How do I use QuantContext?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@QuantContext Screen S\&P 500 for value stocks with PE under 15 and ROE above 12%" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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

Links

Docs — full reference, examples, methodology
PyPI

License

MIT

Install Server

A

license - permissive license

A

quality

C

maintenance

How are these scores calculated?

Resources

GitHub Repository

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