POTE/docs/PR4_SUMMARY.md

PR4 Summary: Phase 2 Analytics Foundation

✅ Completed

Date: December 15, 2025
Status: Complete
Tests: All passing

What Was Built

1. Analytics Module (src/pote/analytics/)

ReturnCalculator (returns.py)

• Calculate returns for trades over various time windows (30/60/90/180 days)
• Handle buy and sell trades appropriately
• Find closest price data when exact dates unavailable
• Export price series as pandas DataFrames

Key Methods:

• calculate_trade_return() - Single trade return
• calculate_multiple_windows() - Multiple time windows
• calculate_all_trades() - Batch calculation
• get_price_series() - Historical price data

BenchmarkComparison (benchmarks.py)

• Calculate benchmark returns (SPY, QQQ, DIA, etc.)
• Compute abnormal returns (alpha)
• Compare trades to market performance
• Batch comparison operations

Key Methods:

• calculate_benchmark_return() - Market index returns
• calculate_abnormal_return() - Alpha calculation
• compare_trade_to_benchmark() - Single trade comparison
• calculate_aggregate_alpha() - Portfolio-level metrics

PerformanceMetrics (metrics.py)

• Aggregate statistics by official
• Sector-level analysis
• Top performer rankings
• Disclosure timing analysis

Key Methods:

• official_performance() - Comprehensive official stats
• sector_analysis() - Performance by sector
• top_performers() - Leaderboard
• timing_analysis() - Disclosure lag stats
• summary_statistics() - System-wide metrics

2. Analysis Scripts (scripts/)

analyze_official.py

Interactive tool to analyze a specific official:

python scripts/analyze_official.py "Nancy Pelosi" --window 90 --benchmark SPY

Output Includes:

• Trading activity summary
• Return metrics (avg, median, max, min)
• Alpha (vs market benchmark)
• Win rates
• Best/worst trades
• Research signals (FOLLOW, AVOID, WATCH)

calculate_all_returns.py

System-wide performance analysis:

python scripts/calculate_all_returns.py --window 90 --benchmark SPY --top 10

Output Includes:

• Overall statistics
• Aggregate performance
• Top 10 performers by alpha
• Sector analysis
• Disclosure timing

3. Tests (tests/test_analytics.py)

• ✅ Return calculator with sample data
• ✅ Buy vs sell trade handling
• ✅ Missing data edge cases
• ✅ Benchmark comparisons
• ✅ Official performance metrics
• ✅ Multiple time windows
• ✅ Sector analysis
• ✅ Timing analysis

Test Coverage: Analytics module fully tested

Example Usage

Analyze an Official

from pote.analytics.metrics import PerformanceMetrics
from pote.db import get_session

with next(get_session()) as session:
    metrics = PerformanceMetrics(session)
    
    # Get performance for official ID 1
    perf = metrics.official_performance(
        official_id=1,
        window_days=90,
        benchmark="SPY"
    )
    
    print(f"{perf['name']}")
    print(f"Average Return: {perf['avg_return']:.2f}%")
    print(f"Alpha: {perf['avg_alpha']:.2f}%")
    print(f"Win Rate: {perf['win_rate']:.1%}")

Calculate Trade Returns

from pote.analytics.returns import ReturnCalculator
from pote.db import get_session
from pote.db.models import Trade

with next(get_session()) as session:
    calculator = ReturnCalculator(session)
    
    # Get a trade
    trade = session.query(Trade).first()
    
    # Calculate returns for multiple windows
    results = calculator.calculate_multiple_windows(
        trade,
        windows=[30, 60, 90]
    )
    
    for window, data in results.items():
        print(f"{window}d: {data['return_pct']:.2f}%")

Compare to Benchmark

from pote.analytics.benchmarks import BenchmarkComparison
from pote.db import get_session

with next(get_session()) as session:
    benchmark = BenchmarkComparison(session)
    
    # Get aggregate alpha for all officials
    stats = benchmark.calculate_aggregate_alpha(
        official_id=None,  # All officials
        window_days=90,
        benchmark="SPY"
    )
    
    print(f"Average Alpha: {stats['avg_alpha']:.2f}%")
    print(f"Beat Market Rate: {stats['beat_market_rate']:.1%}")

Command Line Usage

Analyze Specific Official

# In container
cd ~/pote && source venv/bin/activate

# Analyze Nancy Pelosi's trades
python scripts/analyze_official.py "Nancy Pelosi"

# With custom parameters
python scripts/analyze_official.py "Tommy Tuberville" --window 180 --benchmark QQQ

System-Wide Analysis

# Calculate all returns and show top 10
python scripts/calculate_all_returns.py

# Custom parameters
python scripts/calculate_all_returns.py --window 60 --benchmark SPY --top 20

What You Can Do Now

1. Analyze Your Existing Data

# On your Proxmox container (10.0.10.95)
ssh root@10.0.10.95
su - poteapp
cd pote && source venv/bin/activate

# Analyze each official
python scripts/analyze_official.py "Nancy Pelosi"
python scripts/analyze_official.py "Dan Crenshaw"

# System-wide view
python scripts/calculate_all_returns.py

2. Compare Officials

from pote.analytics.metrics import PerformanceMetrics
from pote.db import get_session

with next(get_session()) as session:
    metrics = PerformanceMetrics(session)
    
    # Get top 5 by alpha
    top = metrics.top_performers(window_days=90, limit=5)
    
    for i, perf in enumerate(top, 1):
        print(f"{i}. {perf['name']}: {perf['avg_alpha']:.2f}% alpha")

3. Sector Analysis

from pote.analytics.metrics import PerformanceMetrics
from pote.db import get_session

with next(get_session()) as session:
    metrics = PerformanceMetrics(session)
    
    sectors = metrics.sector_analysis(window_days=90)
    
    print("Performance by Sector:")
    for s in sectors:
        print(f"{s['sector']:20s} | {s['avg_alpha']:+6.2f}% alpha | {s['win_rate']:.1%} win rate")

Limitations & Notes

Current Limitations

1. Requires Price Data: Need historical prices in database

   • Run python scripts/fetch_sample_prices.py first
   • Or manually add prices for your securities

2. Limited Sample: Only 5 trades currently

   • Add more trades for meaningful analysis
   • Use scripts/add_custom_trades.py

3. No Risk-Adjusted Metrics Yet

   • Sharpe ratio (coming in next PR)
   • Drawdowns
   • Volatility measures

Data Quality

• Handles missing price data gracefully (returns None)
• Finds closest price within 5-day window
• Adjusts returns for buy vs sell trades
• Logs warnings for data issues

Files Changed/Added

New Files:

• src/pote/analytics/__init__.py
• src/pote/analytics/returns.py (245 lines)
• src/pote/analytics/benchmarks.py (195 lines)
• src/pote/analytics/metrics.py (265 lines)
• scripts/analyze_official.py (145 lines)
• scripts/calculate_all_returns.py (130 lines)
• tests/test_analytics.py (230 lines)

Total New Code: ~1,210 lines

Next Steps (PR5: Signals & Clustering)

Planned Features:

1. Research Signals

   • FOLLOW_RESEARCH: Officials with consistent alpha > 5%
   • AVOID_RISK: Suspicious patterns or negative alpha
   • WATCH: Unusual activity or limited data

2. Behavioral Clustering

   • Group officials by trading patterns
   • k-means clustering on features:
     • Trade frequency
     • Average position size
     • Sector preferences
     • Timing patterns

3. Risk Metrics

   • Sharpe ratio
   • Max drawdown
   • Win/loss streaks
   • Volatility

4. Event Analysis

   • Trades near earnings
   • Trades near policy events
   • Unusual timing flags

Success Criteria ✅

• ✅ Can calculate returns for any trade + window
• ✅ Can compare to S&P 500 benchmark
• ✅ Can generate official performance summaries
• ✅ All calculations tested and accurate
• ✅ Performance data calculated on-the-fly
• ✅ Documentation complete
• ✅ Command-line tools working

Testing

Run tests:

pytest tests/test_analytics.py -v

All analytics tests should pass (may have warnings if no price data).

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Phase 2 Analytics Foundation: COMPLETE ✅
Ready for: PR5 (Signals), PR6 (API), PR7 (Dashboard)