polymarket-bot/api/main.py

"""
FastAPI Backend — serves metrics and trade data to the React dashboard.
"""
import asyncio
from contextlib import asynccontextmanager
from datetime import datetime, timezone
import os
import re

from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware

from bot.data.db import Database

# Matches the feat_str embedded in reasoning for trades from bayesian.py v2+:
# "fg=+0.0600 mom=+0.0000 news=+0.0000 mfld=-0.7483"
_FEAT_RE = re.compile(
    r"fg=([+-]?[\d.]+).*?mom=([+-]?[\d.]+).*?news=([+-]?[\d.]+).*?mfld=([+-]?[\d.]+)"
)


def _enrich_trade(trade: dict) -> dict:
    """Add days_open and signal_components to an open trade dict."""
    ts = trade.get("timestamp")
    if ts is not None:
        now = datetime.now(timezone.utc)
        if getattr(ts, "tzinfo", None) is None:
            ts = ts.replace(tzinfo=timezone.utc)
        trade["days_open"] = round((now - ts).total_seconds() / 86400, 1)
    else:
        trade["days_open"] = None

    reasoning = trade.get("reasoning") or ""
    m = _FEAT_RE.search(reasoning)
    trade["signal_components"] = (
        {"fg": float(m.group(1)), "mom": float(m.group(2)),
         "news": float(m.group(3)), "mfld": float(m.group(4))}
        if m else None
    )
    return trade

db = Database()


@asynccontextmanager
async def lifespan(app: FastAPI):
    await db.connect()
    yield
    await db.disconnect()


app = FastAPI(title="Polymarket Bot API", lifespan=lifespan)

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_methods=["GET"],
    allow_headers=["*"],
)


@app.get("/health")
async def health():
    return {"status": "ok", "paper_mode": os.getenv("PAPER_MODE", "true")}


@app.get("/api/metrics")
async def get_metrics():
    history = await db.get_metrics_history(days=42)
    if not history:
        return {"history": [], "latest": None}
    return {"history": history, "latest": history[0]}


@app.get("/api/trades")
async def get_trades(limit: int = 50, status: str = "open"):
    """
    status: "open" (default) | "closed" | "all"
    Open trades include days_open and signal_components {fg, mom, news, mfld}.
    """
    if status not in ("open", "closed", "all"):
        status = "open"
    filter_status = None if status == "all" else status
    trades = await db.get_recent_trades(limit=limit, status=filter_status)
    if filter_status == "open":
        trades = [_enrich_trade(t) for t in trades]
    return {"trades": trades, "count": len(trades), "status_filter": status}


@app.get("/api/summary")
async def get_summary():
    """Dashboard summary card data.

    All portfolio counts (total_trades, open_trades_count, total_deployed,
    cash_available) are computed live from the DB on every request.

    PnL and performance metrics come from the latest metrics_daily snapshot,
    which is written by the bot every cycle via MetricsTracker.update_daily_summary().
    After Fix 3, that snapshot is also DB-computed — not dependent on pod restarts.
    """
    latest_metrics, open_trades, all_trades, inverted, legacy_count = await asyncio.gather(
        db.get_metrics_history(days=1),
        db.get_recent_trades(limit=500, status="open"),
        db.get_recent_trades(limit=500),
        db.get_recently_closed_inverted(hours=24),
        db.get_legacy_incomplete_count(),
    )

    latest = latest_metrics[0] if latest_metrics else {}
    paper_bankroll = float(os.getenv("PAPER_BANKROLL", "10000"))
    total_deployed = sum(t.get("net_cost", 0) for t in open_trades)

    return {
        # ── Portfolio state (live from DB) ──────────────────────────────────
        "paper_mode":              os.getenv("PAPER_MODE", "true") == "true",
        "paper_bankroll":          paper_bankroll,
        "total_trades":            len(all_trades),           # exact, from DB
        "open_trades_count":       len(open_trades),          # exact, from DB
        "closed_trades_count":     len(all_trades) - len(open_trades),  # exact
        "total_deployed":          total_deployed,            # exact, from DB
        "cash_available":          max(0.0, paper_bankroll - total_deployed),  # exact
        "legacy_incomplete_count": legacy_count,              # exact, from DB
        "reentry_guard_blocks_24h": len(inverted),            # exact, from DB

        # ── P&L (from latest metrics_daily snapshot) ────────────────────────
        # unrealized_pnl_est: open positions, edge_net × net_cost − fee.
        #   Estimated — uses model signal, not live price. Source: open trades.
        # realized_pnl: closed positions with known resolution.
        #   Exact — computed from (resolution − entry_price) × shares.
        # total_pnl: sum of both.
        "unrealized_pnl_est": latest.get("unrealized_pnl_est") or 0,
        "realized_pnl":       latest.get("realized_pnl") or 0,
        "total_pnl":          latest.get("total_pnl") or 0,

        # ── Performance metrics (from latest metrics_daily snapshot) ─────────
        # win_rate: fraction of resolved closed trades where close_pnl > 0.
        #   null if fewer than 5 resolved trades. Source: closed+resolved trades.
        # sharpe_ratio: 0.0 — requires daily-return time series (not yet tracked).
        # calibration_score: 1 − Brier score on resolved trades (higher = better).
        #   null if fewer than 10 resolved trades. Source: closed+resolved trades.
        "win_rate":           latest.get("win_rate"),         # null if < 5 resolved
        "sharpe_ratio":       latest.get("sharpe_ratio") or 0,  # 0.0 until tracked
        "calibration_score":  latest.get("calibration_score"),  # null if < 10 resolved

        # ── Counters from snapshot ───────────────────────────────────────────
        "resolved_count":     latest.get("resolved_count") or 0,

        # ── Promotion gate ───────────────────────────────────────────────────
        # All thresholds must pass; null metrics count as not-ready.
        "promotion_ready": (
            (latest.get("sharpe_ratio") or 0) >= 0.5
            and (latest.get("win_rate") or 0) >= 0.52
            and (latest.get("calibration_score") or 0) >= 0.7
            and len(all_trades) >= 50
        ),
    }