openbb-crypto

name	openbb-crypto
description	Cryptocurrency market analysis using OpenBB - price data, on-chain metrics,...

OpenBB Cryptocurrency Analysis

Comprehensive cryptocurrency analysis using OpenBB Platform's crypto data sources.

Usage

/openbb-crypto SYMBOL [--vs USD|BTC|ETH] [--metrics on-chain|defi|social] [--period 30d]

What This Command Does

Analyzes cryptocurrency markets with price data, on-chain metrics, DeFi analytics, and sentiment analysis.

Workflow

1. Setup OpenBB Connection

from openbb import obb
import pandas as pd
from datetime import datetime, timedelta

# Parse arguments
symbol = sys.argv[1].upper() if len(sys.argv) > 1 else "BTC"
vs_currency = "USD"  # USD, BTC, ETH
metrics_type = "all"  # on-chain, defi, social, all
period = "30d"  # 7d, 30d, 90d, 1y

# Parse flags
for arg in sys.argv[2:]:
    if arg.startswith("--vs="):
        vs_currency = arg.split("=")[1].upper()
    elif arg.startswith("--metrics="):
        metrics_type = arg.split("=")[1]
    elif arg.startswith("--period="):
        period = arg.split("=")[1]

2. Retrieve Price Data

# Get historical crypto prices
crypto_data = obb.crypto.price.historical(
    symbol=f"{symbol}{vs_currency}",
    interval="1d",
    period=period
)

df = crypto_data.to_dataframe()

print(f"\n₿ Crypto Analysis: {symbol}/{vs_currency}")
print(f"{'='*60}")

current_price = df['close'].iloc[-1]
period_high = df['high'].max()
period_low = df['low'].min()
period_return = ((current_price / df['close'].iloc[0]) - 1) * 100

print(f"\n💰 Price Overview:")
print(f"Current Price: ${current_price:,.2f}")
print(f"{period} High: ${period_high:,.2f}")
print(f"{period} Low: ${period_low:,.2f}")
print(f"{period} Return: {period_return:+.2f}%")
print(f"24h Volume: ${df['volume'].iloc[-1]:,.0f}")

3. Technical Indicators

# Calculate crypto-specific indicators
print(f"\n📊 Technical Indicators:")

# Moving averages
df['MA_7'] = df['close'].rolling(window=7).mean()
df['MA_30'] = df['close'].rolling(window=30).mean()
df['MA_90'] = df['close'].rolling(window=90).mean()

ma_7 = df['MA_7'].iloc[-1]
ma_30 = df['MA_30'].iloc[-1]
ma_90 = df['MA_90'].iloc[-1]

print(f"MA 7:  ${ma_7:,.2f} {'🟢' if current_price > ma_7 else '🔴'}")
print(f"MA 30: ${ma_30:,.2f} {'🟢' if current_price > ma_30 else '🔴'}")
print(f"MA 90: ${ma_90:,.2f} {'🟢' if current_price > ma_90 else '🔴'}")

# Volatility
returns = df['close'].pct_change()
volatility = returns.std() * (365 ** 0.5) * 100  # Annualized

print(f"\nVolatility (ann.): %")


delta = df[].diff()
gain = delta.where(delta > , ).rolling(window=).mean()
loss = -delta.where(delta < , ).rolling(window=).mean()
rs = gain / loss
df[] =  - ( / ( + rs))
rsi = df[].iloc[-]

()
 rsi > :
    ()
 rsi < :
    ()

4. On-Chain Metrics (if available)

if metrics_type in ["on-chain", "all"]:
    print(f"\n⛓️  On-Chain Metrics:")

    try:
        # Network activity
        network_data = obb.crypto.onchain.active_addresses(symbol=symbol)
        print(f"Active Addresses (24h): {network_data.active_addresses:,}")
        print(f"Transaction Count: {network_data.tx_count:,}")
        print(f"Transaction Volume: ${network_data.tx_volume:,.0f}")

        # Hash rate (for PoW coins)
        if symbol in ["BTC", "ETH", "LTC", "DOGE"]:
            hash_data = obb.crypto.onchain.hashrate(symbol=symbol)
            print(f"\nHash Rate: {hash_data.hashrate / 1e18:.2f} EH/s")
            print(f"Mining Difficulty: {hash_data.difficulty:,.0f}")

        # Holder distribution
        holders = obb.crypto.onchain.holders(symbol=symbol)
        print(f"\nTop 10 Holders: {holders.top_10_pct:.1f}%")
        print(f"Top 100 Holders: {holders.top_100_pct:.1f}%")

    except Exception as e:
        print(f"On-chain data not available for ")

5. DeFi Metrics (if applicable)

if metrics_type in ["defi", "all"] and symbol in ["ETH", "BNB", "AVAX", "SOL"]:
    print(f"\n🏦 DeFi Metrics:")

    try:
        defi_data = obb.crypto.defi.tvl(chain=symbol)
        print(f"Total Value Locked: ${defi_data.tvl / 1e9:.2f}B")
        print(f"Protocol Count: {defi_data.protocol_count}")
        print(f"Top Protocol: {defi_data.top_protocol}")
        print(f"  - TVL: ${defi_data.top_protocol_tvl / 1e9:.2f}B")

        # Staking data
        staking = obb.crypto.defi.staking(symbol=symbol)
        print(f"\nStaking:")
        print(f"Total Staked: {staking.total_staked_pct:.1f}%")
        print(f"Avg APY: {staking.avg_apy:.2f}%")
    except:
        print(f"DeFi data not available for {symbol}")

6. Social Sentiment & News

if metrics_type in ["social", "all"]:
    print(f"\n📱 Social Sentiment:")

    try:
        social_data = obb.crypto.social.sentiment(symbol=symbol)
        print(f"Twitter Mentions (24h): {social_data.twitter_mentions:,}")
        print(f"Reddit Posts (24h): {social_data.reddit_posts:,}")
        print(f"Sentiment Score: {social_data.sentiment_score:.2f}/5.0")

        sentiment_emoji = "🟢" if social_data.sentiment_score > 3.5 else "🟡" if social_data.sentiment_score > 2.5 else "🔴"
        print(f"Overall Sentiment: {sentiment_emoji}")

        # Recent news
        news = obb.crypto.news(symbol=symbol, limit=3)
        print(f"\n📰 Latest News:")
        for i, article in enumerate(news[:3], 1):
            print(f"{i}. {article.title}")
            print(f"   {article.source} - {article.published_date}")
    except:
        print("Social/news data not available")

7. Whale Activity Tracker

print(f"\n🐋 Whale Activity (Large Transfers):")

try:
    # Get large transactions (>$100k)
    whales = obb.crypto.onchain.large_transactions(
        symbol=symbol,
        min_value=100000,
        limit=5
    )

    if len(whales) > 0:
        print(f"Last {len(whales)} large transfers:")
        for tx in whales:
            print(f"  ${tx.value_usd:,.0f} - {tx.from_address[:10]}...→ {tx.to_address[:10]}...")
            print(f"    {tx.timestamp} ({tx.exchange if tx.exchange else 'Unknown'})")
    else:
        print("No significant whale activity detected")
except:
    print("Whale tracking not available")

8. AI-Powered Market Analysis

print(f"\n🤖 AI Market Analysis for {symbol}:")
print(f"\n📈 Trend Analysis:")

# Determine trend
if current_price > ma_7 > ma_30 > ma_90:
    trend = "Strong Uptrend"
    trend_emoji = "🚀"
elif current_price > ma_30:
    trend = "Bullish"
    trend_emoji = "📈"
elif current_price < ma_7 < ma_30 < ma_90:
    trend = "Strong Downtrend"
    trend_emoji = "📉"
else:
    trend = "Consolidating"
    trend_emoji = "↔️"

print(f"{trend_emoji} Market Trend: {trend}")

# Risk assessment
if volatility > 100:
    risk = "Very High"
    risk_emoji = "🔴"
elif volatility > 60:
    risk = "High"
    risk_emoji = "🟡"
else:
    risk = "Moderate"
    risk_emoji = "🟢"

print(f"{risk_emoji} Volatility Risk: {risk}")

# Trading signals
print(f"\n💡 Trading Signals:")
signals = []

if rsi < 30:
    signals.append("🟢 RSI oversold - potential buy zone")
if rsi > 70:
    signals.append("🔴 RSI overbought - consider taking profits")
if current_price > ma_30  returns.iloc[-] > :
    signals.append()
 df[].iloc[-] > df[].rolling().mean().iloc[-] * :
    signals.append()

 signals:
     signal  signals:
        ()
:
    ()

9. Price Targets & Support/Resistance

print(f"\n🎯 Key Levels:")

# Calculate support and resistance
high_30d = df['high'].tail(30).max()
low_30d = df['low'].tail(30).min()
pivot = (high_30d + low_30d + current_price) / 3

resistance_1 = 2 * pivot - low_30d
support_1 = 2 * pivot - high_30d

print(f"Resistance: ${resistance_1:,.2f} ({((resistance_1/current_price - 1) * 100):+.1f}%)")
print(f"Current:    ${current_price:,.2f}")
print(f"Support:    ${support_1:,.2f} ({((support_1/current_price - 1) * 100):+.1f}%)")

Examples

Basic crypto analysis

/openbb-crypto BTC

Ethereum DeFi metrics

/openbb-crypto ETH --metrics=defi

Altcoin vs BTC

/openbb-crypto LINK --vs=BTC --period=90d

Social sentiment check

/openbb-crypto DOGE --metrics=social

Supported Cryptocurrencies

• Major: BTC, ETH, BNB, SOL, ADA, XRP, DOT, AVAX
• DeFi: UNI, AAVE, LINK, COMP, MKR, SNX
• Meme: DOGE, SHIB, PEPE
• Layer 2: MATIC, ARB, OP
• 1000+ more via OpenBB data providers

Data Sources

• Price data: Multiple exchanges (Binance, Coinbase, etc.)
• On-chain: Glassnode, Santiment, IntoTheBlock
• DeFi: DeFi Llama, The Graph
• Social: LunarCrush, Santiment

Tips

1. Compare to BTC: Use --vs=BTC to see altcoin strength vs Bitcoin
2. Track Whales: Monitor large transfers for market-moving activity
3. DeFi Context: Check TVL and staking for ecosystem health
4. Sentiment Analysis: Social metrics can predict short-term moves
5. Correlation: Compare multiple cryptos to find divergences

Integration

# Portfolio tracking
/openbb-portfolio --add-crypto=BTC,ETH,SOL

# Compare with equity markets
/openbb-macro --crypto-correlation

# AI research
/openbb-research --crypto --symbol=BTC

Notes

• Cryptocurrency markets are 24/7
• High volatility - use appropriate risk management
• Not financial advice - DYOR (Do Your Own Research)
• Consider transaction costs and slippage for trading