Crypto Sentiment & Fear/Greed Analysis

🎯 Alternative.me Fear & Greed Index

The Fear & Greed Index is a free, daily sentiment indicator that measures market psychology by analyzing cryptocurrency volatility, market momentum, dominance, trending, and social volume. Values range from 0 (Extreme Fear) to 100 (Extreme Greed).

Index Zones

0–24: 😱 Extreme Fear — panic selling, potential contrarian buy opportunity
25–44: 😨 Fear — uncertainty, sellers gaining momentum
45–55: 😐 Neutral — balanced sentiment, choppy trading
56–74: 😏 Greed — risk appetite rising, FOMO building
75–100: 🤑 Extreme Greed — euphoria, potential pullback risk

API Endpoint: https://api.alternative.me/fng/?limit=30&format=json (no key required)

import requests
from datetime import datetime

def get_fear_greed(limit: int = 30) -> list:
    """
    Fetch Fear & Greed Index history.
    Values: 0 (Extreme Fear) → 100 (Extreme Greed)
    Zones: 0-24 Extreme Fear | 25-44 Fear | 45-55 Neutral | 56-74 Greed | 75-100 Extreme Greed
    """
    url = f"https://api.alternative.me/fng/?limit={limit}&format=json"
    data = requests.get(url, timeout=10).json()
    return data["data"]

def classify_sentiment(value: int) -> str:
    if value <= 24: return "😱 Extreme Fear"
    if value <= 44: return "😨 Fear"
    if value <= 55: return "😐 Neutral"
    if value <= 74: return "😏 Greed"
    return "🤑 Extreme Greed"

# Current reading
history = get_fear_greed(7)
today = history[0]
score = int(today["value"])
label = today["value_classification"]
ts = datetime.fromtimestamp(int(today["timestamp"]))

print(f"Fear & Greed: {score}/100 — {classify_sentiment(score)}")
print(f"As of: {ts.strftime('%Y-%m-%d')}")
print()

# 7-day trend
print("7-Day History:")
for entry in history:
    val = int(entry["value"])
    bar = "█" * (val // 10)
    print(f"  {datetime.fromtimestamp(int(entry['timestamp'])).strftime('%m-%d')}  {val:>3}  {bar}")

💡 Tip: The Fear & Greed Index updates daily at midnight UTC. Historical data helps identify cycles and reversals. Watch for sustained extremes (>7 days) as they often precede corrections or recoveries.

🔄 Contrarian Signal Logic

Extreme sentiment often marks inflection points. When the crowd is maximally fearful or greedy, contrarian traders look for reversals. This approach treats Fear & Greed as a mean-reversion indicator.

Signal Interpretation

CONTRARIAN_BUY_WATCH: Average sentiment < 25 over 14 days — market in capitulation, potential floor-building
CONTRARIAN_CAUTION: Average sentiment > 75 over 14 days — euphoria phase, watch for pullbacks
RECOVERING: Average 25–40 with upward trend — fear easing, recovery phase
NEUTRAL: Mixed signals or mid-range averages

import pandas as pd

def fear_greed_signal(lookback_days: int = 14) -> dict:
    """
    Generate a contrarian signal:
    - Sustained extreme fear (avg < 25): potential buy signal
    - Sustained extreme greed (avg > 75): potential caution signal
    """
    history = get_fear_greed(lookback_days)
    values = [int(h["value"]) for h in history]

    avg = sum(values) / len(values)
    trend = values[0] - values[-1]  # positive = rising (more greed recently)

    signal = "NEUTRAL"
    if avg < 25:
        signal = "CONTRARIAN_BUY_WATCH"  # Market extremely fearful
    elif avg > 75:
        signal = "CONTRARIAN_CAUTION"    # Market extremely greedy
    elif avg < 40 and trend > 10:
        signal = "RECOVERING"            # Fear recovering toward neutral

    return {
        "avg_14d": round(avg, 1),
        "current": values[0],
        "trend_direction": "↑ Rising" if trend > 0 else "↓ Falling",
        "signal": signal,
    }

result = fear_greed_signal()
print(f"14-day avg:  {result['avg_14d']}")
print(f"Current:     {result['current']}")
print(f"Trend:       {result['trend_direction']}")
print(f"Signal:      {result['signal']}")

⚠️ Remember: Contrarian signals work best in cyclical markets. In strong uptrends or downtrends, sustained extreme sentiment can persist for weeks. Pair with technical support/resistance and volume confirmation.

🔴 Reddit Crypto Sentiment (OAuth)

Reddit's crypto communities (r/CryptoCurrency, r/Bitcoin, r/ethereum) reflect real-time community mood. Using the PRAW library and free Reddit OAuth credentials, you can score posts based on bullish/bearish language.

Setup Reddit OAuth

Visit reddit.com/prefs/apps
Create a new app (type: "script")
Note your client_id and client_secret
Install: pip install praw

API Rate Limit: ~60 requests/minute (free tier). Analyze 50–100 recent posts per subreddit for stability.

import praw

# Free Reddit app at reddit.com/prefs/apps — choose "script" type
reddit = praw.Reddit(
    client_id="YOUR_CLIENT_ID",
    client_secret="YOUR_CLIENT_SECRET",
    user_agent="OpenClaw/1.0 (crypto-sentiment-bot)",
)

BULL_WORDS = {"moon","bullish","buy","long","pump","breakout","accumulate","hodl","ath","rally"}
BEAR_WORDS = {"crash","bearish","sell","short","dump","correction","rug","scam","dead","rekt"}

def score_post(text: str) -> int:
    """Simple bag-of-words sentiment: +1 bull, -1 bear."""
    words = set(text.lower().split())
    bull = len(words & BULL_WORDS)
    bear = len(words & BEAR_WORDS)
    return bull - bear

def subreddit_sentiment(sub_name: str, limit: int = 100) -> dict:
    """Score recent hot posts in a subreddit."""
    sub = reddit.subreddit(sub_name)
    scores = []

    for post in sub.hot(limit=limit):
        text = f"{post.title} {post.selftext}"
        score = score_post(text)
        scores.append({
            "title": post.title[:60],
            "score": score,
            "upvotes": post.score,
            "comments": post.num_comments,
        })

    avg = sum(s["score"] for s in scores) / len(scores)
    bull_count = sum(1 for s in scores if s["score"] > 0)
    bear_count = sum(1 for s in scores if s["score"] < 0)

    return {
        "subreddit": sub_name,
        "posts_analyzed": len(scores),
        "avg_sentiment": round(avg, 3),
        "bullish_posts": bull_count,
        "bearish_posts": bear_count,
        "bull_pct": round(bull_count / len(scores) * 100, 1),
    }

for sub in ["CryptoCurrency", "Bitcoin", "ethereum"]:
    result = subreddit_sentiment(sub, limit=50)
    emoji = "🟢" if result["avg_sentiment"] > 0 else "🔴"
    print(f"{emoji} r/{result['subreddit']:<20} avg: {result['avg_sentiment']:+.3f} | "
          f"bull: {result['bull_pct']}%")

Limitations & Tuning

Sarcasm: "This will moon" vs. "Yeah, this coin is mooning" (sarcasm) look identical to simple bag-of-words
Meme speak: "HODL," "REKT," "diamond hands" require context dictionaries
Spam/bots: Filter by upvote count or comment count to weight quality
Language models: Consider VADER (rule-based) or transformer models (TextBlob, Hugging Face) for nuance

🎓 Advanced: Replace bag-of-words with VADER sentiment analyzer (pip install nltk) or fine-tuned transformers for more accurate sentiment classification.

🔥 CoinGecko Trending Coins

CoinGecko's trending endpoint shows the top 7 coins by recent search volume. This reflects what retail investors are chasing right now — useful for detecting alt-season (altcoins rallying) vs. blue-chip dominance.

API Endpoint: https://api.coingecko.com/api/v3/search/trending (free, no key, updates ~every 10 minutes)

def get_trending_coins() -> list:
    """
    Top 7 trending coins on CoinGecko (by search volume).
    Updates every ~10 minutes. No API key needed.
    """
    BASE = "https://api.coingecko.com/api/v3"
    data = requests.get(f"{BASE}/search/trending", timeout=10).json()
    coins = data.get("coins", [])

    print("🔥 Trending Coins (by search volume):")
    results = []
    for item in coins:
        c = item["item"]
        results.append({
            "rank":   c["market_cap_rank"],
            "name":   c["name"],
            "symbol": c["symbol"].upper(),
            "score":  c.get("score", 0),
            "price_btc": c.get("price_btc", 0),
        })

    for r in results:
        print(f"  #{r['rank']:>5}  {r['symbol']:<8}  {r['name']}")

    return results

trending = get_trending_coins()

Interpreting Trending

Major coins trending (BTC, ETH, SOL, BNB): Institutional interest, broad market conviction
Mostly altcoins trending: Alt-season indicator — retail FOMO, higher risk appetite
Niche/meme coins trending: Speculative fervor, potential bubble zone
Stable trends (same 7 coins daily): Low volatility in interest, consolidation phase

💡 Signal: Compare trending composition week-over-week. A shift from 70% major coins to 30% major coins signals a sentiment change toward risk-on behavior.

📊 Composite Crypto Sentiment Score

Combine Fear & Greed, Reddit bullishness, and trending diversity into a single 0–100 momentum metric. This weighted approach balances multiple signal sources and reduces false positives from single indicators.

Weighting Methodology

40% Fear & Greed Index: Market structure & volatility (official, daily update)
40% Reddit Bullishness: Community mood across 3 major subreddits (real-time, grassroots)
20% Trending Diversity: Retail interest & alt-season phase (volume-based, 10-min updates)

def composite_crypto_sentiment() -> dict:
    """
    Combine Fear/Greed + Reddit sentiment + trending signal
    into a single 0-100 momentum score.

    Weights:
      40% Fear & Greed Index (normalized 0-100)
      40% Reddit bullishness (bull_pct across 3 subs)
      20% Trending diversity (are top 7 major coins or alts?)
    """
    # Fear & Greed (already 0-100)
    fg = get_fear_greed(1)
    fg_score = int(fg[0]["value"])

    # Reddit (average bull_pct across 3 subs → normalize to 0-100)
    reddit_scores = []
    for sub in ["CryptoCurrency", "Bitcoin", "ethereum"]:
        try:
            r = subreddit_sentiment(sub, limit=30)
            reddit_scores.append(r["bull_pct"])
        except Exception:
            reddit_scores.append(50)  # neutral fallback
    reddit_score = sum(reddit_scores) / len(reddit_scores)

    # Trending (are top coins well-known? proxy for alt-season)
    trending = get_trending_coins()
    major_symbols = {"BTC","ETH","SOL","BNB","XRP","ADA","DOT","AVAX","MATIC","LINK"}
    major_count = sum(1 for t in trending if t["symbol"] in major_symbols)
    # Alt-season = mostly unknowns trending = more speculative/risky mood
    trending_score = (major_count / len(trending)) * 100 if trending else 50

    composite = (fg_score * 0.4) + (reddit_score * 0.4) + (trending_score * 0.2)

    return {
        "fear_greed": fg_score,
        "reddit_bullish_pct": round(reddit_score, 1),
        "major_trending_pct": round(trending_score, 1),
        "composite_score": round(composite, 1),
        "interpretation": classify_sentiment(int(composite)),
    }

result = composite_crypto_sentiment()
print(f"\n=== Composite Crypto Sentiment ===")
for k, v in result.items():
    print(f"  {k:<25}: {v}")

Example Output

                === Composite Crypto Sentiment ===
  fear_greed              : 42
  reddit_bullish_pct      : 58.3
  major_trending_pct      : 71.4
  composite_score         : 54.7
  interpretation          : 😐 Neutral
            

📈 Actionable: Composite scores >60 suggest position-taking into strength; scores <40 may favor accumulation or hedging. Monitor day-over-day changes to detect sentiment shifts before they're priced in.

📋 Sentiment Signal Reference

Free sentiment signals and their strengths:

Signal	Source	Update Freq	Best For
Fear & Greed Index	Alternative.me	Daily (UTC midnight)	Market cycle positioning, extremes
Subreddit hot posts	Reddit OAuth	Real-time	Short-term crowd mood, community focus
Trending search	CoinGecko	Every 10 min	Momentum, alt-season detection, retail interest
Google Trends	pytrends (free)	Daily	Mainstream retail interest, top-of-market froth
Twitter/X mentions	Limited free access	Real-time	News-driven spikes, influencer impact

⚠️ Sentiment Caveats & Limitations

Sentiment Lags Price: Fear & Greed often reflects yesterday's volatility and dominance. By the time sentiment shifts, a significant move may already be underway. Use sentiment for confirmation, not prediction.

Reddit Word Lists Need Tuning: "Moon" can be sarcasm ("this dog coin will moon to $0.000001"), and "hodl" vs. "hold" skew results. Context-aware models (VADER, transformer-based) improve accuracy.

Fear & Greed Is Backward-Looking: The index is built from yesterday's volatility, momentum, and dominance data. It doesn't predict tomorrow's market behavior; it rates how scared/greedy traders were historically.

Best Use Cases: Position sizing (reduce size in extreme greed), portfolio rebalancing (sell into euphoria), and hedging (buy puts in extremes). Sentiment works best as a macro filter, not entry/exit logic.

Advanced Improvements

Sentiment velocity: Track rate of change (dF&G/dt) to detect fast reversals
Divergences: Compare Fear & Greed vs. price — bullish divergence = uptrend likely to resume
Macro context: Combine with Fed announcements, CPI data, macro risk-off/risk-on signals
Transformer models: Fine-tune BERT/RoBERTa on labeled crypto text for state-of-art sentiment