CausalAlpha: A Real-Time Geopolitical Risk Index from OSINT Channels for Causal Discovery in Financial Markets

We introduce CausalAlpha, an open-source framework that constructs a high-frequency Geopolitical Risk (GPR) index from Telegram OSINT channels using natural language processing, and applies causal discovery methods to identify the directed causal structure between geopolitical uncertainty and financial market variables. Unlike standard sentiment indices or Granger-causality approaches, CausalAlpha employs the Peter-Clark (PC) algorithm to recover the directed acyclic graph (DAG) of causal dependencies between five category-specific GPR indicators and a set of financial variables spanning commodity prices, equity indices, and credit instruments, estimated across four DAG specifications and three significance levels with 500 block-bootstrap resamples. Two findings emerge as globally robust across all DAG specifications at alpha = 0.10: political instability and energy media coverage independently and causally precede conflict coverage, establishing conflict as the primary causal sink of geopolitical narrative escalation in real-time OSINT channels. At the strictest significance level (alpha = 0.05), conflict coverage causally precedes energy sector equity returns (delta XLE), consistent with geopolitical escalation transmitting to energy markets. A Structural VAR on the core macro panel confirms that dynamic transmission from geopolitical NLP signals to financial market prices is statistically weak at daily frequency, suggesting that geopolitical news signals operate primarily within the media narrative system. The framework is deployed as a production application on Google Cloud Run with automated data collection and index construction, representing a step toward real-time macrofinancial risk monitoring using OSINT.