How to Perform Three-Scenario Valuation with AI Berkshire

AI Berkshire computes exact target prices across optimistic, base-case, and pessimistic scenarios using the three-scenario sub-command in tools/financial_rigor.py, leveraging decimal.Decimal arithmetic to eliminate floating-point drift.

The xbtlin/ai-berkshire repository provides a deterministic financial analysis toolkit designed for rigorous investment research. Performing Three-Scenario Valuation with AI Berkshire enables analysts to generate audit-ready price targets by compounding EPS growth rates and applying scenario-specific PE multiples without precision errors.

What is Three-Scenario Valuation?

Three-Scenario Valuation projects future earnings per share (EPS) across three distinct forward-looking assumptions—optimistic (bull), neutral (base), and pessimistic (bear)—then calculates corresponding target prices based on assigned PE multiples. Unlike spreadsheet-based models prone to rounding errors, AI Berkshire implements this logic in tools/financial_rigor.py using a pre-configured decimal Context with 28-digit precision (prec=28), ensuring reproducible results across all computing environments.

The valuation engine resides in the three_scenario_valuation function (lines 33-70 of tools/financial_rigor.py), which iterates through each scenario to compound EPS over a configurable time horizon and compute percentage changes against current market price.

Required Inputs and Parameters

To execute a three-scenario valuation, gather the following fundamental data:

• Current Price: Trading price per share
• Current EPS: Trailing twelve-month earnings per share
• Total Shares: Outstanding share count in billions (as standardized throughout the repository)
• Growth Rates: Three decimal values representing annual EPS growth for optimistic, neutral, and pessimistic scenarios (e.g., 0.18 0.06 -0.12)
• Target PE Multiples: Three corresponding PE ratios (e.g., 25 16 9)
• Forecast Horizon: Number of years to compound growth (default: 3)
• Currency Label: Optional ticker for display purposes (e.g., "CNY", "USD")

Running the Three-Scenario Valuation Command

CLI Syntax

Invoke the financial rigor tool directly from the repository root:

python3 tools/financial_rigor.py three-scenario \
    --price <current_price> \
    --eps <current_eps> \
    --shares <shares_in_billions> \
    --growth <optimistic> <neutral> <pessimistic> \
    --pe <pe_optimistic> <pe_neutral> <pe_pessimistic> \
    --years <forecast_years> \
    --currency <currency_code>

Practical Example

The following command evaluates a security trading at 74.72 CNY with current EPS of 2.455, projecting three years forward:

python3 tools/financial_rigor.py three-scenario \
    --price 74.72 \
    --eps 2.455 \
    --shares 15.689 \
    --growth 0.18 0.06 -0.12 \
    --pe 25 16 9 \
    --years 3 \
    --currency "CNY"

Output Explanation:

The CLI prints a Markdown-compatible table containing:

• Scenario: Bull/Base/Bear classification
• Annual Growth: Compounded yearly rate
• Target PE: Assigned multiple
• Projected EPS: Future earnings per share after compounding
• Target Price: Projected EPS × Target PE
• Price Change: Percentage delta versus current price

Sample output format:

============================================================
三情景估值模型 (Three-Scenario Valuation)
============================================================
  当前股价: 74.72 CNY
  当前EPS:  2.455
  预测期:   3年

  情景           年增速   目标PE   目标EPS     目标股价   涨跌幅
  ------------  -------  -------  ----------  ---------  -------
  乐观 (Bull)       18%      25x      4.55      113.8     +52.5%
  中性 (Base)        6%      16x      2.92       46.7     -37.5%
  悲观 (Bear)        0%       9x      2.46       22.1    -70.4%

Programmatic Integration

Using the Python Function Directly

Import the valuation logic into notebooks or custom scripts:

from tools.financial_rigor import three_scenario_valuation

three_scenario_valuation(
    current_price=74.72,
    current_eps=2.455,
    shares_billion=15.689,
    growth_optimistic=0.18,
    growth_neutral=0.06,
    growth_pessimistic=-0.12,
    pe_optimistic=25,
    pe_neutral=16,
    pe_pessimistic=9,
    years=3,
    currency="CNY",
)

This executes the same decimal.Decimal-based calculations as the CLI, printing the formatted table to stdout.

Embedding in Research Reports

According to the AI Berkshire workflow specifications in skills/investment-team.md (line 83) and skills/thesis-drift.md (line 85), analysts embed the CLI command directly into research tasks. The deterministic output allows verbatim insertion into Markdown reports, as demonstrated in reports/藏格矿业/藏格矿业投资研究报告.md (line 292), where the valuation table appears alongside the exact command used for generation.

Best Practice Workflow:

1. Execute the CLI within a sandboxed Bash environment
2. Copy the output table into the valuation section of your report
3. Cite the command line for audit purposes: "All values generated via python3 tools/financial_rigor.py three-scenario ..."

Summary

• Primary Tool: The three-scenario sub-command in tools/financial_rigor.py serves as the single source of truth for multi-scenario valuation in AI Berkshire.
• Precision Architecture: All calculations use decimal.Decimal with 28-digit precision to prevent floating-point drift and ensure reproducibility.
• Input Requirements: Current price, EPS, shares (in billions), three growth rates, three PE multiples, and optional forecast period (default 3 years).
• Workflow Integration: Skill definitions in skills/investment-team.md and skills/thesis-drift.md standardize the command invocation across research teams.
• Output Format: Markdown-compatible tables suitable for direct insertion into investment reports, with built-in audit trails via the deterministic CLI.

Frequently Asked Questions

What decimal precision does AI Berkshire use for scenario calculations?

AI Berkshire configures a dedicated decimal Context with prec=28 (28 significant digits) in tools/financial_rigor.py. This ensures that even large market-capitalization calculations or high-magnitude EPS figures maintain exact precision without floating-point rounding errors common in standard Python float operations.

How do I adjust the forecast horizon beyond the default 3 years?

Append the --years flag followed by your desired integer. For example, --years 5 extends the EPS compounding period to five annual cycles. If omitted, the three_scenario_valuation function defaults to a 3-year horizon, aligning with standard medium-term equity research conventions.

Can I run Three-Scenario Valuation without installing external dependencies?

Yes. The tools/financial_rigor.py utility relies exclusively on the Python standard library, specifically the decimal and argparse modules. No external APIs, database connections, or third-party packages are required, making the tool fully portable across any Python 3 environment.

Where can I find real-world examples of this valuation method in use?

The repository contains completed research reports demonstrating implementation. Specifically, reports/藏格矿业/藏格矿业投资研究报告.md (line 292) showcases the three-scenario command embedded within a full investment thesis, displaying both the CLI invocation and the resulting valuation table for a Chinese mining sector analysis.

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