QuantContext

Name: QuantContext
Author: zomma-dev

by zomma-devversion 0.1.2i★ –iRuns on your computer

Registry nameio.github.zomma-dev/quantcontext

Install

Overview

Description from the maker (original English)

Deterministic stock screening, backtesting, and factor analysis for AI trading agents

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Accounting, finance & tax

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "quantcontext": {
      "command": "uvx",
      "args": [
        "quantcontext-mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add quantcontext -- uvx quantcontext-mcp

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "quantcontext": {
      "command": "uvx",
      "args": [
        "quantcontext-mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "quantcontext": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "quantcontext-mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account zomma-dev, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Accounting, finance & taxi

More stars than 0% of the other 1720 servers in this category
Published to the registry more recently than 2% of the other 1720 servers in this category
Basic checks equal to or better than 100% of the other 1720 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is QuantContext safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.1.2) reached the registry 8 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Finance Toolkit, SolidInvoice, Korea Tax Calculator

Alternatives

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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

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SolidInvoice

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

QuantContext

QuantContext is an MCP server that turns plain-English strategy descriptions into executable quant research: screen stocks by any criteria, backtest over historical data, and run factor analysis to see where the returns come from. Every number is computed from real market data, not generated by an LLM. Results are fully reproducible.

Works with Claude, Codex, OpenCode, or any other MCP-compatible coding agent.

Install

pip install quantcontext-mcp

Claude Code:

claude mcp add quantcontext -- quantcontext

Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json):

"mcpServers": { "quantcontext": { "command": "quantcontext" } } }

No API keys. No configuration.

Tools

Three tools that compose into a full research workflow:

screen_stocks -> backtest_strategy -> factor_analysis

Tool	What it does
`screen_stocks`	Filter S&P 500, Nasdaq 100, or Russell 2000 by fundamentals, momentum, quality, technical signals, or a multi-factor blend. Returns ranked candidates.
`backtest_strategy`	Test a strategy over history with a rebalance-loop engine. Returns CAGR, Sharpe, max drawdown, equity curve, and trade log.
`factor_analysis`	Decompose strategy returns into Fama-French factors (market, size, value, momentum). Returns alpha with t-statistic, factor loadings, and R-squared.

Sample Prompts

Stock screening:

Screen S&P 500 for value stocks: PE under 15, ROE above 12%

Find the top 20% momentum stocks in the Nasdaq 100 over the last 200 days

Rank S&P 500 stocks by a blend of value, momentum, and quality, equal weight each factor

Find S&P 500 stocks with RSI under 40 and price above the 200-day moving average

Backtesting:

Backtest a top-20% momentum strategy on Nasdaq 100, monthly rebalance, last 2 years

How would a value screen (PE under 15, ROE above 12%) have performed on S&P 500 over the last 3 years?

Test a momentum strategy with a 15% stop loss and 20% max portfolio drawdown circuit breaker

Full research workflow:

Screen S&P 500 for cheap, high-quality stocks. Backtest monthly over 3 years,

then run factor analysis. Is the return real alpha or just factor exposure?

Screen Types

Screen	Description	Key parameters
`fundamental_screen`	Filter by PE, ROE, leverage, revenue growth	`pe_lt`, `roe_gt`, `debt_equity_lt`, `revenue_growth_gt`
`quality_screen`	Profitability and balance sheet health	`roe_gt`, `debt_equity_lt`, `profit_margin_gt`
`momentum_screen`	Rank by N-day price momentum	`lookback_days`, `top_pct`
`value_screen`	Cheapest stocks by valuation	`pe_lt`, `top_n`
`factor_model`	Multi-factor composite score	`weights` (value/momentum/quality/volatility), `top_n`
`technical_signal`	RSI and SMA crossover signals	`rsi_period`, `sma_short`, `sma_long`
`mean_reversion`	Stocks below z-score threshold	`lookback_days`, `z_threshold`

Use from Python

The tools are also importable directly — no agent required. Useful if you have an existing script and want to plug in backtesting or factor analysis.

from quantcontext.server import screen_stocks, backtest_strategy, factor_analysis

import asyncio, json

Screen

result = json.loads(asyncio.run(screen_stocks( universe="sp500", screen_type="fundamental_screen", config={"pe_lt": 15, "roe_gt": 12}, )))

Backtest

bt = json.loads(asyncio.run(backtest_strategy( stages=[{"order": 1, "type": "screen", "skill": "fundamental_screen", "config": {"pe_lt": 15, "roe_gt": 12}}], universe="sp500", rebalance="monthly", start_date="2022-01-01", ))) print(bt["metrics"])

Factor analysis — pipe the equity curve straight in

fa = json.loads(asyncio.run(factor_analysis( equity_curve=bt["full_equity_curve"] ))) print(fa["alpha_annualized"], fa["alpha_tstat"])

Strategies are expressed using the built-in screen types from the table above. All functions are async and return JSON strings.

Data

All public data, no API keys required.

Data	Source	Cache
Daily OHLCV prices	Yahoo Finance (`yfinance`)	`~/.cache/quantcontext/prices.parquet`
Fundamentals (PE, ROE, margins, etc.)	Yahoo Finance	`~/.cache/quantcontext/financials/`, 24h TTL
Fama-French factors (Mkt-RF, SMB, HML, Mom)	Kenneth French Data Library	`~/.cache/quantcontext/ff_factors.parquet`
Universe lists (S&P 500, Nasdaq 100)	Wikipedia	`~/.cache/quantcontext/sp500_tickers.json`

The first tool call downloads and caches data (10-30 seconds). All subsequent calls use the local cache: screening under 1s, backtesting 3-8s.

To skip the cold start, run once after install:

quantcontext-warmup --url https://quantcontext.ai/api/data

License

MIT

QuantContext

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Accounting, finance & taxi

Common questions

Alternatives

Finance Toolkit

SolidInvoice

Korea Tax Calculator

README from the maker

QuantContext

Install

Tools

Sample Prompts

Screen Types

Use from Python

Screen

Backtest

Factor analysis — pipe the equity curve straight in

Data

Links

License