claude mcp add asset-management -- npx -y asset-management
A pasteable skeleton generated from the repository name. Check the project README for the exact package and arguments.
Star count
1 ★ · disin7c9/asset-management · read from the GitHub search API · (checked 2026-10-16 20:00:20)
Source
collected from the punkpeye/awesome-mcp-servers list · type: MCP server
Harvest history
first seen 2026-10-09 · last seen 2026-10-16 · seen 32× · listed by 7 sources
Score
score 703 · 1 ★ · 7 sources · mcp
FAQ (answers come from our records)
How do I install it?
We did not find an install command for this repository (our install coverage is only about 4%). Check its README or releases page.
Who is it for?
We filed it under these tags: MCP 服务. The tags come from the entry itself, not a taxonomy we imposed.
Who maintains it?
repository disin7c9/asset-management;first catalogued 2026-10-09;last seen 2026-10-16。There is no official certification or quality rating here — we record sources and dates only.
**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: 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 - 🚀 Start here - ⚡ Try it in 60 seconds (bundled fake portfolio, no setup) - 💵 Use it with your own money — four steps - 🔍 Why you can trust the numbers - 🚫 Not financial advice — by the shape of the output, not as fine print - ✅ Correctness is a claim you can check - 🔬 Validation — reconciled against two independent tools - 💬 Chat with your portfolio — the Claude Desktop addon (read-only MCP) - 🛟 If something goes wrong - 📖 Reference - 💻 Core brief - 🔁 Rebalance modes - 📈 Backtest details - 🔭 Discovery & the curated universe - 🔍 Screen a candidate - 📝 Narration (optional plain-language summary) - 🔑 Configuration — every key in one place - 🔧 Project - 🧪 Develop - 📁 Layout - 🔒 Privacy Policy - 📜 License 🚀 Start here ⚡ Try it in 60 seconds (bundled fake portfolio, no setup) or from a clone: uv sync && uv run python -m app --demo (needs Python 3.12 and uv). **The full tour** (still the bundled book, ~a minute online) — the two commands below show the tool's characteristic features end to end: a preset target is *proposed*, then *validated* against a known 60-40 reference with a held-out recent-window verdict, and threshold-band rebalance suggestions are laid out with the named rule behind every line, plus per-fund facts: Note what it *doesn't* say: the verdict reads like "no clear drawdown difference from 60-40; the paired bootstrap does not confirm the gap" when the evidence is thin — never "beats the benchmark". When the output earns your trust, the four steps below point it at your own money. 💵 Use it with your own money — four steps Step 1 — your book (the input) Everything is derived from one transaction log. The CSV format is **Ghostfolio's own CSV-import schema** (so a book you keep here imports straight into Ghostfolio too) — columns Date, Code, DataSource, Currency, Price, Quantity, Action, Fee, Note. Action is one of buy, sell, dividend, fee, interest, deposit, withdraw. Cash flows (deposit/withdraw) use a CASH code and put the amount in the Price column. **This tool is USD-only** (long-only stock/ETF): Currency must be USD — a non-USD row is refused with an error naming the row, never silently booked as dollars 1:1. Empty cells in numeric columns are treated as zero. Non-ISO dates are rejected with a clear error. UTF-8 BOM is tolerated. The bundled example (data/sample_data/transactions.csv) shows every row type.