chemavx
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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>
98 lines
4.1 KiB
Python
98 lines
4.1 KiB
Python
"""
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Metrics Tracker — computes and persists trading performance metrics from the DB.
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All metrics are derived directly from the `trades` table on every cycle call.
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No in-memory trade state is kept: the tracker is stateless across pod restarts.
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Metric definitions
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──────────────────
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unrealized_pnl_est Estimated PnL for OPEN positions: edge_net × net_cost − fee.
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Source: open trades with edge_net. Estimated (model signal).
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realized_pnl Exact PnL for CLOSED positions: computed from resolution.
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Source: closed trades with known resolution. Exact.
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total_pnl unrealized_pnl_est + realized_pnl.
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win_rate Fraction of resolved closed trades with close_pnl > 0.
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NULL if fewer than 5 resolved trades.
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calibration_score 1 − AVG((final_prob − resolution)²) on resolved trades.
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Brier score (higher = better calibration). NULL if < 10 resolved.
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sharpe_ratio 0.0 — requires a daily-return time series, not yet tracked.
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"""
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import logging
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from datetime import datetime, UTC
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from bot.data.db import Database
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from bot.executor.paper import Trade
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log = logging.getLogger(__name__)
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class MetricsTracker:
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def __init__(self, db: Database) -> None:
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self._db = db
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async def record_trade(self, trade: Trade) -> None:
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"""Persist a trade to the DB. No in-memory accumulation."""
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await self._db.save_trade(trade)
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log.info("Trade recorded: %s", trade)
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async def update_daily_summary(self) -> None:
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"""Compute metrics from DB and write a metrics_daily snapshot.
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Called every cycle by the trading loop. Safe after pod restarts:
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reads the full trade history from DB, not from in-memory state.
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"""
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raw = await self._db.compute_metrics_from_db()
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if not raw["total_trades"]:
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return
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open_count = int(raw["open_count"] or 0)
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closed_count = int(raw["closed_count"] or 0)
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resolved = int(raw["resolved_count"] or 0)
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wins = int(raw["wins_realized"] or 0)
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unrealized = float(raw["unrealized_pnl_est"] or 0)
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realized = float(raw["realized_pnl"] or 0)
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total_deployed = float(raw["total_deployed"] or 0)
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total_fees = float(raw["total_fees"] or 0)
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total_pnl = unrealized + realized
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# win_rate: only over resolved closed trades; null if sample too small
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win_rate = (wins / resolved) if resolved >= 5 else None
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# calibration: Brier score from DB; null if sample too small
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calibration = (
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float(raw["calibration_score"])
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if raw["calibration_score"] is not None and resolved >= 10
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else None
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)
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avg_edge = total_pnl / total_deployed if total_deployed > 0 else 0.0
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metrics = {
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"timestamp": datetime.now(UTC),
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"total_trades": int(raw["total_trades"]),
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"open_count": open_count,
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"closed_count": closed_count,
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"resolved_count": resolved,
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"total_deployed": total_deployed,
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"total_fees": total_fees,
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"unrealized_pnl_est": unrealized,
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"realized_pnl": realized,
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"total_pnl": total_pnl,
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"win_rate": win_rate,
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"avg_edge": avg_edge,
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"sharpe_ratio": 0.0, # requires daily-return series (not yet tracked)
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"calibration_score": calibration,
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"paper_mode": True,
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}
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await self._db.save_daily_metrics(metrics)
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log.info(
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"Daily metrics | trades=%d (open=%d closed=%d resolved=%d) | "
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"unrealized=$%.2f realized=$%.2f total=$%.2f | "
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"win_rate=%s calibration=%s",
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metrics["total_trades"], open_count, closed_count, resolved,
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unrealized, realized, total_pnl,
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f"{win_rate:.1%}" if win_rate is not None else "n/a (<5)",
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f"{calibration:.3f}" if calibration is not None else "n/a (<10)",
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)
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