How the Cross-Validation Function in AI Berkshire Ensures Data Consistency

The cross_validate routine in tools/financial_rigor.py guarantees data consistency by converting all inputs to high-precision Decimal types, calculating median-based reference values, and automatically flagging any source that deviates beyond a configurable tolerance threshold.

The xbtlin/ai-berkshire repository implements a rigorous financial data validation pipeline designed to prevent silent divergence across multiple data sources. At the heart of this system lies the cross-validation function in AI Berkshire, which transforms manual spot-checking into an automated, reproducible code-driven process. This routine serves as a standard checkpoint in every research workflow, ensuring that critical metrics like revenue or market cap remain consistent regardless of their origin.

High-Precision Decimal Standardization

The function eliminates floating-point arithmetic errors by immediately converting all incoming values to Python’s Decimal type.

This standardization occurs in tools/financial_rigor.py before any comparative analysis begins. An optional unit string (e.g., "亿" for hundreds of millions) is appended solely for human-readable output, while the underlying calculations maintain exact precision. This approach prevents the subtle rounding errors that plague binary-float representations, ensuring that deviation calculations remain deterministic across repeated executions.

Statistical Robustness via Median Reference

Rather than using a simple average that could be skewed by outliers, the function derives a reference value by computing the median of all supplied sources.

From the source_values dictionary provided to the function, the median calculation creates a statistically robust baseline. This median-based approach ensures that a single anomalous data point from one source cannot distort the reference against which all other sources are measured.

Automated Deviation Detection

The consistency check follows a strict mathematical protocol:

1. Calculate deviation: For each source, the function computes abs(value - reference) / reference * 100
2. Apply tolerance: A configurable threshold (defaulting to 2%) determines acceptable variance
3. Flag inconsistencies: Any source exceeding the tolerance receives a "❗" marker in the output table

The function returns a Boolean indicating overall success—True only when all sources fall within the specified tolerance.

Implementation in tools/financial_rigor.py

The core logic resides in tools/financial_rigor.py, which exposes both a Python API and a command-line interface.

from tools.financial_rigor import cross_validate

# Validate revenue figures from multiple sources

field = "revenue"
values = {
    "年报": 7518,
    "Yahoo": 7500,
    "StockAnalysis": 7520
}
unit = "亿"

# Returns True if all sources within 2% tolerance

consistent = cross_validate(field, values, unit)

The deviation calculation implemented in the source code ensures that percentage differences are computed against the median reference, not an arbitrary baseline.

Command-Line Integration and Workflow Automation

The repository integrates cross-validation directly into research workflows through a CLI wrapper that registers the cross-validate sub-command.

Research teams invoke validation through markdown files such as skills/investment-team.md and skills/earnings-review.md using standardized commands:

python3 tools/financial_rigor.py cross-validate \
    --field revenue \
    --values '{"年报": 7518, "Yahoo": 7500, "StockAnalysis": 7520}' \
    --unit 亿

For stricter validation requirements, the tolerance parameter accepts floating-point values:

python3 tools/financial_rigor.py cross-validate \
    --field eps \
    --values '{"Morningstar": 3.45, "Yahoo": 3.41, "GuruFocus": 3.44}' \
    --unit "" \
    --tolerance 1.0

Typical output displays a formatted table showing field name, source, raw value, deviation percentage, and status indicators:

Field: revenue (单位: 亿)
-------------------------------------------------
Source           Value   Deviation   Status
-------------------------------------------------
年报                7518      0.00%      ✅
Yahoo               7500      0.24%      ✅
StockAnalysis       7520      0.26%      ✅
-------------------------------------------------
All values within 2.0% tolerance.

Audit Trail and Reproducibility

The architecture ensures long-term data integrity through deterministic re-execution capabilities.

Because the routine relies on Decimal arithmetic and fixed tolerance parameters, subsequent re-runs produce identical results, creating a reproducible audit trail. The validation output is routinely captured in research reports within the reports/* directory (e.g., reports/拼多多/拼多多-research-20260702.md), then later re-checked by tools/report_audit.py to produce final audit verdicts.

This integration between tools/financial_rigor.py and tools/report_audit.py transforms cross-validation from a one-time check into a persistent accountability mechanism.

Summary

• High-precision arithmetic: The cross_validate function converts all inputs to Decimal types to eliminate floating-point errors
• Robust statistical baseline: Uses median calculation from source_values to establish outlier-resistant reference points
• Automated enforcement: Applies configurable tolerance thresholds (default 2%) to flag inconsistent sources with "❗" markers
• Workflow integration: Accessible via CLI at tools/financial_rigor.py cross-validate and invoked from skill files like skills/investment-team.md
• Deterministic auditing: Decimal arithmetic and fixed tolerances ensure reproducible results for tools/report_audit.py verification

Frequently Asked Questions

What is the default tolerance threshold in AI Berkshire's cross-validation function?

The default tolerance is 2%, meaning any data source deviating more than 2% from the median reference value is flagged as inconsistent. This threshold is configurable via the --tolerance parameter when using the command-line interface or the tolerance argument when calling the Python function directly.

How does the cross-validation function handle different units or scales?

The function accepts an optional unit parameter that appends a string label (such as "亿" for hundreds of millions) to the output for readability. However, all mathematical comparisons occur after converting values to Decimal types, ensuring that scale differences are normalized before deviation calculations begin.

Where is the cross-validation output stored for audit purposes?

Validation results are captured in research reports within the reports/* directory structure (e.g., reports/拼多多/拼多多-research-20260702.md). These outputs are subsequently consumed by tools/report_audit.py, which re-checks the validation logs to produce final audit verdicts, creating a complete accountability chain from data collection to final report approval.

Can the cross-validation function be used outside of the standard research workflow?

Yes. While the function is designed to integrate with markdown-based workflows in skills/investment-team.md and skills/earnings-review.md, the cross_validate routine in tools/financial_rigor.py exposes a clean Python API suitable for custom scripts. Import the function directly to validate data in ad-hoc analyses or third-party data pipelines while maintaining the same consistency guarantees.

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