diff --git a/docs/.nojekyll b/docs/.nojekyll new file mode 100644 index 0000000..e69de29 diff --git a/docs/index.html b/docs/index.html new file mode 100644 index 0000000..bb3404e --- /dev/null +++ b/docs/index.html @@ -0,0 +1,168 @@ + + + + + +Divvy — backtest your real investing history against a different portfolio + + + + + + + + +
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Divvy

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"What if I'd invested my money into that portfolio instead?"

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Replay the exact money you actually invested — the real dates, the real amounts — into a different basket of ETFs/stocks, and see the dividends and returns you would have earned.

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+ PyPI + MIT + Python 3.12+ +
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+ 🚀 Try the live demo + ⭐ Star on GitHub + 📦 pip install +
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The live demo runs on synthetic data. For your real numbers, run it locally — your financial data never leaves your machine.

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Why it's different

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Every backtester on the internet (Portfolio Visualizer and friends) simulates a make-believe "$X every month." Nobody's contributions look like that — real life is lumpy dates and amounts. Divvy replays your genuine contribution calendar (from a broker export, a plain date,amount CSV, or a 1099 dividend import) into whatever portfolio you're curious about, with dividend reinvestment (DRIP).

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💡 "If I'd put the exact money I actually invested into this basket instead, how much more — in dividends specifically, and in total — would I have made?"

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See it in action

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Add portfolios, tweak the knobs, and compare dividends & returns live.

+ Divvy Portfolio Experiment Lab demo +
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Features

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🔁 Real-ledger replay

Your actual contribution calendar, not a synthetic assumption.

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💵 Dividends first-class

Lifetime and trailing-12-month run-rate — the number income investors care about.

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📈 Honest comparison

Money-weighted XIRR, an SPY benchmark row, max drawdown & volatility.

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🧪 Experiment Lab

A local web app to add/remove tickers, tweak weights, and compare live.

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🎛️ Realism knobs

Optional after-tax dividends, rebalancing, and expense-ratio drag.

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🔒 Private by default

All financial data stays on your machine. Nothing uploaded, nothing committed.

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Install & try it in 30 seconds

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No data required — kick the tires with synthetic mode:

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pip install 'divvy-backtest[ui]'
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+# zero-data quickstart
+divvy compare --synthetic-monthly 500 --synthetic-start 2019-01-01 \
+  --bucket examples/buckets/dividend_etf_core.yaml \
+  --bucket examples/buckets/high_yield_tilt.yaml
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+# or launch the interactive Experiment Lab
+divvy ui
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How it works

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Contributions and each holding's ex-dividend dates are merged into one chronological event stream. Contributions buy shares at that day's close; dividends are reinvested (DRIP) into the same holding. Returns use money-weighted XIRR so lumpy cash flows are handled honestly, and risk metrics come from a contribution-free NAV so drawdown/volatility reflect the basket itself.

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⚠️ A backtester, not a crystal ball. Divvy describes what already happened over one window. Past performance does not predict the future, and nothing here is personalized financial advice — do your own research.
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+ + + + diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..59949f8 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,2 @@ +# For Streamlit Community Cloud deploys only. Local dev uses `uv sync` from pyproject.toml. +divvy-backtest[ui] diff --git a/streamlit_app.py b/streamlit_app.py new file mode 100644 index 0000000..b591054 --- /dev/null +++ b/streamlit_app.py @@ -0,0 +1,9 @@ +"""Entry point for Streamlit Community Cloud. + +When deploying at https://share.streamlit.io, set the "Main file path" to `streamlit_app.py`. +Dependencies come from `requirements.txt` (the published `divvy-backtest[ui]` package). +""" + +from divvy.ui import render + +render()