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Hermes Agent + Polymarket: How I Built a Self-Learning Weather Trading Bot ($100 $5,000+)

Weather markets on Polymarket are among the most predictable yet under-automated verticals. Temperature ranges, precipitation, and extreme weather events create repeatable statistical edges — especially when you combine multiple forecast sources with Expected Value (EV) and Kelly Criterion sizing.

Using Hermes Agent (an open-source, self-hosted AI agent from Nous Research), you can build a fully autonomous, self-improving weather trading system with almost no manual coding.

Why Hermes Agent Excels for This Use Case

Hermes stands out from earlier agents (like OpenClaw) due to three architectural strengths:

  • Persistent Memory — Maintains MEMORY.md, USER.md, and full conversation history in SQLite with semantic search
  • Self-Improving Skills — After every complex task (~5+ tool calls), it automatically generates reusable Markdown skills that improve future execution
  • Always-On Execution — Runs 24/7 on a VPS with Telegram/Discord gateway, cron scheduling, and multi-agent orchestration

This creates a true closed learning loop: the agent gets measurably better at your specific weather trading workflow over time.

Core Bot Architecture

The system uses an open-source base (e.g., AlterEgo’s weatherbot) orchestrated by Hermes:

  • Data Layer: Multiple weather APIs (Visual Crossing recommended) + historical Polymarket resolution data
  • Probability Engine: Ensemble of forecast sources → EV calculation per temperature bin
  • Sizing Engine: Kelly Criterion adjusted for edge strength and bankroll
  • Execution Layer: Polymarket CLOB V2 via py-clob-client with pUSD handling
  • Learning Layer: Hermes reviews every trade cycle, refines prompts, and creates new skills

Step-by-Step Technical Implementation (Copy-Paste Prompts)

1. Deploy Hermes Agent on VPS

# On fresh Ubuntu VPS
curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash
hermes gateway setup   # Connect to Telegram
hermes                 # Start interactive session
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2. Initialize the Weather Bot

Prompt Hermes with:

Clone https://github.com/alteregoeth-ai/weatherbot.git
Set up Python 3.12 venv and install: py-clob-client python-dotenv requests web3
Create a dedicated Polygon wallet and save private key to .env
Approve USDC.e and Conditional Tokens contracts for Polymarket spenders
Configure config.json with your balance, max_bet, min_ev, and Visual Crossing API key
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3. Launch Self-Learning Mode

Final prompt:

Start the weather bot in continuous background mode.
Scan every 60 minutes across target cities.
Use EV + Kelly sizing.
After each trade cycle, review performance and create/update skills for better forecast blending and risk management.
Send daily Telegram report with PnL, win rate, and skill improvements.
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Production Enhancements

  • Multi-City Diversification — Run 15–30 cities across continents to reduce variance
  • Ensemble Forecasting — Blend 3+ sources with dynamic weighting based on historical accuracy
  • Risk Engine — Hard caps (max 2% per trade, daily drawdown limits)
  • Monitoring — Hermes automatically logs trades and generates performance Markdown reports
  • Self-Improvement Loop — After 20–30 trades, ask Hermes to analyze losing trades and generate mitigation skills

Realistic Expectations

With disciplined sizing and multiple cities:

  • Starting capital: $100–$500
  • Conservative monthly target: 40–100%+ (highly variable)
  • Key success factor: consistent execution + letting the agent compound its own skills over weeks

Weather markets remain one of the cleanest edges on Polymarket because they combine:

  • Structured, quantifiable data (forecast APIs)
  • Clear resolution windows
  • Lower narrative noise than politics or crypto

Hermes Agent turns this into a true autonomous system that improves daily without constant human intervention.

The future of prediction market trading isn’t just faster bots — it’s self-improving agents that learn your exact risk preferences, execution style, and edge conditions over time.

If you have more questions, please feel free to contact me at any time: https://t.me/FatherSon97

Tags: #Polymarket #HermesAgent #WeatherTrading #TradingBots #AI Agents #SelfLearning #PredictionMarkets #DeFi #Web3 #QuantitativeTrading

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