Part 5: Production and Practice
This stage's goal: Production deployment. From backtesting to live trading, from development to operations, build a multi-agent trading system that actually runs.
Lesson List
| Lesson | Topic | Deliverables |
|---|---|---|
| Lesson 18 | Trading Cost Modeling and Tradability | Cost estimation model, tradability assessment |
| Lesson 19 | Execution System | Execution Agent |
| Lesson 20 | Production Operations | Monitoring and alerting system |
| Lesson 21 | Project Implementation | Complete system integration |
| Lesson 22 | Summary and Advanced Directions | Learning roadmap |
Background Knowledge
| Document | Description | Suggested Reading Time |
|---|---|---|
| Execution Simulator Implementation | 4-level execution simulator, slippage and market impact modeling | 25 minutes |
| Strategy Homogenization and Capacity Bottlenecks | Strategy crowding, liquidity crises, capacity estimation methods | 20 minutes |
| Algorithmic Trading Regulations (2024-2025) | US/China/EU regulatory frameworks, high-frequency trading criteria, compliance requirements | 20 minutes |
Appendix
| Document | Description |
|---|---|
| Appendix A: Live Trading Logging Standards | Minimum log checklist for live trading (feedback for execution and RL) |
| Appendix B: 12 Ways Quant Systems Die | Institutional-level failure case summary (must read) |
| Appendix C: Human Decision and Automation Boundaries | Boundary division for human-machine collaboration |
| Appendix D: Quantitative Trading FAQ | Frequently asked questions |
After Completing This Stage
You will be able to:
- Build trading cost models: slippage, market impact, opportunity cost
- Evaluate strategy tradability: Gross Alpha to Net Alpha
- Implement Execution Agent: TWAP, VWAP and other execution algorithms
- Set up monitoring systems: PnL tracking, drawdown alerts, model drift detection
- Use time-travel debugging: precisely replay "why did this trade lose money"
- Complete end-to-end system integration: full loop from data to execution
- Identify the 12 typical ways quant systems die
- Understand advanced directions: high-frequency trading, cross-market arbitrage, DeFi quant
Final Achievement
After reading the entire book, you will have:
+-------------------------------------------------------------+
| Multi-Agent Trading System Prototype |
+-------------------------------------------------------------+
| |
| Data Pipeline -> Beta/Hedge -> Regime Detection -> Portfolio|
| | |
| Multi-Agent Signals -> Risk Review -> Cost Assessment -> |
| Execution -> Monitoring |
| |
+-------------------------------------------------------------+This is not a toy, but a system architecture that can be continuously iterated and truly deployed.