chemavx
5a3df975d9
CI/CD / build-and-push (push) Failing after 1m20s
total_pnl now uses edge_net × net_cost instead of (0.5 - entry_price) × shares. The old formula overestimated BUY_NO trades at low entry prices by 3–10× because buying at price 0.158 yields 3164 shares — any exit-at-0.5 assumption produced $1072 PnL on $500 deployed. edge_net × net_cost is bounded by net_cost per trade and uses the model's own signal, giving $122 for the same position. calibration_score is now None (null in API) instead of 1 - 2×|avg_edge|. That formula was not a real calibration: it requires knowing market resolutions (YES=1/NO=0) which we do not store yet. Returning null is more honest than returning 0.0 or a meaningless proxy. Fix 3 will compute it from closed trades. check_promotion_thresholds updated to handle None calibration (null → not ready). Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
132 lines
4.5 KiB
Python
132 lines
4.5 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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# Matches the feat_str embedded in reasoning for trades from bayesian.py v2+:
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# "fg=+0.0600 mom=+0.0000 news=+0.0000 mfld=-0.7483"
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_FEAT_RE = re.compile(
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r"fg=([+-]?[\d.]+).*?mom=([+-]?[\d.]+).*?news=([+-]?[\d.]+).*?mfld=([+-]?[\d.]+)"
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)
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def _enrich_trade(trade: dict) -> dict:
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"""Add days_open and signal_components to an open trade dict."""
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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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reasoning = trade.get("reasoning") or ""
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m = _FEAT_RE.search(reasoning)
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trade["signal_components"] = (
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{"fg": float(m.group(1)), "mom": float(m.group(2)),
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"news": float(m.group(3)), "mfld": float(m.group(4))}
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if m else None
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)
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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/summary")
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async def get_summary():
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"""Dashboard summary card data."""
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history = await db.get_metrics_history(days=1)
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open_trades, all_trades, inverted, legacy_count = await asyncio.gather(
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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 = history[0] if history 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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"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),
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"open_trades_count": len(open_trades),
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"closed_trades_count": len(all_trades) - len(open_trades),
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"total_deployed": total_deployed,
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"cash_available": max(0.0, paper_bankroll - total_deployed),
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"legacy_incomplete_count": legacy_count,
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"reentry_guard_blocks_24h": len(inverted),
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# Metrics from latest metrics_daily snapshot (computed by MetricsTracker).
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# total_pnl: estimated unrealized PnL for open trades in the current bot
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# session — uses edge_net × net_cost (model edge on deployed
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# capital). Resets to 0 on pod restart until Fix 3 is applied.
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# calibration_score: null until market resolution data is available
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# (requires close_price / outcome per closed trade).
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"total_pnl": latest.get("total_pnl", 0),
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"win_rate": latest.get("win_rate", 0),
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"sharpe_ratio": latest.get("sharpe_ratio", 0),
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"calibration_score": latest.get("calibration_score"), # null if unavailable
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"promotion_ready": (
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latest.get("sharpe_ratio", 0) >= 0.5
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and latest.get("win_rate", 0) >= 0.52
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and (latest.get("calibration_score") or 0) >= 0.7 # null → not ready
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and len(all_trades) >= 50
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),
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}
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