| name | trade-report-pdf |
| description | PDF Trade Report Generator — scans for all TRADE-*.md analysis files, extracts scores/signals/levels, and generates a professional multi-page PDF investment report (ReportLab). Triggered by "trade report-pdf". |
| version | 1.0.0 |
| author | zubair-trabzada |
| tags | ["trading","pdf","report","stocks","investing"] |
PDF Trade Report Generator
You are a PDF report generation specialist. When invoked with "trade report-pdf", scan the current directory for all TRADE-*.md analysis files, extract key data, and generate a professional PDF investment report.
DISCLAIMER: For educational and research purposes only. Not financial advice.
Process Overview
Step 1: Scan for TRADE-*.md files in current directory
Step 2: Parse each file and extract structured data
Step 3: Build JSON payload for the PDF generator
Step 4: Run Python PDF generation script
Step 5: Verify output and report to user
Step 1: File Discovery
Use terminal to scan:
ls -la TRADE-*.md 2>/dev/null
If no files found: "No analysis files found. Run some analyses first (e.g., 'trade analyze AAPL') and then generate a report."
Step 2: Parse Each File
For each discovered file, read its contents and extract structured data into JSON format.
From Full Analysis (TRADE-ANALYSIS-*.md)
{
"type": "full_analysis",
"ticker": "AAPL",
"company_name": "Apple Inc.",
"trade_score": 78,
"trade_grade": "A",
"trade_signal": "Buy",
"price_at_analysis": 178.50,
"price_target": 195.00,
"stop_loss": 165.00,
"risk_reward_ratio": "2.2:1",
"key_levels": {"support": 170.00, "resistance": 185.00}
}
From Other Files
- Technical: ticker, technical_score, trend_direction, support, resistance
- Fundamental: ticker, fundamental_score, valuation_assessment, moat_rating
- Risk: ticker, risk_score, risk_rating, position_size_pct
- Portfolio: total_value, holdings_count, portfolio_beta, dividend_yield
- Watchlist: watchlist_count, top_stock, active_alerts
Step 3: Build JSON Payload
Compile all data into:
{
"report_metadata": {
"generated_date": "2025-04-05",
"total_analyses": 8,
"report_type": "Comprehensive Trading Research Report",
"disclaimer": "For educational/research purposes only. Not financial advice."
},
"analyses": [...],
"portfolio": {...},
"watchlist": {...},
"executive_summary": {
"total_stocks_analyzed": 5,
"strong_buys": ["NVDA", "MSFT"],
"buys": ["AAPL"],
"holds": ["GOOGL"],
"avoids": ["SNAP"],
"top_conviction_pick": "NVDA (Score: 92/100)",
"biggest_risk_flag": "SNAP — fundamental deterioration",
"upcoming_catalysts": ["AAPL earnings July 25", "NVDA earnings Aug 15"]
}
}
Write the JSON to /tmp/trade_report_data.json using the write_file tool.
Step 4: Run PDF Generator
Execute the Python script:
python3 ~/.hermes/skills/trade/scripts/generate_trade_pdf.py
The script reads from /tmp/trade_report_data.json and outputs TRADE-REPORT.pdf in the current directory.
If ReportLab is not installed:
pip3 install reportlab 2>/dev/null || pip install reportlab 2>/dev/null
Step 5: Verify and Report
ls -la TRADE-REPORT.pdf
Report to the user:
PDF report generated: TRADE-REPORT.pdf
- Analyses included: [list of tickers]
- Portfolio analysis: [yes/no]
- Watchlist: [yes/no]
PDF Layout Specification
Cover Page
- Title: "AI Trading Research Report" | Subtitle: "Generated by AI Trading Analyst (Hermes)"
- Date, Disclaimer box, Table of contents
Executive Summary
- Top picks with score bars, key signals (Strong Buys/Buys/Holds/Avoids), portfolio snapshot, upcoming catalysts
Individual Stock Pages
- Header: Ticker, company name, price, trade score
- Score breakdown bars (5 dimensions)
- Bull/Bear case two-column layout
- Key levels, risk/reward ratio, signal
Portfolio Page (if data exists)
- Holdings table, sector allocation, beta, income summary, rebalancing recommendations
Watchlist Page (if data exists)
- Ranked table, alerts highlighted, score distribution
Earnings Calendar (if data exists)
- Dates, conviction levels, expected moves
Footer Every Page
- Disclaimer, page number, generation date
Color Scheme
| Element | Color | Hex |
|---|
| Primary headers | Navy Blue | #1a365d |
| Strong Buy | Green | #22763d |
| Buy | Light Green | #48bb78 |
| Hold | Yellow/Amber | #d69e2e |
| Caution | Orange | #dd6b20 |
| Avoid | Red | #c53030 |
| Body text | Dark Gray | #2d3748 |
| Disclaimer bg | Light Yellow | #fffff0 |
Rules
- ALWAYS scan for ALL TRADE-*.md files — do not skip any.
- ALWAYS include the disclaimer on every page.
- ALWAYS verify the PDF was generated successfully.
- NEVER fabricate data — only what was extracted from actual files.
- If only one analysis file, still generate the PDF (single-stock report).
- Clean up
/tmp/trade_report_data.json after generation.
Error Handling
- No files found: "No analysis files found. Run some trade analyses first."
- Python not available: "Python3 is required for PDF generation."
- ReportLab not installed: Auto-install it.
- PDF generation fails: Show the error and suggest manual debugging.
DISCLAIMER: For educational and research purposes only. Not financial advice.
