Merge pull request 'Add AGENTS.md orientation for Cursor agents' (#9 ) from chore/bootstrap-agents-md into main

Add AGENTS.md orientation for Cursor agents
Merge pull request 'Humanize README and docs tone' (#8 ) from docs/humanize-prose into main
2026-08-05 17:51:42 -05:00 · 2026-08-05 18:14:15 -04:00 · 2026-08-05 14:18:01 -05:00 · 2026-08-05 14:52:53 -04:00 · 2026-07-26 15:43:38 -05:00 · 2026-07-26 15:40:19 -05:00
58 changed files with 1162 additions and 1339 deletions
@@ -46,10 +46,16 @@ jobs:
          python3 -m pip install --upgrade pip --break-system-packages
          if [ -f requirements.txt ]; then pip install -r requirements.txt --break-system-packages; fi
          if [ -f requirements-dev.txt ]; then pip install -r requirements-dev.txt --break-system-packages; fi
-          pip install bandit pip-audit ruff --break-system-packages
+          # Install the project + dev tools so lint/type/test gates run for real
          pip install -e ".[dev]" --break-system-packages
          pip install bandit pip-audit --break-system-packages
      # Lint, type-check, and tests are hard gates — no `|| true`.
      - name: Ruff lint
-        run: ruff check . || true
+        run: ruff check src tests
      - name: Mypy
        run: mypy src
      - name: Bandit (advisory)
        run: bandit -r . -q || true
@@ -58,13 +64,7 @@ jobs:
        run: pip-audit -r requirements.txt 2>/dev/null || pip-audit 2>/dev/null || true
      - name: Pytest
-        run: |
+        run: pytest -q
          if [ -d tests ] || ls test_*.py *_test.py 2>/dev/null; then
            pip install pytest --break-system-packages
            pytest -q || true
          else
            echo "No tests found — skip"
          fi
  secret-scan:
    needs: skip-ci-check
@@ -34,14 +34,15 @@ jobs:
          .venv/bin/pip install --upgrade pip
          .venv/bin/pip install -e ".[dev]"
      # Linters are hard gates — no `|| true`.
      - name: Run linters
        run: |
          echo "Running ruff..."
-          .venv/bin/ruff check src/ tests/ || true
+          .venv/bin/ruff check src/ tests/
          echo "Running black check..."
-          .venv/bin/black --check src/ tests/ || true
+          .venv/bin/black --check src/ tests/
          echo "Running mypy..."
-          .venv/bin/mypy src/ --install-types --non-interactive || true
+          .venv/bin/mypy src/ --install-types --non-interactive
      - name: Run tests with coverage
        env:
@@ -0,0 +1,20 @@
 # AGENTS.md — POTE
 Short orientation for Cursor agents. Prefer this over rediscovering the repo.
 ## Defaults
 - Read `README.md` for run/install; use `make help` when a Makefile exists.
 - Lint and tests are **hard gates** in CI — never add `|| true` to them.
 - **Do not** commit secrets; app creds live in Infisical (`secrets.levkin.ca`, `/apps/POTE`).
 - Multi-step or ambiguous work → Plan mode first, then Agent mode.
 ## Docs voice
 READMEs/guides: `~/Documents/code/project-template/docs/writing-docs.md` (no emoji decoration).
 ## Close-out
 1. Run the narrowest verify that fits (`make test`, `npm test`, `pytest`, CI workflow locally).
 2. Public GitHub mirror (`Gitilia/*`): after README/docs release merges, sync mirrors — see `ansible/docs/guides/github-mirrors.md`.
 3. Non-trivial PR: offer review; merge only when the user asks.
@@ -0,0 +1,21 @@
 MIT License
 Copyright (c) 2026 Ilia Dobkin
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
 in the Software without restriction, including without limitation the rights
 to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 copies of the Software, and to permit persons to whom the Software is
 furnished to do so, subject to the following conditions:
 The above copyright notice and this permission notice shall be included in all
 copies or substantial portions of the Software.
 THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.
@@ -1,204 +1,67 @@
-# POTE – Public Officials Trading Explorer
+# POTE (Public Officials Trading Explorer)
-**Research-only tool for tracking and analyzing public stock trades by government officials.**
+Research tool for tracking publicly disclosed stock trades by government
 officials (starting with U.S. Congress). Computes descriptive metrics and
 risk/ethics flags from lawfully available public data.
-⚠️ **Important**: This project is for personal research and transparency analysis only. It is **NOT** for investment advice or live trading.
+Not investment advice. Not for live trading. Public disclosures only; data
 may be delayed or incomplete. No claims about inside information.
-## What is this?
+Status: active. Homelab LXC deploy documented under `docs/`.
 POTE tracks stock trading activity of government officials (starting with U.S. Congress) using lawfully available public data sources. It computes research metrics, descriptive signals, and risk/ethics flags to help understand trading patterns.
 ## Key constraints
 - **Public data only**: House Stock Watcher (free!), yfinance (free!), QuiverQuant/FMP (optional)
 - **Research framing**: All outputs are descriptive analytics, not trading recommendations
 - **No inside information claims**: We use public disclosures that may be delayed or incomplete
 ## Current Status
 ✅ **PR1 Complete**: Project scaffold, DB models, price loader  
 ✅ **PR2 Complete**: Congressional trade ingestion (House Stock Watcher)  
 ✅ **PR3 Complete**: Security enrichment + deployment infrastructure  
 ✅ **PR4 Complete**: Phase 2 analytics - returns, benchmarks, performance metrics  
 **45+ passing tests, 88%+ coverage**
 ## Quick start
 **🚀 Already deployed?** See **[QUICKSTART.md](QUICKSTART.md)** for full usage guide!
 **📦 Deploying?** See **[PROXMOX_QUICKSTART.md](PROXMOX_QUICKSTART.md)** for Proxmox LXC deployment (recommended).
 **📧 Want automated reports?** See **[AUTOMATION_QUICKSTART.md](AUTOMATION_QUICKSTART.md)** for email reporting setup!
 **🏠 Homelab deploy (LXC 236)?** See **[docs/HANDOFF-2026-05-27.md](docs/HANDOFF-2026-05-27.md)** for ops handoff and next steps.
 ### Local Development
 ```bash
-# Install
+git clone <repository-url>
-git clone <your-repo>
+cd pote   # or POTE
 cd pote
 make install
 source venv/bin/activate
 # Run migrations
 make migrate
 python scripts/ingest_from_fixtures.py   # offline sample
 make test
 ```
-# Ingest sample data (offline, for testing)
+With network:
 python scripts/ingest_from_fixtures.py
-# Enrich securities with company info
+```bash
 python scripts/enrich_securities.py
 # With internet:
 python scripts/fetch_congressional_trades.py
 python scripts/fetch_sample_prices.py
 # Run tests
 make test
 # Lint & format
 make lint format
 ```
-### Production Deployment
+Deploy: `bash scripts/proxmox_setup.sh` or `docker-compose up -d`.
 Ops handoff: [docs/HANDOFF-2026-05-27.md](docs/HANDOFF-2026-05-27.md).
 ## Useful commands
 ```bash
 # Proxmox LXC (Recommended - 5 minutes)
 bash scripts/proxmox_setup.sh
 # Docker
 docker-compose up -d
 ```
 ## Tech stack
 - **Language**: Python 3.10+
 - **Database**: PostgreSQL or SQLite (dev)
 - **Data**: House Stock Watcher (free!), yfinance (free!), QuiverQuant/FMP (optional)
 - **Libraries**: SQLAlchemy, Alembic, pandas, numpy, httpx, yfinance, scikit-learn
 - **Testing**: pytest (28 tests, 87%+ coverage)
 ## Documentation
 **Getting Started**:
 - [`README.md`](README.md) – This file
 - [`QUICKSTART.md`](QUICKSTART.md) – ⭐ **How to use your deployed POTE instance**
 - [`STATUS.md`](STATUS.md) – Current project status
 - [`FREE_TESTING_QUICKSTART.md`](FREE_TESTING_QUICKSTART.md) – Test for $0
 - [`OFFLINE_DEMO.md`](OFFLINE_DEMO.md) – Works without internet!
 **Deployment**:
 - [`PROXMOX_QUICKSTART.md`](PROXMOX_QUICKSTART.md) – ⭐ **Proxmox quick deployment (5 min)**
 - [`AUTOMATION_QUICKSTART.md`](AUTOMATION_QUICKSTART.md) – ⭐ **Automated reporting setup (5 min)**
 - [`docs/07_deployment.md`](docs/07_deployment.md) – Full deployment guide (all platforms)
 - [`docs/08_proxmox_deployment.md`](docs/08_proxmox_deployment.md) – Proxmox detailed guide
 - [`docs/12_automation_and_reporting.md`](docs/12_automation_and_reporting.md) – Automation & CI/CD guide
 - [`Dockerfile`](Dockerfile) + [`docker-compose.yml`](docker-compose.yml) – Docker setup
 **Technical**:
 - [`docs/00_mvp.md`](docs/00_mvp.md) – MVP roadmap
 - [`docs/01_architecture.md`](docs/01_architecture.md) – Architecture
 - [`docs/02_data_model.md`](docs/02_data_model.md) – Database schema
 - [`docs/03_data_sources.md`](docs/03_data_sources.md) – Data sources
 - [`docs/04_safety_ethics.md`](docs/04_safety_ethics.md) – Research-only guardrails
 - [`docs/05_dev_setup.md`](docs/05_dev_setup.md) – Dev conventions
 - [`docs/06_free_testing_data.md`](docs/06_free_testing_data.md) – Testing strategies
 **PR Summaries**:
 - [`docs/PR1_SUMMARY.md`](docs/PR1_SUMMARY.md) – Scaffold + price loader
 - [`docs/PR2_SUMMARY.md`](docs/PR2_SUMMARY.md) – Congressional trades
 - [`docs/PR3_SUMMARY.md`](docs/PR3_SUMMARY.md) – Enrichment + deployment
 - [`docs/PR4_SUMMARY.md`](docs/PR4_SUMMARY.md) – ⭐ **Analytics foundation (returns, benchmarks, metrics)**
 ## What's Working Now
 - ✅ SQLAlchemy models for officials, securities, trades, prices
 - ✅ Alembic migrations
 - ✅ Price loader with yfinance (idempotent, upsert)
 - ✅ Congressional trade ingestion from House Stock Watcher (FREE!)
 - ✅ Security enrichment (company names, sectors, industries)
 - ✅ ETL to populate officials & trades tables
 - ✅ Docker + deployment infrastructure
 - ✅ 93 passing tests with 88%+ coverage
 - ✅ Linting (ruff + mypy) all green
 - ✅ Works 100% offline with fixtures
 - ✅ Real-time market monitoring & alert system
 - ✅ Disclosure timing correlation engine
 - ✅ Pattern detection & comparative analysis
 - ✅ Automated email reporting (daily/weekly)
 - ✅ CI/CD pipeline (GitHub/Gitea Actions)
 ## What You Can Do Now
 ### Analyze Performance
 ```bash
 # Analyze specific official
 python scripts/analyze_official.py "Nancy Pelosi" --window 90
 # System-wide analysis
 python scripts/calculate_all_returns.py
 ```
 ### Market Monitoring
 ```bash
 # Run market scan
 python scripts/monitor_market.py --scan
 # Analyze timing of recent disclosures
 python scripts/analyze_disclosure_timing.py --recent 7
 # Generate pattern report
 python scripts/generate_pattern_report.py --days 365
 ```
 ### Automated Reporting
 ```bash
 # Set up daily/weekly email reports (5 minutes!)
 ./scripts/setup_cron.sh
 # Send manual report
 python scripts/send_daily_report.py --to your@email.com
 ```
-### Add More Data
+## Stack
 ```bash
 # Manual entry
 python scripts/add_custom_trades.py
-# CSV import
+Python 3.10+, SQLAlchemy, Alembic, pandas/numpy, httpx, yfinance.
-python scripts/scrape_alternative_sources.py import trades.csv
+PostgreSQL or SQLite for local. pytest + ruff + mypy.
 ```
-## System Architecture
+Data sources: House Stock Watcher, yfinance; QuiverQuant/FMP optional.
-POTE now includes a complete 3-phase monitoring system:
+## Docs
-**Phase 1: Real-Time Market Monitoring**
+| Doc | Purpose |
- Tracks ~50 most-traded congressional stocks
+|-----|---------|
- Detects unusual volume, price spikes, volatility
+| [docs/QUICKSTART.md](docs/QUICKSTART.md) | Using a deployed instance |
- Logs all alerts with timestamps and severity
+| [docs/PROXMOX_QUICKSTART.md](docs/PROXMOX_QUICKSTART.md) | Proxmox LXC |
 | [docs/AUTOMATION_QUICKSTART.md](docs/AUTOMATION_QUICKSTART.md) | Email reports |
 | [docs/01_architecture.md](docs/01_architecture.md) | Architecture |
 | [docs/02_data_model.md](docs/02_data_model.md) | Schema |
 | [docs/04_safety_ethics.md](docs/04_safety_ethics.md) | Research guardrails |
 | [docs/07_deployment.md](docs/07_deployment.md) | Full deploy |
 | [docs/00_mvp.md](docs/00_mvp.md) | Roadmap |
-**Phase 2: Disclosure Correlation**
+PR writeups live under `docs/archive/` when moved.
 - Matches trades with prior market alerts (30-45 day lookback)
 - Calculates "timing advantage score" (0-100)
 - Identifies suspicious timing patterns
-**Phase 3: Pattern Detection**
+## License
 - Ranks officials by consistent suspicious timing
 - Analyzes by ticker, sector, and political party
 - Generates comprehensive reports
-**Full Documentation**: See [`MONITORING_SYSTEM_COMPLETE.md`](MONITORING_SYSTEM_COMPLETE.md)
+MIT for research/educational use. Not investment advice.
 ## Next Steps
 - [ ] Signals: "follow_research", "avoid_risk", "watch" with confidence scores
 - [ ] Clustering: group officials by trading behavior patterns
 - [ ] API: FastAPI backend for queries
 - [ ] Dashboard: React/Streamlit visualization
 See [`docs/00_mvp.md`](docs/00_mvp.md) for the full roadmap.
 ---
 **License**: MIT (for research/educational use only)  
 **Disclaimer**: Not investment advice. Use public data only. No claims about inside information.
@@ -6,7 +6,7 @@ Great question! Here are multiple strategies for testing the full pipeline **wit
 ---
-## Strategy 1: Mock/Fixture Data (Current Approach ✅)
+## Strategy 1: Mock/Fixture Data (Current Approach )
 **What we already have:**
 - `tests/conftest.py` creates in-memory SQLite DB with sample officials, securities, trades
@@ -99,7 +99,7 @@ def test_etl_with_real_data():
 ---
-## Strategy 4: Hybrid Testing (Recommended 🌟)
+## Strategy 4: Hybrid Testing (Recommended )
 **Combine all strategies**:
@@ -164,7 +164,7 @@ def test_etl_with_real_data():
 | Source | Free Tier | Paid Tier | Best For |
 |--------|-----------|-----------|----------|
-| **yfinance** | Unlimited | N/A | Prices (already working ✅) |
+| **yfinance** | Unlimited | N/A | Prices (already working ) |
 | **House Stock Watcher** | Unlimited scraping | N/A | Free trades (best option) |
 | **Quiver Free** | 500 calls/mo | $30/mo (5k calls) | Testing, not production |
 | **FMP Free** | 250 calls/day | $15/mo | Alternative for trades |
@@ -222,5 +222,5 @@ class HouseWatcherClient:
        }
 ```
-Let me know if you want me to implement this scraper now for PR2! 🚀
+Let me know if you want me to implement this scraper now for PR2! 
@@ -4,14 +4,14 @@
 POTE can be deployed in several ways depending on your needs:
-1. **Local Development** (SQLite) - What you have now ✅
+1. **Local Development** (SQLite) - What you have now 
 2. **Single Server** (PostgreSQL + cron jobs)
 3. **Docker** (Containerized, easy to move)
 4. **Cloud** (AWS/GCP/Azure with managed DB)
 ---
-## Option 1: Local Development (Current Setup) ✅
+## Option 1: Local Development (Current Setup)
 **You're already running this!**
@@ -2,17 +2,17 @@
 ## Why Proxmox is Perfect for POTE
-✅ **Full control** - Your hardware, your rules  
+ **Full control** - Your hardware, your rules 
-✅ **No monthly costs** - Just electricity  
+ **No monthly costs** - Just electricity 
-✅ **Isolated VMs/LXC** - Clean environments  
+ **Isolated VMs/LXC** - Clean environments 
-✅ **Snapshots** - Easy rollback if needed  
+ **Snapshots** - Easy rollback if needed 
-✅ **Resource efficient** - Run alongside other services  
+ **Resource efficient** - Run alongside other services 
 ---
 ## Deployment Options on Proxmox
-### Option 1: LXC Container (Recommended) ⭐
+### Option 1: LXC Container (Recommended)
 **Pros**: Lightweight, fast, efficient resource usage  
 **Cons**: Linux only (fine for POTE)
@@ -501,14 +501,14 @@ vs.
 ## Next Steps
-1. ✅ Create LXC container
+1. Create LXC container
-2. ✅ Install dependencies
+2. Install dependencies
-3. ✅ Setup PostgreSQL
+3. Setup PostgreSQL
-4. ✅ Deploy POTE
+4. Deploy POTE
-5. ✅ Configure cron jobs
+5. Configure cron jobs
-6. ✅ Setup backups
+6. Setup backups
-7. ⏭️ Build Phase 2 (Analytics)
+7. ⏭ Build Phase 2 (Analytics)
-8. ⏭️ Add FastAPI dashboard (optional)
+8. ⏭ Add FastAPI dashboard (optional)
 ---
@@ -583,7 +583,7 @@ EOF
 sudo -u poteapp mkdir -p /home/poteapp/logs
 echo ""
-echo "✅ Setup complete!"
+echo " Setup complete!"
 echo ""
 echo "Next steps:"
 echo "1. su - poteapp"
@@ -600,5 +600,5 @@ chmod +x proxmox_setup.sh
 ---
-**Your Proxmox setup gives you enterprise-grade infrastructure at hobby costs!** 🚀
+**Your Proxmox setup gives you enterprise-grade infrastructure at hobby costs!** 
@@ -137,13 +137,13 @@ python scripts/fetch_sample_prices.py
 ## Data Sources
 ### Currently Working:
- ✅ yfinance (prices, company info)
+- yfinance (prices, company info)
- ✅ Manual entry
+- Manual entry
- ✅ CSV import
+- CSV import
- ✅ Fixture files (testing)
+- Fixture files (testing)
 ### Currently Down:
- ❌ House Stock Watcher API (domain issues)
+- House Stock Watcher API (domain issues)
 ### Future Options:
 - QuiverQuant (requires $30/month subscription)
@@ -8,10 +8,10 @@
 ### **Reality Check: No Real-Time Data Exists**
 **Federal Law (STOCK Act):**
- 📅 Congress members have **30-45 days** to disclose trades
+- Congress members have **30-45 days** to disclose trades
- 📅 Disclosures are filed as **Periodic Transaction Reports (PTRs)**
+- Disclosures are filed as **Periodic Transaction Reports (PTRs)**
- 📅 Public databases update **after** filing (usually next day)
+- Public databases update **after** filing (usually next day)
- 📅 **No real-time feed exists by design**
+- **No real-time feed exists by design**
 **Example Timeline:**
 ```
@@ -25,15 +25,15 @@ Feb 17, 2024  →  Your system fetches it
 Since trades appear in batches (not continuously), **running once per day is optimal**:
-✅ **Daily (7 AM)** - Catches overnight filings  
+ **Daily (7 AM)** - Catches overnight filings 
-✅ **After market close** - Prices are final  
+ **After market close** - Prices are final 
-✅ **Low server load** - Off-peak hours  
+ **Low server load** - Off-peak hours 
-❌ **Hourly** - Wasteful, no new data  
+ **Hourly** - Wasteful, no new data 
-❌ **Real-time** - Impossible, not how disclosures work  
+ **Real-time** - Impossible, not how disclosures work 
 ---
-## 🤖 Automated Setup Options
+## Automated Setup Options
 ### **Option 1: Cron Job (Linux/Proxmox) - Recommended**
@@ -149,7 +149,7 @@ Or from anywhere:
 ---
-## 📊 What Gets Updated?
+## What Gets Updated?
 ### **1. Congressional Trades**
 **Script:** `fetch_congressional_trades.py`  
@@ -182,7 +182,7 @@ Or from anywhere:
 ---
-## ⚙️ Customizing the Schedule
+## Customizing the Schedule
 ### **Different Frequencies**
@@ -215,7 +215,7 @@ Or from anywhere:
 ---
-## 📧 Email Notifications (Optional)
+## Email Notifications (Optional)
 ### **Setup Email Alerts**
@@ -310,7 +310,7 @@ python scripts/email_summary.py
 ---
-## 🔍 Monitoring & Logging
+## Monitoring & Logging
 ### **Check Cron Job Status**
@@ -355,7 +355,7 @@ Add to `/etc/logrotate.d/pote`:
 ---
-## 🚨 Handling Failures
+## Handling Failures
 ### **What If House Stock Watcher Is Down?**
@@ -363,7 +363,7 @@ The script is designed to continue even if one step fails:
 ```bash
 # Script continues and logs warnings
-⚠️  WARNING: Failed to fetch congressional trades
+ WARNING: Failed to fetch congressional trades
   This is likely because House Stock Watcher API is down
   Continuing with other steps...
 ```
@@ -399,7 +399,7 @@ for attempt in range(MAX_RETRIES):
 ---
-## 📈 Performance Optimization
+## Performance Optimization
 ### **Batch Processing**
@@ -439,7 +439,7 @@ CREATE INDEX IF NOT EXISTS ix_prices_security_id ON prices(security_id);
 ---
-## 🎯 Recommended Setup
+## Recommended Setup
 ### **For Proxmox Production:**
@@ -475,7 +475,7 @@ pote-update
 ---
-## 📝 Summary
+## Summary
 ### **Key Points:**
@@ -1,13 +1,13 @@
 # Live Market Monitoring + Congressional Trading Analysis
-## 🎯 What's Possible vs Impossible
+## What's Possible vs Impossible
-### ❌ **NOT Possible:**
+### **NOT Possible:**
 - Identify WHO is buying/selling in real-time
 - Match live trades to specific Congress members
 - See congressional trades before they're disclosed
-### ✅ **IS Possible:**
+### **IS Possible:**
 - Track unusual market activity in real-time
 - Monitor stocks Congress members historically trade
 - Compare unusual activity to later disclosures
@@ -15,7 +15,7 @@
 ---
-## 🔄 **Two-Phase Monitoring System**
+## **Two-Phase Monitoring System**
 ### **Phase 1: Real-Time Market Monitoring**
 Monitor unusual activity in stocks Congress trades:
@@ -33,7 +33,7 @@ When disclosures come in:
 ---
-## 📊 **Implementation: Watchlist-Based Monitoring**
+## **Implementation: Watchlist-Based Monitoring**
 ### **Concept:**
@@ -57,38 +57,38 @@ Step 3: When Disclosure Appears (30-45 days later)
 ---
-## 🛠️ **Data Sources for Live Market Monitoring**
+## **Data Sources for Live Market Monitoring**
 ### **Free/Low-Cost Options:**
 1. **Yahoo Finance (yfinance)**
-   - ✅ Real-time quotes (15-min delay free)
+   - Real-time quotes (15-min delay free)
-   - ✅ Historical options data
+   - Historical options data
-   - ✅ Volume data
+   - Volume data
-   - ❌ Not true real-time for options flow
+   - Not true real-time for options flow
 2. **Unusual Whales API**
-   - ✅ Options flow data
+   - Options flow data
-   - ✅ Unusual activity alerts
+   - Unusual activity alerts
-   - 💰 Paid ($50-200/month)
+   - Paid ($50-200/month)
   - https://unusualwhales.com/
 3. **Tradier API**
-   - ✅ Real-time market data
+   - Real-time market data
-   - ✅ Options chains
+   - Options chains
-   - 💰 Paid but affordable ($10-50/month)
+   - Paid but affordable ($10-50/month)
   - https://tradier.com/
 4. **FlowAlgo**
-   - ✅ Options flow tracking
+   - Options flow tracking
-   - ✅ Dark pool data
+   - Dark pool data
-   - 💰 Paid ($99-399/month)
+   - Paid ($99-399/month)
   - https://www.flowalgo.com/
 5. **Polygon.io**
-   - ✅ Real-time stock data
+   - Real-time stock data
-   - ✅ Options data
+   - Options data
-   - 💰 Free tier + paid plans
+   - Free tier + paid plans
   - https://polygon.io/
 ### **Best Free Option: Build Your Own with yfinance**
@@ -97,7 +97,7 @@ Track volume/price changes every 5 minutes for congressional watchlist tickers.
 ---
-## 💡 **Practical Hybrid System**
+## **Practical Hybrid System**
 ### **What We Can Build:**
@@ -141,37 +141,37 @@ for disclosure in new_disclosures:
 ---
-## 📈 **Example: Nancy Pelosi NVDA Trade Analysis**
+## **Example: Nancy Pelosi NVDA Trade Analysis**
 ### **Timeline:**
 ```
 Nov 10, 2024:
-  🔔 ALERT: NVDA unusual call options activity
+ ALERT: NVDA unusual call options activity
     Volume: 10x average
     Strike: $500 (2 weeks out)
 Nov 15, 2024:
-  💰 Someone buys NVDA (unknown who at the time)
+ Someone buys NVDA (unknown who at the time)
 Nov 18, 2024:
-  📰 NVDA announces new AI chip
+ NVDA announces new AI chip
-  📈 Stock jumps 15%
+ Stock jumps 15%
 Dec 15, 2024:
-  📋 Disclosure: Nancy Pelosi bought NVDA on Nov 15
+ Disclosure: Nancy Pelosi bought NVDA on Nov 15
     Value: $15,001-$50,000
 ANALYSIS:
-  ✅ She bought AFTER unusual options activity (Nov 10)
+ She bought AFTER unusual options activity (Nov 10)
-  ❓ She bought BEFORE announcement (Nov 18)
+ She bought BEFORE announcement (Nov 18)
-  ⏱️ Timing: 3 days before major news
+ ⏱ Timing: 3 days before major news
-  🚩 Flag: Investigate if announcement was public knowledge
+ Flag: Investigate if announcement was public knowledge
 ```
 ---
-## 🎯 **Recommended Approach**
+## **Recommended Approach**
 ### **Phase 1: Build Congressional Ticker Watchlist**
@@ -230,12 +230,12 @@ def monitor_tickers(tickers, interval_minutes=5):
                    # Check for unusual volume
                    avg_volume = current['Volume'].mean()
                    if latest['Volume'] > avg_volume * 3:
-                        alert(f"🔔 {ticker}: Unusual volume spike!")
+ alert(f" {ticker}: Unusual volume spike!")
                    # Check for price movement
                    price_change = (latest['Close'] - current['Open'].iloc[0]) / current['Open'].iloc[0]
                    if abs(price_change) > 0.05:  # 5% move
-                        alert(f"📈 {ticker}: {price_change:.2%} move today!")
+ alert(f" {ticker}: {price_change:.2%} move today!")
            except Exception as e:
                print(f"Error monitoring {ticker}: {e}")
@@ -290,31 +290,31 @@ def analyze_disclosure_timing(disclosure):
 ---
-## 🚨 **Realistic Expectations**
+## **Realistic Expectations**
 ### **What This System Will Do:**
-✅ Monitor stocks Congress members historically trade
+ Monitor stocks Congress members historically trade
-✅ Alert on unusual market activity in those stocks
+ Alert on unusual market activity in those stocks
-✅ Retroactively correlate disclosures with earlier alerts
+ Retroactively correlate disclosures with earlier alerts
-✅ Identify timing patterns and potential advantages
+ Identify timing patterns and potential advantages
-✅ Build database of congressional trading patterns
+ Build database of congressional trading patterns
 ### **What This System WON'T Do:**
-❌ Identify WHO is buying in real-time
+ Identify WHO is buying in real-time
-❌ Give you advance notice of congressional trades
+ Give you advance notice of congressional trades
-❌ Provide real-time inside information
+ Provide real-time inside information
-❌ Allow you to "front-run" Congress
+ Allow you to "front-run" Congress
 ### **Legal & Ethical:**
-✅ All data is public
+ All data is public
-✅ Analysis is retrospective
+ Analysis is retrospective
-✅ For research and transparency
+ For research and transparency
-✅ Not market manipulation
+ Not market manipulation
-❌ Cannot and should not be used to replicate potentially illegal trades
+ Cannot and should not be used to replicate potentially illegal trades
 ---
-## 📊 **Proposed Implementation**
+## **Proposed Implementation**
 ### **New Scripts to Create:**
@@ -365,15 +365,15 @@ CREATE TABLE disclosure_timing_analysis (
 ---
-## 🎯 **Summary**
+## **Summary**
 ### **Your Question:**
 > "Can we read live trades being made and compare them to a name?"
 ### **Answer:**
-❌ **No** - Live trades are anonymous, can't identify individuals
+ **No** - Live trades are anonymous, can't identify individuals
-✅ **BUT** - You CAN:
+ **BUT** - You CAN:
 1. Monitor unusual activity in stocks Congress trades
 2. Log these alerts in real-time
 3. When disclosures appear (30-45 days later), correlate them
@@ -381,25 +381,25 @@ CREATE TABLE disclosure_timing_analysis (
 5. Build patterns database of timing and performance
 ### **This Gives You:**
- ✅ Transparency on timing advantages
+- Transparency on timing advantages
- ✅ Pattern detection across officials
+- Pattern detection across officials
- ✅ Research-grade analysis
+- Research-grade analysis
- ✅ Historical correlation data
+- Historical correlation data
 ### **This Does NOT Give You:**
- ❌ Real-time identity of traders
+- Real-time identity of traders
- ❌ Advance notice of congressional trades
+- Advance notice of congressional trades
- ❌ Ability to "front-run" disclosures
+- Ability to "front-run" disclosures
 ---
-## 🚀 **Would You Like Me To Build This?**
+## **Would You Like Me To Build This?**
 I can create:
-1. ✅ Real-time monitoring system for congressional tickers
+1. Real-time monitoring system for congressional tickers
-2. ✅ Alert logging and analysis
+2. Alert logging and analysis
-3. ✅ Timing correlation when disclosures appear
+3. Timing correlation when disclosures appear
-4. ✅ Pattern detection and reporting
+4. Pattern detection and reporting
 This would be **Phase 2.5** of POTE - the "timing analysis" module.
@@ -205,18 +205,18 @@ Output:
 POTE HEALTH CHECK
 ============================================================
 Timestamp: 2025-12-15T10:30:00
-Overall Status: ✓ OK
+Overall Status: OK
-✓ Database Connection: Database connection successful
+ Database Connection: Database connection successful
-✓ Data Freshness: Data is fresh (2 days old)
+ Data Freshness: Data is fresh (2 days old)
    latest_trade_date: 2025-12-13
-✓ Data Counts: Database has 1,234 trades
+ Data Counts: Database has 1,234 trades
    officials: 45
    securities: 123
    trades: 1,234
    prices: 12,345
    market_alerts: 567
-✓ Recent Alerts: 23 alerts in last 24 hours
+ Recent Alerts: 23 alerts in last 24 hours
 ============================================================
 ```
@@ -22,18 +22,18 @@ chmod 600 .env
 chown poteapp:poteapp .env
 ```
-### ✅ Pros
+### Pros
 - Simple, works immediately
 - No additional setup
 - Standard practice for Python projects
-### ⚠️ Cons
+### Cons
 - Secrets stored in plain text on disk
 - Risk if server is compromised
 - No audit trail
-### 🔒 Security Checklist
+### Security Checklist
- [ ] `.env` in `.gitignore` (already done ✅)
+- [ ] `.env` in `.gitignore` (already done )
 - [ ] File permissions: `chmod 600 .env`
 - [ ] Never commit to git
 - [ ] Backup securely (encrypted)
@@ -76,12 +76,12 @@ sudo chmod 600 /etc/systemd/system/pote.service
 sudo systemctl daemon-reload
 ```
-### ✅ Pros
+### Pros
 - Secrets not in git or project directory
 - Standard Linux practice
 - Works with systemd timers
-### ⚠️ Cons
+### Cons
 - Still visible in `systemctl show`
 - Requires root to edit
@@ -128,12 +128,12 @@ source .env
 python scripts/send_daily_report.py
 ```
-### ✅ Pros
+### Pros
 - Secrets separate from code
 - Easy to rotate
 - Can be backed up separately
-### ⚠️ Cons
+### Cons
 - Extra file to manage
 - Still plain text
@@ -191,12 +191,12 @@ class Settings(BaseSettings):
    smtp_password: str = Field(default_factory=lambda: get_secret("SMTP_PASSWORD"))
 ```
-### ✅ Pros
+### Pros
 - Docker-native solution
 - Encrypted in Swarm mode
 - Never in logs
-### ⚠️ Cons
+### Cons
 - Requires Docker
 - More complex setup
@@ -228,13 +228,13 @@ secrets = client.secrets.kv.v2.read_secret_version(path='pote')
 smtp_password = secrets['data']['data']['smtp_password']
 ```
-### ✅ Pros
+### Pros
 - Centralized secrets management
 - Audit logs
 - Dynamic secrets
 - Access control
-### ⚠️ Cons
+### Cons
 - Complex setup
 - Requires Vault infrastructure
 - Overkill for single user
@@ -260,14 +260,14 @@ env:
  DATABASE_URL: postgresql://user:${{ secrets.DB_PASSWORD }}@postgres/db
 ```
-### ⚠️ Important
+### Important
 - **Only for CI/CD pipelines**
 - **NOT for deployed servers**
 - Secrets are injected during workflow runs
 ---
-## 🎯 Recommendation for Your Setup
+## Recommendation for Your Setup
 ### Personal/Research Use (Current)
@@ -305,9 +305,9 @@ gpg -c .env  # Creates .env.gpg
 ---
-## 🔒 General Security Best Practices
+## General Security Best Practices
-### ✅ DO
+### DO
 - Use strong, unique passwords
 - Restrict file permissions (`chmod 600`)
@@ -316,7 +316,7 @@ gpg -c .env  # Creates .env.gpg
 - Use encrypted backups
 - Audit who has server access
-### ❌ DON'T
+### DON'T
 - Commit secrets to git (even private repos)
 - Store passwords in code
@@ -327,7 +327,7 @@ gpg -c .env  # Creates .env.gpg
 ---
-## 🧪 Test Your Security
+## Test Your Security
 ### Check if `.env` is protected
@@ -352,7 +352,7 @@ git log --all --full-history --source --pickaxe-all -S 'smtp_password'
 ---
-## 🔄 Password Rotation Procedure
+## Password Rotation Procedure
 ### Every 90 days (or if compromised):
@@ -369,32 +369,32 @@ git log --all --full-history --source --pickaxe-all -S 'smtp_password'
 ---
-## 📊 Security Level Comparison
+## Security Level Comparison
 | Level | Method | Effort | Protection |
 |-------|--------|--------|------------|
-| 🔓 Basic | `.env` (default perms) | None | Low |
+| Basic | `.env` (default perms) | None | Low |
-| 🔒 Good | `.env` (chmod 600) | 1 min | Medium |
+| Good | `.env` (chmod 600) | 1 min | Medium |
-| 🔒 Better | Environment variables | 10 min | Good |
+| Better | Environment variables | 10 min | Good |
-| 🔒 Better | Separate secrets file | 10 min | Good |
+| Better | Separate secrets file | 10 min | Good |
-| 🔐 Best | Docker Secrets | 30 min | Very Good |
+| Best | Docker Secrets | 30 min | Very Good |
-| 🔐 Best | Vault | 2+ hours | Excellent |
+| Best | Vault | 2+ hours | Excellent |
 ---
-## 🎯 Your Current Status
+## Your Current Status
-✅ **Already secure enough for personal use:**
+ **Already secure enough for personal use:**
- `.env` in `.gitignore` ✅
+- `.env` in `.gitignore` 
- Not committed to git ✅
+- Not committed to git 
- Local server only ✅
+- Local server only 
-⚠️ **Recommended improvement (2 minutes):**
+ **Recommended improvement (2 minutes):**
 ```bash
 chmod 600 .env
 ```
-🔐 **Optional (if paranoid):**
+ **Optional (if paranoid):**
 - Use separate secrets file in `/etc/pote/`
 - Encrypt backups with GPG
 - Set up password rotation schedule
@@ -405,9 +405,9 @@ chmod 600 .env
 **For your levkin.ca setup:**
-1. **Current approach (`.env` file) is fine** ✅
+1. **Current approach (`.env` file) is fine** 
 2. **Add `chmod 600 .env`** for better security (2 minutes)
-3. **Don't commit `.env` to git** (already protected ✅)
+3. **Don't commit `.env` to git** (already protected )
 4. **Consider upgrading to environment variables** if you deploy to production
 Your current setup is **appropriate for a personal research project**. Don't over-engineer it unless you have specific compliance requirements or a team.
@@ -62,7 +62,7 @@ Follow the prompts:
 2. Choose daily report time (recommend 6 AM)
 3. Confirm
-That's it! 🎉
+That's it! 
 ---
@@ -112,7 +112,7 @@ Run the daily script manually to test:
 ./scripts/automated_daily_run.sh
 ```
-Check if email arrived! 📧
+Check if email arrived! 
 ---
@@ -244,5 +244,5 @@ Add to cron for regular health checks:
 ---
-**You're all set! POTE will now run automatically and send you daily/weekly reports. 🚀**
+**You're all set! POTE will now run automatically and send you daily/weekly reports. **
@@ -1,17 +1,17 @@
 # POTE Deployment & Automation Guide
-## 🎯 Quick Answer to Your Questions
+## Quick Answer to Your Questions
 ### After Deployment, What Happens?
 **By default: NOTHING automatic happens.** You need to set up automation.
 The deployed system is:
- ✅ Running (database, code installed)
+- Running (database, code installed)
- ✅ Accessible via SSH at your Proxmox IP
+- Accessible via SSH at your Proxmox IP
- ❌ NOT fetching data automatically
+- NOT fetching data automatically
- ❌ NOT sending reports automatically
+- NOT sending reports automatically
- ❌ NOT monitoring markets automatically
+- NOT monitoring markets automatically
 **You must either:**
 1. **Run scripts manually** when you want updates, OR
@@ -19,7 +19,7 @@ The deployed system is:
 ---
-## 🚀 Option 1: Automated Email Reports (Recommended)
+## Option 1: Automated Email Reports (Recommended)
 ### What You Get
@@ -53,13 +53,13 @@ Run the interactive setup:
 Follow prompts:
 1. Enter your email address
 2. Choose report time (default: 6 AM)
-3. Done! ✅
+3. Done! 
 **See full guide:** [`AUTOMATION_QUICKSTART.md`](AUTOMATION_QUICKSTART.md)
 ---
-## 📍 Option 2: Access Reports via IP (No Email)
+## Option 2: Access Reports via IP (No Email)
 If you don't want email, you can:
@@ -107,7 +107,7 @@ python scripts/health_check.py
 ---
-## 🌐 Option 3: Build a Web Interface (Future)
+## Option 3: Build a Web Interface (Future)
 Currently, POTE is **command-line only**. No web UI yet.
@@ -122,16 +122,16 @@ For now, use SSH or email reports.
 ---
-## 🔄 Do You Need CI/CD Pipelines?
+## Do You Need CI/CD Pipelines?
 ### What the Pipeline Does
 The included CI/CD pipeline (`.github/workflows/ci.yml`) runs on **every git push**:
-1. ✅ Lint & test (93 tests)
+1. Lint & test (93 tests)
-2. ✅ Security scanning
+2. Security scanning
-3. ✅ Dependency scanning
+3. Dependency scanning
-4. ✅ Docker build test
+4. Docker build test
 ### Should You Use It?
@@ -174,18 +174,18 @@ docker build -t pote:test .
 ---
-## 📊 Comparison of Options
+## Comparison of Options
 | Method | Pros | Cons | Best For |
 |--------|------|------|----------|
-| **Automated Email** | ✅ Convenient<br>✅ No SSH needed<br>✅ Daily/weekly updates | ❌ Requires SMTP setup | Most users |
+| **Automated Email** | Convenient<br> No SSH needed<br> Daily/weekly updates | Requires SMTP setup | Most users |
-| **SSH + Manual Scripts** | ✅ Full control<br>✅ No email needed | ❌ Manual work<br>❌ Must remember to run | Power users |
+| **SSH + Manual Scripts** | Full control<br> No email needed | Manual work<br> Must remember to run | Power users |
-| **Saved Reports (SSH access)** | ✅ Automated<br>✅ No email | ❌ Must SSH to view | Users without email |
+| **Saved Reports (SSH access)** | Automated<br> No email | Must SSH to view | Users without email |
-| **Web Interface** | ✅ User-friendly | ❌ Not implemented yet | Future |
+| **Web Interface** | User-friendly | Not implemented yet | Future |
 ---
-## 🛠️ Your Ansible Pipeline
+## Your Ansible Pipeline
 Your existing Ansible CI/CD pipeline is **NOT directly usable** for POTE because:
@@ -197,11 +197,11 @@ Your existing Ansible CI/CD pipeline is **NOT directly usable** for POTE because
 **Concepts from your pipeline that ARE used in POTE's CI/CD:**
- ✅ Security scanning (Trivy, Bandit instead of Gitleaks)
+- Security scanning (Trivy, Bandit instead of Gitleaks)
- ✅ Dependency scanning (Trivy instead of npm audit)
+- Dependency scanning (Trivy instead of npm audit)
- ✅ SAST scanning (Bandit instead of Semgrep)
+- SAST scanning (Bandit instead of Semgrep)
- ✅ Container scanning (Docker build test)
+- Container scanning (Docker build test)
- ✅ Workflow summary generation
+- Workflow summary generation
 **The POTE pipeline (`.github/workflows/ci.yml`) already includes all of these!**
@@ -217,7 +217,7 @@ Your Gitea server can run the POTE pipeline using Gitea Actions:
 ---
-## 📧 Email Setup Examples
+## Email Setup Examples
 ### Gmail (Most Common)
@@ -258,7 +258,7 @@ REPORT_RECIPIENTS=admin@yourdomain.com
 ---
-## ✅ Recommended Setup for Most Users
+## Recommended Setup for Most Users
 1. **Deploy to Proxmox** (5 min)
   ```bash
@@ -283,7 +283,7 @@ REPORT_RECIPIENTS=admin@yourdomain.com
 ---
-## 🔍 Monitoring & Health Checks
+## Monitoring & Health Checks
 ### Add System Health Monitoring
@@ -310,17 +310,17 @@ python scripts/health_check.py
 ---
-## 📚 Full Documentation
+## Full Documentation
- **Automation Setup**: [`AUTOMATION_QUICKSTART.md`](AUTOMATION_QUICKSTART.md) ⭐
+- **Automation Setup**: [`AUTOMATION_QUICKSTART.md`](AUTOMATION_QUICKSTART.md) 
- **Deployment**: [`PROXMOX_QUICKSTART.md`](PROXMOX_QUICKSTART.md) ⭐
+- **Deployment**: [`PROXMOX_QUICKSTART.md`](PROXMOX_QUICKSTART.md) 
- **Usage**: [`QUICKSTART.md`](QUICKSTART.md) ⭐
+- **Usage**: [`QUICKSTART.md`](QUICKSTART.md) 
 - **Detailed Automation Guide**: [`docs/12_automation_and_reporting.md`](docs/12_automation_and_reporting.md)
 - **Monitoring System**: [`MONITORING_SYSTEM_COMPLETE.md`](MONITORING_SYSTEM_COMPLETE.md)
 ---
-## 🎉 Summary
+## Summary
 **After deployment:**
 - Reports are NOT sent automatically by default
@@ -334,5 +334,5 @@ python scripts/health_check.py
 1. Deploy to Proxmox
 2. Run `./scripts/setup_cron.sh`
 3. Receive daily/weekly email reports
-4. Done! 🚀
+4. Done! 
@@ -2,7 +2,7 @@
 Homelab POTE sends via Mailcow **`mail.levkine.ca`** using the shared **`alerts@levkine.ca`** mailbox (same as Kuma/Beszel). See ansible `docs/guides/smtp-inventory.md`.
-## ✅ Configuration Done
+## Configuration Done
 The `.env` file has been created with these settings:
@@ -15,7 +15,7 @@ FROM_EMAIL=alerts@levkine.ca
 REPORT_RECIPIENTS=idobkin@gmail.com
 ```
-## 🔑 Next Steps
+## Next Steps
 ### 1. Add Your Password
@@ -38,7 +38,7 @@ python scripts/send_daily_report.py --to test@levkin.ca --test-smtp
 If successful, you'll see:
 ```
 SMTP connection test successful!
-✓ Daily report sent successfully!
+ Daily report sent successfully!
 ```
 And you should receive a test email at `test@levkin.ca`!
@@ -57,7 +57,7 @@ This will:
 - Schedule daily reports (default: 6 AM)
 - Schedule weekly reports (Sundays at 8 AM)
-## 📧 Email Server Details (For Reference)
+## Email Server Details (For Reference)
 Based on your Thunderbird setup:
@@ -76,10 +76,10 @@ Based on your Thunderbird setup:
 POTE only uses **SMTP (outgoing)** to send reports.
-## 🔒 Security Notes
+## Security Notes
 1. **Never commit `.env` to git!**
-   - Already in `.gitignore` ✅
+ - Already in `.gitignore` 
   - Contains sensitive password
 2. **Password Security:**
@@ -92,7 +92,7 @@ POTE only uses **SMTP (outgoing)** to send reports.
   chmod 600 .env  # Only owner can read/write
   ```
-## 🎯 Change Recipients
+## Change Recipients
 To send reports to different email addresses (not just test@levkin.ca):
@@ -109,14 +109,14 @@ python scripts/send_daily_report.py --to someone-else@example.com
 # The FROM address will still be test@levkin.ca
 ```
-## ✅ Testing Checklist
+## Testing Checklist
 - [ ] Updated `.env` with your actual password
 - [ ] Run `python scripts/send_daily_report.py --to test@levkin.ca --test-smtp`
 - [ ] Checked inbox at test@levkin.ca (check spam folder!)
 - [ ] If successful, run `./scripts/setup_cron.sh` to automate
-## 🐛 Troubleshooting
+## Troubleshooting
 ### Error: "SMTP connection failed"
@@ -139,5 +139,5 @@ Check:
 ---
-**You're all set! POTE will send reports from test@levkin.ca 📧**
+**You're all set! POTE will send reports from test@levkin.ca **
@@ -2,14 +2,14 @@
 ## TL;DR: You can test everything for $0
-### Already Working (PR1 ✅)
+### Already Working (PR1 )
 - **Price data**: `yfinance` (free, unlimited)
 - **Unit tests**: Mocked data in `tests/` (15 passing tests)
 - **Coverage**: 87% without any paid APIs
 ### For PR2 (Congressional Trades) - FREE Options
-#### Best Option: House Stock Watcher 🌟
+#### Best Option: House Stock Watcher
 ```bash
 # No API key needed, just scrape their public JSON
 curl https://housestockwatcher.com/api/all_transactions
@@ -44,10 +44,10 @@ python scripts/fetch_house_watcher_sample.py  # We'll build this in PR2
 ### What You DON'T Need to Pay For
-❌ QuiverQuant Pro ($30/mo) - free tier is enough for dev/testing  
+ QuiverQuant Pro ($30/mo) - free tier is enough for dev/testing 
-❌ Financial Modeling Prep paid tier - free tier works  
+ Financial Modeling Prep paid tier - free tier works 
-❌ Any paid database hosting - SQLite works great locally  
+ Any paid database hosting - SQLite works great locally 
-❌ Any cloud services - runs 100% locally
+ Any cloud services - runs 100% locally
 ### When You MIGHT Want Paid (Way Later)
@@ -56,7 +56,7 @@ python scripts/fetch_house_watcher_sample.py  # We'll build this in PR2
 - Multiple concurrent users on a dashboard
 - Commercial use (check each API's terms)
-**For personal research? Stay free forever. 🎉**
+**For personal research? Stay free forever. **
 ---
@@ -1,20 +1,20 @@
-# 🔐 Gitea Secrets Guide for POTE
+# Gitea Secrets Guide for POTE
-## ✅ YES! You Can Store Passwords in Gitea
+## YES! You Can Store Passwords in Gitea
 Gitea has a **Secrets** feature (like GitHub Actions secrets) that lets you store passwords securely and use them in:
-1. **CI/CD pipelines** (Gitea Actions workflows) ✅
+1. **CI/CD pipelines** (Gitea Actions workflows) 
-2. **Deployment workflows** ✅
+2. **Deployment workflows** 
 **BUT NOT:**
- ❌ Directly in your running application on Proxmox
+- Directly in your running application on Proxmox
- ❌ Accessed by scripts outside of workflows
+- Accessed by scripts outside of workflows
 ---
-## 🎯 What Gitea Secrets Are Good For
+## What Gitea Secrets Are Good For
-### ✅ Perfect Use Cases
+### Perfect Use Cases
 1. **CI/CD Testing** - Run tests with real credentials
 2. **Automated Deployment** - Deploy to Proxmox with SSH keys
@@ -22,7 +22,7 @@ Gitea has a **Secrets** feature (like GitHub Actions secrets) that lets you stor
 4. **Docker Registry** - Push images with credentials
 5. **API Keys** - Access external services during builds
-### ❌ NOT Good For
+### NOT Good For
 1. **Runtime secrets** - Your deployed app on Proxmox can't access them
 2. **Local development** - Can't use secrets on your laptop
@@ -30,7 +30,7 @@ Gitea has a **Secrets** feature (like GitHub Actions secrets) that lets you stor
 ---
-## 🔧 How to Set Up Gitea Secrets
+## How to Set Up Gitea Secrets
 ### Step 1: Add Secrets to Gitea
@@ -57,7 +57,7 @@ Secrets are accessed with `${{ secrets.SECRET_NAME }}` syntax.
 ---
-## 📝 Example: CI Pipeline with Secrets
+## Example: CI Pipeline with Secrets
 **File:** `.github/workflows/ci.yml`
@@ -92,11 +92,11 @@ jobs:
            --smtp-password "${{ secrets.SMTP_PASSWORD }}"
 ```
-**✅ I've already updated your CI pipeline to use secrets!**
+** I've already updated your CI pipeline to use secrets!**
 ---
-## 🚀 Example: Automated Deployment Workflow
+## Example: Automated Deployment Workflow
 Create `.github/workflows/deploy.yml`:
@@ -152,7 +152,7 @@ jobs:
 ---
-## 🔄 How Secrets Flow to Your Server
+## How Secrets Flow to Your Server
 ### Option 1: Deploy Workflow Updates `.env` (Recommended)
@@ -201,11 +201,11 @@ jobs:
 ---
-## 🎯 Recommended Setup for Your POTE Project
+## Recommended Setup for Your POTE Project
 ### For CI/CD (Testing):
-**Use Gitea Secrets** ✅
+**Use Gitea Secrets** 
 ```yaml
 # .github/workflows/ci.yml (already updated!)
@@ -216,7 +216,7 @@ env:
 ### For Deployed Server (Proxmox):
-**Keep using `.env` file** ✅
+**Keep using `.env` file** 
 Why?
 - Simpler for manual SSH access
@@ -227,7 +227,7 @@ Why?
 ---
-## 🚀 Complete Workflow: Gitea → Proxmox
+## Complete Workflow: Gitea → Proxmox
 ### 1. Store Secrets in Gitea
@@ -264,27 +264,27 @@ git push origin main
 ---
-## ⚠️ Important Limitations
+## Important Limitations
 ### Gitea Secrets CAN'T:
-❌ Be accessed outside of workflows
+ Be accessed outside of workflows
-❌ Be used in local `python script.py` runs
+ Be used in local `python script.py` runs
-❌ Be read by cron jobs on Proxmox (directly)
+ Be read by cron jobs on Proxmox (directly)
-❌ Replace `.env` for runtime application config
+ Replace `.env` for runtime application config
 ### Gitea Secrets CAN:
-✅ Secure your CI/CD pipeline
+ Secure your CI/CD pipeline
-✅ Deploy safely without exposing passwords in git
+ Deploy safely without exposing passwords in git
-✅ Update `.env` on server during deployment
+ Update `.env` on server during deployment
-✅ Run automated tests with real credentials
+ Run automated tests with real credentials
 ---
-## 🔒 Security Best Practices
+## Security Best Practices
-### ✅ DO:
+### DO:
 1. **Store ALL sensitive data as Gitea secrets**
   - SMTP passwords
@@ -300,10 +300,10 @@ git push origin main
 3. **Never echo secrets**
   ```yaml
-   # ❌ BAD - exposes in logs
+ # BAD - exposes in logs
   - run: echo "${{ secrets.PASSWORD }}"
-   # ✅ GOOD - masked automatically
+ # GOOD - masked automatically
   - run: use_password "${{ secrets.PASSWORD }}"
   ```
@@ -311,7 +311,7 @@ git push origin main
   - Update in Gitea UI
   - Re-run deployment workflow
-### ❌ DON'T:
+### DON'T:
 1. **Commit secrets to git** (even private repos)
 2. **Share secrets via Slack/email**
@@ -320,18 +320,18 @@ git push origin main
 ---
-## 📊 Comparison: Where to Store Secrets
+## Comparison: Where to Store Secrets
 | Storage | CI/CD | Deployed App | Easy Updates | Security |
 |---------|-------|--------------|--------------|----------|
-| **Gitea Secrets** | ✅ Perfect | ❌ No | ✅ Via workflow | ⭐⭐⭐⭐⭐ |
+| **Gitea Secrets** | Perfect | No | Via workflow | |
-| **`.env` file** | ❌ No | ✅ Perfect | ✅ `nano .env` | ⭐⭐⭐ |
+| **`.env` file** | No | Perfect | `nano .env` | |
-| **Environment Vars** | ✅ Yes | ✅ Yes | ❌ Harder | ⭐⭐⭐⭐ |
+| **Environment Vars** | Yes | Yes | Harder | |
-| **Both (Recommended)** | ✅ Yes | ✅ Yes | ✅ Automated | ⭐⭐⭐⭐⭐ |
+| **Both (Recommended)** | Yes | Yes | Automated | |
 ---
-## 🎯 My Recommendation for You
+## My Recommendation for You
 ### Use BOTH:
@@ -345,21 +345,21 @@ git push origin main
 2. Commit code changes
 3. Push to Gitea
 4. Workflow runs:
-   - Tests with Gitea secrets ✅
+ - Tests with Gitea secrets 
-   - Deploys to Proxmox ✅
+ - Deploys to Proxmox 
-   - Updates .env with secrets ✅
+ - Updates .env with secrets 
-5. Proxmox app reads from .env ✅
+5. Proxmox app reads from .env 
 ```
 **This gives you:**
- ✅ Secure CI/CD
+- Secure CI/CD
- ✅ Easy manual SSH access
+- Easy manual SSH access
- ✅ Automated deployments
+- Automated deployments
- ✅ No passwords in git
+- No passwords in git
 ---
-## 🚀 Next Steps
+## Next Steps
 ### 1. Add Secrets to Gitea (5 minutes)
@@ -387,7 +387,7 @@ I can create `.github/workflows/deploy.yml` if you want automated deployments!
 ---
-## 💡 Quick Commands
+## Quick Commands
 ### Add SSH Key to Gitea (for deployment):
@@ -409,12 +409,12 @@ git commit --allow-empty -m "Test secrets"
 git push
 # Check Gitea Actions tab
-# Look for green checkmarks ✅
+# Look for green checkmarks
 ```
 ---
-## 📚 See Also
+## See Also
 - **[docs/13_secrets_management.md](docs/13_secrets_management.md)** - All secrets options
 - **[.github/workflows/ci.yml](.github/workflows/ci.yml)** - Updated with secrets support
@@ -422,16 +422,16 @@ git push
 ---
-## ✅ Summary
+## Summary
 **YES, use Gitea secrets!** They're perfect for:
- ✅ CI/CD pipelines
+- CI/CD pipelines
- ✅ Automated deployments
+- Automated deployments
- ✅ Keeping passwords out of git
+- Keeping passwords out of git
 **But ALSO keep `.env` on Proxmox** for:
- ✅ Runtime application config
+- Runtime application config
- ✅ Manual SSH access
+- Manual SSH access
- ✅ Cron jobs
+- Cron jobs
-**Best of both worlds:** Gitea secrets deploy and update the `.env` file automatically! 🚀
+**Best of both worlds:** Gitea secrets deploy and update the `.env` file automatically! 
@@ -150,9 +150,9 @@ make beszel-install-agents BESZEL_ONLY=pote-236   # if agent not yet installed
 | Doc | Purpose |
 |-----|---------|
-| [EMAIL_SETUP.md](../EMAIL_SETUP.md) | SMTP / Mailcow / levkine.ca |
+| [EMAIL_SETUP.md](EMAIL_SETUP.md) | SMTP / Mailcow / levkine.ca |
-| [AUTOMATION_QUICKSTART.md](../AUTOMATION_QUICKSTART.md) | Cron + reports |
+| [AUTOMATION_QUICKSTART.md](AUTOMATION_QUICKSTART.md) | Cron + reports |
-| [PROXMOX_QUICKSTART.md](../PROXMOX_QUICKSTART.md) | Original LXC provisioning |
+| [PROXMOX_QUICKSTART.md](PROXMOX_QUICKSTART.md) | Original LXC provisioning |
 | Ansible `docs/guides/projects-handoff-2026-05-26.md` | Multi-project homelab context |
 | Ansible `docs/guides/smtp-inventory.md` | Mailboxes |
@@ -1,6 +1,6 @@
 # Local Testing Guide for POTE
-## ✅ Testing Locally Before Deployment
+## Testing Locally Before Deployment
 ### Quick Test - Run Full Suite
@@ -10,18 +10,18 @@ source venv/bin/activate
 pytest -v
 ```
-**Expected Result:** All 55 tests should pass ✅
+**Expected Result:** All 55 tests should pass 
 ---
-## 📊 Current Data Status
+## Current Data Status
-### Live Data Status: ❌ **NOT LIVE YET**
+### Live Data Status:  **NOT LIVE YET**
 **Why?**
- 🔴 **House Stock Watcher API is DOWN** (domain issues, unreachable)
+- **House Stock Watcher API is DOWN** (domain issues, unreachable)
- 🟢 **yfinance works** (for price data)
+- **yfinance works** (for price data)
- 🟡 **Sample data available** (5 trades from fixtures)
+- **Sample data available** (5 trades from fixtures)
 ### What Data Do You Have?
@@ -40,7 +40,7 @@ This will show:
 ---
-## 🧪 Testing Analytics Locally
+## Testing Analytics Locally
 ### 1. Unit Tests (Fast, No External Dependencies)
@@ -53,10 +53,10 @@ pytest tests/test_analytics_integration.py -v
 ```
 These tests:
- ✅ Create synthetic price data
+- Create synthetic price data
- ✅ Simulate trades with known returns
+- Simulate trades with known returns
- ✅ Verify calculations are correct
+- Verify calculations are correct
- ✅ Test edge cases (missing data, sell trades, etc.)
+- Test edge cases (missing data, sell trades, etc.)
 ### 2. Manual Test with Local Database
@@ -92,27 +92,27 @@ calc = ReturnCalculator(session)
 ---
-## 📦 What Gets Tested?
+## What Gets Tested?
-### Core Functionality (All Working ✅)
+### Core Functionality (All Working )
 1. **Database Models** - Officials, Securities, Trades, Prices
 2. **Data Ingestion** - Trade loading, security enrichment
 3. **Analytics Engine** - Returns, benchmarks, metrics
 4. **Edge Cases** - Missing data, sell trades, disclosure lags
 ### Integration Tests Cover:
- ✅ Return calculations over multiple time windows (30/60/90/180 days)
+- Return calculations over multiple time windows (30/60/90/180 days)
- ✅ Benchmark comparisons (stock vs SPY/QQQ)
+- Benchmark comparisons (stock vs SPY/QQQ)
- ✅ Abnormal return (alpha) calculations
+- Abnormal return (alpha) calculations
- ✅ Official performance summaries
+- Official performance summaries
- ✅ Sector analysis
+- Sector analysis
- ✅ Disclosure timing analysis
+- Disclosure timing analysis
- ✅ Top performer rankings
+- Top performer rankings
- ✅ System-wide statistics
+- System-wide statistics
 ---
-## 🔄 Getting Live Data
+## Getting Live Data
 ### Option 1: Wait for House Stock Watcher API
 The API is currently down. Once it's back up:
@@ -164,7 +164,7 @@ export QUIVER_API_KEY="your_key_here"
 ---
-## 📈 After Adding Data, Fetch Prices
+## After Adding Data, Fetch Prices
 ```bash
 # This will fetch prices for all securities in your database
@@ -176,12 +176,12 @@ python scripts/enrich_securities.py
 ---
-## 🎯 Complete Local Test Workflow
+## Complete Local Test Workflow
 ```bash
 # 1. Run all tests
 pytest -v
-# ✅ All 55 tests should pass
+# All 55 tests should pass
 # 2. Check local database
 python -c "
@@ -210,7 +210,7 @@ python scripts/calculate_all_returns.py --window 90
 ---
-## 🚀 Deploy to Proxmox
+## Deploy to Proxmox
 Once local tests pass:
@@ -240,7 +240,7 @@ alembic upgrade head
 ---
-## 🐛 Common Issues
+## Common Issues
 ### "No price data found"
 **Fix:** Run `python scripts/fetch_sample_prices.py`
@@ -260,7 +260,7 @@ sudo -u postgres psql -c "\l"
 ---
-## 📊 Test Coverage
+## Test Coverage
 Run tests with coverage report:
@@ -277,19 +277,19 @@ firefox htmlcov/index.html  # View coverage report
 ---
-## ✨ Summary
+## Summary
 **Before Deploying:**
-1. ✅ Run `pytest -v` - all tests pass
+1. Run `pytest -v` - all tests pass
-2. ✅ Run `make lint` - no errors
+2. Run `make lint` - no errors
-3. ✅ Test locally with sample data
+3. Test locally with sample data
-4. ✅ Verify analytics work with synthetic prices
+4. Verify analytics work with synthetic prices
 **Getting Live Data:**
- 🔴 House Stock Watcher API is down (external issue)
+- House Stock Watcher API is down (external issue)
- 🟢 Manual CSV import works NOW
+- Manual CSV import works NOW
- 🟢 yfinance for prices works NOW
+- yfinance for prices works NOW
- 🟡 QuiverQuant available (requires free API key)
+- QuiverQuant available (requires free API key)
 **You can deploy and use the system NOW with:**
 - Manual data entry
@@ -1,6 +1,6 @@
 # Offline Demo - Works Without Internet!
-## ✅ Full System Working Without Network Access
+## Full System Working Without Network Access
 Even though your environment doesn't have external internet, **everything works perfectly** using fixture files.
@@ -10,9 +10,9 @@ Even though your environment doesn't have external internet, **everything works
 python scripts/ingest_from_fixtures.py
 # Output:
-# ✓ Officials created/updated: 4
+# Officials created/updated: 4
-# ✓ Securities created/updated: 2
+# Securities created/updated: 2
-# ✓ Trades ingested: 5
+# Trades ingested: 5
 #
 # Database totals:
 #   Total officials: 4
@@ -33,7 +33,7 @@ python scripts/ingest_from_fixtures.py
   - NVDA, MSFT, AAPL, TSLA, GOOGL tickers
 2. **Offline Scripts**
-   - `scripts/ingest_from_fixtures.py` - Ingest sample trades (✅ works now!)
+ - `scripts/ingest_from_fixtures.py` - Ingest sample trades ( works now!)
   - `scripts/fetch_sample_prices.py` - Would need network (yfinance)
 3. **28 Passing Tests** - All use mocks, no network required
@@ -67,14 +67,14 @@ with SessionLocal() as session:
 ### What You Can Do Offline
-✅ **Run all tests**: `make test`  
+ **Run all tests**: `make test` 
-✅ **Ingest fixture data**: `python scripts/ingest_from_fixtures.py`  
+ **Ingest fixture data**: `python scripts/ingest_from_fixtures.py` 
-✅ **Query the database**: Use Python REPL or SQLite browser  
+ **Query the database**: Use Python REPL or SQLite browser 
-✅ **Lint & format**: `make lint format`  
+ **Lint & format**: `make lint format` 
-✅ **Run migrations**: `make migrate`  
+ **Run migrations**: `make migrate` 
-✅ **Build analytics** (Phase 2): All math/ML works offline!  
+ **Build analytics** (Phase 2): All math/ML works offline! 
-❌ **Can't do (needs network)**:  
+ **Can't do (needs network)**: 
 - Fetch live congressional trades from House Stock Watcher  
 - Fetch stock prices from yfinance  
 - (But you can add more fixture files to simulate this!)
@@ -108,9 +108,9 @@ You can expand the fixtures for offline development:
 ## Summary
 **The network error is not a problem!** The entire system is designed to work with:
- ✅ Fixtures for development/testing
+- Fixtures for development/testing
- ✅ Real APIs for production (when network available)
+- Real APIs for production (when network available)
- ✅ Same code paths for both
+- Same code paths for both
-This is **by design** - makes development fast and tests reliable! 🚀
+This is **by design** - makes development fast and tests reliable! 
@@ -223,12 +223,12 @@ print(f"Win Rate: {pelosi_stats['win_rate']:.1%}")
 ## Success Criteria
- ✅ Can calculate returns for any trade + window
+- Can calculate returns for any trade + window
- ✅ Can compare to S&P 500 benchmark
+- Can compare to S&P 500 benchmark
- ✅ Can generate official performance summaries
+- Can generate official performance summaries
- ✅ All calculations tested and accurate
+- All calculations tested and accurate
- ✅ Performance data stored efficiently
+- Performance data stored efficiently
- ✅ Documentation complete
+- Documentation complete
 ## Timeline
@@ -1,4 +1,4 @@
-# Proxmox Quick Start ⚡
+# Proxmox Quick Start
 **Got Proxmox? Deploy POTE in 5 minutes!**
@@ -17,7 +17,7 @@ su - poteapp
 cd pote && source venv/bin/activate
 python scripts/ingest_from_fixtures.py
-# Done! ✅
+# Done!
 ```
 ---
@@ -81,8 +81,8 @@ source venv/bin/activate
 python scripts/ingest_from_fixtures.py
 # Should see:
-# ✓ Officials created: 4
+# Officials created: 4
-# ✓ Trades ingested: 5
+# Trades ingested: 5
 ```
 ### 5. Setup Cron Jobs
@@ -96,7 +96,7 @@ crontab -e
 15 6 * * * cd /home/poteapp/pote && /home/poteapp/pote/venv/bin/python scripts/enrich_securities.py >> /home/poteapp/logs/enrich.log 2>&1
 ```
-### 6. Done! 🎉
+### 6. Done!
 Your POTE instance is now running and will:
 - Fetch congressional trades daily at 6 AM
@@ -107,12 +107,12 @@ Your POTE instance is now running and will:
 ## What You Get
-✅ **Full PostgreSQL database**  
+ **Full PostgreSQL database** 
-✅ **Automated daily updates** (via cron)  
+ **Automated daily updates** (via cron) 
-✅ **Isolated environment** (LXC container)  
+ **Isolated environment** (LXC container) 
-✅ **Easy backups** (Proxmox snapshots)  
+ **Easy backups** (Proxmox snapshots) 
-✅ **Low resource usage** (~500MB RAM)  
+ **Low resource usage** (~500MB RAM) 
-✅ **Cost**: Just electricity (~$5-10/mo)
+ **Cost**: Just electricity (~$5-10/mo)
 ---
@@ -231,13 +231,13 @@ pip install -e .
 ## Next Steps
-1. ✅ Container running
+1. Container running
-2. ✅ POTE installed
+2. POTE installed
-3. ✅ Data ingested
+3. Data ingested
-4. ⏭️ Setup Proxmox backups (Web UI → Datacenter → Backup)
+4. ⏭ Setup Proxmox backups (Web UI → Datacenter → Backup)
-5. ⏭️ Configure static IP (if needed)
+5. ⏭ Configure static IP (if needed)
-6. ⏭️ Build Phase 2 analytics
+6. ⏭ Build Phase 2 analytics
-7. ⏭️ Add FastAPI dashboard
+7. ⏭ Add FastAPI dashboard
 ---
@@ -264,10 +264,10 @@ pct restart 100
 ---
-**Your Proxmox = Enterprise infrastructure at hobby prices!** 🚀
+**Your Proxmox = Enterprise infrastructure at hobby prices!** 
 Cost breakdown:
 - Cloud VPS: $20/mo
 - Your Proxmox: ~$10/mo (power)
- **Savings: $120/year** ✨
+- **Savings: $120/year** 
@@ -1,15 +1,15 @@
 # POTE Quick Start Guide
-## 🚀 Your System is Ready!
+## Your System is Ready!
 **Container IP**: Check with `ip addr show eth0 | grep "inet"`  
 **Database**: PostgreSQL on port 5432  
 **Username**: `poteuser`  
-**Password**: `changeme123` (⚠️ change in production!)
+**Password**: `changeme123` ( change in production!)
 ---
-## 📊 How to Use POTE
+## How to Use POTE
 ### Option 1: Command Line (SSH into container)
@@ -51,7 +51,7 @@ with engine.connect() as conn:
 ---
-## 🎯 Common Tasks
+## Common Tasks
 ### 1. Check System Status
@@ -145,7 +145,7 @@ ORDER BY trade_count DESC;
 ---
-## 📈 Example Workflows
+## Example Workflows
 ### Workflow 1: Daily Update
@@ -237,7 +237,7 @@ print(f"Exported {len(df)} trades to trades_export.csv")
 ---
-## 🔧 Maintenance
+## Maintenance
 ### Update POTE Code
@@ -287,7 +287,7 @@ nano ~/pote/.env
 ---
-## 🌐 Access Methods Summary
+## Access Methods Summary
 | Method | From Where | Command |
 |--------|-----------|---------|
@@ -298,22 +298,22 @@ nano ~/pote/.env
 ---
-## 📚 What Data Do You Have?
+## What Data Do You Have?
 Right now (Phase 1 complete):
- ✅ **Congressional trading data** (from House Stock Watcher)
+- **Congressional trading data** (from House Stock Watcher)
- ✅ **Security information** (tickers, names, sectors)
+- **Security information** (tickers, names, sectors)
- ✅ **Historical prices** (OHLCV data from yfinance)
+- **Historical prices** (OHLCV data from yfinance)
- ✅ **Official profiles** (name, party, chamber, state)
+- **Official profiles** (name, party, chamber, state)
 Coming next (Phase 2):
- 📊 **Abnormal return calculations**
+- **Abnormal return calculations**
- 🤖 **Behavioral clustering**
+- **Behavioral clustering**
- 🚨 **Research signals** (follow_research, avoid_risk, watch)
+- **Research signals** (follow_research, avoid_risk, watch)
 ---
-## 🎓 Learning SQL for POTE
+## Learning SQL for POTE
 ### Count Records
 ```sql
@@ -349,7 +349,7 @@ GROUP BY o.party;
 ---
-## ❓ Troubleshooting
+## Troubleshooting
 ### Can't connect remotely?
 ```bash
@@ -380,12 +380,12 @@ pip install -e .
 ---
-## 🚀 Next Steps
+## Next Steps
 1. **Populate with real data**: Run `fetch_congressional_trades.py` regularly
 2. **Set up cron job** for automatic daily updates
 3. **Build analytics** (Phase 2) - abnormal returns, signals
 4. **Create dashboard** (Phase 3) - web interface for exploration
-Ready to build Phase 2 analytics? Just ask! 📈
+Ready to build Phase 2 analytics? Just ask! 
@@ -1,15 +1,15 @@
-# 🚀 POTE Quick Setup Card
+# POTE Quick Setup Card
-## 📍 Your Configuration
+## Your Configuration
 **Email Server:** `mail.levkin.ca`  
 **Email Account:** `test@levkin.ca`  
 **Database:** PostgreSQL (configured)  
-**Status:** ✅ Ready for deployment
+**Status:** Ready for deployment
 ---
-## ⚡ 3-Step Setup
+## 3-Step Setup
 ### Step 1: Add Your Password (30 seconds)
@@ -27,7 +27,7 @@ source venv/bin/activate
 python scripts/send_daily_report.py --to test@levkin.ca --test-smtp
 ```
-✅ **Check test@levkin.ca inbox** - you should receive a test email!
+ **Check test@levkin.ca inbox** - you should receive a test email!
 ### Step 3: Automate (2 minutes)
@@ -37,13 +37,13 @@ python scripts/send_daily_report.py --to test@levkin.ca --test-smtp
 # Choose time: 6 AM (recommended)
 ```
-**Done!** 🎉 You'll now receive:
+**Done!** You'll now receive:
 - Daily reports at 6 AM
 - Weekly reports on Sundays
 ---
-## 📦 Deployment to Proxmox (5 minutes)
+## Deployment to Proxmox (5 minutes)
 ### On Proxmox Host:
@@ -79,7 +79,7 @@ bash scripts/proxmox_setup.sh
 ---
-## 🔍 Quick Commands
+## Quick Commands
 ### On Deployed Server (SSH)
@@ -105,7 +105,7 @@ ls -lh ~/logs/*.txt
 ---
-## 📧 Email Configuration (.env)
+## Email Configuration (.env)
 ```env
 SMTP_HOST=mail.levkin.ca
@@ -123,27 +123,27 @@ REPORT_RECIPIENTS=test@levkin.ca,user2@example.com,user3@example.com
 ---
-## 📊 What You'll Receive
+## What You'll Receive
 ### Daily Report (6 AM)
 ```
-✅ New congressional trades
+ New congressional trades
-✅ Market alerts (unusual activity)
+ Market alerts (unusual activity)
-✅ Suspicious timing detections
+ Suspicious timing detections
-✅ Summary statistics
+ Summary statistics
 ```
 ### Weekly Report (Sunday 8 AM)
 ```
-✅ Most active officials
+ Most active officials
-✅ Most traded securities
+ Most traded securities
-✅ Repeat offenders
+ Repeat offenders
-✅ Pattern analysis
+ Pattern analysis
 ```
 ---
-## 🔧 Troubleshooting
+## Troubleshooting
 ### Email Not Working?
@@ -191,19 +191,19 @@ tail -50 ~/logs/daily_run.log
 ---
-## 📚 Documentation
+## Documentation
 | Document | Purpose |
 |----------|---------|
-| **[EMAIL_SETUP.md](EMAIL_SETUP.md)** | ⭐ Your levkin.ca setup guide |
+| **[EMAIL_SETUP.md](EMAIL_SETUP.md)** | Your levkin.ca setup guide |
-| **[DEPLOYMENT_AND_AUTOMATION.md](DEPLOYMENT_AND_AUTOMATION.md)** | ⭐ Answers all questions |
+| **[DEPLOYMENT_AND_AUTOMATION.md](DEPLOYMENT_AND_AUTOMATION.md)** | Answers all questions |
 | **[AUTOMATION_QUICKSTART.md](AUTOMATION_QUICKSTART.md)** | Quick automation guide |
 | **[PROXMOX_QUICKSTART.md](PROXMOX_QUICKSTART.md)** | Proxmox deployment |
 | **[QUICKSTART.md](QUICKSTART.md)** | Usage guide |
 ---
-## ✅ Checklist
+## Checklist
 **Local Development:**
 - [ ] `.env` file created with password
@@ -227,18 +227,18 @@ tail -50 ~/logs/daily_run.log
 ---
-## 🎯 Your Current Status
+## Your Current Status
-✅ **Code:** Complete (93 tests passing)  
+ **Code:** Complete (93 tests passing) 
-✅ **Monitoring:** 3-phase system operational  
+ **Monitoring:** 3-phase system operational 
-✅ **CI/CD:** Pipeline ready (.github/workflows/ci.yml)  
+ **CI/CD:** Pipeline ready (.github/workflows/ci.yml) 
-✅ **Email:** Configured for test@levkin.ca  
+ **Email:** Configured for test@levkin.ca 
 ⏳ **Deployment:** Ready to deploy to Proxmox  
 ⏳ **Automation:** Ready to set up with `setup_cron.sh`
 ---
-## 🚀 Next Action
+## Next Action
 **Right now (local testing):**
 ```bash
@@ -255,9 +255,9 @@ cd ~/pote
 ./scripts/setup_cron.sh
 ```
-**That's it! 🎉**
+**That's it! **
 ---
-**Everything is ready - just add your password and test!** 📧
+**Everything is ready - just add your password and test!** 
@@ -3,25 +3,25 @@
 **Last Updated**: 2025-12-14  
 **Version**: Phase 1 Complete (PR1 + PR2)
-## 🎉 What's Working Now
+## What's Working Now
 ### Data Ingestion (FREE!)
-✅ **Congressional Trades**: Live ingestion from House Stock Watcher  
+ **Congressional Trades**: Live ingestion from House Stock Watcher 
-✅ **Stock Prices**: Daily OHLCV from yfinance  
+ **Stock Prices**: Daily OHLCV from yfinance 
-✅ **Officials**: Auto-populated from trade disclosures  
+ **Officials**: Auto-populated from trade disclosures 
-✅ **Securities**: Auto-created, ready for enrichment  
+ **Securities**: Auto-created, ready for enrichment 
 ### Database
-✅ **Schema**: Normalized (officials, securities, trades, prices, metrics stubs)  
+ **Schema**: Normalized (officials, securities, trades, prices, metrics stubs) 
-✅ **Migrations**: Alembic configured and applied  
+ **Migrations**: Alembic configured and applied 
-✅ **DB**: SQLite for dev, PostgreSQL-ready  
+ **DB**: SQLite for dev, PostgreSQL-ready 
 ### Code Quality
-✅ **Tests**: 28 passing (86% coverage)  
+ **Tests**: 28 passing (86% coverage) 
-✅ **Linting**: ruff + mypy all green  
+ **Linting**: ruff + mypy all green 
-✅ **Format**: black applied consistently  
+ **Format**: black applied consistently 
-## 📊 Current Stats
+## Current Stats
 ```bash
 # Test Suite
@@ -43,7 +43,7 @@ All free/open-source:
 - pytest (testing)
 ```
-## 🚀 Quick Commands
+## Quick Commands
 ### Fetch Live Data (FREE!)
 ```bash
@@ -75,7 +75,7 @@ make format    # Format with black
 make migrate   # Run Alembic migrations
 ```
-## 🏠 Deployment
+## Deployment
 **Your Proxmox?** Perfect! See [`docs/08_proxmox_deployment.md`](docs/08_proxmox_deployment.md) for:
 - LXC container setup (lightweight, recommended)
@@ -90,7 +90,7 @@ Other options in [`docs/07_deployment.md`](docs/07_deployment.md):
 - Railway/Fly.io - $5-15/mo
 - AWS/GCP - $20-50/mo
-## 📂 Project Structure
+## Project Structure
 ```
 pote/
@@ -138,7 +138,7 @@ pote/
    └── fetch_sample_prices.py         # Live price fetch
 ```
-## 💰 Cost Breakdown
+## Cost Breakdown
 | Component | Cost | Notes |
 |-----------|------|-------|
@@ -154,7 +154,7 @@ Optional paid upgrades (NOT needed):
 - Financial Modeling Prep: $15/mo (250 calls/day free tier available)
 - PostgreSQL hosting: $7+/mo (only if deploying)
-## ✅ Completed PRs
+## Completed PRs
 ### PR1: Project Scaffold + Price Loader
 - [x] Project structure (`src/`, `tests/`, docs)
@@ -176,7 +176,7 @@ Optional paid upgrades (NOT needed):
 **See**: [`docs/PR2_SUMMARY.md`](docs/PR2_SUMMARY.md)
-## 📋 Next Steps (Phase 2 - Analytics)
+## Next Steps (Phase 2 - Analytics)
 ### PR3: Security Enrichment
 - [ ] Enrich securities table with yfinance (names, sectors, exchanges)
@@ -206,20 +206,20 @@ Optional paid upgrades (NOT needed):
 **See**: [`docs/00_mvp.md`](docs/00_mvp.md) for full roadmap
-## 🔬 Research-Only Reminder
+## Research-Only Reminder
 **This tool is for private research and transparency analysis only.**
- ❌ Not investment advice
+- Not investment advice
- ❌ Not a trading system
+- Not a trading system
- ❌ No claims about inside information
+- No claims about inside information
- ✅ Public data only
+- Public data only
- ✅ Descriptive analytics
+- Descriptive analytics
- ✅ Research transparency
+- Research transparency
 See [`docs/04_safety_ethics.md`](docs/04_safety_ethics.md) for guardrails.
-## 🤝 Contributing
+## Contributing
 This is a personal research project, but if you want to use it:
@@ -229,7 +229,7 @@ This is a personal research project, but if you want to use it:
 4. `python scripts/fetch_congressional_trades.py --days 7`
 5. Start exploring!
-## 📄 License
+## License
 MIT License (for research/educational use only)
@@ -1,12 +1,12 @@
 # POTE Testing Status Report
 **Date:** December 15, 2025  
-**Status:** ✅ All Systems Operational - Ready for Deployment
+**Status:** All Systems Operational - Ready for Deployment
 ---
-## 🎯 Test Suite Summary
+## Test Suite Summary
-### **55 Tests - All Passing ✅**
+### **55 Tests - All Passing **
 ```
 Platform: Python 3.13.5, pytest-9.0.2
@@ -18,17 +18,17 @@ Coverage: ~85% overall
 | Module | Tests | Status | Coverage |
 |--------|-------|--------|----------|
-| **Analytics** | 18 tests | ✅ PASS | 80% |
+| **Analytics** | 18 tests | PASS | 80% |
-| **Models** | 7 tests | ✅ PASS | 90% |
+| **Models** | 7 tests | PASS | 90% |
-| **Ingestion** | 14 tests | ✅ PASS | 85% |
+| **Ingestion** | 14 tests | PASS | 85% |
-| **Price Loader** | 8 tests | ✅ PASS | 90% |
+| **Price Loader** | 8 tests | PASS | 90% |
-| **Security Enricher** | 8 tests | ✅ PASS | 85% |
+| **Security Enricher** | 8 tests | PASS | 85% |
 ---
-## 📊 What's Been Tested?
+## What's Been Tested?
-### ✅ Core Database Operations
+### Core Database Operations
 - [x] Creating and querying Officials
 - [x] Creating and querying Securities  
 - [x] Creating and querying Trades
@@ -36,7 +36,7 @@ Coverage: ~85% overall
 - [x] Unique constraints and relationships
 - [x] Database migrations (Alembic)
-### ✅ Data Ingestion
+### Data Ingestion
 - [x] House Stock Watcher client (with fixtures)
 - [x] Trade loading from JSON
 - [x] Security enrichment from yfinance
@@ -44,7 +44,7 @@ Coverage: ~85% overall
 - [x] Idempotent operations (no duplicates)
 - [x] Error handling for missing/invalid data
-### ✅ Analytics Engine
+### Analytics Engine
 - [x] Return calculations (buy trades)
 - [x] Return calculations (sell trades)
 - [x] Multiple time windows (30/60/90/180 days)
@@ -56,7 +56,7 @@ Coverage: ~85% overall
 - [x] Top performer rankings
 - [x] System-wide statistics
-### ✅ Edge Cases
+### Edge Cases
 - [x] Missing price data handling
 - [x] Trades with no exit price yet
 - [x] Sell trades (inverted returns)
@@ -67,7 +67,7 @@ Coverage: ~85% overall
 ---
-## 🧪 Test Types
+## Test Types
 ### 1. Unit Tests (Fast, Isolated)
 **Location:** `tests/test_*.py` (excluding integration)  
@@ -100,7 +100,7 @@ Coverage: ~85% overall
 ---
-## 🔧 How to Run Tests Locally
+## How to Run Tests Locally
 ### Quick Test
 ```bash
@@ -136,7 +136,7 @@ ptw
 ---
-## 🚨 Known Limitations
+## Known Limitations
 ### 1. External API Dependency
 **Issue:** House Stock Watcher API is currently DOWN  
@@ -161,7 +161,7 @@ ptw
 ---
-## 📈 Performance Benchmarks
+## Performance Benchmarks
 ### Test Execution Time
 - **Full suite:** 1.8 seconds
@@ -178,7 +178,7 @@ ptw
 ---
-## 🎯 Pre-Deployment Checklist
+## Pre-Deployment Checklist
 ### Before Deploying to Proxmox:
@@ -215,7 +215,7 @@ python scripts/enrich_securities.py
 ---
-## 🔄 Continuous Testing
+## Continuous Testing
 ### Git Pre-Commit Hook (Optional)
 ```bash
@@ -247,7 +247,7 @@ jobs:
 ---
-## 📝 Test Maintenance
+## Test Maintenance
 ### Adding New Tests
@@ -281,36 +281,36 @@ Fixtures are in `tests/conftest.py`:
 ---
-## 🎉 Summary
+## Summary
-### Current Status: **PRODUCTION READY** ✅
+### Current Status: **PRODUCTION READY**
 **What Works:**
- ✅ All 55 tests passing
+- All 55 tests passing
- ✅ Full analytics pipeline functional
+- Full analytics pipeline functional
- ✅ Database operations solid
+- Database operations solid
- ✅ Data ingestion from multiple sources
+- Data ingestion from multiple sources
- ✅ Price fetching from yfinance
+- Price fetching from yfinance
- ✅ Security enrichment
+- Security enrichment
- ✅ Return calculations
+- Return calculations
- ✅ Benchmark comparisons
+- Benchmark comparisons
- ✅ Performance metrics
+- Performance metrics
- ✅ CLI scripts operational
+- CLI scripts operational
 **What's Missing:**
- ❌ Live congressional trade API (external issue - House Stock Watcher down)
+- Live congressional trade API (external issue - House Stock Watcher down)
  - **Workaround:** Manual import, CSV, or alternative APIs available
 **Next Steps:**
-1. ✅ Tests are complete
+1. Tests are complete
-2. ✅ Code is ready
+2. Code is ready
-3. ➡️ **Deploy to Proxmox** (or continue with Phase 2 features)
+3. **Deploy to Proxmox** (or continue with Phase 2 features)
-4. ➡️ Add more data sources
+4. Add more data sources
-5. ➡️ Build dashboard (Phase 3)
+5. Build dashboard (Phase 3)
 ---
-## 📞 Need Help?
+## Need Help?
 See:
 - `LOCAL_TEST_GUIDE.md` - Detailed local testing instructions
@@ -1,6 +1,6 @@
 # POTE Watchlist & Trading Reports
-## 🎯 Get Trading Reports 1 Hour Before Market Close
+## Get Trading Reports 1 Hour Before Market Close
 ### Quick Setup
@@ -25,7 +25,7 @@ crontab -e
 ---
-## 📋 Watchlist System
+## Watchlist System
 ### Who's on the Default Watchlist?
@@ -48,7 +48,7 @@ crontab -e
 ---
-## 🔧 Managing Your Watchlist
+## Managing Your Watchlist
 ### View Current Watchlist
@@ -104,7 +104,7 @@ This fetches all 535 members of Congress (100 Senate + 435 House).
 ---
-## 📊 Generating Reports
+## Generating Reports
 ### Manual Report Generation
@@ -135,24 +135,24 @@ python scripts/generate_trading_report.py --format json --output report.json
 ================================================================================
 ────────────────────────────────────────────────────────────────────────────────
-👤 Nancy Pelosi (D-CA, House)
+ Nancy Pelosi (D-CA, House)
 ────────────────────────────────────────────────────────────────────────────────
 Side      Ticker  Company                     Sector        Value              Trade Date  Filed
 --------  ------  --------------------------  ----------  -------------------  ----------  ----------
-🟢 BUY     NVDA    NVIDIA Corporation          Technology  $15,001 - $50,000   2024-11-15  2024-12-01
+ BUY NVDA NVIDIA Corporation Technology $15,001 - $50,000 2024-11-15 2024-12-01
-🔴 SELL    MSFT    Microsoft Corporation       Technology  $50,001 - $100,000  2024-11-20  2024-12-01
+ SELL MSFT Microsoft Corporation Technology $50,001 - $100,000 2024-11-20 2024-12-01
 ────────────────────────────────────────────────────────────────────────────────
-👤 Tommy Tuberville (R-AL, Senate)
+ Tommy Tuberville (R-AL, Senate)
 ────────────────────────────────────────────────────────────────────────────────
 Side      Ticker  Company                     Sector        Value              Trade Date  Filed
 --------  ------  --------------------------  ----------  -------------------  ----------  ----------
-🟢 BUY     SPY     SPDR S&P 500 ETF            Financial   $100,001 - $250,000 2024-11-18  2024-12-02
+ BUY SPY SPDR S&P 500 ETF Financial $100,001 - $250,000 2024-11-18 2024-12-02
-🟢 BUY     AAPL    Apple Inc.                  Technology  $50,001 - $100,000  2024-11-22  2024-12-02
+ BUY AAPL Apple Inc. Technology $50,001 - $100,000 2024-11-22 2024-12-02
-🔴 SELL    TSLA    Tesla, Inc.                 Automotive  $15,001 - $50,000   2024-11-25  2024-12-02
+ SELL TSLA Tesla, Inc. Automotive $15,001 - $50,000 2024-11-25 2024-12-02
 ================================================================================
-📊 SUMMARY
+ SUMMARY
 ================================================================================
 Total Trades:    5
@@ -223,7 +223,7 @@ Runs at 8 AM and 3 PM daily (weekdays).
 ---
-## 📧 Email Reports (Optional)
+## Email Reports (Optional)
 ### Setup Email Notifications
@@ -256,7 +256,7 @@ python scripts/send_email.py /tmp/report.html your-email@example.com
 ---
-## 🎯 Typical Workflow
+## Typical Workflow
 ### Daily Routine (3 PM ET)
@@ -282,7 +282,7 @@ python scripts/send_email.py /tmp/report.html your-email@example.com
 ---
-## 🔍 Finding More Officials
+## Finding More Officials
 ### Public Resources
@@ -318,7 +318,7 @@ Add committee members to your watchlist.
 ---
-## 📈 Example Cron Setup
+## Example Cron Setup
 ```bash
 # Edit crontab
@@ -341,7 +341,7 @@ This gives you:
 ---
-## 🚀 Quick Start Summary
+## Quick Start Summary
 ```bash
 # 1. Create watchlist
@@ -363,7 +363,7 @@ cat reports/trading_report_$(date +%Y%m%d).txt
 ---
-## ❓ FAQ
+## FAQ
 **Q: Why are all the trades old (30-45 days)?**  
 **A:** Federal law (STOCK Act) gives Congress 30-45 days to file. This is normal.
@@ -1,8 +1,8 @@
-# 🎉 POTE Monitoring System - ALL PHASES COMPLETE!
+# POTE Monitoring System - ALL PHASES COMPLETE!
-## ✅ **What Was Built (3 Phases)**
+## **What Was Built (3 Phases)**
-### **Phase 1: Real-Time Market Monitoring** ✅
+### **Phase 1: Real-Time Market Monitoring**
 **Detects unusual market activity in congressional tickers**
 **Features:**
@@ -20,11 +20,11 @@
 - `MarketAlert` model - Database storage
 - `monitor_market.py` - CLI tool
-**Tests:** 14 passing ✅
+**Tests:** 14 passing 
 ---
-### **Phase 2: Disclosure Timing Correlation** ✅
+### **Phase 2: Disclosure Timing Correlation**
 **Matches trades to prior market alerts when disclosures appear**
 **Features:**
@@ -50,11 +50,11 @@
 - `DisclosureCorrelator` - Correlation engine
 - `analyze_disclosure_timing.py` - CLI tool
-**Tests:** 13 passing ✅
+**Tests:** 13 passing 
 ---
-### **Phase 3: Pattern Detection & Rankings** ✅
+### **Phase 3: Pattern Detection & Rankings**
 **Cross-official analysis and comparative rankings**
 **Features:**
@@ -71,19 +71,19 @@
 - `PatternDetector` - Pattern analysis engine
 - `generate_pattern_report.py` - CLI tool
-**Tests:** 11 passing ✅
+**Tests:** 11 passing 
 ---
-## 📊 **Complete System Architecture**
+## **Complete System Architecture**
 ```
 ┌─────────────────────────────────────────────────────────────┐
 │  PHASE 1: Real-Time Monitoring                              │
 │  ────────────────────────────────────                       │
-│  🔔 Monitor congressional tickers                            │
+│ Monitor congressional tickers │
-│  📊 Detect unusual activity                                  │
+│ Detect unusual activity │
-│  💾 Log alerts to database                                   │
+│ Log alerts to database │
 └─────────────────────────────────────────────────────────────┘
                            ↓
                     [30-45 days pass]
@@ -91,25 +91,25 @@
 ┌─────────────────────────────────────────────────────────────┐
 │  PHASE 2: Disclosure Correlation                            │
 │  ───────────────────────────────                           │
-│  📋 New congressional trades filed                          │
+│ New congressional trades filed │
-│  🔗 Match to prior alerts                                   │
+│ Match to prior alerts │
-│  📈 Calculate timing scores                                  │
+│ Calculate timing scores │
-│  🚩 Flag suspicious trades                                   │
+│ Flag suspicious trades │
 └─────────────────────────────────────────────────────────────┘
                            ↓
 ┌─────────────────────────────────────────────────────────────┐
 │  PHASE 3: Pattern Detection                                 │
 │  ──────────────────────────                                │
-│  📊 Rank officials by timing                                │
+│ Rank officials by timing │
-│  🔥 Identify repeat offenders                               │
+│ Identify repeat offenders │
-│  📈 Compare parties, sectors, tickers                        │
+│ Compare parties, sectors, tickers │
-│  📋 Generate comprehensive reports                           │
+│ Generate comprehensive reports │
 └─────────────────────────────────────────────────────────────┘
 ```
 ---
-## 🚀 **Usage Guide**
+## **Usage Guide**
 ### **1. Set Up Monitoring (Run Daily)**
@@ -169,7 +169,7 @@ python scripts/generate_pattern_report.py --days 365 --format json --output patt
 ---
-## 📋 **Example Reports**
+## **Example Reports**
 ### **Timing Analysis Report**
@@ -179,7 +179,7 @@ python scripts/generate_pattern_report.py --days 365 --format json --output patt
  3 Trades with Timing Advantages Detected
 ================================================================================
-🚨 #1 - HIGHLY SUSPICIOUS (Timing Score: 85/100)
+ #1 - HIGHLY SUSPICIOUS (Timing Score: 85/100)
 ────────────────────────────────────────────────────────────────────────────────
 Official:    Nancy Pelosi
 Ticker:      NVDA
@@ -187,16 +187,16 @@ Side:        BUY
 Trade Date:  2024-01-15
 Value:       $15,001-$50,000
-📊 Timing Analysis:
+ Timing Analysis:
   Prior Alerts:        3
   Recent Alerts (7d):  2
   High Severity:       2
   Avg Severity:        7.5/10
-💡 Assessment: Trade occurred after 3 alerts, including 2 high-severity.
+ Assessment: Trade occurred after 3 alerts, including 2 high-severity.
               High likelihood of timing advantage.
-🔔 Prior Market Alerts:
+ Prior Market Alerts:
 Timestamp            Type             Severity  Timing
 2024-01-12 10:30:00  Unusual Volume   8/10      3 days before
 2024-01-13 14:15:00  Price Spike      7/10      2 days before
@@ -211,27 +211,27 @@ Timestamp            Type             Severity  Timing
  Period: 365 days
 ================================================================================
-📊 SUMMARY
+ SUMMARY
 ────────────────────────────────────────────────────────────────────────────────
 Officials Analyzed:        45
 Repeat Offenders:          8
 Average Timing Score:      42.3/100
-🚨 TOP 10 MOST SUSPICIOUS OFFICIALS (By Timing Score)
+ TOP 10 MOST SUSPICIOUS OFFICIALS (By Timing Score)
 ================================================================================
 Rank  Official                 Party-State  Chamber  Trades  Suspicious  Rate    Avg Score
 ────  ───────────────────────  ───────────  ───────  ──────  ──────────  ──────  ─────────
-🚨 1   Tommy Tuberville         R-AL         Senate   47      35/47       74.5%   72.5/100
+ 1 Tommy Tuberville R-AL Senate 47 35/47 74.5% 72.5/100
-🚨 2   Nancy Pelosi             D-CA         House    38      28/38       73.7%   71.2/100
+ 2 Nancy Pelosi D-CA House 38 28/38 73.7% 71.2/100
-🔴 3   Dan Crenshaw             R-TX         House    25      15/25       60.0%   65.8/100
+ 3 Dan Crenshaw R-TX House 25 15/25 60.0% 65.8/100
-🔴 4   Marjorie Taylor Greene   R-GA         House    19      11/19       57.9%   63.2/100
+ 4 Marjorie Taylor Greene R-GA House 19 11/19 57.9% 63.2/100
-🟡 5   Josh Gottheimer          D-NJ         House    31      14/31       45.2%   58.7/100
+ 5 Josh Gottheimer D-NJ House 31 14/31 45.2% 58.7/100
-🔥 REPEAT OFFENDERS (50%+ Suspicious Trades)
+ REPEAT OFFENDERS (50%+ Suspicious Trades)
 ================================================================================
-🚨 Tommy Tuberville (R-AL, Senate)
+ Tommy Tuberville (R-AL, Senate)
   Trades: 47 | Suspicious: 35 (74.5%)
   Avg Timing Score: 72.5/100
   Pattern: HIGHLY SUSPICIOUS - Majority of trades show timing advantage
@@ -239,9 +239,9 @@ Rank  Official                 Party-State  Chamber  Trades  Suspicious  Rate
 ---
-## 📈 **Test Coverage**
+## **Test Coverage**
-**Total: 93 tests, all passing ✅**
+**Total: 93 tests, all passing **
 - **Phase 1 (Monitoring):** 14 tests
 - **Phase 2 (Correlation):** 13 tests
@@ -252,7 +252,7 @@ Rank  Official                 Party-State  Chamber  Trades  Suspicious  Rate
 ---
-## 🎯 **Key Insights the System Provides**
+## **Key Insights the System Provides**
 ### **1. Individual Official Analysis**
 - Which officials consistently trade before unusual activity?
@@ -282,7 +282,7 @@ Rank  Official                 Party-State  Chamber  Trades  Suspicious  Rate
 ---
-## 🔧 **Automated Workflow**
+## **Automated Workflow**
 ### **Daily Routine (Recommended)**
@@ -299,7 +299,7 @@ Rank  Official                 Party-State  Chamber  Trades  Suspicious  Rate
 ---
-## 📊 **Database Schema**
+## **Database Schema**
 **New Table: `market_alerts`**
 ```sql
@@ -315,7 +315,7 @@ Rank  Official                 Party-State  Chamber  Trades  Suspicious  Rate
 ---
-## 🎓 **Interpretation Guide**
+## **Interpretation Guide**
 ### **Timing Scores**
 - **80-100:** Highly suspicious - Multiple high-severity alerts before trade
@@ -337,15 +337,15 @@ Rank  Official                 Party-State  Chamber  Trades  Suspicious  Rate
 ---
-## ⚠️ **Important Disclaimers**
+## **Important Disclaimers**
 ### **Legal & Ethical**
-1. ✅ All data is public and legally obtained
+1. All data is public and legally obtained
-2. ✅ Analysis is retrospective (30-45 day lag)
+2. Analysis is retrospective (30-45 day lag)
-3. ✅ For research and transparency only
+3. For research and transparency only
-4. ❌ NOT investment advice
+4. NOT investment advice
-5. ❌ NOT proof of illegal activity (requires investigation)
+5. NOT proof of illegal activity (requires investigation)
-6. ❌ Statistical patterns ≠ legal evidence
+6. Statistical patterns ≠ legal evidence
 ### **Technical Limitations**
 1. Cannot identify WHO is trading in real-time
@@ -356,7 +356,7 @@ Rank  Official                 Party-State  Chamber  Trades  Suspicious  Rate
 ---
-## 🚀 **Deployment Checklist**
+## **Deployment Checklist**
 ### **On Proxmox Container**
@@ -383,7 +383,7 @@ python scripts/generate_pattern_report.py --days 365
 ---
-## 📚 **Documentation**
+## **Documentation**
 - **`docs/11_live_market_monitoring.md`** - Deep dive into monitoring
 - **`LOCAL_TEST_GUIDE.md`** - Testing instructions
@@ -392,23 +392,23 @@ python scripts/generate_pattern_report.py --days 365
 ---
-## 🎉 **Achievement Unlocked!**
+## **Achievement Unlocked!**
 **You now have a complete system that:**
-✅ Monitors real-time market activity  
+ Monitors real-time market activity 
-✅ Correlates trades to prior alerts  
+ Correlates trades to prior alerts 
-✅ Calculates timing advantage scores  
+ Calculates timing advantage scores 
-✅ Identifies repeat offenders  
+ Identifies repeat offenders 
-✅ Ranks officials by suspicion  
+ Ranks officials by suspicion 
-✅ Generates comprehensive reports  
+ Generates comprehensive reports 
-✅ 93 tests confirming it works  
+ 93 tests confirming it works 
-**This is a production-ready transparency and research tool!** 🚀
+**This is a production-ready transparency and research tool!** 
 ---
-## 🔜 **Potential Future Enhancements**
+## **Potential Future Enhancements**
 ### **Phase 4 Ideas (Optional)**
 - Email/SMS alerts for high-severity patterns
@@ -420,6 +420,6 @@ python scripts/generate_pattern_report.py --days 365
 - Automated PDF reports
 - Historical performance tracking
-**But the core system is COMPLETE and FUNCTIONAL now!** ✅
+**But the core system is COMPLETE and FUNCTIONAL now!** 
@@ -1,6 +1,6 @@
 # PR1 Summary: Project Scaffold + DB + Price Loader
-**Status**: ✅ Complete  
+**Status**: Complete 
 **Date**: 2025-12-13
 ## What was built
@@ -36,7 +36,7 @@ Includes proper indexes, unique constraints, and relationships.
 - `tests/conftest.py`: fixtures for in-memory DB, sample officials/securities/trades/prices
 - `tests/test_models.py`: model creation, relationships, unique constraints, queries (7 tests)
 - `tests/test_price_loader.py`: loader logic, idempotency, upsert, mocking yfinance (8 tests)
- **Result**: 15 tests, all passing ✅
+- **Result**: 15 tests, all passing 
 ### 6. Tooling
 - **Black** + **ruff** configured and run (all code formatted + linted)
@@ -1,6 +1,6 @@
 # PR2 Summary: Congressional Trade Ingestion
-**Status**: ✅ Complete  
+**Status**: Complete 
 **Date**: 2025-12-14
 ## What was built
@@ -26,7 +26,7 @@
 - `tests/fixtures/sample_house_watcher.json`: 5 realistic sample transactions
 - Includes House + Senate, Democrats + Republicans, various tickers
-### 4. Tests (13 new tests, all passing ✅)
+### 4. Tests (13 new tests, all passing )
 **`tests/test_house_watcher.py` (8 tests)**:
 - Amount range parsing (with range, single value, invalid)
 - Transaction type normalization
@@ -58,9 +58,9 @@
 python scripts/fetch_congressional_trades.py --days 30
 # Sample output:
-# ✓ Officials created/updated: 47
+# Officials created/updated: 47
-# ✓ Securities created/updated: 89
+# Securities created/updated: 89
-# ✓ Trades ingested: 234
+# Trades ingested: 234
 ```
 ### Database Queries
@@ -1,6 +1,6 @@
 # PR3 Summary: Security Enrichment + Deployment
-**Status**: ✅ Complete  
+**Status**: Complete 
 **Date**: 2025-12-14
 ## What was built
@@ -32,7 +32,7 @@
  python scripts/enrich_securities.py --force
  ```
-### 3. Tests (9 new tests, all passing ✅)
+### 3. Tests (9 new tests, all passing )
 **`tests/test_security_enricher.py`**:
 - Successful enrichment with complete data
 - ETF detection and classification
@@ -72,7 +72,7 @@ python scripts/enrich_securities.py
 # Enriched AAPL: Apple Inc. (Technology)
 # Enriched TSLA: Tesla, Inc. (Consumer Cyclical)
 # Enriched GOOGL: Alphabet Inc. (Communication Services)
-# ✓ Successfully enriched: 5
+# Successfully enriched: 5
 ```
 ### Query Enriched Data
@@ -159,10 +159,10 @@ python scripts/update_all_prices.py  # To be built in PR4
 | Option | Complexity | Cost/month | Best For |
 |--------|-----------|------------|----------|
-| **Local** | ⭐ | $0 | Development |
+| **Local** | | $0 | Development |
-| **VPS + Docker** | ⭐⭐ | $10-20 | Personal deployment |
+| **VPS + Docker** | | $10-20 | Personal deployment |
-| **Railway/Fly.io** | ⭐ | $5-15 | Easy cloud |
+| **Railway/Fly.io** | | $5-15 | Easy cloud |
-| **AWS** | ⭐⭐⭐ | $20-50 | Scalable production |
+| **AWS** | | $20-50 | Scalable production |
 See [`docs/07_deployment.md`](07_deployment.md) for detailed guides.
@@ -1,6 +1,6 @@
 # PR4 Summary: Phase 2 Analytics Foundation
-## ✅ Completed
+## Completed
 **Date**: December 15, 2025  
 **Status**: Complete  
@@ -78,14 +78,14 @@ python scripts/calculate_all_returns.py --window 90 --benchmark SPY --top 10
 ### 3. Tests (`tests/test_analytics.py`)
- ✅ Return calculator with sample data
+- Return calculator with sample data
- ✅ Buy vs sell trade handling
+- Buy vs sell trade handling
- ✅ Missing data edge cases
+- Missing data edge cases
- ✅ Benchmark comparisons
+- Benchmark comparisons
- ✅ Official performance metrics
+- Official performance metrics
- ✅ Multiple time windows
+- Multiple time windows
- ✅ Sector analysis
+- Sector analysis
- ✅ Timing analysis
+- Timing analysis
 **Test Coverage**: Analytics module fully tested
@@ -288,15 +288,15 @@ with next(get_session()) as session:
   - Trades near policy events
   - Unusual timing flags
-## Success Criteria ✅
+## Success Criteria
- ✅ Can calculate returns for any trade + window
+- Can calculate returns for any trade + window
- ✅ Can compare to S&P 500 benchmark
+- Can compare to S&P 500 benchmark
- ✅ Can generate official performance summaries
+- Can generate official performance summaries
- ✅ All calculations tested and accurate
+- All calculations tested and accurate
- ✅ Performance data calculated on-the-fly
+- Performance data calculated on-the-fly
- ✅ Documentation complete
+- Documentation complete
- ✅ Command-line tools working
+- Command-line tools working
 ## Testing
@@ -309,7 +309,7 @@ All analytics tests should pass (may have warnings if no price data).
 ---
-**Phase 2 Analytics Foundation: COMPLETE** ✅  
+**Phase 2 Analytics Foundation: COMPLETE** 
 **Ready for**: PR5 (Signals), PR6 (API), PR7 (Dashboard)
@@ -0,0 +1,3 @@
 # Archive
 PR summaries and one-shot status writeups. Prefer the numbered guides and QUICKSTART files.
@@ -41,6 +41,8 @@ where = ["src"]
 [tool.ruff]
 line-length = 100
 target-version = "py311"
 [tool.ruff.lint]
 select = ["E", "F", "W", "I", "N", "UP", "B", "A", "C4", "SIM", "RET"]
 ignore = ["E501"]  # Line too long (handled by black)
@@ -74,7 +74,7 @@ def main(tickers, interval, once, min_severity, save_report, lookback):
                if filtered:
                    # Generate report
-                    report = alert_mgr.generate_summary_report(filtered, format="text")
+                    report = alert_mgr.generate_summary_report(filtered, output_format="text")
                    print("\n" + report)
                    # Save report if requested
@@ -2,14 +2,12 @@
 Analytics module for calculating returns, performance metrics, and signals.
 """
 from .returns import ReturnCalculator
 from .benchmarks import BenchmarkComparison
 from .metrics import PerformanceMetrics
 from .returns import ReturnCalculator
 __all__ = [
    "ReturnCalculator",
    "BenchmarkComparison",
    "PerformanceMetrics",
 ]
@@ -3,7 +3,7 @@ Benchmark comparison for calculating abnormal returns (alpha).
 """
 import logging
-from datetime import date, timedelta
+from datetime import date
 from decimal import Decimal
 from sqlalchemy.orm import Session
@@ -60,8 +60,7 @@ class BenchmarkComparison:
            return None
        # Calculate return
-        return_pct = ((end_price - start_price) / start_price) * 100
+        return ((end_price - start_price) / start_price) * 100
        return return_pct
    def calculate_abnormal_return(
        self,
@@ -219,5 +218,3 @@ class BenchmarkComparison:
            "benchmark": self.BENCHMARKS.get(benchmark, benchmark),
            "window_days": window_days,
        }
@@ -4,7 +4,6 @@ Performance metrics and aggregations.
 import logging
 from collections import defaultdict
 from datetime import date
 from sqlalchemy import func
 from sqlalchemy.orm import Session
@@ -52,11 +51,7 @@ class PerformanceMetrics:
        if not official:
            return {"error": "Official not found"}
-        trades = (
+        trades = self.session.query(Trade).filter(Trade.official_id == official_id).all()
            self.session.query(Trade)
            .filter(Trade.official_id == official_id)
            .all()
        )
        if not trades:
            return {
@@ -70,9 +65,7 @@ class PerformanceMetrics:
        # Calculate returns for all trades
        returns_data = []
        for trade in trades:
-            result = self.benchmark.compare_trade_to_benchmark(
+            result = self.benchmark.compare_trade_to_benchmark(trade, window_days, benchmark)
                trade, window_days, benchmark
            )
            if result:
                returns_data.append(result)
@@ -96,9 +89,7 @@ class PerformanceMetrics:
        worst_trade = min(returns_data, key=lambda x: x["trade_return"])
        # Total value traded
-        total_value = sum(
+        total_value = sum(float(t.value_min or 0) for t in trades if t.value_min)
            float(t.value_min or 0) for t in trades if t.value_min
        )
        return {
            "name": official.name,
@@ -154,20 +145,14 @@ class PerformanceMetrics:
            List of sector performance dictionaries
        """
        # Get all trades with security info
-        trades = (
+        trades = self.session.query(Trade).join(Security).all()
            self.session.query(Trade)
            .join(Security)
            .all()
        )
        # Group by sector
        sector_data = defaultdict(list)
        for trade in trades:
            sector = trade.security.sector or "Unknown"
-            result = self.benchmark.compare_trade_to_benchmark(
+            result = self.benchmark.compare_trade_to_benchmark(trade, window_days, benchmark)
                trade, window_days, benchmark
            )
            if result:
                sector_data[sector].append(result)
@@ -180,14 +165,16 @@ class PerformanceMetrics:
            returns = [d["trade_return"] for d in data]
            alphas = [d["abnormal_return"] for d in data]
-            results.append({
+            results.append(
                {
                    "sector": sector,
                    "trade_count": len(data),
                    "avg_return": sum(returns) / len(returns),
                    "avg_alpha": sum(alphas) / len(alphas),
                    "win_rate": sum(1 for r in returns if r > 0) / len(returns),
                    "beat_market_rate": sum(1 for a in alphas if a > 0) / len(alphas),
-            })
+                }
            )
        # Sort by average alpha
        results.sort(key=lambda x: x["avg_alpha"], reverse=True)
@@ -229,11 +216,7 @@ class PerformanceMetrics:
        Returns:
            Dictionary with timing statistics
        """
-        trades = (
+        trades = self.session.query(Trade).filter(Trade.filing_date.isnot(None)).all()
            self.session.query(Trade)
            .filter(Trade.filing_date.isnot(None))
            .all()
        )
        if not trades:
            return {"error": "No trades with disclosure dates"}
@@ -288,5 +271,3 @@ class PerformanceMetrics:
            "benchmark": benchmark,
            **aggregate,
        }
@@ -94,18 +94,20 @@ class ReturnCalculator:
    def calculate_multiple_windows(
        self,
        trade: Trade,
-        windows: list[int] = [30, 60, 90, 180],
+        windows: list[int] | None = None,
    ) -> dict[int, dict]:
        """
        Calculate returns for multiple time windows.
        Args:
            trade: Trade object
-            windows: List of window sizes in days
+            windows: List of window sizes in days (defaults to 30/60/90/180)
        Returns:
            Dictionary mapping window_days to return metrics
        """
        if windows is None:
            windows = [30, 60, 90, 180]
        results = {}
        for window in windows:
            result = self.calculate_trade_return(trade, window)
@@ -223,12 +225,13 @@ class ReturnCalculator:
        if not prices:
            return pd.DataFrame()
        # open/high/low are nullable columns; use NaN (pandas-native) when absent.
        data = [
            {
                "date": p.date,
-                "open": float(p.open),
+                "open": float(p.open) if p.open is not None else float("nan"),
-                "high": float(p.high),
+                "high": float(p.high) if p.high is not None else float("nan"),
-                "low": float(p.low),
+                "low": float(p.low) if p.low is not None else float("nan"),
                "close": float(p.close),
                "volume": p.volume,
            }
@@ -236,4 +239,3 @@ class ReturnCalculator:
        ]
        return pd.DataFrame(data)
@@ -3,17 +3,17 @@ SQLAlchemy ORM models for POTE.
 Matches the schema defined in docs/02_data_model.md.
 """
-from datetime import date, datetime, timezone
+from datetime import UTC, date, datetime
 from decimal import Decimal
 from sqlalchemy import (
    DECIMAL,
    JSON,
    Date,
    DateTime,
    ForeignKey,
    Index,
    Integer,
    JSON,
    String,
    Text,
    UniqueConstraint,
@@ -35,13 +35,11 @@ class Official(Base):
    state: Mapped[str | None] = mapped_column(String(2))
    bioguide_id: Mapped[str | None] = mapped_column(String(20), unique=True)
    external_ids: Mapped[str | None] = mapped_column(Text)  # JSON blob for other IDs
-    created_at: Mapped[datetime] = mapped_column(
+    created_at: Mapped[datetime] = mapped_column(DateTime, default=lambda: datetime.now(UTC))
        DateTime, default=lambda: datetime.now(timezone.utc)
    )
    updated_at: Mapped[datetime] = mapped_column(
        DateTime,
-        default=lambda: datetime.now(timezone.utc),
+        default=lambda: datetime.now(UTC),
-        onupdate=lambda: datetime.now(timezone.utc),
+        onupdate=lambda: datetime.now(UTC),
    )
    # Relationships
@@ -63,13 +61,11 @@ class Security(Base):
    sector: Mapped[str | None] = mapped_column(String(100))
    industry: Mapped[str | None] = mapped_column(String(100))
    asset_type: Mapped[str] = mapped_column(String(50), default="stock")  # stock, bond, etc.
-    created_at: Mapped[datetime] = mapped_column(
+    created_at: Mapped[datetime] = mapped_column(DateTime, default=lambda: datetime.now(UTC))
        DateTime, default=lambda: datetime.now(timezone.utc)
    )
    updated_at: Mapped[datetime] = mapped_column(
        DateTime,
-        default=lambda: datetime.now(timezone.utc),
+        default=lambda: datetime.now(UTC),
-        onupdate=lambda: datetime.now(timezone.utc),
+        onupdate=lambda: datetime.now(UTC),
    )
    # Relationships
@@ -107,13 +103,11 @@ class Trade(Base):
    # Quality flags (JSON or enum list)
    quality_flags: Mapped[str | None] = mapped_column(Text)  # e.g., "range_only,delayed_filing"
-    created_at: Mapped[datetime] = mapped_column(
+    created_at: Mapped[datetime] = mapped_column(DateTime, default=lambda: datetime.now(UTC))
        DateTime, default=lambda: datetime.now(timezone.utc)
    )
    updated_at: Mapped[datetime] = mapped_column(
        DateTime,
-        default=lambda: datetime.now(timezone.utc),
+        default=lambda: datetime.now(UTC),
-        onupdate=lambda: datetime.now(timezone.utc),
+        onupdate=lambda: datetime.now(UTC),
    )
    # Relationships
@@ -156,9 +150,7 @@ class Price(Base):
    adjusted_close: Mapped[Decimal | None] = mapped_column(DECIMAL(15, 4))
    source: Mapped[str] = mapped_column(String(50), default="yfinance")
-    created_at: Mapped[datetime] = mapped_column(
+    created_at: Mapped[datetime] = mapped_column(DateTime, default=lambda: datetime.now(UTC))
        DateTime, default=lambda: datetime.now(timezone.utc)
    )
    # Relationships
    security: Mapped["Security"] = relationship("Security", back_populates="prices")
@@ -188,9 +180,7 @@ class MetricOfficial(Base):
    avg_abnormal_return_1m: Mapped[Decimal | None] = mapped_column(DECIMAL(10, 6))
    cluster_label: Mapped[str | None] = mapped_column(String(50))
-    created_at: Mapped[datetime] = mapped_column(
+    created_at: Mapped[datetime] = mapped_column(DateTime, default=lambda: datetime.now(UTC))
        DateTime, default=lambda: datetime.now(timezone.utc)
    )
    __table_args__ = (
        UniqueConstraint("official_id", "calc_date", "calc_version", name="uq_metrics_official"),
@@ -212,9 +202,7 @@ class MetricTrade(Base):
    abnormal_return_1m: Mapped[Decimal | None] = mapped_column(DECIMAL(10, 6))
    signal_flags: Mapped[str | None] = mapped_column(Text)  # JSON list
-    created_at: Mapped[datetime] = mapped_column(
+    created_at: Mapped[datetime] = mapped_column(DateTime, default=lambda: datetime.now(UTC))
        DateTime, default=lambda: datetime.now(timezone.utc)
    )
    __table_args__ = (
        UniqueConstraint("trade_id", "calc_date", "calc_version", name="uq_metrics_trade"),
@@ -242,18 +230,14 @@ class MarketAlert(Base):
    # Metrics at time of alert
    price: Mapped[Decimal | None] = mapped_column(DECIMAL(15, 4))
    volume: Mapped[int | None] = mapped_column(Integer)
-    change_pct: Mapped[Decimal | None] = mapped_column(
+    change_pct: Mapped[Decimal | None] = mapped_column(DECIMAL(10, 4))  # Price change %
        DECIMAL(10, 4)
    )  # Price change %
    # Severity scoring
    severity: Mapped[int | None] = mapped_column(Integer)  # 1-10 scale
    # Metadata
    source: Mapped[str] = mapped_column(String(50), default="market_monitor")
-    created_at: Mapped[datetime] = mapped_column(
+    created_at: Mapped[datetime] = mapped_column(DateTime, default=lambda: datetime.now(UTC))
        DateTime, default=lambda: datetime.now(timezone.utc)
    )
    # Indexes for efficient queries
    __table_args__ = (
@@ -14,6 +14,7 @@ import httpx
 logger = logging.getLogger(__name__)
 def _default_data_urls() -> tuple[str, ...]:
    override = os.environ.get("POTE_HOUSE_DATA_URL", "").strip()
    if override:
@@ -235,10 +236,9 @@ def normalize_transaction_type(txn_type: str) -> str:
    if "purchase" in txn_lower or "buy" in txn_lower:
        return "buy"
-    elif "sale" in txn_lower or "sell" in txn_lower:
+    if "sale" in txn_lower or "sell" in txn_lower:
        return "sell"
-    elif "exchange" in txn_lower:
+    if "exchange" in txn_lower:
        return "exchange"
    else:
    # Default to the original, lowercased
    return txn_lower
@@ -4,7 +4,7 @@ Fetches daily OHLCV data for securities and stores in the prices table.
 """
 import logging
-from datetime import date, datetime, timedelta, timezone
+from datetime import UTC, date, datetime, timedelta
 from decimal import Decimal
 import pandas as pd
@@ -158,7 +158,7 @@ class PriceLoader:
                "volume": int(row["volume"]) if pd.notna(row.get("volume")) else None,
                "adjusted_close": None,  # We'll compute this later if needed
                "source": "yfinance",
-                "created_at": datetime.now(timezone.utc),
+                "created_at": datetime.now(UTC),
            }
            records.append(record)
@@ -9,4 +9,3 @@ from .market_monitor import MarketMonitor
 from .pattern_detector import PatternDetector
 __all__ = ["MarketMonitor", "AlertManager", "DisclosureCorrelator", "PatternDetector"]
@@ -4,8 +4,7 @@ Handles alert filtering, formatting, and delivery.
 """
 import logging
-from datetime import datetime, timezone
+from datetime import UTC, datetime
 from typing import Any
 from sqlalchemy.orm import Session
@@ -75,21 +74,24 @@ class AlertManager:
        Returns:
            HTML formatted alert
        """
-        severity_class = "high" if (alert.severity or 0) >= 7 else "medium" if (alert.severity or 0) >= 4 else "low"
+        severity_class = (
            "high"
            if (alert.severity or 0) >= 7
            else "medium" if (alert.severity or 0) >= 4 else "low"
        )
-        html = f"""
+        return f"""
        <div class="alert {severity_class}">
            <h3>{alert.ticker} - {alert.alert_type.replace('_', ' ').title()}</h3>
            <p class="timestamp">{alert.timestamp.strftime('%Y-%m-%d %H:%M:%S')}</p>
            <p class="severity">Severity: {alert.severity}/10</p>
            <div class="metrics">
-                <span>Price: ${float(alert.price):.2f}</span>
+                <span>Price: ${float(alert.price or 0):.2f}</span>
                <span>Volume: {alert.volume:,}</span>
-                <span>Change: {float(alert.change_pct):+.2f}%</span>
+                <span>Change: {float(alert.change_pct or 0):+.2f}%</span>
            </div>
        </div>
        """
        return html
    def filter_alerts(
        self,
@@ -117,7 +119,7 @@ class AlertManager:
        # Filter by ticker
        if tickers:
-            ticker_set = set(t.upper() for t in tickers)
+            ticker_set = {t.upper() for t in tickers}
            filtered = [a for a in filtered if a.ticker.upper() in ticker_set]
        # Filter by alert type
@@ -128,21 +130,20 @@ class AlertManager:
        return filtered
    def generate_summary_report(
-        self, alerts: list[MarketAlert], format: str = "text"
+        self, alerts: list[MarketAlert], output_format: str = "text"
    ) -> str:
        """
        Generate summary report of alerts.
        Args:
            alerts: List of alerts
-            format: Output format ('text' or 'html')
+            output_format: Output format ('text' or 'html')
        Returns:
            Formatted summary report
        """
-        if format == "html":
+        if output_format == "html":
            return self._generate_html_summary(alerts)
        else:
        return self._generate_text_summary(alerts)
    def _generate_text_summary(self, alerts: list[MarketAlert]) -> str:
@@ -152,7 +153,7 @@ class AlertManager:
        lines = [
            "=" * 80,
-            f"  MARKET ACTIVITY ALERTS - {datetime.now(timezone.utc).strftime('%Y-%m-%d %H:%M:%S')} UTC",
+            f"  MARKET ACTIVITY ALERTS - {datetime.now(UTC).strftime('%Y-%m-%d %H:%M:%S')} UTC",
            f"  {len(alerts)} Alerts",
            "=" * 80,
            "",
@@ -180,9 +181,7 @@ class AlertManager:
            lines.append(f"🎯 {ticker} - {len(ticker_alerts)} alerts (Max Severity: {max_sev}/10)")
            lines.append("─" * 80)
-            for alert in sorted(
+            for alert in sorted(ticker_alerts, key=lambda a: a.severity or 0, reverse=True):
                ticker_alerts, key=lambda a: a.severity or 0, reverse=True
            ):
                lines.append("")
                lines.append(self.format_alert_text(alert))
@@ -202,9 +201,7 @@ class AlertManager:
            type_counts[alert.alert_type] = type_counts.get(alert.alert_type, 0) + 1
        lines.append("\nAlert Types:")
-        for alert_type, count in sorted(
+        for alert_type, count in sorted(type_counts.items(), key=lambda x: x[1], reverse=True):
            type_counts.items(), key=lambda x: x[1], reverse=True
        ):
            lines.append(f"  {alert_type.replace('_', ' ').title():20s}: {count}")
        # Top severity alerts
@@ -232,8 +229,8 @@ class AlertManager:
            ".timestamp { color: #666; font-size: 0.9em; }",
            ".metrics span { margin-right: 20px; }",
            "</style></head><body>",
-            f"<h1>Market Activity Alerts</h1>",
+            "<h1>Market Activity Alerts</h1>",
-            f"<p><strong>{len(alerts)} Alerts</strong> | {datetime.now(timezone.utc).strftime('%Y-%m-%d %H:%M:%S')} UTC</p>",
+            f"<p><strong>{len(alerts)} Alerts</strong> | {datetime.now(UTC).strftime('%Y-%m-%d %H:%M:%S')} UTC</p>",
        ]
        for alert in sorted(alerts, key=lambda a: a.severity or 0, reverse=True):
@@ -241,5 +238,3 @@ class AlertManager:
        html_parts.append("</body></html>")
        return "\n".join(html_parts)
@@ -5,14 +5,13 @@ Calculates timing advantage and suspicious activity scores.
 """
 import logging
-from datetime import date, timedelta, timezone
+from datetime import UTC, date, timedelta
 from decimal import Decimal
 from typing import Any
-from sqlalchemy import and_, func
+from sqlalchemy import and_
 from sqlalchemy.orm import Session
-from pote.db.models import MarketAlert, Official, Security, Trade
+from pote.db.models import MarketAlert, Security, Trade
 logger = logging.getLogger(__name__)
@@ -27,9 +26,7 @@ class DisclosureCorrelator:
        """Initialize disclosure correlator."""
        self.session = session
-    def get_alerts_before_trade(
+    def get_alerts_before_trade(self, trade: Trade, lookback_days: int = 30) -> list[MarketAlert]:
        self, trade: Trade, lookback_days: int = 30
    ) -> list[MarketAlert]:
        """
        Get market alerts that occurred BEFORE a trade.
@@ -50,14 +47,10 @@ class DisclosureCorrelator:
        # Convert dates to datetime for comparison
        from datetime import datetime
-        start_dt = datetime.combine(start_date, datetime.min.time()).replace(
+        start_dt = datetime.combine(start_date, datetime.min.time()).replace(tzinfo=UTC)
-            tzinfo=timezone.utc
+        end_dt = datetime.combine(end_date, datetime.max.time()).replace(tzinfo=UTC)
        )
        end_dt = datetime.combine(end_date, datetime.max.time()).replace(
            tzinfo=timezone.utc
        )
-        alerts = (
+        return (
            self.session.query(MarketAlert)
            .filter(
                and_(
@@ -70,8 +63,6 @@ class DisclosureCorrelator:
            .all()
        )
        return alerts
    def calculate_timing_score(
        self, trade: Trade, prior_alerts: list[MarketAlert]
    ) -> dict[str, Any]:
@@ -131,8 +122,7 @@ class DisclosureCorrelator:
            )
        elif suspicious:
            reason = (
-                f"Trade occurred after {len(prior_alerts)} alerts. "
+                f"Trade occurred after {len(prior_alerts)} alerts. " f"Possible timing advantage."
                f"Possible timing advantage."
            )
        else:
            reason = (
@@ -170,35 +160,27 @@ class DisclosureCorrelator:
        timing_analysis = self.calculate_timing_score(trade, prior_alerts)
        # Build full analysis
-        analysis = {
+        return {
            "trade_id": trade.id,
            "official_name": trade.official.name if trade.official else None,
            "ticker": trade.security.ticker if trade.security else None,
            "side": trade.side,
            "transaction_date": str(trade.transaction_date),
            "filing_date": str(trade.filing_date) if trade.filing_date else None,
-            "value_range": f"${float(trade.value_min):,.0f}"
+            "value_range": f"${float(trade.value_min or 0):,.0f}"
-            + (
+            + (f"-${float(trade.value_max):,.0f}" if trade.value_max else "+"),
                f"-${float(trade.value_max):,.0f}"
                if trade.value_max
                else "+"
            ),
            **timing_analysis,
            "prior_alerts": [
                {
                    "timestamp": str(alert.timestamp),
                    "alert_type": alert.alert_type,
                    "severity": alert.severity,
-                    "days_before_trade": (
+                    "days_before_trade": (trade.transaction_date - alert.timestamp.date()).days,
                        trade.transaction_date - alert.timestamp.date()
                    ).days,
                }
                for alert in prior_alerts
            ],
        }
        return analysis
    def analyze_recent_disclosures(
        self, days: int = 7, min_timing_score: float = 50
    ) -> list[dict[str, Any]]:
@@ -237,9 +219,7 @@ class DisclosureCorrelator:
            f"Found {len(suspicious_trades)} trades with timing score >= {min_timing_score}"
        )
-        return sorted(
+        return sorted(suspicious_trades, key=lambda x: x["timing_score"], reverse=True)
            suspicious_trades, key=lambda x: x["timing_score"], reverse=True
        )
    def get_official_timing_pattern(
        self, official_id: int, lookback_days: int = 365
@@ -258,9 +238,7 @@ class DisclosureCorrelator:
        trades = (
            self.session.query(Trade)
-            .filter(
+            .filter(and_(Trade.official_id == official_id, Trade.transaction_date >= since_date))
                and_(Trade.official_id == official_id, Trade.transaction_date >= since_date)
            )
            .join(Trade.security)
            .all()
        )
@@ -285,9 +263,7 @@ class DisclosureCorrelator:
        highly_suspicious = sum(1 for a in analyses if a.get("highly_suspicious", False))
        avg_timing_score = (
-            sum(a["timing_score"] for a in analyses) / total_trades
+            sum(a["timing_score"] for a in analyses) / total_trades if total_trades > 0 else 0
            if total_trades > 0
            else 0
        )
        # Determine pattern
@@ -311,9 +287,7 @@ class DisclosureCorrelator:
            "analyses": analyses,
        }
-    def get_ticker_timing_analysis(
+    def get_ticker_timing_analysis(self, ticker: str, lookback_days: int = 365) -> dict[str, Any]:
        self, ticker: str, lookback_days: int = 365
    ) -> dict[str, Any]:
        """
        Analyze timing patterns for a specific ticker.
@@ -329,9 +303,7 @@ class DisclosureCorrelator:
        trades = (
            self.session.query(Trade)
            .join(Trade.security)
-            .filter(
+            .filter(and_(Security.ticker == ticker, Trade.transaction_date >= since_date))
                and_(Security.ticker == ticker, Trade.transaction_date >= since_date)
            )
            .join(Trade.official)
            .all()
        )
@@ -350,10 +322,6 @@ class DisclosureCorrelator:
            "trade_count": len(analyses),
            "trades_with_alerts": sum(1 for a in analyses if a["alert_count"] > 0),
            "suspicious_count": sum(1 for a in analyses if a["suspicious"]),
-            "avg_timing_score": round(
+            "avg_timing_score": round(sum(a["timing_score"] for a in analyses) / len(analyses), 2),
                sum(a["timing_score"] for a in analyses) / len(analyses), 2
            ),
            "analyses": sorted(analyses, key=lambda x: x["timing_score"], reverse=True),
        }
@@ -4,7 +4,7 @@ Detects unusual activity: volume spikes, price movements, volatility.
 """
 import logging
-from datetime import datetime, timedelta, timezone
+from datetime import UTC, datetime, timedelta
 from decimal import Decimal
 from typing import Any
@@ -59,7 +59,7 @@ class MarketMonitor:
        Returns:
            List of alerts detected
        """
-        alerts = []
+        alerts: list[dict[str, Any]] = []
        try:
            stock = yf.Ticker(ticker)
@@ -90,7 +90,7 @@ class MarketMonitor:
                    {
                        "ticker": ticker,
                        "alert_type": "unusual_volume",
-                        "timestamp": datetime.now(timezone.utc),
+                        "timestamp": datetime.now(UTC),
                        "details": {
                            "current_volume": int(current_volume),
                            "avg_volume": int(avg_volume),
@@ -109,10 +109,8 @@ class MarketMonitor:
                alerts.append(
                    {
                        "ticker": ticker,
-                        "alert_type": "price_spike"
+                        "alert_type": "price_spike" if price_change > 0 else "price_drop",
-                        if price_change > 0
+                        "timestamp": datetime.now(UTC),
                        else "price_drop",
                        "timestamp": datetime.now(timezone.utc),
                        "details": {
                            "current_price": float(current_price),
                            "prev_price": float(prev["Close"]),
@@ -129,14 +127,12 @@ class MarketMonitor:
            if len(hist) >= 5:
                recent_volatility = hist["Close"].iloc[-5:].pct_change().abs().mean()
                if recent_volatility > avg_price_change * 2 and avg_price_change > 0:
-                    severity = min(
+                    severity = min(10, int((recent_volatility / avg_price_change) - 1))
                        10, int((recent_volatility / avg_price_change) - 1)
                    )
                    alerts.append(
                        {
                            "ticker": ticker,
                            "alert_type": "high_volatility",
-                            "timestamp": datetime.now(timezone.utc),
+                            "timestamp": datetime.now(UTC),
                            "details": {
                                "recent_volatility": round(recent_volatility * 100, 2),
                                "avg_volatility": round(avg_price_change * 100, 2),
@@ -227,7 +223,7 @@ class MarketMonitor:
        Returns:
            List of MarketAlert objects
        """
-        since = datetime.now(timezone.utc) - timedelta(days=days)
+        since = datetime.now(UTC) - timedelta(days=days)
        query = self.session.query(MarketAlert).filter(MarketAlert.timestamp >= since)
@@ -252,7 +248,7 @@ class MarketMonitor:
        Returns:
            Dict mapping ticker to alert summary
        """
-        since = datetime.now(timezone.utc) - timedelta(days=days)
+        since = datetime.now(UTC) - timedelta(days=days)
        from sqlalchemy import func
@@ -278,5 +274,3 @@ class MarketMonitor:
            }
        return summary
@@ -5,13 +5,12 @@ Identifies recurring suspicious behavior and trading patterns.
 import logging
 from datetime import date, timedelta
 from decimal import Decimal
 from typing import Any
-from sqlalchemy import and_, func
+from sqlalchemy import func
 from sqlalchemy.orm import Session
-from pote.db.models import MarketAlert, Official, Security, Trade
+from pote.db.models import Official, Security, Trade
 from pote.monitoring.disclosure_correlator import DisclosureCorrelator
 logger = logging.getLogger(__name__)
@@ -60,9 +59,7 @@ class PatternDetector:
            .all()
        )
-        logger.info(
+        logger.info(f"Analyzing {len(officials_with_trades)} officials with {min_trades}+ trades")
            f"Analyzing {len(officials_with_trades)} officials with {min_trades}+ trades"
        )
        rankings = []
@@ -70,9 +67,7 @@ class PatternDetector:
            official_id, name, chamber, party, state, trade_count = official_data
            # Get timing pattern
-            pattern = self.correlator.get_official_timing_pattern(
+            pattern = self.correlator.get_official_timing_pattern(official_id, lookback_days)
                official_id, lookback_days
            )
            if pattern["trade_count"] == 0:
                continue
@@ -128,9 +123,7 @@ class PatternDetector:
        rankings = self.rank_officials_by_timing(lookback_days, min_trades=5)
        # Filter for high suspicious rates
-        offenders = [
+        offenders = [r for r in rankings if r["suspicious_rate"] >= min_suspicious_rate * 100]
            r for r in rankings if r["suspicious_rate"] >= min_suspicious_rate * 100
        ]
        logger.info(
            f"Found {len(offenders)} officials with {min_suspicious_rate*100}%+ suspicious trades"
@@ -155,9 +148,7 @@ class PatternDetector:
        # Get tickers with enough trades
        tickers_with_trades = (
-            self.session.query(
+            self.session.query(Security.ticker, func.count(Trade.id).label("trade_count"))
                Security.ticker, func.count(Trade.id).label("trade_count")
            )
            .join(Trade)
            .filter(Trade.transaction_date >= since_date)
            .group_by(Security.ticker)
@@ -169,10 +160,8 @@ class PatternDetector:
        ticker_patterns = []
-        for ticker, trade_count in tickers_with_trades:
+        for ticker, _trade_count in tickers_with_trades:
-            analysis = self.correlator.get_ticker_timing_analysis(
+            analysis = self.correlator.get_ticker_timing_analysis(ticker, lookback_days)
                ticker, lookback_days
            )
            if analysis["trade_count"] == 0:
                continue
@@ -199,9 +188,7 @@ class PatternDetector:
        return ticker_patterns
-    def get_sector_timing_analysis(
+    def get_sector_timing_analysis(self, lookback_days: int = 365) -> dict[str, dict[str, Any]]:
        self, lookback_days: int = 365
    ) -> dict[str, dict[str, Any]]:
        """
        Analyze timing patterns by sector.
@@ -252,7 +239,7 @@ class PatternDetector:
                sector_stats[sector]["suspicious_count"] += 1
        # Calculate averages
-        for sector, stats in sector_stats.items():
+        for stats in sector_stats.values():
            if stats["trade_count"] > 0:
                stats["avg_timing_score"] = round(
                    stats["total_timing_score"] / stats["trade_count"], 2
@@ -266,9 +253,7 @@ class PatternDetector:
        return sector_stats
-    def get_party_comparison(
+    def get_party_comparison(self, lookback_days: int = 365) -> dict[str, dict[str, Any]]:
        self, lookback_days: int = 365
    ) -> dict[str, dict[str, Any]]:
        """
        Compare timing patterns between political parties.
@@ -303,7 +288,7 @@ class PatternDetector:
            party_stats[party]["officials"].append(ranking)
        # Calculate averages
-        for party, stats in party_stats.items():
+        for stats in party_stats.values():
            if stats["total_trades"] > 0:
                stats["avg_timing_score"] = round(
                    stats["total_timing_score"] / stats["total_trades"], 2
@@ -356,5 +341,3 @@ class PatternDetector:
            "sector_analysis": sector_analysis,
            "party_comparison": party_comparison,
        }
@@ -8,5 +8,3 @@ from .email_reporter import EmailReporter
 from .report_generator import ReportGenerator
 __all__ = ["EmailReporter", "ReportGenerator"]
@@ -8,7 +8,6 @@ import logging
 import smtplib
 from email.mime.multipart import MIMEMultipart
 from email.mime.text import MIMEText
 from typing import List, Optional
 from pote.config import settings
@@ -20,31 +19,30 @@ class EmailReporter:
    def __init__(
        self,
-        smtp_host: Optional[str] = None,
+        smtp_host: str | None = None,
-        smtp_port: Optional[int] = None,
+        smtp_port: int | None = None,
-        smtp_user: Optional[str] = None,
+        smtp_user: str | None = None,
-        smtp_password: Optional[str] = None,
+        smtp_password: str | None = None,
-        from_email: Optional[str] = None,
+        from_email: str | None = None,
    ):
        """
        Initialize email reporter.
        If parameters are not provided, will attempt to use settings from config.
        """
-        self.smtp_host = smtp_host or getattr(settings, "smtp_host", "localhost")
+        # Settings always defines these fields (with defaults), so direct access is safe.
-        self.smtp_port = smtp_port or getattr(settings, "smtp_port", 587)
+        self.smtp_host: str = smtp_host or settings.smtp_host
-        self.smtp_user = smtp_user or getattr(settings, "smtp_user", None)
+        self.smtp_port: int = smtp_port or settings.smtp_port
-        self.smtp_password = smtp_password or getattr(settings, "smtp_password", None)
+        self.smtp_user: str = smtp_user or settings.smtp_user
-        self.from_email = from_email or getattr(
+        self.smtp_password: str = smtp_password or settings.smtp_password
-            settings, "from_email", "pote@localhost"
+        self.from_email: str = from_email or settings.from_email or "pote@localhost"
        )
    def send_report(
        self,
-        to_emails: List[str],
+        to_emails: list[str],
        subject: str,
        body_text: str,
-        body_html: Optional[str] = None,
+        body_html: str | None = None,
    ) -> bool:
        """
        Send an email report.
@@ -113,4 +111,3 @@ class EmailReporter:
        except Exception as e:
            logger.error(f"SMTP connection test failed: {e}")
            return False
@@ -6,7 +6,7 @@ Generates formatted reports from database data.
 import logging
 from datetime import date, datetime, timedelta
-from typing import Any, Dict, List, Optional
+from typing import Any
 from sqlalchemy import func
 from sqlalchemy.orm import Session
@@ -27,8 +27,8 @@ class ReportGenerator:
        self.detector = PatternDetector(session)
    def generate_daily_summary(
-        self, report_date: Optional[date] = None, *, lookback_days: int = 1
+        self, report_date: date | None = None, *, lookback_days: int = 1
-    ) -> Dict[str, Any]:
+    ) -> dict[str, Any]:
        """
        Generate a daily summary report.
@@ -69,7 +69,7 @@ class ReportGenerator:
        )
        # Get high-severity alerts
-        critical_alerts = [a for a in new_alerts if a.severity >= 7]
+        critical_alerts = [a for a in new_alerts if (a.severity or 0) >= 7]
        # Get suspicious timing matches
        suspicious_trades = []
@@ -110,7 +110,7 @@ class ReportGenerator:
            "suspicious_trades": suspicious_trades,
        }
-    def generate_weekly_summary(self) -> Dict[str, Any]:
+    def generate_weekly_summary(self) -> dict[str, Any]:
        """
        Generate a weekly summary report.
@@ -121,9 +121,7 @@ class ReportGenerator:
        # Most active officials
        active_officials = (
-            self.session.query(
+            self.session.query(Official.name, func.count(Trade.id).label("trade_count"))
                Official.name, func.count(Trade.id).label("trade_count")
            )
            .join(Trade)
            .filter(Trade.filing_date >= week_ago)
            .group_by(Official.id, Official.name)
@@ -134,9 +132,7 @@ class ReportGenerator:
        # Most traded securities
        active_securities = (
-            self.session.query(
+            self.session.query(Security.ticker, func.count(Trade.id).label("trade_count"))
                Security.ticker, func.count(Trade.id).label("trade_count")
            )
            .join(Trade)
            .filter(Trade.filing_date >= week_ago)
            .group_by(Security.id, Security.ticker)
@@ -146,8 +142,10 @@ class ReportGenerator:
        )
        # Get top suspicious patterns
        # Same fix as branch docs/deploy-email-closed: the old kwarg names never
        # existed on identify_repeat_offenders and failed every weekly run.
        repeat_offenders = self.detector.identify_repeat_offenders(
-            days_lookback=7, min_suspicious_trades=2, min_timing_score=40
+            lookback_days=7, min_suspicious_rate=0.4
        )
        return {
@@ -157,14 +155,13 @@ class ReportGenerator:
                {"name": name, "trade_count": count} for name, count in active_officials
            ],
            "most_traded_securities": [
-                {"ticker": ticker, "trade_count": count}
+                {"ticker": ticker, "trade_count": count} for ticker, count in active_securities
                for ticker, count in active_securities
            ],
            "repeat_offenders_count": len(repeat_offenders),
            "repeat_offenders": repeat_offenders[:5],  # Top 5
        }
-    def format_as_text(self, report_data: Dict[str, Any], report_type: str) -> str:
+    def format_as_text(self, report_data: dict[str, Any], report_type: str) -> str:
        """
        Format report data as plain text.
@@ -177,12 +174,11 @@ class ReportGenerator:
        """
        if report_type == "daily":
            return self._format_daily_text(report_data)
-        elif report_type == "weekly":
+        if report_type == "weekly":
            return self._format_weekly_text(report_data)
        else:
        return str(report_data)
-    def _format_daily_text(self, data: Dict[str, Any]) -> str:
+    def _format_daily_text(self, data: dict[str, Any]) -> str:
        """Format daily report as plain text."""
        if data.get("lookback_days", 1) > 1:
            trades_label = (
@@ -245,7 +241,7 @@ class ReportGenerator:
        return "\n".join(lines)
-    def _format_weekly_text(self, data: Dict[str, Any]) -> str:
+    def _format_weekly_text(self, data: dict[str, Any]) -> str:
        """Format weekly report as plain text."""
        lines = [
            "=" * 70,
@@ -264,9 +260,7 @@ class ReportGenerator:
            lines.append(f"  • {security['ticker']}: {security['trade_count']} trades")
        if data["repeat_offenders"]:
-            lines.extend(
+            lines.extend(["", f"⚠️  REPEAT OFFENDERS ({data['repeat_offenders_count']} total)"])
                ["", f"⚠️  REPEAT OFFENDERS ({data['repeat_offenders_count']} total)"]
            )
            for offender in data["repeat_offenders"]:
                lines.append(
                    f"  • {offender['official_name']}: "
@@ -285,7 +279,7 @@ class ReportGenerator:
        return "\n".join(lines)
-    def format_as_html(self, report_data: Dict[str, Any], report_type: str) -> str:
+    def format_as_html(self, report_data: dict[str, Any], report_type: str) -> str:
        """
        Format report data as HTML.
@@ -298,12 +292,11 @@ class ReportGenerator:
        """
        if report_type == "daily":
            return self._format_daily_html(report_data)
-        elif report_type == "weekly":
+        if report_type == "weekly":
            return self._format_weekly_html(report_data)
        else:
        return f"<pre>{report_data}</pre>"
-    def _format_daily_html(self, data: Dict[str, Any]) -> str:
+    def _format_daily_html(self, data: dict[str, Any]) -> str:
        """Format daily report as HTML."""
        if data.get("lookback_days", 1) > 1:
            new_trades_label = f"Trades Filed (last {data['lookback_days']} days):"
@@ -377,7 +370,7 @@ class ReportGenerator:
        return html
-    def _format_weekly_html(self, data: Dict[str, Any]) -> str:
+    def _format_weekly_html(self, data: dict[str, Any]) -> str:
        """Format weekly report as HTML."""
        html = f"""
        <html>
@@ -443,4 +436,3 @@ class ReportGenerator:
        """
        return html
@@ -22,8 +22,8 @@ def test_db_session() -> Session:
    engine = create_engine("sqlite:///:memory:", echo=False)
    Base.metadata.create_all(engine)
-    TestSessionLocal = sessionmaker(bind=engine)
+    session_factory = sessionmaker(bind=engine)
-    session = TestSessionLocal()
+    session = session_factory()
    yield session
@@ -1,13 +1,14 @@
 """Tests for analytics module."""
 import pytest
 from datetime import date, timedelta
 from decimal import Decimal
-from pote.analytics.returns import ReturnCalculator
+import pytest
 from pote.analytics.benchmarks import BenchmarkComparison
 from pote.analytics.metrics import PerformanceMetrics
-from pote.db.models import Official, Security, Trade, Price
+from pote.analytics.returns import ReturnCalculator
 from pote.db.models import Price, Security, Trade
@pytest.fixture
@@ -80,7 +81,9 @@ def test_return_calculator_basic(test_db_session, sample_official, sample_securi
    assert "exit_price" in result
-def test_return_calculator_sell_trade(test_db_session, sample_official, sample_security, sample_prices):
+def test_return_calculator_sell_trade(
    test_db_session, sample_official, sample_security, sample_prices
 ):
    session = test_db_session
    """Test return calculation for sell trade."""
    trade = Trade(
@@ -154,7 +157,9 @@ def test_benchmark_comparison(test_db_session, sample_official, sample_security,
    assert "beat_market" in result
-def test_performance_metrics_official(test_db_session, sample_official, sample_security, sample_prices):
+def test_performance_metrics_official(
    test_db_session, sample_official, sample_security, sample_prices
 ):
    session = test_db_session
    """Test official performance metrics."""
    # Create multiple trades
@@ -265,4 +270,3 @@ def test_timing_analysis(test_db_session, sample_official, sample_security):
    assert "avg_disclosure_lag_days" in timing
    assert timing["avg_disclosure_lag_days"] > 0
@@ -1,13 +1,14 @@
 """Integration tests for analytics with real-ish data."""
 import pytest
 from datetime import date, timedelta
 from decimal import Decimal
-from pote.analytics.returns import ReturnCalculator
+import pytest
 from pote.analytics.benchmarks import BenchmarkComparison
 from pote.analytics.metrics import PerformanceMetrics
-from pote.db.models import Official, Security, Trade, Price
+from pote.analytics.returns import ReturnCalculator
 from pote.db.models import Official, Price, Security, Trade
@pytest.fixture
@@ -129,7 +130,9 @@ def test_return_calculation_with_real_data(test_db_session, full_test_data):
    # Entry around day 15, exit around day 105
    # Expected return: (720 - 532.5) / 532.5 = ~35%
-    assert 30 < float(result["return_pct"]) < 50, f"Expected ~35% return, got {result['return_pct']}"
+    assert (
        30 < float(result["return_pct"]) < 50
    ), f"Expected ~35% return, got {result['return_pct']}"
    print(f"\n✅ NVDA 90-day return: {result['return_pct']:.2f}%")
    print(f"   Entry: ${result['entry_price']} on {result['transaction_date']}")
@@ -155,7 +158,7 @@ def test_benchmark_comparison_with_real_data(test_db_session, full_test_data):
    assert result["beat_market"] is True
    assert float(result["abnormal_return"]) > 10, "NVDA should have strong alpha vs SPY"
-    print(f"\n✅ Benchmark Comparison:")
+    print("\n✅ Benchmark Comparison:")
    print(f"   NVDA Return:  {result['trade_return']:.2f}%")
    print(f"   SPY Return:   {result['benchmark_return']:.2f}%")
    print(f"   Alpha:        {result['abnormal_return']:+.2f}%")
@@ -201,7 +204,7 @@ def test_multiple_windows(test_db_session, full_test_data):
    # Returns should generally increase with longer windows (given upward trend)
    if 30 in results and 90 in results:
-        print(f"\n✅ Multiple Windows:")
+        print("\n✅ Multiple Windows:")
        for window in [30, 60, 90]:
            if window in results:
                print(f"   {window:3d} days: {results[window]['return_pct']:+7.2f}%")
@@ -217,7 +220,7 @@ def test_top_performers(test_db_session, full_test_data):
    assert isinstance(top, list)
    assert len(top) > 0
-    print(f"\n✅ Top Performers:")
+    print("\n✅ Top Performers:")
    for i, perf in enumerate(top, 1):
        if perf.get("trades_analyzed", 0) > 0:
            print(f"   {i}. {perf['name']:20s} | Alpha: {perf['avg_alpha']:+6.2f}%")
@@ -234,7 +237,7 @@ def test_system_statistics(test_db_session, full_test_data):
    assert stats["total_trades"] >= 2
    assert stats["total_securities"] >= 2
-    print(f"\n✅ System Statistics:")
+    print("\n✅ System Statistics:")
    print(f"   Officials:  {stats['total_officials']}")
    print(f"   Trades:     {stats['total_trades']}")
    print(f"   Securities: {stats['total_securities']}")
@@ -254,7 +257,7 @@ def test_disclosure_timing(test_db_session, full_test_data):
    assert "avg_disclosure_lag_days" in timing
    assert timing["avg_disclosure_lag_days"] > 0
-    print(f"\n✅ Disclosure Timing:")
+    print("\n✅ Disclosure Timing:")
    print(f"   Average Lag:  {timing['avg_disclosure_lag_days']:.1f} days")
    print(f"   Median Lag:   {timing['median_disclosure_lag_days']} days")
@@ -269,9 +272,11 @@ def test_sector_analysis(test_db_session, full_test_data):
    assert isinstance(sectors, list)
    if sectors:
-        print(f"\n✅ Sector Analysis:")
+        print("\n✅ Sector Analysis:")
        for s in sectors:
-            print(f"   {s['sector']:15s} | {s['trade_count']} trades | Alpha: {s['avg_alpha']:+6.2f}%")
+            print(
                f"   {s['sector']:15s} | {s['trade_count']} trades | Alpha: {s['avg_alpha']:+6.2f}%"
            )
 def test_edge_case_missing_exit_price(test_db_session, full_test_data):
@@ -328,4 +333,3 @@ def test_sell_trade_logic(test_db_session, full_test_data):
        # Selling during uptrend = negative return
        assert result["return_pct"] < 0, "Sell during uptrend should show negative return"
        print(f"\n✅ Sell trade return correctly inverted: {result['return_pct']:.2f}%")
@@ -1,11 +1,12 @@
 """Tests for disclosure correlation module."""
-import pytest
+from datetime import UTC, date, datetime
 from datetime import date, datetime, timedelta, timezone
 from decimal import Decimal
 import pytest
 from pote.db.models import MarketAlert, Official, Security, Trade
 from pote.monitoring.disclosure_correlator import DisclosureCorrelator
 from pote.db.models import Official, Security, Trade, MarketAlert
@pytest.fixture
@@ -38,7 +39,7 @@ def trade_with_alerts(test_db_session):
        MarketAlert(
            ticker="NVDA",
            alert_type="unusual_volume",
-            timestamp=datetime(2024, 1, 10, 10, 30, tzinfo=timezone.utc),  # 5 days before
+            timestamp=datetime(2024, 1, 10, 10, 30, tzinfo=UTC),  # 5 days before
            details={"multiplier": 3.5},
            price=Decimal("490.00"),
            volume=100000000,
@@ -48,7 +49,7 @@ def trade_with_alerts(test_db_session):
        MarketAlert(
            ticker="NVDA",
            alert_type="price_spike",
-            timestamp=datetime(2024, 1, 12, 14, 15, tzinfo=timezone.utc),  # 3 days before
+            timestamp=datetime(2024, 1, 12, 14, 15, tzinfo=UTC),  # 3 days before
            details={"change_pct": 5.5},
            price=Decimal("505.00"),
            volume=85000000,
@@ -58,7 +59,7 @@ def trade_with_alerts(test_db_session):
        MarketAlert(
            ticker="NVDA",
            alert_type="high_volatility",
-            timestamp=datetime(2024, 1, 14, 16, 20, tzinfo=timezone.utc),  # 1 day before
+            timestamp=datetime(2024, 1, 14, 16, 20, tzinfo=UTC),  # 1 day before
            details={"multiplier": 2.5},
            price=Decimal("510.00"),
            volume=90000000,
@@ -208,13 +209,13 @@ def test_calculate_timing_score_factors(test_db_session):
        MarketAlert(
            ticker="TEST",
            alert_type="test",
-            timestamp=datetime(2024, 1, 10, 12, 0, tzinfo=timezone.utc),
+            timestamp=datetime(2024, 1, 10, 12, 0, tzinfo=UTC),
            severity=3,
        ),
        MarketAlert(
            ticker="TEST",
            alert_type="test",
-            timestamp=datetime(2024, 1, 11, 12, 0, tzinfo=timezone.utc),
+            timestamp=datetime(2024, 1, 11, 12, 0, tzinfo=UTC),
            severity=4,
        ),
    ]
@@ -228,13 +229,13 @@ def test_calculate_timing_score_factors(test_db_session):
        MarketAlert(
            ticker="TEST",
            alert_type="test",
-            timestamp=datetime(2024, 1, 13, 12, 0, tzinfo=timezone.utc),  # Recent
+            timestamp=datetime(2024, 1, 13, 12, 0, tzinfo=UTC),  # Recent
            severity=9,
        ),
        MarketAlert(
            ticker="TEST",
            alert_type="test",
-            timestamp=datetime(2024, 1, 14, 12, 0, tzinfo=timezone.utc),  # Very recent
+            timestamp=datetime(2024, 1, 14, 12, 0, tzinfo=UTC),  # Very recent
            severity=8,
        ),
    ]
@@ -284,8 +285,7 @@ def test_analyze_recent_disclosures(test_db_session, trade_with_alerts, trade_wi
    # Both trades were created "recently" (in fixture setup)
    suspicious_trades = correlator.analyze_recent_disclosures(
-        days=365,  # Wide window to catch test data
+        days=365, min_timing_score=50  # Wide window to catch test data
        min_timing_score=50
    )
    # Should find at least the suspicious trade
@@ -384,7 +384,7 @@ def test_alerts_outside_lookback_window(test_db_session):
    recent_alert = MarketAlert(
        ticker="TEST",
        alert_type="test",
-        timestamp=datetime(2024, 1, 13, 12, 0, tzinfo=timezone.utc),
+        timestamp=datetime(2024, 1, 13, 12, 0, tzinfo=UTC),
        severity=7,
    )
@@ -392,7 +392,7 @@ def test_alerts_outside_lookback_window(test_db_session):
    old_alert = MarketAlert(
        ticker="TEST",
        alert_type="test",
-        timestamp=datetime(2023, 12, 6, 12, 0, tzinfo=timezone.utc),
+        timestamp=datetime(2023, 12, 6, 12, 0, tzinfo=UTC),
        severity=8,
    )
@@ -433,14 +433,14 @@ def test_different_ticker_alerts_excluded(test_db_session):
    nvda_alert = MarketAlert(
        ticker="NVDA",
        alert_type="test",
-        timestamp=datetime(2024, 1, 10, 12, 0, tzinfo=timezone.utc),
+        timestamp=datetime(2024, 1, 10, 12, 0, tzinfo=UTC),
        severity=7,
    )
    msft_alert = MarketAlert(
        ticker="MSFT",
        alert_type="test",
-        timestamp=datetime(2024, 1, 10, 12, 0, tzinfo=timezone.utc),
+        timestamp=datetime(2024, 1, 10, 12, 0, tzinfo=UTC),
        severity=8,
    )
@@ -452,4 +452,3 @@ def test_different_ticker_alerts_excluded(test_db_session):
    assert len(alerts) == 1
    assert alerts[0].ticker == "NVDA"
@@ -5,6 +5,7 @@ Tests for database models.
 from datetime import date
 from decimal import Decimal
 import pytest
 from sqlalchemy import select
 from pote.db.models import Price, Security, Trade
@@ -56,12 +57,9 @@ def test_unique_constraints(test_db_session, sample_security):
    dup_security = Security(ticker="AAPL", name="Apple Duplicate")
    test_db_session.add(dup_security)
-    try:
+    with pytest.raises(IntegrityError):
        test_db_session.commit()
        assert False, "Should have raised IntegrityError"
    except IntegrityError:
    test_db_session.rollback()
        # Expected behavior
 def test_price_unique_per_security_date(test_db_session, sample_security):
@@ -83,12 +81,9 @@ def test_price_unique_per_security_date(test_db_session, sample_security):
    )
    test_db_session.add(price2)
-    try:
+    with pytest.raises(IntegrityError):
        test_db_session.commit()
        assert False, "Should have raised IntegrityError"
    except IntegrityError:
    test_db_session.rollback()
        # Expected behavior
 def test_trade_queries(test_db_session, sample_official, sample_security):
@@ -1,12 +1,13 @@
 """Tests for market monitoring module."""
-import pytest
+from datetime import UTC, date, datetime, timedelta
 from datetime import date, datetime, timedelta, timezone
 from decimal import Decimal
-from pote.monitoring.market_monitor import MarketMonitor
+import pytest
 from pote.db.models import MarketAlert, Official, Security, Trade
 from pote.monitoring.alert_manager import AlertManager
-from pote.db.models import Official, Security, Trade, MarketAlert
+from pote.monitoring.market_monitor import MarketMonitor
@pytest.fixture
@@ -31,21 +32,51 @@ def sample_congressional_trades(test_db_session):
    # Create multiple trades (NVDA is most traded)
    trades = [
-        Trade(official_id=pelosi.id, security_id=nvda.id, source="test",
+        Trade(
-              transaction_date=date(2024, 1, 15), side="buy",
+            official_id=pelosi.id,
-              value_min=Decimal("15001"), value_max=Decimal("50000")),
+            security_id=nvda.id,
-        Trade(official_id=pelosi.id, security_id=nvda.id, source="test",
+            source="test",
-              transaction_date=date(2024, 2, 1), side="buy",
+            transaction_date=date(2024, 1, 15),
-              value_min=Decimal("15001"), value_max=Decimal("50000")),
+            side="buy",
-        Trade(official_id=tuberville.id, security_id=nvda.id, source="test",
+            value_min=Decimal("15001"),
-              transaction_date=date(2024, 2, 15), side="buy",
+            value_max=Decimal("50000"),
-              value_min=Decimal("50001"), value_max=Decimal("100000")),
+        ),
-        Trade(official_id=pelosi.id, security_id=msft.id, source="test",
+        Trade(
-              transaction_date=date(2024, 1, 20), side="sell",
+            official_id=pelosi.id,
-              value_min=Decimal("15001"), value_max=Decimal("50000")),
+            security_id=nvda.id,
-        Trade(official_id=tuberville.id, security_id=aapl.id, source="test",
+            source="test",
-              transaction_date=date(2024, 2, 10), side="buy",
+            transaction_date=date(2024, 2, 1),
-              value_min=Decimal("15001"), value_max=Decimal("50000")),
+            side="buy",
            value_min=Decimal("15001"),
            value_max=Decimal("50000"),
        ),
        Trade(
            official_id=tuberville.id,
            security_id=nvda.id,
            source="test",
            transaction_date=date(2024, 2, 15),
            side="buy",
            value_min=Decimal("50001"),
            value_max=Decimal("100000"),
        ),
        Trade(
            official_id=pelosi.id,
            security_id=msft.id,
            source="test",
            transaction_date=date(2024, 1, 20),
            side="sell",
            value_min=Decimal("15001"),
            value_max=Decimal("50000"),
        ),
        Trade(
            official_id=tuberville.id,
            security_id=aapl.id,
            source="test",
            transaction_date=date(2024, 2, 10),
            side="buy",
            value_min=Decimal("15001"),
            value_max=Decimal("50000"),
        ),
    ]
    session.add_all(trades)
    session.commit()
@@ -62,7 +93,7 @@ def sample_alerts(test_db_session):
    """Create sample market alerts."""
    session = test_db_session
-    now = datetime.now(timezone.utc)
+    now = datetime.now(UTC)
    alerts = [
        MarketAlert(
@@ -145,7 +176,7 @@ def test_scan_watchlist_with_mock(test_db_session, sample_congressional_trades,
                {
                    "ticker": ticker,
                    "alert_type": "unusual_volume",
-                    "timestamp": datetime.now(timezone.utc),
+                    "timestamp": datetime.now(UTC),
                    "details": {"multiplier": 3.5},
                    "price": Decimal("500.00"),
                    "volume": 100000000,
@@ -174,7 +205,7 @@ def test_save_alerts(test_db_session):
        {
            "ticker": "TSLA",
            "alert_type": "price_spike",
-            "timestamp": datetime.now(timezone.utc),
+            "timestamp": datetime.now(UTC),
            "details": {"change_pct": 7.5},
            "price": Decimal("250.00"),
            "volume": 75000000,
@@ -184,7 +215,7 @@ def test_save_alerts(test_db_session):
        {
            "ticker": "TSLA",
            "alert_type": "unusual_volume",
-            "timestamp": datetime.now(timezone.utc),
+            "timestamp": datetime.now(UTC),
            "details": {"multiplier": 4.0},
            "price": Decimal("250.00"),
            "volume": 120000000,
@@ -297,7 +328,7 @@ def test_alert_manager_filter_alerts(test_db_session, sample_alerts):
        sample_alerts,
        min_severity=4,
        tickers=["NVDA"],
-        alert_types=["unusual_volume", "price_spike"]
+        alert_types=["unusual_volume", "price_spike"],
    )
    assert len(filtered) == 2
@@ -307,7 +338,7 @@ def test_alert_manager_generate_summary_text(test_db_session, sample_alerts):
    session = test_db_session
    alert_mgr = AlertManager(session)
-    report = alert_mgr.generate_summary_report(sample_alerts, format="text")
+    report = alert_mgr.generate_summary_report(sample_alerts, output_format="text")
    assert "MARKET ACTIVITY ALERTS" in report
    assert "3 Alerts" in report
@@ -321,7 +352,7 @@ def test_alert_manager_generate_summary_html(test_db_session, sample_alerts):
    session = test_db_session
    alert_mgr = AlertManager(session)
-    report = alert_mgr.generate_summary_report(sample_alerts, format="html")
+    report = alert_mgr.generate_summary_report(sample_alerts, output_format="html")
    assert "<html>" in report
    assert "<head>" in report
@@ -334,7 +365,7 @@ def test_alert_manager_empty_alerts(test_db_session):
    session = test_db_session
    alert_mgr = AlertManager(session)
-    report = alert_mgr.generate_summary_report([], format="text")
+    report = alert_mgr.generate_summary_report([], output_format="text")
    assert "No alerts" in report
@@ -346,7 +377,7 @@ def test_market_alert_model(test_db_session):
    alert = MarketAlert(
        ticker="GOOGL",
        alert_type="price_spike",
-        timestamp=datetime.now(timezone.utc),
+        timestamp=datetime.now(UTC),
        details={"test": "data"},
        price=Decimal("140.50"),
        volume=25000000,
@@ -373,7 +404,7 @@ def test_alert_timestamp_filtering(test_db_session):
    """Test filtering alerts by timestamp."""
    session = test_db_session
-    now = datetime.now(timezone.utc)
+    now = datetime.now(UTC)
    # Create alerts at different times
    old_alert = MarketAlert(
@@ -404,4 +435,3 @@ def test_alert_timestamp_filtering(test_db_session):
    alerts_30_days = monitor.get_recent_alerts(days=30)
    test_alerts = [a for a in alerts_30_days if a.ticker.startswith("TEST")]
    assert len(test_alerts) == 2
@@ -1,11 +1,12 @@
 """Tests for pattern detection module."""
-import pytest
+from datetime import UTC, date, datetime, timedelta
 from datetime import date, datetime, timedelta, timezone
 from decimal import Decimal
 import pytest
 from pote.db.models import MarketAlert, Official, Security, Trade
 from pote.monitoring.pattern_detector import PatternDetector
 from pote.db.models import Official, Security, Trade, MarketAlert
@pytest.fixture
@@ -31,7 +32,7 @@ def multiple_officials_with_patterns(test_db_session):
    # Pelosi - Suspicious pattern (trades with alerts)
    for i in range(5):
-        trade_date = date(2024, 1, 15) + timedelta(days=i*30)
+        trade_date = date(2024, 1, 15) + timedelta(days=i * 30)
        # Create trade
        trade = Trade(
@@ -52,16 +53,15 @@ def multiple_officials_with_patterns(test_db_session):
                ticker="NVDA",
                alert_type="unusual_volume",
                timestamp=datetime.combine(
-                    trade_date - timedelta(days=3+j),
+                    trade_date - timedelta(days=3 + j), datetime.min.time()
-                    datetime.min.time()
+                ).replace(tzinfo=UTC),
                ).replace(tzinfo=timezone.utc),
                severity=7 + j,
            )
            session.add(alert)
    # Tuberville - Mixed pattern
    for i in range(4):
-        trade_date = date(2024, 2, 1) + timedelta(days=i*30)
+        trade_date = date(2024, 2, 1) + timedelta(days=i * 30)
        trade = Trade(
            official_id=tuberville.id,
@@ -81,16 +81,15 @@ def multiple_officials_with_patterns(test_db_session):
                ticker="MSFT",
                alert_type="price_spike",
                timestamp=datetime.combine(
-                    trade_date - timedelta(days=5),
+                    trade_date - timedelta(days=5), datetime.min.time()
-                    datetime.min.time()
+                ).replace(tzinfo=UTC),
                ).replace(tzinfo=timezone.utc),
                severity=6,
            )
            session.add(alert)
    # Clean trader - No suspicious activity
    for i in range(3):
-        trade_date = date(2024, 3, 1) + timedelta(days=i*30)
+        trade_date = date(2024, 3, 1) + timedelta(days=i * 30)
        trade = Trade(
            official_id=clean_trader.id,
@@ -141,8 +140,7 @@ def test_identify_repeat_offenders(test_db_session, multiple_officials_with_patt
    # Set low threshold to catch Pelosi (who has 100% suspicious rate)
    offenders = detector.identify_repeat_offenders(
-        lookback_days=3650,
+        lookback_days=3650, min_suspicious_rate=0.7  # 70%+
        min_suspicious_rate=0.7  # 70%+
    )
    # Should find at least Pelosi (all trades with alerts)
@@ -158,10 +156,7 @@ def test_analyze_ticker_patterns(test_db_session, multiple_officials_with_patter
    session = test_db_session
    detector = PatternDetector(session)
-    ticker_patterns = detector.analyze_ticker_patterns(
+    ticker_patterns = detector.analyze_ticker_patterns(lookback_days=3650, min_trades=3)
        lookback_days=3650,
        min_trades=3
    )
    assert isinstance(ticker_patterns, list)
    assert len(ticker_patterns) >= 1  # At least NVDA should qualify
@@ -208,7 +203,7 @@ def test_get_party_comparison(test_db_session, multiple_officials_with_patterns)
    assert len(party_stats) >= 2  # Democrat, Republican, Independent
    # Check that we have data for each party
-    for party, stats in party_stats.items():
+    for stats in party_stats.values():
        assert "official_count" in stats
        assert "total_trades" in stats
        assert "avg_timing_score" in stats
@@ -322,5 +317,3 @@ def test_party_stats_completeness(test_db_session, multiple_officials_with_patte
        assert dem["official_count"] >= 1
        assert dem["total_trades"] >= 5  # Pelosi has 5 trades
        assert dem["total_suspicious"] > 0  # Pelosi has suspicious trades
Author	SHA1	Message	Date
ilia	332e38dbe8	Merge pull request 'Add AGENTS.md orientation for Cursor agents' (#9 ) from chore/bootstrap-agents-md into main CI / skip-ci-check (push) Successful in 34s Details CI / secret-scan (push) Successful in 30s Details CI / python-ci (push) Successful in 1m42s Details	2026-08-05 17:51:42 -05:00
ilia	68a14d5d19	Add AGENTS.md orientation for Cursor agents CI / skip-ci-check (pull_request) Successful in 34s Details CI / secret-scan (pull_request) Successful in 29s Details CI / python-ci (pull_request) Successful in 2m57s Details	2026-08-05 18:14:15 -04:00
ilia	c8a10b4270	Merge pull request 'Humanize README and docs tone' (#8 ) from docs/humanize-prose into main CI / skip-ci-check (push) Successful in 32s Details CI / secret-scan (push) Successful in 30s Details CI / python-ci (push) Successful in 2m24s Details	2026-08-05 14:18:01 -05:00
ilia	662cb1b1b8	Humanize READMEs and guides for clearer, professional tone CI / skip-ci-check (pull_request) Successful in 32s Details CI / secret-scan (pull_request) Successful in 30s Details CI / python-ci (pull_request) Successful in 2m5s Details Align with project-template docs/writing-docs.md: plain openings, no emoji decoration, archive scratch plans where they cluttered live docs.	2026-08-05 14:52:53 -04:00
ilia	d790dc753b	Merge pull request 'Add MIT LICENSE' (#7 ) from chore/add-license into main CI / skip-ci-check (push) Successful in 19s Details CI / secret-scan (push) Successful in 17s Details CI / python-ci (push) Successful in 1m10s Details	2026-07-26 15:43:38 -05:00
ilia	821d4d58d8	Add MIT LICENSE CI / skip-ci-check (pull_request) Successful in 18s Details CI / secret-scan (pull_request) Successful in 17s Details CI / python-ci (pull_request) Successful in 1m10s Details	2026-07-26 15:40:19 -05:00
ilia	1665885927	Merge pull request 'Fix lint debt and make CI gates honest' (#6 ) from chore/lint-debt-and-honest-ci into main CI / skip-ci-check (push) Successful in 22s Details CI / python-ci (push) Successful in 1m40s Details CI / secret-scan (push) Successful in 19s Details	2026-07-26 14:47:32 -05:00
ilia	5df21d82f4	Fix lint debt and make CI gates honest CI / skip-ci-check (pull_request) Successful in 19s Details CI / secret-scan (pull_request) Successful in 19s Details CI / python-ci (pull_request) Successful in 2m38s Details - ruff: fix all 328 errors (autofix + manual); move config to [tool.ruff.lint] - mypy: fix all errors (annotations, nullable-column guards, stale kwargs in weekly report caller) - black: reformat src/tests so black --check passes - CI: remove `\|\| true` from ruff/black/mypy/pytest steps in both .gitea and .github workflows; install project deps and add mypy step in gitea python-ci so gates actually run - docs: move 14 root status/guide markdown files into docs/, update links	2026-07-26 15:41:07 -04:00
ilia	6e5e69ed10	ci: add local pre-commit gitleaks hook CI / skip-ci-check (push) Successful in 11s Details CI / secret-scan (push) Successful in 11s Details CI / python-ci (push) Successful in 42s Details	2026-07-13 15:00:18 -05:00
		`@@ -0,0 +1,3 @@`
							`# Archive`

							`PR summaries and one-shot status writeups. Prefer the numbered guides and QUICKSTART files.`
`@@ -9,4 +9,3 @@ from .market_monitor import MarketMonitor`
	`from .pattern_detector import PatternDetector`	`from .pattern_detector import PatternDetector`

	`__all__ = ["MarketMonitor", "AlertManager", "DisclosureCorrelator", "PatternDetector"]`	`__all__ = ["MarketMonitor", "AlertManager", "DisclosureCorrelator", "PatternDetector"]`
`@@ -8,5 +8,3 @@ from .email_reporter import EmailReporter`
	`from .report_generator import ReportGenerator`	`from .report_generator import ReportGenerator`

	`__all__ = ["EmailReporter", "ReportGenerator"]`	`__all__ = ["EmailReporter", "ReportGenerator"]`