feat(macro): Macro Vibes — 8-indicator daily snapshot + composite score
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>
This commit is contained in:
k
@@ -0,0 +1,335 @@
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"""Individual fetchers for each macro indicator.
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Each function is an async coroutine that returns a dict shaped like:
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{ "value": float | int | None,
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"label": Optional[str], # only some indicators
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"raw": <upstream payload> } # for debugging / re-scoring
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Every fetcher MUST tolerate upstream failure — return {"value": None} rather
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than raise — so one dead API can't take down the whole snapshot.
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Public, free, no-key sources only:
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AHR999 : derived from BTC daily closes (Binance fapi)
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Altcoin Season Index : CoinGecko top-50 90-day relative performance
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Fear & Greed : api.alternative.me/fng (no auth)
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BTC Dominance : CoinGecko /global
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ETH/BTC Ratio : Binance kline ETHBTC daily
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Stablecoin Supply : DeFiLlama /stablecoins
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ETF Net Flow (1d) : Farside Investors HTML scrape
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BTC Open Interest : Binance fapi /futures/data/openInterestHist
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"""
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from __future__ import annotations
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import logging
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import math
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import re
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from datetime import datetime, timedelta, timezone
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from typing import Any, Optional
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import httpx
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logger = logging.getLogger(__name__)
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# A vanilla User-Agent. CoinGecko + alternative.me + DeFiLlama all happily
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# serve "Mozilla/5.0"; some get suspicious of anything that looks bot-like
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# (e.g. python-httpx default UA returns 400 on /global).
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UA = {"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 14_0) AppleWebKit/605.1.15"}
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DEFAULT_TIMEOUT = 20
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def _none_on_fail(name: str):
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"""Decorator: log+swallow exceptions from a fetcher and return {value: None}."""
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def deco(fn):
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async def wrapper(*a, **kw):
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try:
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return await fn(*a, **kw)
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except Exception as exc:
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logger.warning("macro fetch %s failed: %s (%s)",
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name, type(exc).__name__, exc)
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return {"value": None, "raw": {"error": f"{type(exc).__name__}: {exc}"}}
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return wrapper
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return deco
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def _utc_midnight_ms(now: Optional[datetime] = None) -> int:
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dt = now or datetime.now(timezone.utc)
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midnight = dt.replace(hour=0, minute=0, second=0, microsecond=0)
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return int(midnight.timestamp() * 1000)
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def _drop_in_progress_daily_klines(rows: list[list], now: Optional[datetime] = None) -> list[list]:
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"""Binance daily klines are keyed by OPEN time. If the latest row opened at
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today's 00:00 UTC, that candle is still in progress and should not be used
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for daily snapshots."""
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if not rows:
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return rows
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cutoff = _utc_midnight_ms(now)
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return [row for row in rows if int(row[0]) < cutoff]
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def _latest_closed_daily_point(rows: list[dict], now: Optional[datetime] = None) -> Optional[dict]:
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"""Same idea as `_drop_in_progress_daily_klines`, but for daily point
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series keyed by `timestamp`."""
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if not rows:
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return None
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cutoff = _utc_midnight_ms(now)
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closed = [row for row in rows if int(row.get("timestamp", 0)) < cutoff]
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return closed[-1] if closed else None
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def _parse_farside_latest_total(html: str) -> dict:
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"""Extract the most recent dated row from Farside's historical table.
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The all-data table is chronological from oldest to newest, so the first
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date row is NOT the latest one.
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"""
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m = re.search(r"<tbody[^>]*>(.*?)</tbody>", html, re.DOTALL | re.IGNORECASE)
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if not m:
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return {"value": None, "raw": {"error": "tbody not found"}}
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body = m.group(1)
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rows = re.findall(r"<tr[^>]*>(.*?)</tr>", body, re.DOTALL | re.IGNORECASE)
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latest: Optional[dict] = None
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for row in rows:
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cells = re.findall(r"<td[^>]*>(.*?)</td>", row, re.DOTALL | re.IGNORECASE)
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if not cells:
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continue
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date_text = re.sub(r"<[^>]+>", "", cells[0]).strip()
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if not re.match(r"\d{1,2}\s+[A-Za-z]+\s+\d{4}", date_text):
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continue
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last_text = re.sub(r"<[^>]+>", "", cells[-1]).strip()
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num = last_text.replace(",", "").replace("(", "-").replace(")", "")
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try:
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millions = float(num)
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row_date = datetime.strptime(date_text, "%d %b %Y").date()
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except ValueError:
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continue
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candidate = {
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"value": round(millions * 1_000_000, 2),
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"raw": {"date": date_text, "millions_usd": millions},
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"_date": row_date,
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}
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if latest is None or candidate["_date"] > latest["_date"]:
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latest = candidate
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if latest is None:
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return {"value": None, "raw": {"error": "no parseable rows"}}
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latest.pop("_date", None)
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return latest
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# ── 1. AHR999 ───────────────────────────────────────────────────────────────
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# Formula: AHR999 = (price / 200d MA) × (price / age_fit_price)
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# age_fit_price = 10 ** (5.84 * log10(days_since_2009_01_03) - 17.01)
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# Below 0.45 historically marks accumulation zones; above 1.2 marks
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# "expensive" regime that invalidates a bottom thesis.
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_AHR999_GENESIS = datetime(2009, 1, 3, tzinfo=timezone.utc)
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@_none_on_fail("ahr999")
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async def fetch_ahr999() -> dict:
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"""Compute AHR999 from the last 200 daily BTC closes (Binance fapi)."""
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end_ms = int(datetime.now(timezone.utc).timestamp() * 1000)
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start_ms = end_ms - 260 * 24 * 3600 * 1000 # extra buffer after dropping in-progress day
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async with httpx.AsyncClient(timeout=DEFAULT_TIMEOUT) as c:
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r = await c.get(
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"https://fapi.binance.com/fapi/v1/klines",
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params={"symbol": "BTCUSDT", "interval": "1d",
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"startTime": start_ms, "endTime": end_ms, "limit": 300},
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)
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r.raise_for_status()
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rows = _drop_in_progress_daily_klines(r.json())
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closes = [float(row[4]) for row in rows]
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if len(closes) < 200:
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return {"value": None, "raw": {"error": "insufficient candles", "have": len(closes)}}
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price = closes[-1]
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ma200 = sum(closes[-200:]) / 200
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days = (datetime.now(timezone.utc) - _AHR999_GENESIS).total_seconds() / 86400
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age_fit = 10 ** (5.84 * math.log10(days) - 17.01)
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ahr = (price / ma200) * (price / age_fit)
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return {
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"value": round(ahr, 4),
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"raw": {"price": price, "ma200": round(ma200, 2),
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"age_fit": round(age_fit, 2), "days": round(days, 1)},
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}
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# ── 2. Altcoin Season Index (blockchaincenter.net formula) ───────────────────
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# Take top-50 coins by market cap (excluding stablecoins + wrapped). Count how
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# many beat BTC's 90-day return. Result is the count, projected to 0-100.
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# 75+ = altseason, <25 = bitcoin season, middle = neutral.
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_STABLE_OR_WRAPPED = {
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"USDT", "USDC", "DAI", "BUSD", "TUSD", "USDD", "FDUSD", "PYUSD", "USDE",
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"WBTC", "WETH", "STETH", "WSTETH", "WEETH", "RETH",
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}
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@_none_on_fail("altcoin_season_index")
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async def fetch_altcoin_season_index() -> dict:
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"""Compute the Altcoin Season Index from CoinGecko /coins/markets.
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Original blockchaincenter.net formula uses a 90-day window, but
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CoinGecko's /coins/markets `price_change_percentage` parameter only
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accepts 1h/24h/7d/14d/30d/200d/1y — 90d returns HTTP 400. We use 30d
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as the closest practical proxy. Long-horizon altseason (which 90d
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captures better) would need per-coin /market_chart calls — 50× the
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API budget for a marginal definition improvement.
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"""
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async with httpx.AsyncClient(timeout=DEFAULT_TIMEOUT, headers=UA) as c:
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r = await c.get(
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"https://api.coingecko.com/api/v3/coins/markets",
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params={"vs_currency": "usd", "order": "market_cap_desc",
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"per_page": 60, "page": 1,
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"price_change_percentage": "30d"},
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)
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r.raise_for_status()
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rows = r.json()
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# Drop stablecoins + wrapped, keep top 50 of the remainder.
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eligible = [
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row for row in rows
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if (row.get("symbol") or "").upper() not in _STABLE_OR_WRAPPED
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and row.get("price_change_percentage_30d_in_currency") is not None
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][:50]
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if len(eligible) < 30:
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return {"value": None, "raw": {"error": "insufficient eligible coins",
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"have": len(eligible)}}
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btc_row = next((x for x in rows if x.get("symbol", "").upper() == "BTC"), None)
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btc_30d = btc_row.get("price_change_percentage_30d_in_currency") if btc_row else None
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if btc_30d is None:
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return {"value": None, "raw": {"error": "BTC 30d return missing"}}
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n_outperform = sum(
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1 for row in eligible
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if (row["price_change_percentage_30d_in_currency"] or -999) > btc_30d
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)
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# Project the count over `len(eligible)` to a 0–100 scale.
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index = (n_outperform / len(eligible)) * 100
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return {
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"value": round(index, 1),
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"raw": {"n_outperform": n_outperform, "of": len(eligible),
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"btc_30d_pct": round(btc_30d, 2), "window": "30d"},
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}
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# ── 3. Fear & Greed (alternative.me) ────────────────────────────────────────
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@_none_on_fail("fear_greed")
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async def fetch_fear_greed() -> dict:
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async with httpx.AsyncClient(timeout=DEFAULT_TIMEOUT, headers=UA) as c:
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r = await c.get("https://api.alternative.me/fng/?limit=1")
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r.raise_for_status()
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data = r.json()
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item = (data.get("data") or [None])[0]
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if not item:
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return {"value": None, "raw": data}
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return {
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"value": int(item.get("value", 0)),
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"label": item.get("value_classification"),
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"raw": item,
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}
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# ── 4. BTC Dominance (CoinGecko /global) ────────────────────────────────────
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@_none_on_fail("btc_dominance")
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async def fetch_btc_dominance() -> dict:
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async with httpx.AsyncClient(timeout=DEFAULT_TIMEOUT, headers=UA) as c:
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r = await c.get("https://api.coingecko.com/api/v3/global")
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r.raise_for_status()
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data = r.json()
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pct = (data.get("data", {}).get("market_cap_percentage", {}) or {}).get("btc")
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if pct is None:
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return {"value": None, "raw": data}
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return {"value": round(float(pct), 2),
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"raw": {"total_mcap_usd": data["data"]["total_market_cap"].get("usd")}}
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# ── 5. ETH/BTC Ratio (Binance ETHBTC daily) ──────────────────────────────────
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@_none_on_fail("eth_btc_ratio")
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async def fetch_eth_btc_ratio() -> dict:
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async with httpx.AsyncClient(timeout=DEFAULT_TIMEOUT) as c:
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r = await c.get(
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"https://fapi.binance.com/fapi/v1/klines",
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params={"symbol": "ETHBTC", "interval": "1d", "limit": 3},
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)
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# fapi may 404 ETHBTC; fall back to spot kline endpoint via data-api host.
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if r.status_code == 400 or r.status_code == 404:
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r = await c.get(
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"https://data-api.binance.vision/api/v3/klines",
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params={"symbol": "ETHBTC", "interval": "1d", "limit": 3},
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)
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r.raise_for_status()
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rows = _drop_in_progress_daily_klines(r.json())
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if not rows:
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return {"value": None, "raw": rows}
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close = float(rows[-1][4])
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return {"value": round(close, 6), "raw": {"close": close, "n_rows": len(rows)}}
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# ── 6. Stablecoin Total Supply (DeFiLlama) ───────────────────────────────────
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@_none_on_fail("stablecoin_supply")
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async def fetch_stablecoin_supply() -> dict:
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async with httpx.AsyncClient(timeout=DEFAULT_TIMEOUT, headers=UA) as c:
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r = await c.get(
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"https://stablecoins.llama.fi/stablecoins",
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params={"includePrices": "true"},
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)
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r.raise_for_status()
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data = r.json()
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# Sum circulating peggedUSD across all stables.
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total = 0.0
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for stable in data.get("peggedAssets", []):
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circ = stable.get("circulating", {}).get("peggedUSD")
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if isinstance(circ, (int, float)):
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total += float(circ)
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if total <= 0:
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return {"value": None, "raw": {"error": "no peggedUSD totals found"}}
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return {"value": round(total, 2),
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"raw": {"n_stables": len(data.get("peggedAssets", []))}}
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# ── 7. BTC Spot ETF Net Flow 1d (Farside) ────────────────────────────────────
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# Farside doesn't have a JSON API but their daily flow page is parseable. We
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# pull the most recent row from the All ETFs sum.
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@_none_on_fail("etf_flow")
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async def fetch_etf_flow() -> dict:
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async with httpx.AsyncClient(timeout=DEFAULT_TIMEOUT, headers=UA,
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follow_redirects=True) as c:
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r = await c.get("https://farside.co.uk/bitcoin-etf-flow-all-data/")
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r.raise_for_status()
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return _parse_farside_latest_total(r.text)
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# ── 8. BTC Open Interest (Binance fapi) ──────────────────────────────────────
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@_none_on_fail("btc_open_interest")
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async def fetch_btc_open_interest() -> dict:
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async with httpx.AsyncClient(timeout=DEFAULT_TIMEOUT) as c:
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r = await c.get(
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"https://fapi.binance.com/futures/data/openInterestHist",
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params={"symbol": "BTCUSDT", "period": "1d", "limit": 4},
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)
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r.raise_for_status()
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rows = r.json()
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latest = _latest_closed_daily_point(rows)
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if not latest:
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return {"value": None, "raw": rows}
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notional = float(latest.get("sumOpenInterestValue", 0))
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return {"value": round(notional, 2),
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"raw": {"contracts": float(latest.get("sumOpenInterest", 0)),
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"timestamp": latest.get("timestamp")}}
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@@ -0,0 +1,102 @@
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"""Daily macro snapshot orchestrator.
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Runs every fetcher in parallel (each is independently fail-tolerant), computes
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the composite score against whatever came back, and UPSERTs one row keyed by
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today's date. Subsequent same-day invocations overwrite the row with newer
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data — i.e. you can re-run by hand to refresh after fixing a fetcher without
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duplicating snapshots.
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Schedule: app/main.py wires this to cron at 03:00 UTC (after KOL polls).
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"""
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from __future__ import annotations
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import asyncio
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import json
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import logging
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from datetime import datetime, timezone
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from sqlalchemy import select
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from app.database import AsyncSessionLocal
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from app.models import MacroSnapshot
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from . import fetchers, scoring
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logger = logging.getLogger(__name__)
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async def run_macro_poll() -> dict:
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"""Fetch every indicator and write today's snapshot row. Idempotent per
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calendar day — re-runs overwrite the existing row instead of inserting."""
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logger.info("[macro] poll starting")
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# Run every fetcher in parallel — they're all independent.
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results = await asyncio.gather(
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fetchers.fetch_ahr999(),
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fetchers.fetch_altcoin_season_index(),
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fetchers.fetch_fear_greed(),
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fetchers.fetch_btc_dominance(),
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fetchers.fetch_eth_btc_ratio(),
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fetchers.fetch_stablecoin_supply(),
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fetchers.fetch_etf_flow(),
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fetchers.fetch_btc_open_interest(),
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return_exceptions=False, # the decorator already catches everything
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)
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(ahr, alt, fg, dom, ebr, stab, etf, oi) = results
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# Pack into a values dict matching MacroSnapshot's columns.
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values = {
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"ahr999": ahr["value"],
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"altcoin_season_index": alt["value"],
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"fear_greed": fg["value"],
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"fear_greed_label": fg.get("label"),
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"btc_dominance_pct": dom["value"],
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"eth_btc_ratio": ebr["value"],
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"stablecoin_supply_usd": stab["value"],
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"etf_flow_net_usd_1d": etf["value"],
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"btc_open_interest_usd": oi["value"],
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}
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composite, regime = scoring.compute_composite(values)
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values["composite_score"] = composite
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values["regime_label"] = regime
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raw_blob = json.dumps({
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"ahr999": ahr.get("raw"),
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"altcoin_season": alt.get("raw"),
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"fear_greed": fg.get("raw"),
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"btc_dominance": dom.get("raw"),
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||||
"eth_btc_ratio": ebr.get("raw"),
|
||||
"stablecoin_supply": stab.get("raw"),
|
||||
"etf_flow": etf.get("raw"),
|
||||
"btc_open_interest": oi.get("raw"),
|
||||
}, default=str)[:8000] # cap to a sane Text column size
|
||||
|
||||
today = datetime.now(timezone.utc).date()
|
||||
now = datetime.now(timezone.utc).replace(tzinfo=None)
|
||||
|
||||
async with AsyncSessionLocal() as db:
|
||||
existing = (await db.execute(
|
||||
select(MacroSnapshot).where(MacroSnapshot.snapshot_date == today)
|
||||
)).scalar_one_or_none()
|
||||
|
||||
if existing:
|
||||
# Update in place — same-day re-run.
|
||||
for k, v in values.items():
|
||||
setattr(existing, k, v)
|
||||
existing.captured_at = now
|
||||
existing.raw_json = raw_blob
|
||||
else:
|
||||
db.add(MacroSnapshot(
|
||||
snapshot_date=today,
|
||||
captured_at=now,
|
||||
raw_json=raw_blob,
|
||||
**values,
|
||||
))
|
||||
await db.commit()
|
||||
|
||||
n_alive = sum(1 for v in [ahr, alt, fg, dom, ebr, stab, etf, oi] if v["value"] is not None)
|
||||
logger.info("[macro] poll done: %d/8 indicators OK, composite=%s (%s)",
|
||||
n_alive, composite, regime)
|
||||
return {"date": today.isoformat(), "alive": n_alive, "of": 8,
|
||||
"composite": composite, "regime": regime,
|
||||
"values": values}
|
||||
@@ -0,0 +1,149 @@
|
||||
"""Composite "regime" score over the macro indicators.
|
||||
|
||||
Goal: one -100..+100 number that says whether the overall macro setup tilts
|
||||
risk-on (positive) or risk-off (negative). Used purely as advisory display on
|
||||
the BTC page — does NOT (yet) feed sizing or trade decisions.
|
||||
|
||||
Design choices:
|
||||
* Each indicator contributes a per-indicator signal in [-1, +1].
|
||||
* Weights sum to 1.0 across the indicators we got. Missing indicators are
|
||||
excluded and the remaining weights are renormalised — so a single dead
|
||||
API doesn't drag the whole score toward zero.
|
||||
* The contrarian indicators (fear/greed, AHR999, funding) are intentionally
|
||||
inverted: extreme fear / cheap AHR999 / extreme funding all add positive
|
||||
score (buy-the-fear logic).
|
||||
|
||||
The weights here are seeded by hand from rough conviction. Re-tune after a
|
||||
few weeks of live data — see `composite_score` history vs realised forward
|
||||
returns.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Optional
|
||||
|
||||
# (weight, signal_function) per indicator. signal_function takes the raw value
|
||||
# and returns a number in [-1, +1].
|
||||
#
|
||||
# Inputs may be None if the upstream fetch failed; the orchestrator filters
|
||||
# them out and renormalises before summing.
|
||||
|
||||
|
||||
def _ahr999_signal(v: Optional[float]) -> Optional[float]:
|
||||
"""< 0.45 cheap → +1; 0.45–1.2 neutral; > 1.2 expensive → -1."""
|
||||
if v is None: return None
|
||||
if v < 0.45: return 1.0
|
||||
if v > 1.2: return -1.0
|
||||
# Linear interpolation between 0.45 and 1.2 → +1 to -1.
|
||||
return 1.0 - 2 * ((v - 0.45) / (1.2 - 0.45))
|
||||
|
||||
|
||||
def _altseason_signal(v: Optional[float]) -> Optional[float]:
|
||||
"""High altseason index = risk-on (+1), low = BTC season (defensive but
|
||||
not bearish, so a mild positive)."""
|
||||
if v is None: return None
|
||||
if v >= 75: return 0.7 # altseason — generally bullish risk
|
||||
if v <= 25: return 0.3 # BTC-only — defensive but not a bear signal
|
||||
return (v - 50) / 50 # linear, -0.5 to +0.5
|
||||
|
||||
|
||||
def _fear_greed_signal(v: Optional[int]) -> Optional[float]:
|
||||
"""Contrarian: extreme fear → buy (+1), extreme greed → sell (-1)."""
|
||||
if v is None: return None
|
||||
# Map 0..100 → +1..-1 (inverted, contrarian).
|
||||
return (50 - v) / 50
|
||||
|
||||
|
||||
def _btc_dominance_signal(v: Optional[float]) -> Optional[float]:
|
||||
"""Hard to read in isolation — only inform on extremes.
|
||||
Very high dominance often signals fear (risk-off into BTC) → mild bearish.
|
||||
Very low = altseason froth → also mild bearish (cycle late). Mid = neutral."""
|
||||
if v is None: return None
|
||||
if v >= 60: return -0.3
|
||||
if v <= 40: return -0.3
|
||||
return 0.0
|
||||
|
||||
|
||||
def _eth_btc_signal(v: Optional[float]) -> Optional[float]:
|
||||
"""Rising ETH/BTC = risk-on. No persistent absolute level matters; this is
|
||||
really a trend indicator. We approximate with absolute thresholds for the
|
||||
current cycle (2025-2026): > 0.04 risk-on, < 0.025 risk-off."""
|
||||
if v is None: return None
|
||||
if v >= 0.04: return 0.7
|
||||
if v <= 0.025: return -0.7
|
||||
return (v - 0.0325) / 0.0075 * 0.7 # linear in the middle
|
||||
|
||||
|
||||
def _stablecoin_supply_signal(v: Optional[float]) -> Optional[float]:
|
||||
"""Absolute supply tells us little day-over-day; we need the delta. Since
|
||||
this scorer sees only the snapshot, we treat presence as 0 and let the
|
||||
visual chart show the trend. Returns 0 if we have any value at all."""
|
||||
if v is None: return None
|
||||
return 0.0 # contribution = 0 until we wire in a trend lookup
|
||||
|
||||
|
||||
def _etf_flow_signal(v: Optional[float]) -> Optional[float]:
|
||||
"""Net inflow = institutional bid → +1, outflow → -1. Scale by magnitude."""
|
||||
if v is None: return None
|
||||
# Daily prints over $200M are notable; over $500M unusually large.
|
||||
abs_v = abs(v)
|
||||
sign = 1 if v > 0 else (-1 if v < 0 else 0)
|
||||
if abs_v >= 500_000_000: return 1.0 * sign
|
||||
if abs_v >= 200_000_000: return 0.7 * sign
|
||||
if abs_v >= 50_000_000: return 0.4 * sign
|
||||
return 0.1 * sign
|
||||
|
||||
|
||||
def _open_interest_signal(v: Optional[float]) -> Optional[float]:
|
||||
"""OI in isolation doesn't tell us direction — we'd need OI vs price
|
||||
correlation. Until we have a trend window, contribute 0."""
|
||||
if v is None: return None
|
||||
return 0.0
|
||||
|
||||
|
||||
# Weights (sum to 1.0 across all). When an indicator is missing, we drop its
|
||||
# weight and renormalise the rest.
|
||||
WEIGHTS = {
|
||||
"ahr999": 0.20,
|
||||
"altcoin_season": 0.10,
|
||||
"fear_greed": 0.20,
|
||||
"btc_dominance": 0.05,
|
||||
"eth_btc": 0.15,
|
||||
"stablecoin_supply": 0.05,
|
||||
"etf_flow": 0.20,
|
||||
"btc_open_interest": 0.05,
|
||||
}
|
||||
|
||||
|
||||
def compute_composite(values: dict) -> tuple[Optional[float], Optional[str]]:
|
||||
"""Return (score in [-100, +100], regime_label) or (None, None) if there
|
||||
isn't enough data to score.
|
||||
|
||||
`values` keys must match WEIGHTS keys (without "_signal" suffix).
|
||||
"""
|
||||
pairs = [
|
||||
("ahr999", _ahr999_signal(values.get("ahr999"))),
|
||||
("altcoin_season", _altseason_signal(values.get("altcoin_season_index"))),
|
||||
("fear_greed", _fear_greed_signal(values.get("fear_greed"))),
|
||||
("btc_dominance", _btc_dominance_signal(values.get("btc_dominance_pct"))),
|
||||
("eth_btc", _eth_btc_signal(values.get("eth_btc_ratio"))),
|
||||
("stablecoin_supply", _stablecoin_supply_signal(values.get("stablecoin_supply_usd"))),
|
||||
("etf_flow", _etf_flow_signal(values.get("etf_flow_net_usd_1d"))),
|
||||
("btc_open_interest", _open_interest_signal(values.get("btc_open_interest_usd"))),
|
||||
]
|
||||
alive = [(k, s) for k, s in pairs if s is not None]
|
||||
if not alive:
|
||||
return None, None
|
||||
|
||||
total_w = sum(WEIGHTS[k] for k, _ in alive)
|
||||
if total_w <= 0:
|
||||
return None, None
|
||||
|
||||
score = sum(WEIGHTS[k] * s for k, s in alive) / total_w * 100
|
||||
|
||||
if score >= 60: label = "BULL"
|
||||
elif score >= 20: label = "BULLISH"
|
||||
elif score > -20: label = "NEUTRAL"
|
||||
elif score > -60: label = "BEARISH"
|
||||
else: label = "BEAR"
|
||||
|
||||
return round(score, 1), label
|
||||
Reference in New Issue
Block a user