Screen Stocks by Economic Sensitivity

Add the FutureSearch connector if you haven't already. Then upload your S&P 500 CSV and ask Claude:

Screen to find companies whose profit margins fall when oil prices go up. Consider energy-intensive operations, transportation dependence, consumer discretionary sensitivity, and historical correlation with oil price spikes. Exclude energy companies and those with strong pricing power.

Results take about 17.5 minutes.

Go to futuresearch.ai/app, upload your S&P 500 CSV, and enter:

Screen to find companies whose profit margins fall when oil prices go up. Consider energy-intensive operations, transportation dependence, consumer discretionary sensitivity, and historical correlation with oil price spikes. Exclude energy companies and those with strong pricing power.

Screening is binary classification: use classify() with categories=["yes", "no"] and only passing rows are returned.

pip install futuresearch
export FUTURESEARCH_API_KEY=your_key_here  # Get one at futuresearch.ai/app/api-key

import asyncio
import pandas as pd
from futuresearch import create_session
from futuresearch.ops import classify

stocks = pd.read_csv("sp500.csv")

async def main():
    async with create_session(name="Stock Screening: Oil Margin Sensitivity") as session:
        result = await classify(
            session=session,
            task="""
                Find large cap companies whose profit margins fall when oil prices go up.

                Criteria: High energy costs, transportation dependence, consumer
                discretionary sensitivity, energy-intensive manufacturing, historical
                correlation with oil price spikes.

                Exclude: Energy companies, strong pricing power, minimal energy exposure,
                effective energy hedging.
            """,
            categories=["yes", "no"],
            input=stocks,
        )
        return result.data

results = asyncio.run(main())