digital-oracle

To predict recession probability using market trading data, you aggregate price signals from prediction markets, Treasury yield curves, and institutional positioning. This approach grounds macroeconomic forecasting in actual market consensus rather than noisy sentiment, producing structured probability reports with full reasoning chains.

Assessing geopolitical risk probability using prediction markets involves extracting consensus estimates from platforms where real money is at stake. By cross-validating live trading data from derivatives exchanges and central bank databases, you generate multi-signal reports with contradiction analysis for events like war or election outcomes.

Multi-signal cross-validation for financial risk assessment works by aggregating independent data sources like CFTC positioning, SEC insider trades, and yield curves. It synthesizes these conflicting price signals into a structured probability-weighted scenario analysis, revealing how real money prices macro event outcomes.

Yes, you need Python with the uv runtime to generate prediction market reports. The Skill requires this specific environment to access its 12 zero-dependency financial APIs, fetch live trading data from prediction markets and derivatives exchanges, and produce structured multi-signal probability outputs.

Yes, you can analyze commodity price direction and bubble assessment using financial data APIs. By pulling price history, smart-money positioning, and sentiment indices, the process synthesizes a probability-weighted scenario analysis to determine if assets like gold will rally or if AI is in a bubble.