pip install quantcontext-mcp

claude mcp add quantcontext -- quantcontext

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

screen_stocks -> backtest_strategy -> factor_analysis

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

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

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?

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"])

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