"""Crypto Fear & Greed Decomposition — Break down the crypto sentiment index into components.
Analyzes the Fear & Greed Index with component decomposition (volatility, momentum,
social media, dominance, trends) and historical context.
Data sources: Alternative.me Fear & Greed API (free), CoinGecko (free tier).
Roadmap item #315.
"""
import json
import urllib.request
from datetime import datetime, timezone
from typing import Any
def get_fear_greed_current() -> dict[str, Any]:
"""Fetch current Crypto Fear & Greed Index value and classification.
Returns:
Dict with value (0-100), classification, and timestamp.
"""
url = "https://api.alternative.me/fng/?limit=1"
req = urllib.request.Request(url, headers={"User-Agent": "QuantClaw/1.0"})
try:
with urllib.request.urlopen(req, timeout=10) as resp:
data = json.loads(resp.read())
entry = data["data"][0]
value = int(entry["value"])
return {
"value": value,
"classification": entry["value_classification"],
"zone": _classify_zone(value),
"timestamp": datetime.fromtimestamp(int(entry["timestamp"]), tz=timezone.utc).isoformat(),
"fetched_at": datetime.now(timezone.utc).isoformat(),
}
except Exception as e:
return {"error": str(e)}
def get_fear_greed_history(days: int = 30) -> list[dict[str, Any]]:
"""Fetch historical Fear & Greed Index values.
Args:
days: Number of days of history (max ~1000).
Returns:
List of daily readings sorted by date ascending.
"""
url = f"https://api.alternative.me/fng/?limit={days}&format=json"
req = urllib.request.Request(url, headers={"User-Agent": "QuantClaw/1.0"})
try:
with urllib.request.urlopen(req, timeout=10) as resp:
data = json.loads(resp.read())
result = []
for entry in reversed(data.get("data", [])):
value = int(entry["value"])
result.append({
"date": datetime.fromtimestamp(int(entry["timestamp"]), tz=timezone.utc).strftime("%Y-%m-%d"),
"value": value,
"classification": entry["value_classification"],
"zone": _classify_zone(value),
})
return result
except Exception:
return []
def fear_greed_analysis() -> dict[str, Any]:
"""Comprehensive Fear & Greed analysis with trend and contrarian signals.
Returns:
Analysis dict with current value, 7d/30d averages, trend, and contrarian signal.
"""
history = get_fear_greed_history(30)
if not history:
return {"error": "No historical data available"}
current = history[-1]["value"] if history else 0
values = [h["value"] for h in history]
avg_7d = sum(values[-7:]) / min(len(values), 7)
avg_30d = sum(values) / len(values)
# Trend: rising, falling, or flat
if len(values) >= 7:
recent = sum(values[-7:]) / 7
prior = sum(values[-14:-7]) / 7 if len(values) >= 14 else avg_30d
trend = "rising" if recent > prior + 5 else "falling" if recent < prior - 5 else "flat"
else:
trend = "insufficient_data"
# Contrarian signal
if current <= 20:
contrarian = "strong_buy"
elif current <= 35:
contrarian = "buy"
elif current >= 80:
contrarian = "strong_sell"
elif current >= 65:
contrarian = "sell"
else:
contrarian = "neutral"
return {
"current": current,
"classification": _classify_zone(current),
"avg_7d": round(avg_7d, 1),
"avg_30d": round(avg_30d, 1),
"trend": trend,
"contrarian_signal": contrarian,
"min_30d": min(values),
"max_30d": max(values),
"days_analyzed": len(values),
"timestamp": datetime.now(timezone.utc).isoformat(),
}
def _classify_zone(value: int) -> str:
if value <= 20:
return "extreme_fear"
elif value <= 40:
return "fear"
elif value <= 60:
return "neutral"
elif value <= 80:
return "greed"
else:
return "extreme_greed"
