# Trade Report PDF

> 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".

- Skill: `zubair-trabzada/trade-report-pdf` (Agent Skill)
- Install (CLI): `npx skillmds@latest add zubair-trabzada/trade-report-pdf`
- Raw SKILL.md: https://api.skillmd.com/api/skills/zubair-trabzada/trade-report-pdf/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Docs & Writing
- Author: zubair-trabzada (https://skillmd.com/u/zubair-trabzada)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/zubair-trabzada/trade-report-pdf

---


# 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:

```bash
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)
```json
{
  "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:

```json
{
  "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:

```bash
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:
```bash
pip3 install reportlab 2>/dev/null || pip install reportlab 2>/dev/null
```

---

## Step 5: Verify and Report

```bash
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

1. ALWAYS scan for ALL TRADE-*.md files — do not skip any.
2. ALWAYS include the disclaimer on every page.
3. ALWAYS verify the PDF was generated successfully.
4. NEVER fabricate data — only what was extracted from actual files.
5. If only one analysis file, still generate the PDF (single-stock report).
6. 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.**