360 Secure SkillHub
360 安全技能中心

Simmer Weather Trader

面向 Simmer 天气预测市场的自动交易机器人:抓取活跃市场,汇总 NOAA、Open-Meteo、Wunderground、NVIDIA FourcastNet 四源温度预报,按预报一致性、分桶匹配、平台 edge 建议与结算时间计算 0–100 置信度,仅在满分时执行 YES 交易;默认 dry-run,可选用 Telegram 界面。

mrjoeteam
mrjoeteam
热度 42.66
最近更新 2026-05-18 12:39:04
分类:自动化依赖:代码执行 / 联网浏览搜索 / 第三方 API应用场景:市场分析 / 自动化脚本 / 工作流 / 数据处理

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add simmer-weather-trader
Metadata
name
simmer-weather-trader
description
Automated weather prediction market trading skill for Simmer/Polymarket. Cross-references 4 weather sources (NOAA, Open-Meteo, Wunderground, NVIDIA FourcastNet) and only trades when all forecasts agree within ±1°F. Remixable — add your own data sources or adjust the confidence thresholds.
author
Joe
version
1.0.0
displayName
Simmer Weather Trader
difficulty
advanced

Simmer Weather Trader

An automated trading bot for Simmer weather prediction markets. Fetches active weather markets, cross-references temperature forecasts from 4 independent sources, and only trades when the consensus is strong.

How it works

  1. Market Discovery — fetches active weather markets from Simmer via the SDK
  2. Multi-Source Forecast — gets high temperature predictions from:
    • NOAA (US government weather API)
    • Open-Meteo (free global weather API)
    • Wunderground (scraped via Playwright for broader coverage)
    • NVIDIA FourcastNet (physics-based atmospheric model)
  3. Confidence Scoring — computes a 0–100 score based on:
    • Source agreement (all within ±1°F)
    • Market bucket fit
    • Simmer edge recommendation
    • Time to resolution
  4. Execution — only trades when score reaches 100 (maximum confidence)

Default signal

The default strategy is conservative multi-source consensus:

  • All 3 weather sources must agree within ±1°F
  • FourcastNet must confirm the bucket
  • Simmer edge must recommend TRADE
  • Only YES trades (betting the temp falls within the market bucket)

This is a template. The default signal uses 4 weather models. Remix it by:

  • Adding more weather sources (AccuWeather, Weather.com, etc.)
  • Adjusting the agreement threshold (currently strict ±1°F)
  • Adding NO trades (betting the temp falls outside the bucket)
  • Using ML models trained on historical forecast accuracy per source

Setup

Environment variables

SIMMER_API_KEY=your_key        # Required — from simmer.markets
NVIDIA_API_KEY=your_key        # Required — for FourcastNet
TELEGRAM_BOT_TOKEN=your_token  # Optional — for Telegram UI
TRADE_AMOUNT=10.0              # Optional — default $10
CONFIDENCE_THRESHOLD=100       # Optional — default max confidence only
SIMMER_BASE_URL=https://api.simmer.markets  # Optional
SIMMER_VENUE=sim               # Optional — default "sim"

Dependencies

pip install httpx python-telegram-bot python-dotenv numpy
pip install netCDF4              # For FourcastNet output parsing
pip install playwright           # For Wunderground scraping
playwright install chromium      # Required for Wunderground

Supported cities

New York, Los Angeles, Chicago, Miami, Houston, Phoenix, Philadelphia, San Francisco, Seattle, Denver, Boston, Atlanta, Dallas, Minneapolis, Las Vegas, Detroit, Portland, San Antonio, San Diego, Milan, Madrid, Tel Aviv, London, Paris, Berlin, Tokyo.

Add more in city_map.py.

Remix guide

Swap in your own signals:

  • Different weather sources: Replace or add forecast functions in the simmer_weather_bot/ folder
  • Different scoring: Modify compute_confidence() in strategy.py
  • Add NO trades: Extend the strategy to also bet against consensus
  • ML-based: Train a model on historical forecast accuracy and replace the simple agreement check

The plumbing (market discovery, trade execution, Telegram UI, health checks) stays the same.

Hard rules

  • Always defaults to dry-run. Pass --live for real trades.
  • Always tags trades with source and skill_slug for tracking.
  • Always includes reasoning with the weather data used.
  • Reads API keys from env — never hardcodes credentials.