QuantContext MCP Server

QuantContext is an MCP server for deterministic stock screening, backtesting, and factor analysis used in AI trading agents. It converts plain-English strategy descriptions into executable quant research and computes numbers from real market data, not generated by an LLM. Results are designed to be fully reproducible.

🛠️ Key Features

Deterministic stock screening
Backtesting over historical data
Factor analysis to explain return sources
Reproducible outputs based on real market data

🚀 Use Cases

Screen stocks using specified criteria
Evaluate strategies via historical backtests
Attribute performance using factor analysis

⚡ Developer Benefits

Turns plain-English strategy descriptions into executable research
Works with Claude, Codex, OpenCode, or any MCP-compatible coding agent
Suitable for AI trading agent workflows

⚠️ Limitations

Documentation excerpts provided do not specify supported markets, data sources, tool list details, or configuration options.

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:

code

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:

code

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

code

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

code

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

code

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

Backtesting:

code

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

code

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

code

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

Full research workflow:

code

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.

python

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:

bash

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

License

MIT

QuantContext

QuantContext MCP Server

🛠️ Key Features

🚀 Use Cases

⚡ Developer Benefits

⚠️ Limitations

QuantContext

Install

Tools

Sample Prompts

Screen Types

Use from Python

Data

Links

License

Install

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