Financial analysis tools
Greg Pomerantz
328855a926
fundlab/report.py -> reports/fund_report.html (20 MB, plotly inlined, opens offline). Per fund: max-history equity curve (fund vs fitted reference vs IVV); performance table (full/5y/1y, the 5 market episodes, calendar years) with the fund-minus-reference period-alpha column; drivers (reference-model R²/alpha/t + 34-sleeve signature + curated decomposition verdict and N-PORT cross-check notes); the reference mix explained sleeve-by-sleeve (what each exposure actually is, plus net-cash/net-levered read); tax character + taxable/IRA placement; and a peer table of the 4 best funds in the same k=30 return-driver cluster with computed advantages/disadvantages. Weak-fit (R²<0.5) funds anchor their tables to CASH rather than the statistically-thin forward-selected mix (which can be an offsetting VIX/duration spec combination whose path is meaningless); the loadings are still shown with a 'weak fit' caveat. |
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|---|---|---|
| fundlab | ||
| reports | ||
| tests | ||
| .gitignore | ||
| adx-split.csv | ||
| aef-split.csv | ||
| app.py | ||
| asa-split.csv | ||
| brw-split.csv | ||
| bto-split.csv | ||
| chart_widget.py | ||
| clm-split.csv | ||
| crf-split.csv | ||
| data.py | ||
| evg-split.csv | ||
| families.py | ||
| fxby-split.csv | ||
| grf-split.csv | ||
| herz-split.csv | ||
| iaf-split.csv | ||
| kf-split.csv | ||
| mci-split.csv | ||
| metrics.py | ||
| mxf-split.csv | ||
| nro-split.csv | ||
| peo-split.csv | ||
| portfolio.py | ||
| portfolios.json | ||
| portfolios.py | ||
| README.md | ||
| requirements.txt | ||
| run_tests.sh | ||
| run.sh | ||
| rvt-split.csv | ||
| saba-split.csv | ||
| swz-split.csv | ||
| tax.py | ||
| tyg-split.csv | ||
| utf-split.csv | ||
| vlt-split.csv | ||
| ztr-split.csv | ||
Stock & Portfolio Analyzer
Interactive tool for analyzing individual securities and portfolios
against local Yahoo Finance dumps (~/prog/fin/stocks, ~4k symbols).
Quick start
python3 -m venv .venv
.venv/bin/pip install -r requirements.txt
./run.sh # serves the UI on the fixed port 8599 (http://localhost:8599)
First run builds a parquet cache in .cache/ (~1 min for 4k symbols);
later runs load in well under a second. The cache tracks the data dir
per-file (mtime + size in .cache/manifest.json), so when the download
is updated, only the changed/added/removed symbols are re-read — a
partial refresh takes seconds instead of a full ~1 min rebuild.
Modules
| Module | Purpose |
|---|---|
data.py |
Ingest {sym}-history/dividend/capitalGain.csv -> cached parquet panels (date x symbol), with manifest-based incremental refresh when the data dir changes. Adj Close already includes distributions, so it drives pre-tax total returns. |
metrics.py |
Total/annualized return, vol, Sharpe, Sortino, max drawdown, Calmar, CAPM beta/alpha. Pure pandas, all transparent. |
portfolio.py |
Weighted portfolios with drift and periodic rebalancing to target weights (1W/1ME/QE/YE), one-way cost in bps. Spec grammar: commas join the elements of ONE portfolio (SYM or SYM:w, bare = equal weight), spaces separate DISTINCT symbols/portfolios (parse_items). |
tax.py |
Simplified DAS after-tax engine: FIFO lots, 365-day long/short split, separate LT/ST/dividend rates. Headline curve = what you keep if you sell everything today (unrealized gains taxed daily by lot age). |
chart_widget.py |
Self-contained plotly.js chart in an iframe: mouse zoom/pan, x clamped to the data, view edges snapped to first/last data points with day-precise labels, y tight-fit, every line re-based to 1.0 at the left edge. |
portfolios.py |
Saved portfolio definitions in portfolios.json (name, spec, scheme, cost). |
settings.json |
Persisted UI inputs (symbol/benchmark specs, scheme, costs, tax rates, period, curve/window mode) — restored on every page load and server restart; delete to reset. |
app.py |
Streamlit UI: single "symbol or portfolio" spec field (page updates as soon as the input is valid; unknown symbols get click-to-fix "did you mean" suggestions) + a benchmark box with the same grammar (one benchmark per line; a line is a single symbol or a comma-joined portfolio, simulated with the same scheme/cost/tax rules — pre- and after-tax curves, first one drives beta/alpha), scheme/costs/tax rates, save + load/compare/delete portfolios (overlaid pre/after-tax curves), curve toggle (both / pre-tax only / after-tax only), stats table, allocation, per-year tax detail. |
Development
- Run:
./run.sh→ http://localhost:8599 (fixed port; no-ops if a server is already running). The chart loads plotly.js from a CDN; for fully offline use setF_INLINE_PLOTLY=1inrun.sh. - Test:
./run_tests.shtests/test_app.py— app-level tests via Streamlit AppTest (no browser). Memory: one data bundle is ~2.3 GB, so this process keeps at most ONE AppTest alive (see its header comment).tests/test_e2e_browser.py— Playwright + headless Chromium driving the real page with real keystrokes; needs the server running on 8599. One-time setup:.venv/bin/pip install playwrightand.venv/bin/python -m playwright install chromium.
- Gotchas
- Streamlit caches imported modules per process: restart the server
after editing any
.py(kill the old one first —run.shrefuses to double-start). st.cache_datacaches the portfolio + tax simulations: they recompute only when symbols/scheme/cost/tax rates change, not on window or curve toggles.settings.json(gitignored) persists UI inputs across reloads and restarts; delete it to reset. Saved portfolios live inportfolios.json.- Data cache:
.cache/*.parquet; rebuild via the sidebar checkbox (first build ~1 min for ~4k symbols).
- Streamlit caches imported modules per process: restart the server
after editing any
Known simplifications (roadmap)
- No loss carryover or carryforward across years; no wash-sale rules.
- Distributed capital gains taxed entirely at the long-term rate.
- Single (federal-like) tax bracket; no state taxes, no AMT.
- Equal treatment of benchmark for beta/alpha (CAPM, rf = 0 by default).
Ideas: vectorbt sweeps over rebalance schemes, NiceGUI/Textual frontend, empyrical-reloaded metrics, monthly (not yearly) loss netting, tax-loss harvesting simulation.