diff --git a/.gitignore b/.gitignore
index 4095452..a7cce28 100644
--- a/.gitignore
+++ b/.gitignore
@@ -85,16 +85,30 @@ secrets/
 static_site/
 k8s/secret.yaml
 
-# Tool artifacts
-CLAUDE.md
-**/CLAUDE.md
-
-# LaTeX build artifacts
-*.aux
-*.bbl
-*.blg
-*.fdb_latexmk
-*.fls
-*.out
-*.toc
+# Tool artifacts
+CLAUDE.md
+**/CLAUDE.md
+
+# Development/session notes (temporary artifacts)
+CHECKLIST.md
+PROGRESS.md
+SESSION_NOTES.md
+TODO.md
+PROJECT_SUMMARY.txt
+REVISION_NOTES.md
+
+# Archive/submission files
+*.tar.gz
+*.zip
+!**/fixtures/*.zip
+!**/test-data/*.zip
+
+# LaTeX build artifacts
+*.aux
+*.bbl
+*.blg
+*.fdb_latexmk
+*.fls
+*.out
+*.toc
 *.synctex.gz
diff --git a/ASRI_arxiv.tar.gz b/ASRI_arxiv.tar.gz
deleted file mode 100644
index 457968e..0000000
Binary files a/ASRI_arxiv.tar.gz and /dev/null differ
diff --git a/CHECKLIST.md b/CHECKLIST.md
deleted file mode 100644
index 6fd07cf..0000000
--- a/CHECKLIST.md
+++ /dev/null
@@ -1,373 +0,0 @@
-# ASRI Development Checklist ✅
-
-## Setup Complete ✅
-
-- [x] Repository cloned to `/home/andrewroyce/asri`
-- [x] Python virtual environment created
-- [x] All dependencies installed (64 packages)
-- [x] `.env` file created
-- [x] Docker Compose file for PostgreSQL
-- [x] Documentation created
-
----
-
-## Your First Hour (Start Here!)
-
-### ☐ 1. Activate Environment (30 seconds)
-```bash
-cd /home/andrewroyce/asri
-source .venv/bin/activate
-```
-
-### ☐ 2. Test Current System (2 minutes)
-```bash
-# Run tests
-pytest
-
-# Start API server (Ctrl+C to stop)
-uvicorn asri.api.main:app --reload
-```
-**Expected:** Server starts, visit http://localhost:8000/docs
-
-### ☐ 3. Start PostgreSQL (2 minutes)
-```bash
-# Make sure Docker is running, then:
-docker-compose up -d
-```
-**Expected:** PostgreSQL running on port 5432
-
-### ☐ 4. Get FRED API Key (5 minutes)
-1. Visit: https://fred.stlouisfed.org/docs/api/api_key.html
-2. Create free account
-3. Copy your API key
-4. Edit `.env` and paste: `FRED_API_KEY=your_key_here`
-
----
-
-## Your First Day (Core Setup)
-
-### ☐ 5. Create Database Models (30 minutes)
-
-Create `src/asri/models/base.py`:
-```python
-from sqlalchemy.ext.asyncio import AsyncSession, create_async_engine
-from sqlalchemy.orm import declarative_base, sessionmaker
-from pydantic_settings import BaseSettings
-
-Base = declarative_base()
-
-class Settings(BaseSettings):
-    database_url: str
-    
-    class Config:
-        env_file = ".env"
-
-settings = Settings()
-engine = create_async_engine(settings.database_url, echo=True)
-async_session = sessionmaker(engine, class_=AsyncSession, expire_on_commit=False)
-```
-
-Create `src/asri/models/asri.py`:
-```python
-from datetime import datetime
-from sqlalchemy import Column, Integer, Float, DateTime, String
-from .base import Base
-
-class ASRIDaily(Base):
-    __tablename__ = "asri_daily"
-    
-    id = Column(Integer, primary_key=True)
-    date = Column(DateTime, unique=True, nullable=False, index=True)
-    asri = Column(Float, nullable=False)
-    asri_normalized = Column(Float, nullable=False)
-    alert_level = Column(String(20), nullable=False)
-    
-    stablecoin_risk = Column(Float, nullable=False)
-    defi_liquidity_risk = Column(Float, nullable=False)
-    contagion_risk = Column(Float, nullable=False)
-    arbitrage_opacity = Column(Float, nullable=False)
-    
-    created_at = Column(DateTime, default=datetime.utcnow)
-```
-
-### ☐ 6. Set Up Database Migrations (10 minutes)
-```bash
-# Install Alembic
-pip install alembic
-
-# Initialize
-alembic init alembic
-
-# Edit alembic.ini - set sqlalchemy.url to your DATABASE_URL
-# Or better, edit alembic/env.py to read from .env
-
-# Create first migration
-alembic revision --autogenerate -m "Initial schema"
-
-# Apply migration
-alembic upgrade head
-```
-
-### ☐ 7. Test Database Connection (5 minutes)
-Create `scripts/test_db.py`:
-```python
-import asyncio
-from src.asri.models.base import engine
-from src.asri.models.asri import ASRIDaily, Base
-
-async def test_connection():
-    async with engine.begin() as conn:
-        await conn.run_sync(Base.metadata.create_all)
-    print("✅ Database connection successful!")
-
-if __name__ == "__main__":
-    asyncio.run(test_connection())
-```
-
-Run: `python scripts/test_db.py`
-
----
-
-## Your First Week (Core Features)
-
-### ☐ 8. Build FRED Connector (2 hours)
-
-Create `src/asri/ingestion/fred.py`:
-```python
-import httpx
-from typing import Dict, Any
-from datetime import datetime
-
-class FREDConnector:
-    BASE_URL = "https://api.stlouisfed.org/fred/series/observations"
-    
-    def __init__(self, api_key: str):
-        self.api_key = api_key
-        self.client = httpx.AsyncClient()
-    
-    async def fetch_series(self, series_id: str, start_date: str) -> Dict[str, Any]:
-        params = {
-            "series_id": series_id,
-            "api_key": self.api_key,
-            "file_type": "json",
-            "observation_start": start_date,
-        }
-        response = await self.client.get(self.BASE_URL, params=params)
-        response.raise_for_status()
-        return response.json()
-    
-    async def get_treasury_rates(self, start_date: str):
-        # DGS10 = 10-Year Treasury Rate
-        return await self.fetch_series("DGS10", start_date)
-    
-    async def get_vix(self, start_date: str):
-        # VIXCLS = CBOE VIX
-        return await self.fetch_series("VIXCLS", start_date)
-```
-
-Test it:
-```python
-import asyncio
-from src.asri.ingestion.fred import FREDConnector
-from dotenv import load_dotenv
-import os
-
-load_dotenv()
-
-async def test():
-    fred = FREDConnector(os.getenv("FRED_API_KEY"))
-    data = await fred.get_treasury_rates("2024-01-01")
-    print(data)
-
-asyncio.run(test())
-```
-
-### ☐ 9. Create Data Pipeline (3 hours)
-
-Create `src/asri/pipeline/orchestrator.py`:
-```python
-from datetime import datetime
-from src.asri.ingestion.fred import FREDConnector
-from src.asri.ingestion.defillama import DefiLlamaConnector
-from src.asri.signals.calculator import compute_asri
-from src.asri.models.asri import ASRIDaily
-from src.asri.models.base import async_session
-
-async def run_daily_calculation():
-    """Run the complete ASRI calculation pipeline."""
-    
-    # 1. Fetch data from sources
-    fred = FREDConnector(api_key=...)
-    defillama = DefiLlamaConnector()
-    
-    treasury_data = await fred.get_treasury_rates(...)
-    defi_data = await defillama.get_protocol_tvls(...)
-    
-    # 2. Transform raw data (implement this)
-    # transformed = transform_data(treasury_data, defi_data)
-    
-    # 3. Calculate ASRI
-    # result = compute_asri(...)
-    
-    # 4. Store in database
-    async with async_session() as session:
-        daily_record = ASRIDaily(
-            date=datetime.utcnow(),
-            asri=result.asri,
-            asri_normalized=result.asri_normalized,
-            alert_level=result.alert_level,
-            stablecoin_risk=result.sub_indices.stablecoin_risk,
-            # ... other fields
-        )
-        session.add(daily_record)
-        await session.commit()
-```
-
-### ☐ 10. Wire Up Scheduler (1 hour)
-
-Create `src/asri/scheduler/jobs.py`:
-```python
-from apscheduler.schedulers.asyncio import AsyncIOScheduler
-from src.asri.pipeline.orchestrator import run_daily_calculation
-
-scheduler = AsyncIOScheduler()
-
-def start_scheduler():
-    # Run daily at 1 AM UTC
-    scheduler.add_job(
-        run_daily_calculation,
-        'cron',
-        hour=1,
-        minute=0,
-        id='daily_asri_calculation'
-    )
-    scheduler.start()
-    
-def stop_scheduler():
-    scheduler.shutdown()
-```
-
-Update `src/asri/api/main.py` lifespan:
-```python
-from src.asri.scheduler.jobs import start_scheduler, stop_scheduler
-
-@asynccontextmanager
-async def lifespan(app: FastAPI):
-    print(f"Starting ASRI API v{__version__}")
-    start_scheduler()  # Add this
-    yield
-    print("Shutting down ASRI API")
-    stop_scheduler()  # Add this
-```
-
-### ☐ 11. Connect API to Database (2 hours)
-
-Update `src/asri/api/main.py`:
-```python
-from src.asri.models.base import async_session
-from src.asri.models.asri import ASRIDaily
-from sqlalchemy import select, desc
-
-@app.get("/asri/current", response_model=ASRIResponse)
-async def get_current_asri():
-    async with async_session() as session:
-        stmt = select(ASRIDaily).order_by(desc(ASRIDaily.date)).limit(1)
-        result = await session.execute(stmt)
-        record = result.scalar_one_or_none()
-        
-        if not record:
-            raise HTTPException(status_code=404, detail="No ASRI data available")
-        
-        return ASRIResponse(
-            timestamp=record.date,
-            asri=record.asri,
-            asri_30d_avg=record.asri,  # TODO: Calculate actual 30-day avg
-            trend="rising",  # TODO: Calculate trend
-            sub_indices=SubIndices(
-                stablecoin_risk=record.stablecoin_risk,
-                defi_liquidity_risk=record.defi_liquidity_risk,
-                contagion_risk=record.contagion_risk,
-                arbitrage_opacity=record.arbitrage_opacity,
-            ),
-            alert_level=record.alert_level,
-            last_update=record.created_at,
-        )
-```
-
----
-
-## Testing Checklist
-
-### ☐ Unit Tests
-- [ ] Test FRED connector with mock responses
-- [ ] Test ASRI calculator with known inputs
-- [ ] Test database models CRUD operations
-- [ ] Test data transformations
-
-### ☐ Integration Tests
-- [ ] Test full pipeline with real API calls
-- [ ] Test API endpoints with database
-- [ ] Test scheduler job execution
-
-### ☐ Code Quality
-- [ ] Run `ruff check src/` - no errors
-- [ ] Run `mypy src/` - no type errors
-- [ ] Run `pytest --cov=src/asri` - >80% coverage
-- [ ] All docstrings added
-
----
-
-## Launch Checklist
-
-### ☐ Pre-Production
-- [ ] All API keys secured in environment
-- [ ] Database backups configured
-- [ ] Logging configured (structlog)
-- [ ] Error monitoring (Sentry)
-- [ ] Rate limiting implemented
-- [ ] CORS properly configured
-
-### ☐ Deployment
-- [ ] Dockerfile created
-- [ ] Docker image built and tested
-- [ ] Production database provisioned
-- [ ] SSL certificate configured
-- [ ] Domain name configured
-- [ ] CI/CD pipeline set up
-
-### ☐ Documentation
-- [ ] API documentation complete
-- [ ] User guide written
-- [ ] Developer guide written
-- [ ] README updated
-
----
-
-## Progress Tracker
-
-**Foundation:** ✅✅✅✅✅ 100% (Setup complete!)
-
-**Week 1:** ⬜⬜⬜⬜⬜ 0% (Database models, migrations)
-
-**Week 2:** ⬜⬜⬜⬜⬜ 0% (Data connectors)
-
-**Week 3:** ⬜⬜⬜⬜⬜ 0% (Pipeline & calculator)
-
-**Week 4:** ⬜⬜⬜⬜⬜ 0% (Scheduler & automation)
-
-**Week 5:** ⬜⬜⬜⬜⬜ 0% (API integration)
-
-**Week 6:** ⬜⬜⬜⬜⬜ 0% (Testing & polish)
-
-**Overall:** ████⬜⬜⬜⬜⬜⬜⬜⬜⬜⬜⬜⬜⬜⬜⬜ 15%
-
----
-
-## Need Help?
-
-📖 Read `SETUP_GUIDE.md` - comprehensive documentation
-⚡ Read `QUICK_START.md` - quick reference
-📋 Read `TODO.md` - detailed task breakdown
-📁 Read `docs/ASRI-PROPOSAL.md` - full methodology
-
-🚀 **You've got everything you need. Time to build!**
diff --git a/PROGRESS.md b/PROGRESS.md
deleted file mode 100644
index 9ee2cc3..0000000
--- a/PROGRESS.md
+++ /dev/null
@@ -1,249 +0,0 @@
-# ASRI Development Progress
-
-**Last Updated:** 2025-12-12
-
-## 🎉 What We Just Built
-
-### ✅ Phase 1: Database Foundation (COMPLETE!)
-
-1. **Configuration System** ✅
-   - Created `src/asri/config.py` with Pydantic settings
-   - Environment variable management via `.env`
-   - Database URL configuration
-
-2. **Database Models** ✅
-   - `src/asri/models/base.py` - SQLAlchemy async setup
-   - `src/asri/models/asri.py` - ASRIDaily table with all sub-indices
-   - `src/asri/models/raw_data.py` - RawDataSource for API responses
-
-3. **Database Migrations** ✅
-   - Alembic initialized and configured
-   - Initial migration created and applied
-   - Tables created in PostgreSQL
-
-4. **Database Testing** ✅
-   - Connection test script successful
-   - Sample data inserted and retrieved
-   - Async operations working perfectly
-
-### ✅ Phase 2: Data Ingestion (Started!)
-
-1. **Base Connector** ✅
-   - `src/asri/ingestion/base.py` - Abstract base with retry logic
-   - Rate limiting support
-   - Error handling
-
-2. **FRED Connector** ✅
-   - `src/asri/ingestion/fred.py` - Federal Reserve data
-   - Treasury rates, VIX, yield curve spread
-   - Ready for API key
-
-### ✅ Phase 3: API Integration (COMPLETE!)
-
-1. **API Updates** ✅
-   - Connected to real database
-   - `/asri/current` returns live data from PostgreSQL
-   - Dependency injection for database sessions
-   - Proper error handling
-
-2. **API Testing** ✅
-   - Server starts successfully
-   - Health check working
-   - Current ASRI endpoint returning real data
-   - Swagger docs at http://localhost:8000/docs
-
-### ✅ Phase 4: Calculation Pipeline (COMPLETE!)
-
-1. **Pipeline Module** ✅
-   - `src/asri/pipeline/calculate.py` created
-   - `calculate_and_store_asri()` function working
-   - `run_daily_calculation()` pipeline ready
-   - Integration with calculator module
-
-2. **End-to-End Test** ✅
-   - Calculated ASRI from sub-indices
-   - Stored in database
-   - Retrieved via API
-   - Full data flow working!
-
----
-
-## 🎯 What Works Right Now
-
-```bash
-cd /home/andrewroyce/asri
-source .venv/bin/activate
-
-# Start PostgreSQL
-docker-compose up -d
-
-# Calculate ASRI
-python -m asri.pipeline.calculate
-
-# Start API
-uvicorn asri.api.main:app --reload
-
-# Test endpoints
-curl http://localhost:8000/health
-curl http://localhost:8000/asri/current
-```
-
-**API Response Example:**
-```json
-{
-  "timestamp": "2025-12-12T20:36:43.218842",
-  "asri": 61.6,
-  "asri_30d_avg": 61.6,
-  "trend": "stable",
-  "sub_indices": {
-    "stablecoin_risk": 65.0,
-    "defi_liquidity_risk": 58.0,
-    "contagion_risk": 72.0,
-    "arbitrage_opacity": 48.0
-  },
-  "alert_level": "moderate"
-}
-```
-
----
-
-## 📊 Progress Tracker
-
-- **Phase 1**: ████████████████████ 100% (Database - COMPLETE!)
-- **Phase 2**: ████⬜⬜⬜⬜⬜⬜⬜⬜⬜⬜⬜ 25% (Data Ingestion - Base + FRED)
-- **Phase 3**: ████████████████████ 100% (Pipeline - COMPLETE!)
-- **Phase 4**: ⬜⬜⬜⬜⬜⬜⬜⬜⬜⬜⬜⬜⬜⬜⬜⬜ 0% (Scheduler)
-- **Phase 5**: ████████████████████ 100% (API Integration - COMPLETE!)
-- **Phase 6**: ⬜⬜⬜⬜⬜⬜⬜⬜⬜⬜⬜⬜⬜⬜⬜⬜ 0% (Testing)
-
-**Overall Progress: 55% Complete** 🚀
-
----
-
-## 🎯 Next Steps
-
-### Immediate (Next Session):
-
-1. **Get FRED API Key** (2 minutes)
-   - Visit: https://fred.stlouisfed.org/docs/api/api_key.html
-   - Add to `.env`: `FRED_API_KEY=your_key`
-
-2. **Test FRED Connector** (10 minutes)
-   ```python
-   from asri.ingestion.fred import FREDConnector
-   import asyncio
-   
-   async def test():
-       fred = FREDConnector("YOUR_KEY")
-       data = await fred.get_treasury_rates("2024-01-01")
-       print(data)
-   
-   asyncio.run(test())
-   ```
-
-3. **Build Data Transformation** (30 minutes)
-   - Create `src/asri/pipeline/transform.py`
-   - Transform FRED data → sub-index inputs
-   - Transform DeFi Llama data → sub-index inputs
-
-4. **Expand DeFi Llama Connector** (1 hour)
-   - Complete `src/asri/ingestion/defillama.py`
-   - Add TVL fetching
-   - Add stablecoin data
-
-### Short Term (This Week):
-
-5. **Add Scheduler** (2 hours)
-   - Create `src/asri/scheduler/jobs.py`
-   - Use APScheduler for daily runs
-   - Integrate with API lifespan
-
-6. **Add Time Series Endpoint** (1 hour)
-   - Implement `/asri/timeseries` with date ranges
-   - Add pagination
-   - Test with multiple records
-
-7. **Calculate 30-Day Average** (30 minutes)
-   - Query last 30 days from database
-   - Calculate rolling average
-   - Update ASRI records
-
-### Medium Term (Next Week):
-
-8. **Add More Data Sources**
-   - Token Terminal connector
-   - Messari connector
-   - Chainalysis (manual/crawler)
-
-9. **Sub-Index Calculation Logic**
-   - Implement actual sub-index formulas
-   - Use real data inputs
-   - Validate against methodology
-
-10. **Testing Suite**
-    - Unit tests for all connectors
-    - Integration tests for pipeline
-    - API endpoint tests
-
----
-
-## 🔥 What's Impressive
-
-In one session, we built:
-- **Full database layer** with async PostgreSQL
-- **Database migrations** with Alembic
-- **Working API** connected to real data
-- **Calculation pipeline** that stores results
-- **End-to-end data flow** from calculation → storage → API
-
-The foundation is **rock solid**. Now we just need to:
-1. Connect real data sources
-2. Implement sub-index logic
-3. Add automation (scheduler)
-4. Polish and test
-
----
-
-## 📁 Files Created/Modified
-
-### Created:
-- `src/asri/config.py`
-- `src/asri/models/base.py`
-- `src/asri/models/asri.py`
-- `src/asri/models/raw_data.py`
-- `src/asri/ingestion/base.py`
-- `src/asri/ingestion/fred.py`
-- `src/asri/pipeline/__init__.py`
-- `src/asri/pipeline/calculate.py`
-- `scripts/test_db_connection.py`
-- `alembic/` (directory with migrations)
-
-### Modified:
-- `.env` (updated database URLs)
-- `alembic.ini` (configured for project)
-- `alembic/env.py` (added model imports)
-- `src/asri/api/main.py` (connected to database)
-
----
-
-## 💡 Key Learnings
-
-1. **Async SQLAlchemy** works great with FastAPI
-2. **Alembic** makes database changes easy
-3. **Structured logging** helps track pipeline execution
-4. **Dependency injection** keeps API code clean
-5. **End-to-end testing** validates the full stack
-
----
-
-## 🚀 You Can Now:
-
-✅ Start the API server  
-✅ Query real ASRI data from PostgreSQL  
-✅ Calculate and store new ASRI values  
-✅ View results in JSON format  
-✅ Access interactive API docs  
-
-**The system is ALIVE!** 🎉
-
-Next: Add real data sources and automate calculations.
diff --git a/PROJECT_SUMMARY.txt b/PROJECT_SUMMARY.txt
deleted file mode 100644
index 062ba3b..0000000
--- a/PROJECT_SUMMARY.txt
+++ /dev/null
@@ -1,219 +0,0 @@
-================================================================================
-ASRI PROJECT - READY TO BUILD
-================================================================================
-
-Location: /home/andrewroyce/asri
-Status: ✅ FULLY SET UP AND READY FOR DEVELOPMENT
-
-================================================================================
-WHAT'S DONE
-================================================================================
-
-✅ Repository cloned from GitHub
-✅ Python 3.13 virtual environment created (.venv)
-✅ All dependencies installed:
-   - FastAPI (web framework)
-   - PostgreSQL drivers (asyncpg, psycopg)
-   - Data processing (Pandas, Polars, NumPy)
-   - Testing (Pytest, coverage)
-   - Code quality (Ruff, MyPy)
-   - Scheduling (APScheduler)
-   
-✅ Working components:
-   - FastAPI API server (with placeholder data)
-   - ASRI calculation engine (4 sub-indices)
-   - DeFi Llama connector (skeleton)
-   - Test suite
-   - Swagger documentation at /docs
-
-✅ Configuration files:
-   - .env (from template, needs API keys)
-   - docker-compose.yml (PostgreSQL + pgAdmin)
-   - pyproject.toml (dependencies)
-
-✅ Documentation created:
-   - SETUP_GUIDE.md (comprehensive setup instructions)
-   - QUICK_START.md (get started in 5 minutes)
-   - TODO.md (detailed task breakdown)
-   - PROJECT_SUMMARY.txt (this file)
-
-================================================================================
-TEST IT NOW
-================================================================================
-
-cd /home/andrewroyce/asri
-source .venv/bin/activate
-uvicorn asri.api.main:app --reload
-
-Then open: http://localhost:8000/docs
-
-================================================================================
-WHAT YOU NEED
-================================================================================
-
-1. POSTGRESQL DATABASE
-   - Option A: docker-compose up -d (easiest!)
-   - Option B: sudo apt install postgresql
-
-2. API KEYS (to get data)
-   - FRED API: https://fred.stlouisfed.org/docs/api/api_key.html (FREE!)
-   - Token Terminal: https://tokenterminal.com/ (paid)
-   - Messari: https://messari.io/api (paid)
-   - DeFi Llama: No key needed (free but rate limited)
-
-3. YOUR TIME TO BUILD
-   - Database models (30 min)
-   - Data connectors (1-2 hours each)
-   - ETL pipeline (3-4 hours)
-   - Wire everything together (2-3 hours)
-
-================================================================================
-IMMEDIATE NEXT STEPS
-================================================================================
-
-1. Start PostgreSQL:
-   cd /home/andrewroyce/asri
-   docker-compose up -d
-
-2. Get free FRED API key (2 minutes)
-
-3. Create database models:
-   - src/asri/models/base.py
-   - src/asri/models/asri.py
-   - src/asri/models/sub_indices.py
-
-4. Build FRED connector:
-   - src/asri/ingestion/fred.py
-
-5. Test end-to-end pipeline
-
-================================================================================
-PROJECT STRUCTURE
-================================================================================
-
-asri/
-├── .env                     ✅ Config (add your API keys)
-├── .venv/                   ✅ Python environment
-├── docker-compose.yml       ✅ Database setup
-├── pyproject.toml          ✅ Dependencies
-│
-├── SETUP_GUIDE.md          ✅ Full documentation
-├── QUICK_START.md          ✅ Quick reference
-├── TODO.md                 ✅ Task breakdown
-├── PROJECT_SUMMARY.txt     ✅ This file
-│
-├── src/asri/
-│   ├── api/
-│   │   └── main.py         ✅ FastAPI app (working!)
-│   ├── ingestion/
-│   │   └── defillama.py    🔨 DeFi Llama (started)
-│   ├── signals/
-│   │   └── calculator.py   ✅ ASRI math (complete!)
-│   ├── models/
-│   │   └── __init__.py     ⚠️  Need to build database models
-│   └── __init__.py         ✅
-│
-├── tests/
-│   └── test_calculator.py  ✅ Tests (working)
-│
-└── docs/
-    └── ASRI-PROPOSAL.md    ✅ Full methodology
-
-================================================================================
-KEY COMMANDS
-================================================================================
-
-# Activate environment
-cd /home/andrewroyce/asri
-source .venv/bin/activate
-
-# Start database
-docker-compose up -d
-
-# Start API server
-uvicorn asri.api.main:app --reload
-
-# Run tests
-pytest
-
-# Check code quality
-ruff check src/
-mypy src/
-
-# Access services
-API:     http://localhost:8000/docs
-pgAdmin: http://localhost:5050
-
-================================================================================
-TECHNOLOGY STACK
-================================================================================
-
-Backend:      FastAPI (Python 3.11+)
-Database:     PostgreSQL 15
-Data:         Pandas, Polars, NumPy
-Async:        asyncpg, httpx
-Scheduling:   APScheduler
-Testing:      Pytest
-Code Quality: Ruff, MyPy
-Docs:         Swagger/OpenAPI
-
-Data Sources:
-- DeFi Llama (TVL, volumes)
-- FRED (macro indicators)
-- Token Terminal (protocol metrics)
-- Messari (on-chain data)
-
-================================================================================
-RESOURCES
-================================================================================
-
-📁 Project: /home/andrewroyce/asri
-🌐 GitHub: https://github.com/studiofarzulla/asri
-📖 Setup: SETUP_GUIDE.md
-⚡ Quick: QUICK_START.md
-📋 Tasks: TODO.md
-
-Documentation:
-- FastAPI:      https://fastapi.tiangolo.com/
-- SQLAlchemy:   https://docs.sqlalchemy.org/
-- DeFi Llama:   https://defillama.com/docs/api
-- FRED:         https://fred.stlouisfed.org/docs/api/
-
-================================================================================
-DEVELOPMENT PHASES
-================================================================================
-
-Phase 1: Database (1 week)
-  → Create models, migrations, test connection
-
-Phase 2: Data Ingestion (2 weeks)
-  → Build connectors for all data sources
-
-Phase 3: Processing Pipeline (1 week)
-  → Transform raw data → calculate ASRI → store
-
-Phase 4: Scheduling (1 week)
-  → Automated daily updates
-
-Phase 5: API Integration (1 week)
-  → Connect endpoints to real data
-
-Phase 6: Testing & Polish (1 week)
-  → Full test coverage, documentation
-
-Total: ~6-8 weeks for MVP
-
-================================================================================
-YOU'RE ALL SET! 🚀
-================================================================================
-
-Everything is installed, configured, and ready.
-Read QUICK_START.md to begin building.
-
-The hardest part (setup) is done.
-Now comes the fun part (building)!
-
-Start with: docker-compose up -d
-Then build your first database model.
-
-================================================================================
diff --git a/REVISION_NOTES.md b/REVISION_NOTES.md
deleted file mode 100644
index e61361f..0000000
--- a/REVISION_NOTES.md
+++ /dev/null
@@ -1,164 +0,0 @@
-# ASRI Major Revision: Stanford ML Reviewer Response
-
-## Summary
-
-This revision addresses all 11+ reviewer questions/concerns. The paper now compiles to 72 pages with comprehensive documentation of methodology, validation, and limitations.
-
----
-
-## Reviewer Question Mapping
-
-| Q# | Question | Solution | Status |
-|----|----------|----------|--------|
-| 1 | Confusion matrix reconciliation (3/4 vs 4/4) | Unified detection table (Tab. detection_matrix) | ✓ |
-| 2 | Event study protocol details | Appendix: Event Study Protocol Specification | ✓ |
-| 3 | Bank_t proxy validation vs OCC/ECB | Proxy validation module + documentation | ✓ |
-| 4 | Corr_t/connectedness specifics | VCoVaR implementation + documentation | ✓ |
-| 5 | NLP pipeline for Sent_t | Appendix documentation + sensitivity analysis | ✓ |
-| 6 | Duration-adjusted SCR | Duration sensitivity table + interpretation | ✓ |
-| 7 | Non-linear aggregation (CES/geometric) | Aggregation comparison table + CES implementation | ✓ |
-| 8 | Uncertainty propagation | Confidence sequences module + bands | ✓ |
-| 9 | Composability metrics | Future work section expanded | ✓ |
-| 10 | HMM diagnostics | Full diagnostics table (Tab. hmm_diagnostics) | ✓ |
-| 11 | VCoVaR/VCoES for tail risk | Tail risk module created | ✓ |
-
-### Additional AI Reviewer Questions (Round 2)
-
-| Q# | Question | Solution | Status |
-|----|----------|----------|--------|
-| AI-5 | Collinearity/VIF assessment | New Section 5.2.x + Tables (correlation, VIF, PCA loadings) | ✓ |
-
----
-
-## New Files Created
-
-### Scripts
-- `scripts/generate_detection_table.py` - Unified detection matrix
-- `scripts/extract_hmm_diagnostics.py` - HMM diagnostics extraction
-- `scripts/duration_sensitivity.py` - SCR duration adjustment analysis
-- `scripts/compare_aggregation.py` - Non-linear aggregation comparison
-- `scripts/compute_uncertainty.py` - Uncertainty quantification
-- `scripts/collinearity_analysis.py` - VIF, correlation matrix, PCA loadings
-
-### Source Modules
-- `src/asri/statistics/tail_risk.py` - VCoVaR estimation (~350 LOC)
-- `src/asri/statistics/confidence_sequences.py` - Anytime-valid CIs (~200 LOC)
-- `src/asri/aggregation/nonlinear.py` - CES/geometric aggregation (~350 LOC)
-- `src/asri/validation/proxy_validation.py` - Bank_t validation framework (~250 LOC)
-
-### Result Tables
-- `results/tables/detection_matrix.tex` - Per-event breakdown by method
-- `results/tables/confusion_summary.tex` - TP/FP/FN by threshold
-- `results/tables/hmm_diagnostics.tex` - Full HMM diagnostics
-- `results/tables/transition_matrix.tex` - Updated transition matrix
-- `results/tables/duration_sensitivity.tex` - Duration adjustment impact
-- `results/tables/aggregation_nonlinear.tex` - CES/geometric comparison
-- `results/tables/uncertainty_bands.tex` - Confidence band statistics
-- `results/tables/correlation_matrix.tex` - Sub-index pairwise correlations
-- `results/tables/collinearity_diagnostics.tex` - VIF, condition number, eigenvalues
-- `results/tables/pca_loadings.tex` - Principal component loadings
-
----
-
-## Paper Modifications
-
-### New Sections
-1. **Appendix: Event Study Protocol Specification** - Complete methodology:
-   - Pre-registration and event selection criteria
-   - Window selection justification (estimation: -90 to -31, event: -30 to +10)
-   - Multiple testing correction (Bonferroni α = 0.0125)
-   - Placebo testing results (10% false positive rate)
-   - Lead time measurement definitions
-   - Robustness to specification changes
-
-### Section Updates
-1. **Section 5.3 (Event Study)**: Added unified detection matrix and reconciliation
-2. **Section 5.4 (Aggregation)**: Added non-linear aggregation paragraph and table
-3. **Section 5.x (Regime Detection)**: Added HMM diagnostics table reference
-4. **Appendix A (Components)**: Enhanced Sent_t documentation with sensitivity analysis
-5. **Conclusion**: Expanded future work with composability, VCoVaR, proxy validation
-
----
-
-## Key Findings from New Analyses
-
-### Detection Method Comparison
-| Method | Detection Rate | Note |
-|--------|---------------|------|
-| Threshold τ=50 | 3/4 | Terra/Luna peak 48.7 misses |
-| Event Study (p<0.01) | 4/4 | All statistically significant |
-| Walk-Forward OOS | 4/4 | Conservative baseline calibration |
-| Max-based aggregation | 4/4 | 29-day lead time |
-
-### Duration Sensitivity
-- Duration adjustment (0.25y → 5y) changes ASRI max by +0.6 points
-- Detection rate unchanged across all duration scenarios
-- SVB crisis detected regardless of duration assumption
-
-### Aggregation Methods
-- Linear: 3/4 detection, 18-day lead
-- CES (ρ=-0.5): 3/4 detection, 17-day lead
-- Max-based: **4/4 detection, 29-day lead** (only method detecting Terra/Luna)
-
-### HMM Diagnostics
-- Log-likelihood: -21,631.8
-- AIC: 43,363.6, BIC: 43,639.5
-- Regime persistence: 97-98% (highly sticky regimes)
-- Ergodic distribution: [0.49, 0.33, 0.18]
-
----
-
-## Response Document Template
-
-### Q1: Detection rate inconsistency
-See new Table `detection_matrix` and reconciliation paragraph in Section 5.3.x. The discrepancy arises from different methodologies: threshold-based (3/4) vs event study significance (4/4) vs walk-forward OOS (4/4). Terra/Luna peak of 48.7 falls short of operational threshold but exhibits highly significant abnormal elevation (t=5.47, p<0.001).
-
-### Q2: Event study protocol
-See new Appendix `Event Study Protocol Specification` with pre-registration details, window specifications, multiple testing correction (Bonferroni α=0.0125), and placebo test results.
-
-### Q3: Bank_t proxy validation
-Framework created in `src/asri/validation/proxy_validation.py`. Full validation requires quarterly OCC/ECB filings. Documented approach validates Treasury+VIX proxy against regulatory ground truth with target Spearman ρ > 0.6.
-
-### Q4: Corr_t/connectedness specifics
-VCoVaR implementation added (`src/asri/statistics/tail_risk.py`) capturing tail dependence beyond simple correlation. Rolling ΔCoVaR computation enables dynamic tail risk monitoring.
-
-### Q5: Sent_t NLP pipeline
-See expanded Appendix documentation. Sensitivity analysis shows Sent_t variation (0→100) changes ASRI by ±1.5 points. All crisis detections robust to any Sent_t value.
-
-### Q6: Duration-adjusted SCR
-See Table `duration_sensitivity`. Detection rates unchanged across duration scenarios (0.25y T-bills to 5y T-notes). Framework is robust to duration assumptions.
-
-### Q7: Non-linear aggregation
-See Table `aggregation_comparison`. Max-based aggregation achieves 4/4 detection with 29-day lead times. CES with ρ<0 captures complementary risk dynamics but doesn't improve detection over linear baseline in current sample.
-
-### Q8: Uncertainty propagation
-Confidence sequences module created (`src/asri/statistics/confidence_sequences.py`). Bands widen with lower data quality (Arbitrage Opacity: 50% confidence due to Sent_t placeholder).
-
-### Q9: Composability metrics
-Acknowledged as valuable future extension. Future work section expanded with protocol call-graph extraction, network centrality scoring, and shock propagation simulation requirements.
-
-### Q10: HMM diagnostics
-See Table `hmm_diagnostics` with log-likelihood, AIC/BIC, transition matrix, regime persistence, and ergodic distribution. Convergence criterion: |Δ log L| < 10⁻⁴.
-
-### Q11: VCoVaR for tail risk
-Module created (`src/asri/statistics/tail_risk.py`) with quantile regression-based VCoVaR estimation. ΔCoVaR measures systemic risk contribution from crypto-equity tail dependence.
-
----
-
-## Paper Statistics
-- Total pages: 70
-- Word count: ~25,000 (estimated)
-- Tables: 30+
-- Figures: 7
-- New appendix: Event Study Protocol Specification (~3 pages)
-
-## Compilation
-```bash
-cd paper/
-pdflatex ASRI_Paper.tex
-bibtex ASRI_Paper
-pdflatex ASRI_Paper.tex
-pdflatex ASRI_Paper.tex
-```
-
-All new tables compile cleanly and integrate with existing paper structure.
diff --git a/SESSION_NOTES.md b/SESSION_NOTES.md
deleted file mode 100644
index b1189e3..0000000
--- a/SESSION_NOTES.md
+++ /dev/null
@@ -1,410 +0,0 @@
-# ASRI Implementation Session Notes
-**Date:** 2025-12-16 / 2025-12-17
-**Status:** Production Ready - Fully Automated with Daily Updates
-
----
-
-## What We Built
-
-### Fully Automated Systemic Risk Index
-ASRI calculates from **4 live data sources** with **zero manual inputs**, persists to PostgreSQL, generates a static dashboard, and auto-deploys daily:
-
-```
-ASRI: 46.8 | Alert Level: LOW
-
-Sub-Indices:
-  stablecoin_risk           59.1  (USDT dominance, HHI=4308)
-  defi_liquidity_risk       49.6  (protocol concentration, audit coverage)
-  contagion_risk            27.9  (low BTC-S&P500 correlation)
-  arbitrage_opacity         48.5  (news-based regulatory sentiment)
-```
-
-**Live Dashboard:** https://resurrexi.io/asri/
-
----
-
-## Complete Architecture
-
-```
-┌─────────────────────────────────────────────────────────────────────┐
-│                     DAILY AUTOMATION (01:00 UTC)                    │
-│                     systemd: asri-scheduler.service                 │
-└─────────────────────────────────────────────────────────────────────┘
-                                  ↓
-┌─────────────────────────────────────────────────────────────────────┐
-│                        DATA INGESTION                               │
-│  DeFiLlama → FRED → CoinGecko → Google News                        │
-│     (concurrent asyncio.gather)                                     │
-└─────────────────────────────────────────────────────────────────────┘
-                                  ↓
-┌─────────────────────────────────────────────────────────────────────┐
-│                        TRANSFORM LAYER                              │
-│  src/asri/pipeline/transform.py                                    │
-│  - Raw data → normalized 0-100 inputs                              │
-│  - HHI calculations, correlation, sentiment conversion              │
-└─────────────────────────────────────────────────────────────────────┘
-                                  ↓
-┌─────────────────────────────────────────────────────────────────────┐
-│                        CALCULATOR                                   │
-│  src/asri/signals/calculator.py                                    │
-│  - Sub-index formulas (30/25/25/20 weights)                        │
-│  - Alert level determination                                        │
-└─────────────────────────────────────────────────────────────────────┘
-                                  ↓
-┌─────────────────────────────────────────────────────────────────────┐
-│                        ORCHESTRATOR                                 │
-│  src/asri/pipeline/orchestrator.py                                 │
-│  - Coordinates data fetching                                        │
-│  - calculate_and_save() → PostgreSQL                               │
-└─────────────────────────────────────────────────────────────────────┘
-                                  ↓
-┌─────────────────────────────────────────────────────────────────────┐
-│                        DATABASE                                     │
-│  PostgreSQL (Docker: asri-postgres)                                │
-│  Table: asri_daily (id, date, asri, sub-indices, trend, etc.)      │
-└─────────────────────────────────────────────────────────────────────┘
-                                  ↓
-┌─────────────────────────────────────────────────────────────────────┐
-│                    STATIC SITE GENERATOR                            │
-│  src/asri/scheduler/static_generator.py                            │
-│  - Generates static_site/index.html                                │
-│  - Dark theme, Chart.js, responsive                                │
-└─────────────────────────────────────────────────────────────────────┘
-                                  ↓
-┌─────────────────────────────────────────────────────────────────────┐
-│                        DEPLOYMENT                                   │
-│  scripts/deploy.sh                                                  │
-│  ├── rsync → K3s cluster (immediate)                               │
-│  ├── git push → resurrexi-io (Cloudflare Pages)                    │
-│  └── git push → asri repo (backup)                                 │
-└─────────────────────────────────────────────────────────────────────┘
-```
-
----
-
-## Data Sources
-
-### 1. DeFiLlama (No Auth)
-**File:** `src/asri/ingestion/defillama.py`
-- `/protocols` - 6826 protocols with TVL, category, audits
-- `/v2/historicalChainTvl` - Historical TVL for volatility
-- `stablecoins.llama.fi/stablecoins` - 316 stablecoins
-- `bridges.llama.fi/bridges` - 88 bridges
-
-### 2. FRED (Free API Key)
-**File:** `src/asri/ingestion/fred.py`
-- `DGS10` - 10-Year Treasury Rate
-- `VIXCLS` - VIX Volatility Index
-- `T10Y2Y` - Yield Curve Spread
-- `SP500` - S&P 500 for correlation
-
-### 3. CoinGecko (Free Demo Key)
-**File:** `src/asri/ingestion/coingecko.py`
-- `/coins/bitcoin/market_chart` - 90-day BTC prices
-- Used for BTC-S&P500 correlation calculation
-
-### 4. Google News RSS + VADER NLP (No Auth)
-**File:** `src/asri/ingestion/news.py`
-- RSS feeds for crypto regulation news
-- VADER sentiment analysis
-- Converts sentiment to regulatory risk score
-
----
-
-## Database
-
-### PostgreSQL (Docker)
-```bash
-docker run -d \
-  --name asri-postgres \
-  -e POSTGRES_USER=asri \
-  -e POSTGRES_PASSWORD=asri \
-  -e POSTGRES_DB=asri \
-  -p 5432:5432 \
-  -v asri-pgdata:/var/lib/postgresql/data \
-  postgres:16-alpine
-```
-
-### Schema: `asri_daily`
-| Column | Type | Description |
-|--------|------|-------------|
-| id | SERIAL | Primary key |
-| date | TIMESTAMP | Calculation date (unique) |
-| asri | FLOAT | Aggregate score (0-100) |
-| asri_normalized | FLOAT | Same as asri |
-| asri_30d_avg | FLOAT | 30-day moving average |
-| trend | VARCHAR(20) | increasing/decreasing/stable |
-| alert_level | VARCHAR(20) | low/moderate/elevated/high/critical |
-| stablecoin_risk | FLOAT | Sub-index (30% weight) |
-| defi_liquidity_risk | FLOAT | Sub-index (25% weight) |
-| contagion_risk | FLOAT | Sub-index (25% weight) |
-| arbitrage_opacity | FLOAT | Sub-index (20% weight) |
-| created_at | TIMESTAMP | Record creation time |
-| updated_at | TIMESTAMP | Last update time |
-
----
-
-## API Endpoints
-
-### FastAPI Server
-```bash
-uvicorn asri.api.main:app --reload --port 8000
-```
-
-| Endpoint | Method | Description |
-|----------|--------|-------------|
-| `/health` | GET | Health check |
-| `/asri/current` | GET | Latest ASRI with `?calculate_if_missing=true` |
-| `/asri/calculate` | POST | Trigger live calculation with `?save=true` |
-| `/asri/timeseries` | GET | Historical data `?start=YYYY-MM-DD&end=YYYY-MM-DD` |
-| `/asri/subindex/{name}` | GET | Individual sub-index history |
-| `/asri/stress-test` | GET | Stress test scenarios (TODO) |
-| `/asri/methodology` | GET | Methodology documentation |
-| `/docs` | GET | Swagger UI |
-
----
-
-## Scheduler
-
-### Systemd Service
-**File:** `scripts/asri-scheduler.service`
-
-```bash
-# Install
-sudo cp scripts/asri-scheduler.service /etc/systemd/system/
-sudo systemctl daemon-reload
-sudo systemctl enable asri-scheduler
-sudo systemctl start asri-scheduler
-
-# Check status
-sudo systemctl status asri-scheduler
-sudo journalctl -u asri-scheduler -f
-```
-
-### Daily Job (01:00 UTC)
-**File:** `src/asri/scheduler/daemon.py`
-
-1. Calculate ASRI from live data
-2. Save to PostgreSQL
-3. Generate static HTML dashboard
-4. Deploy via rsync + git push
-
----
-
-## Deployment
-
-### Deploy Script
-**File:** `scripts/deploy.sh`
-
-```bash
-./scripts/deploy.sh
-```
-
-**Actions:**
-1. **rsync to K3s** - Immediate update to `sudosenpai:/mnt/storage/resurrexi-io/public/asri/`
-2. **git push resurrexi-io** - Triggers Cloudflare Pages deploy
-3. **git push asri** - Commits code changes to asri repo
-
-### Targets
-- **K3s:** https://resurrexi.io/asri/ (via Cloudflare Tunnel)
-- **Cloudflare Pages:** Auto-deploys from `studiofarzulla/resurrexi-io`
-- **Source:** `studiofarzulla/asri`
-
----
-
-## Project Structure
-
-```
-asri/
-├── .env                          # API keys (not committed)
-├── .gitignore                    # Ignores static_site/, .venv/, etc.
-├── pyproject.toml                # Python package config
-├── SESSION_NOTES.md              # This file
-│
-├── scripts/
-│   ├── deploy.sh                 # Full deployment script
-│   ├── asri-scheduler.service    # Systemd unit file
-│   └── install-service.sh        # Service installer
-│
-├── static_site/                  # Generated (not committed)
-│   └── index.html                # Static dashboard
-│
-├── src/asri/
-│   ├── __init__.py
-│   ├── config.py                 # Pydantic settings
-│   │
-│   ├── api/
-│   │   └── main.py               # FastAPI application
-│   │
-│   ├── db/
-│   │   └── init_db.py            # Database initialization
-│   │
-│   ├── ingestion/
-│   │   ├── base.py               # Base client class
-│   │   ├── defillama.py          # DeFiLlama client
-│   │   ├── fred.py               # FRED client
-│   │   ├── coingecko.py          # CoinGecko client
-│   │   └── news.py               # Google News + VADER
-│   │
-│   ├── models/
-│   │   ├── base.py               # SQLAlchemy base + engine
-│   │   ├── asri.py               # ASRIDaily model
-│   │   └── raw_data.py           # Raw data models
-│   │
-│   ├── pipeline/
-│   │   ├── calculate.py          # Calculation helpers
-│   │   ├── transform.py          # Data transformation
-│   │   └── orchestrator.py       # Main pipeline coordinator
-│   │
-│   ├── scheduler/
-│   │   ├── __init__.py           # Package exports
-│   │   ├── daemon.py             # APScheduler daemon
-│   │   ├── jobs.py               # Daily job logic
-│   │   └── static_generator.py   # HTML generator
-│   │
-│   └── signals/
-│       └── calculator.py         # ASRI calculation formulas
-```
-
----
-
-## Quick Commands
-
-```bash
-cd /home/purrpower/Resurrexi/projects/resurrexi-projects/asri
-source .venv/bin/activate
-
-# Manual calculation + deploy
-./scripts/deploy.sh
-
-# Just calculate (no deploy)
-python -m asri.pipeline.orchestrator
-
-# Run scheduler daemon (foreground)
-python -m asri.scheduler.daemon
-
-# Initialize database
-python -m asri.db.init_db
-
-# Start API server
-uvicorn asri.api.main:app --reload --port 8000
-
-# Test components
-python -m asri.ingestion.news          # News sentiment
-python -m asri.ingestion.coingecko     # BTC prices
-python -m asri.scheduler.static_generator  # Generate HTML
-```
-
----
-
-## Environment Variables
-
-```bash
-# .env
-DATABASE_URL=postgresql+asyncpg://asri:asri@localhost:5432/asri
-DATABASE_SYNC_URL=postgresql://asri:asri@localhost:5432/asri
-FRED_API_KEY=<your-key>
-COINGECKO_API_KEY=<your-key>
-ENVIRONMENT=development
-DEBUG=true
-SCHEDULER_ENABLED=true
-DAILY_UPDATE_HOUR=1
-```
-
----
-
-## Sub-Index Formulas
-
-### Stablecoin Risk (30% weight)
-```python
-WEIGHTS = {
-    'tvl_ratio': 0.4,           # current_tvl / max_historical
-    'treasury_stress': 0.3,      # normalized 10Y treasury rate
-    'concentration_hhi': 0.2,    # stablecoin HHI → risk score
-    'peg_volatility': 0.1,       # weighted peg deviation
-}
-```
-
-### DeFi Liquidity Risk (25% weight)
-```python
-WEIGHTS = {
-    'top10_concentration': 0.35,  # protocol HHI
-    'tvl_volatility': 0.25,       # std/mean of TVL history
-    'smart_contract_risk': 0.20,  # inverse of audit coverage
-    'flash_loan_proxy': 0.10,     # daily TVL changes
-    'leverage_change': 0.10,      # lending protocol ratio
-}
-```
-
-### Contagion Risk (25% weight)
-```python
-WEIGHTS = {
-    'rwa_growth_rate': 0.30,            # RWA category TVL share
-    'bank_exposure': 0.25,              # treasury + VIX composite
-    'tradfi_linkage': 0.20,             # yield curve signal
-    'crypto_equity_correlation': 0.15,  # BTC-S&P500 correlation
-    'bridge_exploit_frequency': 0.10,   # number of bridges
-}
-```
-
-### Arbitrage/Opacity Risk (20% weight)
-```python
-WEIGHTS = {
-    'unregulated_exposure': 0.25,   # chain distribution estimate
-    'multi_issuer_risk': 0.25,      # stablecoin issuer count
-    'custody_concentration': 0.20,  # top 2 stablecoin share
-    'regulatory_sentiment': 0.15,   # NEWS-BASED (live!)
-    'transparency_score': 0.15,     # audit coverage (inverted)
-}
-```
-
----
-
-## Key Findings (2025-12-16)
-
-1. **Stablecoin Concentration: HIGH**
-   - USDT: 60.4% ($186B), USDC: 25.3% ($78B)
-   - HHI: 4308 (>2500 = highly concentrated)
-
-2. **BTC-S&P500 Correlation: NEAR ZERO (0.056)**
-   - Crypto currently decoupled from equities
-   - Lower contagion risk than typical
-
-3. **Regulatory Sentiment: LOW RISK (25/100)**
-   - News about constructive frameworks
-   - Not crackdown/enforcement tone
-
-4. **TVL at 67% of Historical Max**
-   - Current: $119.5B, Max: $177.5B
-   - Not at peak froth levels
-
-5. **Yield Curve: NOT Inverted (+0.67%)**
-   - No recession signal from bond markets
-
----
-
-## Future Improvements
-
-### Short Term
-- [ ] Backtest against historical crises (Luna, FTX, SVB)
-- [ ] Add email/webhook alerts for threshold breaches
-- [ ] Implement stress test scenarios
-
-### Medium Term
-- [ ] CryptoPanic integration (needs auth token)
-- [ ] LLM-based regulatory analysis
-- [ ] Interactive frontend with filters
-
-### Long Term
-- [ ] Multi-chain analysis
-- [ ] Prediction model for risk spikes
-- [ ] Integration with trading signals
-
----
-
-## References
-
-- **Paper:** [ASRI: Aggregated Systemic Risk Index](https://doi.org/10.5281/zenodo.17918239)
-- **Live Dashboard:** https://resurrexi.io/asri/
-- **API Docs:** http://localhost:8000/docs (when running)
-- **Source Code:** https://github.com/studiofarzulla/asri
diff --git a/TODO.md b/TODO.md
deleted file mode 100644
index ca3983d..0000000
--- a/TODO.md
+++ /dev/null
@@ -1,338 +0,0 @@
-# ASRI Development Roadmap & TODO List
-
-## ✅ COMPLETED
-- [x] Repository structure
-- [x] Core dependencies installed
-- [x] FastAPI application skeleton
-- [x] ASRI calculation logic
-- [x] DeFi Llama connector started
-- [x] Basic test suite
-- [x] API documentation (Swagger)
-- [x] Development environment setup
-
----
-
-## 🚀 PHASE 1: Database Foundation (Priority: HIGH)
-
-### Week 1: Database Setup
-
-- [ ] **1.1 Install Database Dependencies**
-  - [ ] Install Alembic: `pip install alembic`
-  - [ ] Initialize Alembic: `alembic init alembic`
-  - [ ] Configure alembic.ini with DATABASE_URL
-
-- [ ] **1.2 Create Database Models**
-  - [ ] Create `src/asri/models/base.py`
-    - Database connection setup
-    - Async session configuration
-    - Base model class
-  - [ ] Create `src/asri/models/asri.py`
-    - ASRIDaily table (date, asri, normalized, alert_level)
-    - ASRIHistory for backtest data
-  - [ ] Create `src/asri/models/sub_indices.py`
-    - SubIndicesDaily table
-    - Store all 4 sub-index values per day
-  - [ ] Create `src/asri/models/raw_data.py`
-    - RawDataSource table (source, timestamp, data)
-    - For storing raw API responses
-
-- [ ] **1.3 Database Migrations**
-  - [ ] Create initial migration: `alembic revision --autogenerate -m "initial schema"`
-  - [ ] Review generated migration
-  - [ ] Apply migration: `alembic upgrade head`
-  - [ ] Test database connection
-
-- [ ] **1.4 Configuration Management**
-  - [ ] Create `src/asri/config.py`
-    - Load settings from .env
-    - Database connection strings
-    - API keys management
-  - [ ] Update .env with database credentials
-  - [ ] Test configuration loading
-
----
-
-## 🔌 PHASE 2: Data Ingestion (Priority: HIGH)
-
-### Week 2: Build Data Connectors
-
-- [ ] **2.1 Base Connector Class**
-  - [ ] Create `src/asri/ingestion/base.py`
-    - Abstract base class
-    - Rate limiting logic
-    - Error handling
-    - Retry mechanism
-  
-- [ ] **2.2 FRED Connector** (EASIEST - Start Here!)
-  - [ ] Get free FRED API key
-  - [ ] Create `src/asri/ingestion/fred.py`
-    - Fetch Treasury rates
-    - Fetch VIX data
-    - Fetch GDP indicators
-  - [ ] Write tests for FRED connector
-  - [ ] Test with real API calls
-
-- [ ] **2.3 Finish DeFi Llama Connector**
-  - [ ] Complete `src/asri/ingestion/defillama.py`
-    - TVL data for top protocols
-    - Stablecoin market caps
-    - Historical data fetching
-  - [ ] Add error handling
-  - [ ] Write tests
-
-- [ ] **2.4 Token Terminal Connector** (Requires paid API)
-  - [ ] Get Token Terminal API key
-  - [ ] Create `src/asri/ingestion/token_terminal.py`
-    - Protocol revenue data
-    - Active users
-    - TVL metrics
-  - [ ] Write tests
-
-- [ ] **2.5 Messari Connector** (Requires paid API)
-  - [ ] Get Messari API key
-  - [ ] Create `src/asri/ingestion/messari.py`
-    - On-chain metrics
-    - Asset profiles
-    - Market data
-  - [ ] Write tests
-
-- [ ] **2.6 Integration Tests**
-  - [ ] Test all connectors together
-  - [ ] Validate data formats
-  - [ ] Check rate limiting
-  - [ ] Error handling validation
-
----
-
-## ⚙️ PHASE 3: Data Processing Pipeline (Priority: HIGH)
-
-### Week 3: ETL Pipeline
-
-- [ ] **3.1 Create Pipeline Module**
-  - [ ] Create `src/asri/pipeline/__init__.py`
-  - [ ] Create `src/asri/pipeline/transform.py`
-    - Transform raw DeFi Llama data
-    - Transform FRED economic data
-    - Normalize all inputs to 0-100 scale
-  
-- [ ] **3.2 Connect Calculator to Pipeline**
-  - [ ] Update `src/asri/signals/calculator.py`
-    - Accept transformed data as input
-    - Add data validation
-    - Handle missing data gracefully
-  
-- [ ] **3.3 Data Storage Layer**
-  - [ ] Create `src/asri/pipeline/store.py`
-    - Save raw data to database
-    - Save sub-index calculations
-    - Save final ASRI values
-    - Transaction handling
-
-- [ ] **3.4 Pipeline Orchestration**
-  - [ ] Create `src/asri/pipeline/orchestrator.py`
-    - Coordinate: fetch → transform → calculate → store
-    - Error handling for each stage
-    - Logging and monitoring
-  
-- [ ] **3.5 Testing**
-  - [ ] Create `tests/test_pipeline.py`
-  - [ ] Test with mock data
-  - [ ] Test with real API data
-  - [ ] Test error scenarios
-
----
-
-## ⏰ PHASE 4: Scheduling & Automation (Priority: MEDIUM)
-
-### Week 4: Daily Updates
-
-- [ ] **4.1 Create Scheduler Module**
-  - [ ] Create `src/asri/scheduler/__init__.py`
-  - [ ] Create `src/asri/scheduler/jobs.py`
-    - Daily ASRI calculation job
-    - Data source health checks
-    - Cleanup old data job
-
-- [ ] **4.2 APScheduler Integration**
-  - [ ] Create `src/asri/scheduler/runner.py`
-    - Initialize scheduler
-    - Configure job triggers (daily at 1 AM UTC)
-    - Start/stop scheduler
-  
-- [ ] **4.3 Update API Lifespan**
-  - [ ] Modify `src/asri/api/main.py`
-    - Start scheduler on startup
-    - Stop scheduler on shutdown
-    - Add scheduler status endpoint
-
-- [ ] **4.4 Monitoring & Alerts**
-  - [ ] Add logging to all scheduled jobs
-  - [ ] Email alerts on failure (optional)
-  - [ ] Job execution history tracking
-
----
-
-## 🔗 PHASE 5: API Integration (Priority: MEDIUM)
-
-### Week 5: Connect API to Database
-
-- [ ] **5.1 Database Dependencies**
-  - [ ] Create `src/asri/api/dependencies.py`
-    - Database session dependency
-    - Authentication dependency (future)
-    - Rate limiting dependency (future)
-
-- [ ] **5.2 Update API Endpoints**
-  - [ ] `/asri/current` - Query latest ASRI from database
-  - [ ] `/asri/timeseries` - Query historical data with date range
-  - [ ] `/asri/subindex/{name}` - Query specific sub-index history
-  - [ ] `/asri/stress-test` - Implement actual stress test logic
-  - [ ] `/asri/methodology` - Return from database or static file
-
-- [ ] **5.3 Query Optimization**
-  - [ ] Add database indexes
-  - [ ] Optimize date range queries
-  - [ ] Add caching layer (Redis optional)
-
-- [ ] **5.4 Response Models**
-  - [ ] Create `src/asri/api/schemas.py`
-    - Pydantic models for all responses
-    - Validation logic
-    - Example responses
-
----
-
-## 🧪 PHASE 6: Testing & Quality (Priority: HIGH)
-
-### Week 6: Comprehensive Testing
-
-- [ ] **6.1 Unit Tests**
-  - [ ] Test all data connectors
-  - [ ] Test calculator functions
-  - [ ] Test data transformations
-  - [ ] Test database models
-
-- [ ] **6.2 Integration Tests**
-  - [ ] Test full pipeline end-to-end
-  - [ ] Test API endpoints with database
-  - [ ] Test scheduler jobs
-
-- [ ] **6.3 Code Quality**
-  - [ ] Fix all Ruff linting errors
-  - [ ] Fix all MyPy type errors
-  - [ ] Add docstrings to all functions
-  - [ ] Achieve >80% test coverage
-
-- [ ] **6.4 Performance Testing**
-  - [ ] Load test API endpoints
-  - [ ] Optimize slow database queries
-  - [ ] Profile memory usage
-
----
-
-## 📈 PHASE 7: Advanced Features (Priority: LOW)
-
-### Future Enhancements
-
-- [ ] **7.1 Rate Limiting**
-  - [ ] Implement rate limiting middleware
-  - [ ] Create free tier (100 req/day)
-  - [ ] Create pro tier (5000 req/day)
-  - [ ] Add API key authentication
-
-- [ ] **7.2 Stress Testing**
-  - [ ] Implement scenario engine
-  - [ ] Add predefined scenarios (treasury spike, DeFi crash)
-  - [ ] Allow custom scenarios
-  - [ ] Store stress test results
-
-- [ ] **7.3 Historical Backtesting**
-  - [ ] Backfill data from 2020-01-01
-  - [ ] Validate against known crises
-  - [ ] Generate backtest reports
-  - [ ] Compare with other risk indices
-
-- [ ] **7.4 Web Dashboard**
-  - [ ] Create React/Vue frontend
-  - [ ] Real-time ASRI chart
-  - [ ] Sub-index breakdown
-  - [ ] Historical trends
-  - [ ] Alert notifications
-
-- [ ] **7.5 Alerts & Notifications**
-  - [ ] Email alerts on high risk
-  - [ ] Webhook notifications
-  - [ ] Telegram/Discord bot
-  - [ ] SMS alerts (Twilio)
-
----
-
-## 🚀 DEPLOYMENT (Priority: MEDIUM)
-
-### Production Readiness
-
-- [ ] **8.1 Containerization**
-  - [ ] Create production Dockerfile
-  - [ ] Optimize image size
-  - [ ] Multi-stage build
-  - [ ] Health checks
-
-- [ ] **8.2 CI/CD Pipeline**
-  - [ ] GitHub Actions workflow
-  - [ ] Automated testing
-  - [ ] Automated deployment
-  - [ ] Version tagging
-
-- [ ] **8.3 Infrastructure**
-  - [ ] Set up production database (AWS RDS / DigitalOcean)
-  - [ ] Set up application server (AWS EC2 / DigitalOcean)
-  - [ ] Configure DNS
-  - [ ] SSL certificates (Let's Encrypt)
-
-- [ ] **8.4 Monitoring**
-  - [ ] Application monitoring (Sentry)
-  - [ ] Database monitoring
-  - [ ] Uptime monitoring
-  - [ ] Log aggregation
-
-- [ ] **8.5 Documentation**
-  - [ ] API documentation (OpenAPI/Swagger)
-  - [ ] User guide
-  - [ ] Developer setup guide
-  - [ ] Deployment guide
-
----
-
-## 📝 DOCUMENTATION
-
-- [ ] API reference documentation
-- [ ] Architecture diagrams
-- [ ] Data flow diagrams
-- [ ] Contributing guidelines
-- [ ] Change log / Release notes
-
----
-
-## 🎯 QUICK WINS (Do These First!)
-
-1. **Set up PostgreSQL** - 5 minutes with Docker
-2. **Get FRED API key** - 2 minutes, completely free
-3. **Create database models** - 30 minutes
-4. **Build FRED connector** - 1 hour
-5. **Test end-to-end** - Connect FRED → transform → calculate → store
-
-Start with these and you'll have working data flowing through the system!
-
----
-
-## 📊 Progress Tracking
-
-- **Phase 1**: ⬜⬜⬜⬜⬜ 0% (Database)
-- **Phase 2**: ⬜⬜⬜⬜⬜ 0% (Data Ingestion)
-- **Phase 3**: ⬜⬜⬜⬜⬜ 0% (Pipeline)
-- **Phase 4**: ⬜⬜⬜⬜⬜ 0% (Scheduling)
-- **Phase 5**: ⬜⬜⬜⬜⬜ 0% (API Integration)
-- **Phase 6**: ⬜⬜⬜⬜⬜ 0% (Testing)
-- **Overall**: 15% Complete
-
-*Update this as you complete tasks!*
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