Oracle3 prediction-market arbitrage checks

Name: Oracle3 prediction-market arbitrage checks
Author: YichengYang-Ethan

作者：YichengYang-Ethan版本 1.2.2i★ –i在你的电脑上运行

注册名称io.github.YichengYang-Ethan/oracle3

安装

概览

作者提供的说明（英文原文）

Kalshi and Polymarket data, venue fees, no-arbitrage checks and paper execution for AI agents.

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数：意见、测量和人气是不同的东西。

用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
我们的基础检查i1 项中通过 1 项依据公开信息检查；尚未扫描代码
使用情况i尚未统计★ –

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

连接方式: 在你的电脑上运行
传输方式: stdio
分类: 加密货币与区块链

安装

选择你使用的 AI 应用，然后复制设置。

安装之前

基础检查：1 项中通过 1 项
在你的电脑上运行，拥有你的用户账户的权限（文件、网络、应用）。
需要你自己填写： ORACLE3_MCP_LEDGER

你需要

PyPI已安装 Python 的 uv（用于 uvx 命令）

你用的是哪个应用？

ClaudeClaude CodeCursorVS CodeChatGPT

在上方选择你的应用。我们会记住它，下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config，把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "oracle3": {
      "command": "uvx",
      "args": [
        "oracle3",
        "mcp"
      ],
      "env": {
        "ORACLE3_MCP_LEDGER": "your-value"
      }
    }
  }
}

在终端中运行这条命令

claude mcp add oracle3 --env ORACLE3_MCP_LEDGER=your-value -- uvx oracle3 mcp

运行前请先填写空着的值。

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "oracle3": {
      "command": "uvx",
      "args": [
        "oracle3",
        "mcp"
      ],
      "env": {
        "ORACLE3_MCP_LEDGER": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "oracle3": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "oracle3",
        "mcp"
      ],
      "env": {
        "ORACLE3_MCP_LEDGER": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

这个服务器在你的电脑上运行，而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上，无需账户。

需要你填写的值

ORACLE3_MCP_LEDGER env
Path of the local paper-trading ledger (default ~/.oracle3/mcp_paper_ledger.json)

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同，请以 README 为准。

它能做什么

它在你的电脑上运行；安装后，工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日

✓ 通过
作者与仓库一致
注册名称已通过 GitHub 账户 YichengYang-Ethan 验证，该账户拥有此仓库
… 尚未检查
仍在维护
我们还没有从 GitHub 读取这个仓库
… 尚未检查
有许可证
我们还没有从 GitHub 读取这个仓库
– 不适用
端点有响应
它在你的电脑上运行，没有互联网端点
– 不适用
使用 OAuth 登录
在你的电脑上运行，改用本地设置
… 尚未检查
工具描述中没有隐藏指令
它在你的电脑上运行；我们只能读取互联网端点的工具列表

与“加密货币与区块链”中的其他服务器比较i

星标数多于此分类中其他 1612 个服务器的 0%
发布到注册中心的时间比此分类中其他 1612 个服务器的 56% 更新
基础检查结果等于或优于此分类中其他 1612 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。

常见问题

Oracle3 prediction-market arbitrage checks 安全吗？

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前，请先看看这个服务器要求哪些权限，并使用只具备所需权限的密钥。

它需要哪些密钥或代码？

注册中心没有列出必需的密钥，不过有些服务器会在首次使用时要求你登录。

它在我的电脑上运行还是在云端？

它在你的电脑上运行。你的 AI 应用会在需要时启动它，数据不会经过作者的服务器（除非它自己调用在线服务）。

它还在维护吗？

GitHub 仓库最后更新于–，最新版本（1.2.2）于38天前进入注册中心。

哪些应用可以使用它？

支持 MCP 的应用，例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Python (uv)。

有什么可以替代它？

同一分类中有：NEMO x402 Micro-Services, Crypto Derivatives Data: Perp Funding Rates, Open Interest, Basis, IV, Agent Data Tools: Prices and DeFi

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

NEMO x402 Micro-Services

作者：floortomproductions-droid

34 paid tools: site/SEO, weather, crypto+markets, podcasts, YouTube, Polymarket. x402/MPP/Stripe.

在云端运行今天更新

安装

Crypto Derivatives Data: Perp Funding Rates, Open Interest, Basis, IV

作者：derivs.duoleads.com

Perp funding rates, open interest, futures basis, IV, liquidations, order books on 8 exchanges

在云端运行今天更新

安装

Agent Data Tools: Prices and DeFi

作者：Nutshell2000

50 tools: token prices, wallet and contract risk, liquidity pools, FX. 25 free calls.

在云端运行今天更新

安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

Oracle3

Build prediction-market agents that discover markets, analyze opportunities, and test strategies.

Connect AI agents to Kalshi and Polymarket through MCP. Give them reusable Skills for market research and strategy development, then build on a Python engine for paper and live execution.

13 MCP tools · 6 agent Skills · Kalshi & Polymarket data · Python 3.10+

Quickstart · MCP tools · Agent Skills · Project and contributions · Documentation

Why Oracle3

A connected research workflow. Discover contracts, inspect their rules and order books, compare related outcomes, and track simulated positions.
Calculations that include trading costs. Five relationship checks return candidate baskets, per-leg fees, and calculated net edge.
Tools and workflows for agents. Use MCP for structured access and Skills for repeatable research, strategy authoring, and operations.
An engine you can extend. Build strategies with Python interfaces, risk controls, and paper/live venue adapters.
A foundation for experiments. Reproducible software checks, automated tests, and installed-package validation make the implementation inspectable.

Public Kalshi and Polymarket market reads need no exchange credentials. MCP provides market data, calculations, and a local paper ledger; real-order execution uses the separate CLI/Python engine.

Quickstart

Install the current development checkout:

git clone https://github.com/YichengYang-Ethan/oracle3-prediction-market-agent.git

cd oracle3-prediction-market-agent python -m venv .venv .venv/bin/python -m pip install -e .

Connect it to Claude Code from this directory:

claude mcp add oracle3 -- "$PWD/.venv/bin/oracle3-mcp"

For other MCP clients, configure the absolute path to .venv/bin/oracle3-mcp as the command with an empty argument list. On Windows, use .venv\Scripts\python.exe and .venv\Scripts\oracle3-mcp.exe.

Client configuration and first tool call · Published-package installation

Give your agent a first task

Find Kalshi and Polymarket contracts about the next Fed rate decision. Compare their outcome definitions, resolution rules, and order-book quotes. Summarize the markets with their IDs, prices, and the assumptions needed to compare them.

For a calculation that works offline after installation:

Use Oracle3 to check an implication: A implies B. A has a YES bid of $0.60 and B has a YES ask of $0.55. Use the default Kalshi taker fee schedule and 100 contracts per leg. Show the basket, gross edge, fees, and net edge.

The synthetic example returns $5.00 gross edge − $3.4125 fees = $1.5875 calculated net edge, conditional on the supplied implication and quotes. See exact inputs and output.

What agents can do

Task	Tools	Result
Discover markets	`search_markets`, `get_market`	Contracts, metadata, and available resolution rules
Inspect market conditions	`get_quote`, `get_orderbook`	Prices, fee information, and displayed depth
Analyze related contracts	`check_constraint`, `check_constraint_live`	Baskets and net edge under a stated relationship
Calculate fees and model values	`trading_fee`, `fair_value`	Fee estimates and parameterized model outputs
Try a paper entry	`paper_order`, `paper_portfolio`	Simulated fills and holdings in a local ledger

Relationship checks cover implication, exclusivity, complement, same event, and event sum. They expose assumptions with their results, giving agents and researchers a common calculation layer.

All 13 MCP tools · Workflow examples

Reusable agent Skills

The repository includes six workflows that help agents move from individual tool calls to complete tasks:

Skill	Use it to
Data discovery	Find markets and organize research inputs
Constraint arbitrage	Examine related contracts and calculate fee-aware baskets
Quant strategy authoring	Develop strategies with Oracle3's Python interfaces
Agent strategy authoring	Build agent-driven decision workflows
Paper operations	Run and inspect simulated trading sessions
Live operations	Configure and operate the separate live engine

Claude Code project copies are included in .claude/skills/. Open the checkout as your project, or use your client's supported Skill installation mechanism. Skill setup.

For AI agents: start with llms.txt for task routing, the MCP reference for tool schemas, and AGENTS.md for contributing code.

Build with Python and the CLI

Use the same project for research scripts, agent strategies, and engine operations:

.venv/bin/oracle3 market search --exchange kalshi --query "fed" --json

.venv/bin/oracle3 market search --exchange polymarket --query "fed decision" --json

Component	Build with it
MCP server	Structured market access and local paper operations for agents
Relations and fees	Shared calculations for related-contract strategies
Strategies	Logical-relation, statistical, and model-driven approaches
Trading engine	Paper/live adapters for Kalshi, Polymarket, and Solana via DFlow
Pricing	Wang-transform models and estimators for research
oracle3-extras	Optional venue, data-provider, and wallet integrations

CLI guide · Integration guide · Execution reference

Built for experimentation

Oracle3 combines a concrete decision environment with structured tools, observable state, and a software validation harness. This supports work on agent interfaces, workflow guidance, and reproducible evaluation.

CI runs automated tests and checks the built package through real MCP sessions on SDK 1.x and 2.x. The harness documentation explains the reproducible checks. Model-level agent evaluation is a next research direction.

Project overview and Yicheng Yang's contributions · Fee-frontier study · Companion pricing research

Contribute

Help build better agent workflows, labeled evaluation tasks, strategy examples, and client integrations. New venue, data-provider, and wallet integrations start in oracle3-extras.

Contributing guide · Open problems · Discussions

Origin and attribution

Oracle3 began as ulab-uiuc/oracle3, developed by Yicheng Yang and Haofei Yu at U Lab (University of Illinois Urbana-Champaign). It also bundles the lab's Coinjure package. Yicheng Yang continues to develop and maintain the extensions in this repository. Read the project story and contributions.

We acknowledge the foundational work of the Coinjure team at the University of Illinois Urbana-Champaign—Haofei Yu, Yicheng Yang, Yuxiang Liu, and Prof. Jiaxuan You—on which Oracle3 builds.

For software citation, use CITATION.cff and Zenodo. For the pricing model, see the working paper.

License

Apache 2.0; see LICENSE. Original U Lab and Coinjure portions retain their MIT license in NOTICE.

*This software is for research and education. Trading involves financial risk.*

Oracle3 prediction-market arbitrage checks

概览

PopNx MCP 的三大支柱

它能帮你做什么

安装

安装之前

需要你填写的值

它能做什么

基础检查i

作者与仓库一致

仍在维护

有许可证

端点有响应

使用 OAuth 登录

工具描述中没有隐藏指令

与“加密货币与区块链”中的其他服务器比较i

常见问题

替代选择

NEMO x402 Micro-Services

Crypto Derivatives Data: Perp Funding Rates, Open Interest, Basis, IV

Agent Data Tools: Prices and DeFi

作者的 README

Oracle3

Why Oracle3

Quickstart

Give your agent a first task

What agents can do

Reusable agent Skills

Build with Python and the CLI

Built for experimentation

Contribute

Origin and attribution

License