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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@@ -133,19 +133,28 @@ This approach is handy when you still want external links but need to block cert
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### 3.1 Accessing `result.media`
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By default, Crawl4AI collects images, audio, and video URLs it finds on the page. These are stored in `result.media`, a dictionary keyed by media type (e.g., `images`, `videos`, `audio`).
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By default, Crawl4AI collects images, audio, video URLs, and data tables it finds on the page. These are stored in `result.media`, a dictionary keyed by media type (e.g., `images`, `videos`, `audio`, `tables`).
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**Basic Example**:
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```python
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if result.success:
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# Get images
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images_info = result.media.get("images", [])
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print(f"Found {len(images_info)} images in total.")
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for i, img in enumerate(images_info[:5]): # Inspect just the first 5
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for i, img in enumerate(images_info[:3]): # Inspect just the first 3
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print(f"[Image {i}] URL: {img['src']}")
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print(f" Alt text: {img.get('alt', '')}")
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print(f" Score: {img.get('score')}")
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print(f" Description: {img.get('desc', '')}\n")
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# Get tables
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tables = result.media.get("tables", [])
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print(f"Found {len(tables)} data tables in total.")
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for i, table in enumerate(tables):
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print(f"[Table {i}] Caption: {table.get('caption', 'No caption')}")
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print(f" Columns: {len(table.get('headers', []))}")
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print(f" Rows: {len(table.get('rows', []))}")
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```
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**Structure Example**:
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],
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"audio": [
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# Similar structure but with audio-specific fields
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],
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"tables": [
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{
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"headers": ["Name", "Age", "Location"],
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"rows": [
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["John Doe", "34", "New York"],
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["Jane Smith", "28", "San Francisco"],
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["Alex Johnson", "42", "Chicago"]
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],
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"caption": "Employee Directory",
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"summary": "Directory of company employees"
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},
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# More tables if present
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]
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}
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```
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This setting attempts to discard images from outside the primary domain, keeping only those from the site you’re crawling.
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### 3.3 Additional Media Config
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### 3.3 Working with Tables
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Crawl4AI can detect and extract structured data from HTML tables. Tables are analyzed based on various criteria to determine if they are actual data tables (as opposed to layout tables), including:
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- Presence of thead and tbody sections
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- Use of th elements for headers
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- Column consistency
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- Text density
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- And other factors
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Tables that score above the threshold (default: 7) are extracted and stored in `result.media.tables`.
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**Accessing Table Data**:
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```python
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if result.success:
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tables = result.media.get("tables", [])
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print(f"Found {len(tables)} data tables on the page")
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if tables:
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# Access the first table
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first_table = tables[0]
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print(f"Table caption: {first_table.get('caption', 'No caption')}")
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print(f"Headers: {first_table.get('headers', [])}")
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# Print the first 3 rows
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for i, row in enumerate(first_table.get('rows', [])[:3]):
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print(f"Row {i+1}: {row}")
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```
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**Configuring Table Extraction**:
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You can adjust the sensitivity of the table detection algorithm with:
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```python
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crawler_cfg = CrawlerRunConfig(
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table_score_threshold=5 # Lower value = more tables detected (default: 7)
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)
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```
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Each extracted table contains:
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- `headers`: Column header names
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- `rows`: List of rows, each containing cell values
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- `caption`: Table caption text (if available)
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- `summary`: Table summary attribute (if specified)
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### 3.4 Additional Media Config
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- **`screenshot`**: Set to `True` if you want a full-page screenshot stored as `base64` in `result.screenshot`.
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- **`pdf`**: Set to `True` if you want a PDF version of the page in `result.pdf`.
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@@ -273,4 +341,11 @@ if __name__ == "__main__":
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---
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**That’s it for Link & Media Analysis!** You’re now equipped to filter out unwanted sites and zero in on the images and videos that matter for your project.
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**That’s it for Link & Media Analysis!** You’re now equipped to filter out unwanted sites and zero in on the images and videos that matter for your project.
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### Table Extraction Tips
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- Not all HTML tables are extracted - only those detected as "data tables" vs. layout tables.
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- Tables with inconsistent cell counts, nested tables, or those used purely for layout may be skipped.
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- If you're missing tables, try adjusting the `table_score_threshold` to a lower value (default is 7).
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The table detection algorithm scores tables based on features like consistent columns, presence of headers, text density, and more. Tables scoring above the threshold are considered data tables worth extracting.
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