About QuantContext
QuantContext is an MCP server in the Finance category: deterministic stock screening, backtesting, and factor analysis for AI trading agents.
Install
uvx quantcontext-mcppip install quantcontext-mcpThis server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.
Tools (3)
backtest_strategyTest a strategy over history with a rebalance-loop engine. Returns CAGR, Sharpe, max drawdown, equity curve, and trade log.
Parameters not published.
factor_analysisDecompose strategy returns into Fama-French factors (market, size, value, momentum). Returns alpha with t-statistic, factor loadings, and R-squared.
Parameters not published.
screen_stocksFilter S&P 500, Nasdaq 100, or Russell 2000 by fundamentals, momentum, quality, technical signals, or a multi-factor blend. Returns ranked candidates.
Parameters not published.
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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
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
License
MIT
README copy captured when this server was indexed; it may lag the repository. The original is authoritative.