prediction-markets-analysis

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Prediction Markets Analysis

预测市场分析

Generate comprehensive research reports on prediction market events using the Octagon Prediction Markets Agent. This skill combines real-time Kalshi market data with AI-driven analysis to identify mispricings, surface price drivers, and provide actionable trading insights.

借助Octagon Prediction Markets Agent生成关于预测市场事件的全面研究报告。该技能结合实时Kalshi市场数据与AI驱动的分析，识别定价偏差、揭示价格驱动因素，并提供可执行的交易洞察。

Prerequisites

前提条件

Ensure Octagon MCP is configured in your AI agent (Cursor, Claude Desktop, Windsurf, etc.). See references/mcp-setup.md for installation instructions.

Note: Report generation requires Octagon Plus, Pro, or Enterprise subscription (3 credits per report). Cached reports are free for all users.

确保你的AI Agent（Cursor、Claude Desktop、Windsurf等）中已配置Octagon MCP。安装说明请查看references/mcp-setup.md。

注意：报告生成需要Octagon Plus、Pro或Enterprise订阅（每份报告消耗3个积分）。缓存报告对所有用户免费。

Core Functionality

核心功能

The Prediction Markets Agent provides:

Capability	Description
Market vs. Model Probability	Computes independent model probability and compares against live market price
Event-Driven Research	Identifies key claims, catalysts, and decision-flipping events
Historical Resolution Tracking	Surfaces past resolution outcomes for context on base rates
Contract Snapshot	Returns live bid/ask spreads, last traded price, volume, and open interest
Cross-Category Coverage	120+ active markets across Politics, Economics, Crypto, Sports, Entertainment, Science & Tech, Climate

Prediction Markets Agent提供以下功能：

功能	描述
市场与模型概率对比	计算独立模型概率并与实时市场价格对比
事件驱动研究	识别关键论点、催化剂及可能反转决策的事件
历史结果追踪	展示过往事件结果，为基准概率提供背景信息
合约快照	返回实时买卖价差、最新成交价、成交量及持仓量
跨类别覆盖	涵盖政治、经济、加密货币、体育、娱乐、科技与科学、气候等领域的120+活跃市场

Model Variants

模型变体

Variant	Description	Use Case
`octagon-prediction-markets-agent`	Default. Returns cached report if available, otherwise generates new	General use
`octagon-prediction-markets-agent:cache`	Always retrieves cached report	Fastest response, may not be latest
`octagon-prediction-markets-agent:refresh`	Always generates fresh report	Most up-to-date, takes several minutes

变体	描述	使用场景
`octagon-prediction-markets-agent`	默认版本。若存在缓存报告则返回，否则生成新报告	通用场景
`octagon-prediction-markets-agent:cache`	始终获取缓存报告	响应速度最快，但可能不是最新内容
`octagon-prediction-markets-agent:refresh`	始终生成全新报告	内容最新，需耗时数分钟

Example Queries

查询示例

Federal Reserve Policy

美联储政策

Analyze this Fed decision prediction market: https://kalshi.com/markets/kxfeddecision/fed-meeting/kxfeddecision-26jun

Analyze this Fed decision prediction market: https://kalshi.com/markets/kxfeddecision/fed-meeting/kxfeddecision-26jun

Cryptocurrency Price Range

加密货币价格区间

Research this Bitcoin price prediction market: https://kalshi.com/markets/kxbtcminy/how-low-will-bitcoin-fall-this-year/kxbtcminy-27jan01

Research this Bitcoin price prediction market: https://kalshi.com/markets/kxbtcminy/how-low-will-bitcoin-fall-this-year/kxbtcminy-27jan01

Sports Matchup

体育赛事对决

Analyze this NHL playoff prediction market: https://kalshi.com/markets/kxnhleast/eastern-conference-championship/kxnhleast-26

Analyze this NHL playoff prediction market: https://kalshi.com/markets/kxnhleast/eastern-conference-championship/kxnhleast-26

Entertainment Awards

娱乐奖项

Research this Grammy nomination market: https://kalshi.com/markets/kxgrammynomsoty/grammy-nominees-for-song-of-the-year/kxgrammynomsoty-69

Research this Grammy nomination market: https://kalshi.com/markets/kxgrammynomsoty/grammy-nominees-for-song-of-the-year/kxgrammynomsoty-69

IPO Timing

IPO时间预测

Analyze when SpaceX will IPO: https://kalshi.com/markets/kxipospacex/when-will-spacex-ipo/kxipospacex

Analyze when SpaceX will IPO: https://kalshi.com/markets/kxipospacex/when-will-spacex-ipo/kxipospacex

Force Fresh Report

强制生成全新报告

Generate a fresh report (bypass cache) for: https://kalshi.com/markets/kxbtcminy/how-low-will-bitcoin-fall-this-year/kxbtcminy-27jan01

Generate a fresh report (bypass cache) for: https://kalshi.com/markets/kxbtcminy/how-low-will-bitcoin-fall-this-year/kxbtcminy-27jan01

Example Response Structure

响应示例结构

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Market Overview

市场概述

Event: Fed Decision June 2026
Resolution Date: June 18, 2026
Current Market Price: 72% (Hold rates)
Model Probability: 68%
Mispricing Signal: Market slightly overpriced

事件: 2026年6月美联储决议
结算日期: 2026年6月18日
当前市场价格: 72%（维持利率）
模型概率: 68%
定价偏差信号: 市场定价略有偏高

Price Drivers

价格驱动因素

Recent Economic Data: CPI trending lower, supporting rate hold
Fed Communications: Dovish tone in recent FOMC minutes
Labor Market: Unemployment stable at 4.1%

近期经济数据: CPI呈下降趋势，支持维持利率
美联储沟通: 近期FOMC会议纪要释放鸽派信号
劳动力市场: 失业率稳定在4.1%

Key Catalysts

关键催化剂

Date	Event	Potential Impact
June 10	CPI Release	High - could shift probabilities 5-10%
June 12	FOMC Statement	Medium - language on future path

日期	事件	潜在影响
6月10日	CPI数据发布	高影响 - 可能使概率变动5-10%
6月12日	FOMC声明	中影响 - 声明中关于未来政策路径的表述

Historical Resolution Context

历史结果背景

Last 6 meetings: 4 holds, 2 cuts
Model accuracy on this series: 78%

过去6次会议：4次维持利率，2次降息
该系列事件的模型准确率：78%

Contract Details

合约详情

Outcome	Bid	Ask	Last	Volume	OI
Hold	0.71	0.73	0.72	15,234	89,000
Cut 25bp	0.18	0.20	0.19	8,456	45,000
Hike	0.08	0.10	0.09	2,100	12,000

结果	买价	卖价	最新成交价	成交量	持仓量
维持利率	0.71	0.73	0.72	15,234	89,000
降息25基点	0.18	0.20	0.19	8,456	45,000
加息	0.08	0.10	0.09	2,100	12,000

Recommendation

建议

Slight Sell on Hold contract. Model sees 4% overpricing. Consider waiting for CPI data before positioning.

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小幅卖出维持利率合约。模型显示存在4%的定价偏高。建议等待CPI数据发布后再进行仓位布局。

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Market Categories

市场类别

The agent covers 120+ active markets across:

Category	Example Markets
Politics	Elections, legislation, executive actions
Economics	Fed decisions, inflation, employment data
Crypto	Bitcoin/Ethereum price ranges, ETF approvals
Sports	Championship outcomes, playoff matchups
Entertainment	Awards shows, box office, streaming
Science & Tech	Space launches, AI milestones, IPO timing
Climate	Temperature records, hurricane landfalls

该Agent覆盖以下领域的120+活跃市场：

类别	示例市场
政治	选举、立法、行政举措
经济	美联储决议、通胀、就业数据
加密货币	比特币/以太坊价格区间、ETF获批
体育	冠军结果、季后赛对决
娱乐	颁奖典礼、票房、流媒体相关
科技与科学	航天发射、AI里程碑、IPO时间
气候	气温纪录、飓风登陆

Use Cases

使用场景

Identify Mispricings

识别定价偏差

Compare crowd consensus against model-derived probability to find trading opportunities.

对比大众共识与模型推导的概率，寻找交易机会。

Pre-Trade Research

交易前研究

Research key drivers behind a prediction market price before placing a trade.

在进行交易前，研究预测市场价格背后的关键驱动因素。

Catalyst Monitoring

催化剂监控

Monitor upcoming events that could move contract prices.

监控可能影响合约价格的即将发生的事件。

Event Analysis

事件分析

Get structured, sourced research on fast-moving events in politics, finance, and beyond.

获取政治、金融等领域快速变化事件的结构化、有来源的研究内容。

Historical Data Access

历史数据访问

Use the

prediction_markets_history

tool to fetch historical data:

Fetch historical data for prediction market ticker KXFEDDECISION-26JUN from January to June 2026

Parameters:

```
ticker
```
: Market ticker symbol
```
captured_from
```
: Start date filter
```
captured_to
```
: End date filter
```
limit
```
: Number of records
```
cursor
```
: Pagination cursor

使用

prediction_markets_history

工具获取历史数据：

Fetch historical data for prediction market ticker KXFEDDECISION-26JUN from January to June 2026

参数：

```
ticker
```
: 市场代码
```
captured_from
```
: 起始日期筛选
```
captured_to
```
: 结束日期筛选
```
limit
```
: 记录数量
```
cursor
```
: 分页游标

Integration with Other Skills

与其他技能集成

Combine with other Octagon skills for comprehensive analysis:

Skill	Integration Use
`earnings-call-analysis`	Analyze CEO comments relevant to prediction market events
`sec-8k-analysis`	Track material events that could impact market outcomes
`stock-performance`	Correlate stock moves with prediction market probabilities

与其他Octagon技能结合进行全面分析：

技能	集成用途
`earnings-call-analysis`	分析与预测市场事件相关的CEO言论
`sec-8k-analysis`	追踪可能影响市场结果的重大事件
`stock-performance`	将股价变动与预测市场概率关联分析

Analysis Tips

分析技巧

Check model vs. market spread: Larger spreads suggest potential mispricings
Monitor catalyst calendar: Know when decision-flipping events are scheduled
Review resolution history: Past accuracy provides context for current probabilities
Consider liquidity: Check volume and open interest before sizing positions
Use cache wisely: Use
```
:cache
```
for quick checks,
```
:refresh
```
before trading
Cross-reference sources: Combine prediction market analysis with fundamental research

检查模型与市场价差: 价差越大，潜在定价偏差可能性越高
监控催化剂日历: 了解可能反转决策的事件日程
查看历史结果: 过往准确率可为当前概率提供背景参考
考虑流动性: 建仓前查看成交量与持仓量
合理使用缓存: 快速查询用
```
:cache
```
，交易前用
```
:refresh
```
交叉参考来源: 将预测市场分析与基本面研究结合