Asset Management MCP server

asset-management

TL;DR: uvx --from git+https://github.com/disin7c9/asset-management asset-management --demo — a drawdown-first portfolio brief on a bundled example book, one command, no setup. USD-only, long-only stock/ETF; you keep your own transaction log.

Track a personal stock/ETF portfolio and get suggestions you can audit. Python computes every number; the optional AI narrates — it never receives the figures, and every value it quotes is substituted from the computed result.

🔢 The number fence

Many AI finance tools let the model produce the numbers. Here the model may only place {{token}} placeholders: a deterministic renderer substitutes figures from the validated core and refuses the entire narration if the model typed any numeral itself — in any Unicode numeric category, so ½ fails closed alongside 3.2:

the model wrote                             │  the reader gets
─────────────────────────────────────────────┼─────────────────────────────────────────────
 "Your deepest stretch fell                  │  "Your deepest stretch fell -9.84% from
  {{max_drawdown}} from its peak, and an     │   its peak, and an ulcer index of 2.38%
  ulcer index of {{ulcer}} says the ride     │   says the ride stayed shallow."
  stayed shallow."                           │  ✔ every figure substituted from the
                                             │    validated core
─────────────────────────────────────────────┼─────────────────────────────────────────────
 "Your portfolio fell 12% but recovered      │  (no summary at all)
  nicely — don't worry."                     │  ✘ REFUSED — one model-typed numeral voids
                                             │    the entire note; the brief prints
                                             │    without it

Run both sides yourself — it drives the real production fence, no API key needed: uv run python scripts/demo_fence.py

🎁 What you get

• A drawdown-first brief of your real holdings — how far you fell from your peak, how long underwater, what came back — with a bootstrap confidence band on every sampled risk statistic (returns are accounting identities, so they honestly carry none).
• Deterministic buy/sell suggestions toward a target you choose, each line paired to the named rule that produced it — you learn the rule rather than trust a bot.
• Optional, fenced AI narration, and a read-only Claude Desktop addon ("chat with your portfolio") over the same validated core.

Holdings are derived from an append-only transaction log (date, ticker, action, quantity, price, fee per row) — never stored — so the same input always produces the same output. Prices are fetched with a provider fallback (Yahoo Finance primary; Tiingo secondary, via a free API key) and an on-disk cache; every displayed number is traceable to its source, and figures that can't be computed honestly (too short a window, no real solution) print n/a rather than a fabricated number. Stock splits are adjusted automatically (share counts are reconciled with the split-adjusted price history), so a split during your holding period doesn't distort the returns. When the split feed is unavailable, the mismatch detector catches ratios of 2:1 or larger.

📑 Contents

Shortened. The full README is on GitHub.