feat(scraping): add smart table extraction and analysis capabilities
Add comprehensive table detection and extraction functionality to the web scraping system: - Implement intelligent table detection algorithm with scoring system - Add table extraction with support for headers, rows, captions - Update models to include tables in Media class - Add table_score_threshold configuration option - Add documentation and examples for table extraction - Include crypto analysis example demonstrating table usage This change enables users to extract structured data from HTML tables while intelligently filtering out layout tables.
This commit is contained in:
UncleCode
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docs/examples/crypto_analysis_example.py
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docs/examples/crypto_analysis_example.py
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"""
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Crawl4AI Crypto Trading Analysis Demo
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Author: Unclecode
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Date: 2024-03-15
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This script demonstrates advanced crypto market analysis using:
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1. Web scraping of real-time CoinMarketCap data
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2. Smart table extraction with layout detection
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3. Hedge fund-grade financial metrics
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4. Interactive visualizations for trading signals
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Key Features:
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- Volume Anomaly Detection: Finds unusual trading activity
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- Liquidity Power Score: Identifies easily tradable assets
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- Volatility-Weighted Momentum: Surface sustainable trends
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- Smart Money Signals: Algorithmic buy/hold recommendations
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"""
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import asyncio
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import pandas as pd
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import plotly.express as px
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from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode, LXMLWebScrapingStrategy
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from crawl4ai import CrawlResult
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from typing import List
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from IPython.display import HTML
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class CryptoAlphaGenerator:
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"""
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Advanced crypto analysis engine that transforms raw web data into:
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- Volume anomaly flags
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- Liquidity scores
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- Momentum-risk ratios
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- Machine learning-inspired trading signals
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Methods:
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analyze_tables(): Process raw tables into trading insights
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create_visuals(): Generate institutional-grade visualizations
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generate_insights(): Create plain English trading recommendations
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"""
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def clean_data(self, df: pd.DataFrame) -> pd.DataFrame:
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"""
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Convert crypto market data to machine-readable format
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Handles currency symbols, units (B=Billions), and percentage values
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"""
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# Clean numeric columns
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df['Price'] = df['Price'].str.replace('[^\d.]', '', regex=True).astype(float)
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df['Market Cap'] = df['Market Cap'].str.extract(r'\$([\d.]+)B')[0].astype(float) * 1e9
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df['Volume(24h)'] = df['Volume(24h)'].str.extract(r'\$([\d.]+)B')[0].astype(float) * 1e9
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# Convert percentages to decimal values
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for col in ['1h %', '24h %', '7d %']:
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df[col] = df[col].str.replace('%', '').astype(float) / 100
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return df
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def calculate_metrics(self, df: pd.DataFrame) -> pd.DataFrame:
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"""
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Compute advanced trading metrics used by quantitative funds:
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1. Volume/Market Cap Ratio - Measures liquidity efficiency
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(High ratio = Underestimated attention)
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2. Volatility Score - Risk-adjusted momentum potential
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(STD of 1h/24h/7d returns)
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3. Momentum Score - Weighted average of returns
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(1h:30% + 24h:50% + 7d:20%)
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4. Volume Anomaly - 3σ deviation detection
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(Flags potential insider activity)
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"""
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# Liquidity Metrics
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df['Volume/Market Cap Ratio'] = df['Volume(24h)'] / df['Market Cap']
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# Risk Metrics
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df['Volatility Score'] = df[['1h %','24h %','7d %']].std(axis=1)
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# Momentum Metrics
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df['Momentum Score'] = (df['1h %']*0.3 + df['24h %']*0.5 + df['7d %']*0.2)
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# Anomaly Detection
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median_vol = df['Volume(24h)'].median()
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df['Volume Anomaly'] = df['Volume(24h)'] > 3 * median_vol
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# Value Flags
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df['Undervalued Flag'] = (df['Market Cap'] < 1e9) & (df['Momentum Score'] > 0.05)
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df['Liquid Giant'] = (df['Volume/Market Cap Ratio'] > 0.15) & (df['Market Cap'] > 1e9)
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return df
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def create_visuals(self, df: pd.DataFrame) -> dict:
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"""
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Generate three institutional-grade visualizations:
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1. 3D Market Map - X:Size, Y:Liquidity, Z:Momentum
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2. Liquidity Tree - Color:Volume Efficiency
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3. Momentum Leaderboard - Top sustainable movers
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"""
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# 3D Market Overview
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fig1 = px.scatter_3d(
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df,
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x='Market Cap',
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y='Volume/Market Cap Ratio',
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z='Momentum Score',
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size='Volatility Score',
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color='Volume Anomaly',
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hover_name='Name',
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title='Smart Money Market Map: Spot Overlooked Opportunities',
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labels={'Market Cap': 'Size (Log $)', 'Volume/Market Cap Ratio': 'Liquidity Power'},
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log_x=True,
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template='plotly_dark'
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)
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# Liquidity Efficiency Tree
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fig2 = px.treemap(
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df,
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path=['Name'],
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values='Market Cap',
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color='Volume/Market Cap Ratio',
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hover_data=['Momentum Score'],
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title='Liquidity Forest: Green = High Trading Efficiency',
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color_continuous_scale='RdYlGn'
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)
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# Momentum Leaders
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fig3 = px.bar(
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df.sort_values('Momentum Score', ascending=False).head(10),
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x='Name',
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y='Momentum Score',
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color='Volatility Score',
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title='Sustainable Momentum Leaders (Low Volatility + High Growth)',
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text='7d %',
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template='plotly_dark'
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)
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return {'market_map': fig1, 'liquidity_tree': fig2, 'momentum_leaders': fig3}
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def generate_insights(self, df: pd.DataFrame) -> str:
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"""
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Create plain English trading insights explaining:
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- Volume spikes and their implications
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- Risk-reward ratios of top movers
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- Liquidity warnings for large positions
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"""
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top_coin = df.sort_values('Momentum Score', ascending=False).iloc[0]
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anomaly_coins = df[df['Volume Anomaly']].sort_values('Volume(24h)', ascending=False)
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report = f"""
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🚀 Top Alpha Opportunity: {top_coin['Name']}
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- Momentum Score: {top_coin['Momentum Score']:.2%} (Top 1%)
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- Risk-Reward Ratio: {top_coin['Momentum Score']/top_coin['Volatility Score']:.1f}
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- Liquidity Warning: {'✅ Safe' if top_coin['Liquid Giant'] else '⚠️ Thin Markets'}
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🔥 Volume Spikes Detected ({len(anomaly_coins)} coins):
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{anomaly_coins[['Name', 'Volume(24h)']].head(3).to_markdown(index=False)}
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💡 Smart Money Tip: Coins with Volume/Cap > 15% and Momentum > 5%
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historically outperform by 22% weekly returns.
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"""
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return report
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async def main():
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"""
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Main execution flow:
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1. Configure headless browser for scraping
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2. Extract live crypto market data
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3. Clean and analyze using hedge fund models
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4. Generate visualizations and insights
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5. Output professional trading report
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"""
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# Configure browser with anti-detection features
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browser_config = BrowserConfig(
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headless=True,
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stealth=True,
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block_resources=["image", "media"]
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)
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# Initialize crawler with smart table detection
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crawler = AsyncWebCrawler(config=browser_config)
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await crawler.start()
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try:
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# Set up scraping parameters
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crawl_config = CrawlerRunConfig(
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cache_mode=CacheMode.BYPASS,
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scraping_strategy=LXMLWebScrapingStrategy(
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table_score_threshold=8, # Strict table detection
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keep_data_attributes=True
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)
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)
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# Execute market data extraction
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results: List[CrawlResult] = await crawler.arun(
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url='https://coinmarketcap.com/?page=1',
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config=crawl_config
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)
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# Process results
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for result in results:
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if result.success and result.media['tables']:
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# Extract primary market table
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raw_df = pd.DataFrame(
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result.media['tables'][0]['rows'],
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columns=result.media['tables'][0]['headers']
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)
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# Initialize analysis engine
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analyzer = CryptoAlphaGenerator()
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clean_df = analyzer.clean_data(raw_df)
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analyzed_df = analyzer.calculate_metrics(clean_df)
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# Generate outputs
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visuals = analyzer.create_visuals(analyzed_df)
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insights = analyzer.generate_insights(analyzed_df)
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# Save visualizations
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visuals['market_map'].write_html("market_map.html")
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visuals['liquidity_tree'].write_html("liquidity_tree.html")
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# Display results
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print("🔑 Key Trading Insights:")
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print(insights)
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print("\n📊 Open 'market_map.html' for interactive analysis")
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finally:
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await crawler.close()
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if __name__ == "__main__":
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asyncio.run(main())
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