k
123 lines
4.5 KiB
Python
123 lines
4.5 KiB
Python
import logging
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from datetime import datetime, timedelta, timezone
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from fastapi import APIRouter, Depends, Query
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from sqlalchemy import select
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.database import get_db
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from app.models import BotTrade
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from app.schemas import BotPerformance
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from app.services.signed_request import (
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SignedReadCreds, signed_read_creds, verify_signed_request_any)
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router = APIRouter()
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logger = logging.getLogger(__name__)
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PERIOD_DAYS = 30
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ACTION_VIEW_PERFORMANCE = "view_performance"
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ACTION_VIEW_USER = "view_user"
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@router.get("/performance", response_model=BotPerformance)
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async def get_performance(
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wallet: str = Query(..., description="Wallet address (lower-cased internally)"),
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creds: SignedReadCreds = Depends(signed_read_creds),
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include_paper: bool = Query(
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False,
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description="Include paper (simulated) trades. Default false — this "
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"endpoint reports REAL-money performance, so paper fills "
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"(hl_order_id='paper') are excluded unless explicitly asked.",
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),
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db: AsyncSession = Depends(get_db),
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):
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wallet = wallet.lower().strip()
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verify_signed_request_any(
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actions=[ACTION_VIEW_PERFORMANCE, ACTION_VIEW_USER],
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wallet=wallet,
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timestamp_ms=creds.ts,
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signature=creds.sig,
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body=None,
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allow_replay=True,
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)
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since = datetime.now(timezone.utc).replace(tzinfo=None) - timedelta(days=PERIOD_DAYS)
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# Period basis is closed_at (realized-PnL-in-window), NOT opened_at. This
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# matches how the frontend Analytics page filters every time window, so the
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# 30d numbers shown there (and on the dashboard tile) use one consistent
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# basis. Ordering by closed_at also makes the drawdown equity curve reflect
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# realization order. A trade opened before the window but closed inside it
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# correctly counts; one opened inside but still open does not (closed_at IS
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# NOT NULL already excludes it).
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#
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# MONEY-SAFETY: by default exclude paper trades (hl_order_id == "paper").
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# Mixing simulated and real P&L into one "performance" number is misleading
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# — the dashboard tile that consumes this shows it as real performance.
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stmt = (
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select(BotTrade)
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.where(BotTrade.wallet_address == wallet)
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.where(BotTrade.closed_at.is_not(None))
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.where(BotTrade.closed_at >= since)
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)
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if not include_paper:
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stmt = stmt.where(BotTrade.hl_order_id != "paper")
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stmt = stmt.order_by(BotTrade.closed_at.asc())
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result = await db.execute(stmt)
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trades = result.scalars().all()
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total_trades = len(trades)
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if total_trades == 0:
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return BotPerformance(
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period_days=PERIOD_DAYS,
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total_trades=0,
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win_rate=0.0,
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net_pnl_usd=0.0,
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avg_hold_seconds=0.0,
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max_drawdown_pct=0.0,
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)
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# Only include trades with a known PnL in financial statistics.
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# Trades with pnl_usd=NULL were externally closed or unsettled — treating
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# them as 0 silently inflates trade count and distorts win rate / net PnL.
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settled = [t for t in trades if t.pnl_usd is not None]
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if not settled:
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return BotPerformance(
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period_days=PERIOD_DAYS,
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total_trades=total_trades,
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win_rate=0.0,
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net_pnl_usd=0.0,
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avg_hold_seconds=0.0,
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max_drawdown_pct=0.0,
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)
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winning = sum(1 for t in settled if t.pnl_usd > 0) # type: ignore[operator]
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win_rate = winning / len(settled)
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pnl_values = [t.pnl_usd for t in settled] # type: ignore[misc]
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net_pnl = sum(pnl_values)
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# For hold time use all trades (we always have opened_at + closed_at when closed)
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hold_values = [t.hold_seconds for t in trades if t.hold_seconds is not None]
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avg_hold = sum(hold_values) / len(hold_values) if hold_values else 0.0
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# Max drawdown: running peak → trough of cumulative PnL
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cumulative = 0.0
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peak = 0.0
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max_drawdown = 0.0
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for pnl in pnl_values:
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cumulative += pnl
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if cumulative > peak:
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peak = cumulative
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drawdown = (peak - cumulative) / peak * 100 if peak > 0 else 0.0
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if drawdown > max_drawdown:
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max_drawdown = drawdown
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return BotPerformance(
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period_days=PERIOD_DAYS,
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total_trades=total_trades,
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win_rate=round(win_rate, 4),
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net_pnl_usd=round(net_pnl, 2),
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avg_hold_seconds=round(avg_hold, 1),
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max_drawdown_pct=round(max_drawdown, 4),
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)
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