> For the complete documentation index, see [llms.txt](https://prismnetwork.gitbook.io/prismnetwork-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://prismnetwork.gitbook.io/prismnetwork-docs/3.-prism-network-solution/3.1-core-positioning-ai-strategy-account-layer/3.1.1-signal-analysis-and-opportunity-discovery.md).

# 3.1.1 Signal Analysis and Opportunity Discovery

Prism Network's signal analysis system continuously ingests multi-source data:

Data Sources:

* Prediction Market Data: Event prices, implied probabilities, order book depth, and trading volume from platforms such as Polymarket and Kalshi
* News Data: Financial news, real-time updates, policy changes
* On-Chain Data: Address behavior, fund flows, whale transactions
* Social Data: Sentiment from X (Twitter), Discord, and Reddit
* Market Data: Token prices, trading volume, volatility
* Macro Data: Economic indicators, policy announcements

Data Processing Pipeline:

* Data Collection (APIs, WebSockets, on-chain nodes)
* Data Cleaning (deduplication, timestamp alignment, anomaly filtering)
* Entity Recognition (event mapping, keyword extraction)
* Signal Generation (event intensity, sentiment direction, fund flow)
* Confidence Assessment (multi-source cross-validation, weight calculation)


---

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