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2025 - 07

Plan

  1. Prediction Monitoring: Integrate ML predictions into trading engine and establish monitoring via Logfire
  2. Paper Trading: Deploy paper trading environment and launch ML-integrated strategy
  3. Strategy Monitoring: Complete Logfire integration for strategy performance tracking
  4. Prepare Live Trading: Improve strategy architecture and accounting systems for production readiness

Progress

  1. Prediction Monitoring: Done - TE-ML prediction task integration, Endpoint Prediction Metrics Monitoring via Logfire
  2. Paper Trading: Done - Deploy new AWS Env stag-paper, ML-integrated strategy launched, establishing baseline performance
  3. Strategy Monitoring: 🔄 40% Done - Deferred to next month due to strategy architecture changes, pending Logfire integration
  4. Prepare Live Trading:
    • Improve Strategy architecture
    • Upgrade Accounting data model
    • Enhance Reconcile State
    • Simplify Release & Deployment
  5. ML Infra & Model:
    • Integrated pytorch-forecasting
    • Store & Display Endpoint Evaluation artifact

Learning

Paper Trading Deployment Success

Successfully deploying paper trading environment and ML strategy proved June's infrastructure investment was worth it. Having complete ML pipeline enabled rapid deployment of paper trading in one month. The new AWS staging-paper environment provides clean separation for risk-free strategy testing.

Architecture Changes Ripple Effect

Strategy monitoring progress remained 40% due to architecture improvements for live trading. This shows how foundational changes impact all dependent systems. Better to pause monitoring development and get architecture right than build on unstable foundation.

Live Trading Preparation Complexity

Preparing for live trading requires more system changes than expected - strategy architecture, accounting data model, state reconciliation, and deployment processes all need upgrades. Each component affects the others, making this a bigger effort than initially planned.

ML Model Evolution Speed

Integrating pytorch-forecasting and improving evaluation artifacts happened quickly once infrastructure was solid. This validates the "infrastructure first, then rapid iteration" approach from previous months. ML model improvements accelerate when the foundation is complete.