About

Brian Christopher, CFA

Founder & Principal — BlackArbs LLC

I build production trading infrastructure. Since 2013, I've designed and deployed algorithmic trading systems, quantitative research pipelines, and cloud-native automation on AWS for myself and for clients.

With 15 years of Python engineering and a CFA charter, I work at an intersection most people don't: quantitative finance, full-stack software architecture, and cloud infrastructure. My stack runs from FastAPI microservices and React Native mobile apps to AWS Lambda orchestration and real-time portfolio monitoring.

I take on select consulting and development engagements — particularly in algorithmic trading systems, AWS automation pipelines, and quantitative research tooling.

Credentials
  • CFA Charterholder (2016)
  • 15+ years Python engineering
  • 12+ years production systems (since 2013)
  • Full-stack: Python, TypeScript, React Native
Specialization
  • Algorithmic trading systems
  • AWS cloud automation (Lambda, ECS, CDK)
  • Options strategies & risk management
  • Microservice architecture & deployment
  • Broker integrations (IBKR, Schwab/TDA)
Notable Projects

Project Ironclad

Designed and delivered a fully autonomous options trading system for a client. Seven AWS Lambda functions orchestrate position monitoring, option selection, automated rolling, risk assessment, and trade execution via Interactive Brokers through a persistent broker connection proxy. Includes margin verification, naked exposure prevention, idempotent operation tracking, and real-time alerting. Deployed on AWS with CloudFormation.

Client delivery — deployed & operational

Untilt

A behavioral trading platform that helps discretionary traders measure and improve their process discipline. Multi-service architecture (FastAPI, PostgreSQL, Redis) with a React Native mobile app, OAuth2 authentication, and a behavioral scoring engine across seven psychological dimensions and nine trader archetypes.

In development

Project Nightfall

A systematic long-equity algorithm built on Schwab (formerly TD Ameritrade) — rules-based allocation with automated rebalancing, tax-aware transaction management, and risk-adjusted position sizing. Profitable in initial deployment, currently under reconstruction.

Under construction

Alpha Lab

Quantitative alpha factor research pipeline with walk-forward validation, research paper replication framework, and portfolio construction with risk constraints. Screens hundreds of candidate signals against market data from Polygon.io, FRED, and SEC EDGAR.

Project Aegis

Trading research and backtesting platform with ML-driven strategy evaluation, event-sourced portfolio state, and walk-forward overfitting detection. Includes market scanners, regime-change analysis, and a CLI for querying and filtering backtest results. Deployed on AWS (S3, ECS/Fargate, Lambda).

nadis

A static analysis tool that builds dependency graphs from Python source code and applies graph theory algorithms to find structural bugs IDEs and linters miss. Ten analyzers cover blast radius mapping, circular dependency detection, mutation authority conflicts, dead code identification, and module coupling analysis. Interactive HTML reports with navigation and change-impact computation.

Sports Prediction Machines

Multi-sport prediction framework covering CFB, NFL, NBA, and CBB. Walkforward validation engines with multi-model ensembles (LightGBM, XGBoost), Zig-accelerated compute backend for 50–100x speedups on hot paths, and integrated betting strategy analysis with Kelly criterion sizing. PostgreSQL data layer with DuckDB analytics.

Project Meridian

Cross-platform cryptocurrency arbitrage system. Identified and executed price discrepancies across exchanges with automated order routing and fund transfers. Deployed and profitable until exchange fee increases and transfer restrictions eliminated the edge.

Client collaboration — deployed
Open Source

boruta-quant

Temporal-aware feature selection for quantitative finance. Implements Boruta with OOS-only importance scoring and purged temporal cross-validation to prevent lookahead bias in financial time series. Supports LightGBM, XGBoost, and SHAP.

blkarbs-profiling

Python timing and memory profiling library for algorithmic trading backtests and data pipelines. Component-level timers, profiling sessions with summary tables, nanosecond-precision accumulators for tight loops, and cProfile callgraph wrappers.

Connect

Stay in the loop

Get research updates, trading insights, and Python tutorials delivered to your inbox.

By clicking "Subscribe," you agree to our Terms of Use and acknowledge our Privacy Policy. You can unsubscribe at any time.

No spam. Unsubscribe anytime.