JARVIS — crypto analytics and live signal stack

Since 2018 we have evolved JARVIS — a cryptocurrency-focused analytics and signal engine that ingests market and narrative data, applies proprietary models (including NLP-heavy workflows), and connects to live trading paths, today including Bitcoin and additional pairs, with workloads spread across multiple AWS regions and a polyglot PHP and Python estate.

Challenge

Crypto markets generate enormous noise. Building something durable meant combining fast market data, language-heavy sentiment features, and disciplined signal generation — then operating it where milliseconds and regional redundancy matter once capital is live.

Approach

  1. Invested in ingestion and normalization first so downstream models saw consistent time series and text artifacts instead of ad-hoc CSVs.
  2. Separated research sandboxes from production paths so new logic could be promoted only after it survived realistic execution constraints.
  3. Used AI-heavy workflows where they compress analyst time — not as a gimmick — and kept human review on the risk boundaries.
  4. Ran services across AWS regions to balance latency, failover, and data residency expectations for always-on trading infrastructure.

“We don't invent numbers. What we publish matches what clients are comfortable having on the record.”

Outcome

JARVIS remains under active development with live trading participation: a reference architecture for how Craft & Logic treats data-heavy, regulated-adjacent systems that never get to pause for maintenance windows.

Proof

Live systems

Production analytics, signal generation, and execution-side integration.

Public reference

Details of models and positions are intentionally not published.