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"""
MACRO INTELLIGENCE — Buy Signal Context Layer
=============================================
Fetches and caches macro-level signals that enrich every buy decision.
All signals feed INTO the scoring — they never block an entry.
All APIs are free and require no authentication.
Signals gathered:
1. Crypto Fear & Greed Index (alternative.me)
2. Market-wide data (CoinGecko /global)
3. BTC + ETH + XRP 24h trends (CoinGecko /coins/markets)
4. Coin-specific 24h trend (CoinGecko)
5. Trending coins (CoinGecko /search/trending)
6. BTC 6h volume pattern (Kraken OHLC)
Output via get_entry_context(pair):
{
"fear_greed": int, # 0-100 (0=extreme fear, 100=extreme greed)
"fear_label": str, # "Extreme Fear" … "Extreme Greed"
"btc_24h": float, # BTC 24h % change
"eth_24h": float, # ETH 24h % change
"xrp_24h": float, # XRP 24h % change (payment coin correlation)
"market_24h": float, # Total market cap 24h % change
"btc_dominance": float, # BTC dominance %
"coin_24h": float, # This coin's own 24h % change
"coin_vol_usd": float, # This coin's 24h volume USD
"is_trending": bool, # Coin in CoinGecko top-10 trending
"btc_vol_pattern": str, # "rising" | "falling" | "flat" (6h vol trend)
"macro_score": float, # -2.0 → +2.0 composite — adds to candidate score
"signal_age_s": float, # Seconds since last refresh
}
macro_score feeds directly into find_best_target() candidate scoring.
Higher macro_score = stronger buy signal for this coin right now.
Low fear + coin outperforming + trending + rising volume = best buys.
Refresh interval: 10 minutes (MACRO_REFRESH_S).
Never blocks on network — returns last cached value on failure.
"""
import json
import logging
import threading
import time
import urllib.request
from typing import Dict, Optional
logger = logging.getLogger(__name__)
MACRO_REFRESH_S = 600 # Refresh every 10 minutes
_COINGECKO_IDS = { # Kraken base → CoinGecko ID
"XBT": "bitcoin",
"BTC": "bitcoin",
"ETH": "ethereum",
"XRP": "ripple",
"SOL": "solana",
"ADA": "cardano",
"DOGE": "dogecoin",
"DOT": "polkadot",
"LINK": "chainlink",
"LTC": "litecoin",
"XLM": "stellar",
"AVAX": "avalanche-2",
"ATOM": "cosmos",
"UNI": "uniswap",
"ALGO": "algorand",
"DASH": "dash",
"XMR": "monero",
"ZEC": "zcash",
"EOS": "eos",
"COMP": "compound-governance-token",
"FIL": "filecoin",
"NEAR": "near",
"SUI": "sui",
"APT": "aptos",
"INJ": "injective-protocol",
"TIA": "celestia",
}
class MacroIntelligence:
"""
Buy signal context engine — enriches every entry decision with macro data.
All signals feed into scoring; nothing is blocked.
Usage:
macro = MacroIntelligence()
ctx = macro.get_entry_context("SOLUSDT")
score += ctx["macro_score"] # -2.0 → +2.0, part of the buy signal
"""
def __init__(self):
self._cache: dict = {}
self._coin_cache: Dict[str, dict] = {}
self._lock = threading.Lock()
self._last_refresh: float = 0.0
# Background refresh thread
self._thread = threading.Thread(target=self._bg_refresh, daemon=True)
self._thread.start()
# ------------------------------------------------------------------ #
# PUBLIC INTERFACE #
# ------------------------------------------------------------------ #
def get_entry_context(self, kraken_pair: str = "") -> dict:
"""
Return full macro context for a given pair.
kraken_pair: e.g. "SOLUSDT", "ETHUSD", "XBTUSD" or just "SOL"
"""
with self._lock:
cache = dict(self._cache)
if not cache:
# First call before any data — trigger sync fetch
self._refresh()
with self._lock:
cache = dict(self._cache)
# Extract coin-specific data
base = self._extract_base(kraken_pair)
cg_id = _COINGECKO_IDS.get(base, "")
coin_data = self._coin_cache.get(cg_id, {})
fg = cache.get("fear_greed", 50)
fg_label = cache.get("fear_label", "Neutral")
btc_24h = cache.get("btc_24h", 0.0)
eth_24h = cache.get("eth_24h", 0.0)
xrp_24h = cache.get("xrp_24h", 0.0)
market_24h= cache.get("market_24h", 0.0)
btc_dom = cache.get("btc_dominance", 55.0)
trending = cache.get("trending_symbols", [])
vol_pat = cache.get("btc_vol_pattern", "flat")
coin_24h = coin_data.get("price_change_24h", btc_24h * 0.7)
coin_vol = coin_data.get("volume_24h", 0.0)
is_trend = base in trending or cg_id in trending
# ── Composite macro_score (-2 → +2) ──────────────────────────────
score = 0.0
# Fear & Greed contribution (-1 → +1)
if fg >= 60: score += 1.0 # Greed — momentum favours entry
elif fg >= 45: score += 0.5
elif fg >= 30: score += 0.0
elif fg >= 20: score -= 0.5
else: score -= 1.0 # Extreme Fear
# Market trend contribution (-0.5 → +0.5)
if market_24h >= 2: score += 0.5
elif market_24h >= 0: score += 0.2
elif market_24h >= -3:score -= 0.2
else: score -= 0.5
# BTC dominance — high dom = altcoins underperforming (-0.25 → +0.25)
if btc_dom < 48: score += 0.25 # altcoin season
elif btc_dom > 58: score -= 0.25 # BTC-only market
# Trending bonus (+0.25)
if is_trend:
score += 0.25
# BTC volume pattern (+0.0 / +0.25)
if vol_pat == "rising":
score += 0.25
# Coin-specific trend vs BTC
if coin_24h > btc_24h + 1.0: score += 0.25 # outperforming
elif coin_24h < btc_24h - 2.0: score -= 0.25 # lagging badly
macro_score = round(max(-2.0, min(2.0, score)), 3)
return {
"fear_greed": fg,
"fear_label": fg_label,
"btc_24h": btc_24h,
"eth_24h": eth_24h,
"xrp_24h": xrp_24h,
"market_24h": market_24h,
"btc_dominance": btc_dom,
"coin_24h": coin_24h,
"coin_vol_usd": coin_vol,
"is_trending": is_trend,
"btc_vol_pattern": vol_pat,
"macro_score": macro_score, # feeds into candidate score
"signal_age_s": round(time.time() - self._last_refresh, 0),
}
def summary_line(self, kraken_pair: str = "") -> str:
"""One-line human-readable summary logged alongside every buy signal."""
ctx = self.get_entry_context(kraken_pair)
trend_tag = " TRENDING" if ctx["is_trending"] else ""
return (
f"MACRO | F&G={ctx['fear_greed']} {ctx['fear_label']} | "
f"Mkt={ctx['market_24h']:+.1f}% | BTC={ctx['btc_24h']:+.1f}% | "
f"BTC.dom={ctx['btc_dominance']:.1f}% | "
f"Coin={ctx['coin_24h']:+.1f}% | "
f"score={ctx['macro_score']:+.2f} | vol={ctx['btc_vol_pattern']}"
f"{trend_tag}"
)
# ------------------------------------------------------------------ #
# INTERNAL FETCH LOGIC #
# ------------------------------------------------------------------ #
def _bg_refresh(self):
"""Background thread — refreshes every MACRO_REFRESH_S seconds."""
while True:
try:
if time.time() - self._last_refresh >= MACRO_REFRESH_S:
self._refresh()
except Exception:
pass
time.sleep(60)
def _refresh(self):
"""Fetch all signals and update cache."""
new_cache = {}
try:
# 1. Fear & Greed
fg_data = self._fetch("https://api.alternative.me/fng/?limit=1")
if fg_data:
item = fg_data["data"][0]
new_cache["fear_greed"] = int(item["value"])
new_cache["fear_label"] = item["value_classification"]
# 2. CoinGecko global
global_data = self._fetch("https://api.coingecko.com/api/v3/global")
if global_data:
d = global_data["data"]
new_cache["market_24h"] = d.get("market_cap_change_percentage_24h_usd", 0.0)
new_cache["btc_dominance"] = d.get("market_cap_percentage", {}).get("btc", 55.0)
# 3. Core coins — BTC, ETH, XRP + all tracked coins
all_ids = "bitcoin,ethereum,ripple," + ",".join(set(_COINGECKO_IDS.values()))
url = (
"https://api.coingecko.com/api/v3/coins/markets"
f"?vs_currency=usd&ids={all_ids}"
"&order=market_cap_desc&per_page=60&page=1"
"&sparkline=false&price_change_percentage=24h"
)
coins = self._fetch(url)
if coins:
coin_map = {c["id"]: c for c in coins}
btc = coin_map.get("bitcoin", {})
eth = coin_map.get("ethereum", {})
xrp = coin_map.get("ripple", {})
new_cache["btc_24h"] = btc.get("price_change_percentage_24h", 0.0)
new_cache["eth_24h"] = eth.get("price_change_percentage_24h", 0.0)
new_cache["xrp_24h"] = xrp.get("price_change_percentage_24h", 0.0)
# Per-coin cache
with self._lock:
for cid, cdata in coin_map.items():
self._coin_cache[cid] = {
"price_change_24h": cdata.get("price_change_percentage_24h", 0.0),
"volume_24h": cdata.get("total_volume", 0.0),
"current_price": cdata.get("current_price", 0.0),
}
# 4. Trending coins
trending = self._fetch("https://api.coingecko.com/api/v3/search/trending")
if trending:
syms = [c["item"]["symbol"].upper() for c in trending.get("coins", [])[:10]]
ids = [c["item"]["id"] for c in trending.get("coins", [])[:10]]
new_cache["trending_symbols"] = syms + ids
# 5. BTC 6h volume pattern from Kraken
ohlc = self._fetch("https://api.kraken.com/0/public/OHLC?pair=XBTUSD&interval=60")
if ohlc and not ohlc.get("error"):
candles = list(ohlc["result"].values())[0][-7:] # last 7h
vols = [float(c[6]) for c in candles]
if len(vols) >= 4:
recent = sum(vols[-3:])
prior = sum(vols[-6:-3])
if prior > 0:
ratio = recent / prior
if ratio >= 1.25:
new_cache["btc_vol_pattern"] = "rising"
elif ratio <= 0.75:
new_cache["btc_vol_pattern"] = "falling"
else:
new_cache["btc_vol_pattern"] = "flat"
if new_cache:
with self._lock:
self._cache.update(new_cache)
self._last_refresh = time.time()
logger.info(
f"MACRO REFRESH | F&G={new_cache.get('fear_greed','?')} "
f"{new_cache.get('fear_label','?')} | "
f"BTC={new_cache.get('btc_24h',0):+.1f}% | "
f"Mkt={new_cache.get('market_24h',0):+.1f}% | "
f"BTC.dom={new_cache.get('btc_dominance',0):.1f}% | "
f"vol={new_cache.get('btc_vol_pattern','?')}"
)
except Exception as e:
logger.warning(f"Macro refresh error: {e}")
def _fetch(self, url: str, timeout: int = 8):
try:
req = urllib.request.Request(url)
req.add_header("User-Agent", "AureonMacroIntel/1.0")
with urllib.request.urlopen(req, timeout=timeout) as resp:
return json.loads(resp.read().decode())
except Exception as e:
logger.debug(f"Macro fetch failed: {url[:60]}... {e}")
return None
@staticmethod
def _extract_base(pair: str) -> str:
"""Extract base coin symbol from various pair formats."""
pair = pair.upper()
# Strip common quote suffixes
for suffix in ("USD", "USDT", "USDC", "EUR", "BTC", "ETH"):
if pair.endswith(suffix) and len(pair) > len(suffix):
base = pair[:-len(suffix)]
# Kraken internal prefix X for crypto
if base.startswith("X") and len(base) > 1 and base[1:] in _COINGECKO_IDS:
return base[1:]
return base
return pair
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