FP&A Test Data Platform

Python tooling to generate, validate, and load realistic FP&A data into your Go API.

Structure

testing/
├── generators/
│   └── generate_data.py     # Creates all CSV files
├── loaders/
│   └── api_loader.py        # POSTs CSVs to your Go API
├── tests/
│   └── test_fpa.py          # Data integrity + API tests
├── data/
│   └── csv/                 # Generated CSV files land here
└── requirements.txt

Quick Start

pip install -r requirements.txt

# 1. Generate all CSV data
python generators/generate_data.py

# 2. Validate data integrity (no API needed)
pytest tests/test_fpa.py -v

# 3. Load into your Go API (dry-run first)
python loaders/api_loader.py --dry-run

# 4. Load for real
python loaders/api_loader.py --url http://localhost:8080

Generated Datasets

File	Rows	Description
`revenue_budget_vs_actuals.csv`	48	Product & Service revenue — budget vs actuals, 24 months
`opex_budget_vs_actuals.csv`	~2,688	Dept × category opex — budget vs actuals
`pl_income_statement.csv`	24	Monthly P&L: revenue, COGS, gross profit, EBITDA, net income
`cash_flow.csv`	24	Operating / investing / financing cash flows, rolling balance
`headcount_workforce.csv`	~1,000+	Employee snapshots per month, with hire/term dates & salaries

Key Concepts

Budget vs Actuals — Every financial row has a budget_amount (what was planned) and actual_amount (what really happened). The variance = actual − budget. Positive variance on revenue = good. Positive variance on spend = over budget.

Product vs Service revenue — Product is recurring SaaS subscriptions (~70%, higher margin). Service is consulting/support (~30%, lower margin). Both grow monthly at different rates.

Cash Flow — Separate from revenue. Collections can lag invoicing (DSO effect). Includes a simulated Series A raise in June 2023.

API Endpoints (from main.go)

POST   /api/v1/budgets          ← create one budget line
PUT    /api/v1/budgets/{id}     ← update a budget line
DELETE /api/v1/budgets/{id}     ← delete a budget line
POST   /api/v1/actuals/ingest   ← bulk ingest actuals { "records": [...] }
GET    /api/v1/variance         ← variance report (budget vs actuals)
GET    /api/v1/variance/alerts  ← over/under budget alerts
GET    /api/v1/health           ← db health check

Load Order

The seeder always loads in this order — budgets must exist before actuals:

Budgets — POST /api/v1/budgets individually (revenue + opex lines)
Actuals — POST /api/v1/actuals/ingest in batches of 50
Variance check — GET /api/v1/variance to confirm data landed

Loader Options

python loaders/api_loader.py --help

  --url       API base URL (default: http://localhost:8080)
  --batch     Records per actuals/ingest request (default: 50)
  --dry-run   Print payloads without sending
  --token     Bearer token for auth header
  --only      Run one step only: budgets | actuals | variance | alerts

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