Initial commit: Data Intelligence Report Generator

- Oracle ERP ROA integration with sales analytics and margin analysis
- Excel multi-sheet reports with conditional formatting
- PDF executive summaries with charts via ReportLab
- Optimized SQL queries (no cartesian products)
- Docker support for cross-platform deployment
- Configurable alert thresholds for business intelligence

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

CLAUDE.md

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+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Project Overview
+
+Data Intelligence Report Generator for ERP ROA (Oracle Database). Generates Excel and PDF business intelligence reports with sales analytics, margin analysis, stock tracking, and alerts.
+
+## Commands
+
+### Option 1: Virtual Environment (WSL or Windows)
+```bash
+# Create and activate virtual environment
+python -m venv .venv
+source .venv/bin/activate  # Linux/WSL
+# or: .venv\Scripts\activate  # Windows
+
+# Install dependencies
+pip install -r requirements.txt
+
+# Run report
+python main.py
+```
+
+### Option 2: Docker (Windows Docker Desktop / Linux)
+```bash
+# Copy and configure environment
+cp .env.example .env
+# Edit .env with your Oracle credentials
+
+# Run with docker-compose
+docker-compose run --rm report-generator
+
+# Or with custom months
+docker-compose run --rm report-generator python main.py --months 6
+```
+
+### Common Options
+```bash
+# Run with custom period
+python main.py --months 6
+
+# Custom output directory
+python main.py --output-dir /path/to/output
+```
+
+## Oracle Connection from Different Environments
+
+| Environment | ORACLE_HOST value |
+|-------------|-------------------|
+| Windows native | `127.0.0.1` |
+| WSL | Windows IP (run: `cat /etc/resolv.conf \| grep nameserver`) |
+| Docker | `host.docker.internal` (automatic in docker-compose) |
+
+## Architecture
+
+**Entry point**: `main.py` - CLI interface, orchestrates query execution and report generation
+
+**Data flow**:
+1. `config.py` loads Oracle connection settings from `.env` file
+2. `queries.py` contains all SQL queries in a `QUERIES` dictionary with metadata (title, description, params)
+3. `main.py` executes queries via `OracleConnection` context manager, stores results in `results` dict
+4. `report_generator.py` receives dataframes and generates:
+   - `ExcelReportGenerator`: Multi-sheet workbook with conditional formatting
+   - `PDFReportGenerator`: Executive summary with charts via ReportLab
+
+**Key patterns**:
+- Queries use parameterized `:months` for configurable analysis period
+- Sheet order in `main.py:sheet_order` controls Excel tab sequence
+- Charts are generated via matplotlib, converted to images for PDF
+
+## Oracle Database Schema
+
+Required views: `fact_vfacturi2`, `fact_vfacturi_detalii`, `vnom_articole`, `vnom_parteneri`, `vstoc`, `vrul`
+
+Filter conventions:
+- `sters = 0` excludes deleted records
+- `tip NOT IN (7, 8, 9, 24)` excludes returns/credit notes
+- Account codes: `341`, `345` = own production; `301` = raw materials
+
+## Adding New Reports
+
+1. Add SQL query constant in `queries.py`
+2. Add entry to `QUERIES` dict with `sql`, `params`, `title`, `description`
+3. Add query name to `sheet_order` list in `main.py` (line ~143)
+4. For PDF inclusion, add rendering logic in `main.py:generate_reports()`
+
+## Alert Thresholds (in config.py)
+
+- Low margin: < 15%
+- Price variation: > 20%
+- Slow stock: > 90 days without movement
+- Minimum sales for analysis: 1000 RON