AI Berkshire Performance Track Records: Real-Money Returns and Benchmark Analysis

AI Berkshire has generated verified annual returns of +69.29% (2024) and +66.38% (2025) in a real-money, audited portfolio, outperforming the S&P 500 by over 46 percentage points each year according to the repository’s documented track record.

The documented performance track records of AI Berkshire represent one of the few publicly auditable AI-driven investment frameworks backed by live brokerage data. Unlike backtested simulations, these figures derive from a real-money portfolio hosted on Futu Securities, with cumulative live returns exceeding ¥1.46 million over the two-year period. All verification screenshots and calculation methodologies are stored directly in the xbtlin/ai-berkshire repository.

Verified Annual Returns vs. Global Benchmarks

The framework’s performance is benchmarked against major global indices to demonstrate absolute and relative alpha generation.

Year	Full-Year Return	Benchmark Comparison
2024	+69.29%	Hang Seng +17.67% · S&P 500 +23.31% · CSI 300 +14.68% · NASDAQ +28.64%
2025	+66.38%	Hang Seng +27.77% · S&P 500 +16.39% · CSI 300 +17.66% · NASDAQ +20.36%

In 2024, the AI Berkshire portfolio beat the S&P 500 by 46 percentage points. In 2025, the outperformance margin widened to 50 percentage points. These figures are documented in the Real Track Record section of README_EN.md and supported by live account screenshots.

How the Track Record Is Generated and Audited

The performance numbers rely on a rigorous validation pipeline that prevents calculation errors and data inconsistencies.

Real-Money Portfolio Verification

The repository stores actual brokerage screenshots that serve as audit trails:

• assets/2024-returns.jpg – Screenshot of the 2024 live portfolio performance
• assets/2025-returns.jpg – Screenshot of the 2025 live portfolio performance

These images capture the Futu Securities account interface, confirming the documented performance track records reflect real market executions rather than theoretical simulations.

Exact-Decimal Validation Engine

All financial calculations use tools/financial_rigor.py, which enforces exact-decimal arithmetic to eliminate floating-point errors. This engine cross-validates market-cap calculations, position sizing, and return aggregations before they are published in the track record.

Reproducing the Performance Methodology

You can invoke the same skills and tools that generate the research powering these verified returns.

Running the Investment Research Pipeline

The investment-research skill executes multi-agent analysis that feeds into portfolio decisions tracked in the performance record.

# Install the Codex skills (once)

git clone https://github.com/xbtlin/ai-berkshire.git
cd ai-berkshire
./scripts/install-codex-skills.sh

# Launch analysis on a target company (e.g., Tencent)

codex use investment-research "Tencent"

This skill pulls live data and validates financial numbers using tools/financial_rigor.py, outputting research memos that inform the portfolio’s constituents.

Calculating Live Portfolio Returns

The portfolio-review skill aggregates holdings and computes real-time performance using the same engine that produces the audited track record.

codex use portfolio-review \
  "Tencent, 30%" "Meituan, 20%" "Kweichow Moutai, 20%" "Cash, 30%"

This command fetches latest market prices via the built-in web scraper and calculates returns using exact-decimal methods.

Cross-Validating Financial Calculations

Verify individual calculations using the validation engine directly:

python3 tools/financial_rigor.py verify-market-cap \
  --price 510 --shares 9.11e9 --reported 4.65e12 --currency HKD

This reproduces the rigorous cross-validation steps underlying the track record’s credibility.

Key Source Files Behind the Track Record

• README_EN.md (section Real Track Record) – Central documentation for audited returns and benchmark tables
• assets/2024-returns.jpg – Screenshot evidence of 2024 live portfolio performance
• assets/2025-returns.jpg – Screenshot evidence of 2025 live portfolio performance
• tools/financial_rigor.py – Exact-decimal calculation engine ensuring figure reliability
• skills/portfolio-review.md – Skill that aggregates holdings and computes portfolio-level returns
• skills/investment-research.md – Core research skill generating the deep-analysis reports driving investment decisions

Summary

• AI Berkshire’s documented performance track records show +69.29% (2024) and +66.38% (2025) returns in a real-money portfolio.
• The framework outperformed the S&P 500 by 46–50 percentage points annually against major global indices.
• All claims are supported by live brokerage screenshots stored in assets/2024-returns.jpg and assets/2025-returns.jpg.
• Exact-decimal validation via tools/financial_rigor.py guarantees calculation accuracy.
• Users can reproduce the methodology using investment-research and portfolio-review skills.

Frequently Asked Questions

Is AI Berkshire's performance track record independently audited?

The track record is documented through real-money brokerage screenshots from Futu Securities rather than third-party audit statements. The repository stores these screenshots in assets/2024-returns.jpg and assets/2025-returns.jpg, providing visual verification of the live account performance. All calculation methodologies are open-source and stored in tools/financial_rigor.py for public inspection.

How does AI Berkshire ensure the accuracy of its return calculations?

The framework uses tools/financial_rigor.py, an exact-decimal calculation engine that eliminates floating-point errors common in financial software. This tool cross-validates market-cap figures, position weights, and aggregated returns before publication. The portfolio-review skill invokes this engine when computing real-time portfolio performance.

Can investors reproduce the decisions that generated these returns?

Yes. The repository provides Codex skills that replicate the research process. Users can run codex use investment-research to generate deep-analysis reports and codex use portfolio-review to calculate portfolio-level returns using the same validation logic. This allows independent verification of the methodology behind the documented performance track records.

What benchmarks does AI Berkshire use for performance comparison?

The track record compares returns against four major global indices: the Hang Seng Index, S&P 500, CSI 300, and NASDAQ. The framework has outperformed all four benchmarks in both 2024 and 2025, with particular outperformance versus the S&P 500 exceeding 46 percentage points each year.

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