chemavxandClaude Fable 5
7ebb87aede
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Bug #5: metrics.record_trade() only delegated to save_trade(), which executor.execute() already calls — every trade was written twice (deduped only by ON CONFLICT DO NOTHING). Remove the redundant call and the now-dead method. RealExecutor.execute() raises NotImplementedError, so real mode is unaffected. Bug #6 (CYCLE SUMMARY): manifold accepted/rejected counters only increment on the active-signal path, so with MANIFOLD_SIGNAL_ENABLED=false they always printed 0/0 — print 'manifold_signal: disabled' instead. family_conflicts_prevented duplicated blocked_by_family (same counter printed twice); removed. gnews_cap was a dead variable with a misleading comment; removed. Bug #7 (/api/summary): total_trades was len() over a LIMIT-500 query — capped once history grows; counts now come from COUNT(*) via compute_metrics_from_db. cash_available was reimplemented in the API; extract cash_available() in paper.py (same formula, unchanged) and feed it from get_open_position_data() — the exact source/helper PaperExecutor.initialize() uses. Test asserts API and executor report identical cash for the same DB state. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
92 lines
3.8 KiB
Python
92 lines
3.8 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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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 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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