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Private2025 — 2026

diBoaS Analytics

The quant brain: backtesting, Monte Carlo risk, ML anomaly detection.

diboas.com/market, the live macro-regime dashboard fed by the private engine
diboas.com/market, the live macro-regime dashboard fed by the private engine
Role
Sole engineer: architecture, quant methodology, data pipelines

The quantitative backend of diBoaS. It collects market and macro data from multiple financial sources, pushes everything through a gated quality pipeline, and backtests risk-tiered strategies. It runs regime-switching Monte Carlo simulations to stress portfolios against fat-tailed markets, with a machine-learning anomaly layer and real-time protocol health monitoring on top.

The public window into it is diboas.com/market: a live macro-regime dashboard fed by a build-time pipeline with dual-source verification and a fail-closed quality gate.

What it proves

  • Quantitative methodology modeled on the validation practices of institutional finance
  • Data engineering as a discipline: quality gates, freshness SLAs, audit trails, reproducible runs
  • Applied ML where it earns its keep: anomaly detection, not decoration
Technical depth

Stack

Python · pandas · scikit-learn · scipy · 808 tests · 19 CLI commands

  • ~50,000 lines of Python, 808 tests, 19 CLI commands
  • Data from five-plus sources behind circuit breakers and rate limits; a four-gate quality pipeline
  • Ten risk-tiered strategies backtested; Markov regime transitions, Student-t fat tails, VaR and CVaR
  • Isolation-Forest anomaly detection; dual-source verification with a fail-closed build gate