Current status: v1.0 complete — 6 proprietary models, 5 dashboards, 4 Excel workbooks, 4 PDFs, 4 research memos. Zero API keys required.
This roadmap outlines what we're building next. Contributions on any milestone are welcome — see CONTRIBUTING.md to get started.
Goal: make the platform production-grade and contributor-friendly.
- Turn pipeline into a Python package with CLI entrypoint (
cmi run data|analysis|outputs|all) - Add YAML/JSON configuration for tickers, countries, event windows, and scenarios
- Refactor duplicated data-loading logic into shared utility module (
cmi/data_utils.py) - Add structured logging and graceful error handling across the pipeline
- Add pytest test suite with golden-data tests for core models
- Add GitHub Actions CI (tests + lint on every PR)
- Add type hints and mypy checks for core modules
- Add Dockerfile and optional VS Code devcontainer
- Add a
cmi run happy-pathcommand for fast end-to-end validation
Goal: deepen the quantitative models across all four verticals.
- Add configurable estimation windows and additional statistical tests (rank tests, parametric vs non-parametric)
- Add market-adjusted vs market-model comparison
- Cross-check abnormal returns against an external event-study library
- Add multi-event aggregation: CARs by sector, country, underwriter (with boxplots)
- Move from simple correlations to logistic regression / tree-based classification model with held-out validation
- Add sector-level M&A summaries (median premium, completion rate, typical deal size)
- Add time-to-completion analysis as a function of deal characteristics
- Decompose sovereign risk index contributions by indicator (tornado charts per country)
- Add rating-style mapping (IG/BB/B buckets) and watch-list flag for downgrade risk
- Add commodity-linked stress scenarios (oil exporter vs importer differential)
- Add Markov transition probabilities and regime persistence stats (half-life, expected duration)
- Add a toy strategy backtest using regime calls (with appropriate disclaimers)
Goal: make the platform feel like a real institutional research product.
- Add a global navigation "home page" HTML linking all dashboards, reports, and workbooks
- Add interactive filters (country, sector, date range) to Plotly dashboards
- Standardize chart style, color palette, and fonts across all dashboards
- Add TOC, numbered sections, and figure captions to PDF reports
- Add hyperlinks in Excel workbooks (tickers → Yahoo Finance, countries → World Bank)
- Generate a "Data Inventory" appendix (source, coverage period, last updated) in both Excel and PDF
- Add a "Scenario Cookbook" in
docs/(how to add a new IPO, country, or scenario) - Add a full worked-example Jupyter notebook for each vertical (IPO, M&A, sovereign, macro)
Longer-horizon ideas — contributions and proposals welcome:
- Dynamic term structure models (Nelson-Siegel-Svensson)
- Multi-factor risk models for cross-asset portfolios
- Integration with open-source trading agents (FinRL, FinRobot) for research simulation
- Optional LLM-powered memo generation (narrative summaries from structured model output)
- Real-time data refresh mode (scheduled pipeline runs)
- Web-based UI for non-technical users to configure and run analyses
We are actively looking for help with:
- Quant finance: regime-switching models, factor models, advanced event-study statistics
- Python engineering: packaging, CI, type safety, testing
- Visualization: Plotly dashboard polish, cross-asset chart design
- Documentation: notebooks, cookbooks, written explanations of models
See CONTRIBUTING.md and browse open issues — especially those labeled good first issue or help wanted.