k
6d50357d11
- /posts gains an optional ?source= filter. The Macro page was pulling the latest-500 posts globally and filtering client-side; rare scanner signals (btc_bottom_reversal ~2-4/cycle, funding_reversal hourly) got pushed off the page by frequent Trump posts, so Macro falsely showed "no signals". - /performance now filters and orders by closed_at (realized-PnL-in-window) instead of opened_at, so it shares ONE basis with the frontend Analytics page (which filters every window by closed_at). Boundary trades no longer land in one basis but not the other. - funding snapshot returns the actual `venue` (provider.name) so the frontend label follows the real data source instead of hardcoding "Binance". 72 tests pass. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
95 lines
3.1 KiB
Python
95 lines
3.1 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 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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ts: int = Query(..., description="Signed timestamp (ms)"),
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sig: str = Query(..., description="EIP-191 signature"),
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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=ts,
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signature=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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result = await db.execute(
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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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.order_by(BotTrade.closed_at.asc())
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)
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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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winning = sum(1 for t in trades if (t.pnl_usd or 0) > 0)
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win_rate = winning / total_trades
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pnl_values = [(t.pnl_usd or 0.0) for t in trades]
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net_pnl = sum(pnl_values)
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hold_values = [(t.hold_seconds or 0) for t in trades]
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avg_hold = sum(hold_values) / len(hold_values)
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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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