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2025 - 06

Plan

  1. ML Infrastructure: Merge two ML repos and clarify prediction monitoring via logfire
  2. Strategy Monitoring: Finalize metrics and dashboards for strategy performance tracking
  3. Paper Trading: Launch ML-integrated strategy to establish baseline performance
  4. Team Development: Draft job post for trading engine engineer contractor

Progress

  1. ML Infrastructure: ✅ Complete - Merged repos, built model pipeline, integrated MLflow + Logfire monitoring, deployed to AWS
  2. Strategy Monitoring: 🔄 40% Done - Updated frontend dashboard with new metrics, pending Logfire integration
  3. Paper Trading: ❌ Not Started - Delayed due to ML infrastructure scope
  4. Team Development: ❌ Pivoted - Decided against hiring trading engineer, focus on profitability first
  5. QR Success: ✅ Hired Vicky full-time after first ML regression model beat historical mean baseline (single feature, small dataset)
  6. Database Migration: ✅ AWS RDS database migration

Learning

ML Model Validation Success

First ML regression model beating historical baseline with just one feature and small dataset proves our ML approach works. This validates months of infrastructure investment and shows we can build profitable models with minimal data.

Hiring Strategy Discipline

Deciding not to hire trading engineer shows improved resource management. We learned to validate profitability before scaling team. Hiring Vicky full-time after proven ML results was the right sequence - results first, then investment.

Infrastructure Completeness Pays Off

Completing full ML pipeline (development → monitoring → deployment) in one month created foundation for rapid model iteration. The extra effort upfront enables faster future development cycles.

Strategic Sequencing Learning

ML infrastructure had to be fully complete before meaningful paper trading. Trying to do both simultaneously would have compromised both. Sequential execution continues to prove more effective than parallel work.