Datasets:

TheFinAI
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CFO-Env

This document defines the complete simulation framework for the CFO environment, including backend state tracking, financial statement generation, fundraising success simulation, and stochastic evaluation design. All modules share a common monthly time axis t = 0, 1, 2, ..., T, and the agent only observes information up to the current time t.

Module A: Backend Tracking (Ledger)

Purpose

Maintain a running ledger of the company's financial state at each month. The system tracks two parallel views:

P&L Ledger (accrual basis) — revenue and costs recognized when earned/incurred
Cash Ledger (cash basis) — actual money entering/leaving the bank account

The ledger is append-only and the agent can only read [0, t] — no future information is visible.

Simulation boundaries (from config.json):

Start date: company_config.initial_date (e.g., 2005-01-01)
Max duration: environment_config.max_episode_months (e.g., 300 months)
The simulation runs from t = 0 to at most t = max_episode_months, or until cash goes negative.

A.1 Core State Variables

At each month t, the backend tracks:

Business State:

Field	Description	Source / Logic
`t`	Month index (0-based)	System clock
`date`	Calendar month-end date	`combined_economic_data_2015_2025.csv`
`active_users`	Monthly active borrowers	Simulated (see A.2)
`net_new_users`	New borrowers this month	`Users(t) − Users(t-1)`
`loan_portfolio_gross`	Total outstanding loans (gross)	Running balance (see A.6)
`allowance`	Allowance for credit losses	Running balance (see A.6)
`lending_rate`	Annual lending rate	`Tsy2Y(t)/100 + Baa_Yield(t)/100`

P&L (Accrual Basis):

Field	Description	Source / Logic
`revenue`	Interest income accrued	`Loan_Portfolio_Gross × Lending_Rate / 12`
`credit_loss_provision`	Expected loan losses	`Revenue × (1 − Gross_Margin) × Provision_Share`
`cogs`	Cost of goods sold	`Revenue × (1 − Gross_Margin)`
`gross_profit`	Gross profit	`Revenue − COGS`
`opex`	Operating expenses	`Gross_Profit − EBITDA`
`ebitda`	EBITDA	`Revenue × EBITDA_Margin`
`interest_expense`	Interest on company's debt	Sum of `principal × rate / 12` per debt instrument
`net_income`	Bottom line	`EBITDA − Interest_Expense − Taxes`

Cash (Bank Account):

Field	Description	Source / Logic
`cash_balance`	Actual bank balance	Previous + all cash inflows − all cash outflows
`cash_in_interest`	Borrower interest payments received	`Revenue(t-1) × Collection_Rate` (lagged)
`cash_in_principal`	Borrower principal repayments	`Loan_Portfolio_Gross(t-1) / Avg_Loan_Term × Collection_Rate` (lagged)
`cash_out_originations`	New loans funded	`max(0, Net_New_Users(t)) × Avg_Loan_Size` (immediate)
`cash_out_servicing`	Servicing cost payments	`Servicing_Costs(t-1)` (lagged)
`cash_out_opex`	Operating cost payments	`OpEx(t-1)` (lagged)
`cash_out_debt_interest`	Debt interest paid	Same month
`cash_out_debt_repayment`	Debt principal paid	Scheduled payment
`cash_in_fundraising`	Funds received from raises	Same month (if success)

Balance Sheet Running Balances:

Field	Description	Source / Logic
`interest_receivable`	Accrued revenue awaiting collection	`Revenue(t)` — cleared next month
`principal_receivable`	Scheduled repayment awaiting cash	`Loan_Portfolio_Gross(t) / Avg_Loan_Term`
`accounts_payable`	Accrued costs awaiting payment	`OpEx(t) + Servicing_Costs(t)` — paid next month
`write_offs`	Bad loans removed from books	`Credit_Loss_Provision(t-4)` — non-cash

Ownership & Capital:

Field	Description	Source / Logic
`total_debt`	Outstanding debt principal	Cumulative from fundraising events
`total_equity_raised`	Cumulative equity raised	Cumulative from fundraising events
`shares_outstanding`	Current total shares	Updated on equity raises

A.2 Revenue & User Simulation

Revenue and users evolve monthly based on indicators from combined_economic_data_2015_2025.csv.

Adjusted Columns Convention: The simulation uses adj_Monthly_User_Growth (not the original Monthly_User_Growth) to drive user growth. The adj_ columns contain amplified growth bumps at three checkpoint periods (months 23-36, 53-62, 106-117) that create cash liquidity traps for the lending company. Original columns are preserved in the CSV for reference. Similarly, fundraising probabilities use adj_P_debt and adj_P_equity from fundraising_success_probabilities_2015_2025.csv.

Active Users (Borrowers):

Users(t)     = Users(t-1) × (1 + adj_Monthly_User_Growth(t) / 100)
Net_New(t)   = Users(t) − Users(t-1)

Loan Portfolio (Gross) — Running Balance:

The gross loan portfolio is tracked as a running balance (see A.6 for full logic):

Loan_Portfolio_Gross(t) = Loan_Portfolio_Gross(t-1)
                        + New_Originations(t)
                        − Scheduled_Principal_Repayment(t)
                        − Write_Offs(t)

Avg_Loan_Size = $10,000 (fixed, from config.json → company_config.average_loan_size) At t=0: Loan_Portfolio_Gross(0) = initial_customers × Avg_Loan_Size

Revenue (Interest Income) — Accrual:

The company earns interest on its gross loan portfolio. The lending rate = risk-free base rate + credit spread:

Lending_Rate(t) = Tsy2Y(t) / 100 + Baa_Yield(t) / 100
Revenue(t)      = Loan_Portfolio_Gross(t) × Lending_Rate(t) / 12

Tsy2Y(t) = 2-Year Treasury yield from combined_economic_data_2015_2025.csv Baa_Yield(t) = ICE BofA Corporate Bond OAS (credit spread) from combined_economic_data_2015_2025.csv loan_term_years = 2 (from config.json → company_config.loan_term_years) Divide by 12 because rates are annual but revenue is monthly

Why Tsy2Y? The base rate matches the loan duration — 2-year loans use the 2-Year Treasury yield as the risk-free benchmark.

Why Baa_Yield? This is the market credit spread (OAS), not an absolute yield. It captures the risk premium the market demands for lending — widens during crises (e.g., 2.58% in Mar 2020), tightens in calm periods (e.g., 0.77% in Sep 2025). Both components are fully dynamic from the environment.

Cost Breakdown — Accrual:

COGS(t)                  = Revenue(t) × (1 − Gross_Margin(t) / 100)
  └─ Credit_Loss_Provision(t) = COGS(t) × Provision_Share
  └─ Servicing_Costs(t)       = COGS(t) × (1 − Provision_Share)
Gross_Profit(t)          = Revenue(t) − COGS(t)
EBITDA(t)                = Revenue(t) × EBITDA_Margin(t) / 100
OpEx(t)                  = Gross_Profit(t) − EBITDA(t)

Provision_Share = 0.40 (from config.json) — 40% of COGS is credit loss provision (non-cash), 60% is servicing costs

A.3 Accrual vs. Cash: Timing Lags

A lending company has significant timing differences between when items appear on the P&L and when cash actually moves. This section explains each lag.

Interest Revenue → Cash Collection (lag: 1 month)

Interest income is accrued on the P&L in the month it's earned. But cash arrives when borrowers make their monthly payment, which involves processing time (ACH settlement, payment posting). Additionally, not all borrowers pay — some are delinquent or default.

Cash_In_Interest(t) = Revenue(t-1) × Collection_Rate

Collection_Rate = 0.96 (96%, from config.json) — 4% of accrued interest is never collected At t=0, there is no prior month, so Cash_In_Interest(0) = 0

Principal Repayments → Cash In (lag: 1 month)

When borrowers make monthly payments, part goes to interest (above) and part repays principal. Principal repayments are NOT revenue — they reduce the loan asset on the balance sheet. But they are real cash inflows.

Monthly_Principal_Rate = 1 / Avg_Loan_Term_Months
Cash_In_Principal(t)   = Loan_Portfolio_Gross(t-1) × Monthly_Principal_Rate × Collection_Rate

Avg_Loan_Term_Months = 24 (from config.json) — 2-year loan term This is cash coming back from the existing portfolio each month

New Loan Originations → Cash Out (lag: 0 months, immediate)

When new borrowers are onboarded, the company must fund their loans immediately from its bank account. This is the biggest cash drain for a growing lender — it is NOT an expense on the P&L (it becomes an asset), but it consumes cash.

Cash_Out_Originations(t) = max(0, Net_New_Users(t)) × Avg_Loan_Size

If users decline (negative growth), no origination cash outflow occurs This creates the key tension: fast growth = high P&L revenue but heavy cash burn

Credit Loss Provision → Write-off (lag: 4 months, non-cash)

Credit losses are provisioned on the P&L when loans are originated or risk is assessed. This is a non-cash accrual that increases the Allowance for Credit Losses (a contra-asset on the Balance Sheet).

When a borrower becomes 90-120+ days delinquent (roughly 4 months later), the loan is written off. A write-off is a Balance Sheet reclassification only — it does NOT cause cash to leave the bank:

Write_Off(t) = Credit_Loss_Provision(t-4)

Balance Sheet entry:
  Loan Portfolio (Gross)         ↓ by Write_Off amount
  Allowance for Credit Losses    ↓ by Write_Off amount
  Loan Portfolio (Net)           unchanged (gross and allowance both decrease)

At early months (t < 4), no write-offs have matured yet, so Write_Off = 0 The cash impact of defaults is already captured through Collection_Rate < 100%: borrowers who default stop making interest and principal payments, so Cash_In_Interest and Cash_In_Principal are reduced accordingly.

Operating Expenses → Cash Out (lag: 1 month)

OpEx includes salaries, technology, marketing, rent, etc. Salaries are paid same-month, but vendor invoices are typically net-30. Blended across all OpEx:

Cash_Out_OpEx(t) = OpEx(t-1)

At t=0, Cash_Out_OpEx(0) = 0

Servicing Costs → Cash Out (lag: 1 month)

The non-provision portion of COGS (payment processing, customer support, loan servicing):

Cash_Out_Servicing(t) = Servicing_Costs(t-1)

servicing_lag_months = 1 (from config.json)

Company Debt Interest → Cash Out (lag: 0 months, same month)

Interest the company owes on its own debt (from fundraising) is accrued and paid in the same month.

Cash_Out_Debt_Interest(t) = Interest_Expense(t)

Company Debt Repayment → Cash Out (lag: 0 months, scheduled)

Principal repayments on the company's debt follow the amortization schedule.

Cash_Out_Debt_Repayment(t) = scheduled amount per debt_instruments table

Repayment type: environment_config.debt_repayment_type (default: "amortizing") Default maturity: environment_config.debt_maturity_months (default: 36 months)

Fundraising Proceeds → Cash In (lag: 0 months, immediate)

When a fundraising round succeeds, cash arrives immediately.

Cash_In_Fundraising(t) = Amount_Raised (if success, else 0)

Summary Table

P&L Item (Accrual)	Cash Event	Lag	Rate/Adjustment
Revenue (interest accrued)	Borrower interest payments received	t+1	× Collection_Rate (96%)
(not on P&L)	Borrower principal repayments received	t+1	× Collection_Rate (96%)
(not on P&L)	New loan originations funded	t+0	immediate, full amount
Credit Loss Provision	Write-off (BS reclassification, non-cash)	t+4	Gross ↓, Allowance ↓, Net unchanged
Servicing Costs (COGS)	Servicing payments	t+1	accrued amount
OpEx	Vendor/salary payments	t+1	accrued amount
Interest Expense (debt)	Debt interest paid	t+0	same month
(not on P&L)	Debt principal repayment	t+0	per schedule
(not on P&L)	Fundraising proceeds	t+0	if success

A.4 Cash Balance Update

Each month, the actual bank balance is updated using cash-basis items only:

Cash(t) = Cash(t-1)

        + Cash_In_Interest(t)            Revenue(t-1) × Collection_Rate
        + Cash_In_Principal(t)           Loan_Portfolio_Gross(t-1) / Avg_Loan_Term × Collection_Rate
        + Cash_In_Fundraising(t)         if fundraising succeeds this month

        − Cash_Out_Originations(t)       Net_New_Users(t) × Avg_Loan_Size
        − Cash_Out_Servicing(t)          Servicing_Costs(t-1)
        − Cash_Out_OpEx(t)               OpEx(t-1)
        − Cash_Out_Debt_Interest(t)      Interest_Expense(t)
        − Cash_Out_Debt_Repayment(t)     per amortization schedule

Key insight: A lending company can be profitable on the P&L but cash-negative if it grows quickly. New loan originations drain cash immediately, while interest income trickles in over months. This forces the CFO agent to balance growth ambition with liquidity management — and fundraise at the right time.

A.5 Storage Schema

P&L Ledger (accrual basis, one row per month-end):

pnl_ledger (
    t                      INT PRIMARY KEY,
    date                   DATE,       -- month-end date (e.g., 2015-01-31)
    active_users           BIGINT,
    net_new_users            BIGINT,
    lending_rate             DECIMAL,

    -- P&L items (accrual)
    revenue                  DECIMAL,
    credit_loss_provision    DECIMAL,
    servicing_costs          DECIMAL,
    cogs                     DECIMAL,
    gross_profit             DECIMAL,
    opex                     DECIMAL,
    ebitda                   DECIMAL,
    interest_expense         DECIMAL,
    net_income               DECIMAL,

    -- Balance Sheet running balances
    loan_portfolio_gross     DECIMAL,    -- running balance (see A.6)
    allowance                DECIMAL,    -- running balance (see A.6)
    interest_receivable      DECIMAL,    -- Revenue(t), cleared at t+1
    principal_receivable     DECIMAL,    -- scheduled repayment, cleared at t+1
    accounts_payable         DECIMAL,    -- OpEx(t) + Servicing_Costs(t), cleared at t+1
    write_offs               DECIMAL     -- Credit_Loss_Provision(t-4), non-cash
)

Cash Ledger (cash basis, one row per month-end):

cash_ledger (
    t                        INT PRIMARY KEY,
    date                     DATE,       -- month-end date (e.g., 2015-01-31)
    cash_in_interest         DECIMAL,    -- Revenue(t-1) × Collection_Rate
    cash_in_principal        DECIMAL,    -- portfolio principal repayments
    cash_in_fundraising      DECIMAL,    -- 0 unless fundraising succeeds
    cash_out_originations    DECIMAL,    -- new loans funded
    cash_out_servicing       DECIMAL,    -- Servicing_Costs(t-1)
    cash_out_opex            DECIMAL,    -- OpEx(t-1)
    cash_out_debt_interest   DECIMAL,    -- same month
    cash_out_debt_repayment  DECIMAL,    -- per schedule
    net_cash_flow            DECIMAL,    -- sum of all above
    cash_balance             DECIMAL     -- running bank balance
)

Auxiliary tables:

debt_instruments (
    id                  INT PRIMARY KEY,
    issued_at           INT,       -- month t when issued
    principal           DECIMAL,
    rate                DECIMAL,   -- annual interest rate
    maturity_months     INT,
    remaining_principal DECIMAL
)

equity_rounds (
    id              INT PRIMARY KEY,
    issued_at       INT,       -- month t when issued
    amount_raised   DECIMAL,
    shares_issued   BIGINT,
    price_per_share DECIMAL,
    investor_label  TEXT
)

capital_summary (
    t                    INT PRIMARY KEY,
    total_debt           DECIMAL,
    total_equity_raised  DECIMAL,
    shares_outstanding   BIGINT
)

A.6 Loan Portfolio & Allowance Tracking

The loan portfolio and allowance are tracked as running balances to ensure the Balance Sheet always balances.

Loan Portfolio (Gross):

Loan_Portfolio_Gross(t) = Loan_Portfolio_Gross(t-1)
                        + New_Originations(t)
                        − Scheduled_Principal_Repayment(t)
                        − Write_Offs(t)

Where:

New_Originations(t) = max(0, Net_New_Users(t)) × Avg_Loan_Size
Scheduled_Principal_Repayment(t) = Loan_Portfolio_Gross(t-1) / Avg_Loan_Term_Months
Write_Offs(t) = Credit_Loss_Provision(t - 4) (bad loans removed from books after ~4 months delinquent)

Allowance for Credit Losses:

Allowance(t) = Allowance(t-1)
             + Credit_Loss_Provision(t)       provision added (from P&L)
             − Write_Offs(t)                  used up when loan is written off

When a write-off occurs, both Gross and Allowance decrease by the same amount, so Net is unchanged. This is a reclassification, not a new loss — the loss was already recognized when the provision was booked.

Interest Receivable:

Interest_Receivable(t) = Revenue(t)

Accrued this month, collected next month. Cleared when Cash_In_Interest arrives at t+1. Uncollected portion (1 − Collection_Rate) is absorbed by the Allowance.

Principal Receivable:

Principal_Receivable(t) = Scheduled_Principal_Repayment(t)

Scheduled this month, cash arrives next month. Uncollected portion absorbed by Allowance.

Accounts Payable:

Accounts_Payable(t) = OpEx(t) + Servicing_Costs(t)

Accrued this month, paid next month (1-month lag).

A.7 Initial Balance Sheet (t = 0)

At simulation start, the company begins with a pre-existing state. All values must satisfy A = L + E:

Assets:
  Cash                    = initial_cash                           (from config.json)
  Interest Receivable     = 0                                      (no prior accrual)
  Principal Receivable    = 0                                      (no prior schedule)
  Loan Portfolio (Gross)  = initial_customers × Avg_Loan_Size
  Allowance               = 0                                      (no provisions yet)
  Loan Portfolio (Net)    = Loan Portfolio (Gross)
  ────────────────────────────────
  Total Assets            = initial_cash + initial_customers × Avg_Loan_Size

Liabilities:
  Total Debt              = initial_debt                           (from config.json, default 0)
  Accounts Payable        = 0                                      (no prior accrual)
  ────────────────────────────────
  Total Liabilities       = initial_debt

Equity:
  Paid-in Capital         = Total Assets − Total Liabilities       (founders funded everything)
  Retained Earnings       = 0                                      (no prior income)
  ────────────────────────────────
  Total Equity            = Paid-in Capital

Cap Table:
  Shares Outstanding      = initial_shares_outstanding             (from config.json, default 10,500,000)
  Implied Share Price     = initial_share_price                    (from config.json, default $10.0)

Balance check: A = L + E ✓

With initial_cash = $15M and initial_customers = 5,000 × $10K = $50M, Total Assets = $65M, Paid-in Capital = $65M. initial_shares_outstanding = 10,000,000 — matches pre-ESOP Cap Table (Founders 8M + Seed 2M). Implied share price $6.50 × 10M = $65M = Paid-in Capital.

A.8 Balance Sheet Reconciliation

Every monthly event must maintain A = L + E. Here is how each event type preserves the equation:

1. New loan origination (Cash → Loan Portfolio):

Cash ↓ $X    |  Loan Portfolio (Gross) ↑ $X
Assets net: unchanged  |  L+E: unchanged  ✓

2. Interest revenue accrual (P&L → Balance Sheet):

Interest Receivable ↑ $Y         (Asset ↑)
Revenue → Net Income → RE ↑ $Y  (Equity ↑)
A ↑ = E ↑  ✓

3. Interest cash collection at t+1 (Receivable → Cash):

Cash ↑ $Y × 96%                   (Asset ↑)
Interest Receivable ↓ $Y          (Asset ↓)
Uncollected $Y × 4%:
  Allowance ↓ $Y × 4%             (contra-asset ↓ = Asset ↑)
  ... net: absorbed by existing allowance
A net: unchanged  |  L+E: unchanged  ✓

4. Credit loss provision (P&L → Allowance):

Allowance ↑ $Z                    (contra-asset ↑ = Net Assets ↓)
COGS → Net Income → RE ↓ $Z      (Equity ↓)
A ↓ = E ↓  ✓

5. Loan write-off (BS reclassification, non-cash):

Loan Portfolio (Gross) ↓ $W       (Asset ↓)
Allowance ↓ $W                    (contra-asset ↓ = Asset ↑)
Loan Portfolio (Net): unchanged    (↓ and ↑ cancel)
A: unchanged  |  L+E: unchanged  ✓

6. OpEx / Servicing accrual (P&L → AP):

Accounts Payable ↑ $V             (Liability ↑)
OpEx → Net Income → RE ↓ $V      (Equity ↓)
L ↑ by $V, E ↓ by $V → L+E: unchanged  |  A: unchanged  ✓

7. OpEx / Servicing cash payment at t+1:

Cash ↓ $V                         (Asset ↓)
Accounts Payable ↓ $V             (Liability ↓)
A ↓ = L ↓  ✓

8. Principal repayment scheduled (Loan → Receivable):

Loan Portfolio (Gross) ↓ $P       (Asset ↓)
Principal Receivable ↑ $P         (Asset ↑)
A: unchanged  |  L+E: unchanged  ✓

9. Principal cash collection at t+1:

Cash ↑ $P × 96%                   (Asset ↑)
Principal Receivable ↓ $P         (Asset ↓)
Uncollected $P × 4%:
  Allowance ↓ $P × 4%             (absorbed by existing allowance)
A net: unchanged  |  L+E: unchanged  ✓

10. Fundraising (equity):

Cash ↑ $F                         (Asset ↑)
Paid-in Capital ↑ $F              (Equity ↑)
A ↑ = E ↑  ✓

11. Fundraising (debt):

Cash ↑ $F                         (Asset ↑)
Total Debt ↑ $F                   (Liability ↑)
A ↑ = L ↑  ✓

12. Debt interest payment (same month, accrual = cash):

Cash ↓ $I                         (Asset ↓)
Interest Expense → RE ↓ $I       (Equity ↓)
A ↓ = E ↓  ✓

13. Debt principal repayment:

Cash ↓ $D                         (Asset ↓)
Total Debt ↓ $D                   (Liability ↓)
A ↓ = L ↓  ✓

A.9 Visibility Rule

At time t, the agent can query:

pnl_ledger WHERE t <= current_t
cash_ledger WHERE t <= current_t
capital_summary WHERE t <= current_t
debt_instruments WHERE issued_at <= current_t
equity_rounds WHERE issued_at <= current_t

No future rows are ever exposed.

Module B: Financial Statement Simulation

Purpose

Generate the three core financial statements (Income Statement, Balance Sheet, Cash Flow Statement) on demand, triggered by specific agent actions.

B.1 Triggers

There are two actions that trigger financial statement generation:

Action	Triggers	Output
`book_closing`	End-of-period close	3 financial statements (IS, BS, CF)
`fund_raising_request`	Fundraising attempt	3 financial statements + updated Cap Table

B.2 Action: `book_closing`

When: Agent decides to close the books at current month t.

Reporting period: Always Year-to-Date (YTD) — from January of the current year through the current month. For example, if t falls in August 2017, the reporting period is Jan 2017 – Aug 2017.

ytd_start = January of the year that date(t) falls in
ytd_end   = date(t)

Process:

1. Determine YTD period:
   ytd_start = first month of the current calendar year
   ytd_end   = current month t

2. Freeze both ledgers at month t

3. Generate:
   a. Income Statement   (YTD: ytd_start to t)
   b. Balance Sheet       (snapshot: as of t)
   c. Cash Flow Statement (YTD: ytd_start to t)

4. Store statements as a versioned snapshot

5. Return statements to the agent

Income Statement (YTD: [ytd_start, t]):

Line Item	Source
Revenue	`SUM(pnl_ledger.revenue)` from ytd_start to t
Cost of Goods Sold	`SUM(pnl_ledger.cogs)` from ytd_start to t
Gross Profit	Revenue − COGS
Operating Expenses	`SUM(pnl_ledger.opex)` from ytd_start to t
EBITDA	Gross Profit − OpEx
Interest Expense	`SUM(pnl_ledger.interest_expense)` from ytd_start to t
Pre-tax Income	EBITDA − Interest Expense
Tax (simplified %)	Pre-tax Income × tax_rate
Net Income	Pre-tax Income − Tax

Example: book_closing in March 2018 → Income Statement covers Jan–Mar 2018 Example: book_closing in December 2018 → full-year Income Statement for 2018

Balance Sheet (snapshot at t):

Line Item	Source
Assets
Cash & Equivalents	`cash_ledger.cash_balance` at t
Interest Receivable	`Revenue(t)` — accrued this month, cash arrives at t+1
Principal Receivable	`Loan_Portfolio_Gross(t) / Avg_Loan_Term` — scheduled repayment, cash arrives at t+1
Loan Portfolio (Gross)	Running balance (see A.6)
Less: Allowance for Credit Losses	Running balance (see A.6)
Loan Portfolio (Net)	Gross − Allowance
Total Assets	Sum of above
Liabilities
Total Debt	`SUM(debt_instruments.remaining_principal)` at t
Accounts Payable	`OpEx(t) + Servicing_Costs(t)` — accrued this month, paid at t+1
Total Liabilities	Sum of above
Equity
Paid-in Capital	Initial capital + `SUM(equity_rounds.amount_raised)`
Retained Earnings	Cumulative `SUM(pnl_ledger.net_income)` from t=0 to t
Total Equity	Paid-in Capital + Retained Earnings

Balance Sheet is always a point-in-time snapshot — not affected by the YTD period. Balance check: Total Assets = Total Liabilities + Total Equity

Why Interest Receivable? Revenue is accrued in month t (P&L), but cash arrives in t+1. Without this asset, the balance sheet would have equity (via retained earnings) increasing without a matching asset increase. Interest Receivable bridges the 1-month gap.

Why Principal Receivable? Same logic — principal repayments are scheduled at t but cash arrives at t+1. This is the portion of the loan portfolio that is "due this month" and waiting for payment processing.

Cash Flow Statement (YTD: [ytd_start, t], built from cash_ledger):

Section	Line Items	Source
Operating Activities
Interest received from borrowers	`SUM(cash_in_interest)` ytd_start to t	cash_ledger
Servicing & OpEx paid	`−SUM(cash_out_servicing + cash_out_opex)` ytd_start to t	cash_ledger
Net Cash from Operations	Sum of above
Investing Activities
New loans originated	`−SUM(cash_out_originations)` ytd_start to t	cash_ledger
Principal repayments received	`+SUM(cash_in_principal)` ytd_start to t	cash_ledger
Net Cash from Investing	Sum of above
Financing Activities
Debt raised	`+SUM(cash_in_fundraising)` where type=debt, ytd_start to t	cash_ledger
Equity raised	`+SUM(cash_in_fundraising)` where type=equity, ytd_start to t	cash_ledger
Debt interest paid	`−SUM(cash_out_debt_interest)` ytd_start to t	cash_ledger
Debt principal repaid	`−SUM(cash_out_debt_repayment)` ytd_start to t	cash_ledger
Net Cash from Financing	Sum of above
Net Change in Cash	Operating + Investing + Financing
Beginning Cash	`cash_ledger.cash_balance` at ytd_start − 1 (prior year-end)
Ending Cash	Beginning + Net Change

Cross-check with Balance Sheet: Ending Cash on the CF Statement MUST equal Cash & Equivalents on the Balance Sheet. Both are sourced from cash_ledger.cash_balance at t.

Cross-check with Income Statement: Net Income (IS) + non-cash items (provision) − working capital changes (ΔReceivables, ΔPayables) ≈ Net Cash from Operations (CF). This is the indirect method relationship and can be verified as a sanity check.

B.3 Action: `fund_raising_request`

When: Agent requests debt or equity fundraising.

Input parameters from agent:

Parameter	Description
`type`	`"debt"` or `"equity"`
`amount`	Requested amount
`terms`	Rate / valuation / maturity (depending on type)

Process:

1. Generate YTD financial statements (same as book_closing, steps 1-5)
   → These represent the company's state "as presented to investors"

2. Determine fundraising success:
   a. Look up P_debt or P_equity for current month t
      (from Module C: Fundraising Success Simulation)
   b. Draw random number r ~ Uniform(0, 1)
   c. success = (r < P_success)

3. If SUCCESS:
   a. For DEBT:
      - Insert into debt_instruments (principal, rate, maturity)
      - Update ledger.total_debt
      - Update ledger.cash_balance += amount_raised
   b. For EQUITY:
      - Calculate new shares: shares_new = amount / price_per_share
      - Insert into equity_rounds
      - Update shares_outstanding += shares_new
      - Update cap_table (recalculate all ownership %)
      - Update ledger.total_equity_raised
      - Update ledger.cash_balance += amount_raised

4. If FAILURE:
   - No changes to ledger, debt_instruments, or equity_rounds
   - Return failure notice to agent
   - Agent may retry in a future month

5. Return:
   - Financial statements (always)
   - Fundraising result (success/failure)
   - Updated cap table (if equity success)

B.4 Cap Table Update Logic (Equity Only)

On a successful equity raise:

new_shares = amount_raised / price_per_share
total_shares_after = shares_outstanding + new_shares

For each existing shareholder:
    new_ownership% = their_shares / total_shares_after

New investor:
    ownership% = new_shares / total_shares_after

Example:

Before	Shares	%	After ($2M at $20M val)	Shares	%
Founders	8,000,000	76%	Founders	8,000,000	72.7%
Seed	2,000,000	19%	Seed	2,000,000	18.2%
ESOP	500,000	5%	ESOP	500,000	4.5%
			Series A	500,000	4.5%
Total	10,500,000	100%	Total	11,000,000	100%

B.5 Statement Versioning

Each generation is stored with metadata:

financial_snapshots (
    snapshot_id      INT PRIMARY KEY,
    trigger          TEXT,         -- 'book_closing' or 'fund_raising_request'
    t                INT,          -- month index when generated
    date             DATE,         -- month-end date (e.g., 2018-03-31)
    ytd_start_date   DATE,         -- YTD period start (e.g., 2018-01-31)
    fiscal_year      INT,          -- e.g., 2018
    months_in_period INT,          -- e.g., 3 for Jan-Mar
    income_stmt      JSON/BLOB,    -- YTD: ytd_start to t
    balance_sheet    JSON/BLOB,    -- Point-in-time snapshot at t
    cashflow_stmt    JSON/BLOB,    -- YTD: ytd_start to t
    cap_table        JSON/BLOB,    -- NULL for book_closing
    balance_check    BOOLEAN       -- Total Assets = Total Liabilities + Total Equity
)

Example: book_closing called in March 2018 → date = 2018-03-31, ytd_start_date = 2018-01-31, fiscal_year = 2018, months_in_period = 3

Module C: Fundraising Success Simulation

Fundraising probability is determined by two layers:

Macro layer — base probability from CSV, driven by market conditions (VIX, interest rates)
Company-state layer — modifiers that reduce probability based on the company's own financial state

P_adjusted = P_base_csv × company_modifier

C.1 Macro Layer: Base Probabilities

Debt

Key driver: SOFR / Fed Funds (lower rate → higher success probability)
Use SOFR when available (Apr 2018+), fallback to FEDFUNDS

P_debt_base = 1 − (Rate − Rate_min) / (Rate_max − Rate_min)

Equity

Key driver: VIX (lower VIX → higher success probability)

P_equity_base = 1 − (VIX − VIX_min) / (VIX_max − VIX_min)

C.2 Company-State Layer: Difficulty Progression

Equity: Round-Count Decay

Each completed equity round makes the next one harder (models "Series A is easier than Series D"):

equity_modifier = equity_round_decay ^ num_completed_equity_rounds
P_equity = P_equity_base × equity_modifier

With equity_round_decay = 0.75 (configurable in environment_config):

Completed Rounds	Modifier	Effect
0 (first raise)	1.00x	No penalty
1	0.75x
2	0.56x
3	0.42x
4	0.32x
5	0.24x
6+	~0.18x	Nearly impossible

Debt: Leverage Penalty on Probability

High leverage ratio (total_debt / total_equity) reduces debt approval odds. Below a safe threshold, no penalty applies; above it, the modifier declines linearly to zero:

leverage_ratio = total_debt / (paid_in_capital + retained_earnings)

if leverage_ratio <= debt_safe_leverage:
    debt_modifier = 1.0
else:
    excess = leverage_ratio - debt_safe_leverage
    debt_modifier = max(0, 1 - debt_leverage_prob_decay × excess)

P_debt = P_debt_base × debt_modifier

With debt_safe_leverage = 0.5 and debt_leverage_prob_decay = 1.5:

Leverage	Modifier	Effect
0.0–0.5	1.00x	Safe zone, no penalty
0.7	0.70x
1.0	0.25x
1.17+	0.00	Impossible

Debt: Leverage Spread on Cost

Higher leverage increases the interest rate the company pays, on top of the market rate:

base_rate = (Tsy2Y + Baa_Yield) / 100
leverage_spread = max(0, leverage_ratio - debt_safe_leverage) × debt_leverage_spread_bps / 10000
cost_rate = base_rate + leverage_spread

With debt_leverage_spread_bps = 500 (5% per unit of excess leverage):

Leverage	Spread	Effect
0.3	+0.0%	Below safe threshold
0.7	+1.0%
1.0	+2.5%

C.3 Simulate Success

For each fundraising attempt at month t:

prob = P_base_csv × company_modifier   # adjusted probability
roll = random()
success = roll < prob

C.4 Amount Raised & Cost

Amount Raised (on success):

fill_rate = Uniform(0.7, 1.0)
Amount_Raised = Fund_Ask × fill_rate

Cost:

Debt_Cost   = (Tsy2Y + Baa_Yield) / 100 + leverage_spread   (annual interest rate, includes company risk)
Equity_Cost = Fund_Ask / Implied_Valuation                    (dilution %)

C.5 Deferred Settlement

Fundraising results are not immediate. Upon submission, the outcome (success/failure) and delivery month are determined but not revealed to the agent:

delivery_month = current_month + randint(fundraising_delivery_min, fundraising_delivery_max)

The agent receives a "submitted" confirmation. At the delivery month, a notification is sent with the result:

If approved: funds are deposited, balance sheet updated, cap table refreshed (for equity)
If declined: no changes, agent may retry

Config parameters: fundraising_delivery_min = 1, fundraising_delivery_max = 6 (in environment_config).

C.6 Configuration Parameters

All fundraising difficulty parameters are in config.json → environment_config:

"equity_round_decay": 0.75,
"debt_safe_leverage": 0.5,
"debt_leverage_prob_decay": 1.5,
"debt_leverage_spread_bps": 500,
"fundraising_delivery_min": 1,
"fundraising_delivery_max": 6

C.7 Historical Reference (2015–2025)

Normalization Parameters

Metric	Min	Max
Rate (SOFR/FEDFUNDS)	0.01%	5.34%
VIX	10.13	57.74

Summary Statistics

Metric	P_debt	P_equity
Min	0.00	0.00
Max	1.00	1.00
Mean	0.63	0.83

Notable Periods

Event	Date	Probability	Driver
Best for Debt	Apr 2021	P_debt = 1.00	Rate = 0.01% (near-zero rates)
Worst for Debt	Jul 2024	P_debt = 0.00	Rate = 5.34% (peak rates)
Best for Equity	Oct 2017	P_equity = 1.00	VIX = 10.13 (lowest volatility)
Worst for Equity	Mar 2020	P_equity = 0.00	VIX = 57.74 (COVID crash)

Data source: fundraising_success_probabilities_2015_2025.csv

Module D: Stochastic Simulation & Evaluation Design

D.1 Deterministic vs. Stochastic Components

当前模拟器中各环节可分为两类：

确定性逻辑（Deterministic）— 不应加入随机性

这些要素由环境数据或会计公式直接驱动，是"游戏规则"本身，加入随机性会破坏逻辑一致性：

组件	原因
宏观经济指标 (Tsy2Y, Baa_Yield, VIX, FEDFUNDS 等)	来自真实历史数据，是所有 agent 面对的相同环境
Lending Rate = Tsy2Y + Baa_Yield	纯公式，由环境决定
Revenue = Portfolio × Rate / 12	会计恒等式，给定 portfolio 和 rate 后无歧义
COGS / Gross Profit / EBITDA / OpEx 分解	给定 margin 后是纯算术
所有 Timing Lag 结构 (1 month, 4 month)	系统规则，不应随机化
Cash Balance 更新公式	会计恒等式
三表生成与配平 (IS, BS, CF)	会计规则，不容差异
P_debt / P_equity 计算	由环境指标确定的概率值本身是确定的

可引入随机性（Stochastic）— 增加 episode 间差异

这些要素在现实中本身存在波动，加入噪声可以测试 agent 在不确定环境中的鲁棒性：

组件	当前设计	随机化方案	理由
Fundraising 成功/失败	`random() < P_success`	保持不变（已经是随机的）	核心随机性来源
Collection Rate	固定 0.96	`clip(N(0.96, σ₁), 0.85, 1.0)` 每月独立抽样	真实中催收率受经济环境和借款人行为波动
User Growth	直接用 CSV 中的 `Monthly_User_Growth`	`Monthly_User_Growth(t) + N(0, σ₂)`	用户增长有行业噪声，同一经济环境下不同公司有差异
Gross Margin	直接用 CSV 中的 `Gross_Margin`	`Gross_Margin(t) + N(0, σ₃)`	信用损失和服务成本有月度波动
EBITDA Margin	直接用 CSV 中的 `EBITDA_Margin`	`EBITDA_Margin(t) + N(0, σ₄)`	运营效率有随机波动
Fundraising 到账金额	`Fund_Ask × P_success` (全额或零)	`Fund_Ask × Uniform(0.7, 1.0)` (部分到账)	现实中很少恰好拿到全额

注意：Lending Rate、Revenue 公式、Cash Formula、三表逻辑不加噪声。这些是确定性的"物理定律"。随机性只加在输入参数上，公式本身保持精确。

D.2 噪声参数 (Noise Parameters)

所有噪声参数集中管理，写入 config.json → stochastic_config：

stochastic_config: {
    "enabled": true,                  -- 开关：false 时退化为完全确定性
    "seed": null,                     -- 全局种子，null 表示使用 episode_id
    "n_episodes": 1000,               -- 每个 agent 的运行次数

    "collection_rate_std": 0.04,      -- σ₁: Collection Rate 标准差
    "user_growth_std": 0.5,           -- σ₂: Monthly User Growth 标准差 (百分点)
    "gross_margin_std": 2.0,          -- σ₃: Gross Margin 标准差 (百分点)
    "ebitda_margin_std": 1.5,         -- σ₄: EBITDA Margin 标准差 (百分点)
    "fundraising_fill_range": [0.7, 1.0]  -- 到账比例的均匀分布范围
}

D.3 可比性保证：Common Random Numbers (CRN)

核心问题： 加入随机性后，如何确保 Agent A 和 Agent B 的结果是可比的？

方案：种子控制 + 配对比较 (Paired Comparison with CRN)

对于 episode i = 1, 2, ..., 1000:
    seed_i = base_seed + i
    rng_i  = RandomGenerator(seed_i)

    用 rng_i 预生成该 episode 的所有随机序列：
      collection_rates[0..T]    ← N(0.96, σ₁) per month
      user_growth_noise[0..T]   ← N(0, σ₂) per month
      gross_margin_noise[0..T]  ← N(0, σ₃) per month
      ebitda_margin_noise[0..T] ← N(0, σ₄) per month
      fundraising_rolls[0..T]   ← Uniform(0, 1) per month  (用于判定成功/失败)
      fundraising_fills[0..T]   ← Uniform(0.7, 1.0) per month

    Agent A 和 Agent B 在 episode i 中面对**完全相同**的随机序列
    唯一的差异来自 agent 的决策（何时融资、融多少、选 debt 还是 equity）

关键： 随机序列是环境的属性，不是 agent 的属性。同一 episode 中所有 agent 面对相同的"天气"，区别只在于他们如何应对。

为什么这有效？

消除运气差异： 如果 Agent A 碰巧遇到高 Collection Rate 的 episode 而 Agent B 没有，比较不公平。CRN 确保两者面对同样的随机环境
降低所需样本量： 配对比较的方差远小于独立比较。Var(A-B|paired) << Var(A) + Var(B)。1000 次可能就足够，而独立比较可能需要 10000+
可复现： 给定 seed，任何 episode 都可以精确复现，便于 debug

D.4 评估指标与统计方法

每个 episode 产出一个 score：

Score_i = (TTM_Revenue × Valuation_Multiple) + Cash_Balance    if cash never went negative
Score_i = 0                                                     if cash went negative at any point

Valuation_Multiple = 10 (from config.json → environment_config.valuation_multiple) TTM = Trailing Twelve Months revenue (sum of last 12 months from pnl_ledger.revenue)

跨 episode 聚合：

指标	计算	用途
Mean Score	`mean(Score_1..Score_N)`	主要排名指标
Survival Rate	`count(Score > 0) / N`	agent 避免现金危机的能力
Std Dev	`std(Score_1..Score_N)`	策略稳定性
Median Score	`median(Score_1..Score_N)`	抗极端值的稳健指标
5th Percentile	`quantile(Score, 0.05)`	最差情况表现（tail risk）

Agent 间比较：

对于每个 episode i:
    Δ_i = Score_A_i − Score_B_i       (配对差值)

统计检验:
    H₀: mean(Δ) = 0  (两个 agent 无差异)
    H₁: mean(Δ) ≠ 0

    使用 paired t-test 或 Wilcoxon signed-rank test
    报告 p-value 和 95% confidence interval for mean(Δ)

因为使用了 CRN，配对差值 Δ_i 的方差很小，即使两个 agent 的平均表现差异不大也能检测出来。

D.5 实验流程

输入:
  agents = [Agent_A, Agent_B, Agent_C, ...]
  N = 1000
  base_seed = 42

流程:
  for i in 1..N:
      env_i = generate_environment(seed = base_seed + i)
        → 预生成所有月份的随机参数
        → 加载确定性的宏观数据

      for agent in agents:
          reset agent state
          score_i[agent] = run_episode(agent, env_i)

输出:
  for each agent:
      mean, std, median, survival_rate, p5, p95

  for each agent pair (A, B):
      paired_diff = score[A] − score[B]
      t_stat, p_value = paired_ttest(paired_diff)
      → "Agent A is significantly better/worse than Agent B (p < 0.05)"

System Flow Diagram

  ──── Every month (automatic) ────
           │
           ▼
    Module A: update both ledgers
    ┌──────────────────────────────────┐
    │  1. Compute Users(t), Revenue(t) │  ← P&L ledger (accrual)
    │     COGS(t), OpEx(t), etc.       │
    │                                  │
    │  2. Compute cash flows with lags │  ← Cash ledger (bank account)
    │     Revenue(t-1) → cash in       │
    │     New loans(t) → cash out      │
    │     OpEx(t-1) → cash out         │
    │                                  │
    │  3. Update BS running balances   │  ← Loan Portfolio, Allowance,
    │     Provision(t-4) → write-off   │     Receivables, Payables
    │     (non-cash BS reclassification)│
    │                                  │
    │  4. Update cash_balance          │
    └──────────────────────────────────┘
           │
           ▼
  Agent decides action at month t
           │
           ├──── action = "book_closing"
           │         │
           │         ▼
           │    Freeze both ledgers [0, t]
           │         │
           │         ▼
           │    Generate 3 statements
           │    (IS from pnl_ledger,
           │     BS from both,
           │     CF from cash_ledger)
           │         │
           │         ▼
           │    Return to agent
           │
           └──── action = "fund_raising_request"
                     │
                     ▼
                Generate 3 statements (same as above)
                     │
                     ▼
              Module C: evaluate P_success
                     │
                ┌────┴────┐
                ▼         ▼
            SUCCESS    FAILURE
                │         │
                ▼         │
          Update:         │
          - cash_ledger   │
          - capital_summary│
          - debt/equity   │
          - cap table     │
                │         │
                ▼         ▼
            Return results to agent

Data Dependencies

Module	Reads From	Writes To
A: Tracking	`data_paths.combined_econ_data` (Tsy2Y, Baa_Yield, Gross_Margin, EBITDA_Margin, adj_Monthly_User_Growth) + `company_config` (loan params, lag params)	`pnl_ledger`, `cash_ledger`, `capital_summary`, `debt_instruments`, `equity_rounds`
B: Statements	`pnl_ledger`, `cash_ledger`, `capital_summary`, `debt_instruments`, `equity_rounds`	`financial_snapshots`
C: Fundraising	`data_paths.fundraising_probs` (adj_P_debt, adj_P_equity)	Results fed into Module A & B
D: Stochastic	`stochastic_config` (noise params, seed, n_episodes)	Pre-generated random sequences per episode

Appendix: Accounting Review — Issues Found & Fixed

在设计三表配平检查时，发现并修正了以下三个问题：

Issue 1: Write-off 被错误归类为现金流出

原始设计： Cash_Out_Writeoffs(t) = Credit_Loss_Provision(t-4) 作为现金流出项出现在 cash_ledger 和现金流量表中。

问题： Write-off 是资产负债表内部的重新分类（Gross ↓, Allowance ↓, Net 不变），不会导致现金离开银行账户。违约对现金的影响已经通过 Collection_Rate < 100% 体现——违约借款人停止支付利息和本金，导致 Cash_In_Interest 和 Cash_In_Principal 减少。将 write-off 同时计入现金流出会造成重复计算。

修正： 从 cash_ledger、A.4 现金公式、现金流量表中移除 Cash_Out_Writeoffs。Write-off 仅作为资产负债表非现金操作保留在 A.6 的 running balance 逻辑中。

Issue 2: 缺少 Interest Receivable 和 Principal Receivable

原始设计： 资产负债表资产端只有 Cash、Loan Portfolio、Other Assets。

问题： Revenue 在月 t 通过 P&L 确认（accrual），增加 Retained Earnings（Equity ↑），但现金要到 t+1 才到账。如果资产端没有对应的 Receivable，则 Equity 增加了但 Asset 没有同步增加，A ≠ L + E。同理，本金还款在 t 月安排但 t+1 到账，Loan Portfolio Gross 已经减少但 Cash 尚未增加，中间需要 Principal Receivable 来桥接。

修正： 在资产负债表中新增 Interest Receivable = Revenue(t) 和 Principal Receivable = Loan_Portfolio_Gross(t) / Avg_Loan_Term，并在 A.6 中定义其清算逻辑。

Issue 3: Accounts Payable 不完整

原始设计： Accounts Payable = OpEx(t) — 仅包含运营费用。

问题： Servicing Costs（COGS 中的非 provision 部分）同样有 1 个月的现金滞后（月 t 确认，t+1 支付）。如果只计入 OpEx 的应付而遗漏 Servicing Costs，则 P&L 确认的 Servicing_Costs 减少了 Equity（通过 RE），但负债端没有对应增加，L + E 不等式被打破。

修正： 扩展为 Accounts_Payable(t) = OpEx(t) + Servicing_Costs(t)。

Environment Simulator Logic

Module A: Backend Tracking (Ledger)

Purpose

A.1 Core State Variables

A.2 Revenue & User Simulation

A.3 Accrual vs. Cash: Timing Lags

Interest Revenue → Cash Collection (lag: 1 month)

Principal Repayments → Cash In (lag: 1 month)

New Loan Originations → Cash Out (lag: 0 months, immediate)

Credit Loss Provision → Write-off (lag: 4 months, non-cash)

Operating Expenses → Cash Out (lag: 1 month)

Servicing Costs → Cash Out (lag: 1 month)

Company Debt Interest → Cash Out (lag: 0 months, same month)

Company Debt Repayment → Cash Out (lag: 0 months, scheduled)

Fundraising Proceeds → Cash In (lag: 0 months, immediate)

Summary Table

A.4 Cash Balance Update

A.5 Storage Schema

A.6 Loan Portfolio & Allowance Tracking

A.7 Initial Balance Sheet (t = 0)

A.8 Balance Sheet Reconciliation

A.9 Visibility Rule

Module B: Financial Statement Simulation

Purpose

B.1 Triggers

B.2 Action: book_closing

B.3 Action: fund_raising_request

B.4 Cap Table Update Logic (Equity Only)

B.5 Statement Versioning

Module C: Fundraising Success Simulation

C.1 Macro Layer: Base Probabilities

Debt

Equity

C.2 Company-State Layer: Difficulty Progression

Equity: Round-Count Decay

Debt: Leverage Penalty on Probability

Debt: Leverage Spread on Cost

C.3 Simulate Success

C.4 Amount Raised & Cost

C.5 Deferred Settlement

C.6 Configuration Parameters

C.7 Historical Reference (2015–2025)

Normalization Parameters

Summary Statistics

Notable Periods

Module D: Stochastic Simulation & Evaluation Design

D.1 Deterministic vs. Stochastic Components

确定性逻辑（Deterministic）— 不应加入随机性

可引入随机性（Stochastic）— 增加 episode 间差异

D.2 噪声参数 (Noise Parameters)

D.3 可比性保证：Common Random Numbers (CRN)

D.4 评估指标与统计方法

D.5 实验流程

System Flow Diagram

Data Dependencies

Appendix: Accounting Review — Issues Found & Fixed

Issue 1: Write-off 被错误归类为现金流出

Issue 2: 缺少 Interest Receivable 和 Principal Receivable

Issue 3: Accounts Payable 不完整

B.2 Action: `book_closing`

B.3 Action: `fund_raising_request`