The 7 Hard Metrics for Quality Screening in AI Berkshire: A Technical Deep Dive

AI Berkshire's Quality-Screen skill applies seven strict quantitative filters—including ROE ≥15%, debt-to-equity ≤0.5, and positive operating cash flow—that a company must satisfy simultaneously to pass the initial screening gate.

The AI Berkshire repository (xbtlin/ai-berkshire) implements a defensive investing framework inspired by Warren Buffett's selection criteria. At its core lies the /quality-screen command, which uses hard-coded financial thresholds to eliminate low-quality stocks before deeper analysis. These seven hard metrics for quality screening in AI Berkshire are defined in skills/quality-screen.md and enforced by the quality_screen() function in tools/stock_screener.py.

The Seven Hard Metrics Explained

The quality screen treats these metrics as hard filters, meaning failure on any single criterion results in immediate exclusion. According to the repository's reference implementation, companies must satisfy all seven conditions to proceed.

1. Positive Net Profit

The most fundamental requirement validates that a company is currently profitable. The filter checks that net profit > 0, ensuring the business generates bottom-line earnings rather than losses.

2. Return on Equity (ROE) ≥ 15%

This metric measures management's efficiency in converting shareholder equity into profits. The threshold of ROE ≥ 15% identifies companies with strong capital allocation skills and competitive moats.

3. Debt-to-Equity Ratio ≤ 0.5

Financial conservatism is enforced through a debt-to-equity ≤ 0.5 limit. This low-leverage requirement reduces bankruptcy risk and ensures companies can withstand economic downturns without excessive interest burdens.

4. Gross Margin ≥ 30%

Pricing power and cost discipline are captured by the gross margin ≥ 30% threshold. High margins indicate either strong brand value or operational efficiency that protects profits during competitive pressures.

5. Consistent Earnings Growth (≥ 10% YoY)

Sustainability requires a three-year average year-over-year earnings growth ≥ 10%. This filter eliminates cyclical anomalies and one-time windfalls, focusing on companies with durable profit trajectories.

6. Positive Operating Cash Flow

Accounting profits must translate to actual cash. The screen requires positive operating cash flow, verifying that earnings are backed by real cash generation rather than accrual accounting adjustments.

7. Institutional Ownership ≥ 20%

Smart money validation comes from the institutional ownership ≥ 20% requirement. Significant holdings by professional investors suggest thorough due diligence and confidence in long-term viability.

Implementation in the Source Code

The screening logic is implemented as a strict conjunction in tools/stock_screener.py. The quality_screen() function fetches fundamental data from data/fundamentals.json and applies all seven filters simultaneously.

# Example: run the quality screen on a list of tickers

from tools.stock_screener import quality_screen

tickers = ["AAPL", "MSFT", "TSLA", "BABA"]
good, bad = quality_screen(tickers)

print("Passed:", good)   # tickers that satisfied all 7 hard metrics

print("Failed:", bad)    # tickers rejected by at least one metric

Inside the screening function, the hard metrics are evaluated as boolean conditions that must all evaluate to True:

# Inside tools/stock_screener.py (simplified)

def quality_screen(tickers: List[str]) -> Tuple[List[str], List[str]]:
    passed, failed = [], []
    for t in tickers:
        data = fetch_fundamentals(t)          # pulls financials from data/fundamentals.json

        if (data["net_profit"] > 0 and
            data["roe"] >= 0.15 and
            data["debt_to_equity"] <= 0.5 and
            data["gross_margin"] >= 0.30 and
            data["earnings_yoy_growth"] >= 0.10 and
            data["operating_cf"] > 0 and
            data["institutional_ownership"] >= 0.20):
            passed.append(t)
        else:
            failed.append(t)
    return passed, failed

Key Files for Configuration

The quality screening system spans three critical files in the repository:

• skills/quality-screen.md — Documents the seven hard metrics and their default thresholds for users interacting with the /quality-screen command.
• tools/stock_screener.py — Contains the quality_screen() function that implements the filtering logic.
• data/fundamentals.json — Stores the fundamental financial data (net profit, ROE, debt ratios, etc.) queried by the screening algorithm.

Summary

AI Berkshire's quality screening system uses seven hard metrics to enforce strict financial discipline:

• All seven criteria must be satisfied for a ticker to pass the initial screen.
• Profitability metrics (net profit, ROE, gross margin) ensure earnings quality.
• Financial health metrics (debt-to-equity, operating cash flow) verify balance sheet strength.
• Growth and validation metrics (earnings growth, institutional ownership) confirm sustainability and professional confidence.
• Thresholds are configurable in skills/quality-screen.md, though the defaults reflect conservative Buffett-style criteria.

Frequently Asked Questions

What happens if a company fails just one of the seven hard metrics?

The screen operates on a strict rejection principle. If a company fails any single metric—such as having a debt-to-equity ratio of 0.6 or ROE of 14%—it is immediately placed in the failed list. This "hard" approach ensures only companies meeting all quality benchmarks proceed to further analysis.

Can the threshold values for the seven metrics be customized?

Yes. While the quality_screen() function in tools/stock_screener.py uses hardcoded defaults (ROE ≥ 0.15, debt-to-equity ≤ 0.5, etc.), these values are documented as configurable in skills/quality-screen.md. Users can modify these thresholds to match different risk tolerances or sector-specific requirements.

Where does AI Berkshire source the financial data for these metrics?

The screening algorithm pulls fundamental data from data/fundamentals.json, which contains processed financial statements including net profit, ROE calculations, debt ratios, and ownership statistics. This centralized data store ensures the quality_screen() function evaluates consistent metrics across all tickers.

How does the AI Berkshire quality screen differ from soft screening methods?

Unlike soft screens that rank or score companies on a gradient, AI Berkshire's hard metrics function as binary gates. As implemented in tools/stock_screener.py, the logic uses strict boolean comparisons (>, >=, <=) that result in immediate exclusion if any condition is not met, eliminating subjective judgment from the initial filtering phase.

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