data_collect/simulator_logic.md

# Environment Simulator Logic

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**:

1. **P&L Ledger** (accrual basis) — revenue and costs recognized when *earned/incurred*
2. **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:

1. **Macro layer** — base probability from CSV, driven by market conditions (VIX, interest rates)
2. **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`:

```python
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`:

```json
"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？**

1. **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
2. **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+
3. **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)`。