Screen Stocks by Economic Sensitivity

Add the everyrow 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.

150 of 502 companies pass (29.9%). Results take about 17.5 minutes.

Claude Code is great at researching one company's oil price sensitivity. It can find margin history, check hedging disclosures, and assess energy exposure. Doing that same five-factor analysis for 502 companies requires more parallel web research than a single session can support.

Here, we get Claude Code to screen the S&P 500 for companies whose margins compress when oil prices rise.

Add everyrow to Claude Code if you haven't already:

claude mcp add futuresearch --scope project --transport http https://mcp.futuresearch.ai/mcp

With your S&P 500 CSV in the working directory, tell Claude:

Screen this S&P 500 dataset 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.

Claude calls everyrow's screen MCP tool:

Tool: everyrow_screen
├─ task: "Find large cap companies whose profit margins fall when oil prices go up..."
├─ input_csv: "/Users/you/sp500.csv"
└─ response_schema: null

→ Submitted: 502 rows for screening.
  Session: https://futuresearch.ai/sessions/c460711d-cb8c-44e2-aac7-b5d6bae2e02b
  Task ID: a8c9...

Tool: everyrow_progress
→ Running: 0/502 complete (30s elapsed)

...

Tool: everyrow_progress
→ Completed: 502/502 (0 failed) in 1048s.

Tool: everyrow_results
├─ task_id: "a8c9..."
├─ output_path: "/Users/you/oil_sensitive_stocks.csv"
→ Saved 150 rows to /Users/you/oil_sensitive_stocks.csv

150 of 502 companies are margin-sensitive to oil prices. View the session.

Sample passing companies:

For Amazon: "Retail business is highly transportation-dependent, with billions in shipping and fulfillment costs. Analysts note profits can be 'hammered' by higher gas and diesel prices."

The everyrow SDK screens every row with LLM-powered web research agents to assess margin sensitivity to oil prices.

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

import asyncio
import pandas as pd
from pydantic import BaseModel, Field
from everyrow import create_session
from everyrow.ops import screen

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

class ScreenResult(BaseModel):
    passes: bool = Field(
        description="True if company's margins fall when oil prices go up"
    )

async def main():
    async with create_session(name="Stock Screening: Oil Margin Sensitivity") as session:
        result = await screen(
            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.
            """,
            input=stocks,
            response_model=ScreenResult,
        )
        return result.data

results = asyncio.run(main())

150 companies identified. The affected sectors are Consumer Discretionary (39), Industrials (39), Consumer Staples (28), and Materials (18). Airlines, retailers with heavy logistics, and energy-intensive manufacturers dominate the results.