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schema.sql
trades: + close_pnl, resolution (market outcome storage)
metrics_daily: + unrealized_pnl_est, realized_pnl, open/closed/resolved_count
db.py
close_paper_position(): accepts resolution; computes close_pnl in SQL
BUY_YES: (resolution − entry_price) × shares
BUY_NO: ((1 − resolution) − entry_price) × shares
save_daily_metrics(): persists new columns
compute_metrics_from_db(): single DB query for all metrics; no in-memory state
tracker.py — complete rewrite (stateless)
Removed self._trades, self._daily_returns, compute_metrics(), _compute_sharpe(),
check_promotion_thresholds(), _empty_metrics()
update_daily_summary() now reads compute_metrics_from_db() every cycle
Safe across pod restarts: always reflects full DB history
paper.py
close_position(): passes resolution to close_paper_position()
api/main.py /api/summary
Added unrealized_pnl_est (estimated, open trades) and realized_pnl (exact,
closed+resolved) as separate fields alongside total_pnl
win_rate: null if < 5 resolved trades (was proxy on entry_price < 0.5)
calibration_score: Brier-based, null if < 10 resolved trades
resolved_count exposed as field
Each field annotated with: exact/estimated, source, null conditions
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Description
Polymarket trading bot
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