chemavx
adf2917cda
CI/CD / build-and-push (push) Successful in 1m52s
Adds alpha attribution by dominant signal feature — which feat_*_lo had the largest absolute log-odds value on each trade. Changes: - _dominant_feature() helper in api/main.py: picks the winning feature from signal_components (threshold 0.0001, same as "triggered" in /api/metrics/features) - _enrich_trade() refactored to single exit point; adds dominant_feature field to every open trade in /api/trades - compute_attribution_from_db() in db.py: VALUES subquery finds dominant feature per trade in SQL, then aggregates trade_count/avg_edge_net/ unrealized_pnl_est/realized_pnl/resolved_count/win_rate per group - /api/metrics/attribution endpoint: returns attribution dict + total_attributed_trades No schema changes, no strategy changes. Pure observability. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
279 lines
12 KiB
Python
279 lines
12 KiB
Python
"""
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FastAPI Backend — serves metrics and trade data to the React dashboard.
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"""
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import asyncio
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from contextlib import asynccontextmanager
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from datetime import datetime, timezone
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import os
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import re
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from fastapi import FastAPI
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from fastapi.middleware.cors import CORSMiddleware
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from bot.data.db import Database
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# Phase 6 format (Phase 6+): values already in log-odds space.
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# "fg_lo=+0.1200 mom_lo=+0.0000 news_lo=+0.0000 mfld_lo=-0.7483 btc_dom_lo=+0.0000"
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_FEAT_RE_LO = re.compile(
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r"fg_lo=([+-]?[\d.]+).*?mom_lo=([+-]?[\d.]+).*?"
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r"news_lo=([+-]?[\d.]+).*?mfld_lo=([+-]?[\d.]+).*?btc_dom_lo=([+-]?[\d.]+)"
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)
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# Pre-Phase-6 format: raw probability-delta values (fg/mom need ×2 for log-odds).
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# "fg=+0.0600 mom=+0.0000 news=+0.0000 mfld=-0.7483"
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_FEAT_RE_RAW = re.compile(
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r"fg=([+-]?[\d.]+).*?mom=([+-]?[\d.]+).*?news=([+-]?[\d.]+).*?mfld=([+-]?[\d.]+)"
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)
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def _dominant_feature(sc: dict | None) -> str | None:
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"""Return the name of the signal_components key with the largest abs log-odds value.
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Returns None if signal_components is absent or all features are below threshold.
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Threshold 0.0001 matches the "triggered" definition used in /api/metrics/features.
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"""
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if not sc:
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return None
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candidates = {
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k: abs(v)
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for k, v in sc.items()
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if k != "unit" and v is not None and abs(v) > 0.0001
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}
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if not candidates:
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return None
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return max(candidates, key=candidates.__getitem__)
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def _enrich_trade(trade: dict) -> dict:
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"""Add days_open, signal_components (log-odds), and dominant_feature to a trade."""
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ts = trade.get("timestamp")
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if ts is not None:
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now = datetime.now(timezone.utc)
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if getattr(ts, "tzinfo", None) is None:
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ts = ts.replace(tzinfo=timezone.utc)
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trade["days_open"] = round((now - ts).total_seconds() / 86400, 1)
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else:
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trade["days_open"] = None
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# Prefer DB columns (Phase 6+) — exact, no parsing required.
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if trade.get("feat_fg_lo") is not None:
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sc = {
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"unit": "log_odds",
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"fg": trade["feat_fg_lo"],
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"mom": trade["feat_mom_lo"],
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"news": trade["feat_news_lo"],
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"mfld": trade["feat_mfld_lo"],
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"btc_dom": trade.get("feat_btc_dom_lo"),
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}
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else:
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# Fallback: parse reasoning string (trades before Phase 6 DB columns exist).
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reasoning = trade.get("reasoning") or ""
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m_lo = _FEAT_RE_LO.search(reasoning)
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m_raw = _FEAT_RE_RAW.search(reasoning)
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if m_lo:
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sc = {
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"unit": "log_odds",
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"fg": float(m_lo.group(1)),
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"mom": float(m_lo.group(2)),
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"news": float(m_lo.group(3)),
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"mfld": float(m_lo.group(4)),
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"btc_dom": float(m_lo.group(5)),
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}
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elif m_raw:
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# Pre-Phase-6: fg/mom are raw probability-deltas → multiply ×2.
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sc = {
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"unit": "log_odds",
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"fg": float(m_raw.group(1)) * 2,
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"mom": float(m_raw.group(2)) * 2,
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"news": float(m_raw.group(3)),
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"mfld": float(m_raw.group(4)),
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"btc_dom": None,
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}
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else:
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sc = None
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trade["signal_components"] = sc
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trade["dominant_feature"] = _dominant_feature(sc)
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return trade
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db = Database()
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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await db.connect()
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yield
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await db.disconnect()
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app = FastAPI(title="Polymarket Bot API", lifespan=lifespan)
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_methods=["GET"],
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allow_headers=["*"],
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)
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@app.get("/health")
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async def health():
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return {"status": "ok", "paper_mode": os.getenv("PAPER_MODE", "true")}
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@app.get("/api/metrics")
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async def get_metrics():
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history = await db.get_metrics_history(days=42)
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if not history:
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return {"history": [], "latest": None}
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return {"history": history, "latest": history[0]}
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@app.get("/api/trades")
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async def get_trades(limit: int = 50, status: str = "open"):
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"""
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status: "open" (default) | "closed" | "all"
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Open trades include days_open and signal_components {fg, mom, news, mfld}.
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"""
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if status not in ("open", "closed", "all"):
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status = "open"
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filter_status = None if status == "all" else status
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trades = await db.get_recent_trades(limit=limit, status=filter_status)
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if filter_status == "open":
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trades = [_enrich_trade(t) for t in trades]
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return {"trades": trades, "count": len(trades), "status_filter": status}
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@app.get("/api/metrics/features")
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async def get_feature_metrics():
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"""Per-signal-feature performance breakdown — all values in log-odds space.
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Each feature key contains:
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unit "log_odds" (common unit for all features)
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materiality_threshold |lo| threshold for "material" classification
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triggered_count trades where |feat_lo| > 0.0001 (signal fired)
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material_count trades where |feat_lo| >= threshold (moved the model)
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avg_contribution_lo mean signed contribution (triggered trades)
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avg_abs_contribution_lo mean absolute contribution (triggered trades)
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avg_edge_net_when_material mean edge_net for material trades
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unrealized_pnl_est estimated open-position PnL (triggered trades)
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realized_pnl sum close_pnl for resolved triggered trades
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resolved_count closed triggered trades with known outcome
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win_rate null if resolved_count < 5
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net_positive_count triggered trades where feature pushed BUY direction
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net_negative_count triggered trades where feature pushed SELL direction
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NULL values in resolved_count / win_rate are expected early in the paper run.
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"""
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features = await db.compute_feature_metrics_from_db()
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return {"features": features}
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@app.get("/api/trades/legacy")
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async def get_legacy_trades():
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"""Trades with NULL edge_net — pre-Phase-1 data, excluded from PnL estimates.
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These trades have no signal quality information (edge_net, final_prob)
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and are excluded from unrealized_pnl_est in /api/summary.
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They may also be missing feat_*_lo columns if the reasoning string
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predates the Phase 6 format.
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"""
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trades = await db.get_legacy_incomplete_trades()
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return {"trades": trades, "count": len(trades)}
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@app.get("/api/metrics/attribution")
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async def get_attribution():
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"""Alpha attribution by dominant signal feature.
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Groups Phase 6 trades by their dominant feature — the feat_*_lo with the
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largest absolute log-odds value — to reveal which signal is actually
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generating opportunities.
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Each key in "attribution" contains:
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trade_count trades where this feature was dominant
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avg_edge_net mean net edge for those trades
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unrealized_pnl_est open-position PnL estimate (edge_net × net_cost − fee)
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realized_pnl sum close_pnl for resolved trades in this group
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resolved_count closed trades with known outcome
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win_rate null if resolved_count < 5
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"none" appears for Phase 6 trades where all feat_*_lo values are < 0.0001
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(signals fired but below trigger threshold).
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Only Phase 6 trades (feat_fg_lo IS NOT NULL) are included.
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Pre-Phase-6 trades appear in /api/trades/legacy instead.
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"""
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attribution = await db.compute_attribution_from_db()
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total = sum(v["trade_count"] for v in attribution.values())
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return {"attribution": attribution, "total_attributed_trades": total}
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@app.get("/api/summary")
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async def get_summary():
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"""Dashboard summary card data.
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All portfolio counts (total_trades, open_trades_count, total_deployed,
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cash_available) are computed live from the DB on every request.
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PnL and performance metrics come from the latest metrics_daily snapshot,
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which is written by the bot every cycle via MetricsTracker.update_daily_summary().
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After Fix 3, that snapshot is also DB-computed — not dependent on pod restarts.
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"""
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latest_metrics, open_trades, all_trades, inverted, legacy_count = await asyncio.gather(
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db.get_metrics_history(days=1),
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db.get_recent_trades(limit=500, status="open"),
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db.get_recent_trades(limit=500),
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db.get_recently_closed_inverted(hours=24),
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db.get_legacy_incomplete_count(),
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)
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latest = latest_metrics[0] if latest_metrics else {}
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paper_bankroll = float(os.getenv("PAPER_BANKROLL", "10000"))
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total_deployed = sum(t.get("net_cost", 0) for t in open_trades)
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return {
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# ── Portfolio state (live from DB) ──────────────────────────────────
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"paper_mode": os.getenv("PAPER_MODE", "true") == "true",
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"paper_bankroll": paper_bankroll,
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"total_trades": len(all_trades), # exact, from DB
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"open_trades_count": len(open_trades), # exact, from DB
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"closed_trades_count": len(all_trades) - len(open_trades), # exact
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"total_deployed": total_deployed, # exact, from DB
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"cash_available": max(0.0, paper_bankroll - total_deployed), # exact
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"legacy_incomplete_count": legacy_count, # exact, from DB
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"reentry_guard_blocks_24h": len(inverted), # exact, from DB
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# ── P&L (from latest metrics_daily snapshot) ────────────────────────
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# unrealized_pnl_est: open positions, edge_net × net_cost − fee.
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# Estimated — uses model signal, not live price. Source: open trades.
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# realized_pnl: closed positions with known resolution.
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# Exact — computed from (resolution − entry_price) × shares.
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# total_pnl: sum of both.
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"unrealized_pnl_est": latest.get("unrealized_pnl_est") or 0,
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"realized_pnl": latest.get("realized_pnl") or 0,
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"total_pnl": latest.get("total_pnl") or 0,
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# ── Performance metrics (from latest metrics_daily snapshot) ─────────
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# win_rate: fraction of resolved closed trades where close_pnl > 0.
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# null if fewer than 5 resolved trades. Source: closed+resolved trades.
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# sharpe_ratio: 0.0 — requires daily-return time series (not yet tracked).
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# calibration_score: 1 − Brier score on resolved trades (higher = better).
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# null if fewer than 10 resolved trades. Source: closed+resolved trades.
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"win_rate": latest.get("win_rate"), # null if < 5 resolved
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"sharpe_ratio": latest.get("sharpe_ratio") or 0, # 0.0 until tracked
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"calibration_score": latest.get("calibration_score"), # null if < 10 resolved
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# ── Counters from snapshot ───────────────────────────────────────────
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"resolved_count": latest.get("resolved_count") or 0,
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# ── Promotion gate ───────────────────────────────────────────────────
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# All thresholds must pass; null metrics count as not-ready.
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"promotion_ready": (
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(latest.get("sharpe_ratio") or 0) >= 0.5
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and (latest.get("win_rate") or 0) >= 0.52
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and (latest.get("calibration_score") or 0) >= 0.7
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and len(all_trades) >= 50
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),
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}
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