Open Net Worth
Open Net Worth is an open-source personal AI wealth assistant you run yourself: one ledger for your whole financial life, worked by an AI agent you control.
A live quantitative macro terminal: FRED → classifier → signals → Claude briefings, refreshed daily.
Translation pending
FinanceTypeScript · PythonGitHub @maxkomen-macroOnlineOwner unconfirmed
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Automated macro data pipeline and live dashboard that tracks key economic indicators, classifies macro regimes (Goldilocks/Overheating/Stagflation/Recession Risk), and generates alerts for yield curve inversions, VIX spikes, and labor deterioration. Built with Python, SQLite, and Streamlit.
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A Bloomberg-terminal-style quantitative macro platform — regime classification, recession modeling, portfolio optimization, LBO analysis, and AI-augmented news intelligence across 11 interactive tabs. Refreshed automatically; zero manual intervention.
Macro Regime Radar ingests macroeconomic time series from FRED, equity / rates / commodity prices from yfinance, and breaking financial headlines from Finnhub and NewsAPI. It then runs a stack of quantitative models on top of that data:Read the full README ↗A live macro terminal that reads the US economy into one of four regimes every month, then shows the evidence, the models and the data trail underneath.
Open the live site → · Analyst Desk · Methodology · Latest daily memo
Macro Regime Radar is a one-person project: a React and TypeScript front end, a FastAPI service, and a data pipeline that runs itself on GitHub Actions. It pulls macro data from FRED, market prices and headlines; classifies the economy as Goldilocks, Overheating, Stagflation or Recession Risk; scores recession odds with a separate model; and publishes daily and weekly briefings. Every number on the site carries the date of the data behind it.
It is built for three kinds of visitors: someone who wants the macro picture in ten seconds, an analyst who wants to test an idea against history, and an engineer who wants to see how a small, fully automated data product is put together.
The site has two parts: the terminal, with seven tabs plus Methodology, and the Analyst Desk, a research and trading workspace.
| Tab | What you can do there |
|---|---|
| Dashboard | See the current regime with its odds, the five monitored signals, key market levels, what markets are pricing, and the main macro charts. |
| Regime Lab | Read the regime's playbook, where it sits in the cycle, the odds of moving to another regime, historical analogues, a scenario builder, the full regime history, and backtests with factor attribution. |
| Markets | Research any US stock or ETF, watch the macro tape, scan the sector heatmap, and see what markets are pricing and the biggest data surprises. |
| Credit | Track investment-grade and high-yield spreads by rating, the quality ladder, and financing conditions. |
| Recession | See the recession model's score, the yield-curve monitor, and sliders that show how each input moves the odds. |
| News & Calendar | Read priority headlines, each scored for significance and the top ones with a Claude interpretation and cited research, beside the macro release calendar. |
| Tools | Run an LBO calculator, or an asset allocator with seven optimizers (mean-variance, minimum variance, risk parity, Black-Litterman, HRP, HERC and CVaR) built on how each asset class did in each regime. |
| Methodology | How every model works, what it can and cannot claim, and where each number comes from. |
Around the tabs: a market strip, an "Ask the analyst" assistant that answers questions from the stored data, a ⌘K palette to jump to any section, an alerts drawer, and a Data status drawer that shows how current each feed is.
The Desk turns a macro view into a testable trade idea.
A few Desk cards (options hedging, positioning, an event study on your basket, the volatility column on Technicals, and a Snowflake sync) are prototypes: they are drawn finished with illustrative values and say so in a footnote. They will be wired to live data over the coming weeks.
The Methodology page covers the signals, the backtests and the limits of each model.
This is the beginning of the README. Read the rest at the original. Read the full README ↗