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
61 changed files with 1247 additions and 1340 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: |
-          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
+        run: pytest -q

  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:
@@ -1,6 +1,15 @@
 # Homelab bootstrap — gitleaks allowlist (tests, examples, placeholders)
+#
+# IMPORTANT: `useDefault = true` is required — without it gitleaks loads ONLY
+# this file (title + allowlist) with ZERO detection rules, so it would never
+# flag a real secret. Fixed 2026-07 (security-hardening track); if you're
+# re-pushing this template to a repo that already had the old version, that
+# repo's secret scanning was a no-op until this lands.
 title = "homelab gitea bootstrap"

+[extend]
+useDefault = true
+
 [allowlist]
 description = "Test fixtures and example configs are not production secrets"
 paths = [
@@ -11,9 +20,11 @@ paths = [
  '''(?i).*\.example\.(yml|yaml|env|json|toml)$''',
  '''(?i).*vault\.example\.(yml|yaml)$''',
  '''(?i).*\.env\.example$''',
+  '''(?i)CUSTOMIZATION_CHECKLIST\.md$''',
 ]
 regexes = [
  '''(?i)(invalid|fake|dummy|placeholder|example|changeme|change_me|not-a-real)''',
  '''(?i)sk-or-invalid''',
  '''(?i)msk-or-invalid''',
+  '''(?i)your_ssh_private_key_here''',
 ]
@@ -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?
-
-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**
+Status: active. Homelab LXC deploy documented under `docs/`.

 ## 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 <your-repo>
-cd pote
+git clone <repository-url>
+cd pote   # or 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)
-python scripts/ingest_from_fixtures.py
+With network:

-# Enrich securities with company info
-python scripts/enrich_securities.py
-
-# With internet:
+```bash
 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
-```bash
-# Manual entry
-python scripts/add_custom_trades.py
+## Stack

-# CSV import
-python scripts/scrape_alternative_sources.py import trades.csv
-```
+Python 3.10+, SQLAlchemy, Alembic, pandas/numpy, httpx, yfinance.
+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**
- Tracks ~50 most-traded congressional stocks
- Detects unusual volume, price spikes, volatility
- Logs all alerts with timestamps and severity
+| Doc | Purpose |
+|-----|---------|
+| [docs/QUICKSTART.md](docs/QUICKSTART.md) | Using a deployed instance |
+| [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**
- Matches trades with prior market alerts (30-45 day lookback)
- Calculates "timing advantage score" (0-100)
- Identifies suspicious timing patterns
+PR writeups live under `docs/archive/` when moved.

-**Phase 3: Pattern Detection**
- Ranks officials by consistent suspicious timing
- Analyzes by ticker, sector, and political party
- Generates comprehensive reports
+## License

-**Full Documentation**: See [`MONITORING_SYSTEM_COMPLETE.md`](MONITORING_SYSTEM_COMPLETE.md)
-
-## 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.
+MIT for research/educational use. Not investment advice.
@@ -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  
-✅ **No monthly costs** - Just electricity  
-✅ **Isolated VMs/LXC** - Clean environments  
-✅ **Snapshots** - Easy rollback if needed  
-✅ **Resource efficient** - Run alongside other services  
+ **Full control** - Your hardware, your rules 
+ **No monthly costs** - Just electricity 
+ **Isolated VMs/LXC** - Clean environments 
+ **Snapshots** - Easy rollback if needed 
+ **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
-2. ✅ Install dependencies
-3. ✅ Setup PostgreSQL
-4. ✅ Deploy POTE
-5. ✅ Configure cron jobs
-6. ✅ Setup backups
-7. ⏭️ Build Phase 2 (Analytics)
-8. ⏭️ Add FastAPI dashboard (optional)
+1. Create LXC container
+2. Install dependencies
+3. Setup PostgreSQL
+4. Deploy POTE
+5. Configure cron jobs
+6. Setup backups
+7. ⏭ Build Phase 2 (Analytics)
+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)
- ✅ Manual entry
- ✅ CSV import
- ✅ Fixture files (testing)
+- yfinance (prices, company info)
+- Manual entry
+- CSV import
+- 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
- 📅 Disclosures are filed as **Periodic Transaction Reports (PTRs)**
- 📅 Public databases update **after** filing (usually next day)
- 📅 **No real-time feed exists by design**
+- Congress members have **30-45 days** to disclose trades
+- Disclosures are filed as **Periodic Transaction Reports (PTRs)**
+- Public databases update **after** filing (usually next day)
+- **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  
-✅ **After market close** - Prices are final  
-✅ **Low server load** - Off-peak hours  
-❌ **Hourly** - Wasteful, no new data  
-❌ **Real-time** - Impossible, not how disclosures work  
+ **Daily (7 AM)** - Catches overnight filings 
+ **After market close** - Prices are final 
+ **Low server load** - Off-peak hours 
+ **Hourly** - Wasteful, no new data 
+ **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)
-   - ✅ Historical options data
-   - ✅ Volume data
-   - ❌ Not true real-time for options flow
+   - Real-time quotes (15-min delay free)
+   - Historical options data
+   - Volume data
+   - Not true real-time for options flow

 2. **Unusual Whales API**
-   - ✅ Options flow data
-   - ✅ Unusual activity alerts
-   - 💰 Paid ($50-200/month)
+   - Options flow data
+   - Unusual activity alerts
+   - Paid ($50-200/month)
   - https://unusualwhales.com/

 3. **Tradier API**
-   - ✅ Real-time market data
-   - ✅ Options chains
-   - 💰 Paid but affordable ($10-50/month)
+   - Real-time market data
+   - Options chains
+   - Paid but affordable ($10-50/month)
   - https://tradier.com/

 4. **FlowAlgo**
-   - ✅ Options flow tracking
-   - ✅ Dark pool data
-   - 💰 Paid ($99-399/month)
+   - Options flow tracking
+   - Dark pool data
+   - Paid ($99-399/month)
   - https://www.flowalgo.com/

 5. **Polygon.io**
-   - ✅ Real-time stock data
-   - ✅ Options data
-   - 💰 Free tier + paid plans
+   - Real-time stock data
+   - Options data
+   - 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
-  📈 Stock jumps 15%
+ NVDA announces new AI chip
+ 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 BEFORE announcement (Nov 18)
-  ⏱️ Timing: 3 days before major news
-  🚩 Flag: Investigate if announcement was public knowledge
+ She bought AFTER unusual options activity (Nov 10)
+ She bought BEFORE announcement (Nov 18)
+ ⏱ Timing: 3 days before major news
+ 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
-✅ Alert on unusual market activity in those stocks
-✅ Retroactively correlate disclosures with earlier alerts
-✅ Identify timing patterns and potential advantages
-✅ Build database of congressional trading patterns
+ Monitor stocks Congress members historically trade
+ Alert on unusual market activity in those stocks
+ Retroactively correlate disclosures with earlier alerts
+ Identify timing patterns and potential advantages
+ Build database of congressional trading patterns

 ### **What This System WON'T Do:**
-❌ Identify WHO is buying in real-time
-❌ Give you advance notice of congressional trades
-❌ Provide real-time inside information
-❌ Allow you to "front-run" Congress
+ Identify WHO is buying in real-time
+ Give you advance notice of congressional trades
+ Provide real-time inside information
+ Allow you to "front-run" Congress

 ### **Legal & Ethical:**
-✅ All data is public
-✅ Analysis is retrospective
-✅ For research and transparency
-✅ Not market manipulation
-❌ Cannot and should not be used to replicate potentially illegal trades
+ All data is public
+ Analysis is retrospective
+ For research and transparency
+ Not market manipulation
+ 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
- ✅ Pattern detection across officials
- ✅ Research-grade analysis
- ✅ Historical correlation data
+- Transparency on timing advantages
+- Pattern detection across officials
+- Research-grade analysis
+- Historical correlation data

 ### **This Does NOT Give You:**
- ❌ Real-time identity of traders
- ❌ Advance notice of congressional trades
- ❌ Ability to "front-run" disclosures
+- Real-time identity of traders
+- Advance notice of congressional trades
+- 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
-2. ✅ Alert logging and analysis
-3. ✅ Timing correlation when disclosures appear
-4. ✅ Pattern detection and reporting
+1. Real-time monitoring system for congressional tickers
+2. Alert logging and analysis
+3. Timing correlation when disclosures appear
+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
-✓ Data Freshness: Data is fresh (2 days old)
+ Database Connection: Database connection successful
+ 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
- [ ] `.env` in `.gitignore` (already done ✅)
+### Security Checklist
+- [ ] `.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 |
-| 🔒 Good | `.env` (chmod 600) | 1 min | Medium |
-| 🔒 Better | Environment variables | 10 min | Good |
-| 🔒 Better | Separate secrets file | 10 min | Good |
-| 🔐 Best | Docker Secrets | 30 min | Very Good |
-| 🔐 Best | Vault | 2+ hours | Excellent |
+| Basic | `.env` (default perms) | None | Low |
+| Good | `.env` (chmod 600) | 1 min | Medium |
+| Better | Environment variables | 10 min | Good |
+| Better | Separate secrets file | 10 min | Good |
+| Best | Docker Secrets | 30 min | Very Good |
+| Best | Vault | 2+ hours | Excellent |

 ---

-## 🎯 Your Current Status
+## Your Current Status

-✅ **Already secure enough for personal use:**
- `.env` in `.gitignore` ✅
- Not committed to git ✅
- Local server only ✅
+ **Already secure enough for personal use:**
+- `.env` in `.gitignore` 
+- Not committed to git 
+- 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)
- ✅ Accessible via SSH at your Proxmox IP
- ❌ NOT fetching data automatically
- ❌ NOT sending reports automatically
- ❌ NOT monitoring markets automatically
+- Running (database, code installed)
+- Accessible via SSH at your Proxmox IP
+- NOT fetching data automatically
+- NOT sending reports 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)
-2. ✅ Security scanning
-3. ✅ Dependency scanning
-4. ✅ Docker build test
+1. Lint & test (93 tests)
+2. Security scanning
+3. Dependency scanning
+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 |
-| **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 |
-| **Web Interface** | ✅ User-friendly | ❌ Not implemented yet | Future |
+| **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 |
+| **Saved Reports (SSH access)** | Automated<br> No email | Must SSH to view | Users without email |
+| **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)
- ✅ Dependency scanning (Trivy instead of npm audit)
- ✅ SAST scanning (Bandit instead of Semgrep)
- ✅ Container scanning (Docker build test)
- ✅ Workflow summary generation
+- Security scanning (Trivy, Bandit instead of Gitleaks)
+- Dependency scanning (Trivy instead of npm audit)
+- SAST scanning (Bandit instead of Semgrep)
+- Container scanning (Docker build test)
+- 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) ⭐
- **Deployment**: [`PROXMOX_QUICKSTART.md`](PROXMOX_QUICKSTART.md) ⭐
- **Usage**: [`QUICKSTART.md`](QUICKSTART.md) ⭐
+- **Automation Setup**: [`AUTOMATION_QUICKSTART.md`](AUTOMATION_QUICKSTART.md) 
+- **Deployment**: [`PROXMOX_QUICKSTART.md`](PROXMOX_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  
-❌ Financial Modeling Prep paid tier - free tier works  
-❌ Any paid database hosting - SQLite works great locally  
-❌ Any cloud services - runs 100% locally
+ QuiverQuant Pro ($30/mo) - free tier is enough for dev/testing 
+ Financial Modeling Prep paid tier - free tier works 
+ Any paid database hosting - SQLite works great 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) ✅
-2. **Deployment workflows** ✅
+1. **CI/CD pipelines** (Gitea Actions workflows) 
+2. **Deployment workflows** 

 **BUT NOT:**
- ❌ Directly in your running application on Proxmox
- ❌ Accessed by scripts outside of workflows
+- Directly in your running application on Proxmox
+- 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 used in local `python script.py` runs
-❌ Be read by cron jobs on Proxmox (directly)
-❌ Replace `.env` for runtime application config
+ Be accessed outside of workflows
+ Be used in local `python script.py` runs
+ Be read by cron jobs on Proxmox (directly)
+ Replace `.env` for runtime application config

 ### Gitea Secrets CAN:

-✅ Secure your CI/CD pipeline
-✅ Deploy safely without exposing passwords in git
-✅ Update `.env` on server during deployment
-✅ Run automated tests with real credentials
+ Secure your CI/CD pipeline
+ Deploy safely without exposing passwords in git
+ Update `.env` on server during deployment
+ 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 | ⭐⭐⭐⭐⭐ |
-| **`.env` file** | ❌ No | ✅ Perfect | ✅ `nano .env` | ⭐⭐⭐ |
-| **Environment Vars** | ✅ Yes | ✅ Yes | ❌ Harder | ⭐⭐⭐⭐ |
-| **Both (Recommended)** | ✅ Yes | ✅ Yes | ✅ Automated | ⭐⭐⭐⭐⭐ |
+| **Gitea Secrets** | Perfect | No | Via workflow | |
+| **`.env` file** | No | Perfect | `nano .env` | |
+| **Environment Vars** | Yes | Yes | Harder | |
+| **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 ✅
-   - Deploys to Proxmox ✅
-   - Updates .env with secrets ✅
-5. Proxmox app reads from .env ✅
+ - Tests with Gitea secrets 
+ - Deploys to Proxmox 
+ - Updates .env with secrets 
+5. Proxmox app reads from .env 
 ```

 **This gives you:**
- ✅ Secure CI/CD
- ✅ Easy manual SSH access
- ✅ Automated deployments
- ✅ No passwords in git
+- Secure CI/CD
+- Easy manual SSH access
+- Automated deployments
+- 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
- ✅ Automated deployments
- ✅ Keeping passwords out of git
+- CI/CD pipelines
+- Automated deployments
+- Keeping passwords out of git

 **But ALSO keep `.env` on Proxmox** for:
- ✅ Runtime application config
- ✅ Manual SSH access
- ✅ Cron jobs
+- Runtime application config
+- Manual SSH access
+- 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! 
@@ -117,6 +117,7 @@ make beszel-install-agents BESZEL_ONLY=pote-236   # if agent not yet installed

 | P | Task | Owner | Status |
 |---|------|-------|--------|
+| **P1** | Proxmox backup schedule for LXC **236** on pve10 | @you | ⏳ |
 | **P3** | Dedicated `pote@levkine.ca` mailbox (vs shared `alerts@`) | @you | optional / low |
 | **P3** | Git deploy on LXC (replace rsync-only) | @agent | optional / low |
 | **P3** | Kuma LAN health monitor | @agent | optional / low |
@@ -125,7 +126,6 @@ make beszel-install-agents BESZEL_ONLY=pote-236   # if agent not yet installed

 **Closed 2026-07-11:** Beszel agent on pote-236 (`beszel-agent` active).  
 **Closed 2026-07-12:** `make deploy-pote` synced to LXC 236; 07:00 cron email delivered (`idobkin@gmail.com`, log OK). DB ~1129 trades / 80 officials.
-**Closed 2026-07-15:** Proxmox backup — LXC 236 confirmed in pve10 vzdump job vmid list. Daily email re-verified through Jul 15 (44 successes logged); DB now 85 officials / 1161 trades. **Weekly report bug fixed** — caller in `report_generator.py` passed stale kwarg names (`days_lookback`/`min_suspicious_trades`/`min_timing_score`) to `PatternDetector.identify_repeat_offenders(lookback_days, min_suspicious_rate)`; had been failing silently every Sun 08:00 since ≥2026-07-05. Fixed + deployed to `.48` + verified `generate_weekly_summary()`/`format_as_text()` run clean (next real send: Sun 08:00).

 ### Not planned (unless you want them)

@@ -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 |
-| [AUTOMATION_QUICKSTART.md](../AUTOMATION_QUICKSTART.md) | Cron + reports |
-| [PROXMOX_QUICKSTART.md](../PROXMOX_QUICKSTART.md) | Original LXC provisioning |
+| [EMAIL_SETUP.md](EMAIL_SETUP.md) | SMTP / Mailcow / levkine.ca |
+| [AUTOMATION_QUICKSTART.md](AUTOMATION_QUICKSTART.md) | Cron + reports |
+| [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)
- 🟢 **yfinance works** (for price data)
- 🟡 **Sample data available** (5 trades from fixtures)
+- **House Stock Watcher API is DOWN** (domain issues, unreachable)
+- **yfinance works** (for price data)
+- **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
- ✅ Simulate trades with known returns
- ✅ Verify calculations are correct
- ✅ Test edge cases (missing data, sell trades, etc.)
+- Create synthetic price data
+- Simulate trades with known returns
+- Verify calculations are correct
+- 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)
- ✅ Benchmark comparisons (stock vs SPY/QQQ)
- ✅ Abnormal return (alpha) calculations
- ✅ Official performance summaries
- ✅ Sector analysis
- ✅ Disclosure timing analysis
- ✅ Top performer rankings
- ✅ System-wide statistics
+- Return calculations over multiple time windows (30/60/90/180 days)
+- Benchmark comparisons (stock vs SPY/QQQ)
+- Abnormal return (alpha) calculations
+- Official performance summaries
+- Sector analysis
+- Disclosure timing analysis
+- Top performer rankings
+- 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
-2. ✅ Run `make lint` - no errors
-3. ✅ Test locally with sample data
-4. ✅ Verify analytics work with synthetic prices
+1. Run `pytest -v` - all tests pass
+2. Run `make lint` - no errors
+3. Test locally with sample data
+4. Verify analytics work with synthetic prices

 **Getting Live Data:**
- 🔴 House Stock Watcher API is down (external issue)
- 🟢 Manual CSV import works NOW
- 🟢 yfinance for prices works NOW
- 🟡 QuiverQuant available (requires free API key)
+- House Stock Watcher API is down (external issue)
+- Manual CSV import works NOW
+- yfinance for prices works NOW
+- 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
-# ✓ Securities created/updated: 2
-# ✓ Trades ingested: 5
+# Officials created/updated: 4
+# Securities created/updated: 2
+# 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`  
-✅ **Ingest fixture data**: `python scripts/ingest_from_fixtures.py`  
-✅ **Query the database**: Use Python REPL or SQLite browser  
-✅ **Lint & format**: `make lint format`  
-✅ **Run migrations**: `make migrate`  
-✅ **Build analytics** (Phase 2): All math/ML works offline!  
+ **Run all tests**: `make test` 
+ **Ingest fixture data**: `python scripts/ingest_from_fixtures.py` 
+ **Query the database**: Use Python REPL or SQLite browser 
+ **Lint & format**: `make lint format` 
+ **Run migrations**: `make migrate` 
+ **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
- ✅ Real APIs for production (when network available)
- ✅ Same code paths for both
+- Fixtures for development/testing
+- Real APIs for production (when network available)
+- 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 compare to S&P 500 benchmark
- ✅ Can generate official performance summaries
- ✅ All calculations tested and accurate
- ✅ Performance data stored efficiently
- ✅ Documentation complete
+- Can calculate returns for any trade + window
+- Can compare to S&P 500 benchmark
+- Can generate official performance summaries
+- All calculations tested and accurate
+- Performance data stored efficiently
+- 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
-# ✓ Trades ingested: 5
+# Officials created: 4
+# 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**  
-✅ **Automated daily updates** (via cron)  
-✅ **Isolated environment** (LXC container)  
-✅ **Easy backups** (Proxmox snapshots)  
-✅ **Low resource usage** (~500MB RAM)  
-✅ **Cost**: Just electricity (~$5-10/mo)
+ **Full PostgreSQL database** 
+ **Automated daily updates** (via cron) 
+ **Isolated environment** (LXC container) 
+ **Easy backups** (Proxmox snapshots) 
+ **Low resource usage** (~500MB RAM) 
+ **Cost**: Just electricity (~$5-10/mo)

 ---

@@ -231,13 +231,13 @@ pip install -e .

 ## Next Steps

-1. ✅ Container running
-2. ✅ POTE installed
-3. ✅ Data ingested
-4. ⏭️ Setup Proxmox backups (Web UI → Datacenter → Backup)
-5. ⏭️ Configure static IP (if needed)
-6. ⏭️ Build Phase 2 analytics
-7. ⏭️ Add FastAPI dashboard
+1. Container running
+2. POTE installed
+3. Data ingested
+4. ⏭ Setup Proxmox backups (Web UI → Datacenter → Backup)
+5. ⏭ Configure static IP (if needed)
+6. ⏭ Build Phase 2 analytics
+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)
- ✅ **Security information** (tickers, names, sectors)
- ✅ **Historical prices** (OHLCV data from yfinance)
- ✅ **Official profiles** (name, party, chamber, state)
+- **Congressional trading data** (from House Stock Watcher)
+- **Security information** (tickers, names, sectors)
+- **Historical prices** (OHLCV data from yfinance)
+- **Official profiles** (name, party, chamber, state)

 Coming next (Phase 2):
- 📊 **Abnormal return calculations**
- 🤖 **Behavioral clustering**
- 🚨 **Research signals** (follow_research, avoid_risk, watch)
+- **Abnormal return calculations**
+- **Behavioral clustering**
+- **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
-✅ Market alerts (unusual activity)
-✅ Suspicious timing detections
-✅ Summary statistics
+ New congressional trades
+ Market alerts (unusual activity)
+ Suspicious timing detections
+ Summary statistics
 ```

 ### Weekly Report (Sunday 8 AM)
 ```
-✅ Most active officials
-✅ Most traded securities
-✅ Repeat offenders
-✅ Pattern analysis
+ Most active officials
+ Most traded securities
+ Repeat offenders
+ 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 |
-| **[DEPLOYMENT_AND_AUTOMATION.md](DEPLOYMENT_AND_AUTOMATION.md)** | ⭐ Answers all questions |
+| **[EMAIL_SETUP.md](EMAIL_SETUP.md)** | Your levkin.ca setup guide |
+| **[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)  
-✅ **Monitoring:** 3-phase system operational  
-✅ **CI/CD:** Pipeline ready (.github/workflows/ci.yml)  
-✅ **Email:** Configured for test@levkin.ca  
+ **Code:** Complete (93 tests passing) 
+ **Monitoring:** 3-phase system operational 
+ **CI/CD:** Pipeline ready (.github/workflows/ci.yml) 
+ **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  
-✅ **Stock Prices**: Daily OHLCV from yfinance  
-✅ **Officials**: Auto-populated from trade disclosures  
-✅ **Securities**: Auto-created, ready for enrichment  
+ **Congressional Trades**: Live ingestion from House Stock Watcher 
+ **Stock Prices**: Daily OHLCV from yfinance 
+ **Officials**: Auto-populated from trade disclosures 
+ **Securities**: Auto-created, ready for enrichment 

 ### Database
-✅ **Schema**: Normalized (officials, securities, trades, prices, metrics stubs)  
-✅ **Migrations**: Alembic configured and applied  
-✅ **DB**: SQLite for dev, PostgreSQL-ready  
+ **Schema**: Normalized (officials, securities, trades, prices, metrics stubs) 
+ **Migrations**: Alembic configured and applied 
+ **DB**: SQLite for dev, PostgreSQL-ready 

 ### Code Quality
-✅ **Tests**: 28 passing (86% coverage)  
-✅ **Linting**: ruff + mypy all green  
-✅ **Format**: black applied consistently  
+ **Tests**: 28 passing (86% coverage) 
+ **Linting**: ruff + mypy all green 
+ **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 a trading system
- ❌ No claims about inside information
- ✅ Public data only
- ✅ Descriptive analytics
- ✅ Research transparency
+- Not investment advice
+- Not a trading system
+- No claims about inside information
+- Public data only
+- Descriptive analytics
+- 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% |
-| **Models** | 7 tests | ✅ PASS | 90% |
-| **Ingestion** | 14 tests | ✅ PASS | 85% |
-| **Price Loader** | 8 tests | ✅ PASS | 90% |
-| **Security Enricher** | 8 tests | ✅ PASS | 85% |
+| **Analytics** | 18 tests | PASS | 80% |
+| **Models** | 7 tests | PASS | 90% |
+| **Ingestion** | 14 tests | PASS | 85% |
+| **Price Loader** | 8 tests | PASS | 90% |
+| **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
- ✅ Full analytics pipeline functional
- ✅ Database operations solid
- ✅ Data ingestion from multiple sources
- ✅ Price fetching from yfinance
- ✅ Security enrichment
- ✅ Return calculations
- ✅ Benchmark comparisons
- ✅ Performance metrics
- ✅ CLI scripts operational
+- All 55 tests passing
+- Full analytics pipeline functional
+- Database operations solid
+- Data ingestion from multiple sources
+- Price fetching from yfinance
+- Security enrichment
+- Return calculations
+- Benchmark comparisons
+- Performance metrics
+- 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
-2. ✅ Code is ready
-3. ➡️ **Deploy to Proxmox** (or continue with Phase 2 features)
-4. ➡️ Add more data sources
-5. ➡️ Build dashboard (Phase 3)
+1. Tests are complete
+2. Code is ready
+3. **Deploy to Proxmox** (or continue with Phase 2 features)
+4. Add more data sources
+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
-🔴 SELL    MSFT    Microsoft Corporation       Technology  $50,001 - $100,000  2024-11-20  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

 ────────────────────────────────────────────────────────────────────────────────
-👤 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     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
+ 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
+ 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                            │
-│  📊 Detect unusual activity                                  │
-│  💾 Log alerts to database                                   │
+│ Monitor congressional tickers │
+│ Detect unusual activity │
+│ Log alerts to database │
 └─────────────────────────────────────────────────────────────┘
                            ↓
                     [30-45 days pass]
@@ -91,25 +91,25 @@
 ┌─────────────────────────────────────────────────────────────┐
 │  PHASE 2: Disclosure Correlation                            │
 │  ───────────────────────────────                           │
-│  📋 New congressional trades filed                          │
-│  🔗 Match to prior alerts                                   │
-│  📈 Calculate timing scores                                  │
-│  🚩 Flag suspicious trades                                   │
+│ New congressional trades filed │
+│ Match to prior alerts │
+│ Calculate timing scores │
+│ Flag suspicious trades │
 └─────────────────────────────────────────────────────────────┘
                            ↓
 ┌─────────────────────────────────────────────────────────────┐
 │  PHASE 3: Pattern Detection                                 │
 │  ──────────────────────────                                │
-│  📊 Rank officials by timing                                │
-│  🔥 Identify repeat offenders                               │
-│  📈 Compare parties, sectors, tickers                        │
-│  📋 Generate comprehensive reports                           │
+│ Rank officials by timing │
+│ Identify repeat offenders │
+│ Compare parties, sectors, tickers │
+│ 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
-🚨 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
-🔴 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
+ 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
+ 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
+ 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
-2. ✅ Analysis is retrospective (30-45 day lag)
-3. ✅ For research and transparency only
-4. ❌ NOT investment advice
-5. ❌ NOT proof of illegal activity (requires investigation)
-6. ❌ Statistical patterns ≠ legal evidence
+1. All data is public and legally obtained
+2. Analysis is retrospective (30-45 day lag)
+3. For research and transparency only
+4. NOT investment advice
+5. NOT proof of illegal activity (requires investigation)
+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  
-✅ Correlates trades to prior alerts  
-✅ Calculates timing advantage scores  
-✅ Identifies repeat offenders  
-✅ Ranks officials by suspicion  
-✅ Generates comprehensive reports  
-✅ 93 tests confirming it works  
+ Monitors real-time market activity 
+ Correlates trades to prior alerts 
+ Calculates timing advantage scores 
+ Identifies repeat offenders 
+ Ranks officials by suspicion 
+ Generates comprehensive reports 
+ 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
-# ✓ Securities created/updated: 89
-# ✓ Trades ingested: 234
+# Officials created/updated: 47
+# Securities created/updated: 89
+# 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 |
-| **VPS + Docker** | ⭐⭐ | $10-20 | Personal deployment |
-| **Railway/Fly.io** | ⭐ | $5-15 | Easy cloud |
-| **AWS** | ⭐⭐⭐ | $20-50 | Scalable production |
+| **Local** | | $0 | Development |
+| **VPS + Docker** | | $10-20 | Personal deployment |
+| **Railway/Fly.io** | | $5-15 | Easy cloud |
+| **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
- ✅ Buy vs sell trade handling
- ✅ Missing data edge cases
- ✅ Benchmark comparisons
- ✅ Official performance metrics
- ✅ Multiple time windows
- ✅ Sector analysis
- ✅ Timing analysis
+- Return calculator with sample data
+- Buy vs sell trade handling
+- Missing data edge cases
+- Benchmark comparisons
+- Official performance metrics
+- Multiple time windows
+- Sector 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 compare to S&P 500 benchmark
- ✅ Can generate official performance summaries
- ✅ All calculations tested and accurate
- ✅ Performance data calculated on-the-fly
- ✅ Documentation complete
- ✅ Command-line tools working
+- Can calculate returns for any trade + window
+- Can compare to S&P 500 benchmark
+- Can generate official performance summaries
+- All calculations tested and accurate
+- Performance data calculated on-the-fly
+- Documentation complete
+- 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)

@@ -0,0 +1,46 @@
+#!/usr/bin/env bash
+# One-time installer for the local pre-commit gitleaks hook.
+# Run once per clone: bash scripts/git-hooks/install.sh
+#
+# Respects `core.hooksPath` if you've set one (local or global) — some setups
+# point git at a hooks dir outside `.git/hooks/` (e.g. a machine-wide
+# `~/.git-hooks/`), and installing to `.git/hooks/` in that case would be a
+# silent no-op. If an existing hook is already at that path, this chains to
+# it so nothing already relying on it breaks.
+set -euo pipefail
+
+REPO_ROOT="$(git rev-parse --show-toplevel)"
+cd "$REPO_ROOT"
+
+HOOKS_DIR="$(git config --get core.hooksPath || true)"
+if [ -z "$HOOKS_DIR" ]; then
+  HOOKS_DIR=".git/hooks"
+elif [[ "$HOOKS_DIR" != /* ]]; then
+  HOOKS_DIR="$REPO_ROOT/$HOOKS_DIR"
+fi
+mkdir -p "$HOOKS_DIR"
+
+TARGET="$HOOKS_DIR/pre-commit"
+if [ -f "$TARGET" ] && ! grep -q "gitleaks" "$TARGET" 2>/dev/null; then
+  echo "⚠️  Existing pre-commit hook found at $TARGET that isn't ours — chaining instead of overwriting."
+  CHAINED="$HOOKS_DIR/pre-commit.d-gitleaks"
+  cp scripts/git-hooks/pre-commit "$CHAINED"
+  chmod +x "$CHAINED"
+  if ! grep -q "pre-commit.d-gitleaks" "$TARGET" 2>/dev/null; then
+    printf '\n# Added by levkinops ansible repo (scripts/git-hooks/install.sh)\n"%s"\n' "$CHAINED" >> "$TARGET"
+  fi
+else
+  cp scripts/git-hooks/pre-commit "$TARGET"
+  chmod +x "$TARGET"
+fi
+
+echo "✓ Installed pre-commit gitleaks hook → $TARGET"
+if [ "$HOOKS_DIR" != ".git/hooks" ] && [ "$HOOKS_DIR" != "$REPO_ROOT/.git/hooks" ]; then
+  echo "  (using core.hooksPath=$HOOKS_DIR — applies to every repo that shares this hooksPath)"
+fi
+if ! command -v gitleaks >/dev/null 2>&1; then
+  echo "  Note: gitleaks isn't installed locally yet. Install it for the hook to actually run:"
+  echo "    macOS:  brew install gitleaks"
+  echo "    Linux:  see https://github.com/gitleaks/gitleaks#installing"
+  echo "  Until then this hook is a no-op locally (CI still scans every push)."
+fi
@@ -0,0 +1,27 @@
+#!/usr/bin/env bash
+# Local pre-commit secret scan — mirrors the `secret-scanning` CI job (gitleaks)
+# so leaked secrets are caught before they ever leave your machine, not just at
+# CI time. Installed via `make install-git-hooks`.
+#
+# Uses the same .gitleaks.toml allowlist as CI. Only scans staged content.
+set -euo pipefail
+
+REPO_ROOT="$(git rev-parse --show-toplevel)"
+cd "$REPO_ROOT"
+
+if ! command -v gitleaks >/dev/null 2>&1; then
+  echo "⚠️  gitleaks not installed locally — skipping local secret scan."
+  echo "    Install: brew install gitleaks   (CI will still catch it either way)"
+  exit 0
+fi
+
+echo "🔐 Running gitleaks on staged changes..."
+if ! gitleaks protect --staged --config .gitleaks.toml --no-banner --redact; then
+  echo ""
+  echo "❌ gitleaks found a potential secret in your staged changes."
+  echo "   Fix it, or if it's a false positive, add an allowlist entry to .gitleaks.toml."
+  echo "   Bypass (not recommended): git commit --no-verify"
+  exit 1
+fi
+
+echo "✓ gitleaks: no secrets found in staged changes"
@@ -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 return_pct
+        return ((end_price - start_price) / start_price) * 100

    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 = (
-            self.session.query(Trade)
-            .filter(Trade.official_id == official_id)
-            .all()
-        )
+        trades = 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(
-                trade, window_days, benchmark
-            )
+            result = self.benchmark.compare_trade_to_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(
-            float(t.value_min or 0) for t in trades if t.value_min
-        )
+        total_value = sum(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 = (
-            self.session.query(Trade)
-            .join(Security)
-            .all()
-        )
+        trades = 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(
-                trade, window_days, benchmark
-            )
+            result = self.benchmark.compare_trade_to_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 = (
-            self.session.query(Trade)
-            .filter(Trade.filing_date.isnot(None))
-            .all()
-        )
+        trades = 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),
-                "high": float(p.high),
-                "low": float(p.low),
+                "open": float(p.open) if p.open is not None else float("nan"),
+                "high": float(p.high) if p.high is not None else float("nan"),
+                "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(
-        DateTime, default=lambda: datetime.now(timezone.utc)
-    )
+    created_at: Mapped[datetime] = mapped_column(DateTime, default=lambda: datetime.now(UTC))
    updated_at: Mapped[datetime] = mapped_column(
        DateTime,
-        default=lambda: datetime.now(timezone.utc),
-        onupdate=lambda: datetime.now(timezone.utc),
+        default=lambda: datetime.now(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(
-        DateTime, default=lambda: datetime.now(timezone.utc)
-    )
+    created_at: Mapped[datetime] = mapped_column(DateTime, default=lambda: datetime.now(UTC))
    updated_at: Mapped[datetime] = mapped_column(
        DateTime,
-        default=lambda: datetime.now(timezone.utc),
-        onupdate=lambda: datetime.now(timezone.utc),
+        default=lambda: datetime.now(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(
-        DateTime, default=lambda: datetime.now(timezone.utc)
-    )
+    created_at: Mapped[datetime] = mapped_column(DateTime, default=lambda: datetime.now(UTC))
    updated_at: Mapped[datetime] = mapped_column(
        DateTime,
-        default=lambda: datetime.now(timezone.utc),
-        onupdate=lambda: datetime.now(timezone.utc),
+        default=lambda: datetime.now(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(
-        DateTime, default=lambda: datetime.now(timezone.utc)
-    )
+    created_at: Mapped[datetime] = mapped_column(DateTime, default=lambda: datetime.now(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(
-        DateTime, default=lambda: datetime.now(timezone.utc)
-    )
+    created_at: Mapped[datetime] = mapped_column(DateTime, default=lambda: datetime.now(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(
-        DateTime, default=lambda: datetime.now(timezone.utc)
-    )
+    created_at: Mapped[datetime] = mapped_column(DateTime, default=lambda: datetime.now(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(
-        DECIMAL(10, 4)
-    )  # Price change %
+    change_pct: Mapped[Decimal | None] = mapped_column(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(
-        DateTime, default=lambda: datetime.now(timezone.utc)
-    )
+    created_at: Mapped[datetime] = mapped_column(DateTime, default=lambda: datetime.now(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 typing import Any
+from datetime import UTC, datetime

 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(
-                ticker_alerts, key=lambda a: a.severity or 0, reverse=True
-            ):
+            for alert in sorted(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(
-            type_counts.items(), key=lambda x: x[1], reverse=True
-        ):
+        for alert_type, count in sorted(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>",
-            f"<p><strong>{len(alerts)} Alerts</strong> | {datetime.now(timezone.utc).strftime('%Y-%m-%d %H:%M:%S')} UTC</p>",
+            "<h1>Market Activity Alerts</h1>",
+            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 decimal import Decimal
+from datetime import UTC, date, timedelta
 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(
-        self, trade: Trade, lookback_days: int = 30
-    ) -> list[MarketAlert]:
+    def get_alerts_before_trade(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(
-            tzinfo=timezone.utc
-        )
-        end_dt = datetime.combine(end_date, datetime.max.time()).replace(
-            tzinfo=timezone.utc
-        )
+        start_dt = datetime.combine(start_date, datetime.min.time()).replace(tzinfo=UTC)
+        end_dt = datetime.combine(end_date, datetime.max.time()).replace(tzinfo=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"Possible timing advantage."
+                f"Trade occurred after {len(prior_alerts)} alerts. " 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"${float(trade.value_min):,.0f}"
-            + (
-                f"-${float(trade.value_max):,.0f}"
-                if trade.value_max
-                else "+"
-            ),
+            "value_range": f"${float(trade.value_min or 0):,.0f}"
+            + (f"-${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": (
-                        trade.transaction_date - alert.timestamp.date()
-                    ).days,
+                    "days_before_trade": (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(
-            suspicious_trades, key=lambda x: x["timing_score"], reverse=True
-        )
+        return sorted(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(
-                and_(Trade.official_id == official_id, Trade.transaction_date >= since_date)
-            )
+            .filter(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
-            if total_trades > 0
-            else 0
+            sum(a["timing_score"] for a in analyses) / total_trades if total_trades > 0 else 0
        )

        # Determine pattern
@@ -311,9 +287,7 @@ class DisclosureCorrelator:
            "analyses": analyses,
        }

-    def get_ticker_timing_analysis(
-        self, ticker: str, lookback_days: int = 365
-    ) -> dict[str, Any]:
+    def get_ticker_timing_analysis(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(
-                and_(Security.ticker == ticker, Trade.transaction_date >= since_date)
-            )
+            .filter(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(
-                sum(a["timing_score"] for a in analyses) / len(analyses), 2
-            ),
+            "avg_timing_score": round(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"
-                        if price_change > 0
-                        else "price_drop",
-                        "timestamp": datetime.now(timezone.utc),
+                        "alert_type": "price_spike" if price_change > 0 else "price_drop",
+                        "timestamp": datetime.now(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(
-                        10, int((recent_volatility / avg_price_change) - 1)
-                    )
+                    severity = min(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(
-            f"Analyzing {len(officials_with_trades)} officials with {min_trades}+ trades"
-        )
+        logger.info(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(
-                official_id, lookback_days
-            )
+            pattern = self.correlator.get_official_timing_pattern(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 = [
-            r for r in rankings if r["suspicious_rate"] >= min_suspicious_rate * 100
-        ]
+        offenders = [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(
-                Security.ticker, func.count(Trade.id).label("trade_count")
-            )
+            self.session.query(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:
-            analysis = self.correlator.get_ticker_timing_analysis(
-                ticker, lookback_days
-            )
+        for ticker, _trade_count in tickers_with_trades:
+            analysis = self.correlator.get_ticker_timing_analysis(ticker, lookback_days)

            if analysis["trade_count"] == 0:
                continue
@@ -199,9 +188,7 @@ class PatternDetector:

        return ticker_patterns

-    def get_sector_timing_analysis(
-        self, lookback_days: int = 365
-    ) -> dict[str, dict[str, Any]]:
+    def get_sector_timing_analysis(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(
-        self, lookback_days: int = 365
-    ) -> dict[str, dict[str, Any]]:
+    def get_party_comparison(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_port: Optional[int] = None,
-        smtp_user: Optional[str] = None,
-        smtp_password: Optional[str] = None,
-        from_email: Optional[str] = None,
+        smtp_host: str | None = None,
+        smtp_port: int | None = None,
+        smtp_user: str | None = None,
+        smtp_password: str | None = 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")
-        self.smtp_port = smtp_port or getattr(settings, "smtp_port", 587)
-        self.smtp_user = smtp_user or getattr(settings, "smtp_user", None)
-        self.smtp_password = smtp_password or getattr(settings, "smtp_password", None)
-        self.from_email = from_email or getattr(
-            settings, "from_email", "pote@localhost"
-        )
+        # Settings always defines these fields (with defaults), so direct access is safe.
+        self.smtp_host: str = smtp_host or settings.smtp_host
+        self.smtp_port: int = smtp_port or settings.smtp_port
+        self.smtp_user: str = smtp_user or settings.smtp_user
+        self.smtp_password: str = smtp_password or settings.smtp_password
+        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
-    ) -> Dict[str, Any]:
+        self, report_date: date | None = None, *, lookback_days: int = 1
+    ) -> 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(
-                Official.name, func.count(Trade.id).label("trade_count")
-            )
+            self.session.query(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(
-                Security.ticker, func.count(Trade.id).label("trade_count")
-            )
+            self.session.query(Security.ticker, func.count(Trade.id).label("trade_count"))
            .join(Trade)
            .filter(Trade.filing_date >= week_ago)
            .group_by(Security.id, Security.ticker)
@@ -146,6 +142,8 @@ 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(
            lookback_days=7, min_suspicious_rate=0.4
        )
@@ -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}
-                for ticker, count in active_securities
+                {"ticker": ticker, "trade_count": count} 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(
-                ["", f"⚠️  REPEAT OFFENDERS ({data['repeat_offenders_count']} total)"]
-            )
+            lines.extend(["", 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 = TestSessionLocal()
+    session_factory = sessionmaker(bind=engine)
+    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 date, datetime, timedelta, timezone
+from datetime import UTC, date, datetime
 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
-        min_timing_score=50
+        days=365, min_timing_score=50  # Wide window to catch test data
    )

    # 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 date, datetime, timedelta, timezone
+from datetime import UTC, date, datetime, timedelta
 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",
-              transaction_date=date(2024, 1, 15), side="buy",
-              value_min=Decimal("15001"), value_max=Decimal("50000")),
-        Trade(official_id=pelosi.id, security_id=nvda.id, source="test",
-              transaction_date=date(2024, 2, 1), 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")),
+        Trade(
+            official_id=pelosi.id,
+            security_id=nvda.id,
+            source="test",
+            transaction_date=date(2024, 1, 15),
+            side="buy",
+            value_min=Decimal("15001"),
+            value_max=Decimal("50000"),
+        ),
+        Trade(
+            official_id=pelosi.id,
+            security_id=nvda.id,
+            source="test",
+            transaction_date=date(2024, 2, 1),
+            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 date, datetime, timedelta, timezone
+from datetime import UTC, date, datetime, timedelta
 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),
-                    datetime.min.time()
-                ).replace(tzinfo=timezone.utc),
+                    trade_date - timedelta(days=3 + j), datetime.min.time()
+                ).replace(tzinfo=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),
-                    datetime.min.time()
-                ).replace(tzinfo=timezone.utc),
+                    trade_date - timedelta(days=5), datetime.min.time()
+                ).replace(tzinfo=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,
-        min_suspicious_rate=0.7  # 70%+
+        lookback_days=3650, 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(
-        lookback_days=3650,
-        min_trades=3
-    )
+    ticker_patterns = detector.analyze_ticker_patterns(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
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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
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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
ilia	781a45f1a3	Mark deploy and daily email verified (#4 ) CI / skip-ci-check (push) Successful in 9s Details CI / secret-scan (push) Successful in 8s Details CI / python-ci (push) Successful in 22s Details	2026-07-12 10:38:51 -05:00