QuantContext

io.github.zomma-devPublished by GitHub account zomma-devv0.1.2Updated Oct 24, 2026

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

VerifiedSTDIODesktop onlyFinanceData & Analytics

Overview

AI-generated overview

Lets an assistant screen stocks, backtest trading strategies, and run factor analysis on real market data.

What you can do
QuantContext exposes three tools that chain into a research workflow: screen_stocks filters S&P 500, Nasdaq 100, or Russell 2000 names by fundamentals, momentum, quality, technical signals, or a multi-factor blend; backtest_strategy runs a rebalance-loop test and returns CAGR, Sharpe, max drawdown, an equity curve, and a trade log; factor_analysis decomposes returns into Fama-French factors with alpha, t-statistic, loadings, and R-squared. The same functions are importable from Python. Results are computed from market data rather than generated by a model.
When to use it
Useful when an assistant needs to answer quantitative equity questions: ranking stocks by a screen, checking how a rule-based strategy would have performed historically, or separating real alpha from factor exposure. It suits research and exploration, not live trading.
Requirements
Runs locally over stdio as a PyPI package (quantcontext-mcp), typically launched with uvx or installed with pip. No API keys or configuration are declared. It needs network access on first use to download prices, fundamentals, Fama-French factors, and index lists, which are then cached under ~/.cache/quantcontext; the first call takes roughly 10-30 seconds.
Before you install
The README mentions an optional warmup command that fetches data from a remote URL; the default data path pulls from Yahoo Finance, the Kenneth French Data Library, and Wikipedia, so requests leave the machine. Cached market data is written to the local cache directory. The server is described as desktop-only and does not execute trades or move money.

Installation

In SourceWeft

  1. Open QuantContext in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

README

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

bash
pip install quantcontext-mcp

Claude Code:

bash
claude mcp add quantcontext -- quantcontext

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

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
ToolWhat it does
screen_stocksFilter S&P 500, Nasdaq 100, or Russell 2000 by fundamentals, momentum, quality, technical signals, or a multi-factor blend. Returns ranked candidates.
backtest_strategyTest a strategy over history with a rebalance-loop engine. Returns CAGR, Sharpe, max drawdown, equity curve, and trade log.
factor_analysisDecompose 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

ScreenDescriptionKey parameters
fundamental_screenFilter by PE, ROE, leverage, revenue growthpe_lt, roe_gt, debt_equity_lt, revenue_growth_gt
quality_screenProfitability and balance sheet healthroe_gt, debt_equity_lt, profit_margin_gt
momentum_screenRank by N-day price momentumlookback_days, top_pct
value_screenCheapest stocks by valuationpe_lt, top_n
factor_modelMulti-factor composite scoreweights (value/momentum/quality/volatility), top_n
technical_signalRSI and SMA crossover signalsrsi_period, sma_short, sma_long
mean_reversionStocks below z-score thresholdlookback_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.

python
from quantcontext.server import screen_stocks, backtest_strategy, factor_analysisimport asyncio, json
# Screenresult = json.loads(asyncio.run(screen_stocks(    universe="sp500",    screen_type="fundamental_screen",    config={"pe_lt": 15, "roe_gt": 12},)))
# Backtestbt = 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 infa = 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.

DataSourceCache
Daily OHLCV pricesYahoo 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:

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

Links

  • Docs — full reference, examples, methodology
  • PyPI

License

MIT

Source: README.md at commit 00bc2e7

Tools

0
SourceWeft has not started this package in its sandbox yet, so its tools are not known.

Version history

1
  1. v0.1.2LatestSep 16, 2026

Score

50% of the applicable checks met

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  • Reached in the last 30 days25 pts

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  • Auth declared10 pts

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  • Tools listed15 pts

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