Quantitative system development practice

The practical topic of quantitative system development is based on the real Micang Trader project. The focus is not on stacking indicator formulas or code lists, but on explaining how a quantitative trading system evolves from a script to a maintainable engineering system.

Originality and Scope Statement

This topic is an original engineering review based on the author’s Micang Trader project practice. It focuses on architecture boundaries, data governance, event-driven design, testing defenses, performance optimization, and AI-assisted development workflows for quantitative trading systems.

The sample code, diagrams, and process descriptions are used to explain engineering design ideas. They do not constitute a complete strategy, trading signal, or return commitment that can be directly used in live trading. Mentions of third-party frameworks, trading interfaces, or public materials are technical background references only, and the related rights belong to their original authors or project owners.

This content is for learning and technical discussion only. It does not constitute investment advice, trading advice, or any form of financial service. Please do not republish the full text without permission; quote with the author name and original link.

Project background

Micang Trader is a modular and extensible real-time quantitative trading system for layered signal assessment. It is customized on top of vn.py, inherits vn.py’s modular architecture advantages, can connect to multiple market data sources, can deeply customize data processing workflows for specific markets when needed, and displays market data, indicators, strategy state, and runtime results through a desktop UI.

This topic does not expand those capabilities into a full product feature manual. Instead, it uses them as an entry point for understanding system boundaries: which capabilities belong to data infrastructure, which belong to trading business logic, which belong to presentation and operations, and which must be constrained by testing and backtesting evidence.

Core capability modules:

• Market data access
• Historical K-line / real-time K-line aggregation
• Period indicator calculation
• Visual trading
• Data management
• Market data recording
• Strategy engine
• Backtesting framework

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Recommended reading path

This topic gives priority to establishing a reading order according to the series: Part1 -> Part2 -> Part3 -> Part4 -> Part5 -> Part6 -> Part7. Readers first establish system boundaries, then fully see real defects, and then understand testing defense lines, performance governance, architecture evolution, and AI engineering enhancement layers.

table of contents	theme	core content
Part1	Architecture entry	System boundaries, data flow, trading period attribution, backtesting unified interface and seven routes
Part2	Python pitfalls on	Syntax, type, status, exception and concurrency risk groups, used as engineering debugging index
Part3	Python pitfalls under	GUI, network, security, architectural boundaries, and deployment risk groups for system governance reference
Part4	Test lines of defense	Trading Sessions, Data Lineage, Backtest Consistency, Attribute Testing and CI Gate
Part5	Performance optimization	From symptoms, measurement, bottleneck location to optimization solution comparison, benchmark and rollback strategy
Part6	Architecture evolution and refactoring decisions	Five Refactorings, Technical Debt Heat Maps, Target Architecture, and ADR Decision Flow
Part7	AI engineering	speckit, BMAD, human-machine collaboration Gate and traceable closed loop from requirements to implementation

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AI-assisted development

This project uses AI-assisted development methods, including:

• Complex logic design: AI-assisted design of cross-market cycle calculation algorithms
• Bug Fix: AI-assisted root cause analysis and repair solution design
• Performance Optimization: AI-assisted performance analysis and architecture reconstruction
• Documentation: AI-assisted technology documentation and architectural decision records