Files
Bundle OKF 0.1 · 5 conceitos · ArjunDivecha/News
Open source Repository Open in the app JSON README (API)
About
# Files
- [OpenWiki Quickstart](quickstart.md) - Entry point for the repository wiki. Covers the daily market-and-portfolio report system, upstream universe construction, analytics, tag views, scenario risk, and operations.
# Directories
- [analytics](analytics/)
- [architecture](architecture/)
- [domain](domain/)
- [operations](operations/)
Details
- Kind
- OKF bundles
- Topic
- Finance & crypto
- Publisher
- arjundivecha
- Origin
- okf_github
- Category
- dados
- Version
- 0.1
- Last push
- 2026-08-14T22:17:19Z
- Repository state
- ativo
- Language
- Python
- Added
- 2026-09-08 02:20:20
- Updated
- 2026-09-08 02:20:20
- Origin id
ArjunDivecha/News:openwiki/index.md
README
# News - Unified Daily Market & Portfolio Report One command pulls live Schwab + IBKR holdings, fetches ~800 ETF prices from Yahoo Finance (no Bloomberg terminal), runs unit-tested analytics, and has Claude Opus write a single unified market + portfolio report as a PDF: ```bash python3 report/main.py ``` Full documentation: [`report/README.md`](report/README.md) ```bash python3 report/main.py --no-llm # data + analytics only (free, fast) python3 report/main.py --non-interactive # cron mode (stale fallback, no prompts) python3 -m pytest tests/ -v # 25 tests on the financial math ``` ## Repository layout ``` News/ ├── report/ # THE daily report system (start here) │ ├── main.py # one-command pipeline │ ├── prompts/system.md # LLM strategist prompt │ └── README.md # full docs ├── tests/ # pytest suite for the financial math ├── data/ # universe.xlsx, report.db, holdings.xlsx ├── outputs/unified/ # generated reports (PDF/MD + data packages) │ ├── Step 1 Data Collection/ # Universe construction (run rarely) ├── Step 2 Data Processing - Final1000/ # classification & selection ├── Step 3 Data Analysis/ # analytics on the universe ├── fine tuning/ # ML classifier training │ ├── archive/ # Legacy reporting chain (replaced by report/) ├── AGENTS.md # AI agent instructions └── README.md # this file ``` ## The two pipelines ### 1. Daily report (`report/`) - run every day See `report/README.md`. Replaces the old Phase 0 -> Step 4 -> Phase 2 chain (now in `archive/`). Universe: 763 unique ETFs in `data/universe.xlsx`, priced via Yahoo Finance. History: single SQLite db at `data/report.db`. ### 2. Universe construction (Steps 1-3 + fine tuning) - run rarely Builds the Final 1000 Asset Master List from Bloomberg indices, ETFs, and Goldman baskets via LLM classification. Only needed to rebuild/refresh the universe; after changing it, regenerate the report universe with: ```bash python3 report/build_universe.py ``` | Stage | Purpose | Output | |-------|---------|--------| | Step 1 Data Collection | Raw data acquisition | Filtered datasets from 3 sources | | Step 2 Data Processing | Classification & selection | Final 1000 Asset Master List | | Step 3 Data Analysis | Performance analytics | Factor profiles & deduplication | | Fine Tuning | ML model training | Fine-tuned Llama classifier | ## Requirements - **Python** 3.14 (`yfinance`, `pandas`, `anthropic>=0.109`, `schwabdev`, `python-dotenv`, `markdown`, `pytest`) - `.venv-ibkr312/` - Python 3.12 venv with `ib_insync` (IBKR API requirement) - **PrinceXML** (`brew install prince`) for PDF rendering - `.env` at repo root: `ANTHROPIC_API_KEY`, `SCHWAB_APP_KEY`, `SCHWAB_APP_SECRET` - TWS / IB Gateway logged in (auto-launched if not running) ## Documentation | File | Purpose | |------|---------| | `report/README.md` | Daily report system - full guide | | `AGENTS.md` | AI coding agent instructions | | `archive/README.md` | What the legacy code was and what replaced it | | `Step 2 .../README.md` | Classification workflow | | `fine tuning/README.md` | ML training guide | --- **Last Updated**: 2026-06-10 **Version**: 3.0.0 (unified report rearchitecture)