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 set F_INLINE_PLOTLY=1 in run.sh.
• Test: ./run_tests.sh
  1. tests/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).
  2. 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 playwright and .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.sh refuses to double-start).
  • st.cache_data caches 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 in portfolios.json.
  • Data cache: .cache/*.parquet; rebuild via the sidebar checkbox (first build ~1 min for ~4k symbols).

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.