feat: Add PDF report generation with charts for trading analysis
- Add comprehensive PDF report generation with all analysis stages - Include market data visualizations: price chart, volume chart, and technical indicators (RSI/MACD) - Fix text parsing issues with proper HTML/markdown escaping - Add reportlab and matplotlib dependencies for PDF and chart generation - PDF reports saved automatically after analysis completion in results directory 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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"""Chart generation utilities for PDF reports."""
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import io
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from pathlib import Path
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from typing import Optional, Dict, Any
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import datetime
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def generate_stock_price_chart(ticker: str, analysis_date: str) -> Optional[io.BytesIO]:
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"""
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Generate a stock price chart with moving averages.
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Args:
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ticker: Stock ticker symbol
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analysis_date: Analysis date string (YYYY-MM-DD)
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Returns:
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BytesIO buffer containing the chart image, or None if generation fails
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"""
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try:
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import matplotlib.pyplot as plt
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import matplotlib.dates as mdates
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import yfinance as yf
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from datetime import datetime, timedelta
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except ImportError:
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print("Warning: matplotlib required for chart generation")
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return None
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try:
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# Parse the analysis date
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end_date = datetime.strptime(analysis_date, '%Y-%m-%d')
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# Get 90 days of data for context
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start_date = end_date - timedelta(days=90)
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# Fetch stock data
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stock = yf.Ticker(ticker)
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df = stock.history(start=start_date.strftime('%Y-%m-%d'),
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end=end_date.strftime('%Y-%m-%d'))
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if df.empty:
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print(f"Warning: No data available for {ticker}")
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return None
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# Calculate moving averages
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df['SMA_20'] = df['Close'].rolling(window=20).mean()
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df['SMA_50'] = df['Close'].rolling(window=50).mean()
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# Create the figure
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fig, ax = plt.subplots(figsize=(10, 6))
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# Plot price and moving averages
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ax.plot(df.index, df['Close'], label='Close Price', linewidth=2, color='#2E86AB')
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ax.plot(df.index, df['SMA_20'], label='20-day SMA', linewidth=1.5,
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linestyle='--', color='#A23B72', alpha=0.8)
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ax.plot(df.index, df['SMA_50'], label='50-day SMA', linewidth=1.5,
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linestyle='--', color='#F18F01', alpha=0.8)
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# Formatting
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ax.set_title(f'{ticker} Stock Price - Last 90 Days', fontsize=14, fontweight='bold')
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ax.set_xlabel('Date', fontsize=12)
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ax.set_ylabel('Price ($)', fontsize=12)
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ax.legend(loc='best', fontsize=10)
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ax.grid(True, alpha=0.3)
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# Format x-axis dates
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ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
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ax.xaxis.set_major_locator(mdates.MonthLocator())
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plt.xticks(rotation=45)
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# Tight layout to prevent label cutoff
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plt.tight_layout()
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# Save to BytesIO buffer
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buf = io.BytesIO()
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plt.savefig(buf, format='png', dpi=150, bbox_inches='tight')
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buf.seek(0)
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plt.close(fig)
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return buf
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except Exception as e:
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print(f"Error generating stock price chart: {e}")
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return None
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def generate_volume_chart(ticker: str, analysis_date: str) -> Optional[io.BytesIO]:
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"""
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Generate a trading volume chart.
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Args:
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ticker: Stock ticker symbol
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analysis_date: Analysis date string (YYYY-MM-DD)
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Returns:
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BytesIO buffer containing the chart image, or None if generation fails
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"""
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try:
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import matplotlib.pyplot as plt
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import matplotlib.dates as mdates
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import yfinance as yf
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from datetime import datetime, timedelta
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except ImportError:
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return None
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try:
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# Parse the analysis date
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end_date = datetime.strptime(analysis_date, '%Y-%m-%d')
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start_date = end_date - timedelta(days=90)
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# Fetch stock data
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stock = yf.Ticker(ticker)
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df = stock.history(start=start_date.strftime('%Y-%m-%d'),
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end=end_date.strftime('%Y-%m-%d'))
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if df.empty:
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return None
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# Create the figure
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fig, ax = plt.subplots(figsize=(10, 4))
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# Plot volume bars
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colors = ['green' if close >= open_ else 'red'
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for close, open_ in zip(df['Close'], df['Open'])]
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ax.bar(df.index, df['Volume'], color=colors, alpha=0.6)
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# Add average volume line
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avg_volume = df['Volume'].mean()
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ax.axhline(y=avg_volume, color='blue', linestyle='--',
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linewidth=1.5, label=f'Avg Volume: {avg_volume:,.0f}', alpha=0.7)
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# Formatting
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ax.set_title(f'{ticker} Trading Volume - Last 90 Days', fontsize=14, fontweight='bold')
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ax.set_xlabel('Date', fontsize=12)
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ax.set_ylabel('Volume', fontsize=12)
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ax.legend(loc='best', fontsize=10)
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ax.grid(True, alpha=0.3, axis='y')
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# Format y-axis to show volume in millions
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ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, p: f'{x/1e6:.1f}M'))
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# Format x-axis dates
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ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
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ax.xaxis.set_major_locator(mdates.MonthLocator())
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plt.xticks(rotation=45)
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plt.tight_layout()
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# Save to BytesIO buffer
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buf = io.BytesIO()
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plt.savefig(buf, format='png', dpi=150, bbox_inches='tight')
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buf.seek(0)
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plt.close(fig)
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return buf
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except Exception as e:
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print(f"Error generating volume chart: {e}")
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return None
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def generate_technical_indicators_chart(ticker: str, analysis_date: str) -> Optional[io.BytesIO]:
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"""
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Generate a chart showing RSI and MACD technical indicators.
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Args:
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ticker: Stock ticker symbol
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analysis_date: Analysis date string (YYYY-MM-DD)
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Returns:
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BytesIO buffer containing the chart image, or None if generation fails
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"""
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try:
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import matplotlib.pyplot as plt
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import matplotlib.dates as mdates
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import yfinance as yf
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import pandas as pd
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from datetime import datetime, timedelta
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except ImportError:
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return None
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try:
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# Parse the analysis date
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end_date = datetime.strptime(analysis_date, '%Y-%m-%d')
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start_date = end_date - timedelta(days=90)
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# Fetch stock data
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stock = yf.Ticker(ticker)
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df = stock.history(start=start_date.strftime('%Y-%m-%d'),
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end=end_date.strftime('%Y-%m-%d'))
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if df.empty:
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return None
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# Calculate 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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# Calculate MACD
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exp1 = df['Close'].ewm(span=12, adjust=False).mean()
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exp2 = df['Close'].ewm(span=26, adjust=False).mean()
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df['MACD'] = exp1 - exp2
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df['Signal'] = df['MACD'].ewm(span=9, adjust=False).mean()
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# Create subplots
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fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8), sharex=True)
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# RSI plot
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ax1.plot(df.index, df['RSI'], label='RSI', linewidth=2, color='#2E86AB')
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ax1.axhline(y=70, color='red', linestyle='--', linewidth=1, alpha=0.7, label='Overbought (70)')
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ax1.axhline(y=30, color='green', linestyle='--', linewidth=1, alpha=0.7, label='Oversold (30)')
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ax1.fill_between(df.index, 30, 70, alpha=0.1, color='gray')
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ax1.set_ylabel('RSI', fontsize=12)
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ax1.set_title(f'{ticker} Technical Indicators', fontsize=14, fontweight='bold')
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ax1.legend(loc='best', fontsize=10)
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ax1.grid(True, alpha=0.3)
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ax1.set_ylim(0, 100)
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# MACD plot
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ax2.plot(df.index, df['MACD'], label='MACD', linewidth=2, color='#2E86AB')
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ax2.plot(df.index, df['Signal'], label='Signal', linewidth=2, color='#F18F01')
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ax2.bar(df.index, df['MACD'] - df['Signal'], label='Histogram',
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color='gray', alpha=0.3)
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ax2.axhline(y=0, color='black', linestyle='-', linewidth=0.5)
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ax2.set_xlabel('Date', fontsize=12)
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ax2.set_ylabel('MACD', fontsize=12)
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ax2.legend(loc='best', fontsize=10)
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ax2.grid(True, alpha=0.3)
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# Format x-axis dates
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ax2.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
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ax2.xaxis.set_major_locator(mdates.MonthLocator())
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plt.xticks(rotation=45)
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plt.tight_layout()
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# Save to BytesIO buffer
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buf = io.BytesIO()
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plt.savefig(buf, format='png', dpi=150, bbox_inches='tight')
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buf.seek(0)
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plt.close(fig)
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return buf
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except Exception as e:
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print(f"Error generating technical indicators chart: {e}")
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return None
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18
cli/main.py
18
cli/main.py
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@ -28,6 +28,7 @@ from tradingagents.graph.trading_graph import TradingAgentsGraph
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from tradingagents.default_config import DEFAULT_CONFIG
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from cli.models import AnalystType
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from cli.utils import *
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from cli.pdf_generator import generate_pdf_report
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console = Console()
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@ -1099,6 +1100,23 @@ def run_analysis():
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update_display(layout)
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# Generate PDF report after the Live display context ends
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try:
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console.print("\n[bold cyan]Generating PDF report...[/bold cyan]")
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pdf_path = generate_pdf_report(
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final_state,
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selections["ticker"],
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selections["analysis_date"],
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results_dir
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)
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console.print(f"[bold green]PDF report saved to:[/bold green] {pdf_path}")
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except ImportError as e:
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console.print(f"[yellow]Warning: {e}[/yellow]")
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console.print("[yellow]PDF generation skipped. Install reportlab to enable PDF reports:[/yellow]")
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console.print("[dim]pip install reportlab[/dim]")
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except Exception as e:
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console.print(f"[red]Error generating PDF report: {e}[/red]")
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@app.command()
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def analyze():
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"""PDF report generator for TradingAgents analysis results."""
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import datetime
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import re
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from pathlib import Path
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from typing import Dict, Any
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import io
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from cli.chart_generator import (
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generate_stock_price_chart,
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generate_volume_chart,
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generate_technical_indicators_chart
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)
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def generate_pdf_report(
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final_state: Dict[str, Any],
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ticker: str,
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analysis_date: str,
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output_path: Path,
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) -> Path:
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"""
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Generate a comprehensive PDF report from the analysis results.
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Args:
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final_state: The final state dictionary containing all analysis results
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ticker: The stock ticker symbol
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analysis_date: The analysis date
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output_path: Path where the PDF should be saved
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Returns:
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Path to the generated PDF file
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"""
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try:
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from reportlab.lib.pagesizes import letter, A4
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from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
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from reportlab.lib.units import inch
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from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, PageBreak
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from reportlab.platypus import Table, TableStyle, Image
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from reportlab.lib import colors
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from reportlab.lib.enums import TA_CENTER, TA_LEFT, TA_JUSTIFY
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except ImportError:
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raise ImportError(
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"reportlab is required for PDF generation. "
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"Install it with: pip install reportlab"
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)
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# Create the PDF document
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pdf_file = output_path / f"{ticker}_{analysis_date}_analysis_report.pdf"
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doc = SimpleDocTemplate(
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str(pdf_file),
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pagesize=letter,
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rightMargin=72,
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leftMargin=72,
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topMargin=72,
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bottomMargin=18,
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)
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# Container for the 'Flowable' objects
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elements = []
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# Define styles
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styles = getSampleStyleSheet()
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styles.add(ParagraphStyle(
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name='CustomTitle',
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parent=styles['Heading1'],
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fontSize=24,
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textColor=colors.HexColor('#1a1a1a'),
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spaceAfter=30,
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alignment=TA_CENTER,
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))
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styles.add(ParagraphStyle(
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name='CustomHeading1',
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parent=styles['Heading1'],
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fontSize=18,
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textColor=colors.HexColor('#2c3e50'),
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spaceAfter=12,
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spaceBefore=12,
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))
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styles.add(ParagraphStyle(
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name='CustomHeading2',
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parent=styles['Heading2'],
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fontSize=14,
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textColor=colors.HexColor('#34495e'),
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spaceAfter=10,
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spaceBefore=10,
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))
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styles.add(ParagraphStyle(
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name='CustomBody',
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parent=styles['BodyText'],
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fontSize=10,
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alignment=TA_JUSTIFY,
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spaceAfter=12,
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))
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# Title Page
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title = Paragraph(
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f"TradingAgents Analysis Report<br/>{ticker}",
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styles['CustomTitle']
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)
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elements.append(title)
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elements.append(Spacer(1, 12))
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# Metadata table
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metadata = [
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['Analysis Date:', analysis_date],
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['Report Generated:', datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')],
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['Ticker Symbol:', ticker],
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]
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t = Table(metadata, colWidths=[2*inch, 4*inch])
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t.setStyle(TableStyle([
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('ALIGN', (0, 0), (-1, -1), 'LEFT'),
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('FONTNAME', (0, 0), (0, -1), 'Helvetica-Bold'),
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('FONTSIZE', (0, 0), (-1, -1), 10),
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('BOTTOMPADDING', (0, 0), (-1, -1), 6),
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('TOPPADDING', (0, 0), (-1, -1), 6),
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]))
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elements.append(t)
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elements.append(Spacer(1, 30))
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# Generate and add charts
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elements.append(PageBreak())
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elements.append(Paragraph('Market Data Visualizations', styles['CustomHeading1']))
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elements.append(Spacer(1, 12))
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# Stock price chart
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print("Generating stock price chart...")
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price_chart = generate_stock_price_chart(ticker, analysis_date)
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if price_chart:
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img = Image(price_chart, width=6*inch, height=3.6*inch)
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elements.append(img)
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elements.append(Spacer(1, 20))
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# Volume chart
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print("Generating volume chart...")
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volume_chart = generate_volume_chart(ticker, analysis_date)
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if volume_chart:
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img = Image(volume_chart, width=6*inch, height=2.4*inch)
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elements.append(img)
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elements.append(Spacer(1, 20))
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# Technical indicators chart
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print("Generating technical indicators chart...")
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tech_chart = generate_technical_indicators_chart(ticker, analysis_date)
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if tech_chart:
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img = Image(tech_chart, width=6*inch, height=4.8*inch)
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elements.append(img)
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elements.append(Spacer(1, 20))
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# Helper function to clean and escape text for PDF
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def clean_text(text: str) -> str:
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"""Clean text and properly escape for reportlab."""
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# Remove markdown headers
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text = re.sub(r'^#{1,6}\s+', '', text, flags=re.MULTILINE)
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# Escape special XML/HTML characters first
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text = text.replace('&', '&')
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text = text.replace('<', '<')
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text = text.replace('>', '>')
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# Now we can safely add our formatting tags
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# Convert markdown bold (**text** or __text__) to HTML bold
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text = re.sub(r'\*\*(.+?)\*\*', r'<b>\1</b>', text)
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text = re.sub(r'__(.+?)__', r'<b>\1</b>', text)
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# Convert markdown italic (*text* or _text_) to HTML italic
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# Be careful not to match ** or __
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text = re.sub(r'(?<!\*)\*(?!\*)(.+?)(?<!\*)\*(?!\*)', r'<i>\1</i>', text)
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text = re.sub(r'(?<!_)_(?!_)(.+?)(?<!_)_(?!_)', r'<i>\1</i>', text)
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# Remove any remaining problematic characters
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text = text.replace('\r', '')
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return text
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# Helper function to add section
|
||||
def add_section(title: str, content: str):
|
||||
if content:
|
||||
elements.append(Paragraph(title, styles['CustomHeading1']))
|
||||
elements.append(Spacer(1, 12))
|
||||
|
||||
# Clean the content
|
||||
content = clean_text(content)
|
||||
|
||||
# Split content into paragraphs
|
||||
paragraphs = content.split('\n\n')
|
||||
for para in paragraphs:
|
||||
para = para.strip()
|
||||
if para:
|
||||
# Replace single newlines with spaces, but preserve structure
|
||||
para = para.replace('\n', ' ')
|
||||
# Clean up multiple spaces
|
||||
para = re.sub(r'\s+', ' ', para)
|
||||
try:
|
||||
elements.append(Paragraph(para, styles['CustomBody']))
|
||||
except Exception as e:
|
||||
# If there's still an error, add as plain text
|
||||
print(f"Warning: Could not parse paragraph, adding as plain text: {e}")
|
||||
elements.append(Paragraph(para.replace('<', '<').replace('>', '>'), styles['CustomBody']))
|
||||
elements.append(Spacer(1, 20))
|
||||
|
||||
# I. Analyst Team Reports
|
||||
elements.append(PageBreak())
|
||||
elements.append(Paragraph('I. Analyst Team Reports', styles['CustomHeading1']))
|
||||
elements.append(Spacer(1, 20))
|
||||
|
||||
if final_state.get('market_report'):
|
||||
add_section('Market Analysis', final_state['market_report'])
|
||||
|
||||
if final_state.get('sentiment_report'):
|
||||
add_section('Social Sentiment Analysis', final_state['sentiment_report'])
|
||||
|
||||
if final_state.get('news_report'):
|
||||
add_section('News Analysis', final_state['news_report'])
|
||||
|
||||
if final_state.get('fundamentals_report'):
|
||||
add_section('Fundamentals Analysis', final_state['fundamentals_report'])
|
||||
|
||||
# II. Research Team Reports
|
||||
if final_state.get('investment_debate_state'):
|
||||
elements.append(PageBreak())
|
||||
elements.append(Paragraph('II. Research Team Decision', styles['CustomHeading1']))
|
||||
elements.append(Spacer(1, 20))
|
||||
|
||||
debate_state = final_state['investment_debate_state']
|
||||
|
||||
if debate_state.get('bull_history'):
|
||||
add_section('Bull Researcher Analysis', debate_state['bull_history'])
|
||||
|
||||
if debate_state.get('bear_history'):
|
||||
add_section('Bear Researcher Analysis', debate_state['bear_history'])
|
||||
|
||||
if debate_state.get('judge_decision'):
|
||||
add_section('Research Manager Decision', debate_state['judge_decision'])
|
||||
|
||||
# III. Trading Team Reports
|
||||
if final_state.get('trader_investment_plan'):
|
||||
elements.append(PageBreak())
|
||||
elements.append(Paragraph('III. Trading Team Plan', styles['CustomHeading1']))
|
||||
elements.append(Spacer(1, 20))
|
||||
add_section('Trader Plan', final_state['trader_investment_plan'])
|
||||
|
||||
# IV. Risk Management Team Reports
|
||||
if final_state.get('risk_debate_state'):
|
||||
elements.append(PageBreak())
|
||||
elements.append(Paragraph('IV. Risk Management Team Decision', styles['CustomHeading1']))
|
||||
elements.append(Spacer(1, 20))
|
||||
|
||||
risk_state = final_state['risk_debate_state']
|
||||
|
||||
if risk_state.get('risky_history'):
|
||||
add_section('Aggressive Analyst Analysis', risk_state['risky_history'])
|
||||
|
||||
if risk_state.get('safe_history'):
|
||||
add_section('Conservative Analyst Analysis', risk_state['safe_history'])
|
||||
|
||||
if risk_state.get('neutral_history'):
|
||||
add_section('Neutral Analyst Analysis', risk_state['neutral_history'])
|
||||
|
||||
# V. Portfolio Manager Decision
|
||||
if risk_state.get('judge_decision'):
|
||||
elements.append(PageBreak())
|
||||
elements.append(Paragraph('V. Portfolio Manager Decision', styles['CustomHeading1']))
|
||||
elements.append(Spacer(1, 20))
|
||||
add_section('Final Decision', risk_state['judge_decision'])
|
||||
|
||||
# Build PDF
|
||||
doc.build(elements)
|
||||
|
||||
return pdf_file
|
||||
|
|
@ -24,3 +24,5 @@ rich
|
|||
questionary
|
||||
langchain_anthropic
|
||||
langchain-google-genai
|
||||
reportlab
|
||||
matplotlib
|
||||
|
|
|
|||
Loading…
Reference in New Issue