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Contributing to Capital Markets Intelligence Platform

Thank you for your interest in contributing! This platform is built to mirror institutional research standards — contributions that raise analytical depth, code quality, or usability are all welcome.

Table of Contents

  1. Getting Started
  2. Development Setup
  3. Running the Pipeline
  4. How to Contribute
  5. Coding Style
  6. Submitting a PR
  7. Issue Labels

Getting Started

  1. Fork the repository and clone your fork locally.
  2. Browse open issues — look for good first issue or help wanted labels.
  3. Comment on an issue to claim it before starting work.

Development Setup

Requirements: Python 3.13+

# Clone your fork
git clone https://github.com/<your-username>/capital-markets-intelligence.git
cd capital-markets-intelligence

# Create and activate virtual environment
python3 -m venv venv
source venv/bin/activate        # Mac/Linux
# venv\Scripts\activate         # Windows

# Install dependencies
pip install -r requirements.txt

Running the Pipeline

Quick validation (minimal run — recommended for contributors):

python scripts/01_fetch_ipo_data.py
python scripts/05_fetch_sovereign_data.py
python scripts/10_advanced_analysis.py
python scripts/11_generate_plotly_dashboards.py

Full pipeline:

# Phase 1 — Data Collection
python scripts/01_fetch_ipo_data.py
python scripts/02_fetch_mna_data.py
python scripts/03_stress_test_model.py
python scripts/04_case_study_builder.py
python scripts/05_fetch_sovereign_data.py

# Phase 2 — Live API Data
python scripts/08_fetch_fred_data.py
python scripts/09_fetch_worldbank_data.py

# Phase 3 — Advanced Analysis
python scripts/10_advanced_analysis.py

# Phase 4 — Outputs
python scripts/06_generate_visualizations.py
python scripts/07_generate_memos.py
python scripts/11_generate_plotly_dashboards.py
python scripts/12_generate_excel_reports.py
python scripts/13_generate_pdf_reports.py

Outputs appear in output/ (dashboards, Excel, PDFs, memos).

How to Contribute

  1. Pick an issue or open a new one describing your idea.
  2. Create a feature branch: git checkout -b feature/your-feature-name
  3. Make your changes (keep PRs focused — one feature or fix per PR).
  4. Run the quick validation pipeline to confirm nothing is broken.
  5. Commit with a clear message: feat: add sector-level M&A summaries
  6. Push and open a Pull Request against main.

Coding Style

  • Formatter: black (line length 100)
  • Linter: ruff or flake8
  • Docstrings: NumPy or Google style
  • Paths: use pathlib.Path, not raw string concatenation
  • Logging: use Python logging module, not print()
  • Type hints: encouraged for all new functions

To format before committing:

pip install black ruff
black scripts/ analysis/
ruff check scripts/ analysis/

Submitting a PR

Please fill in the PR template when opening a PR. Key points:

  • What changed and why.
  • How you tested it (e.g., "ran quick validation, checked Excel output").
  • Screenshots or sample output if the change touches dashboards/reports.

Issue Labels

Label Meaning
good first issue Small, well-scoped — great for new contributors
help wanted Higher-impact work the maintainer wants community help on
analytics Changes to quantitative models or analysis logic
data Changes to data fetching, cleaning, or schemas
infra Packaging, CI, config, testing, logging
ux Dashboards, Excel/PDF polish, visual output
docs Documentation, notebooks, cookbooks
IPO IPO event study module
M&A M&A deal screening module
sovereign Sovereign risk index and stress testing
macro Cross-asset regime detection and yield curve

Built to institutional research standards. Contributions held to the same bar.
⚡