Self-Directed Product · MarketDNA · Case Study 02
From explainable financial intelligence to context-aware decision and predictive research
A full-stack financial intelligence system evolved from explainable trust metrics into a broader decision architecture spanning market context, strategy ranking, parameter research, and leakage-controlled predictive validation.
AI Product EngineeringExplainable AIDesign Engineering
4MARKET → STOCK LAYERS
120TRADING-DAY VALIDATION
0→1Solo Build
01 / Problem & Context
What had to become true
The challenge
A full-stack financial intelligence system evolved from explainable trust metrics into a broader decision architecture spanning market context, strategy ranking, parameter research, and leakage-controlled predictive validation.
My role
I owned the problem framing, system/product decisions, implementation direction, validation, and iteration represented in this case study. The emphasis is on the decisions that changed the system—not a feature inventory.
02 / Decisions & Tradeoffs
Architecture follows the constraint
Key decisions
- Separate Data Quality, Analysis Reliability, and Evidence Alignment instead of blending uncertainty into one score.
- Use one canonical Decision Engine across scanner and stock workflows to avoid divergent conclusions.
- Separate market context from execution strategy so the same setup can be interpreted differently under different regimes.
- Use chronological out-of-sample validation and a strict Feature Snapshot → Freeze → Future Bars → Outcome Labels temporal contract.
Working loop
PROBLEM → INVESTIGATE → ARCHITECT → BUILD → VALIDATE → ITERATE
The implementation was treated as a measured loop: diagnose the actual constraint, make the smallest architectural change that resolves it, then verify behavior and quality before moving forward.
03 / System Design & Build
What I built
- Market → Sector → Industry → Stock provides a four-level decision hierarchy.
- Recorder-first historical state makes later replay, learning, and validation possible.
- Context-aware strategy ranking evaluates strategy fit rather than assuming one universal strategy.
- Parameter search is evaluated against objective functions rather than highest win rate alone.
- React/TypeScript DOM components, semantic structure, ARIA-aware states, Motion and GSAP form the coded interaction system.
04 / Validation & Outcomes
Evidence over claims
Validation
- 120 trading-day long validation window with shorter-window comparison.
- Leakage controls preserve temporal separation between features and future outcomes.
- Production ownership spans FastAPI, Next.js/React, TypeScript, Python, SQLModel, APIs, auth, Stripe entitlements, admin, and Cloudflare deployment.
What changed
Trustworthy financial AI requires both inspectable uncertainty and disciplined research boundaries. Product UX, system architecture, and validation methodology have to agree on what the system actually knows.
05 / Skills & Positioning
What this project demonstrates
AI Product Engineering · Explainable AI · Design EngineeringThis case sits inside a broader portfolio spanning AI systems and agents, full-stack products and revenue infrastructure, design engineering, and creative AI leadership.
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