How AI Berkshire Performs Multi-Source Cross-Validation for Financial Data

AI Berkshire validates financial data by cross-checking every critical figure against independent providers using a two-tier system: automated statistical median analysis in tools/financial_rigor.py and strict human-verified dual-source audits in tools/report_audit.py, ensuring no single data point relies on just one source.

AI Berkshire is an open-source research framework that guarantees quantitative accuracy through rigorous multi-source cross-validation. Instead of trusting single data feeds, the system programmatically cross-references every financial metric against independent providers to eliminate transcription errors and source-specific biases. The implementation centers on two Python utilities that combine statistical consensus algorithms with human-in-the-loop verification.

Core Validation Components

Statistical Consensus via cross_validate

Located in tools/financial_rigor.py (lines 63-71), the cross_validate function accepts a dictionary of values from different sources and computes a median or weighted median as the reference point. It flags any source whose deviation exceeds a configurable tolerance—defaulting to 2%—and returns a consensus value alongside an "all-consistent" boolean flag. This utility prints a concise comparison table showing per-source deviations, making it easy to spot outliers at a glance.

Human-in-the-Loop Auditing via render_verdict

The render_verdict function in tools/report_audit.py (lines 43-61) handles the strict verification layer. After analysts manually populate fetched_value and fetched_value2 fields from dual independent sources (e.g., macrotrends paired with stockanalysis), the function calculates percentage differences using the internal _pct_diff helper. It applies a rigid 1% tolerance threshold (_TOLERANCE = 0.01) to generate PASS/FAIL verdicts, surfacing WARN status when sources diverge beyond acceptable limits but not both failing.

The Cross-Validation Workflow

The multi-source validation process follows a five-stage pipeline:

1. Data Extraction – The report_audit tool parses Markdown research reports and extracts financial data from tables, key-value lines, and bolded figures.

2. Random Sampling – The system selects a representative 15% subset of all extracted data points for verification, balancing thorough coverage with manageable workload.

3. Dual-Source Lookup – For each sampled datum, analysts retrieve values from two independent providers hard-coded in the validation logic:

   • U.S. equities: macrotrends.net + stockanalysis.com
   • Hong Kong listings: aastocks.com + macrotrends ADR
   • A-shares: eastmoney.com + cninfo.com.cn

4. Cross-Validation Execution – Validation occurs through two parallel tracks:

   • Programmatic: Direct invocation of cross_validate with JSON payloads computes median reference values and flags 2% deviations.
   • Human-Verified: The verdict sub-command evaluates manually populated dual-source data against the strict 1% tolerance, checking _pct_diff calculations.

5. Outcome Classification – Results are categorized as:

   • PASS: All sources within 1% tolerance, report cleared for publication
   • FAIL: Any source exceeds threshold, triggering mandatory corrections
   • WARN: Single source deviation while other complies, indicating potential currency conversion or accounting standard mismatches requiring manual review

Command-Line Usage Examples

Execute direct statistical validation using the cross_validate command:

python3 tools/financial_rigor.py cross-validate \
    --field "每股收益 (EPS)" \
    --values '{"Macrotrends":23.5,"StockAnalysis":23.6,"Yahoo":23.4}' \
    --unit "" \
    --tolerance 2.0

Expected output:

交叉验证: 每股收益 (EPS) (Cross-Validation)
  数据来源数: 3
  参考中位数: 23.50
  ✅ Macrotrends       : 23.50   (偏差 0.00%)
  ✅ StockAnalysis     : 23.60   (偏差 0.43%)
  ✅ Yahoo             : 23.40   (偏差 0.43%)
  ✅ 所有来源偏差 ≤ 2.0%, 数据一致
  共识值 (加权中位数): 23.50

Run the complete audit workflow from extraction to verdict:

# Step A: Extract sample from report

python3 tools/report_audit.py extract \
    --report reports/腾讯/腾讯-research-20260408.md \
    --dry-run

# Step B: Manually populate fetched_value and fetched_value2 fields

# Step C: Evaluate audit

python3 tools/report_audit.py verdict \
    --results '[{"id":1,"label":"营业收入","reported_value":7518,"unit":"亿","fetched_value":7518,"fetched_source":"macrotrends","fetched_value2":7500,"fetched_source2":"stockanalysis"}]'

Sample verdict output:

✅ 通过 [ 1] 营业收入               报告:      7518.00 亿
              macrotrends: 7518.00 (偏差 0.00%)  |  stockanalysis: 7500.00 (偏差 0.24%)
...
【准出】所有抽检数据通过，报告可发布。

Summary

• AI Berkshire implements multi-source cross-validation through tools/financial_rigor.py and tools/report_audit.py, ensuring no financial metric relies on a single data provider.
• Statistical validation uses median-based consensus with a default 2% tolerance in cross_validate, while human verification applies stricter 1% thresholds in render_verdict.
• The workflow randomly samples 15% of report data for dual-source verification against provider pairs like macrotrends/stockanalysis or eastmoney/cninfo.
• Clear outcome classifications (PASS/FAIL/WARN) trigger automatic publication clearance or mandatory manual review when sources diverge.
• All validation logic is open-source and reproducible, located in specific line ranges (financial_rigor.py lines 63-71, report_audit.py lines 43-61) for transparency.

Frequently Asked Questions

What tolerance thresholds does AI Berkshire use for validation?

The system employs tiered tolerances: cross_validate defaults to 2% for statistical outlier detection, while render_verdict enforces a strict 1% limit (_TOLERANCE = 0.01) for final audit approval. This dual-threshold approach catches significant discrepancies early while maintaining rigorous standards for publication.

How does the sampling process work?

The report_audit tool performs random sampling of 15% of all extracted financial data points from a research report. This percentage strikes a balance between statistical significance and operational efficiency, ensuring coverage across the entire document without requiring verification of every single figure.

Which data providers are integrated into the validation system?

Source pairs are hard-coded based on asset class: U.S. equities use macrotrends.net and stockanalysis.com (or Yahoo); Hong Kong securities pair aastocks.com with macrotrends ADR; mainland Chinese A-shares validate against eastmoney.com and cninfo.com.cn. These combinations ensure geographic and methodological diversity.

Can I use cross_validate without the full audit workflow?

Yes. The cross_validate function in tools/financial_rigor.py operates independently via command-line invocation. You can pass any JSON payload of source values using the --values argument to receive immediate consensus calculations and deviation reports, making it suitable for ad-hoc data verification outside the formal report_audit pipeline.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}