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

Align with project-template docs/writing-docs.md: plain openings, no
emoji decoration, archive scratch plans where they cluttered live docs.

.gitea/workflows/ci.yml

@@ -0,0 +1,84 @@
+---
+# ci-sync: 2026-05-30T02:31:20Z
+# Homelab CI — Python lane (git-ci-01) + secret scan (git-ci-02)
+# Skip: @skipci in branch name or commit message
+name: CI
+
+on:
+  push:
+    branches: [master, main]
+  pull_request:
+    types: [opened, synchronize, reopened]
+
+jobs:
+  skip-ci-check:
+    runs-on: [homelab, self-hosted, linux]
+    container:
+      image: node:20-bookworm
+    outputs:
+      should-skip: ${{ steps.check.outputs.skip }}
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 1
+      - id: check
+        run: |
+          SKIP=0
+          BRANCH="${GITHUB_HEAD_REF:-${GITHUB_REF#refs/heads/}}"
+          MSG="${GITHUB_EVENT_HEAD_COMMIT_MESSAGE:-$(git log -1 --pretty=%B 2>/dev/null || true)}"
+          echo "$BRANCH" "$MSG" | grep -qi '@skipci' && SKIP=1
+          echo "skip=$SKIP" >> $GITHUB_OUTPUT
+
+  python-ci:
+    needs: skip-ci-check
+    if: needs.skip-ci-check.outputs.should-skip != '1'
+    runs-on: [homelab, self-hosted, linux, python]
+    container:
+      # node image: actions/checkout@v4 needs Node; install python3 in-job
+      image: node:20-bookworm
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Install Python tooling
+        run: |
+          apt-get update -qq
+          DEBIAN_FRONTEND=noninteractive apt-get install -y -qq python3 python3-pip python3-venv
+          python3 -m pip install --upgrade pip --break-system-packages
+          if [ -f requirements.txt ]; then pip install -r requirements.txt --break-system-packages; fi
+          if [ -f requirements-dev.txt ]; then pip install -r requirements-dev.txt --break-system-packages; fi
+          # Install the project + dev tools so lint/type/test gates run for real
+          pip install -e ".[dev]" --break-system-packages
+          pip install bandit pip-audit --break-system-packages
+
+      # Lint, type-check, and tests are hard gates — no `|| true`.
+      - name: Ruff lint
+        run: ruff check src tests
+
+      - name: Mypy
+        run: mypy src
+
+      - name: Bandit (advisory)
+        run: bandit -r . -q || true
+
+      - name: pip-audit (advisory)
+        run: pip-audit -r requirements.txt 2>/dev/null || pip-audit 2>/dev/null || true
+
+      - name: Pytest
+        run: pytest -q
+
+  secret-scan:
+    needs: skip-ci-check
+    if: needs.skip-ci-check.outputs.should-skip != '1'
+    runs-on: [homelab, self-hosted, linux, heavy]
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+      - name: Gitleaks
+        run: |
+          extra=""
+          if [ -f .gitleaks.toml ]; then
+            extra="--config /repo/.gitleaks.toml"
+          fi
+          docker run --rm -v "$PWD:/repo" ghcr.io/gitleaks/gitleaks:latest \
+            detect --source /repo --no-banner --redact ${extra}

.github/workflows/ci.yml

@@ -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:

.gitleaks.toml

@@ -0,0 +1,30 @@
+# 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 = [
+  '''(?i).*\.test\.(ts|tsx|js|jsx|py)$''',
+  '''(?i).*\.spec\.(ts|tsx|js|jsx)$''',
+  '''(?i).*/tests/.*''',
+  '''(?i).*/__tests__/.*''',
+  '''(?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''',
+]

AGENTS.md

@@ -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.

LICENSE

@@ -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.

README.md

@@ -1,202 +1,67 @@
-# POTE – Public Officials Trading Explorer
+# POTE (Public Officials Trading Explorer)
 
-**Research-only tool for tracking and analyzing public stock trades by government officials.**
+Research tool for tracking publicly disclosed stock trades by government
+officials (starting with U.S. Congress). Computes descriptive metrics and
+risk/ethics flags from lawfully available public data.
 
-⚠️ **Important**: This project is for personal research and transparency analysis only. It is **NOT** for investment advice or live trading.
+Not investment advice. Not for live trading. Public disclosures only; data
+may be delayed or incomplete. No claims about inside information.
 
-## What is this?
+Status: active. Homelab LXC deploy documented under `docs/`.
-
-POTE tracks stock trading activity of government officials (starting with U.S. Congress) using lawfully available public data sources. It computes research metrics, descriptive signals, and risk/ethics flags to help understand trading patterns.
-
-## Key constraints
-
-- **Public data only**: House Stock Watcher (free!), yfinance (free!), QuiverQuant/FMP (optional)
-- **Research framing**: All outputs are descriptive analytics, not trading recommendations
-- **No inside information claims**: We use public disclosures that may be delayed or incomplete
-
-## Current Status
-
-✅ **PR1 Complete**: Project scaffold, DB models, price loader  
-✅ **PR2 Complete**: Congressional trade ingestion (House Stock Watcher)  
-✅ **PR3 Complete**: Security enrichment + deployment infrastructure  
-✅ **PR4 Complete**: Phase 2 analytics - returns, benchmarks, performance metrics  
-**45+ passing tests, 88%+ coverage**
 
 ## Quick start
 
-**🚀 Already deployed?** See **[QUICKSTART.md](QUICKSTART.md)** for full usage guide!
-
-**📦 Deploying?** See **[PROXMOX_QUICKSTART.md](PROXMOX_QUICKSTART.md)** for Proxmox LXC deployment (recommended).
-
-**📧 Want automated reports?** See **[AUTOMATION_QUICKSTART.md](AUTOMATION_QUICKSTART.md)** for email reporting setup!
-
-### Local Development
 ```bash
-# Install
+git clone <repository-url>
-git clone <your-repo>
+cd pote   # or POTE
-cd pote
 make install
 source venv/bin/activate
-
-# Run migrations
 make migrate
+python scripts/ingest_from_fixtures.py   # offline sample
+make test
+```
 
-# Ingest sample data (offline, for testing)
+With network:
-python scripts/ingest_from_fixtures.py
 
-# Enrich securities with company info
+```bash
-python scripts/enrich_securities.py
-
-# With internet:
 python scripts/fetch_congressional_trades.py
 python scripts/fetch_sample_prices.py
-
-# Run tests
-make test
-
-# Lint & format
-make lint format
 ```
 
-### Production Deployment
+Deploy: `bash scripts/proxmox_setup.sh` or `docker-compose up -d`.
+Ops handoff: [docs/HANDOFF-2026-05-27.md](docs/HANDOFF-2026-05-27.md).
+
+## Useful commands
+
 ```bash
-# Proxmox LXC (Recommended - 5 minutes)
-bash scripts/proxmox_setup.sh
-
-# Docker
-docker-compose up -d
-```
-
-## Tech stack
-
-- **Language**: Python 3.10+
-- **Database**: PostgreSQL or SQLite (dev)
-- **Data**: House Stock Watcher (free!), yfinance (free!), QuiverQuant/FMP (optional)
-- **Libraries**: SQLAlchemy, Alembic, pandas, numpy, httpx, yfinance, scikit-learn
-- **Testing**: pytest (28 tests, 87%+ coverage)
-
-## Documentation
-
-**Getting Started**:
-- [`README.md`](README.md) – This file
-- [`QUICKSTART.md`](QUICKSTART.md) – ⭐ **How to use your deployed POTE instance**
-- [`STATUS.md`](STATUS.md) – Current project status
-- [`FREE_TESTING_QUICKSTART.md`](FREE_TESTING_QUICKSTART.md) – Test for $0
-- [`OFFLINE_DEMO.md`](OFFLINE_DEMO.md) – Works without internet!
-
-**Deployment**:
-- [`PROXMOX_QUICKSTART.md`](PROXMOX_QUICKSTART.md) – ⭐ **Proxmox quick deployment (5 min)**
-- [`AUTOMATION_QUICKSTART.md`](AUTOMATION_QUICKSTART.md) – ⭐ **Automated reporting setup (5 min)**
-- [`docs/07_deployment.md`](docs/07_deployment.md) – Full deployment guide (all platforms)
-- [`docs/08_proxmox_deployment.md`](docs/08_proxmox_deployment.md) – Proxmox detailed guide
-- [`docs/12_automation_and_reporting.md`](docs/12_automation_and_reporting.md) – Automation & CI/CD guide
-- [`Dockerfile`](Dockerfile) + [`docker-compose.yml`](docker-compose.yml) – Docker setup
-
-**Technical**:
-- [`docs/00_mvp.md`](docs/00_mvp.md) – MVP roadmap
-- [`docs/01_architecture.md`](docs/01_architecture.md) – Architecture
-- [`docs/02_data_model.md`](docs/02_data_model.md) – Database schema
-- [`docs/03_data_sources.md`](docs/03_data_sources.md) – Data sources
-- [`docs/04_safety_ethics.md`](docs/04_safety_ethics.md) – Research-only guardrails
-- [`docs/05_dev_setup.md`](docs/05_dev_setup.md) – Dev conventions
-- [`docs/06_free_testing_data.md`](docs/06_free_testing_data.md) – Testing strategies
-
-**PR Summaries**:
-- [`docs/PR1_SUMMARY.md`](docs/PR1_SUMMARY.md) – Scaffold + price loader
-- [`docs/PR2_SUMMARY.md`](docs/PR2_SUMMARY.md) – Congressional trades
-- [`docs/PR3_SUMMARY.md`](docs/PR3_SUMMARY.md) – Enrichment + deployment
-- [`docs/PR4_SUMMARY.md`](docs/PR4_SUMMARY.md) – ⭐ **Analytics foundation (returns, benchmarks, metrics)**
-
-## What's Working Now
-
-- ✅ SQLAlchemy models for officials, securities, trades, prices
-- ✅ Alembic migrations
-- ✅ Price loader with yfinance (idempotent, upsert)
-- ✅ Congressional trade ingestion from House Stock Watcher (FREE!)
-- ✅ Security enrichment (company names, sectors, industries)
-- ✅ ETL to populate officials & trades tables
-- ✅ Docker + deployment infrastructure
-- ✅ 93 passing tests with 88%+ coverage
-- ✅ Linting (ruff + mypy) all green
-- ✅ Works 100% offline with fixtures
-- ✅ Real-time market monitoring & alert system
-- ✅ Disclosure timing correlation engine
-- ✅ Pattern detection & comparative analysis
-- ✅ Automated email reporting (daily/weekly)
-- ✅ CI/CD pipeline (GitHub/Gitea Actions)
-
-## What You Can Do Now
-
-### Analyze Performance
-```bash
-# Analyze specific official
 python scripts/analyze_official.py "Nancy Pelosi" --window 90
-
-# System-wide analysis
 python scripts/calculate_all_returns.py
-```
-
-### Market Monitoring
-```bash
-# Run market scan
 python scripts/monitor_market.py --scan
-
-# Analyze timing of recent disclosures
-python scripts/analyze_disclosure_timing.py --recent 7
-
-# Generate pattern report
-python scripts/generate_pattern_report.py --days 365
-```
-
-### Automated Reporting
-```bash
-# Set up daily/weekly email reports (5 minutes!)
-./scripts/setup_cron.sh
-
-# Send manual report
 python scripts/send_daily_report.py --to your@email.com
 ```
 
-### Add More Data
+## Stack
-```bash
-# Manual entry
-python scripts/add_custom_trades.py
 
-# CSV import
+Python 3.10+, SQLAlchemy, Alembic, pandas/numpy, httpx, yfinance.
-python scripts/scrape_alternative_sources.py import trades.csv
+PostgreSQL or SQLite for local. pytest + ruff + mypy.
-```
 
-## System Architecture
+Data sources: House Stock Watcher, yfinance; QuiverQuant/FMP optional.
 
-POTE now includes a complete 3-phase monitoring system:
+## Docs
 
-**Phase 1: Real-Time Market Monitoring**
+| Doc | Purpose |
-- Tracks ~50 most-traded congressional stocks
+|-----|---------|
-- Detects unusual volume, price spikes, volatility
+| [docs/QUICKSTART.md](docs/QUICKSTART.md) | Using a deployed instance |
-- Logs all alerts with timestamps and severity
+| [docs/PROXMOX_QUICKSTART.md](docs/PROXMOX_QUICKSTART.md) | Proxmox LXC |
+| [docs/AUTOMATION_QUICKSTART.md](docs/AUTOMATION_QUICKSTART.md) | Email reports |
+| [docs/01_architecture.md](docs/01_architecture.md) | Architecture |
+| [docs/02_data_model.md](docs/02_data_model.md) | Schema |
+| [docs/04_safety_ethics.md](docs/04_safety_ethics.md) | Research guardrails |
+| [docs/07_deployment.md](docs/07_deployment.md) | Full deploy |
+| [docs/00_mvp.md](docs/00_mvp.md) | Roadmap |
 
-**Phase 2: Disclosure Correlation**
+PR writeups live under `docs/archive/` when moved.
-- Matches trades with prior market alerts (30-45 day lookback)
-- Calculates "timing advantage score" (0-100)
-- Identifies suspicious timing patterns
 
-**Phase 3: Pattern Detection**
+## License
-- Ranks officials by consistent suspicious timing
-- Analyzes by ticker, sector, and political party
-- Generates comprehensive reports
 
-**Full Documentation**: See [`MONITORING_SYSTEM_COMPLETE.md`](MONITORING_SYSTEM_COMPLETE.md)
+MIT for research/educational use. Not investment advice.
-
-## Next Steps
-
-- [ ] Signals: "follow_research", "avoid_risk", "watch" with confidence scores
-- [ ] Clustering: group officials by trading behavior patterns
-- [ ] API: FastAPI backend for queries
-- [ ] Dashboard: React/Streamlit visualization
-
-See [`docs/00_mvp.md`](docs/00_mvp.md) for the full roadmap.
-
----
-
-**License**: MIT (for research/educational use only)  
-**Disclaimer**: Not investment advice. Use public data only. No claims about inside information.

docs/06_free_testing_data.md

@@ -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! 
 

docs/07_deployment.md

@@ -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!**
 

docs/08_proxmox_deployment.md

@@ -2,17 +2,17 @@
 
 ## Why Proxmox is Perfect for POTE
 
-✅ **Full control** - Your hardware, your rules  
+ **Full control** - Your hardware, your rules 
-✅ **No monthly costs** - Just electricity  
+ **No monthly costs** - Just electricity 
-✅ **Isolated VMs/LXC** - Clean environments  
+ **Isolated VMs/LXC** - Clean environments 
-✅ **Snapshots** - Easy rollback if needed  
+ **Snapshots** - Easy rollback if needed 
-✅ **Resource efficient** - Run alongside other services  
+ **Resource efficient** - Run alongside other services 
 
 ---
 
 ## Deployment Options on Proxmox
 
-### Option 1: LXC Container (Recommended) ⭐
+### Option 1: LXC Container (Recommended)
 
 **Pros**: Lightweight, fast, efficient resource usage  
 **Cons**: Linux only (fine for POTE)
@@ -501,14 +501,14 @@ vs.
 
 ## Next Steps
 
-1. ✅ Create LXC container
+1. Create LXC container
-2. ✅ Install dependencies
+2. Install dependencies
-3. ✅ Setup PostgreSQL
+3. Setup PostgreSQL
-4. ✅ Deploy POTE
+4. Deploy POTE
-5. ✅ Configure cron jobs
+5. Configure cron jobs
-6. ✅ Setup backups
+6. Setup backups
-7. ⏭️ Build Phase 2 (Analytics)
+7. ⏭ Build Phase 2 (Analytics)
-8. ⏭️ Add FastAPI dashboard (optional)
+8. ⏭ Add FastAPI dashboard (optional)
 
 ---
 
@@ -583,7 +583,7 @@ EOF
 sudo -u poteapp mkdir -p /home/poteapp/logs
 
 echo ""
-echo "✅ Setup complete!"
+echo " Setup complete!"
 echo ""
 echo "Next steps:"
 echo "1. su - poteapp"
@@ -600,5 +600,5 @@ chmod +x proxmox_setup.sh
 
 ---
 
-**Your Proxmox setup gives you enterprise-grade infrastructure at hobby costs!** 🚀
+**Your Proxmox setup gives you enterprise-grade infrastructure at hobby costs!** 
 

docs/09_data_updates.md

@@ -137,13 +137,13 @@ python scripts/fetch_sample_prices.py
 ## Data Sources
 
 ### Currently Working:
-- ✅ yfinance (prices, company info)
+- yfinance (prices, company info)
-- ✅ Manual entry
+- Manual entry
-- ✅ CSV import
+- CSV import
-- ✅ Fixture files (testing)
+- Fixture files (testing)
 
 ### Currently Down:
-- ❌ House Stock Watcher API (domain issues)
+- House Stock Watcher API (domain issues)
 
 ### Future Options:
 - QuiverQuant (requires $30/month subscription)

docs/10_automation.md

@@ -8,10 +8,10 @@
 ### **Reality Check: No Real-Time Data Exists**
 
 **Federal Law (STOCK Act):**
-- 📅 Congress members have **30-45 days** to disclose trades
+- Congress members have **30-45 days** to disclose trades
-- 📅 Disclosures are filed as **Periodic Transaction Reports (PTRs)**
+- Disclosures are filed as **Periodic Transaction Reports (PTRs)**
-- 📅 Public databases update **after** filing (usually next day)
+- Public databases update **after** filing (usually next day)
-- 📅 **No real-time feed exists by design**
+- **No real-time feed exists by design**
 
 **Example Timeline:**
 ```
@@ -25,15 +25,15 @@ Feb 17, 2024  →  Your system fetches it
 
 Since trades appear in batches (not continuously), **running once per day is optimal**:
 
-✅ **Daily (7 AM)** - Catches overnight filings  
+ **Daily (7 AM)** - Catches overnight filings 
-✅ **After market close** - Prices are final  
+ **After market close** - Prices are final 
-✅ **Low server load** - Off-peak hours  
+ **Low server load** - Off-peak hours 
-❌ **Hourly** - Wasteful, no new data  
+ **Hourly** - Wasteful, no new data 
-❌ **Real-time** - Impossible, not how disclosures work  
+ **Real-time** - Impossible, not how disclosures work 
 
 ---
 
-## 🤖 Automated Setup Options
+## Automated Setup Options
 
 ### **Option 1: Cron Job (Linux/Proxmox) - Recommended**
 
@@ -149,7 +149,7 @@ Or from anywhere:
 
 ---
 
-## 📊 What Gets Updated?
+## What Gets Updated?
 
 ### **1. Congressional Trades**
 **Script:** `fetch_congressional_trades.py`  
@@ -182,7 +182,7 @@ Or from anywhere:
 
 ---
 
-## ⚙️ Customizing the Schedule
+## Customizing the Schedule
 
 ### **Different Frequencies**
 
@@ -215,7 +215,7 @@ Or from anywhere:
 
 ---
 
-## 📧 Email Notifications (Optional)
+## Email Notifications (Optional)
 
 ### **Setup Email Alerts**
 
@@ -310,7 +310,7 @@ python scripts/email_summary.py
 
 ---
 
-## 🔍 Monitoring & Logging
+## Monitoring & Logging
 
 ### **Check Cron Job Status**
 
@@ -355,7 +355,7 @@ Add to `/etc/logrotate.d/pote`:
 
 ---
 
-## 🚨 Handling Failures
+## Handling Failures
 
 ### **What If House Stock Watcher Is Down?**
 
@@ -363,7 +363,7 @@ The script is designed to continue even if one step fails:
 
 ```bash
 # Script continues and logs warnings
-⚠️  WARNING: Failed to fetch congressional trades
+ WARNING: Failed to fetch congressional trades
    This is likely because House Stock Watcher API is down
    Continuing with other steps...
 ```
@@ -399,7 +399,7 @@ for attempt in range(MAX_RETRIES):
 
 ---
 
-## 📈 Performance Optimization
+## Performance Optimization
 
 ### **Batch Processing**
 
@@ -439,7 +439,7 @@ CREATE INDEX IF NOT EXISTS ix_prices_security_id ON prices(security_id);
 
 ---
 
-## 🎯 Recommended Setup
+## Recommended Setup
 
 ### **For Proxmox Production:**
 
@@ -475,7 +475,7 @@ pote-update
 
 ---
 
-## 📝 Summary
+## Summary
 
 ### **Key Points:**
 

docs/11_live_market_monitoring.md

@@ -1,13 +1,13 @@
 # Live Market Monitoring + Congressional Trading Analysis
 
-## 🎯 What's Possible vs Impossible
+## What's Possible vs Impossible
 
-### ❌ **NOT Possible:**
+### **NOT Possible:**
 - Identify WHO is buying/selling in real-time
 - Match live trades to specific Congress members
 - See congressional trades before they're disclosed
 
-### ✅ **IS Possible:**
+### **IS Possible:**
 - Track unusual market activity in real-time
 - Monitor stocks Congress members historically trade
 - Compare unusual activity to later disclosures
@@ -15,7 +15,7 @@
 
 ---
 
-## 🔄 **Two-Phase Monitoring System**
+## **Two-Phase Monitoring System**
 
 ### **Phase 1: Real-Time Market Monitoring**
 Monitor unusual activity in stocks Congress trades:
@@ -33,7 +33,7 @@ When disclosures come in:
 
 ---
 
-## 📊 **Implementation: Watchlist-Based Monitoring**
+## **Implementation: Watchlist-Based Monitoring**
 
 ### **Concept:**
 
@@ -57,38 +57,38 @@ Step 3: When Disclosure Appears (30-45 days later)
 
 ---
 
-## 🛠️ **Data Sources for Live Market Monitoring**
+## **Data Sources for Live Market Monitoring**
 
 ### **Free/Low-Cost Options:**
 
 1. **Yahoo Finance (yfinance)**
-   - ✅ Real-time quotes (15-min delay free)
+   - Real-time quotes (15-min delay free)
-   - ✅ Historical options data
+   - Historical options data
-   - ✅ Volume data
+   - Volume data
-   - ❌ Not true real-time for options flow
+   - Not true real-time for options flow
 
 2. **Unusual Whales API**
-   - ✅ Options flow data
+   - Options flow data
-   - ✅ Unusual activity alerts
+   - Unusual activity alerts
-   - 💰 Paid ($50-200/month)
+   - Paid ($50-200/month)
    - https://unusualwhales.com/
 
 3. **Tradier API**
-   - ✅ Real-time market data
+   - Real-time market data
-   - ✅ Options chains
+   - Options chains
-   - 💰 Paid but affordable ($10-50/month)
+   - Paid but affordable ($10-50/month)
    - https://tradier.com/
 
 4. **FlowAlgo**
-   - ✅ Options flow tracking
+   - Options flow tracking
-   - ✅ Dark pool data
+   - Dark pool data
-   - 💰 Paid ($99-399/month)
+   - Paid ($99-399/month)
    - https://www.flowalgo.com/
 
 5. **Polygon.io**
-   - ✅ Real-time stock data
+   - Real-time stock data
-   - ✅ Options data
+   - Options data
-   - 💰 Free tier + paid plans
+   - Free tier + paid plans
    - https://polygon.io/
 
 ### **Best Free Option: Build Your Own with yfinance**
@@ -97,7 +97,7 @@ Track volume/price changes every 5 minutes for congressional watchlist tickers.
 
 ---
 
-## 💡 **Practical Hybrid System**
+## **Practical Hybrid System**
 
 ### **What We Can Build:**
 
@@ -141,37 +141,37 @@ for disclosure in new_disclosures:
 
 ---
 
-## 📈 **Example: Nancy Pelosi NVDA Trade Analysis**
+## **Example: Nancy Pelosi NVDA Trade Analysis**
 
 ### **Timeline:**
 
 ```
 Nov 10, 2024:
-  🔔 ALERT: NVDA unusual call options activity
+ ALERT: NVDA unusual call options activity
      Volume: 10x average
      Strike: $500 (2 weeks out)
      
 Nov 15, 2024:
-  💰 Someone buys NVDA (unknown who at the time)
+ Someone buys NVDA (unknown who at the time)
   
 Nov 18, 2024:
-  📰 NVDA announces new AI chip
+ NVDA announces new AI chip
-  📈 Stock jumps 15%
+ Stock jumps 15%
   
 Dec 15, 2024:
-  📋 Disclosure: Nancy Pelosi bought NVDA on Nov 15
+ Disclosure: Nancy Pelosi bought NVDA on Nov 15
      Value: $15,001-$50,000
      
 ANALYSIS:
-  ✅ She bought AFTER unusual options activity (Nov 10)
+ She bought AFTER unusual options activity (Nov 10)
-  ❓ She bought BEFORE announcement (Nov 18)
+ She bought BEFORE announcement (Nov 18)
-  ⏱️ Timing: 3 days before major news
+ ⏱ Timing: 3 days before major news
-  🚩 Flag: Investigate if announcement was public knowledge
+ Flag: Investigate if announcement was public knowledge
 ```
 
 ---
 
-## 🎯 **Recommended Approach**
+## **Recommended Approach**
 
 ### **Phase 1: Build Congressional Ticker Watchlist**
 
@@ -230,12 +230,12 @@ def monitor_tickers(tickers, interval_minutes=5):
                     # Check for unusual volume
                     avg_volume = current['Volume'].mean()
                     if latest['Volume'] > avg_volume * 3:
-                        alert(f"🔔 {ticker}: Unusual volume spike!")
+ alert(f" {ticker}: Unusual volume spike!")
                     
                     # Check for price movement
                     price_change = (latest['Close'] - current['Open'].iloc[0]) / current['Open'].iloc[0]
                     if abs(price_change) > 0.05:  # 5% move
-                        alert(f"📈 {ticker}: {price_change:.2%} move today!")
+ alert(f" {ticker}: {price_change:.2%} move today!")
                         
             except Exception as e:
                 print(f"Error monitoring {ticker}: {e}")
@@ -290,31 +290,31 @@ def analyze_disclosure_timing(disclosure):
 
 ---
 
-## 🚨 **Realistic Expectations**
+## **Realistic Expectations**
 
 ### **What This System Will Do:**
-✅ Monitor stocks Congress members historically trade
+ Monitor stocks Congress members historically trade
-✅ Alert on unusual market activity in those stocks
+ Alert on unusual market activity in those stocks
-✅ Retroactively correlate disclosures with earlier alerts
+ Retroactively correlate disclosures with earlier alerts
-✅ Identify timing patterns and potential advantages
+ Identify timing patterns and potential advantages
-✅ Build database of congressional trading patterns
+ Build database of congressional trading patterns
 
 ### **What This System WON'T Do:**
-❌ Identify WHO is buying in real-time
+ Identify WHO is buying in real-time
-❌ Give you advance notice of congressional trades
+ Give you advance notice of congressional trades
-❌ Provide real-time inside information
+ Provide real-time inside information
-❌ Allow you to "front-run" Congress
+ Allow you to "front-run" Congress
 
 ### **Legal & Ethical:**
-✅ All data is public
+ All data is public
-✅ Analysis is retrospective
+ Analysis is retrospective
-✅ For research and transparency
+ For research and transparency
-✅ Not market manipulation
+ Not market manipulation
-❌ Cannot and should not be used to replicate potentially illegal trades
+ Cannot and should not be used to replicate potentially illegal trades
 
 ---
 
-## 📊 **Proposed Implementation**
+## **Proposed Implementation**
 
 ### **New Scripts to Create:**
 
@@ -365,15 +365,15 @@ CREATE TABLE disclosure_timing_analysis (
 
 ---
 
-## 🎯 **Summary**
+## **Summary**
 
 ### **Your Question:**
 > "Can we read live trades being made and compare them to a name?"
 
 ### **Answer:**
-❌ **No** - Live trades are anonymous, can't identify individuals
+ **No** - Live trades are anonymous, can't identify individuals
 
-✅ **BUT** - You CAN:
+ **BUT** - You CAN:
 1. Monitor unusual activity in stocks Congress trades
 2. Log these alerts in real-time
 3. When disclosures appear (30-45 days later), correlate them
@@ -381,25 +381,25 @@ CREATE TABLE disclosure_timing_analysis (
 5. Build patterns database of timing and performance
 
 ### **This Gives You:**
-- ✅ Transparency on timing advantages
+- Transparency on timing advantages
-- ✅ Pattern detection across officials
+- Pattern detection across officials
-- ✅ Research-grade analysis
+- Research-grade analysis
-- ✅ Historical correlation data
+- Historical correlation data
 
 ### **This Does NOT Give You:**
-- ❌ Real-time identity of traders
+- Real-time identity of traders
-- ❌ Advance notice of congressional trades
+- Advance notice of congressional trades
-- ❌ Ability to "front-run" disclosures
+- Ability to "front-run" disclosures
 
 ---
 
-## 🚀 **Would You Like Me To Build This?**
+## **Would You Like Me To Build This?**
 
 I can create:
-1. ✅ Real-time monitoring system for congressional tickers
+1. Real-time monitoring system for congressional tickers
-2. ✅ Alert logging and analysis
+2. Alert logging and analysis
-3. ✅ Timing correlation when disclosures appear
+3. Timing correlation when disclosures appear
-4. ✅ Pattern detection and reporting
+4. Pattern detection and reporting
 
 This would be **Phase 2.5** of POTE - the "timing analysis" module.
 

docs/12_automation_and_reporting.md

@@ -205,18 +205,18 @@ Output:
 POTE HEALTH CHECK
 ============================================================
 Timestamp: 2025-12-15T10:30:00
-Overall Status: ✓ OK
+Overall Status: OK
 
-✓ Database Connection: Database connection successful
+ Database Connection: Database connection successful
-✓ Data Freshness: Data is fresh (2 days old)
+ Data Freshness: Data is fresh (2 days old)
     latest_trade_date: 2025-12-13
-✓ Data Counts: Database has 1,234 trades
+ Data Counts: Database has 1,234 trades
     officials: 45
     securities: 123
     trades: 1,234
     prices: 12,345
     market_alerts: 567
-✓ Recent Alerts: 23 alerts in last 24 hours
+ Recent Alerts: 23 alerts in last 24 hours
 ============================================================
 ```
 

docs/13_secrets_management.md

@@ -22,18 +22,18 @@ chmod 600 .env
 chown poteapp:poteapp .env
 ```
 
-### ✅ Pros
+### Pros
 - Simple, works immediately
 - No additional setup
 - Standard practice for Python projects
 
-### ⚠️ Cons
+### Cons
 - Secrets stored in plain text on disk
 - Risk if server is compromised
 - No audit trail
 
-### 🔒 Security Checklist
+### Security Checklist
-- [ ] `.env` in `.gitignore` (already done ✅)
+- [ ] `.env` in `.gitignore` (already done )
 - [ ] File permissions: `chmod 600 .env`
 - [ ] Never commit to git
 - [ ] Backup securely (encrypted)
@@ -76,12 +76,12 @@ sudo chmod 600 /etc/systemd/system/pote.service
 sudo systemctl daemon-reload
 ```
 
-### ✅ Pros
+### Pros
 - Secrets not in git or project directory
 - Standard Linux practice
 - Works with systemd timers
 
-### ⚠️ Cons
+### Cons
 - Still visible in `systemctl show`
 - Requires root to edit
 
@@ -128,12 +128,12 @@ source .env
 python scripts/send_daily_report.py
 ```
 
-### ✅ Pros
+### Pros
 - Secrets separate from code
 - Easy to rotate
 - Can be backed up separately
 
-### ⚠️ Cons
+### Cons
 - Extra file to manage
 - Still plain text
 
@@ -191,12 +191,12 @@ class Settings(BaseSettings):
     smtp_password: str = Field(default_factory=lambda: get_secret("SMTP_PASSWORD"))
 ```
 
-### ✅ Pros
+### Pros
 - Docker-native solution
 - Encrypted in Swarm mode
 - Never in logs
 
-### ⚠️ Cons
+### Cons
 - Requires Docker
 - More complex setup
 
@@ -228,13 +228,13 @@ secrets = client.secrets.kv.v2.read_secret_version(path='pote')
 smtp_password = secrets['data']['data']['smtp_password']
 ```
 
-### ✅ Pros
+### Pros
 - Centralized secrets management
 - Audit logs
 - Dynamic secrets
 - Access control
 
-### ⚠️ Cons
+### Cons
 - Complex setup
 - Requires Vault infrastructure
 - Overkill for single user
@@ -260,14 +260,14 @@ env:
   DATABASE_URL: postgresql://user:${{ secrets.DB_PASSWORD }}@postgres/db
 ```
 
-### ⚠️ Important
+### Important
 - **Only for CI/CD pipelines**
 - **NOT for deployed servers**
 - Secrets are injected during workflow runs
 
 ---
 
-## 🎯 Recommendation for Your Setup
+## Recommendation for Your Setup
 
 ### Personal/Research Use (Current)
 
@@ -305,9 +305,9 @@ gpg -c .env  # Creates .env.gpg
 
 ---
 
-## 🔒 General Security Best Practices
+## General Security Best Practices
 
-### ✅ DO
+### DO
 
 - Use strong, unique passwords
 - Restrict file permissions (`chmod 600`)
@@ -316,7 +316,7 @@ gpg -c .env  # Creates .env.gpg
 - Use encrypted backups
 - Audit who has server access
 
-### ❌ DON'T
+### DON'T
 
 - Commit secrets to git (even private repos)
 - Store passwords in code
@@ -327,7 +327,7 @@ gpg -c .env  # Creates .env.gpg
 
 ---
 
-## 🧪 Test Your Security
+## Test Your Security
 
 ### Check if `.env` is protected
 
@@ -352,7 +352,7 @@ git log --all --full-history --source --pickaxe-all -S 'smtp_password'
 
 ---
 
-## 🔄 Password Rotation Procedure
+## Password Rotation Procedure
 
 ### Every 90 days (or if compromised):
 
@@ -369,32 +369,32 @@ git log --all --full-history --source --pickaxe-all -S 'smtp_password'
 
 ---
 
-## 📊 Security Level Comparison
+## Security Level Comparison
 
 | Level | Method | Effort | Protection |
 |-------|--------|--------|------------|
-| 🔓 Basic | `.env` (default perms) | None | Low |
+| Basic | `.env` (default perms) | None | Low |
-| 🔒 Good | `.env` (chmod 600) | 1 min | Medium |
+| Good | `.env` (chmod 600) | 1 min | Medium |
-| 🔒 Better | Environment variables | 10 min | Good |
+| Better | Environment variables | 10 min | Good |
-| 🔒 Better | Separate secrets file | 10 min | Good |
+| Better | Separate secrets file | 10 min | Good |
-| 🔐 Best | Docker Secrets | 30 min | Very Good |
+| Best | Docker Secrets | 30 min | Very Good |
-| 🔐 Best | Vault | 2+ hours | Excellent |
+| Best | Vault | 2+ hours | Excellent |
 
 ---
 
-## 🎯 Your Current Status
+## Your Current Status
 
-✅ **Already secure enough for personal use:**
+ **Already secure enough for personal use:**
-- `.env` in `.gitignore` ✅
+- `.env` in `.gitignore` 
-- Not committed to git ✅
+- Not committed to git 
-- Local server only ✅
+- Local server only 
 
-⚠️ **Recommended improvement (2 minutes):**
+ **Recommended improvement (2 minutes):**
 ```bash
 chmod 600 .env
 ```
 
-🔐 **Optional (if paranoid):**
+ **Optional (if paranoid):**
 - Use separate secrets file in `/etc/pote/`
 - Encrypt backups with GPG
 - Set up password rotation schedule
@@ -405,9 +405,9 @@ chmod 600 .env
 
 **For your levkin.ca setup:**
 
-1. **Current approach (`.env` file) is fine** ✅
+1. **Current approach (`.env` file) is fine** 
 2. **Add `chmod 600 .env`** for better security (2 minutes)
-3. **Don't commit `.env` to git** (already protected ✅)
+3. **Don't commit `.env` to git** (already protected )
 4. **Consider upgrading to environment variables** if you deploy to production
 
 Your current setup is **appropriate for a personal research project**. Don't over-engineer it unless you have specific compliance requirements or a team.

docs/AUTOMATION_QUICKSTART.md

@@ -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. **
 

docs/DEPLOYMENT_AND_AUTOMATION.md

@@ -1,17 +1,17 @@
 # POTE Deployment & Automation Guide
 
-## 🎯 Quick Answer to Your Questions
+## Quick Answer to Your Questions
 
 ### After Deployment, What Happens?
 
 **By default: NOTHING automatic happens.** You need to set up automation.
 
 The deployed system is:
-- ✅ Running (database, code installed)
+- Running (database, code installed)
-- ✅ Accessible via SSH at your Proxmox IP
+- Accessible via SSH at your Proxmox IP
-- ❌ NOT fetching data automatically
+- NOT fetching data automatically
-- ❌ NOT sending reports automatically
+- NOT sending reports automatically
-- ❌ NOT monitoring markets automatically
+- NOT monitoring markets automatically
 
 **You must either:**
 1. **Run scripts manually** when you want updates, OR
@@ -19,7 +19,7 @@ The deployed system is:
 
 ---
 
-## 🚀 Option 1: Automated Email Reports (Recommended)
+## Option 1: Automated Email Reports (Recommended)
 
 ### What You Get
 
@@ -53,13 +53,13 @@ Run the interactive setup:
 Follow prompts:
 1. Enter your email address
 2. Choose report time (default: 6 AM)
-3. Done! ✅
+3. Done! 
 
 **See full guide:** [`AUTOMATION_QUICKSTART.md`](AUTOMATION_QUICKSTART.md)
 
 ---
 
-## 📍 Option 2: Access Reports via IP (No Email)
+## Option 2: Access Reports via IP (No Email)
 
 If you don't want email, you can:
 
@@ -107,7 +107,7 @@ python scripts/health_check.py
 
 ---
 
-## 🌐 Option 3: Build a Web Interface (Future)
+## Option 3: Build a Web Interface (Future)
 
 Currently, POTE is **command-line only**. No web UI yet.
 
@@ -122,16 +122,16 @@ For now, use SSH or email reports.
 
 ---
 
-## 🔄 Do You Need CI/CD Pipelines?
+## Do You Need CI/CD Pipelines?
 
 ### What the Pipeline Does
 
 The included CI/CD pipeline (`.github/workflows/ci.yml`) runs on **every git push**:
 
-1. ✅ Lint & test (93 tests)
+1. Lint & test (93 tests)
-2. ✅ Security scanning
+2. Security scanning
-3. ✅ Dependency scanning
+3. Dependency scanning
-4. ✅ Docker build test
+4. Docker build test
 
 ### Should You Use It?
 
@@ -174,18 +174,18 @@ docker build -t pote:test .
 
 ---
 
-## 📊 Comparison of Options
+## Comparison of Options
 
 | Method | Pros | Cons | Best For |
 |--------|------|------|----------|
-| **Automated Email** | ✅ Convenient<br>✅ No SSH needed<br>✅ Daily/weekly updates | ❌ Requires SMTP setup | Most users |
+| **Automated Email** | Convenient<br> No SSH needed<br> Daily/weekly updates | Requires SMTP setup | Most users |
-| **SSH + Manual Scripts** | ✅ Full control<br>✅ No email needed | ❌ Manual work<br>❌ Must remember to run | Power users |
+| **SSH + Manual Scripts** | Full control<br> No email needed | Manual work<br> Must remember to run | Power users |
-| **Saved Reports (SSH access)** | ✅ Automated<br>✅ No email | ❌ Must SSH to view | Users without email |
+| **Saved Reports (SSH access)** | Automated<br> No email | Must SSH to view | Users without email |
-| **Web Interface** | ✅ User-friendly | ❌ Not implemented yet | Future |
+| **Web Interface** | User-friendly | Not implemented yet | Future |
 
 ---
 
-## 🛠️ Your Ansible Pipeline
+## Your Ansible Pipeline
 
 Your existing Ansible CI/CD pipeline is **NOT directly usable** for POTE because:
 
@@ -197,11 +197,11 @@ Your existing Ansible CI/CD pipeline is **NOT directly usable** for POTE because
 
 **Concepts from your pipeline that ARE used in POTE's CI/CD:**
 
-- ✅ Security scanning (Trivy, Bandit instead of Gitleaks)
+- Security scanning (Trivy, Bandit instead of Gitleaks)
-- ✅ Dependency scanning (Trivy instead of npm audit)
+- Dependency scanning (Trivy instead of npm audit)
-- ✅ SAST scanning (Bandit instead of Semgrep)
+- SAST scanning (Bandit instead of Semgrep)
-- ✅ Container scanning (Docker build test)
+- Container scanning (Docker build test)
-- ✅ Workflow summary generation
+- Workflow summary generation
 
 **The POTE pipeline (`.github/workflows/ci.yml`) already includes all of these!**
 
@@ -217,7 +217,7 @@ Your Gitea server can run the POTE pipeline using Gitea Actions:
 
 ---
 
-## 📧 Email Setup Examples
+## Email Setup Examples
 
 ### Gmail (Most Common)
 
@@ -258,7 +258,7 @@ REPORT_RECIPIENTS=admin@yourdomain.com
 
 ---
 
-## ✅ Recommended Setup for Most Users
+## Recommended Setup for Most Users
 
 1. **Deploy to Proxmox** (5 min)
    ```bash
@@ -283,7 +283,7 @@ REPORT_RECIPIENTS=admin@yourdomain.com
 
 ---
 
-## 🔍 Monitoring & Health Checks
+## Monitoring & Health Checks
 
 ### Add System Health Monitoring
 
@@ -310,17 +310,17 @@ python scripts/health_check.py
 
 ---
 
-## 📚 Full Documentation
+## Full Documentation
 
-- **Automation Setup**: [`AUTOMATION_QUICKSTART.md`](AUTOMATION_QUICKSTART.md) ⭐
+- **Automation Setup**: [`AUTOMATION_QUICKSTART.md`](AUTOMATION_QUICKSTART.md) 
-- **Deployment**: [`PROXMOX_QUICKSTART.md`](PROXMOX_QUICKSTART.md) ⭐
+- **Deployment**: [`PROXMOX_QUICKSTART.md`](PROXMOX_QUICKSTART.md) 
-- **Usage**: [`QUICKSTART.md`](QUICKSTART.md) ⭐
+- **Usage**: [`QUICKSTART.md`](QUICKSTART.md) 
 - **Detailed Automation Guide**: [`docs/12_automation_and_reporting.md`](docs/12_automation_and_reporting.md)
 - **Monitoring System**: [`MONITORING_SYSTEM_COMPLETE.md`](MONITORING_SYSTEM_COMPLETE.md)
 
 ---
 
-## 🎉 Summary
+## Summary
 
 **After deployment:**
 - Reports are NOT sent automatically by default
@@ -334,5 +334,5 @@ python scripts/health_check.py
 1. Deploy to Proxmox
 2. Run `./scripts/setup_cron.sh`
 3. Receive daily/weekly email reports
-4. Done! 🚀
+4. Done! 
 

docs/EMAIL_SETUP.md

@@ -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 **
 

docs/FREE_TESTING_QUICKSTART.md

@@ -2,14 +2,14 @@
 
 ## TL;DR: You can test everything for $0
 
-### Already Working (PR1 ✅)
+### Already Working (PR1 )
 - **Price data**: `yfinance` (free, unlimited)
 - **Unit tests**: Mocked data in `tests/` (15 passing tests)
 - **Coverage**: 87% without any paid APIs
 
 ### For PR2 (Congressional Trades) - FREE Options
 
-#### Best Option: House Stock Watcher 🌟
+#### Best Option: House Stock Watcher
 ```bash
 # No API key needed, just scrape their public JSON
 curl https://housestockwatcher.com/api/all_transactions
@@ -44,10 +44,10 @@ python scripts/fetch_house_watcher_sample.py  # We'll build this in PR2
 
 ### What You DON'T Need to Pay For
 
-❌ QuiverQuant Pro ($30/mo) - free tier is enough for dev/testing  
+ QuiverQuant Pro ($30/mo) - free tier is enough for dev/testing 
-❌ Financial Modeling Prep paid tier - free tier works  
+ Financial Modeling Prep paid tier - free tier works 
-❌ Any paid database hosting - SQLite works great locally  
+ Any paid database hosting - SQLite works great locally 
-❌ Any cloud services - runs 100% locally
+ Any cloud services - runs 100% locally
 
 ### When You MIGHT Want Paid (Way Later)
 
@@ -56,7 +56,7 @@ python scripts/fetch_house_watcher_sample.py  # We'll build this in PR2
 - Multiple concurrent users on a dashboard
 - Commercial use (check each API's terms)
 
-**For personal research? Stay free forever. 🎉**
+**For personal research? Stay free forever. **
 
 ---
 

docs/GITEA_SECRETS_GUIDE.md

@@ -1,20 +1,20 @@
-# 🔐 Gitea Secrets Guide for POTE
+# Gitea Secrets Guide for POTE
 
-## ✅ YES! You Can Store Passwords in Gitea
+## YES! You Can Store Passwords in Gitea
 
 Gitea has a **Secrets** feature (like GitHub Actions secrets) that lets you store passwords securely and use them in:
-1. **CI/CD pipelines** (Gitea Actions workflows) ✅
+1. **CI/CD pipelines** (Gitea Actions workflows) 
-2. **Deployment workflows** ✅
+2. **Deployment workflows** 
 
 **BUT NOT:**
-- ❌ Directly in your running application on Proxmox
+- Directly in your running application on Proxmox
-- ❌ Accessed by scripts outside of workflows
+- Accessed by scripts outside of workflows
 
 ---
 
-## 🎯 What Gitea Secrets Are Good For
+## What Gitea Secrets Are Good For
 
-### ✅ Perfect Use Cases
+### Perfect Use Cases
 
 1. **CI/CD Testing** - Run tests with real credentials
 2. **Automated Deployment** - Deploy to Proxmox with SSH keys
@@ -22,7 +22,7 @@ Gitea has a **Secrets** feature (like GitHub Actions secrets) that lets you stor
 4. **Docker Registry** - Push images with credentials
 5. **API Keys** - Access external services during builds
 
-### ❌ NOT Good For
+### NOT Good For
 
 1. **Runtime secrets** - Your deployed app on Proxmox can't access them
 2. **Local development** - Can't use secrets on your laptop
@@ -30,7 +30,7 @@ Gitea has a **Secrets** feature (like GitHub Actions secrets) that lets you stor
 
 ---
 
-## 🔧 How to Set Up Gitea Secrets
+## How to Set Up Gitea Secrets
 
 ### Step 1: Add Secrets to Gitea
 
@@ -57,7 +57,7 @@ Secrets are accessed with `${{ secrets.SECRET_NAME }}` syntax.
 
 ---
 
-## 📝 Example: CI Pipeline with Secrets
+## Example: CI Pipeline with Secrets
 
 **File:** `.github/workflows/ci.yml`
 
@@ -92,11 +92,11 @@ jobs:
             --smtp-password "${{ secrets.SMTP_PASSWORD }}"
 ```
 
-**✅ I've already updated your CI pipeline to use secrets!**
+** I've already updated your CI pipeline to use secrets!**
 
 ---
 
-## 🚀 Example: Automated Deployment Workflow
+## Example: Automated Deployment Workflow
 
 Create `.github/workflows/deploy.yml`:
 
@@ -152,7 +152,7 @@ jobs:
 
 ---
 
-## 🔄 How Secrets Flow to Your Server
+## How Secrets Flow to Your Server
 
 ### Option 1: Deploy Workflow Updates `.env` (Recommended)
 
@@ -201,11 +201,11 @@ jobs:
 
 ---
 
-## 🎯 Recommended Setup for Your POTE Project
+## Recommended Setup for Your POTE Project
 
 ### For CI/CD (Testing):
 
-**Use Gitea Secrets** ✅
+**Use Gitea Secrets** 
 
 ```yaml
 # .github/workflows/ci.yml (already updated!)
@@ -216,7 +216,7 @@ env:
 
 ### For Deployed Server (Proxmox):
 
-**Keep using `.env` file** ✅
+**Keep using `.env` file** 
 
 Why?
 - Simpler for manual SSH access
@@ -227,7 +227,7 @@ Why?
 
 ---
 
-## 🚀 Complete Workflow: Gitea → Proxmox
+## Complete Workflow: Gitea → Proxmox
 
 ### 1. Store Secrets in Gitea
 
@@ -264,27 +264,27 @@ git push origin main
 
 ---
 
-## ⚠️ Important Limitations
+## Important Limitations
 
 ### Gitea Secrets CAN'T:
 
-❌ Be accessed outside of workflows
+ Be accessed outside of workflows
-❌ Be used in local `python script.py` runs
+ Be used in local `python script.py` runs
-❌ Be read by cron jobs on Proxmox (directly)
+ Be read by cron jobs on Proxmox (directly)
-❌ Replace `.env` for runtime application config
+ Replace `.env` for runtime application config
 
 ### Gitea Secrets CAN:
 
-✅ Secure your CI/CD pipeline
+ Secure your CI/CD pipeline
-✅ Deploy safely without exposing passwords in git
+ Deploy safely without exposing passwords in git
-✅ Update `.env` on server during deployment
+ Update `.env` on server during deployment
-✅ Run automated tests with real credentials
+ Run automated tests with real credentials
 
 ---
 
-## 🔒 Security Best Practices
+## Security Best Practices
 
-### ✅ DO:
+### DO:
 
 1. **Store ALL sensitive data as Gitea secrets**
    - SMTP passwords
@@ -300,10 +300,10 @@ git push origin main
 
 3. **Never echo secrets**
    ```yaml
-   # ❌ BAD - exposes in logs
+ # BAD - exposes in logs
    - run: echo "${{ secrets.PASSWORD }}"
    
-   # ✅ GOOD - masked automatically
+ # GOOD - masked automatically
    - run: use_password "${{ secrets.PASSWORD }}"
    ```
 
@@ -311,7 +311,7 @@ git push origin main
    - Update in Gitea UI
    - Re-run deployment workflow
 
-### ❌ DON'T:
+### DON'T:
 
 1. **Commit secrets to git** (even private repos)
 2. **Share secrets via Slack/email**
@@ -320,18 +320,18 @@ git push origin main
 
 ---
 
-## 📊 Comparison: Where to Store Secrets
+## Comparison: Where to Store Secrets
 
 | Storage | CI/CD | Deployed App | Easy Updates | Security |
 |---------|-------|--------------|--------------|----------|
-| **Gitea Secrets** | ✅ Perfect | ❌ No | ✅ Via workflow | ⭐⭐⭐⭐⭐ |
+| **Gitea Secrets** | Perfect | No | Via workflow | |
-| **`.env` file** | ❌ No | ✅ Perfect | ✅ `nano .env` | ⭐⭐⭐ |
+| **`.env` file** | No | Perfect | `nano .env` | |
-| **Environment Vars** | ✅ Yes | ✅ Yes | ❌ Harder | ⭐⭐⭐⭐ |
+| **Environment Vars** | Yes | Yes | Harder | |
-| **Both (Recommended)** | ✅ Yes | ✅ Yes | ✅ Automated | ⭐⭐⭐⭐⭐ |
+| **Both (Recommended)** | Yes | Yes | Automated | |
 
 ---
 
-## 🎯 My Recommendation for You
+## My Recommendation for You
 
 ### Use BOTH:
 
@@ -345,21 +345,21 @@ git push origin main
 2. Commit code changes
 3. Push to Gitea
 4. Workflow runs:
-   - Tests with Gitea secrets ✅
+ - Tests with Gitea secrets 
-   - Deploys to Proxmox ✅
+ - Deploys to Proxmox 
-   - Updates .env with secrets ✅
+ - Updates .env with secrets 
-5. Proxmox app reads from .env ✅
+5. Proxmox app reads from .env 
 ```
 
 **This gives you:**
-- ✅ Secure CI/CD
+- Secure CI/CD
-- ✅ Easy manual SSH access
+- Easy manual SSH access
-- ✅ Automated deployments
+- Automated deployments
-- ✅ No passwords in git
+- No passwords in git
 
 ---
 
-## 🚀 Next Steps
+## Next Steps
 
 ### 1. Add Secrets to Gitea (5 minutes)
 
@@ -387,7 +387,7 @@ I can create `.github/workflows/deploy.yml` if you want automated deployments!
 
 ---
 
-## 💡 Quick Commands
+## Quick Commands
 
 ### Add SSH Key to Gitea (for deployment):
 
@@ -409,12 +409,12 @@ git commit --allow-empty -m "Test secrets"
 git push
 
 # Check Gitea Actions tab
-# Look for green checkmarks ✅
+# Look for green checkmarks
 ```
 
 ---
 
-## 📚 See Also
+## See Also
 
 - **[docs/13_secrets_management.md](docs/13_secrets_management.md)** - All secrets options
 - **[.github/workflows/ci.yml](.github/workflows/ci.yml)** - Updated with secrets support
@@ -422,16 +422,16 @@ git push
 
 ---
 
-## ✅ Summary
+## Summary
 
 **YES, use Gitea secrets!** They're perfect for:
-- ✅ CI/CD pipelines
+- CI/CD pipelines
-- ✅ Automated deployments
+- Automated deployments
-- ✅ Keeping passwords out of git
+- Keeping passwords out of git
 
 **But ALSO keep `.env` on Proxmox** for:
-- ✅ Runtime application config
+- Runtime application config
-- ✅ Manual SSH access
+- Manual SSH access
-- ✅ Cron jobs
+- Cron jobs
 
-**Best of both worlds:** Gitea secrets deploy and update the `.env` file automatically! 🚀
+**Best of both worlds:** Gitea secrets deploy and update the `.env` file automatically! 

docs/HANDOFF-2026-05-27.md

@@ -0,0 +1,163 @@
+# POTE homelab handoff — 2026-05-27
+
+**Status:** Production LXC running; PR #1 merged to `main`; CI green on Gitea Actions.  
+**Research only — not investment advice.**
+
+---
+
+## What’s live
+
+| Item | Value |
+|------|--------|
+| Host | LXC **236** `pote` @ **10.0.10.48** (pve10) |
+| App | `/home/poteapp/pote` (venv, **no git clone** — deploy via rsync) |
+| DB | PostgreSQL `pote` / `poteuser` (password rotated; in Ansible vault) |
+| Data | ~55 officials, ~329 trades (30-day live ingest, May 2026) |
+| SMTP | `10.0.10.132` (Mailcow), send as **`alerts@levkine.ca`** |
+| Reports | **`idobkin@gmail.com`** daily 07:00, weekly Sun 08:00 |
+
+### Cron (`crontab -u poteapp -l`)
+
+| Time | Script |
+|------|--------|
+| 06:00 | `fetch_congressional_trades.py --days 7` |
+| 06:15 | `enrich_securities.py` |
+| 06:30 | `monitor_market.py --scan` |
+| 07:00 | `send_daily_report.py --to idobkin@gmail.com` |
+| Sun 08:00 | `send_weekly_report.py --to idobkin@gmail.com` |
+
+### Data source (important)
+
+Legacy **housestockwatcher.com** and S3 buckets are dead/blocked. Ingest uses public JSON from [congress-trading-monitor](https://github.com/kadoa-org/congress-trading-monitor) (~5000 rows cap). Override with env `POTE_HOUSE_DATA_URL` if you add another feed.
+
+---
+
+## Repos & branches
+
+| Repo | Branch | Notes |
+|------|--------|--------|
+| **POTE** | `main` @ `git.levkin.ca/ilia/POTE` | Merged PR #1 — ingest, email, CI, deps |
+| **ansible** | `feature/outline-setup-api` (or `master`) | Inventory, `deploy-pote.sh`, vault — may need merge to homelab default branch |
+
+Local:
+
+```bash
+cd ~/Documents/code/POTE && git checkout main && git pull
+```
+
+---
+
+## Quick access
+
+```bash
+ssh root@10.0.10.48
+su - poteapp
+cd pote && source venv/bin/activate
+
+# Logs
+tail -f ~/logs/daily_report.log
+tail -f ~/logs/trades.log
+
+# Manual run
+python scripts/fetch_congressional_trades.py --days 30
+python scripts/send_daily_report.py --to idobkin@gmail.com --test-smtp
+```
+
+Deploy code from laptop (preserves server `.env`):
+
+```bash
+cd ~/Documents/code/ansible
+make deploy-pote
+# or: RUN_FETCH=1 make deploy-pote
+```
+
+---
+
+## Ansible / homelab inventory
+
+Already wired (ansible repo):
+
+- `inventories/production/hosts` — `pote` @ `.48`, VMID 236
+- `docs/guides/host-list.md` — LXC 236 row
+- `scripts/beszel-install-agents.sh` — `pote-236`
+- `scripts/deploy-pote.sh`, `make deploy-pote`
+- `scripts/vault-update-pote.py`, `make vault-update-pote`
+- `docs/guides/smtp-inventory.md` — POTE uses `alerts@levkine.ca`
+- Vault: `vault_pote_db_password_prod`, `vault_pote_smtp_password`
+
+```bash
+make vault-export-env
+make beszel-install-agents BESZEL_ONLY=pote-236   # if agent not yet installed
+```
+
+---
+
+## Verify after first automated day
+
+1. **07:00+** — Email in Gmail (From: `alerts@levkine.ca`, subject `POTE Daily Report - YYYY-MM-DD`). Check spam once.
+2. **Logs** — `~/logs/daily_report.log`, `trades.log` — no tracebacks.
+3. **DB growth** — trade count should tick up on weekdays:
+
+   ```bash
+   su - poteapp -c 'cd pote && source venv/bin/activate && python -c "
+   from sqlalchemy import func, select
+   from pote.db import SessionLocal
+   from pote.db.models import Trade, Official
+   with SessionLocal() as s:
+       print(\"trades\", s.scalar(select(func.count(Trade.id))))
+       print(\"officials\", s.scalar(select(func.count(Official.id))))
+   "'
+   ```
+
+---
+
+**Vikunja:** [todo.levkin.ca → Business → POTE](https://todo.levkin.ca) (`POTE`)
+
+## Open tasks (source of truth)
+
+| 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 |
+| **P3** | Mattermost / webhook alerts (email only today) | @agent | optional / low |
+| **P3** | Full history ingest (second data source beyond kadoa cap) | @agent | optional / low |
+
+**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.
+
+### Not planned (unless you want them)
+
+- Public URL / Caddy vhost (LAN-only by design)
+- Investment signals exposed as advice (research descriptors only)
+
+---
+
+## Known issues / caveats
+
+| Topic | Detail |
+|-------|--------|
+| **Disclosure lag** | STOCK Act filings appear weeks after trades; reports are descriptive, not timely trading signals. |
+| **Amount ranges** | Disclosure buckets only ($1k–$15k, etc.), not exact sizes. |
+| **Empty tickers** | Some filings skipped when ticker missing. |
+| **CI vs prod** | CI uses venv + Postgres service; prod uses host Postgres — both should pass after merge. |
+| **Gitea deploy workflow** | `.github/workflows/deploy.yml` still references `git pull` on Proxmox; prod uses **rsync** via ansible `deploy-pote.sh`. |
+
+---
+
+## Related docs
+
+| 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 |
+| Ansible `docs/guides/projects-handoff-2026-05-26.md` | Multi-project homelab context |
+| Ansible `docs/guides/smtp-inventory.md` | Mailboxes |
+
+---
+
+## One-line summary
+
+**POTE on 10.0.10.48 ingests public congressional trades daily, emails a research summary to Gmail at 07:00, and is maintained via `main` + `make deploy-pote` — verify tomorrow’s cron email, then Beszel, backups, and optional data-source expansion.**

docs/LOCAL_TEST_GUIDE.md

@@ -1,6 +1,6 @@
 # Local Testing Guide for POTE
 
-## ✅ Testing Locally Before Deployment
+## Testing Locally Before Deployment
 
 ### Quick Test - Run Full Suite
 
@@ -10,18 +10,18 @@ source venv/bin/activate
 pytest -v
 ```
 
-**Expected Result:** All 55 tests should pass ✅
+**Expected Result:** All 55 tests should pass 
 
 ---
 
-## 📊 Current Data Status
+## Current Data Status
 
-### Live Data Status: ❌ **NOT LIVE YET**
+### Live Data Status:  **NOT LIVE YET**
 
 **Why?**
-- 🔴 **House Stock Watcher API is DOWN** (domain issues, unreachable)
+- **House Stock Watcher API is DOWN** (domain issues, unreachable)
-- 🟢 **yfinance works** (for price data)
+- **yfinance works** (for price data)
-- 🟡 **Sample data available** (5 trades from fixtures)
+- **Sample data available** (5 trades from fixtures)
 
 ### What Data Do You Have?
 
@@ -40,7 +40,7 @@ This will show:
 
 ---
 
-## 🧪 Testing Analytics Locally
+## Testing Analytics Locally
 
 ### 1. Unit Tests (Fast, No External Dependencies)
 
@@ -53,10 +53,10 @@ pytest tests/test_analytics_integration.py -v
 ```
 
 These tests:
-- ✅ Create synthetic price data
+- Create synthetic price data
-- ✅ Simulate trades with known returns
+- Simulate trades with known returns
-- ✅ Verify calculations are correct
+- Verify calculations are correct
-- ✅ Test edge cases (missing data, sell trades, etc.)
+- Test edge cases (missing data, sell trades, etc.)
 
 ### 2. Manual Test with Local Database
 
@@ -92,27 +92,27 @@ calc = ReturnCalculator(session)
 
 ---
 
-## 📦 What Gets Tested?
+## What Gets Tested?
 
-### Core Functionality (All Working ✅)
+### Core Functionality (All Working )
 1. **Database Models** - Officials, Securities, Trades, Prices
 2. **Data Ingestion** - Trade loading, security enrichment
 3. **Analytics Engine** - Returns, benchmarks, metrics
 4. **Edge Cases** - Missing data, sell trades, disclosure lags
 
 ### Integration Tests Cover:
-- ✅ Return calculations over multiple time windows (30/60/90/180 days)
+- Return calculations over multiple time windows (30/60/90/180 days)
-- ✅ Benchmark comparisons (stock vs SPY/QQQ)
+- Benchmark comparisons (stock vs SPY/QQQ)
-- ✅ Abnormal return (alpha) calculations
+- Abnormal return (alpha) calculations
-- ✅ Official performance summaries
+- Official performance summaries
-- ✅ Sector analysis
+- Sector analysis
-- ✅ Disclosure timing analysis
+- Disclosure timing analysis
-- ✅ Top performer rankings
+- Top performer rankings
-- ✅ System-wide statistics
+- System-wide statistics
 
 ---
 
-## 🔄 Getting Live Data
+## Getting Live Data
 
 ### Option 1: Wait for House Stock Watcher API
 The API is currently down. Once it's back up:
@@ -164,7 +164,7 @@ export QUIVER_API_KEY="your_key_here"
 
 ---
 
-## 📈 After Adding Data, Fetch Prices
+## After Adding Data, Fetch Prices
 
 ```bash
 # This will fetch prices for all securities in your database
@@ -176,12 +176,12 @@ python scripts/enrich_securities.py
 
 ---
 
-## 🎯 Complete Local Test Workflow
+## Complete Local Test Workflow
 
 ```bash
 # 1. Run all tests
 pytest -v
-# ✅ All 55 tests should pass
+# All 55 tests should pass
 
 # 2. Check local database
 python -c "
@@ -210,7 +210,7 @@ python scripts/calculate_all_returns.py --window 90
 
 ---
 
-## 🚀 Deploy to Proxmox
+## Deploy to Proxmox
 
 Once local tests pass:
 
@@ -240,7 +240,7 @@ alembic upgrade head
 
 ---
 
-## 🐛 Common Issues
+## Common Issues
 
 ### "No price data found"
 **Fix:** Run `python scripts/fetch_sample_prices.py`
@@ -260,7 +260,7 @@ sudo -u postgres psql -c "\l"
 
 ---
 
-## 📊 Test Coverage
+## Test Coverage
 
 Run tests with coverage report:
 
@@ -277,19 +277,19 @@ firefox htmlcov/index.html  # View coverage report
 
 ---
 
-## ✨ Summary
+## Summary
 
 **Before Deploying:**
-1. ✅ Run `pytest -v` - all tests pass
+1. Run `pytest -v` - all tests pass
-2. ✅ Run `make lint` - no errors
+2. Run `make lint` - no errors
-3. ✅ Test locally with sample data
+3. Test locally with sample data
-4. ✅ Verify analytics work with synthetic prices
+4. Verify analytics work with synthetic prices
 
 **Getting Live Data:**
-- 🔴 House Stock Watcher API is down (external issue)
+- House Stock Watcher API is down (external issue)
-- 🟢 Manual CSV import works NOW
+- Manual CSV import works NOW
-- 🟢 yfinance for prices works NOW
+- yfinance for prices works NOW
-- 🟡 QuiverQuant available (requires free API key)
+- QuiverQuant available (requires free API key)
 
 **You can deploy and use the system NOW with:**
 - Manual data entry

docs/OFFLINE_DEMO.md

@@ -1,6 +1,6 @@
 # Offline Demo - Works Without Internet!
 
-## ✅ Full System Working Without Network Access
+## Full System Working Without Network Access
 
 Even though your environment doesn't have external internet, **everything works perfectly** using fixture files.
 
@@ -10,9 +10,9 @@ Even though your environment doesn't have external internet, **everything works
 python scripts/ingest_from_fixtures.py
 
 # Output:
-# ✓ Officials created/updated: 4
+# Officials created/updated: 4
-# ✓ Securities created/updated: 2
+# Securities created/updated: 2
-# ✓ Trades ingested: 5
+# Trades ingested: 5
 #
 # Database totals:
 #   Total officials: 4
@@ -33,7 +33,7 @@ python scripts/ingest_from_fixtures.py
    - NVDA, MSFT, AAPL, TSLA, GOOGL tickers
 
 2. **Offline Scripts**
-   - `scripts/ingest_from_fixtures.py` - Ingest sample trades (✅ works now!)
+ - `scripts/ingest_from_fixtures.py` - Ingest sample trades ( works now!)
    - `scripts/fetch_sample_prices.py` - Would need network (yfinance)
 
 3. **28 Passing Tests** - All use mocks, no network required
@@ -67,14 +67,14 @@ with SessionLocal() as session:
 
 ### What You Can Do Offline
 
-✅ **Run all tests**: `make test`  
+ **Run all tests**: `make test` 
-✅ **Ingest fixture data**: `python scripts/ingest_from_fixtures.py`  
+ **Ingest fixture data**: `python scripts/ingest_from_fixtures.py` 
-✅ **Query the database**: Use Python REPL or SQLite browser  
+ **Query the database**: Use Python REPL or SQLite browser 
-✅ **Lint & format**: `make lint format`  
+ **Lint & format**: `make lint format` 
-✅ **Run migrations**: `make migrate`  
+ **Run migrations**: `make migrate` 
-✅ **Build analytics** (Phase 2): All math/ML works offline!  
+ **Build analytics** (Phase 2): All math/ML works offline! 
 
-❌ **Can't do (needs network)**:  
+ **Can't do (needs network)**: 
 - Fetch live congressional trades from House Stock Watcher  
 - Fetch stock prices from yfinance  
 - (But you can add more fixture files to simulate this!)
@@ -108,9 +108,9 @@ You can expand the fixtures for offline development:
 ## Summary
 
 **The network error is not a problem!** The entire system is designed to work with:
-- ✅ Fixtures for development/testing
+- Fixtures for development/testing
-- ✅ Real APIs for production (when network available)
+- Real APIs for production (when network available)
-- ✅ Same code paths for both
+- Same code paths for both
 
-This is **by design** - makes development fast and tests reliable! 🚀
+This is **by design** - makes development fast and tests reliable! 
 

docs/PR4_PLAN.md

@@ -223,12 +223,12 @@ print(f"Win Rate: {pelosi_stats['win_rate']:.1%}")
 
 ## Success Criteria
 
-- ✅ Can calculate returns for any trade + window
+- Can calculate returns for any trade + window
-- ✅ Can compare to S&P 500 benchmark
+- Can compare to S&P 500 benchmark
-- ✅ Can generate official performance summaries
+- Can generate official performance summaries
-- ✅ All calculations tested and accurate
+- All calculations tested and accurate
-- ✅ Performance data stored efficiently
+- Performance data stored efficiently
-- ✅ Documentation complete
+- Documentation complete
 
 ## Timeline
 

docs/PROXMOX_QUICKSTART.md

@@ -1,4 +1,4 @@
-# Proxmox Quick Start ⚡
+# Proxmox Quick Start
 
 **Got Proxmox? Deploy POTE in 5 minutes!**
 
@@ -17,7 +17,7 @@ su - poteapp
 cd pote && source venv/bin/activate
 python scripts/ingest_from_fixtures.py
 
-# Done! ✅
+# Done!
 ```
 
 ---
@@ -81,8 +81,8 @@ source venv/bin/activate
 python scripts/ingest_from_fixtures.py
 
 # Should see:
-# ✓ Officials created: 4
+# Officials created: 4
-# ✓ Trades ingested: 5
+# Trades ingested: 5
 ```
 
 ### 5. Setup Cron Jobs
@@ -96,7 +96,7 @@ crontab -e
 15 6 * * * cd /home/poteapp/pote && /home/poteapp/pote/venv/bin/python scripts/enrich_securities.py >> /home/poteapp/logs/enrich.log 2>&1
 ```
 
-### 6. Done! 🎉
+### 6. Done!
 
 Your POTE instance is now running and will:
 - Fetch congressional trades daily at 6 AM
@@ -107,12 +107,12 @@ Your POTE instance is now running and will:
 
 ## What You Get
 
-✅ **Full PostgreSQL database**  
+ **Full PostgreSQL database** 
-✅ **Automated daily updates** (via cron)  
+ **Automated daily updates** (via cron) 
-✅ **Isolated environment** (LXC container)  
+ **Isolated environment** (LXC container) 
-✅ **Easy backups** (Proxmox snapshots)  
+ **Easy backups** (Proxmox snapshots) 
-✅ **Low resource usage** (~500MB RAM)  
+ **Low resource usage** (~500MB RAM) 
-✅ **Cost**: Just electricity (~$5-10/mo)
+ **Cost**: Just electricity (~$5-10/mo)
 
 ---
 
@@ -231,13 +231,13 @@ pip install -e .
 
 ## Next Steps
 
-1. ✅ Container running
+1. Container running
-2. ✅ POTE installed
+2. POTE installed
-3. ✅ Data ingested
+3. Data ingested
-4. ⏭️ Setup Proxmox backups (Web UI → Datacenter → Backup)
+4. ⏭ Setup Proxmox backups (Web UI → Datacenter → Backup)
-5. ⏭️ Configure static IP (if needed)
+5. ⏭ Configure static IP (if needed)
-6. ⏭️ Build Phase 2 analytics
+6. ⏭ Build Phase 2 analytics
-7. ⏭️ Add FastAPI dashboard
+7. ⏭ Add FastAPI dashboard
 
 ---
 
@@ -264,10 +264,10 @@ pct restart 100
 
 ---
 
-**Your Proxmox = Enterprise infrastructure at hobby prices!** 🚀
+**Your Proxmox = Enterprise infrastructure at hobby prices!** 
 
 Cost breakdown:
 - Cloud VPS: $20/mo
 - Your Proxmox: ~$10/mo (power)
-- **Savings: $120/year** ✨
+- **Savings: $120/year** 
 

docs/QUICKSTART.md

@@ -1,15 +1,15 @@
 # POTE Quick Start Guide
 
-## 🚀 Your System is Ready!
+## Your System is Ready!
 
 **Container IP**: Check with `ip addr show eth0 | grep "inet"`  
 **Database**: PostgreSQL on port 5432  
 **Username**: `poteuser`  
-**Password**: `changeme123` (⚠️ change in production!)
+**Password**: `changeme123` ( change in production!)
 
 ---
 
-## 📊 How to Use POTE
+## How to Use POTE
 
 ### Option 1: Command Line (SSH into container)
 
@@ -51,7 +51,7 @@ with engine.connect() as conn:
 
 ---
 
-## 🎯 Common Tasks
+## Common Tasks
 
 ### 1. Check System Status
 
@@ -145,7 +145,7 @@ ORDER BY trade_count DESC;
 
 ---
 
-## 📈 Example Workflows
+## Example Workflows
 
 ### Workflow 1: Daily Update
 
@@ -237,7 +237,7 @@ print(f"Exported {len(df)} trades to trades_export.csv")
 
 ---
 
-## 🔧 Maintenance
+## Maintenance
 
 ### Update POTE Code
 
@@ -287,7 +287,7 @@ nano ~/pote/.env
 
 ---
 
-## 🌐 Access Methods Summary
+## Access Methods Summary
 
 | Method | From Where | Command |
 |--------|-----------|---------|
@@ -298,22 +298,22 @@ nano ~/pote/.env
 
 ---
 
-## 📚 What Data Do You Have?
+## What Data Do You Have?
 
 Right now (Phase 1 complete):
-- ✅ **Congressional trading data** (from House Stock Watcher)
+- **Congressional trading data** (from House Stock Watcher)
-- ✅ **Security information** (tickers, names, sectors)
+- **Security information** (tickers, names, sectors)
-- ✅ **Historical prices** (OHLCV data from yfinance)
+- **Historical prices** (OHLCV data from yfinance)
-- ✅ **Official profiles** (name, party, chamber, state)
+- **Official profiles** (name, party, chamber, state)
 
 Coming next (Phase 2):
-- 📊 **Abnormal return calculations**
+- **Abnormal return calculations**
-- 🤖 **Behavioral clustering**
+- **Behavioral clustering**
-- 🚨 **Research signals** (follow_research, avoid_risk, watch)
+- **Research signals** (follow_research, avoid_risk, watch)
 
 ---
 
-## 🎓 Learning SQL for POTE
+## Learning SQL for POTE
 
 ### Count Records
 ```sql
@@ -349,7 +349,7 @@ GROUP BY o.party;
 
 ---
 
-## ❓ Troubleshooting
+## Troubleshooting
 
 ### Can't connect remotely?
 ```bash
@@ -380,12 +380,12 @@ pip install -e .
 
 ---
 
-## 🚀 Next Steps
+## Next Steps
 
 1. **Populate with real data**: Run `fetch_congressional_trades.py` regularly
 2. **Set up cron job** for automatic daily updates
 3. **Build analytics** (Phase 2) - abnormal returns, signals
 4. **Create dashboard** (Phase 3) - web interface for exploration
 
-Ready to build Phase 2 analytics? Just ask! 📈
+Ready to build Phase 2 analytics? Just ask! 
 

docs/QUICK_SETUP_CARD.md

@@ -1,15 +1,15 @@
-# 🚀 POTE Quick Setup Card
+# POTE Quick Setup Card
 
-## 📍 Your Configuration
+## Your Configuration
 
 **Email Server:** `mail.levkin.ca`  
 **Email Account:** `test@levkin.ca`  
 **Database:** PostgreSQL (configured)  
-**Status:** ✅ Ready for deployment
+**Status:** Ready for deployment
 
 ---
 
-## ⚡ 3-Step Setup
+## 3-Step Setup
 
 ### Step 1: Add Your Password (30 seconds)
 
@@ -27,7 +27,7 @@ source venv/bin/activate
 python scripts/send_daily_report.py --to test@levkin.ca --test-smtp
 ```
 
-✅ **Check test@levkin.ca inbox** - you should receive a test email!
+ **Check test@levkin.ca inbox** - you should receive a test email!
 
 ### Step 3: Automate (2 minutes)
 
@@ -37,13 +37,13 @@ python scripts/send_daily_report.py --to test@levkin.ca --test-smtp
 # Choose time: 6 AM (recommended)
 ```
 
-**Done!** 🎉 You'll now receive:
+**Done!** You'll now receive:
 - Daily reports at 6 AM
 - Weekly reports on Sundays
 
 ---
 
-## 📦 Deployment to Proxmox (5 minutes)
+## Deployment to Proxmox (5 minutes)
 
 ### On Proxmox Host:
 
@@ -79,7 +79,7 @@ bash scripts/proxmox_setup.sh
 
 ---
 
-## 🔍 Quick Commands
+## Quick Commands
 
 ### On Deployed Server (SSH)
 
@@ -105,7 +105,7 @@ ls -lh ~/logs/*.txt
 
 ---
 
-## 📧 Email Configuration (.env)
+## Email Configuration (.env)
 
 ```env
 SMTP_HOST=mail.levkin.ca
@@ -123,27 +123,27 @@ REPORT_RECIPIENTS=test@levkin.ca,user2@example.com,user3@example.com
 
 ---
 
-## 📊 What You'll Receive
+## What You'll Receive
 
 ### Daily Report (6 AM)
 ```
-✅ New congressional trades
+ New congressional trades
-✅ Market alerts (unusual activity)
+ Market alerts (unusual activity)
-✅ Suspicious timing detections
+ Suspicious timing detections
-✅ Summary statistics
+ Summary statistics
 ```
 
 ### Weekly Report (Sunday 8 AM)
 ```
-✅ Most active officials
+ Most active officials
-✅ Most traded securities
+ Most traded securities
-✅ Repeat offenders
+ Repeat offenders
-✅ Pattern analysis
+ Pattern analysis
 ```
 
 ---
 
-## 🔧 Troubleshooting
+## Troubleshooting
 
 ### Email Not Working?
 
@@ -191,19 +191,19 @@ tail -50 ~/logs/daily_run.log
 
 ---
 
-## 📚 Documentation
+## Documentation
 
 | Document | Purpose |
 |----------|---------|
-| **[EMAIL_SETUP.md](EMAIL_SETUP.md)** | ⭐ Your levkin.ca setup guide |
+| **[EMAIL_SETUP.md](EMAIL_SETUP.md)** | Your levkin.ca setup guide |
-| **[DEPLOYMENT_AND_AUTOMATION.md](DEPLOYMENT_AND_AUTOMATION.md)** | ⭐ Answers all questions |
+| **[DEPLOYMENT_AND_AUTOMATION.md](DEPLOYMENT_AND_AUTOMATION.md)** | Answers all questions |
 | **[AUTOMATION_QUICKSTART.md](AUTOMATION_QUICKSTART.md)** | Quick automation guide |
 | **[PROXMOX_QUICKSTART.md](PROXMOX_QUICKSTART.md)** | Proxmox deployment |
 | **[QUICKSTART.md](QUICKSTART.md)** | Usage guide |
 
 ---
 
-## ✅ Checklist
+## Checklist
 
 **Local Development:**
 - [ ] `.env` file created with password
@@ -227,18 +227,18 @@ tail -50 ~/logs/daily_run.log
 
 ---
 
-## 🎯 Your Current Status
+## Your Current Status
 
-✅ **Code:** Complete (93 tests passing)  
+ **Code:** Complete (93 tests passing) 
-✅ **Monitoring:** 3-phase system operational  
+ **Monitoring:** 3-phase system operational 
-✅ **CI/CD:** Pipeline ready (.github/workflows/ci.yml)  
+ **CI/CD:** Pipeline ready (.github/workflows/ci.yml) 
-✅ **Email:** Configured for test@levkin.ca  
+ **Email:** Configured for test@levkin.ca 
 ⏳ **Deployment:** Ready to deploy to Proxmox  
 ⏳ **Automation:** Ready to set up with `setup_cron.sh`
 
 ---
 
-## 🚀 Next Action
+## Next Action
 
 **Right now (local testing):**
 ```bash
@@ -255,9 +255,9 @@ cd ~/pote
 ./scripts/setup_cron.sh
 ```
 
-**That's it! 🎉**
+**That's it! **
 
 ---
 
-**Everything is ready - just add your password and test!** 📧
+**Everything is ready - just add your password and test!** 
 

docs/STATUS.md

@@ -3,25 +3,25 @@
 **Last Updated**: 2025-12-14  
 **Version**: Phase 1 Complete (PR1 + PR2)
 
-## 🎉 What's Working Now
+## What's Working Now
 
 ### Data Ingestion (FREE!)
-✅ **Congressional Trades**: Live ingestion from House Stock Watcher  
+ **Congressional Trades**: Live ingestion from House Stock Watcher 
-✅ **Stock Prices**: Daily OHLCV from yfinance  
+ **Stock Prices**: Daily OHLCV from yfinance 
-✅ **Officials**: Auto-populated from trade disclosures  
+ **Officials**: Auto-populated from trade disclosures 
-✅ **Securities**: Auto-created, ready for enrichment  
+ **Securities**: Auto-created, ready for enrichment 
 
 ### Database
-✅ **Schema**: Normalized (officials, securities, trades, prices, metrics stubs)  
+ **Schema**: Normalized (officials, securities, trades, prices, metrics stubs) 
-✅ **Migrations**: Alembic configured and applied  
+ **Migrations**: Alembic configured and applied 
-✅ **DB**: SQLite for dev, PostgreSQL-ready  
+ **DB**: SQLite for dev, PostgreSQL-ready 
 
 ### Code Quality
-✅ **Tests**: 28 passing (86% coverage)  
+ **Tests**: 28 passing (86% coverage) 
-✅ **Linting**: ruff + mypy all green  
+ **Linting**: ruff + mypy all green 
-✅ **Format**: black applied consistently  
+ **Format**: black applied consistently 
 
-## 📊 Current Stats
+## Current Stats
 
 ```bash
 # Test Suite
@@ -43,7 +43,7 @@ All free/open-source:
 - pytest (testing)
 ```
 
-## 🚀 Quick Commands
+## Quick Commands
 
 ### Fetch Live Data (FREE!)
 ```bash
@@ -75,7 +75,7 @@ make format    # Format with black
 make migrate   # Run Alembic migrations
 ```
 
-## 🏠 Deployment
+## Deployment
 
 **Your Proxmox?** Perfect! See [`docs/08_proxmox_deployment.md`](docs/08_proxmox_deployment.md) for:
 - LXC container setup (lightweight, recommended)
@@ -90,7 +90,7 @@ Other options in [`docs/07_deployment.md`](docs/07_deployment.md):
 - Railway/Fly.io - $5-15/mo
 - AWS/GCP - $20-50/mo
 
-## 📂 Project Structure
+## Project Structure
 
 ```
 pote/
@@ -138,7 +138,7 @@ pote/
     └── fetch_sample_prices.py         # Live price fetch
 ```
 
-## 💰 Cost Breakdown
+## Cost Breakdown
 
 | Component | Cost | Notes |
 |-----------|------|-------|
@@ -154,7 +154,7 @@ Optional paid upgrades (NOT needed):
 - Financial Modeling Prep: $15/mo (250 calls/day free tier available)
 - PostgreSQL hosting: $7+/mo (only if deploying)
 
-## ✅ Completed PRs
+## Completed PRs
 
 ### PR1: Project Scaffold + Price Loader
 - [x] Project structure (`src/`, `tests/`, docs)
@@ -176,7 +176,7 @@ Optional paid upgrades (NOT needed):
 
 **See**: [`docs/PR2_SUMMARY.md`](docs/PR2_SUMMARY.md)
 
-## 📋 Next Steps (Phase 2 - Analytics)
+## Next Steps (Phase 2 - Analytics)
 
 ### PR3: Security Enrichment
 - [ ] Enrich securities table with yfinance (names, sectors, exchanges)
@@ -206,20 +206,20 @@ Optional paid upgrades (NOT needed):
 
 **See**: [`docs/00_mvp.md`](docs/00_mvp.md) for full roadmap
 
-## 🔬 Research-Only Reminder
+## Research-Only Reminder
 
 **This tool is for private research and transparency analysis only.**
 
-- ❌ Not investment advice
+- Not investment advice
-- ❌ Not a trading system
+- Not a trading system
-- ❌ No claims about inside information
+- No claims about inside information
-- ✅ Public data only
+- Public data only
-- ✅ Descriptive analytics
+- Descriptive analytics
-- ✅ Research transparency
+- Research transparency
 
 See [`docs/04_safety_ethics.md`](docs/04_safety_ethics.md) for guardrails.
 
-## 🤝 Contributing
+## Contributing
 
 This is a personal research project, but if you want to use it:
 
@@ -229,7 +229,7 @@ This is a personal research project, but if you want to use it:
 4. `python scripts/fetch_congressional_trades.py --days 7`
 5. Start exploring!
 
-## 📄 License
+## License
 
 MIT License (for research/educational use only)
 

docs/TESTING_STATUS.md

@@ -1,12 +1,12 @@
 # POTE Testing Status Report
 **Date:** December 15, 2025  
-**Status:** ✅ All Systems Operational - Ready for Deployment
+**Status:** All Systems Operational - Ready for Deployment
 
 ---
 
-## 🎯 Test Suite Summary
+## Test Suite Summary
 
-### **55 Tests - All Passing ✅**
+### **55 Tests - All Passing **
 
 ```
 Platform: Python 3.13.5, pytest-9.0.2
@@ -18,17 +18,17 @@ Coverage: ~85% overall
 
 | Module | Tests | Status | Coverage |
 |--------|-------|--------|----------|
-| **Analytics** | 18 tests | ✅ PASS | 80% |
+| **Analytics** | 18 tests | PASS | 80% |
-| **Models** | 7 tests | ✅ PASS | 90% |
+| **Models** | 7 tests | PASS | 90% |
-| **Ingestion** | 14 tests | ✅ PASS | 85% |
+| **Ingestion** | 14 tests | PASS | 85% |
-| **Price Loader** | 8 tests | ✅ PASS | 90% |
+| **Price Loader** | 8 tests | PASS | 90% |
-| **Security Enricher** | 8 tests | ✅ PASS | 85% |
+| **Security Enricher** | 8 tests | PASS | 85% |
 
 ---
 
-## 📊 What's Been Tested?
+## What's Been Tested?
 
-### ✅ Core Database Operations
+### Core Database Operations
 - [x] Creating and querying Officials
 - [x] Creating and querying Securities  
 - [x] Creating and querying Trades
@@ -36,7 +36,7 @@ Coverage: ~85% overall
 - [x] Unique constraints and relationships
 - [x] Database migrations (Alembic)
 
-### ✅ Data Ingestion
+### Data Ingestion
 - [x] House Stock Watcher client (with fixtures)
 - [x] Trade loading from JSON
 - [x] Security enrichment from yfinance
@@ -44,7 +44,7 @@ Coverage: ~85% overall
 - [x] Idempotent operations (no duplicates)
 - [x] Error handling for missing/invalid data
 
-### ✅ Analytics Engine
+### Analytics Engine
 - [x] Return calculations (buy trades)
 - [x] Return calculations (sell trades)
 - [x] Multiple time windows (30/60/90/180 days)
@@ -56,7 +56,7 @@ Coverage: ~85% overall
 - [x] Top performer rankings
 - [x] System-wide statistics
 
-### ✅ Edge Cases
+### Edge Cases
 - [x] Missing price data handling
 - [x] Trades with no exit price yet
 - [x] Sell trades (inverted returns)
@@ -67,7 +67,7 @@ Coverage: ~85% overall
 
 ---
 
-## 🧪 Test Types
+## Test Types
 
 ### 1. Unit Tests (Fast, Isolated)
 **Location:** `tests/test_*.py` (excluding integration)  
@@ -100,7 +100,7 @@ Coverage: ~85% overall
 
 ---
 
-## 🔧 How to Run Tests Locally
+## How to Run Tests Locally
 
 ### Quick Test
 ```bash
@@ -136,7 +136,7 @@ ptw
 
 ---
 
-## 🚨 Known Limitations
+## Known Limitations
 
 ### 1. External API Dependency
 **Issue:** House Stock Watcher API is currently DOWN  
@@ -161,7 +161,7 @@ ptw
 
 ---
 
-## 📈 Performance Benchmarks
+## Performance Benchmarks
 
 ### Test Execution Time
 - **Full suite:** 1.8 seconds
@@ -178,7 +178,7 @@ ptw
 
 ---
 
-## 🎯 Pre-Deployment Checklist
+## Pre-Deployment Checklist
 
 ### Before Deploying to Proxmox:
 
@@ -215,7 +215,7 @@ python scripts/enrich_securities.py
 
 ---
 
-## 🔄 Continuous Testing
+## Continuous Testing
 
 ### Git Pre-Commit Hook (Optional)
 ```bash
@@ -247,7 +247,7 @@ jobs:
 
 ---
 
-## 📝 Test Maintenance
+## Test Maintenance
 
 ### Adding New Tests
 
@@ -281,36 +281,36 @@ Fixtures are in `tests/conftest.py`:
 
 ---
 
-## 🎉 Summary
+## Summary
 
-### Current Status: **PRODUCTION READY** ✅
+### Current Status: **PRODUCTION READY**
 
 **What Works:**
-- ✅ All 55 tests passing
+- All 55 tests passing
-- ✅ Full analytics pipeline functional
+- Full analytics pipeline functional
-- ✅ Database operations solid
+- Database operations solid
-- ✅ Data ingestion from multiple sources
+- Data ingestion from multiple sources
-- ✅ Price fetching from yfinance
+- Price fetching from yfinance
-- ✅ Security enrichment
+- Security enrichment
-- ✅ Return calculations
+- Return calculations
-- ✅ Benchmark comparisons
+- Benchmark comparisons
-- ✅ Performance metrics
+- Performance metrics
-- ✅ CLI scripts operational
+- CLI scripts operational
 
 **What's Missing:**
-- ❌ Live congressional trade API (external issue - House Stock Watcher down)
+- Live congressional trade API (external issue - House Stock Watcher down)
   - **Workaround:** Manual import, CSV, or alternative APIs available
 
 **Next Steps:**
-1. ✅ Tests are complete
+1. Tests are complete
-2. ✅ Code is ready
+2. Code is ready
-3. ➡️ **Deploy to Proxmox** (or continue with Phase 2 features)
+3. **Deploy to Proxmox** (or continue with Phase 2 features)
-4. ➡️ Add more data sources
+4. Add more data sources
-5. ➡️ Build dashboard (Phase 3)
+5. Build dashboard (Phase 3)
 
 ---
 
-## 📞 Need Help?
+## Need Help?
 
 See:
 - `LOCAL_TEST_GUIDE.md` - Detailed local testing instructions

docs/WATCHLIST_GUIDE.md

@@ -1,6 +1,6 @@
 # POTE Watchlist & Trading Reports
 
-## 🎯 Get Trading Reports 1 Hour Before Market Close
+## Get Trading Reports 1 Hour Before Market Close
 
 ### Quick Setup
 
@@ -25,7 +25,7 @@ crontab -e
 
 ---
 
-## 📋 Watchlist System
+## Watchlist System
 
 ### Who's on the Default Watchlist?
 
@@ -48,7 +48,7 @@ crontab -e
 
 ---
 
-## 🔧 Managing Your Watchlist
+## Managing Your Watchlist
 
 ### View Current Watchlist
 
@@ -104,7 +104,7 @@ This fetches all 535 members of Congress (100 Senate + 435 House).
 
 ---
 
-## 📊 Generating Reports
+## Generating Reports
 
 ### Manual Report Generation
 
@@ -135,24 +135,24 @@ python scripts/generate_trading_report.py --format json --output report.json
 ================================================================================
 
 ────────────────────────────────────────────────────────────────────────────────
-👤 Nancy Pelosi (D-CA, House)
+ Nancy Pelosi (D-CA, House)
 ────────────────────────────────────────────────────────────────────────────────
 Side      Ticker  Company                     Sector        Value              Trade Date  Filed
 --------  ------  --------------------------  ----------  -------------------  ----------  ----------
-🟢 BUY     NVDA    NVIDIA Corporation          Technology  $15,001 - $50,000   2024-11-15  2024-12-01
+ BUY NVDA NVIDIA Corporation Technology $15,001 - $50,000 2024-11-15 2024-12-01
-🔴 SELL    MSFT    Microsoft Corporation       Technology  $50,001 - $100,000  2024-11-20  2024-12-01
+ SELL MSFT Microsoft Corporation Technology $50,001 - $100,000 2024-11-20 2024-12-01
 
 ────────────────────────────────────────────────────────────────────────────────
-👤 Tommy Tuberville (R-AL, Senate)
+ Tommy Tuberville (R-AL, Senate)
 ────────────────────────────────────────────────────────────────────────────────
 Side      Ticker  Company                     Sector        Value              Trade Date  Filed
 --------  ------  --------------------------  ----------  -------------------  ----------  ----------
-🟢 BUY     SPY     SPDR S&P 500 ETF            Financial   $100,001 - $250,000 2024-11-18  2024-12-02
+ BUY SPY SPDR S&P 500 ETF Financial $100,001 - $250,000 2024-11-18 2024-12-02
-🟢 BUY     AAPL    Apple Inc.                  Technology  $50,001 - $100,000  2024-11-22  2024-12-02
+ BUY AAPL Apple Inc. Technology $50,001 - $100,000 2024-11-22 2024-12-02
-🔴 SELL    TSLA    Tesla, Inc.                 Automotive  $15,001 - $50,000   2024-11-25  2024-12-02
+ SELL TSLA Tesla, Inc. Automotive $15,001 - $50,000 2024-11-25 2024-12-02
 
 ================================================================================
-📊 SUMMARY
+ SUMMARY
 ================================================================================
 
 Total Trades:    5
@@ -223,7 +223,7 @@ Runs at 8 AM and 3 PM daily (weekdays).
 
 ---
 
-## 📧 Email Reports (Optional)
+## Email Reports (Optional)
 
 ### Setup Email Notifications
 
@@ -256,7 +256,7 @@ python scripts/send_email.py /tmp/report.html your-email@example.com
 
 ---
 
-## 🎯 Typical Workflow
+## Typical Workflow
 
 ### Daily Routine (3 PM ET)
 
@@ -282,7 +282,7 @@ python scripts/send_email.py /tmp/report.html your-email@example.com
 
 ---
 
-## 🔍 Finding More Officials
+## Finding More Officials
 
 ### Public Resources
 
@@ -318,7 +318,7 @@ Add committee members to your watchlist.
 
 ---
 
-## 📈 Example Cron Setup
+## Example Cron Setup
 
 ```bash
 # Edit crontab
@@ -341,7 +341,7 @@ This gives you:
 
 ---
 
-## 🚀 Quick Start Summary
+## Quick Start Summary
 
 ```bash
 # 1. Create watchlist
@@ -363,7 +363,7 @@ cat reports/trading_report_$(date +%Y%m%d).txt
 
 ---
 
-## ❓ FAQ
+## FAQ
 
 **Q: Why are all the trades old (30-45 days)?**  
 **A:** Federal law (STOCK Act) gives Congress 30-45 days to file. This is normal.

docs/archive/MONITORING_SYSTEM_COMPLETE.md

@@ -1,8 +1,8 @@
-# 🎉 POTE Monitoring System - ALL PHASES COMPLETE!
+# POTE Monitoring System - ALL PHASES COMPLETE!
 
-## ✅ **What Was Built (3 Phases)**
+## **What Was Built (3 Phases)**
 
-### **Phase 1: Real-Time Market Monitoring** ✅
+### **Phase 1: Real-Time Market Monitoring**
 **Detects unusual market activity in congressional tickers**
 
 **Features:**
@@ -20,11 +20,11 @@
 - `MarketAlert` model - Database storage
 - `monitor_market.py` - CLI tool
 
-**Tests:** 14 passing ✅
+**Tests:** 14 passing 
 
 ---
 
-### **Phase 2: Disclosure Timing Correlation** ✅
+### **Phase 2: Disclosure Timing Correlation**
 **Matches trades to prior market alerts when disclosures appear**
 
 **Features:**
@@ -50,11 +50,11 @@
 - `DisclosureCorrelator` - Correlation engine
 - `analyze_disclosure_timing.py` - CLI tool
 
-**Tests:** 13 passing ✅
+**Tests:** 13 passing 
 
 ---
 
-### **Phase 3: Pattern Detection & Rankings** ✅
+### **Phase 3: Pattern Detection & Rankings**
 **Cross-official analysis and comparative rankings**
 
 **Features:**
@@ -71,19 +71,19 @@
 - `PatternDetector` - Pattern analysis engine
 - `generate_pattern_report.py` - CLI tool
 
-**Tests:** 11 passing ✅
+**Tests:** 11 passing 
 
 ---
 
-## 📊 **Complete System Architecture**
+## **Complete System Architecture**
 
 ```
 ┌─────────────────────────────────────────────────────────────┐
 │  PHASE 1: Real-Time Monitoring                              │
 │  ────────────────────────────────────                       │
-│  🔔 Monitor congressional tickers                            │
+│ Monitor congressional tickers │
-│  📊 Detect unusual activity                                  │
+│ Detect unusual activity │
-│  💾 Log alerts to database                                   │
+│ Log alerts to database │
 └─────────────────────────────────────────────────────────────┘
                             ↓
                      [30-45 days pass]
@@ -91,25 +91,25 @@
 ┌─────────────────────────────────────────────────────────────┐
 │  PHASE 2: Disclosure Correlation                            │
 │  ───────────────────────────────                           │
-│  📋 New congressional trades filed                          │
+│ New congressional trades filed │
-│  🔗 Match to prior alerts                                   │
+│ Match to prior alerts │
-│  📈 Calculate timing scores                                  │
+│ Calculate timing scores │
-│  🚩 Flag suspicious trades                                   │
+│ Flag suspicious trades │
 └─────────────────────────────────────────────────────────────┘
                             ↓
 ┌─────────────────────────────────────────────────────────────┐
 │  PHASE 3: Pattern Detection                                 │
 │  ──────────────────────────                                │
-│  📊 Rank officials by timing                                │
+│ Rank officials by timing │
-│  🔥 Identify repeat offenders                               │
+│ Identify repeat offenders │
-│  📈 Compare parties, sectors, tickers                        │
+│ Compare parties, sectors, tickers │
-│  📋 Generate comprehensive reports                           │
+│ Generate comprehensive reports │
 └─────────────────────────────────────────────────────────────┘
 ```
 
 ---
 
-## 🚀 **Usage Guide**
+## **Usage Guide**
 
 ### **1. Set Up Monitoring (Run Daily)**
 
@@ -169,7 +169,7 @@ python scripts/generate_pattern_report.py --days 365 --format json --output patt
 
 ---
 
-## 📋 **Example Reports**
+## **Example Reports**
 
 ### **Timing Analysis Report**
 
@@ -179,7 +179,7 @@ python scripts/generate_pattern_report.py --days 365 --format json --output patt
   3 Trades with Timing Advantages Detected
 ================================================================================
 
-🚨 #1 - HIGHLY SUSPICIOUS (Timing Score: 85/100)
+ #1 - HIGHLY SUSPICIOUS (Timing Score: 85/100)
 ────────────────────────────────────────────────────────────────────────────────
 Official:    Nancy Pelosi
 Ticker:      NVDA
@@ -187,16 +187,16 @@ Side:        BUY
 Trade Date:  2024-01-15
 Value:       $15,001-$50,000
 
-📊 Timing Analysis:
+ Timing Analysis:
    Prior Alerts:        3
    Recent Alerts (7d):  2
    High Severity:       2
    Avg Severity:        7.5/10
 
-💡 Assessment: Trade occurred after 3 alerts, including 2 high-severity.
+ Assessment: Trade occurred after 3 alerts, including 2 high-severity.
                High likelihood of timing advantage.
 
-🔔 Prior Market Alerts:
+ Prior Market Alerts:
 Timestamp            Type             Severity  Timing
 2024-01-12 10:30:00  Unusual Volume   8/10      3 days before
 2024-01-13 14:15:00  Price Spike      7/10      2 days before
@@ -211,27 +211,27 @@ Timestamp            Type             Severity  Timing
   Period: 365 days
 ================================================================================
 
-📊 SUMMARY
+ SUMMARY
 ────────────────────────────────────────────────────────────────────────────────
 Officials Analyzed:        45
 Repeat Offenders:          8
 Average Timing Score:      42.3/100
 
-🚨 TOP 10 MOST SUSPICIOUS OFFICIALS (By Timing Score)
+ TOP 10 MOST SUSPICIOUS OFFICIALS (By Timing Score)
 ================================================================================
 
 Rank  Official                 Party-State  Chamber  Trades  Suspicious  Rate    Avg Score
 ────  ───────────────────────  ───────────  ───────  ──────  ──────────  ──────  ─────────
-🚨 1   Tommy Tuberville         R-AL         Senate   47      35/47       74.5%   72.5/100
+ 1 Tommy Tuberville R-AL Senate 47 35/47 74.5% 72.5/100
-🚨 2   Nancy Pelosi             D-CA         House    38      28/38       73.7%   71.2/100
+ 2 Nancy Pelosi D-CA House 38 28/38 73.7% 71.2/100
-🔴 3   Dan Crenshaw             R-TX         House    25      15/25       60.0%   65.8/100
+ 3 Dan Crenshaw R-TX House 25 15/25 60.0% 65.8/100
-🔴 4   Marjorie Taylor Greene   R-GA         House    19      11/19       57.9%   63.2/100
+ 4 Marjorie Taylor Greene R-GA House 19 11/19 57.9% 63.2/100
-🟡 5   Josh Gottheimer          D-NJ         House    31      14/31       45.2%   58.7/100
+ 5 Josh Gottheimer D-NJ House 31 14/31 45.2% 58.7/100
 
-🔥 REPEAT OFFENDERS (50%+ Suspicious Trades)
+ REPEAT OFFENDERS (50%+ Suspicious Trades)
 ================================================================================
 
-🚨 Tommy Tuberville (R-AL, Senate)
+ Tommy Tuberville (R-AL, Senate)
    Trades: 47 | Suspicious: 35 (74.5%)
    Avg Timing Score: 72.5/100
    Pattern: HIGHLY SUSPICIOUS - Majority of trades show timing advantage
@@ -239,9 +239,9 @@ Rank  Official                 Party-State  Chamber  Trades  Suspicious  Rate
 
 ---
 
-## 📈 **Test Coverage**
+## **Test Coverage**
 
-**Total: 93 tests, all passing ✅**
+**Total: 93 tests, all passing **
 
 - **Phase 1 (Monitoring):** 14 tests
 - **Phase 2 (Correlation):** 13 tests
@@ -252,7 +252,7 @@ Rank  Official                 Party-State  Chamber  Trades  Suspicious  Rate
 
 ---
 
-## 🎯 **Key Insights the System Provides**
+## **Key Insights the System Provides**
 
 ### **1. Individual Official Analysis**
 - Which officials consistently trade before unusual activity?
@@ -282,7 +282,7 @@ Rank  Official                 Party-State  Chamber  Trades  Suspicious  Rate
 
 ---
 
-## 🔧 **Automated Workflow**
+## **Automated Workflow**
 
 ### **Daily Routine (Recommended)**
 
@@ -299,7 +299,7 @@ Rank  Official                 Party-State  Chamber  Trades  Suspicious  Rate
 
 ---
 
-## 📊 **Database Schema**
+## **Database Schema**
 
 **New Table: `market_alerts`**
 ```sql
@@ -315,7 +315,7 @@ Rank  Official                 Party-State  Chamber  Trades  Suspicious  Rate
 
 ---
 
-## 🎓 **Interpretation Guide**
+## **Interpretation Guide**
 
 ### **Timing Scores**
 - **80-100:** Highly suspicious - Multiple high-severity alerts before trade
@@ -337,15 +337,15 @@ Rank  Official                 Party-State  Chamber  Trades  Suspicious  Rate
 
 ---
 
-## ⚠️ **Important Disclaimers**
+## **Important Disclaimers**
 
 ### **Legal & Ethical**
-1. ✅ All data is public and legally obtained
+1. All data is public and legally obtained
-2. ✅ Analysis is retrospective (30-45 day lag)
+2. Analysis is retrospective (30-45 day lag)
-3. ✅ For research and transparency only
+3. For research and transparency only
-4. ❌ NOT investment advice
+4. NOT investment advice
-5. ❌ NOT proof of illegal activity (requires investigation)
+5. NOT proof of illegal activity (requires investigation)
-6. ❌ Statistical patterns ≠ legal evidence
+6. Statistical patterns ≠ legal evidence
 
 ### **Technical Limitations**
 1. Cannot identify WHO is trading in real-time
@@ -356,7 +356,7 @@ Rank  Official                 Party-State  Chamber  Trades  Suspicious  Rate
 
 ---
 
-## 🚀 **Deployment Checklist**
+## **Deployment Checklist**
 
 ### **On Proxmox Container**
 
@@ -383,7 +383,7 @@ python scripts/generate_pattern_report.py --days 365
 
 ---
 
-## 📚 **Documentation**
+## **Documentation**
 
 - **`docs/11_live_market_monitoring.md`** - Deep dive into monitoring
 - **`LOCAL_TEST_GUIDE.md`** - Testing instructions
@@ -392,23 +392,23 @@ python scripts/generate_pattern_report.py --days 365
 
 ---
 
-## 🎉 **Achievement Unlocked!**
+## **Achievement Unlocked!**
 
 **You now have a complete system that:**
 
-✅ Monitors real-time market activity  
+ Monitors real-time market activity 
-✅ Correlates trades to prior alerts  
+ Correlates trades to prior alerts 
-✅ Calculates timing advantage scores  
+ Calculates timing advantage scores 
-✅ Identifies repeat offenders  
+ Identifies repeat offenders 
-✅ Ranks officials by suspicion  
+ Ranks officials by suspicion 
-✅ Generates comprehensive reports  
+ Generates comprehensive reports 
-✅ 93 tests confirming it works  
+ 93 tests confirming it works 
 
-**This is a production-ready transparency and research tool!** 🚀
+**This is a production-ready transparency and research tool!** 
 
 ---
 
-## 🔜 **Potential Future Enhancements**
+## **Potential Future Enhancements**
 
 ### **Phase 4 Ideas (Optional)**
 - Email/SMS alerts for high-severity patterns
@@ -420,6 +420,6 @@ python scripts/generate_pattern_report.py --days 365
 - Automated PDF reports
 - Historical performance tracking
 
-**But the core system is COMPLETE and FUNCTIONAL now!** ✅
+**But the core system is COMPLETE and FUNCTIONAL now!** 
 
 

docs/archive/PR1_SUMMARY.md

@@ -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)

docs/archive/PR2_SUMMARY.md

@@ -1,6 +1,6 @@
 # PR2 Summary: Congressional Trade Ingestion
 
-**Status**: ✅ Complete  
+**Status**: Complete 
 **Date**: 2025-12-14
 
 ## What was built
@@ -26,7 +26,7 @@
 - `tests/fixtures/sample_house_watcher.json`: 5 realistic sample transactions
 - Includes House + Senate, Democrats + Republicans, various tickers
 
-### 4. Tests (13 new tests, all passing ✅)
+### 4. Tests (13 new tests, all passing )
 **`tests/test_house_watcher.py` (8 tests)**:
 - Amount range parsing (with range, single value, invalid)
 - Transaction type normalization
@@ -58,9 +58,9 @@
 python scripts/fetch_congressional_trades.py --days 30
 
 # Sample output:
-# ✓ Officials created/updated: 47
+# Officials created/updated: 47
-# ✓ Securities created/updated: 89
+# Securities created/updated: 89
-# ✓ Trades ingested: 234
+# Trades ingested: 234
 ```
 
 ### Database Queries

docs/archive/PR3_SUMMARY.md

@@ -1,6 +1,6 @@
 # PR3 Summary: Security Enrichment + Deployment
 
-**Status**: ✅ Complete  
+**Status**: Complete 
 **Date**: 2025-12-14
 
 ## What was built
@@ -32,7 +32,7 @@
   python scripts/enrich_securities.py --force
   ```
 
-### 3. Tests (9 new tests, all passing ✅)
+### 3. Tests (9 new tests, all passing )
 **`tests/test_security_enricher.py`**:
 - Successful enrichment with complete data
 - ETF detection and classification
@@ -72,7 +72,7 @@ python scripts/enrich_securities.py
 # Enriched AAPL: Apple Inc. (Technology)
 # Enriched TSLA: Tesla, Inc. (Consumer Cyclical)
 # Enriched GOOGL: Alphabet Inc. (Communication Services)
-# ✓ Successfully enriched: 5
+# Successfully enriched: 5
 ```
 
 ### Query Enriched Data
@@ -159,10 +159,10 @@ python scripts/update_all_prices.py  # To be built in PR4
 
 | Option | Complexity | Cost/month | Best For |
 |--------|-----------|------------|----------|
-| **Local** | ⭐ | $0 | Development |
+| **Local** | | $0 | Development |
-| **VPS + Docker** | ⭐⭐ | $10-20 | Personal deployment |
+| **VPS + Docker** | | $10-20 | Personal deployment |
-| **Railway/Fly.io** | ⭐ | $5-15 | Easy cloud |
+| **Railway/Fly.io** | | $5-15 | Easy cloud |
-| **AWS** | ⭐⭐⭐ | $20-50 | Scalable production |
+| **AWS** | | $20-50 | Scalable production |
 
 See [`docs/07_deployment.md`](07_deployment.md) for detailed guides.
 

docs/archive/PR4_SUMMARY.md

@@ -1,6 +1,6 @@
 # PR4 Summary: Phase 2 Analytics Foundation
 
-## ✅ Completed
+## Completed
 
 **Date**: December 15, 2025  
 **Status**: Complete  
@@ -78,14 +78,14 @@ python scripts/calculate_all_returns.py --window 90 --benchmark SPY --top 10
 
 ### 3. Tests (`tests/test_analytics.py`)
 
-- ✅ Return calculator with sample data
+- Return calculator with sample data
-- ✅ Buy vs sell trade handling
+- Buy vs sell trade handling
-- ✅ Missing data edge cases
+- Missing data edge cases
-- ✅ Benchmark comparisons
+- Benchmark comparisons
-- ✅ Official performance metrics
+- Official performance metrics
-- ✅ Multiple time windows
+- Multiple time windows
-- ✅ Sector analysis
+- Sector analysis
-- ✅ Timing analysis
+- Timing analysis
 
 **Test Coverage**: Analytics module fully tested
 
@@ -288,15 +288,15 @@ with next(get_session()) as session:
    - Trades near policy events
    - Unusual timing flags
 
-## Success Criteria ✅
+## Success Criteria
 
-- ✅ Can calculate returns for any trade + window
+- Can calculate returns for any trade + window
-- ✅ Can compare to S&P 500 benchmark
+- Can compare to S&P 500 benchmark
-- ✅ Can generate official performance summaries
+- Can generate official performance summaries
-- ✅ All calculations tested and accurate
+- All calculations tested and accurate
-- ✅ Performance data calculated on-the-fly
+- Performance data calculated on-the-fly
-- ✅ Documentation complete
+- Documentation complete
-- ✅ Command-line tools working
+- Command-line tools working
 
 ## Testing
 
@@ -309,7 +309,7 @@ All analytics tests should pass (may have warnings if no price data).
 
 ---
 
-**Phase 2 Analytics Foundation: COMPLETE** ✅  
+**Phase 2 Analytics Foundation: COMPLETE** 
 **Ready for**: PR5 (Signals), PR6 (API), PR7 (Dashboard)
 
 

docs/archive/README.md

@@ -0,0 +1,3 @@
+# Archive
+
+PR summaries and one-shot status writeups. Prefer the numbered guides and QUICKSTART files.

pyproject.toml

@@ -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)
 

scripts/git-hooks/install.sh

@@ -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

scripts/git-hooks/pre-commit

@@ -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"

scripts/monitor_market.py

@@ -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

scripts/send_daily_report.py

@@ -43,6 +43,12 @@ def main():
         action="store_true",
         help="Test SMTP connection before sending",
     )
+    parser.add_argument(
+        "--lookback-days",
+        type=int,
+        default=1,
+        help="Include trades filed in the last N days ending on the report date (default: 1)",
+    )
     parser.add_argument(
         "--save-to-file",
         help="Also save report to this file path",
@@ -81,7 +87,9 @@ def main():
     logger.info(f"Generating daily report for {report_date or date.today()}...")
     with get_session() as session:
         generator = ReportGenerator(session)
-        report_data = generator.generate_daily_summary(report_date)
+        report_data = generator.generate_daily_summary(
+            report_date, lookback_days=args.lookback_days
+        )
 
         # Format as text and HTML
         text_body = generator.format_as_text(report_data, "daily")

src/pote/analytics/__init__.py

@@ -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",
 ]
-
-

src/pote/analytics/benchmarks.py

@@ -3,7 +3,7 @@ Benchmark comparison for calculating abnormal returns (alpha).
 """
 
 import logging
-from datetime import date, timedelta
+from datetime import date
 from decimal import Decimal
 
 from sqlalchemy.orm import Session
@@ -60,8 +60,7 @@ class BenchmarkComparison:
             return None
 
         # Calculate return
-        return_pct = ((end_price - start_price) / start_price) * 100
+        return ((end_price - start_price) / start_price) * 100
-        return return_pct
 
     def calculate_abnormal_return(
         self,
@@ -219,5 +218,3 @@ class BenchmarkComparison:
             "benchmark": self.BENCHMARKS.get(benchmark, benchmark),
             "window_days": window_days,
         }
-
-

src/pote/analytics/metrics.py

@@ -4,7 +4,6 @@ Performance metrics and aggregations.
 
 import logging
 from collections import defaultdict
-from datetime import date
 
 from sqlalchemy import func
 from sqlalchemy.orm import Session
@@ -52,11 +51,7 @@ class PerformanceMetrics:
         if not official:
             return {"error": "Official not found"}
 
-        trades = (
+        trades = self.session.query(Trade).filter(Trade.official_id == official_id).all()
-            self.session.query(Trade)
-            .filter(Trade.official_id == official_id)
-            .all()
-        )
 
         if not trades:
             return {
@@ -70,9 +65,7 @@ class PerformanceMetrics:
         # Calculate returns for all trades
         returns_data = []
         for trade in trades:
-            result = self.benchmark.compare_trade_to_benchmark(
+            result = self.benchmark.compare_trade_to_benchmark(trade, window_days, benchmark)
-                trade, window_days, benchmark
-            )
             if result:
                 returns_data.append(result)
 
@@ -96,9 +89,7 @@ class PerformanceMetrics:
         worst_trade = min(returns_data, key=lambda x: x["trade_return"])
 
         # Total value traded
-        total_value = sum(
+        total_value = sum(float(t.value_min or 0) for t in trades if t.value_min)
-            float(t.value_min or 0) for t in trades if t.value_min
-        )
 
         return {
             "name": official.name,
@@ -154,20 +145,14 @@ class PerformanceMetrics:
             List of sector performance dictionaries
         """
         # Get all trades with security info
-        trades = (
+        trades = self.session.query(Trade).join(Security).all()
-            self.session.query(Trade)
-            .join(Security)
-            .all()
-        )
 
         # Group by sector
         sector_data = defaultdict(list)
 
         for trade in trades:
             sector = trade.security.sector or "Unknown"
-            result = self.benchmark.compare_trade_to_benchmark(
+            result = self.benchmark.compare_trade_to_benchmark(trade, window_days, benchmark)
-                trade, window_days, benchmark
-            )
             if result:
                 sector_data[sector].append(result)
 
@@ -180,14 +165,16 @@ class PerformanceMetrics:
             returns = [d["trade_return"] for d in data]
             alphas = [d["abnormal_return"] for d in data]
 
-            results.append({
+            results.append(
-                "sector": sector,
+                {
-                "trade_count": len(data),
+                    "sector": sector,
-                "avg_return": sum(returns) / len(returns),
+                    "trade_count": len(data),
-                "avg_alpha": sum(alphas) / len(alphas),
+                    "avg_return": sum(returns) / len(returns),
-                "win_rate": sum(1 for r in returns if r > 0) / len(returns),
+                    "avg_alpha": sum(alphas) / len(alphas),
-                "beat_market_rate": sum(1 for a in alphas if a > 0) / len(alphas),
+                    "win_rate": sum(1 for r in returns if r > 0) / len(returns),
-            })
+                    "beat_market_rate": sum(1 for a in alphas if a > 0) / len(alphas),
+                }
+            )
 
         # Sort by average alpha
         results.sort(key=lambda x: x["avg_alpha"], reverse=True)
@@ -229,11 +216,7 @@ class PerformanceMetrics:
         Returns:
             Dictionary with timing statistics
         """
-        trades = (
+        trades = self.session.query(Trade).filter(Trade.filing_date.isnot(None)).all()
-            self.session.query(Trade)
-            .filter(Trade.filing_date.isnot(None))
-            .all()
-        )
 
         if not trades:
             return {"error": "No trades with disclosure dates"}
@@ -288,5 +271,3 @@ class PerformanceMetrics:
             "benchmark": benchmark,
             **aggregate,
         }
-
-

src/pote/analytics/returns.py

@@ -94,18 +94,20 @@ class ReturnCalculator:
     def calculate_multiple_windows(
         self,
         trade: Trade,
-        windows: list[int] = [30, 60, 90, 180],
+        windows: list[int] | None = None,
     ) -> dict[int, dict]:
         """
         Calculate returns for multiple time windows.
 
         Args:
             trade: Trade object
-            windows: List of window sizes in days
+            windows: List of window sizes in days (defaults to 30/60/90/180)
 
         Returns:
             Dictionary mapping window_days to return metrics
         """
+        if windows is None:
+            windows = [30, 60, 90, 180]
         results = {}
         for window in windows:
             result = self.calculate_trade_return(trade, window)
@@ -223,12 +225,13 @@ class ReturnCalculator:
         if not prices:
             return pd.DataFrame()
 
+        # open/high/low are nullable columns; use NaN (pandas-native) when absent.
         data = [
             {
                 "date": p.date,
-                "open": float(p.open),
+                "open": float(p.open) if p.open is not None else float("nan"),
-                "high": float(p.high),
+                "high": float(p.high) if p.high is not None else float("nan"),
-                "low": float(p.low),
+                "low": float(p.low) if p.low is not None else float("nan"),
                 "close": float(p.close),
                 "volume": p.volume,
             }
@@ -236,4 +239,3 @@ class ReturnCalculator:
         ]
 
         return pd.DataFrame(data)
-

src/pote/db/models.py

@@ -3,17 +3,17 @@ SQLAlchemy ORM models for POTE.
 Matches the schema defined in docs/02_data_model.md.
 """
 
-from datetime import date, datetime, timezone
+from datetime import UTC, date, datetime
 from decimal import Decimal
 
 from sqlalchemy import (
     DECIMAL,
+    JSON,
     Date,
     DateTime,
     ForeignKey,
     Index,
     Integer,
-    JSON,
     String,
     Text,
     UniqueConstraint,
@@ -35,13 +35,11 @@ class Official(Base):
     state: Mapped[str | None] = mapped_column(String(2))
     bioguide_id: Mapped[str | None] = mapped_column(String(20), unique=True)
     external_ids: Mapped[str | None] = mapped_column(Text)  # JSON blob for other IDs
-    created_at: Mapped[datetime] = mapped_column(
+    created_at: Mapped[datetime] = mapped_column(DateTime, default=lambda: datetime.now(UTC))
-        DateTime, default=lambda: datetime.now(timezone.utc)
-    )
     updated_at: Mapped[datetime] = mapped_column(
         DateTime,
-        default=lambda: datetime.now(timezone.utc),
+        default=lambda: datetime.now(UTC),
-        onupdate=lambda: datetime.now(timezone.utc),
+        onupdate=lambda: datetime.now(UTC),
     )
 
     # Relationships
@@ -63,13 +61,11 @@ class Security(Base):
     sector: Mapped[str | None] = mapped_column(String(100))
     industry: Mapped[str | None] = mapped_column(String(100))
     asset_type: Mapped[str] = mapped_column(String(50), default="stock")  # stock, bond, etc.
-    created_at: Mapped[datetime] = mapped_column(
+    created_at: Mapped[datetime] = mapped_column(DateTime, default=lambda: datetime.now(UTC))
-        DateTime, default=lambda: datetime.now(timezone.utc)
-    )
     updated_at: Mapped[datetime] = mapped_column(
         DateTime,
-        default=lambda: datetime.now(timezone.utc),
+        default=lambda: datetime.now(UTC),
-        onupdate=lambda: datetime.now(timezone.utc),
+        onupdate=lambda: datetime.now(UTC),
     )
 
     # Relationships
@@ -107,13 +103,11 @@ class Trade(Base):
     # Quality flags (JSON or enum list)
     quality_flags: Mapped[str | None] = mapped_column(Text)  # e.g., "range_only,delayed_filing"
 
-    created_at: Mapped[datetime] = mapped_column(
+    created_at: Mapped[datetime] = mapped_column(DateTime, default=lambda: datetime.now(UTC))
-        DateTime, default=lambda: datetime.now(timezone.utc)
-    )
     updated_at: Mapped[datetime] = mapped_column(
         DateTime,
-        default=lambda: datetime.now(timezone.utc),
+        default=lambda: datetime.now(UTC),
-        onupdate=lambda: datetime.now(timezone.utc),
+        onupdate=lambda: datetime.now(UTC),
     )
 
     # Relationships
@@ -156,9 +150,7 @@ class Price(Base):
     adjusted_close: Mapped[Decimal | None] = mapped_column(DECIMAL(15, 4))
 
     source: Mapped[str] = mapped_column(String(50), default="yfinance")
-    created_at: Mapped[datetime] = mapped_column(
+    created_at: Mapped[datetime] = mapped_column(DateTime, default=lambda: datetime.now(UTC))
-        DateTime, default=lambda: datetime.now(timezone.utc)
-    )
 
     # Relationships
     security: Mapped["Security"] = relationship("Security", back_populates="prices")
@@ -188,9 +180,7 @@ class MetricOfficial(Base):
     avg_abnormal_return_1m: Mapped[Decimal | None] = mapped_column(DECIMAL(10, 6))
     cluster_label: Mapped[str | None] = mapped_column(String(50))
 
-    created_at: Mapped[datetime] = mapped_column(
+    created_at: Mapped[datetime] = mapped_column(DateTime, default=lambda: datetime.now(UTC))
-        DateTime, default=lambda: datetime.now(timezone.utc)
-    )
 
     __table_args__ = (
         UniqueConstraint("official_id", "calc_date", "calc_version", name="uq_metrics_official"),
@@ -212,9 +202,7 @@ class MetricTrade(Base):
     abnormal_return_1m: Mapped[Decimal | None] = mapped_column(DECIMAL(10, 6))
     signal_flags: Mapped[str | None] = mapped_column(Text)  # JSON list
 
-    created_at: Mapped[datetime] = mapped_column(
+    created_at: Mapped[datetime] = mapped_column(DateTime, default=lambda: datetime.now(UTC))
-        DateTime, default=lambda: datetime.now(timezone.utc)
-    )
 
     __table_args__ = (
         UniqueConstraint("trade_id", "calc_date", "calc_version", name="uq_metrics_trade"),
@@ -242,18 +230,14 @@ class MarketAlert(Base):
     # Metrics at time of alert
     price: Mapped[Decimal | None] = mapped_column(DECIMAL(15, 4))
     volume: Mapped[int | None] = mapped_column(Integer)
-    change_pct: Mapped[Decimal | None] = mapped_column(
+    change_pct: Mapped[Decimal | None] = mapped_column(DECIMAL(10, 4))  # Price change %
-        DECIMAL(10, 4)
-    )  # Price change %
 
     # Severity scoring
     severity: Mapped[int | None] = mapped_column(Integer)  # 1-10 scale
 
     # Metadata
     source: Mapped[str] = mapped_column(String(50), default="market_monitor")
-    created_at: Mapped[datetime] = mapped_column(
+    created_at: Mapped[datetime] = mapped_column(DateTime, default=lambda: datetime.now(UTC))
-        DateTime, default=lambda: datetime.now(timezone.utc)
-    )
 
     # Indexes for efficient queries
     __table_args__ = (

src/pote/ingestion/house_watcher.py

@@ -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
-        # Default to the original, lowercased
+    return txn_lower
-        return txn_lower

src/pote/ingestion/prices.py

@@ -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)
 

src/pote/monitoring/__init__.py

@@ -9,4 +9,3 @@ from .market_monitor import MarketMonitor
 from .pattern_detector import PatternDetector
 
 __all__ = ["MarketMonitor", "AlertManager", "DisclosureCorrelator", "PatternDetector"]
-

src/pote/monitoring/alert_manager.py

@@ -4,8 +4,7 @@ Handles alert filtering, formatting, and delivery.
 """
 
 import logging
-from datetime import datetime, timezone
+from datetime import UTC, datetime
-from typing import Any
 
 from sqlalchemy.orm import Session
 
@@ -75,21 +74,24 @@ class AlertManager:
         Returns:
             HTML formatted alert
         """
-        severity_class = "high" if (alert.severity or 0) >= 7 else "medium" if (alert.severity or 0) >= 4 else "low"
+        severity_class = (
+            "high"
+            if (alert.severity or 0) >= 7
+            else "medium" if (alert.severity or 0) >= 4 else "low"
+        )
 
-        html = f"""
+        return f"""
         <div class="alert {severity_class}">
             <h3>{alert.ticker} - {alert.alert_type.replace('_', ' ').title()}</h3>
             <p class="timestamp">{alert.timestamp.strftime('%Y-%m-%d %H:%M:%S')}</p>
             <p class="severity">Severity: {alert.severity}/10</p>
             <div class="metrics">
-                <span>Price: ${float(alert.price):.2f}</span>
+                <span>Price: ${float(alert.price or 0):.2f}</span>
                 <span>Volume: {alert.volume:,}</span>
-                <span>Change: {float(alert.change_pct):+.2f}%</span>
+                <span>Change: {float(alert.change_pct or 0):+.2f}%</span>
             </div>
         </div>
         """
-        return html
 
     def filter_alerts(
         self,
@@ -117,7 +119,7 @@ class AlertManager:
 
         # Filter by ticker
         if tickers:
-            ticker_set = set(t.upper() for t in tickers)
+            ticker_set = {t.upper() for t in tickers}
             filtered = [a for a in filtered if a.ticker.upper() in ticker_set]
 
         # Filter by alert type
@@ -128,22 +130,21 @@ 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)
-            return self._generate_text_summary(alerts)
 
     def _generate_text_summary(self, alerts: list[MarketAlert]) -> str:
         """Generate text summary report."""
@@ -152,7 +153,7 @@ class AlertManager:
 
         lines = [
             "=" * 80,
-            f"  MARKET ACTIVITY ALERTS - {datetime.now(timezone.utc).strftime('%Y-%m-%d %H:%M:%S')} UTC",
+            f"  MARKET ACTIVITY ALERTS - {datetime.now(UTC).strftime('%Y-%m-%d %H:%M:%S')} UTC",
             f"  {len(alerts)} Alerts",
             "=" * 80,
             "",
@@ -180,9 +181,7 @@ class AlertManager:
             lines.append(f"🎯 {ticker} - {len(ticker_alerts)} alerts (Max Severity: {max_sev}/10)")
             lines.append("─" * 80)
 
-            for alert in sorted(
+            for alert in sorted(ticker_alerts, key=lambda a: a.severity or 0, reverse=True):
-                ticker_alerts, key=lambda a: a.severity or 0, reverse=True
-            ):
                 lines.append("")
                 lines.append(self.format_alert_text(alert))
 
@@ -202,9 +201,7 @@ class AlertManager:
             type_counts[alert.alert_type] = type_counts.get(alert.alert_type, 0) + 1
 
         lines.append("\nAlert Types:")
-        for alert_type, count in sorted(
+        for alert_type, count in sorted(type_counts.items(), key=lambda x: x[1], reverse=True):
-            type_counts.items(), key=lambda x: x[1], reverse=True
-        ):
             lines.append(f"  {alert_type.replace('_', ' ').title():20s}: {count}")
 
         # Top severity alerts
@@ -232,8 +229,8 @@ class AlertManager:
             ".timestamp { color: #666; font-size: 0.9em; }",
             ".metrics span { margin-right: 20px; }",
             "</style></head><body>",
-            f"<h1>Market Activity Alerts</h1>",
+            "<h1>Market Activity Alerts</h1>",
-            f"<p><strong>{len(alerts)} Alerts</strong> | {datetime.now(timezone.utc).strftime('%Y-%m-%d %H:%M:%S')} UTC</p>",
+            f"<p><strong>{len(alerts)} Alerts</strong> | {datetime.now(UTC).strftime('%Y-%m-%d %H:%M:%S')} UTC</p>",
         ]
 
         for alert in sorted(alerts, key=lambda a: a.severity or 0, reverse=True):
@@ -241,5 +238,3 @@ class AlertManager:
 
         html_parts.append("</body></html>")
         return "\n".join(html_parts)
-
-

src/pote/monitoring/disclosure_correlator.py

@@ -5,14 +5,13 @@ Calculates timing advantage and suspicious activity scores.
 """
 
 import logging
-from datetime import date, timedelta, timezone
+from datetime import UTC, date, timedelta
-from decimal import Decimal
 from typing import Any
 
-from sqlalchemy import and_, func
+from sqlalchemy import and_
 from sqlalchemy.orm import Session
 
-from pote.db.models import MarketAlert, Official, Security, Trade
+from pote.db.models import MarketAlert, Security, Trade
 
 logger = logging.getLogger(__name__)
 
@@ -27,9 +26,7 @@ class DisclosureCorrelator:
         """Initialize disclosure correlator."""
         self.session = session
 
-    def get_alerts_before_trade(
+    def get_alerts_before_trade(self, trade: Trade, lookback_days: int = 30) -> list[MarketAlert]:
-        self, trade: Trade, lookback_days: int = 30
-    ) -> list[MarketAlert]:
         """
         Get market alerts that occurred BEFORE a trade.
 
@@ -50,14 +47,10 @@ class DisclosureCorrelator:
         # Convert dates to datetime for comparison
         from datetime import datetime
 
-        start_dt = datetime.combine(start_date, datetime.min.time()).replace(
+        start_dt = datetime.combine(start_date, datetime.min.time()).replace(tzinfo=UTC)
-            tzinfo=timezone.utc
+        end_dt = datetime.combine(end_date, datetime.max.time()).replace(tzinfo=UTC)
-        )
-        end_dt = datetime.combine(end_date, datetime.max.time()).replace(
-            tzinfo=timezone.utc
-        )
 
-        alerts = (
+        return (
             self.session.query(MarketAlert)
             .filter(
                 and_(
@@ -70,8 +63,6 @@ class DisclosureCorrelator:
             .all()
         )
 
-        return alerts
-
     def calculate_timing_score(
         self, trade: Trade, prior_alerts: list[MarketAlert]
     ) -> dict[str, Any]:
@@ -131,8 +122,7 @@ class DisclosureCorrelator:
             )
         elif suspicious:
             reason = (
-                f"Trade occurred after {len(prior_alerts)} alerts. "
+                f"Trade occurred after {len(prior_alerts)} alerts. " f"Possible timing advantage."
-                f"Possible timing advantage."
             )
         else:
             reason = (
@@ -170,35 +160,27 @@ class DisclosureCorrelator:
         timing_analysis = self.calculate_timing_score(trade, prior_alerts)
 
         # Build full analysis
-        analysis = {
+        return {
             "trade_id": trade.id,
             "official_name": trade.official.name if trade.official else None,
             "ticker": trade.security.ticker if trade.security else None,
             "side": trade.side,
             "transaction_date": str(trade.transaction_date),
             "filing_date": str(trade.filing_date) if trade.filing_date else None,
-            "value_range": f"${float(trade.value_min):,.0f}"
+            "value_range": f"${float(trade.value_min or 0):,.0f}"
-            + (
+            + (f"-${float(trade.value_max):,.0f}" if trade.value_max else "+"),
-                f"-${float(trade.value_max):,.0f}"
-                if trade.value_max
-                else "+"
-            ),
             **timing_analysis,
             "prior_alerts": [
                 {
                     "timestamp": str(alert.timestamp),
                     "alert_type": alert.alert_type,
                     "severity": alert.severity,
-                    "days_before_trade": (
+                    "days_before_trade": (trade.transaction_date - alert.timestamp.date()).days,
-                        trade.transaction_date - alert.timestamp.date()
-                    ).days,
                 }
                 for alert in prior_alerts
             ],
         }
 
-        return analysis
-
     def analyze_recent_disclosures(
         self, days: int = 7, min_timing_score: float = 50
     ) -> list[dict[str, Any]]:
@@ -237,9 +219,7 @@ class DisclosureCorrelator:
             f"Found {len(suspicious_trades)} trades with timing score >= {min_timing_score}"
         )
 
-        return sorted(
+        return sorted(suspicious_trades, key=lambda x: x["timing_score"], reverse=True)
-            suspicious_trades, key=lambda x: x["timing_score"], reverse=True
-        )
 
     def get_official_timing_pattern(
         self, official_id: int, lookback_days: int = 365
@@ -258,9 +238,7 @@ class DisclosureCorrelator:
 
         trades = (
             self.session.query(Trade)
-            .filter(
+            .filter(and_(Trade.official_id == official_id, Trade.transaction_date >= since_date))
-                and_(Trade.official_id == official_id, Trade.transaction_date >= since_date)
-            )
             .join(Trade.security)
             .all()
         )
@@ -285,9 +263,7 @@ class DisclosureCorrelator:
         highly_suspicious = sum(1 for a in analyses if a.get("highly_suspicious", False))
 
         avg_timing_score = (
-            sum(a["timing_score"] for a in analyses) / total_trades
+            sum(a["timing_score"] for a in analyses) / total_trades if total_trades > 0 else 0
-            if total_trades > 0
-            else 0
         )
 
         # Determine pattern
@@ -311,9 +287,7 @@ class DisclosureCorrelator:
             "analyses": analyses,
         }
 
-    def get_ticker_timing_analysis(
+    def get_ticker_timing_analysis(self, ticker: str, lookback_days: int = 365) -> dict[str, Any]:
-        self, ticker: str, lookback_days: int = 365
-    ) -> dict[str, Any]:
         """
         Analyze timing patterns for a specific ticker.
 
@@ -329,9 +303,7 @@ class DisclosureCorrelator:
         trades = (
             self.session.query(Trade)
             .join(Trade.security)
-            .filter(
+            .filter(and_(Security.ticker == ticker, Trade.transaction_date >= since_date))
-                and_(Security.ticker == ticker, Trade.transaction_date >= since_date)
-            )
             .join(Trade.official)
             .all()
         )
@@ -350,10 +322,6 @@ class DisclosureCorrelator:
             "trade_count": len(analyses),
             "trades_with_alerts": sum(1 for a in analyses if a["alert_count"] > 0),
             "suspicious_count": sum(1 for a in analyses if a["suspicious"]),
-            "avg_timing_score": round(
+            "avg_timing_score": round(sum(a["timing_score"] for a in analyses) / len(analyses), 2),
-                sum(a["timing_score"] for a in analyses) / len(analyses), 2
-            ),
             "analyses": sorted(analyses, key=lambda x: x["timing_score"], reverse=True),
         }
-
-

src/pote/monitoring/market_monitor.py

@@ -4,7 +4,7 @@ Detects unusual activity: volume spikes, price movements, volatility.
 """
 
 import logging
-from datetime import datetime, timedelta, timezone
+from datetime import UTC, datetime, timedelta
 from decimal import Decimal
 from typing import Any
 
@@ -59,7 +59,7 @@ class MarketMonitor:
         Returns:
             List of alerts detected
         """
-        alerts = []
+        alerts: list[dict[str, Any]] = []
 
         try:
             stock = yf.Ticker(ticker)
@@ -90,7 +90,7 @@ class MarketMonitor:
                     {
                         "ticker": ticker,
                         "alert_type": "unusual_volume",
-                        "timestamp": datetime.now(timezone.utc),
+                        "timestamp": datetime.now(UTC),
                         "details": {
                             "current_volume": int(current_volume),
                             "avg_volume": int(avg_volume),
@@ -109,10 +109,8 @@ class MarketMonitor:
                 alerts.append(
                     {
                         "ticker": ticker,
-                        "alert_type": "price_spike"
+                        "alert_type": "price_spike" if price_change > 0 else "price_drop",
-                        if price_change > 0
+                        "timestamp": datetime.now(UTC),
-                        else "price_drop",
-                        "timestamp": datetime.now(timezone.utc),
                         "details": {
                             "current_price": float(current_price),
                             "prev_price": float(prev["Close"]),
@@ -129,14 +127,12 @@ class MarketMonitor:
             if len(hist) >= 5:
                 recent_volatility = hist["Close"].iloc[-5:].pct_change().abs().mean()
                 if recent_volatility > avg_price_change * 2 and avg_price_change > 0:
-                    severity = min(
+                    severity = min(10, int((recent_volatility / avg_price_change) - 1))
-                        10, int((recent_volatility / avg_price_change) - 1)
-                    )
                     alerts.append(
                         {
                             "ticker": ticker,
                             "alert_type": "high_volatility",
-                            "timestamp": datetime.now(timezone.utc),
+                            "timestamp": datetime.now(UTC),
                             "details": {
                                 "recent_volatility": round(recent_volatility * 100, 2),
                                 "avg_volatility": round(avg_price_change * 100, 2),
@@ -227,7 +223,7 @@ class MarketMonitor:
         Returns:
             List of MarketAlert objects
         """
-        since = datetime.now(timezone.utc) - timedelta(days=days)
+        since = datetime.now(UTC) - timedelta(days=days)
 
         query = self.session.query(MarketAlert).filter(MarketAlert.timestamp >= since)
 
@@ -252,7 +248,7 @@ class MarketMonitor:
         Returns:
             Dict mapping ticker to alert summary
         """
-        since = datetime.now(timezone.utc) - timedelta(days=days)
+        since = datetime.now(UTC) - timedelta(days=days)
 
         from sqlalchemy import func
 
@@ -278,5 +274,3 @@ class MarketMonitor:
             }
 
         return summary
-
-

src/pote/monitoring/pattern_detector.py

@@ -5,13 +5,12 @@ Identifies recurring suspicious behavior and trading patterns.
 
 import logging
 from datetime import date, timedelta
-from decimal import Decimal
 from typing import Any
 
-from sqlalchemy import and_, func
+from sqlalchemy import func
 from sqlalchemy.orm import Session
 
-from pote.db.models import MarketAlert, Official, Security, Trade
+from pote.db.models import Official, Security, Trade
 from pote.monitoring.disclosure_correlator import DisclosureCorrelator
 
 logger = logging.getLogger(__name__)
@@ -60,9 +59,7 @@ class PatternDetector:
             .all()
         )
 
-        logger.info(
+        logger.info(f"Analyzing {len(officials_with_trades)} officials with {min_trades}+ trades")
-            f"Analyzing {len(officials_with_trades)} officials with {min_trades}+ trades"
-        )
 
         rankings = []
 
@@ -70,9 +67,7 @@ class PatternDetector:
             official_id, name, chamber, party, state, trade_count = official_data
 
             # Get timing pattern
-            pattern = self.correlator.get_official_timing_pattern(
+            pattern = self.correlator.get_official_timing_pattern(official_id, lookback_days)
-                official_id, lookback_days
-            )
 
             if pattern["trade_count"] == 0:
                 continue
@@ -128,9 +123,7 @@ class PatternDetector:
         rankings = self.rank_officials_by_timing(lookback_days, min_trades=5)
 
         # Filter for high suspicious rates
-        offenders = [
+        offenders = [r for r in rankings if r["suspicious_rate"] >= min_suspicious_rate * 100]
-            r for r in rankings if r["suspicious_rate"] >= min_suspicious_rate * 100
-        ]
 
         logger.info(
             f"Found {len(offenders)} officials with {min_suspicious_rate*100}%+ suspicious trades"
@@ -155,9 +148,7 @@ class PatternDetector:
 
         # Get tickers with enough trades
         tickers_with_trades = (
-            self.session.query(
+            self.session.query(Security.ticker, func.count(Trade.id).label("trade_count"))
-                Security.ticker, func.count(Trade.id).label("trade_count")
-            )
             .join(Trade)
             .filter(Trade.transaction_date >= since_date)
             .group_by(Security.ticker)
@@ -169,10 +160,8 @@ class PatternDetector:
 
         ticker_patterns = []
 
-        for ticker, trade_count in tickers_with_trades:
+        for ticker, _trade_count in tickers_with_trades:
-            analysis = self.correlator.get_ticker_timing_analysis(
+            analysis = self.correlator.get_ticker_timing_analysis(ticker, lookback_days)
-                ticker, lookback_days
-            )
 
             if analysis["trade_count"] == 0:
                 continue
@@ -199,9 +188,7 @@ class PatternDetector:
 
         return ticker_patterns
 
-    def get_sector_timing_analysis(
+    def get_sector_timing_analysis(self, lookback_days: int = 365) -> dict[str, dict[str, Any]]:
-        self, lookback_days: int = 365
-    ) -> dict[str, dict[str, Any]]:
         """
         Analyze timing patterns by sector.
 
@@ -252,7 +239,7 @@ class PatternDetector:
                 sector_stats[sector]["suspicious_count"] += 1
 
         # Calculate averages
-        for sector, stats in sector_stats.items():
+        for stats in sector_stats.values():
             if stats["trade_count"] > 0:
                 stats["avg_timing_score"] = round(
                     stats["total_timing_score"] / stats["trade_count"], 2
@@ -266,9 +253,7 @@ class PatternDetector:
 
         return sector_stats
 
-    def get_party_comparison(
+    def get_party_comparison(self, lookback_days: int = 365) -> dict[str, dict[str, Any]]:
-        self, lookback_days: int = 365
-    ) -> dict[str, dict[str, Any]]:
         """
         Compare timing patterns between political parties.
 
@@ -303,7 +288,7 @@ class PatternDetector:
             party_stats[party]["officials"].append(ranking)
 
         # Calculate averages
-        for party, stats in party_stats.items():
+        for stats in party_stats.values():
             if stats["total_trades"] > 0:
                 stats["avg_timing_score"] = round(
                     stats["total_timing_score"] / stats["total_trades"], 2
@@ -336,7 +321,7 @@ class PatternDetector:
         # Calculate summary statistics
         total_officials = len(official_rankings)
         total_offenders = len(repeat_offenders)
-        
+
         avg_timing_score = (
             sum(r["avg_timing_score"] for r in official_rankings) / total_officials
             if total_officials > 0
@@ -356,5 +341,3 @@ class PatternDetector:
             "sector_analysis": sector_analysis,
             "party_comparison": party_comparison,
         }
-
-

src/pote/reporting/__init__.py

@@ -8,5 +8,3 @@ from .email_reporter import EmailReporter
 from .report_generator import ReportGenerator
 
 __all__ = ["EmailReporter", "ReportGenerator"]
-
-

src/pote/reporting/email_reporter.py

@@ -8,7 +8,6 @@ import logging
 import smtplib
 from email.mime.multipart import MIMEMultipart
 from email.mime.text import MIMEText
-from typing import List, Optional
 
 from pote.config import settings
 
@@ -20,31 +19,30 @@ class EmailReporter:
 
     def __init__(
         self,
-        smtp_host: Optional[str] = None,
+        smtp_host: str | None = None,
-        smtp_port: Optional[int] = None,
+        smtp_port: int | None = None,
-        smtp_user: Optional[str] = None,
+        smtp_user: str | None = None,
-        smtp_password: Optional[str] = None,
+        smtp_password: str | None = None,
-        from_email: Optional[str] = None,
+        from_email: str | None = None,
     ):
         """
         Initialize email reporter.
 
         If parameters are not provided, will attempt to use settings from config.
         """
-        self.smtp_host = smtp_host or getattr(settings, "smtp_host", "localhost")
+        # Settings always defines these fields (with defaults), so direct access is safe.
-        self.smtp_port = smtp_port or getattr(settings, "smtp_port", 587)
+        self.smtp_host: str = smtp_host or settings.smtp_host
-        self.smtp_user = smtp_user or getattr(settings, "smtp_user", None)
+        self.smtp_port: int = smtp_port or settings.smtp_port
-        self.smtp_password = smtp_password or getattr(settings, "smtp_password", None)
+        self.smtp_user: str = smtp_user or settings.smtp_user
-        self.from_email = from_email or getattr(
+        self.smtp_password: str = smtp_password or settings.smtp_password
-            settings, "from_email", "pote@localhost"
+        self.from_email: str = from_email or settings.from_email or "pote@localhost"
-        )
 
     def send_report(
         self,
-        to_emails: List[str],
+        to_emails: list[str],
         subject: str,
         body_text: str,
-        body_html: Optional[str] = None,
+        body_html: str | None = None,
     ) -> bool:
         """
         Send an email report.
@@ -113,4 +111,3 @@ class EmailReporter:
         except Exception as e:
             logger.error(f"SMTP connection test failed: {e}")
             return False
-

src/pote/reporting/report_generator.py

@@ -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
@@ -27,13 +27,15 @@ class ReportGenerator:
         self.detector = PatternDetector(session)
 
     def generate_daily_summary(
-        self, report_date: Optional[date] = None
+        self, report_date: date | None = None, *, lookback_days: int = 1
-    ) -> Dict[str, Any]:
+    ) -> dict[str, Any]:
         """
         Generate a daily summary report.
 
         Args:
             report_date: Date to generate report for (defaults to today)
+            lookback_days: Include trades filed in the last N days ending on report_date
+                (defaults to 1, meaning only filings on report_date).
 
         Returns:
             Dictionary containing report data
@@ -41,12 +43,19 @@ class ReportGenerator:
         if report_date is None:
             report_date = date.today()
 
+        if lookback_days < 1:
+            raise ValueError("lookback_days must be >= 1")
+
         start_of_day = datetime.combine(report_date, datetime.min.time())
         end_of_day = datetime.combine(report_date, datetime.max.time())
 
-        # Count new trades filed today
+        filing_start_date = report_date - timedelta(days=lookback_days - 1)
+
+        # Trades filed within the lookback window (inclusive)
         new_trades = (
-            self.session.query(Trade).filter(Trade.filing_date == report_date).all()
+            self.session.query(Trade)
+            .filter(Trade.filing_date >= filing_start_date, Trade.filing_date <= report_date)
+            .all()
         )
 
         # Count market alerts today
@@ -60,7 +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 = []
@@ -71,6 +80,8 @@ class ReportGenerator:
 
         return {
             "date": report_date,
+            "filing_start_date": filing_start_date,
+            "lookback_days": lookback_days,
             "new_trades_count": len(new_trades),
             "new_trades": [
                 {
@@ -99,7 +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.
 
@@ -110,9 +121,7 @@ class ReportGenerator:
 
         # Most active officials
         active_officials = (
-            self.session.query(
+            self.session.query(Official.name, func.count(Trade.id).label("trade_count"))
-                Official.name, func.count(Trade.id).label("trade_count")
-            )
             .join(Trade)
             .filter(Trade.filing_date >= week_ago)
             .group_by(Official.id, Official.name)
@@ -123,9 +132,7 @@ class ReportGenerator:
 
         # Most traded securities
         active_securities = (
-            self.session.query(
+            self.session.query(Security.ticker, func.count(Trade.id).label("trade_count"))
-                Security.ticker, func.count(Trade.id).label("trade_count")
-            )
             .join(Trade)
             .filter(Trade.filing_date >= week_ago)
             .group_by(Security.id, Security.ticker)
@@ -135,8 +142,10 @@ class ReportGenerator:
         )
 
         # Get top suspicious patterns
+        # Same fix as branch docs/deploy-email-closed: the old kwarg names never
+        # existed on identify_repeat_offenders and failed every weekly run.
         repeat_offenders = self.detector.identify_repeat_offenders(
-            days_lookback=7, min_suspicious_trades=2, min_timing_score=40
+            lookback_days=7, min_suspicious_rate=0.4
         )
 
         return {
@@ -146,14 +155,13 @@ class ReportGenerator:
                 {"name": name, "trade_count": count} for name, count in active_officials
             ],
             "most_traded_securities": [
-                {"ticker": ticker, "trade_count": count}
+                {"ticker": ticker, "trade_count": count} for ticker, count in active_securities
-                for ticker, count in active_securities
             ],
             "repeat_offenders_count": len(repeat_offenders),
             "repeat_offenders": repeat_offenders[:5],  # Top 5
         }
 
-    def format_as_text(self, report_data: Dict[str, Any], report_type: str) -> str:
+    def format_as_text(self, report_data: dict[str, Any], report_type: str) -> str:
         """
         Format report data as plain text.
 
@@ -166,20 +174,26 @@ 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)
-            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 = (
+                f"  • Trades Filed (last {data['lookback_days']} days): {data['new_trades_count']}"
+            )
+        else:
+            trades_label = f"  • New Trades Filed: {data['new_trades_count']}"
+
         lines = [
             "=" * 70,
             f"POTE DAILY REPORT - {data['date']}",
             "=" * 70,
             "",
             "📊 SUMMARY",
-            f"  • New Trades Filed: {data['new_trades_count']}",
+            trades_label,
             f"  • Market Alerts: {data['market_alerts_count']}",
             f"  • Critical Alerts (≥7 severity): {data['critical_alerts_count']}",
             f"  • Suspicious Timing Trades: {data['suspicious_trades_count']}",
@@ -227,7 +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,
@@ -246,9 +260,7 @@ class ReportGenerator:
             lines.append(f"  • {security['ticker']}: {security['trade_count']} trades")
 
         if data["repeat_offenders"]:
-            lines.extend(
+            lines.extend(["", f"⚠️  REPEAT OFFENDERS ({data['repeat_offenders_count']} total)"])
-                ["", f"⚠️  REPEAT OFFENDERS ({data['repeat_offenders_count']} total)"]
-            )
             for offender in data["repeat_offenders"]:
                 lines.append(
                     f"  • {offender['official_name']}: "
@@ -267,7 +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.
 
@@ -280,13 +292,17 @@ 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>"
-            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):"
+        else:
+            new_trades_label = "New Trades:"
+
         html = f"""
         <html>
         <head>
@@ -304,10 +320,10 @@ class ReportGenerator:
         </head>
         <body>
             <h1>POTE Daily Report - {data['date']}</h1>
-            
+
             <div class="summary">
                 <h2>📊 Summary</h2>
-                <div class="stat"><strong>New Trades:</strong> {data['new_trades_count']}</div>
+                <div class="stat"><strong>{new_trades_label}</strong> {data['new_trades_count']}</div>
                 <div class="stat"><strong>Market Alerts:</strong> {data['market_alerts_count']}</div>
                 <div class="stat"><strong>Critical Alerts:</strong> {data['critical_alerts_count']}</div>
                 <div class="stat"><strong>Suspicious Trades:</strong> {data['suspicious_trades_count']}</div>
@@ -319,7 +335,7 @@ class ReportGenerator:
             for t in data["new_trades"][:10]:
                 html += f"""
                 <div class="trade">
-                    <strong>{t['official']}</strong>: {t['side']} {t['ticker']} 
+                    <strong>{t['official']}</strong>: {t['side']} {t['ticker']}
                     (${t['value_min']:,.0f} - ${t['value_max']:,.0f}) on {t['transaction_date']}
                 </div>
                 """
@@ -329,7 +345,7 @@ class ReportGenerator:
             for a in data["critical_alerts"][:5]:
                 html += f"""
                 <div class="alert critical">
-                    <strong>{a['ticker']}</strong>: {a['type']} (severity {a['severity']}) 
+                    <strong>{a['ticker']}</strong>: {a['type']} (severity {a['severity']})
                     at {a['timestamp'].strftime('%H:%M:%S')}
                 </div>
                 """
@@ -354,7 +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>
@@ -372,7 +388,7 @@ class ReportGenerator:
         <body>
             <h1>POTE Weekly Report</h1>
             <p><strong>Period:</strong> {data['period_start']} to {data['period_end']}</p>
-            
+
             <h2>👥 Most Active Officials</h2>
             <table>
                 <tr><th>Official</th><th>Trade Count</th></tr>
@@ -383,7 +399,7 @@ class ReportGenerator:
 
         html += """
             </table>
-            
+
             <h2>📈 Most Traded Securities</h2>
             <table>
                 <tr><th>Ticker</th><th>Trade Count</th></tr>
@@ -420,4 +436,3 @@ class ReportGenerator:
         """
 
         return html
-

tests/conftest.py

@@ -22,8 +22,8 @@ def test_db_session() -> Session:
     engine = create_engine("sqlite:///:memory:", echo=False)
     Base.metadata.create_all(engine)
 
-    TestSessionLocal = sessionmaker(bind=engine)
+    session_factory = sessionmaker(bind=engine)
-    session = TestSessionLocal()
+    session = session_factory()
 
     yield session
 

tests/test_analytics.py

@@ -1,27 +1,28 @@
 """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
 def sample_prices(test_db_session, sample_security):
     """Create sample price data for testing."""
     session = test_db_session
-    
+
     # Add SPY (benchmark) prices
     spy = Security(ticker="SPY", name="SPDR S&P 500 ETF")
     session.add(spy)
     session.flush()
-    
+
     base_date = date(2024, 1, 1)
-    
+
     # Create SPY prices
     for i in range(100):
         price = Price(
@@ -34,7 +35,7 @@ def sample_prices(test_db_session, sample_security):
             volume=1000000,
         )
         session.add(price)
-    
+
     # Create prices for sample_security (AAPL)
     for i in range(100):
         price = Price(
@@ -47,7 +48,7 @@ def sample_prices(test_db_session, sample_security):
             volume=50000000,
         )
         session.add(price)
-    
+
     session.commit()
     return session
 
@@ -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(
@@ -130,7 +133,7 @@ def test_benchmark_comparison(test_db_session, sample_official, sample_security,
     """Test benchmark comparison."""
     # Create trade and SPY security
     spy = session.query(Security).filter_by(ticker="SPY").first()
-    
+
     trade = Trade(
         official_id=sample_official.id,
         security_id=spy.id,
@@ -154,12 +157,14 @@ 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
     spy = session.query(Security).filter_by(ticker="SPY").first()
-    
+
     for i in range(3):
         trade = Trade(
             official_id=sample_official.id,
@@ -171,7 +176,7 @@ def test_performance_metrics_official(test_db_session, sample_official, sample_s
             value_max=Decimal("50000"),
         )
         session.add(trade)
-    
+
     session.commit()
 
     # Get performance metrics
@@ -187,7 +192,7 @@ def test_multiple_windows(test_db_session, sample_official, sample_security, sam
     session = test_db_session
     """Test calculating returns for multiple windows."""
     spy = session.query(Security).filter_by(ticker="SPY").first()
-    
+
     trade = Trade(
         official_id=sample_official.id,
         security_id=spy.id,
@@ -231,7 +236,7 @@ def test_sector_analysis(test_db_session, sample_official, sample_prices):
             value_max=Decimal("50000"),
         )
         session.add(trade)
-    
+
     session.commit()
 
     metrics = PerformanceMetrics(session)
@@ -257,7 +262,7 @@ def test_timing_analysis(test_db_session, sample_official, sample_security):
             value_max=Decimal("50000"),
         )
         session.add(trade)
-    
+
     session.commit()
 
     metrics = PerformanceMetrics(session)
@@ -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
-

tests/test_analytics_integration.py

@@ -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
@@ -39,13 +40,13 @@ def full_test_data(test_db_session):
     # Create price data for NVDA (upward trend)
     base_date = date(2024, 1, 1)
     nvda_base_price = Decimal("495.00")
-    
+
     for i in range(120):
         current_date = base_date + timedelta(days=i)
         # Simulate upward trend: +0.5% per day on average
         price_change = Decimal(i) * Decimal("2.50")  # ~50% gain over 120 days
         current_price = nvda_base_price + price_change
-        
+
         price = Price(
             security_id=nvda.id,
             date=current_date,
@@ -59,12 +60,12 @@ def full_test_data(test_db_session):
 
     # Create price data for SPY (slower upward trend - ~10% over 120 days)
     spy_base_price = Decimal("450.00")
-    
+
     for i in range(120):
         current_date = base_date + timedelta(days=i)
         price_change = Decimal(i) * Decimal("0.35")
         current_price = spy_base_price + price_change
-        
+
         price = Price(
             security_id=spy.id,
             date=current_date,
@@ -88,7 +89,7 @@ def full_test_data(test_db_session):
         value_min=Decimal("15001"),
         value_max=Decimal("50000"),
     )
-    
+
     # Tuberville buys NVDA later (still good but less alpha)
     trade2 = Trade(
         official_id=tuberville.id,
@@ -115,22 +116,24 @@ def test_return_calculation_with_real_data(test_db_session, full_test_data):
     session = test_db_session
     """Test return calculation with realistic price data."""
     calculator = ReturnCalculator(session)
-    
+
     # Get Pelosi's NVDA trade
     trade = full_test_data["trades"][0]
-    
+
     # Calculate 90-day return
     result = calculator.calculate_trade_return(trade, window_days=90)
-    
+
     assert result is not None, "Should calculate return with available data"
     assert result["ticker"] == "NVDA"
     assert result["window_days"] == 90
     assert result["return_pct"] > 0, "NVDA should have positive return"
-    
+
     # 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']}")
     print(f"   Exit:  ${result['exit_price']} on {result['exit_date']}")
@@ -140,22 +143,22 @@ def test_benchmark_comparison_with_real_data(test_db_session, full_test_data):
     session = test_db_session
     """Test benchmark comparison with SPY."""
     benchmark = BenchmarkComparison(session)
-    
+
     # Get Pelosi's trade
     trade = full_test_data["trades"][0]
-    
+
     # Compare to SPY
     result = benchmark.compare_trade_to_benchmark(trade, window_days=90, benchmark="SPY")
-    
+
     assert result is not None
     assert result["ticker"] == "NVDA"
     assert result["benchmark"] == "SPY"
-    
+
     # NVDA should beat SPY significantly
     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}%")
@@ -165,21 +168,21 @@ def test_official_performance_summary(test_db_session, full_test_data):
     session = test_db_session
     """Test official performance aggregation."""
     metrics = PerformanceMetrics(session)
-    
+
     pelosi = full_test_data["officials"][0]
-    
+
     # Get performance summary
     perf = metrics.official_performance(pelosi.id, window_days=90)
-    
+
     assert perf["name"] == "Nancy Pelosi"
     assert perf["total_trades"] >= 1
-    
+
     if perf.get("trades_analyzed", 0) > 0:
         assert "avg_return" in perf
         assert "avg_alpha" in perf
         assert "win_rate" in perf
         assert perf["win_rate"] >= 0 and perf["win_rate"] <= 1
-        
+
         print(f"\n✅ {perf['name']} Performance:")
         print(f"   Total Trades:     {perf['total_trades']}")
         print(f"   Average Return:   {perf['avg_return']:.2f}%")
@@ -191,17 +194,17 @@ def test_multiple_windows(test_db_session, full_test_data):
     session = test_db_session
     """Test calculating multiple time windows."""
     calculator = ReturnCalculator(session)
-    
+
     trade = full_test_data["trades"][0]
-    
+
     # Calculate for 30, 60, 90 days
     results = calculator.calculate_multiple_windows(trade, windows=[30, 60, 90])
-    
+
     assert len(results) == 3, "Should calculate all three windows"
-    
+
     # 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}%")
@@ -211,13 +214,13 @@ def test_top_performers(test_db_session, full_test_data):
     session = test_db_session
     """Test top performer ranking."""
     metrics = PerformanceMetrics(session)
-    
+
     top = metrics.top_performers(window_days=90, limit=5)
-    
+
     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}%")
@@ -227,18 +230,18 @@ def test_system_statistics(test_db_session, full_test_data):
     session = test_db_session
     """Test system-wide statistics."""
     metrics = PerformanceMetrics(session)
-    
+
     stats = metrics.summary_statistics(window_days=90)
-    
+
     assert stats["total_officials"] >= 2
     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']}")
-    
+
     if stats.get("avg_alpha") is not None:
         print(f"   Avg Alpha:  {stats['avg_alpha']:+.2f}%")
         print(f"   Beat Market: {stats['beat_market_rate']:.1%}")
@@ -248,13 +251,13 @@ def test_disclosure_timing(test_db_session, full_test_data):
     session = test_db_session
     """Test disclosure lag analysis."""
     metrics = PerformanceMetrics(session)
-    
+
     timing = metrics.timing_analysis()
-    
+
     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")
 
@@ -263,25 +266,27 @@ def test_sector_analysis(test_db_session, full_test_data):
     session = test_db_session
     """Test sector-level analysis."""
     metrics = PerformanceMetrics(session)
-    
+
     sectors = metrics.sector_analysis(window_days=90)
-    
+
     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):
     session = test_db_session
     """Test handling of trade with no exit price available."""
     calculator = ReturnCalculator(session)
-    
+
     nvda = session.query(Security).filter_by(ticker="NVDA").first()
     pelosi = session.query(Official).filter_by(name="Nancy Pelosi").first()
-    
+
     # Create trade with transaction date far in future (no exit price)
     future_trade = Trade(
         official_id=pelosi.id,
@@ -294,9 +299,9 @@ def test_edge_case_missing_exit_price(test_db_session, full_test_data):
     )
     session.add(future_trade)
     session.commit()
-    
+
     result = calculator.calculate_trade_return(future_trade, window_days=90)
-    
+
     assert result is None, "Should return None when price data unavailable"
     print("\n✅ Correctly handles missing price data")
 
@@ -305,10 +310,10 @@ def test_sell_trade_logic(test_db_session, full_test_data):
     session = test_db_session
     """Test that sell trades have inverted return logic."""
     calculator = ReturnCalculator(session)
-    
+
     nvda = session.query(Security).filter_by(ticker="NVDA").first()
     pelosi = session.query(Official).filter_by(name="Nancy Pelosi").first()
-    
+
     # Create sell trade during uptrend (should show negative return)
     sell_trade = Trade(
         official_id=pelosi.id,
@@ -321,11 +326,10 @@ def test_sell_trade_logic(test_db_session, full_test_data):
     )
     session.add(sell_trade)
     session.commit()
-    
+
     result = calculator.calculate_trade_return(sell_trade, window_days=90)
-    
+
     if result:
         # 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}%")
-

tests/test_disclosure_correlator.py

@@ -1,24 +1,25 @@
 """Tests for disclosure correlation module."""
 
-import pytest
+from datetime import UTC, date, datetime
-from datetime import date, datetime, timedelta, timezone
 from decimal import Decimal
 
+import pytest
+
+from pote.db.models import MarketAlert, Official, Security, Trade
 from pote.monitoring.disclosure_correlator import DisclosureCorrelator
-from pote.db.models import Official, Security, Trade, MarketAlert
 
 
 @pytest.fixture
 def trade_with_alerts(test_db_session):
     """Create a trade with prior market alerts."""
     session = test_db_session
-    
+
     # Create official and security
     pelosi = Official(name="Nancy Pelosi", chamber="House", party="Democrat", state="CA")
     nvda = Security(ticker="NVDA", name="NVIDIA Corporation", sector="Technology")
     session.add_all([pelosi, nvda])
     session.flush()
-    
+
     # Create trade on Jan 15
     trade = Trade(
         official_id=pelosi.id,
@@ -32,13 +33,13 @@ def trade_with_alerts(test_db_session):
     )
     session.add(trade)
     session.flush()
-    
+
     # Create alerts BEFORE trade (suspicious)
     alerts = [
         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,
@@ -68,7 +69,7 @@ def trade_with_alerts(test_db_session):
     ]
     session.add_all(alerts)
     session.commit()
-    
+
     return {
         "trade": trade,
         "official": pelosi,
@@ -81,12 +82,12 @@ def trade_with_alerts(test_db_session):
 def trade_without_alerts(test_db_session):
     """Create a trade without prior alerts (clean)."""
     session = test_db_session
-    
+
     official = Official(name="John Smith", chamber="House", party="Republican", state="TX")
     security = Security(ticker="MSFT", name="Microsoft Corporation", sector="Technology")
     session.add_all([official, security])
     session.flush()
-    
+
     trade = Trade(
         official_id=official.id,
         security_id=security.id,
@@ -98,7 +99,7 @@ def trade_without_alerts(test_db_session):
     )
     session.add(trade)
     session.commit()
-    
+
     return {
         "trade": trade,
         "official": official,
@@ -110,12 +111,12 @@ def test_get_alerts_before_trade(test_db_session, trade_with_alerts):
     """Test retrieving alerts before a trade."""
     session = test_db_session
     correlator = DisclosureCorrelator(session)
-    
+
     trade = trade_with_alerts["trade"]
-    
+
     # Get alerts before trade
     prior_alerts = correlator.get_alerts_before_trade(trade, lookback_days=30)
-    
+
     assert len(prior_alerts) == 3
     assert all(alert.ticker == "NVDA" for alert in prior_alerts)
     assert all(alert.timestamp.date() < trade.transaction_date for alert in prior_alerts)
@@ -125,11 +126,11 @@ def test_get_alerts_before_trade_no_alerts(test_db_session, trade_without_alerts
     """Test retrieving alerts when none exist."""
     session = test_db_session
     correlator = DisclosureCorrelator(session)
-    
+
     trade = trade_without_alerts["trade"]
-    
+
     prior_alerts = correlator.get_alerts_before_trade(trade, lookback_days=30)
-    
+
     assert len(prior_alerts) == 0
 
 
@@ -137,12 +138,12 @@ def test_calculate_timing_score_high_suspicion(test_db_session, trade_with_alert
     """Test timing score calculation for suspicious trade."""
     session = test_db_session
     correlator = DisclosureCorrelator(session)
-    
+
     trade = trade_with_alerts["trade"]
     alerts = trade_with_alerts["alerts"]
-    
+
     timing_analysis = correlator.calculate_timing_score(trade, alerts)
-    
+
     assert timing_analysis["timing_score"] > 60, "Should be suspicious with 3 alerts"
     assert timing_analysis["suspicious"] is True
     assert timing_analysis["alert_count"] == 3
@@ -155,13 +156,13 @@ def test_calculate_timing_score_no_alerts(test_db_session):
     """Test timing score with no prior alerts."""
     session = test_db_session
     correlator = DisclosureCorrelator(session)
-    
+
     # Create minimal trade
     official = Official(name="Test", chamber="House", party="Democrat", state="CA")
     security = Security(ticker="TEST", name="Test Corp")
     session.add_all([official, security])
     session.flush()
-    
+
     trade = Trade(
         official_id=official.id,
         security_id=security.id,
@@ -172,9 +173,9 @@ def test_calculate_timing_score_no_alerts(test_db_session):
     )
     session.add(trade)
     session.commit()
-    
+
     timing_analysis = correlator.calculate_timing_score(trade, [])
-    
+
     assert timing_analysis["timing_score"] == 0
     assert timing_analysis["suspicious"] is False
     assert timing_analysis["alert_count"] == 0
@@ -184,13 +185,13 @@ def test_calculate_timing_score_factors(test_db_session):
     """Test that timing score considers all factors correctly."""
     session = test_db_session
     correlator = DisclosureCorrelator(session)
-    
+
     # Create trade
     official = Official(name="Test", chamber="House", party="Democrat", state="CA")
     security = Security(ticker="TEST", name="Test Corp")
     session.add_all([official, security])
     session.flush()
-    
+
     trade_date = date(2024, 1, 15)
     trade = Trade(
         official_id=official.id,
@@ -202,47 +203,47 @@ def test_calculate_timing_score_factors(test_db_session):
     )
     session.add(trade)
     session.flush()
-    
+
     # Test with low severity alerts (should have lower score)
     low_sev_alerts = [
         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,
         ),
     ]
     session.add_all(low_sev_alerts)
     session.commit()
-    
+
     low_score = correlator.calculate_timing_score(trade, low_sev_alerts)
-    
+
     # Test with high severity alerts (should have higher score)
     high_sev_alerts = [
         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,
         ),
     ]
     session.add_all(high_sev_alerts)
     session.commit()
-    
+
     high_score = correlator.calculate_timing_score(trade, high_sev_alerts)
-    
+
     # High severity + recent should score higher
     assert high_score["timing_score"] > low_score["timing_score"]
     assert high_score["recent_alert_count"] > 0
@@ -253,11 +254,11 @@ def test_analyze_trade_full(test_db_session, trade_with_alerts):
     """Test complete trade analysis."""
     session = test_db_session
     correlator = DisclosureCorrelator(session)
-    
+
     trade = trade_with_alerts["trade"]
-    
+
     analysis = correlator.analyze_trade(trade)
-    
+
     # Check all required fields
     assert analysis["trade_id"] == trade.id
     assert analysis["official_name"] == "Nancy Pelosi"
@@ -267,7 +268,7 @@ def test_analyze_trade_full(test_db_session, trade_with_alerts):
     assert analysis["timing_score"] > 0
     assert "prior_alerts" in analysis
     assert len(analysis["prior_alerts"]) == 3
-    
+
     # Check alert details
     for alert_detail in analysis["prior_alerts"]:
         assert "timestamp" in alert_detail
@@ -281,16 +282,15 @@ def test_analyze_recent_disclosures(test_db_session, trade_with_alerts, trade_wi
     """Test batch analysis of recent disclosures."""
     session = test_db_session
     correlator = DisclosureCorrelator(session)
-    
+
     # Both trades were created "recently" (in fixture setup)
     suspicious_trades = correlator.analyze_recent_disclosures(
-        days=365,  # Wide window to catch test data
+        days=365, min_timing_score=50  # Wide window to catch test data
-        min_timing_score=50
     )
-    
+
     # Should find at least the suspicious trade
     assert len(suspicious_trades) >= 1
-    
+
     # Check sorting (highest score first)
     if len(suspicious_trades) > 1:
         for i in range(len(suspicious_trades) - 1):
@@ -301,12 +301,12 @@ def test_get_official_timing_pattern(test_db_session, trade_with_alerts):
     """Test official timing pattern analysis."""
     session = test_db_session
     correlator = DisclosureCorrelator(session)
-    
+
     official = trade_with_alerts["official"]
-    
+
     # Use wide lookback to catch test data (trade is 2024-01-15)
     pattern = correlator.get_official_timing_pattern(official.id, lookback_days=3650)
-    
+
     assert pattern["official_id"] == official.id
     assert pattern["trade_count"] >= 1
     assert pattern["trades_with_prior_alerts"] >= 1
@@ -319,13 +319,13 @@ def test_get_official_timing_pattern_no_trades(test_db_session):
     """Test official with no trades."""
     session = test_db_session
     correlator = DisclosureCorrelator(session)
-    
+
     official = Official(name="No Trades", chamber="House", party="Democrat", state="CA")
     session.add(official)
     session.commit()
-    
+
     pattern = correlator.get_official_timing_pattern(official.id)
-    
+
     assert pattern["trade_count"] == 0
     assert "No trades" in pattern["pattern"]
 
@@ -334,10 +334,10 @@ def test_get_ticker_timing_analysis(test_db_session, trade_with_alerts):
     """Test ticker timing analysis."""
     session = test_db_session
     correlator = DisclosureCorrelator(session)
-    
+
     # Use wide lookback to catch test data
     analysis = correlator.get_ticker_timing_analysis("NVDA", lookback_days=3650)
-    
+
     assert analysis["ticker"] == "NVDA"
     assert analysis["trade_count"] >= 1
     assert analysis["trades_with_alerts"] >= 1
@@ -349,9 +349,9 @@ def test_get_ticker_timing_analysis_no_trades(test_db_session):
     """Test ticker with no trades."""
     session = test_db_session
     correlator = DisclosureCorrelator(session)
-    
+
     analysis = correlator.get_ticker_timing_analysis("ZZZZ")
-    
+
     assert analysis["ticker"] == "ZZZZ"
     assert analysis["trade_count"] == 0
     assert "No trades" in analysis["pattern"]
@@ -361,13 +361,13 @@ def test_alerts_outside_lookback_window(test_db_session):
     """Test that alerts outside lookback window are excluded."""
     session = test_db_session
     correlator = DisclosureCorrelator(session)
-    
+
     # Create trade and alerts
     official = Official(name="Test", chamber="House", party="Democrat", state="CA")
     security = Security(ticker="TEST", name="Test Corp")
     session.add_all([official, security])
     session.flush()
-    
+
     trade_date = date(2024, 1, 15)
     trade = Trade(
         official_id=official.id,
@@ -379,29 +379,29 @@ def test_alerts_outside_lookback_window(test_db_session):
     )
     session.add(trade)
     session.flush()
-    
+
     # Alert 2 days before (within window)
     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,
     )
-    
+
     # Alert 40 days before (outside 30-day window)
     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,
     )
-    
+
     session.add_all([recent_alert, old_alert])
     session.commit()
-    
+
     # Should only get recent alert
     alerts = correlator.get_alerts_before_trade(trade, lookback_days=30)
-    
+
     assert len(alerts) == 1
     assert alerts[0].timestamp.date() == date(2024, 1, 13)
 
@@ -410,14 +410,14 @@ def test_different_ticker_alerts_excluded(test_db_session):
     """Test that alerts for different tickers are excluded."""
     session = test_db_session
     correlator = DisclosureCorrelator(session)
-    
+
     # Create trade for NVDA
     official = Official(name="Test", chamber="House", party="Democrat", state="CA")
     nvda = Security(ticker="NVDA", name="NVIDIA")
     msft = Security(ticker="MSFT", name="Microsoft")
     session.add_all([official, nvda, msft])
     session.flush()
-    
+
     trade = Trade(
         official_id=official.id,
         security_id=nvda.id,
@@ -428,28 +428,27 @@ def test_different_ticker_alerts_excluded(test_db_session):
     )
     session.add(trade)
     session.flush()
-    
+
     # Create alerts for both tickers
     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,
     )
-    
+
     session.add_all([nvda_alert, msft_alert])
     session.commit()
-    
+
     # Should only get NVDA alert
     alerts = correlator.get_alerts_before_trade(trade, lookback_days=30)
-    
+
     assert len(alerts) == 1
     assert alerts[0].ticker == "NVDA"
-

tests/test_models.py

@@ -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"
+    test_db_session.rollback()
-    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"
+    test_db_session.rollback()
-    except IntegrityError:
-        test_db_session.rollback()
-        # Expected behavior
 
 
 def test_trade_queries(test_db_session, sample_official, sample_security):

tests/test_monitoring.py

@@ -1,25 +1,26 @@
 """Tests for market monitoring module."""
 
-import pytest
+from datetime import UTC, date, datetime, timedelta
-from datetime import date, datetime, timedelta, timezone
 from decimal import Decimal
 
-from pote.monitoring.market_monitor import MarketMonitor
+import pytest
+
+from pote.db.models import MarketAlert, Official, Security, Trade
 from pote.monitoring.alert_manager import AlertManager
-from pote.db.models import Official, Security, Trade, MarketAlert
+from pote.monitoring.market_monitor import MarketMonitor
 
 
 @pytest.fixture
 def sample_congressional_trades(test_db_session):
     """Create sample congressional trades for watchlist building."""
     session = test_db_session
-    
+
     # Create officials
     pelosi = Official(name="Nancy Pelosi", chamber="House", party="Democrat", state="CA")
     tuberville = Official(name="Tommy Tuberville", chamber="Senate", party="Republican", state="AL")
     session.add_all([pelosi, tuberville])
     session.flush()
-    
+
     # Create securities
     nvda = Security(ticker="NVDA", name="NVIDIA Corporation", sector="Technology")
     msft = Security(ticker="MSFT", name="Microsoft Corporation", sector="Technology")
@@ -28,28 +29,58 @@ def sample_congressional_trades(test_db_session):
     spy = Security(ticker="SPY", name="SPDR S&P 500 ETF", sector="Financial")
     session.add_all([nvda, msft, aapl, tsla, spy])
     session.flush()
-    
+
     # Create multiple trades (NVDA is most traded)
     trades = [
-        Trade(official_id=pelosi.id, security_id=nvda.id, source="test",
+        Trade(
-              transaction_date=date(2024, 1, 15), side="buy",
+            official_id=pelosi.id,
-              value_min=Decimal("15001"), value_max=Decimal("50000")),
+            security_id=nvda.id,
-        Trade(official_id=pelosi.id, security_id=nvda.id, source="test",
+            source="test",
-              transaction_date=date(2024, 2, 1), side="buy",
+            transaction_date=date(2024, 1, 15),
-              value_min=Decimal("15001"), value_max=Decimal("50000")),
+            side="buy",
-        Trade(official_id=tuberville.id, security_id=nvda.id, source="test",
+            value_min=Decimal("15001"),
-              transaction_date=date(2024, 2, 15), side="buy",
+            value_max=Decimal("50000"),
-              value_min=Decimal("50001"), value_max=Decimal("100000")),
+        ),
-        Trade(official_id=pelosi.id, security_id=msft.id, source="test",
+        Trade(
-              transaction_date=date(2024, 1, 20), side="sell",
+            official_id=pelosi.id,
-              value_min=Decimal("15001"), value_max=Decimal("50000")),
+            security_id=nvda.id,
-        Trade(official_id=tuberville.id, security_id=aapl.id, source="test",
+            source="test",
-              transaction_date=date(2024, 2, 10), side="buy",
+            transaction_date=date(2024, 2, 1),
-              value_min=Decimal("15001"), value_max=Decimal("50000")),
+            side="buy",
+            value_min=Decimal("15001"),
+            value_max=Decimal("50000"),
+        ),
+        Trade(
+            official_id=tuberville.id,
+            security_id=nvda.id,
+            source="test",
+            transaction_date=date(2024, 2, 15),
+            side="buy",
+            value_min=Decimal("50001"),
+            value_max=Decimal("100000"),
+        ),
+        Trade(
+            official_id=pelosi.id,
+            security_id=msft.id,
+            source="test",
+            transaction_date=date(2024, 1, 20),
+            side="sell",
+            value_min=Decimal("15001"),
+            value_max=Decimal("50000"),
+        ),
+        Trade(
+            official_id=tuberville.id,
+            security_id=aapl.id,
+            source="test",
+            transaction_date=date(2024, 2, 10),
+            side="buy",
+            value_min=Decimal("15001"),
+            value_max=Decimal("50000"),
+        ),
     ]
     session.add_all(trades)
     session.commit()
-    
+
     return {
         "officials": [pelosi, tuberville],
         "securities": [nvda, msft, aapl, tsla, spy],
@@ -61,9 +92,9 @@ def sample_congressional_trades(test_db_session):
 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(
             ticker="NVDA",
@@ -96,10 +127,10 @@ def sample_alerts(test_db_session):
             severity=5,
         ),
     ]
-    
+
     session.add_all(alerts)
     session.commit()
-    
+
     return alerts
 
 
@@ -107,9 +138,9 @@ def test_get_congressional_watchlist(test_db_session, sample_congressional_trade
     """Test building watchlist from congressional trades."""
     session = test_db_session
     monitor = MarketMonitor(session)
-    
+
     watchlist = monitor.get_congressional_watchlist(limit=10)
-    
+
     assert len(watchlist) > 0
     assert "NVDA" in watchlist  # Most traded
     assert watchlist[0] == "NVDA"  # Should be first (3 trades)
@@ -119,11 +150,11 @@ def test_check_ticker_basic(test_db_session):
     """Test basic ticker checking (may not find alerts with real data)."""
     session = test_db_session
     monitor = MarketMonitor(session)
-    
+
     # This uses real yfinance data, so alerts depend on current market
     # We test that it doesn't crash
     alerts = monitor.check_ticker("AAPL", lookback_days=5)
-    
+
     assert isinstance(alerts, list)
     # Each alert should have required fields
     for alert in alerts:
@@ -137,7 +168,7 @@ def test_scan_watchlist_with_mock(test_db_session, sample_congressional_trades,
     """Test scanning watchlist with mocked data."""
     session = test_db_session
     monitor = MarketMonitor(session)
-    
+
     # Mock the check_ticker method to return controlled data
     def mock_check_ticker(ticker, lookback_days=5):
         if ticker == "NVDA":
@@ -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,
@@ -154,12 +185,12 @@ def test_scan_watchlist_with_mock(test_db_session, sample_congressional_trades,
                 }
             ]
         return []
-    
+
     monkeypatch.setattr(monitor, "check_ticker", mock_check_ticker)
-    
+
     # Scan with limited watchlist
     alerts = monitor.scan_watchlist(tickers=["NVDA", "MSFT"], lookback_days=5)
-    
+
     assert len(alerts) == 1
     assert alerts[0]["ticker"] == "NVDA"
     assert alerts[0]["alert_type"] == "unusual_volume"
@@ -169,12 +200,12 @@ def test_save_alerts(test_db_session):
     """Test saving alerts to database."""
     session = test_db_session
     monitor = MarketMonitor(session)
-    
+
     alerts_data = [
         {
             "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,
@@ -192,11 +223,11 @@ def test_save_alerts(test_db_session):
             "severity": 9,
         },
     ]
-    
+
     saved_count = monitor.save_alerts(alerts_data)
-    
+
     assert saved_count == 2
-    
+
     # Verify in database
     alerts = session.query(MarketAlert).filter_by(ticker="TSLA").all()
     assert len(alerts) == 2
@@ -206,21 +237,21 @@ def test_get_recent_alerts(test_db_session, sample_alerts):
     """Test querying recent alerts."""
     session = test_db_session
     monitor = MarketMonitor(session)
-    
+
     # Get all alerts
     all_alerts = monitor.get_recent_alerts(days=1)
     assert len(all_alerts) >= 3
-    
+
     # Filter by ticker
     nvda_alerts = monitor.get_recent_alerts(ticker="NVDA", days=1)
     assert len(nvda_alerts) == 2
     assert all(a.ticker == "NVDA" for a in nvda_alerts)
-    
+
     # Filter by alert type
     volume_alerts = monitor.get_recent_alerts(alert_type="unusual_volume", days=1)
     assert len(volume_alerts) == 1
     assert volume_alerts[0].alert_type == "unusual_volume"
-    
+
     # Filter by severity
     high_sev_alerts = monitor.get_recent_alerts(min_severity=6, days=1)
     assert all(a.severity >= 6 for a in high_sev_alerts)
@@ -230,12 +261,12 @@ def test_get_ticker_alert_summary(test_db_session, sample_alerts):
     """Test alert summary by ticker."""
     session = test_db_session
     monitor = MarketMonitor(session)
-    
+
     summary = monitor.get_ticker_alert_summary(days=1)
-    
+
     assert "NVDA" in summary
     assert "MSFT" in summary
-    
+
     nvda_summary = summary["NVDA"]
     assert nvda_summary["alert_count"] == 2
     assert nvda_summary["max_severity"] == 7
@@ -246,11 +277,11 @@ def test_alert_manager_format_text(test_db_session, sample_alerts):
     """Test text formatting of alerts."""
     session = test_db_session
     alert_mgr = AlertManager(session)
-    
+
     alert = sample_alerts[0]  # NVDA unusual volume
-    
+
     text = alert_mgr.format_alert_text(alert)
-    
+
     assert "NVDA" in text
     assert "UNUSUAL VOLUME" in text
     assert "Severity" in text
@@ -261,11 +292,11 @@ def test_alert_manager_format_html(test_db_session, sample_alerts):
     """Test HTML formatting of alerts."""
     session = test_db_session
     alert_mgr = AlertManager(session)
-    
+
     alert = sample_alerts[0]
-    
+
     html = alert_mgr.format_alert_html(alert)
-    
+
     assert "<div" in html
     assert "NVDA" in html
     assert "unusual_volume" in html or "Unusual Volume" in html
@@ -275,29 +306,29 @@ def test_alert_manager_filter_alerts(test_db_session, sample_alerts):
     """Test filtering alerts."""
     session = test_db_session
     alert_mgr = AlertManager(session)
-    
+
     # Filter by severity
     high_sev = alert_mgr.filter_alerts(sample_alerts, min_severity=6)
     assert len(high_sev) == 1
     assert high_sev[0].ticker == "NVDA"
     assert high_sev[0].severity == 7
-    
+
     # Filter by ticker
     nvda_only = alert_mgr.filter_alerts(sample_alerts, min_severity=0, tickers=["NVDA"])
     assert len(nvda_only) == 2
     assert all(a.ticker == "NVDA" for a in nvda_only)
-    
+
     # Filter by alert type
     volume_only = alert_mgr.filter_alerts(sample_alerts, alert_types=["unusual_volume"])
     assert len(volume_only) == 1
     assert volume_only[0].alert_type == "unusual_volume"
-    
+
     # Combined filters
     filtered = alert_mgr.filter_alerts(
         sample_alerts,
         min_severity=4,
         tickers=["NVDA"],
-        alert_types=["unusual_volume", "price_spike"]
+        alert_types=["unusual_volume", "price_spike"],
     )
     assert len(filtered) == 2
 
@@ -306,9 +337,9 @@ def test_alert_manager_generate_summary_text(test_db_session, sample_alerts):
     """Test generating text summary report."""
     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
     assert "NVDA" in report
@@ -320,9 +351,9 @@ def test_alert_manager_generate_summary_html(test_db_session, sample_alerts):
     """Test generating HTML summary report."""
     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
     assert "Market Activity Alerts" in report
@@ -333,20 +364,20 @@ def test_alert_manager_empty_alerts(test_db_session):
     """Test handling empty alert list."""
     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
 
 
 def test_market_alert_model(test_db_session):
     """Test MarketAlert model creation and retrieval."""
     session = 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,
@@ -354,13 +385,13 @@ def test_market_alert_model(test_db_session):
         severity=7,
         source="test",
     )
-    
+
     session.add(alert)
     session.commit()
-    
+
     # Retrieve
     retrieved = session.query(MarketAlert).filter_by(ticker="GOOGL").first()
-    
+
     assert retrieved is not None
     assert retrieved.ticker == "GOOGL"
     assert retrieved.alert_type == "price_spike"
@@ -372,9 +403,9 @@ def test_market_alert_model(test_db_session):
 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(
         ticker="TEST1",
@@ -388,20 +419,19 @@ def test_alert_timestamp_filtering(test_db_session):
         timestamp=now - timedelta(hours=2),
         severity=5,
     )
-    
+
     session.add_all([old_alert, recent_alert])
     session.commit()
-    
+
     monitor = MarketMonitor(session)
-    
+
     # Should only get recent alert
     alerts_1_day = monitor.get_recent_alerts(days=1)
     test_alerts = [a for a in alerts_1_day if a.ticker.startswith("TEST")]
     assert len(test_alerts) == 1
     assert test_alerts[0].ticker == "TEST2"
-    
+
     # Should get both with longer lookback
     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
-

tests/test_pattern_detector.py

@@ -1,38 +1,39 @@
 """Tests for pattern detection module."""
 
-import pytest
+from datetime import UTC, date, datetime, timedelta
-from datetime import date, datetime, timedelta, timezone
 from decimal import Decimal
 
+import pytest
+
+from pote.db.models import MarketAlert, Official, Security, Trade
 from pote.monitoring.pattern_detector import PatternDetector
-from pote.db.models import Official, Security, Trade, MarketAlert
 
 
 @pytest.fixture
 def multiple_officials_with_patterns(test_db_session):
     """Create multiple officials with different timing patterns."""
     session = test_db_session
-    
+
     # Create officials
     pelosi = Official(name="Nancy Pelosi", chamber="House", party="Democrat", state="CA")
     tuberville = Official(name="Tommy Tuberville", chamber="Senate", party="Republican", state="AL")
     clean_trader = Official(name="Clean Trader", chamber="House", party="Independent", state="TX")
-    
+
     session.add_all([pelosi, tuberville, clean_trader])
     session.flush()
-    
+
     # Create securities
     nvda = Security(ticker="NVDA", name="NVIDIA", sector="Technology")
     msft = Security(ticker="MSFT", name="Microsoft", sector="Technology")
     xom = Security(ticker="XOM", name="Exxon", sector="Energy")
-    
+
     session.add_all([nvda, msft, xom])
     session.flush()
-    
+
     # 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(
             official_id=pelosi.id,
@@ -45,24 +46,23 @@ def multiple_officials_with_patterns(test_db_session):
         )
         session.add(trade)
         session.flush()
-        
+
         # Create alerts BEFORE trade (suspicious)
         for j in range(2):
             alert = MarketAlert(
                 ticker="NVDA",
                 alert_type="unusual_volume",
                 timestamp=datetime.combine(
-                    trade_date - timedelta(days=3+j),
+                    trade_date - timedelta(days=3 + j), datetime.min.time()
-                    datetime.min.time()
+                ).replace(tzinfo=UTC),
-                ).replace(tzinfo=timezone.utc),
                 severity=7 + j,
             )
             session.add(alert)
-    
+
     # Tuberville - Mixed pattern
     for i in range(4):
-        trade_date = date(2024, 2, 1) + timedelta(days=i*30)
+        trade_date = date(2024, 2, 1) + timedelta(days=i * 30)
-        
+
         trade = Trade(
             official_id=tuberville.id,
             security_id=msft.id,
@@ -74,24 +74,23 @@ def multiple_officials_with_patterns(test_db_session):
         )
         session.add(trade)
         session.flush()
-        
+
         # Only first 2 trades have alerts
         if i < 2:
             alert = MarketAlert(
                 ticker="MSFT",
                 alert_type="price_spike",
                 timestamp=datetime.combine(
-                    trade_date - timedelta(days=5),
+                    trade_date - timedelta(days=5), datetime.min.time()
-                    datetime.min.time()
+                ).replace(tzinfo=UTC),
-                ).replace(tzinfo=timezone.utc),
                 severity=6,
             )
             session.add(alert)
-    
+
     # Clean trader - No suspicious activity
     for i in range(3):
-        trade_date = date(2024, 3, 1) + timedelta(days=i*30)
+        trade_date = date(2024, 3, 1) + timedelta(days=i * 30)
-        
+
         trade = Trade(
             official_id=clean_trader.id,
             security_id=xom.id,
@@ -102,9 +101,9 @@ def multiple_officials_with_patterns(test_db_session):
             value_max=Decimal("50000"),
         )
         session.add(trade)
-    
+
     session.commit()
-    
+
     return {
         "officials": [pelosi, tuberville, clean_trader],
         "securities": [nvda, msft, xom],
@@ -115,15 +114,15 @@ def test_rank_officials_by_timing(test_db_session, multiple_officials_with_patte
     """Test ranking officials by timing scores."""
     session = test_db_session
     detector = PatternDetector(session)
-    
+
     rankings = detector.rank_officials_by_timing(lookback_days=3650, min_trades=3)
-    
+
     assert len(rankings) >= 2  # At least 2 officials with 3+ trades
-    
+
     # Rankings should be sorted by avg_timing_score (descending)
     for i in range(len(rankings) - 1):
         assert rankings[i]["avg_timing_score"] >= rankings[i + 1]["avg_timing_score"]
-    
+
     # Check required fields
     for ranking in rankings:
         assert "name" in ranking
@@ -138,16 +137,15 @@ def test_identify_repeat_offenders(test_db_session, multiple_officials_with_patt
     """Test identifying repeat offenders."""
     session = test_db_session
     detector = PatternDetector(session)
-    
+
     # Set low threshold to catch Pelosi (who has 100% suspicious rate)
     offenders = detector.identify_repeat_offenders(
-        lookback_days=3650,
+        lookback_days=3650, min_suspicious_rate=0.7  # 70%+
-        min_suspicious_rate=0.7  # 70%+
     )
-    
+
     # Should find at least Pelosi (all trades with alerts)
     assert isinstance(offenders, list)
-    
+
     # All offenders should have high suspicious rates
     for offender in offenders:
         assert offender["suspicious_rate"] >= 70
@@ -157,19 +155,16 @@ def test_analyze_ticker_patterns(test_db_session, multiple_officials_with_patter
     """Test ticker pattern analysis."""
     session = test_db_session
     detector = PatternDetector(session)
-    
+
-    ticker_patterns = detector.analyze_ticker_patterns(
+    ticker_patterns = detector.analyze_ticker_patterns(lookback_days=3650, min_trades=3)
-        lookback_days=3650,
+
-        min_trades=3
-    )
-    
     assert isinstance(ticker_patterns, list)
     assert len(ticker_patterns) >= 1  # At least NVDA should qualify
-    
+
     # Check sorting
     for i in range(len(ticker_patterns) - 1):
         assert ticker_patterns[i]["avg_timing_score"] >= ticker_patterns[i + 1]["avg_timing_score"]
-    
+
     # Check fields
     for pattern in ticker_patterns:
         assert "ticker" in pattern
@@ -182,12 +177,12 @@ def test_get_sector_timing_analysis(test_db_session, multiple_officials_with_pat
     """Test sector timing analysis."""
     session = test_db_session
     detector = PatternDetector(session)
-    
+
     sector_stats = detector.get_sector_timing_analysis(lookback_days=3650)
-    
+
     assert isinstance(sector_stats, dict)
     assert len(sector_stats) >= 2  # Technology and Energy
-    
+
     # Check Technology sector (should have alerts)
     if "Technology" in sector_stats:
         tech = sector_stats["Technology"]
@@ -201,14 +196,14 @@ def test_get_party_comparison(test_db_session, multiple_officials_with_patterns)
     """Test party comparison analysis."""
     session = test_db_session
     detector = PatternDetector(session)
-    
+
     party_stats = detector.get_party_comparison(lookback_days=3650)
-    
+
     assert isinstance(party_stats, dict)
     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
@@ -219,9 +214,9 @@ def test_generate_pattern_report(test_db_session, multiple_officials_with_patter
     """Test comprehensive pattern report generation."""
     session = test_db_session
     detector = PatternDetector(session)
-    
+
     report = detector.generate_pattern_report(lookback_days=3650)
-    
+
     # Check report structure
     assert "period_days" in report
     assert "summary" in report
@@ -230,12 +225,12 @@ def test_generate_pattern_report(test_db_session, multiple_officials_with_patter
     assert "suspicious_tickers" in report
     assert "sector_analysis" in report
     assert "party_comparison" in report
-    
+
     # Check summary
     summary = report["summary"]
     assert summary["total_officials_analyzed"] >= 2
     assert "avg_timing_score" in summary
-    
+
     # Check that lists are populated
     assert len(report["top_suspicious_officials"]) >= 2
     assert isinstance(report["suspicious_tickers"], list)
@@ -245,15 +240,15 @@ def test_rank_officials_min_trades_filter(test_db_session, multiple_officials_wi
     """Test that min_trades filter works correctly."""
     session = test_db_session
     detector = PatternDetector(session)
-    
+
     # With min_trades=5, should only get Pelosi
     rankings_high = detector.rank_officials_by_timing(lookback_days=3650, min_trades=5)
-    
+
     # With min_trades=3, should get at least 2 officials
     rankings_low = detector.rank_officials_by_timing(lookback_days=3650, min_trades=3)
-    
+
     assert len(rankings_low) >= len(rankings_high)
-    
+
     # All officials should meet min_trades requirement
     for ranking in rankings_high:
         assert ranking["trade_count"] >= 5
@@ -263,17 +258,17 @@ def test_empty_data_handling(test_db_session):
     """Test handling of empty dataset."""
     session = test_db_session
     detector = PatternDetector(session)
-    
+
     # With no data, should return empty results
     rankings = detector.rank_officials_by_timing(lookback_days=30, min_trades=1)
     assert rankings == []
-    
+
     offenders = detector.identify_repeat_offenders(lookback_days=30)
     assert offenders == []
-    
+
     tickers = detector.analyze_ticker_patterns(lookback_days=30)
     assert tickers == []
-    
+
     sectors = detector.get_sector_timing_analysis(lookback_days=30)
     assert sectors == {}
 
@@ -282,13 +277,13 @@ def test_ranking_score_accuracy(test_db_session, multiple_officials_with_pattern
     """Test that rankings accurately reflect timing patterns."""
     session = test_db_session
     detector = PatternDetector(session)
-    
+
     rankings = detector.rank_officials_by_timing(lookback_days=3650, min_trades=3)
-    
+
     # Find Pelosi and Clean Trader
     pelosi_rank = next((r for r in rankings if "Pelosi" in r["name"]), None)
     clean_rank = next((r for r in rankings if "Clean" in r["name"]), None)
-    
+
     if pelosi_rank and clean_rank:
         # Pelosi (with alerts) should have higher score than clean trader (no alerts)
         assert pelosi_rank["avg_timing_score"] > clean_rank["avg_timing_score"]
@@ -299,9 +294,9 @@ def test_sector_stats_accuracy(test_db_session, multiple_officials_with_patterns
     """Test sector statistics are calculated correctly."""
     session = test_db_session
     detector = PatternDetector(session)
-    
+
     sector_stats = detector.get_sector_timing_analysis(lookback_days=3650)
-    
+
     # Energy should have clean pattern (no alerts)
     if "Energy" in sector_stats:
         energy = sector_stats["Energy"]
@@ -313,14 +308,12 @@ def test_party_stats_completeness(test_db_session, multiple_officials_with_patte
     """Test party statistics completeness."""
     session = test_db_session
     detector = PatternDetector(session)
-    
+
     party_stats = detector.get_party_comparison(lookback_days=3650)
-    
+
     # Check Democrats (Pelosi)
     if "Democrat" in party_stats:
         dem = party_stats["Democrat"]
         assert dem["official_count"] >= 1
         assert dem["total_trades"] >= 5  # Pelosi has 5 trades
         assert dem["total_suspicious"] > 0  # Pelosi has suspicious trades
-
-