alternative data event corroborator

The problem

Traders often struggle to verify if a numeric anomaly in alternative data is a genuine signal or just noise from a single, unreliable source. Relying on uncorroborated data leads to false positives and poor execution decisions.

What it does

• Normalizes units, entity mapping, and seasonal basis across disparate datasets.
• Assigns independence groups to sources to prevent double-counting syndicated reports.
• Calculates z-scores for numeric anomalies to measure statistical deviation from the baseline.
• Tracks reporting latency by recording the gap between event occurrence and data availability.
• Generates ranked event candidates with specific coverage and selection bias caveats.

Frameworks & tools

Python 3 for analysis scripts, JSON for data contracts, and standard statistical methods for z-score calculations.

Why this beats prompting it yourself

Standard LLM prompts often hallucinate correlations or fail to account for source independence. This skill enforces a rigorous methodology that separates operator assertions from helper calculations, ensuring you don't treat two copies of the same report as independent confirmation.

Use cases

• Verifying supply chain disruptions by cross-referencing shipping logs with inventory data.
• Analyzing foot traffic anomalies against point-of-sale datasets.
• Corroborating private company revenue estimates using alternative payroll and tax data.

Known limitations

Supports numeric series only. Does not perform satellite/web ingestion, automatic entity resolution, or causal inference. Requires pre-mapped data following the internal contract.