34aebb1c2e
Complete analytics module with returns, benchmarks, and performance metrics. New Modules: - src/pote/analytics/returns.py: Return calculator for trades - src/pote/analytics/benchmarks.py: Benchmark comparison & alpha - src/pote/analytics/metrics.py: Performance aggregations Scripts: - scripts/analyze_official.py: Analyze specific official - scripts/calculate_all_returns.py: System-wide analysis Tests: - tests/test_analytics.py: Full coverage of analytics Features: ✅ Calculate returns over 30/60/90/180 day windows ✅ Compare to market benchmarks (SPY, QQQ, etc.) ✅ Calculate abnormal returns (alpha) ✅ Aggregate stats by official, sector ✅ Top performer rankings ✅ Disclosure timing analysis ✅ Command-line analysis tools ~1,210 lines of new code, all tested
133 lines
4.0 KiB
Python
Executable File
133 lines
4.0 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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Scrape congressional trades from alternative sources.
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Options:
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1. Senate Stock Watcher (if available)
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2. QuiverQuant (requires API key)
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3. Capitol Trades (web scraping - be careful)
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4. Manual CSV import
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"""
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import csv
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import logging
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from datetime import datetime
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from pathlib import Path
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from pote.db import get_session
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from pote.ingestion.trade_loader import TradeLoader
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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def import_from_csv(csv_path: str):
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"""
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Import trades from CSV file.
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CSV format:
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name,party,chamber,state,ticker,side,value_min,value_max,transaction_date,disclosure_date
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"""
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logger.info(f"Reading trades from {csv_path}")
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with open(csv_path, 'r') as f:
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reader = csv.DictReader(f)
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transactions = []
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for row in reader:
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# Convert CSV row to transaction format
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txn = {
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"representative": row["name"],
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"party": row["party"],
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"house": row["chamber"], # "House" or "Senate"
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"state": row.get("state", ""),
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"district": row.get("district", ""),
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"ticker": row["ticker"],
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"transaction": row["side"].capitalize(), # "Purchase" or "Sale"
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"amount": f"${row['value_min']} - ${row['value_max']}",
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"transaction_date": row["transaction_date"],
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"disclosure_date": row.get("disclosure_date", row["transaction_date"]),
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}
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transactions.append(txn)
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logger.info(f"Loaded {len(transactions)} transactions from CSV")
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# Ingest into database
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with next(get_session()) as session:
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loader = TradeLoader(session)
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stats = loader.ingest_transactions(transactions, source="csv_import")
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logger.info(f"✅ Ingested: {stats['officials_created']} officials, "
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f"{stats['securities_created']} securities, "
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f"{stats['trades_ingested']} trades")
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def create_sample_csv(output_path: str = "trades_template.csv"):
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"""Create a template CSV file for manual entry."""
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template_data = [
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{
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"name": "Bernie Sanders",
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"party": "Independent",
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"chamber": "Senate",
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"state": "VT",
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"district": "",
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"ticker": "COIN",
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"side": "sell",
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"value_min": "15001",
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"value_max": "50000",
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"transaction_date": "2024-12-01",
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"disclosure_date": "2024-12-15",
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},
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{
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"name": "Alexandria Ocasio-Cortez",
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"party": "Democrat",
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"chamber": "House",
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"state": "NY",
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"district": "NY-14",
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"ticker": "PLTR",
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"side": "buy",
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"value_min": "1001",
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"value_max": "15000",
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"transaction_date": "2024-11-15",
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"disclosure_date": "2024-12-01",
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},
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]
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with open(output_path, 'w', newline='') as f:
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writer = csv.DictWriter(f, fieldnames=template_data[0].keys())
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writer.writeheader()
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writer.writerows(template_data)
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logger.info(f"✅ Created template CSV: {output_path}")
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logger.info("Edit this file and run: python scripts/scrape_alternative_sources.py import <file>")
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def main():
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"""Main entry point."""
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import sys
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if len(sys.argv) < 2:
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print("Usage:")
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print(" python scripts/scrape_alternative_sources.py template # Create CSV template")
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print(" python scripts/scrape_alternative_sources.py import <csv_file> # Import from CSV")
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sys.exit(1)
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command = sys.argv[1]
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if command == "template":
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create_sample_csv()
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elif command == "import":
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if len(sys.argv) < 3:
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print("Error: Please specify CSV file to import")
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sys.exit(1)
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import_from_csv(sys.argv[2])
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else:
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print(f"Unknown command: {command}")
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sys.exit(1)
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if __name__ == "__main__":
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main()
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