2025 - 10¶

Plan¶

Week 1-2: Break-Even Strategy

Cross-Venue Arbitrage (#11): Iterate Cross-Venue Arbitrage Strategy to break-even in paper trading
Risk Management(#7): Strategy capital allocation and account asset usage limit
Performance Analysis: Deep dive into what's working vs what's not

Week 3: Safety Systems & Monitoring

Fix PnL Inconsistency (#46): Resolve PositionGroup vs PositionMetrics discrepancies
Auto Integrity Checks (#1): Basic automated state verification for production safety
Strategy Monitoring: (#5): Logfire dashboard and Alert
Live Trading Prep: Final safety checks and deployment procedures

Week 4: Live Trading Launch

Deploy Live Strategy: Launch break-even or better strategy to production
Monitor & Validate: Track live performance, safety systems, and real P&L

Progress¶

Trading Engine (Completed)¶

✅ Cross-Venue Arbitrage (#11): Achieved profitability in paper trading
✅ PnL Inconsistency Fix (#46): Resolved PositionGroup vs PositionMetrics discrepancies
✅ Risk Management (#7): Implemented strategy capital allocation and account asset usage limits
✅ Strategy Decision Simplification (#66): Reduced execution storage by 95-99% while improving speed and traceability
✅ Hyperliquid Websocket Fix (#72): Fixed order book not updating after websocket disconnection
✅ Strategy Monitoring (#5): Deployed Logfire dashboard and alert system
✅ Auto Integrity Checks (#1): Basic automated state verification for production safety

Machine Learning (Completed)¶

✅ SWA Integration (#122): Added Stochastic Weight Averaging to improve Timesnet generalization
✅ Feature Engineering System (#124): Built iterative feature selection with performance-based incremental addition
✅ Endpoint Rollback (#129): Automated rollback system for failed endpoint evaluations
✅ Metrics Calibration (#83): Domain-calibrated confidence calculation for crypto trading model assessment
✅ Dual-Model Support (#133): Refactored Timesnet structure for dual-model architecture
✅ CI Speed Optimization (#125): Reduced integration test time with minimal epoch training

Frontend (Completed)¶

✅ ML Views Layout (#123): Improved dashboard organization and readability
✅ Unit Test Coverage (#8): Added Streamlit tests for basic page load verification
✅ Trading Page Polish (#28): Fixed coloring and visual indicators

Not Completed¶

❌ Live Trading Prep: Final safety checks and deployment procedures not completed
❌ Live Deployment: Did not launch profitable strategy to production

Learning¶

Infrastructure Work Enabled Arbitrage Profitability

October's system improvements directly contributed to arbitrage profitability. PnL consistency fixes, websocket reconnection, risk management, and account balance checks prevented losses from edge cases that killed earlier attempts. The work was necessary, not procrastination.

Two-Track Strategy: Arbitrage Ready, ML Needs Time

Arbitrage achieved profitability and is ready to launch. ML improvements (SWA, feature engineering, dual-model) haven't yet produced usable trading signals. These are independent tracks - launch arbitrage now while ML development continues separately. Don't let ML block arbitrage revenue.

System Ready But Didn't Launch - Execution Problem

October proved the system works with profitable strategy, monitoring, risk controls, and edge case handling. Yet we didn't launch. The gap isn't technical capability - it's the final decision to deploy with real capital. We need to shift from "making it better" mode to "making it live" mode.

Q4 Window Closing - Progress Too Slow

August to November = four months working toward live trading without deployment. Every month delayed is another month without revenue, validation, or proof of concept for investors. If we don't launch in November, we lose the entire year. This is an execution problem, not a technical problem.

For November: Launch arbitrage live trading in first week. Continue ML development separately. Prove the business model with arbitrage revenue while ML matures.