k
4442e97f28
New backend pipeline: 8 free public macro signals fetched in parallel,
upserted once per calendar day, served via /api/macro/{snapshot,history}.
- AHR999 (computed from Binance 200d klines)
- Altcoin Season Index (CoinGecko top-50 30d)
- Fear & Greed (alternative.me)
- BTC dominance, ETH/BTC ratio
- Stablecoin supply (DeFiLlama)
- Spot BTC ETF net flow (Farside)
- BTC perp open interest (Binance fapi)
Each fetcher is independently @_none_on_fail decorated so one outage
can't take down the snapshot; scoring renormalises across whichever
indicators returned a value. Daily cron at 03:00 UTC; on startup a
fire-and-forget bootstrap fills today's row if missing.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
87 lines
2.9 KiB
Python
87 lines
2.9 KiB
Python
"""Macro indicators API.
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GET /api/macro/snapshot
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Latest macro snapshot (today's row, or the most recent available).
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GET /api/macro/history?days=30
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Time series of every indicator over the last N days. Used by the
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sparklines on the BTC page macro panel.
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"""
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from __future__ import annotations
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from datetime import datetime, timedelta, timezone
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from typing import Optional
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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 MacroSnapshot
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router = APIRouter(prefix="/macro", tags=["macro"])
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def _row_to_dict(r: MacroSnapshot) -> dict:
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"""Snapshot row → JSON-friendly dict, in the canonical indicator order
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the frontend lists them in.
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Order rule (see UI mock-up in BtcPageClient MacroPanel):
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1. AHR999
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2. Altcoin Season Index
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3. Fear & Greed
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4. BTC Dominance
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5. ETH/BTC Ratio
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6. Stablecoin Total Supply
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7. ETF Net Flow (1d)
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8. BTC Open Interest
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"""
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return {
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"date": r.snapshot_date.isoformat() if r.snapshot_date else None,
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"captured_at": r.captured_at.replace(tzinfo=timezone.utc).isoformat() if r.captured_at else None,
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"indicators": {
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"ahr999": r.ahr999,
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"altcoin_season_index": r.altcoin_season_index,
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"fear_greed": r.fear_greed,
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"fear_greed_label": r.fear_greed_label,
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"btc_dominance_pct": r.btc_dominance_pct,
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"eth_btc_ratio": r.eth_btc_ratio,
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"stablecoin_supply_usd": r.stablecoin_supply_usd,
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"etf_flow_net_usd_1d": r.etf_flow_net_usd_1d,
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"btc_open_interest_usd": r.btc_open_interest_usd,
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},
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"composite_score": r.composite_score,
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"regime_label": r.regime_label,
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}
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@router.get("/snapshot")
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async def get_snapshot(db: AsyncSession = Depends(get_db)) -> dict:
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"""Latest macro snapshot. May be null if poll hasn't run yet."""
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row = (await db.execute(
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select(MacroSnapshot).order_by(MacroSnapshot.snapshot_date.desc()).limit(1)
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)).scalar_one_or_none()
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if not row:
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return {"ok": False, "error": "no snapshots yet — poll has not run"}
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return {"ok": True, **_row_to_dict(row)}
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@router.get("/history")
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async def get_history(
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days: int = Query(default=30, ge=1, le=365),
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db: AsyncSession = Depends(get_db),
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) -> dict:
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"""Time series across the last N days — for the panel sparklines."""
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cutoff = (datetime.now(timezone.utc) - timedelta(days=days)).date()
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rows = (await db.execute(
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select(MacroSnapshot)
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.where(MacroSnapshot.snapshot_date >= cutoff)
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.order_by(MacroSnapshot.snapshot_date.asc())
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)).scalars().all()
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return {
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"ok": True,
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"days": days,
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"count": len(rows),
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"items": [_row_to_dict(r) for r in rows],
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}
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