> For the complete documentation index, see [llms.txt](https://docs.cournot.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.cournot.ai/use-cases/verticals-what-cournot-enables/trading-intelligence-and-signals.md).

# Trading Intelligence & Signals

Oracle layers: 2.0 + 4.0 | Status: Live

Traditional market data providers deliver raw feeds. Cournot delivers verified intelligence, signals that carry reasoning provenance, confidence levels, and full evidence chains.

|      **Signal Type**      | **Oracle Layer** |                                                                                              **What Makes It Different**                                                                                              |
| :-----------------------: | :--------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
|  **Quantitative signals** |        2.0       |                                        Multi-source composite metrics, anomaly-scored, with cross-validation, not just a price feed but a reasoned assessment of price quality                                        |
|  **Event-driven signals** |     3.0 + 4.0    | Agent detects a regulatory filing, assesses market impact via PoR, and produces a verified signal, the consumer knows *why* the signal was generated, what evidence supported it, and how confident the assessment is |
| **Continuous monitoring** |        4.0       |                          Agent tracks on-chain flows, social sentiment, governance proposals, and news for a specific token or protocol, produces ongoing intelligence with reasoning trails                          |

The key differentiator from raw data feeds: every signal is verifiable. Consumers can inspect the evidence, replay the reasoning, and assess confidence, rather than trusting an opaque model.