Global Forecast System ensemble from Open-Meteo API running 31 parallel forecasts with slightly different initial conditions. Ensemble agreement represents probability if 25 of 31 members predict temperature exceeding 85°F, implied probability is 80.6%.
Consensus Probability Calculation: Converts ensemble member agreement into calibrated probabilities accounting for forecast bias and historical accuracy. Temperature forecasts show systematic cold bias in winter requiring +1.2°F adjustment, hot bias in summer requiring -0.8°F correction improving probability estimates.
8% Edge Threshold: Only trades when model probability exceeds Polymarket/Kalshi market price by 8 percentage points or more. This conservative threshold ensures positive expected value after 2% Polymarket fees or Kalshi's $1 + 7% fee structure.
Cross-Platform Arbitrage: Identifies same weather events priced differently on Polymarket versus Kalshi. If Polymarket shows "NYC exceeds 85°F" at 45 cents and Kalshi shows an equivalent market at 38 cents, simultaneous positions capture a 7 cent edge.
React Dashboard: Web-based interface displaying active positions, ensemble forecast visualizations, profit/loss tracking, and market scanning results. Non-technical users can monitor automated trading without command line interaction.
FastAPI Backend: RESTful API enabling mobile app development, webhook integrations, and third-party tool connections extending bot capabilities beyond core weather trading.
SQLite Database: Stores historical trades, forecast accuracy, market prices, and performance metrics enabling backtesting, strategy refinement, and tax reporting documentation.
3. hcharper/polyBot-Weather - Home Hardware Friendly
- Category: Multi-Strategy Bot
- Skill Level: Beginner to Intermediate
- GitHub: https://github.com/hcharper/polyBot-Weather
Overview
Designed for regular consumer hardware (Mac Mini, home PC, residential internet) combining weather trading, crypto markets, and whale copy trading in a unified system.
Key Features
Gaussian Probability Model: Builds normal distribution probability curves from NOAA forecast data with standard deviation estimates. For temperature forecasts showing 75°F ± 3°F, creates probability distribution calculating likelihood across all temperature ranges enabling bucket probability estimation.
Multi-Strategy Implementation: Weather markets using NOAA data, crypto markets applying Black-Scholes option pricing theory to binary outcomes, and arbitrage scanner identifying mathematical inconsistencies. Diversification across strategies reduces correlation and portfolio volatility.
Whale Copy Trading: Monitors 10 configurable wallet addresses checking blockchain every 60 seconds for new positions. When a tracked whale opens a position exceeding minimum threshold ($10,000 default), bot automatically mirror position at same or smaller size depending on available capital.
Streamlit Dashboard: Python web framework creating clean dashboard with position monitoring, P&L charts, whale activity feed, and manual override controls. Updates real-time without page refresh providing professional trading interface.
Residential Internet Tolerance: Built with connection failures, API rate limits, and unstable home networks in mind. Implements exponential backoff retry logic, request queuing, and state persistence preventing lost trades or duplicate positions during internet disruptions.
Low Resource Requirements: Runs on Mac Mini M1, Windows 10 PC with 8GB RAM, or Linux laptop without dedicated server infrastructure. Typical CPU usage 5 to 15% and RAM 500MB to 1.2GB enabling concurrent operation with normal computer usage.
4. yangyuan-zhen/PolyWeather - Dynamic Error Balancing
- Category: Weather Trading Bot
- Skill Level: Intermediate to Advanced
- GitHub: https://github.com/yangyuan-zhen/PolyWeather
Overview
Sophisticated multi-city weather bot using Dynamic Error Balancing blending forecasts from multiple models weighted by recent accuracy performance.
Key Features
52-City Coverage: Pre-configured for 52 major global cities including US (NYC, LA, Chicago, Miami, Phoenix, Seattle), European (London, Paris, Berlin, Madrid), Asian (Tokyo, Singapore, Hong Kong, Mumbai), and others. Expandable to additional cities through configuration files.
Dynamic Error Balancing (DEB): Innovative approach tracking forecast error for each model (ECMWF, GFS, HRRR, regional models) per city over trailing 30 days. Weights model contributions inversely to recent errors if ECMWF shows 0.8°F average error in Phoenix while GFS shows 1.4°F, ECMWF receives 63.6% weight and GFS receives 36.4% in final probability calculation.
Adaptive Weighting: Model weights update daily as performance changes. Summer heat events might show HRRR outperforming ECMWF in desert cities, winter cold snaps might reverse pattern. DEB automatically adjusts maximizing forecast accuracy without manual intervention.
Calibrated Probability Buckets: Rather than point forecasts, generates probability distributions across temperature ranges. For "Will temperature exceed 85°F?" calculates probability as the area under distribution curve above 85°F threshold providing statistically rigorous probability estimates.
Web Dashboard: Browser-based interface showing active positions, DEB model weights by city, forecast accuracy trends, and market scanning results. Cleaner design than Streamlit with better performance handling 100+ concurrent positions.
Telegram Bot Integration: Receive instant alerts on mobile devices when new opportunities detected, positions executed, or markets resolve. Commands enable checking portfolio status, forcing manual scans, and adjusting parameters without accessing the web dashboard.
5. Polymarket/agents - Official Reference Implementation
- Category: AI Agent Framework
- Skill Level: Intermediate to Advanced
- GitHub: https://github.com/polymarket/agents
Overview
Official repository from Polymarket providing reference implementation for building AI trading agents with proper API integration, market data access, and execution infrastructure.
Key Features
Gamma API Integration: Complete implementation of Polymarket's Gamma Market Data API providing real-time market prices, order book depth, trade history, and market metadata. Handles authentication, rate limiting, websocket connections, and error recovery production-ready.
CLOB (Central Limit Order Book) Trading: Proper order execution through Polymarket's CLOB including limit orders, market orders, order cancellation, and position management. Implements wallet connection, transaction signing, and gas optimization.
News Source Querying: Framework for connecting external news APIs (NewsAPI, Twitter, RSS feeds) enabling AI agents to ingest market-relevant information for analysis. Includes content filtering, sentiment analysis, and relevance scoring.
LLM Integration Templates: Pre-built connectors for OpenAI GPT-4, Anthropic Claude, and Google Gemini with prompt engineering templates for market analysis, probability estimation, and trading decision generation. Abstracts API complexity enabling focus on strategy logic.
6. caiovicentino/polymarket-mcp-server - Claude Integration
- Category: AI Agent Framework
- Skill Level: Intermediate
- GitHub: https://github.com/caiovicentino/polymarket-mcp-server
Overview
MCP (Model Context Protocol) server connecting Anthropic's Claude AI directly to Polymarket enabling conversational trading, market analysis, and autonomous position management through natural language.
Key Features
Real-Time Market Analysis: Claude accesses live Polymarket data analyzing market descriptions, resolution criteria, current prices, order book depth, and recent trade history providing comprehensive market assessments through conversation.
Price Movement Tracking: Monitors markets continuously alerting when significant price changes occur (10%+ movements), order book imbalances develop, or whale positions appear suggesting informed trading activity.
Natural Language Trading: Execute trades through conversation: "Buy $500 YES on 'Trump wins Pennsylvania' if price is below 60 cents" translates to actual order placement after confirmation. Non-technical users can trade without learning complex interfaces.
Autonomous Trading Mode: Toggle autonomous operation where Claude independently identifies opportunities, analyzes fundamentals, calculates edge, and executes positions within configured risk parameters without human intervention for each trade.
7. aarora4/Awesome-Prediction-Market-Tools - Resource Aggregation
- Category: Tool Collection
- Skill Level: All Levels
- GitHub: https://github.com/aarora4/Awesome-Prediction-Market-Tools
Overview
Curated collection of 100+ prediction market tools, educational resources, data sources, AI agents, trading bots, and analytics platforms aggregated in categorized list.
Key Categories
Educational Resources: Beginner guides, probability theory tutorials, trading strategy articles, YouTube channels, podcasts, and academic papers explaining prediction market fundamentals.
Trading Bots: Links to 30+ open-source bots including weather traders, arbitrage scanners, whale trackers, and multi-strategy systems with brief descriptions and GitHub stars for quality assessment.
AI Agents: Collection of LLM-based analysis tools, autonomous trading frameworks, research assistants, and MCP integrations enabling AI-powered prediction market participation.
Data Sources: Free and paid APIs for market data (Polymarket, Kalshi, PredictIt), news feeds, polling aggregators, weather forecasts, economic calendars, and blockchain explorers.
Analytics Platforms: Dashboards, portfolio trackers, performance analyzers, and visualization tools including Laika Labs for professional whale tracking and AI intelligence.
Browser Extensions: Chrome and Firefox extensions for enhanced Polymarket interface, quick order placement, price alerts, and portfolio monitoring.
Community Resources: Discord servers, Telegram groups, Reddit communities, Twitter accounts, and forums where prediction market traders share strategies and opportunities.
Value Proposition
One-Stop Discovery: Instead of searching GitHub randomly, aggregated collection surfaces highest-quality tools through community curation and maintainer vetting.
Beginner Friendly: Organized by skill level and use case enabling newcomers to identify appropriate starting points versus overwhelming advanced infrastructure.
Maintained and Updated: Active contributors add new tools monthly, remove deprecated projects, and update links ensuring current relevance versus stale awesome lists.
Quality Indicators: Star counts, fork numbers, last update dates, and maintainer notes help assess tool maturity and community adoption before investment time in setup.
8. pydantic/pydantic-ai - AI Agent Building Framework
- Category: AI Agent Framework
- Skill Level: Advanced
- GitHub: https://github.com/pydantic/pydantic-ai
Overview
Production-grade framework for building AI agents with type safety, validation, and testing infrastructure applicable to prediction market autonomous trading systems.
Key Features
Type-Safe Agent Development: Pydantic's data validation ensures agent inputs, outputs, and configurations match expected schemas, preventing runtime errors from malformed data common in LLM integrations.
Multi-LLM Support: Unified interface for OpenAI GPT-4, Anthropic Claude, Google Gemini, Mistral, and open-source models enabling provider switching without code changes and A/B testing different models for accuracy.
Function Calling: Structured output from LLMs enabling agents to call Python functions for market analysis, data retrieval, trade execution, and portfolio management moving beyond pure text generation to actionable operations.
Polymarket Application
Custom Trading Agents: Build sophisticated agents analyzing Polymarket markets using custom probability models, external data sources, and trading strategies beyond generic LLM analysis.
Complex Multi-Market Strategies: Coordinate analysis across dozens of related markets identifying conditional probability violations, cross-market arbitrage, and portfolio optimization opportunities requiring systematic approach.
Research Automation: Automate fundamental research for political markets (polling analysis), economic markets (Fed data parsing), sports markets (injury tracking), and weather markets (ensemble forecast processing).
Backtesting and Simulation: Test agent strategies against historical Polymarket data validating approach profitability before risking capital in live markets.
9. sstklen/trump-code - Social Media Prediction
- Category: Specialized Analysis Tool
- Skill Level: Intermediate
- GitHub: https://github.com/sstklen/trump-code
Overview
Specialized tool analyzing Trump's Truth Social and Twitter posts predicting Polymarket political market reactions based on historical correlation between social media activity and market movements.
Key Features
Real-Time Post Monitoring: Scrapes Truth Social and Twitter every 60 seconds detecting new Trump posts within 1 to 3 minutes of publication providing advance notice before mainstream media coverage and market reaction.
Sentiment Analysis: Natural language processing classifies posts as positive, negative, or neutral toward specific politicians, policies, or events. Positive Trump posts on candidate X historically correlate with 3 to 8 point increases in X's election probability on Polymarket.
Historical Correlation Database: 18-month database tracking Trump social media posts and corresponding Polymarket price movements identifying which post types generate strongest market reactions enabling prediction of future responses.
Market Impact Prediction: Machine learning model trained on historical data predicts probability that new Trump post will move specific Polymarket markets by 5+ points within 24 hours, 10+ points within 48 hours, providing actionable trading signals.
10. txbabaxyz/collectmarkets2 - Wallet History Analyzer
- Category: Analytics Tool
- Skill Level: Beginner to Intermediate
- GitHub: https://github.com/txbabaxyz/collectmarkets2
Overview
Tool extracting complete trading history for any Polymarket wallet address generating detailed statistics, performance charts, and CSV exports for analysis or tax reporting.
Key Features
Complete Trade History: Fetches every trade from wallet's first Polymarket transaction to present including market names, position sizes, entry/exit prices, timestamps, profit/loss, and resolution outcomes.
Performance Statistics: Calculates win rate, total ROI, average position size, largest win/loss, best/worst markets, category-wise performance (politics vs sports vs crypto), and time-series returns enabling comprehensive performance assessment.
CSV Export: Exports all trade data to CSV format compatible with Excel, Google Sheets, or tax software enabling custom analysis, record keeping, and capital gains/losses calculation for IRS reporting.
Visual Analytics: Generates performance charts including equity curve showing cumulative P&L over time, win/loss distribution histograms, category performance comparison bar charts, and position size scatter plots revealing trading patterns.
Frequently Asked Questions
What is best GitHub repo for Polymarket beginners
alteregoeth-ai/weatherbot provides the best beginner experience with pure Python implementation, comprehensive documentation, 45-90 minute setup, and proven 22-35% ROI in weather markets. Alternatively, hcharper/polyBot-Weather offers multi-strategy approach running on regular home computers with Streamlit dashboard accessible to non-programmers requiring only 30-60 minute installation.
Can you really make money with Polymarket GitHub bots
Yes, weather trading bots report 22-35% annual ROI with 58-68% win rates using ECMWF and NOAA forecast data. Repository maintainers and independent users document profitable results though individual performance varies based on configuration, bankroll management, and market conditions. Expect a 3-6 month learning curve before consistent profitability.
What programming skills do I need for Polymarket bots
Weather bots require basic Python knowledge (variables, functions, loops), command line comfort, and ability to follow setup documentation. AI agent frameworks require intermediate to advanced skills including async/await patterns, API integration, and software architecture. Start with beginner-friendly repos like weatherbot or polyBot-Weather building skills before advancing to agent frameworks.
How much does it cost to run Polymarket GitHub bot
Weather bots cost $0-$125 monthly (ECMWF API $25-75, hosting optional), AI agents cost $50-$500+ monthly (Claude/GPT-4 API), whale trackers cost $0 (free blockchain data). Add $500-$10,000 trading capital minimum. Total startup $500-$1,000, monthly operating $0-$500 depending on bot sophistication.
What is most profitable Polymarket bot strategy
Weather trading using ensemble forecasts shows highest consistent profitability (22-35% ROI) due to objective forecast data and lower competition. Whale copy trading adds 15-28% returns following wallets with 60-75% historical win rates. Multi-strategy bots diversifying across weather, whale following, and arbitrage optimize risk-adjusted returns.
Are Polymarket AI agents better than specialized bots
AI agents provide flexibility analyzing any market type and adapting to new situations but cost $50-$500+ monthly for LLM APIs and require advanced programming. Specialized weather bots cost less ($0-$125 monthly), require simpler setup, and show proven profitability. Start with a specialized bot for reliable returns then add an AI agent for advanced strategies.
