prediction-market-oracle-research

affaan-m ｜ 金融與投資 ｜ 原文 ｜ MIT

研究預測市場當資料來源或 oracle 訊號，附品質評估，不給投資建議

---
name: prediction-market-oracle-research
description: 研究預測市場，把它當成產品、agent、儀表板和企業決策情報的資料來源或 oracle 訊號。用來做有來源依據的分析，涵蓋市場隱含機率、注意事項和整合模式，不提供投資建議。評估預測市場能不能當成產品、agent 或儀表板的資料來源或 oracle 訊號時使用。
metadata:
  origin: ECC
---

# Prediction Market Oracle Research

當預測市場正被考慮當成資料來源、預測輸入、類 oracle 訊號，或決策情報層時，使用這個 skill。

## 護欄

- 不要把市場價格當成客觀真相。
- 不要提供投資建議或交易建議。
- 把交易場所的機制、流動性、誘因和結算規則，跟隱含的訊號分開。
- 要點出操縱、流動性薄弱、停滯的市場和模糊的結果。
- 對鏈上或和執行相連的系統，在授予任何寫入權限之前，先跑 `llm-trading-agent-security`。

## 研究流程

1. 定義這個訊號要輔助的決策。
2. 找出相關的市場、事件、標籤和交易場所。
3. 記錄市場隱含的機率，附上時間戳記和來源連結。
4. 評估訊號品質：
   - 流動性
   - 價差
   - 市場存續時間
   - 交易者/誘因的集中程度（如果知道的話）
   - 結算權威
   - 地理或帳號限制
5. 拿它跟非市場的來源比較，例如申報文件、新聞、民調、研究、客戶資料或內部 KPI。
6. 建議這個訊號對所述的決策而言，是可用、薄弱，還是不適用。

## 整合模式

- 研究助理：為人類分析師提供有來源依據的背景。
- 儀表板訊號：把市場隱含機率，跟內部指標並列。
- Agent 記憶輸入：一個有時間戳記、之後可以取回的訊號。
- 警示輸入：機率、價差或流動性越過門檻時發出通知。
- 情境規劃：比較多種事件結果，但不自動化交易。

## 輸出格式

使用：

1. 決策背景
2. 市場來源
3. 訊號品質
4. 比較用的來源
5. 整合建議
6. 注意事項

最後以這句收尾：

```text
Prediction-market signals are informational inputs, not investment advice.
```

---
name: prediction-market-oracle-research
description: Research prediction markets as data sources or oracle signals for products, agents, dashboards, and corporate decision intelligence. Use for source-grounded analysis of market-implied probabilities, caveats, and integration patterns without investment advice. Use when evaluating prediction markets as a data source or oracle signal for a product, agent, or dashboard.
metadata:
  origin: ECC
---

# Prediction Market Oracle Research

Use this skill when prediction markets are being considered as a data source,
forecasting input, oracle-like signal, or decision-intelligence layer.

## Guardrails

- Do not treat market prices as objective truth.
- Do not provide investment advice or trading recommendations.
- Separate venue mechanics, liquidity, incentives, and resolution rules from the
  implied signal.
- Call out manipulation, thin liquidity, stale markets, and ambiguous outcomes.
- For on-chain or execution-linked systems, run `llm-trading-agent-security`
  before granting any write authority.

## Research Workflow

1. Define the decision the signal is meant to inform.
2. Find relevant markets, events, tags, and venues.
3. Record market-implied probabilities with timestamps and source links.
4. Evaluate signal quality:
   - liquidity
   - spread
   - market age
   - trader/incentive concentration if known
   - resolution authority
   - geography or account restrictions
5. Compare against non-market sources such as filings, news, polls, research,
   customer data, or internal KPIs.
6. Recommend whether the signal is usable, weak, or unsuitable for the stated
   decision.

## Integration Patterns

- Research assistant: source-grounded context for a human analyst.
- Dashboard signal: market-implied probability alongside internal metrics.
- Agent memory input: a time-stamped signal that can be retrieved later.
- Alerting input: notify when probabilities, spreads, or liquidity cross a
  threshold.
- Scenario planning: compare multiple event outcomes without automating trades.

## Output Contract

Use:

1. decision context
2. market sources
3. signal quality
4. comparison sources
5. integration recommendation
6. caveats

End with:

```text
Prediction-market signals are informational inputs, not investment advice.
```