DRIVER Help

What is DRIVER?

DRIVER is a methodology for building finance and quantitative analysis tools with AI assistance. It guides you from concept to completion through six stages.

The Philosophy: Cognition Mate (认知伙伴)

Core principle: 互帮互助，因缘合和，互相成就

Chinese	Pinyin	Meaning
互帮互助	hù bāng hù zhù	Mutual help, helping each other
因缘合和	yīn yuán hé hé	Causes and conditions coming together (interdependent arising)
互相成就	hù xiāng chéng jiù	Accomplishing together, mutual achievement

What this means in practice:

•AI is not a tool you command — it's a thinking partner
•You bring vision and domain expertise; AI brings patterns and research
•Neither creates alone — meaning emerges from interaction
•The relationship is collaborative, not transactional

The Six Stages

code

┌─────────────────────────────────────────────────────────┐
│  DEFINE (开题调研)                                        │
│  "What are we building? What already exists?"            │
│  开题 = Open the topic  调研 = Research/investigate       │
└─────────────────────────────────────────────────────────┘
                           ↓
┌─────────────────────────────────────────────────────────┐
│  REPRESENT                                               │
│  "How do we break this into buildable pieces?"          │
│  Roadmap, data model, sections                          │
└─────────────────────────────────────────────────────────┘
                           ↓
┌─────────────────────────────────────────────────────────┐
│  IMPLEMENT                                               │
│  "Build it, run it, show it"                            │
│  Show don't tell — code speaks louder than plans        │
└─────────────────────────────────────────────────────────┘
                           ↓
┌─────────────────────────────────────────────────────────┐
│  VALIDATE                                                │
│  "Does it actually work?"                               │
│  Evidence before claims — see it running                │
└─────────────────────────────────────────────────────────┘
                           ↓
┌─────────────────────────────────────────────────────────┐
│  EVOLVE                                                  │
│  "Package the final deliverable"                        │
│  Self-contained export, ready for production            │
└─────────────────────────────────────────────────────────┘
                           ↓
┌─────────────────────────────────────────────────────────┐
│  REFLECT (Optional)                                      │
│  "What did we learn?"                                   │
│  Capture tech stack lessons, especially failures        │
└─────────────────────────────────────────────────────────┘

Key Concepts

分头研究 (fēn tóu yán jiū)

"Parallel research" — Before building anything, research what exists. You focus on your unique needs; AI researches existing libraries, papers, implementations.

很可能已经有类似的了 = "There's probably something similar already"

Show Don't Tell

Don't explain what you'll build. Build it. Run it. Let them see it.

The fastest feedback loop: See result → Give feedback → Iterate → See updated result

KISS — Keep It Simple, Structured

•Simple and logical beats elegant and fancy
•Quants need clear data tables, not animations
•A 500-line Python script beats a 50-file TypeScript project

Available Skills

Skill	Stage	Purpose
`/driver:init`	Setup	Initialize project structure
`/driver:status`	Any	Check progress, get suggestions
`/driver:help`	Any	This help page
`/driver:define`	DEFINE	Research and define vision
`/driver:represent-roadmap`	REPRESENT	Break into sections
`/driver:represent-datamodel`	REPRESENT	Define core entities
`/driver:represent-tokens`	REPRESENT	Colors/typography (web apps)
`/driver:represent-shell`	REPRESENT	Navigation shell (web apps)
`/driver:represent-section`	REPRESENT	Spec a section
`/driver:implement-data`	IMPLEMENT	Sample data (web apps)
`/driver:implement-screen`	IMPLEMENT	Build and run code
`/driver:validate`	VALIDATE	Capture screenshots
`/driver:evolve`	EVOLVE	Generate export package
`/driver:reflect`	REFLECT	Capture learnings

Recommended Stack for Finance/Quant

code

UI:           Streamlit (or Dash/Panel)
Backend:      FastAPI + Pydantic
Calculations: NumPy, Pandas, SciPy
Finance:      numpy-financial, QuantLib
Data Sources: financialdatasets.ai, Bloomberg, Refinitiv (recommended)
              yfinance, FRED (free alternatives - verify accuracy)
Storage:      SQLite → PostgreSQL, Parquet files
Testing:      pytest + Hypothesis

Data Quality Matters: For reliable financial analysis, use professional data providers. Free sources like yfinance may have gaps, delays, or inaccuracies that affect your results.

Why Python over TypeScript for quant work:

•Vectorized calculations (NumPy) vs manual loops
•Pydantic catches validation errors at boundaries
•streamlit run app.py vs npm/webpack complexity
•Division by zero: np.divide(..., where=b!=0) vs manual guards everywhere

Example Projects

Project	Style	Key Libraries	Data Source
DCF Valuation Tool	Damodaran	numpy-financial	financialdatasets.ai
Portfolio Optimizer	Markowitz	PyPortfolioOpt, scipy.optimize	Professional feed
Factor Research	Open Source AP	pandas, statsmodels, alphalens	WRDS, CRSP
Risk Dashboard	VaR/CVaR	scipy.stats, matplotlib	Professional feed
Data Pipeline	ETL	pandas, SQLAlchemy	Multiple sources

Getting Started

•New project: /driver:init or just describe what you want to build
•Existing project: /driver:status to see where you are
•Stuck? Tell me the finance problem you're solving — we'll figure it out together

Iron Laws (Never Break These)

Stage	Iron Law
DEFINE	NO BUILDING WITHOUT 分头研究 FIRST
REPRESENT	PLAN THE UNIQUE PART — DON'T REINVENT
IMPLEMENT	SHOW DON'T TELL — BUILD AND RUN IT
VALIDATE	EVIDENCE BEFORE CLAIMS
EVOLVE	SELF-CONTAINED DELIVERABLE
REFLECT	CAPTURE WHAT DIDN'T WORK