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Zhongwei Li
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commands/openbb-crypto.md
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348
commands/openbb-crypto.md
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---
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name: openbb-crypto
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description: Cryptocurrency market analysis using OpenBB - price data, on-chain metrics, DeFi analytics, whale tracking, and market sentiment
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---
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# OpenBB Cryptocurrency Analysis
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Comprehensive cryptocurrency analysis using OpenBB Platform's crypto data sources.
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## Usage
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```bash
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/openbb-crypto SYMBOL [--vs USD|BTC|ETH] [--metrics on-chain|defi|social] [--period 30d]
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```
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## What This Command Does
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Analyzes cryptocurrency markets with price data, on-chain metrics, DeFi analytics, and sentiment analysis.
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## Workflow
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### 1. Setup OpenBB Connection
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```python
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from openbb import obb
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import pandas as pd
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from datetime import datetime, timedelta
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# Parse arguments
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symbol = sys.argv[1].upper() if len(sys.argv) > 1 else "BTC"
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vs_currency = "USD" # USD, BTC, ETH
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metrics_type = "all" # on-chain, defi, social, all
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period = "30d" # 7d, 30d, 90d, 1y
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# Parse flags
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for arg in sys.argv[2:]:
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if arg.startswith("--vs="):
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vs_currency = arg.split("=")[1].upper()
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elif arg.startswith("--metrics="):
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metrics_type = arg.split("=")[1]
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elif arg.startswith("--period="):
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period = arg.split("=")[1]
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```
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### 2. Retrieve Price Data
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```python
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# Get historical crypto prices
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crypto_data = obb.crypto.price.historical(
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symbol=f"{symbol}{vs_currency}",
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interval="1d",
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period=period
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)
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df = crypto_data.to_dataframe()
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print(f"\n₿ Crypto Analysis: {symbol}/{vs_currency}")
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print(f"{'='*60}")
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current_price = df['close'].iloc[-1]
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period_high = df['high'].max()
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period_low = df['low'].min()
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period_return = ((current_price / df['close'].iloc[0]) - 1) * 100
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print(f"\n💰 Price Overview:")
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print(f"Current Price: ${current_price:,.2f}")
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print(f"{period} High: ${period_high:,.2f}")
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print(f"{period} Low: ${period_low:,.2f}")
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print(f"{period} Return: {period_return:+.2f}%")
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print(f"24h Volume: ${df['volume'].iloc[-1]:,.0f}")
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```
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### 3. Technical Indicators
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```python
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# Calculate crypto-specific indicators
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print(f"\n📊 Technical Indicators:")
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# Moving averages
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df['MA_7'] = df['close'].rolling(window=7).mean()
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df['MA_30'] = df['close'].rolling(window=30).mean()
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df['MA_90'] = df['close'].rolling(window=90).mean()
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ma_7 = df['MA_7'].iloc[-1]
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ma_30 = df['MA_30'].iloc[-1]
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ma_90 = df['MA_90'].iloc[-1]
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print(f"MA 7: ${ma_7:,.2f} {'🟢' if current_price > ma_7 else '🔴'}")
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print(f"MA 30: ${ma_30:,.2f} {'🟢' if current_price > ma_30 else '🔴'}")
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print(f"MA 90: ${ma_90:,.2f} {'🟢' if current_price > ma_90 else '🔴'}")
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# Volatility
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returns = df['close'].pct_change()
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volatility = returns.std() * (365 ** 0.5) * 100 # Annualized
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print(f"\nVolatility (ann.): {volatility:.1f}%")
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# RSI
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delta = df['close'].diff()
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gain = delta.where(delta > 0, 0).rolling(window=14).mean()
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loss = -delta.where(delta < 0, 0).rolling(window=14).mean()
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rs = gain / loss
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df['RSI'] = 100 - (100 / (1 + rs))
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rsi = df['RSI'].iloc[-1]
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print(f"RSI (14): {rsi:.1f}")
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if rsi > 70:
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print(" ⚠️ Overbought - potential sell signal")
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elif rsi < 30:
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print(" 🟢 Oversold - potential buy signal")
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```
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### 4. On-Chain Metrics (if available)
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```python
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if metrics_type in ["on-chain", "all"]:
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print(f"\n⛓️ On-Chain Metrics:")
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try:
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# Network activity
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network_data = obb.crypto.onchain.active_addresses(symbol=symbol)
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print(f"Active Addresses (24h): {network_data.active_addresses:,}")
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print(f"Transaction Count: {network_data.tx_count:,}")
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print(f"Transaction Volume: ${network_data.tx_volume:,.0f}")
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# Hash rate (for PoW coins)
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if symbol in ["BTC", "ETH", "LTC", "DOGE"]:
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hash_data = obb.crypto.onchain.hashrate(symbol=symbol)
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print(f"\nHash Rate: {hash_data.hashrate / 1e18:.2f} EH/s")
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print(f"Mining Difficulty: {hash_data.difficulty:,.0f}")
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# Holder distribution
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holders = obb.crypto.onchain.holders(symbol=symbol)
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print(f"\nTop 10 Holders: {holders.top_10_pct:.1f}%")
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print(f"Top 100 Holders: {holders.top_100_pct:.1f}%")
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except Exception as e:
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print(f"On-chain data not available for {symbol}")
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```
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### 5. DeFi Metrics (if applicable)
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```python
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if metrics_type in ["defi", "all"] and symbol in ["ETH", "BNB", "AVAX", "SOL"]:
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print(f"\n🏦 DeFi Metrics:")
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try:
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defi_data = obb.crypto.defi.tvl(chain=symbol)
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print(f"Total Value Locked: ${defi_data.tvl / 1e9:.2f}B")
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print(f"Protocol Count: {defi_data.protocol_count}")
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print(f"Top Protocol: {defi_data.top_protocol}")
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print(f" - TVL: ${defi_data.top_protocol_tvl / 1e9:.2f}B")
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# Staking data
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staking = obb.crypto.defi.staking(symbol=symbol)
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print(f"\nStaking:")
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print(f"Total Staked: {staking.total_staked_pct:.1f}%")
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print(f"Avg APY: {staking.avg_apy:.2f}%")
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except:
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print(f"DeFi data not available for {symbol}")
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```
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### 6. Social Sentiment & News
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```python
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if metrics_type in ["social", "all"]:
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print(f"\n📱 Social Sentiment:")
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try:
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social_data = obb.crypto.social.sentiment(symbol=symbol)
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print(f"Twitter Mentions (24h): {social_data.twitter_mentions:,}")
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print(f"Reddit Posts (24h): {social_data.reddit_posts:,}")
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print(f"Sentiment Score: {social_data.sentiment_score:.2f}/5.0")
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sentiment_emoji = "🟢" if social_data.sentiment_score > 3.5 else "🟡" if social_data.sentiment_score > 2.5 else "🔴"
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print(f"Overall Sentiment: {sentiment_emoji}")
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# Recent news
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news = obb.crypto.news(symbol=symbol, limit=3)
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print(f"\n📰 Latest News:")
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for i, article in enumerate(news[:3], 1):
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print(f"{i}. {article.title}")
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print(f" {article.source} - {article.published_date}")
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except:
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print("Social/news data not available")
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```
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### 7. Whale Activity Tracker
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```python
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print(f"\n🐋 Whale Activity (Large Transfers):")
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try:
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# Get large transactions (>$100k)
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whales = obb.crypto.onchain.large_transactions(
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symbol=symbol,
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min_value=100000,
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limit=5
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)
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if len(whales) > 0:
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print(f"Last {len(whales)} large transfers:")
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for tx in whales:
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print(f" ${tx.value_usd:,.0f} - {tx.from_address[:10]}...→ {tx.to_address[:10]}...")
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print(f" {tx.timestamp} ({tx.exchange if tx.exchange else 'Unknown'})")
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else:
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print("No significant whale activity detected")
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except:
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print("Whale tracking not available")
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```
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### 8. AI-Powered Market Analysis
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```python
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print(f"\n🤖 AI Market Analysis for {symbol}:")
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print(f"\n📈 Trend Analysis:")
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# Determine trend
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if current_price > ma_7 > ma_30 > ma_90:
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trend = "Strong Uptrend"
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trend_emoji = "🚀"
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elif current_price > ma_30:
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trend = "Bullish"
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trend_emoji = "📈"
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elif current_price < ma_7 < ma_30 < ma_90:
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trend = "Strong Downtrend"
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trend_emoji = "📉"
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else:
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trend = "Consolidating"
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trend_emoji = "↔️"
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print(f"{trend_emoji} Market Trend: {trend}")
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# Risk assessment
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if volatility > 100:
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risk = "Very High"
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risk_emoji = "🔴"
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elif volatility > 60:
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risk = "High"
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risk_emoji = "🟡"
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else:
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risk = "Moderate"
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risk_emoji = "🟢"
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print(f"{risk_emoji} Volatility Risk: {risk}")
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# Trading signals
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print(f"\n💡 Trading Signals:")
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signals = []
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if rsi < 30:
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signals.append("🟢 RSI oversold - potential buy zone")
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if rsi > 70:
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signals.append("🔴 RSI overbought - consider taking profits")
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if current_price > ma_30 and returns.iloc[-1] > 0.05:
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signals.append("🚀 Strong momentum detected")
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if df['volume'].iloc[-1] > df['volume'].rolling(20).mean().iloc[-1] * 2:
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signals.append("📊 Unusual volume spike")
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if signals:
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for signal in signals:
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print(f" {signal}")
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else:
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print(" No strong signals detected - market in equilibrium")
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```
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### 9. Price Targets & Support/Resistance
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```python
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print(f"\n🎯 Key Levels:")
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# Calculate support and resistance
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high_30d = df['high'].tail(30).max()
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low_30d = df['low'].tail(30).min()
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pivot = (high_30d + low_30d + current_price) / 3
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resistance_1 = 2 * pivot - low_30d
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support_1 = 2 * pivot - high_30d
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print(f"Resistance: ${resistance_1:,.2f} ({((resistance_1/current_price - 1) * 100):+.1f}%)")
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print(f"Current: ${current_price:,.2f}")
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print(f"Support: ${support_1:,.2f} ({((support_1/current_price - 1) * 100):+.1f}%)")
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```
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## Examples
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### Basic crypto analysis
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```bash
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/openbb-crypto BTC
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```
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### Ethereum DeFi metrics
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```bash
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/openbb-crypto ETH --metrics=defi
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```
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### Altcoin vs BTC
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```bash
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/openbb-crypto LINK --vs=BTC --period=90d
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```
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### Social sentiment check
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```bash
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/openbb-crypto DOGE --metrics=social
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```
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## Supported Cryptocurrencies
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- **Major**: BTC, ETH, BNB, SOL, ADA, XRP, DOT, AVAX
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- **DeFi**: UNI, AAVE, LINK, COMP, MKR, SNX
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- **Meme**: DOGE, SHIB, PEPE
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- **Layer 2**: MATIC, ARB, OP
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- **1000+ more via OpenBB data providers**
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## Data Sources
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- Price data: Multiple exchanges (Binance, Coinbase, etc.)
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- On-chain: Glassnode, Santiment, IntoTheBlock
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- DeFi: DeFi Llama, The Graph
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- Social: LunarCrush, Santiment
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## Tips
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1. **Compare to BTC**: Use `--vs=BTC` to see altcoin strength vs Bitcoin
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2. **Track Whales**: Monitor large transfers for market-moving activity
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3. **DeFi Context**: Check TVL and staking for ecosystem health
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4. **Sentiment Analysis**: Social metrics can predict short-term moves
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5. **Correlation**: Compare multiple cryptos to find divergences
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## Integration
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```bash
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# Portfolio tracking
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/openbb-portfolio --add-crypto=BTC,ETH,SOL
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# Compare with equity markets
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/openbb-macro --crypto-correlation
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# AI research
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/openbb-research --crypto --symbol=BTC
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```
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## Notes
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- Cryptocurrency markets are 24/7
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- High volatility - use appropriate risk management
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- Not financial advice - DYOR (Do Your Own Research)
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- Consider transaction costs and slippage for trading
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