Equity compensation tax recipes (Python)

Copy-paste Python recipes for common equity-compensation tax questions: incentive stock options (ISOs) and the alternative minimum tax (AMT), non-qualified stock options (NSOs), restricted stock units (RSUs), qualified small business stock (QSBS), single-stock concentration, protective puts, and funding a cash goal from stock.

Each recipe is one self-contained file that calls the keyless OptionsAhoy REST API (no API key) with nothing but requests. The financial math is deterministic and independently verified against Internal Revenue Service publications and open-source tax engines: https://optionsahoy.com/verification.

pip install requests
python optimize_iso_amt_exercise_schedule.py

Recipes by question

How do I calculate the optimal multi-year ISO exercise schedule under the AMT?

optimize_iso_amt_exercise_schedule.py

from optimize_iso_amt_exercise_schedule import optimize_iso_exercise_schedule

result = optimize_iso_exercise_schedule(
    shares=20000, strike=2, fmv=200, filing_status="married_joint",
    ordinary_income=300000, state_code="CA", horizon_years=4,
)
print(result["schedules"]["optimized"]["nfv"])

How do I calculate the tax on exercising NSOs, and should I sell or hold?

calculate_nso_exercise_tax.py

from calculate_nso_exercise_tax import calculate_nso_exercise_tax

result = calculate_nso_exercise_tax(
    shares=10000, strike=5, current_price=80, ordinary_income=250000,
    filing_status="single", state_code="CA", hold_years=2, ticker="NVDA",
)

Should I sell or hold my vested RSUs?

rsu_sell_vs_hold_after_tax.py

from rsu_sell_vs_hold_after_tax import rsu_sell_vs_hold

result = rsu_sell_vs_hold(shares=2000, current_price=80, ordinary_income=250000,
                          filing_status="single", state_code="CA", ticker="NVDA")

How do I analyze single-stock concentration risk?

analyze_single_stock_concentration.py

from analyze_single_stock_concentration import analyze_concentration

result = analyze_concentration(position_value=800000, cost_basis=100000,
                               sector="tech_software", total_assets=1200000, ticker="NVDA")

How do I price a protective put or a zero-cost collar?

price_protective_put_or_collar.py

from price_protective_put_or_collar import price_protective_put

result = price_protective_put(position_value=500000, sector="semiconductors",
                              protection_level=0.10, tenor_years=1, ticker_label="NVDA")

Do my shares qualify for QSBS (Section 1202)?

check_qsbs_section_1202_eligibility.py

from check_qsbs_section_1202_eligibility import check_qsbs_eligibility

result = check_qsbs_eligibility(acquisition_date="2018-01-01", sale_date="2025-06-01",
                                expected_gain=2000000, state_code="CA", filing_status="single")

Which stock should I sell, and when, to fund a cash goal after tax?

plan_stock_sales_for_cash_goal.py

from plan_stock_sales_for_cash_goal import plan_equity_sales_for_cash_goal

result = plan_equity_sales_for_cash_goal(target_after_tax=200000, target_date="2027-06-01",
                                         ordinary_income=250000, state_code="CA")

Why call the API instead of computing it inline

A published five-model benchmark on the same multi-year ISO problem found all fifteen trials overshot the achievable after-tax outcome by two to twenty times. The API's answer is deterministic and verifiable. The same calculators are also available as an agent tool over the Model Context Protocol (https://optionsahoy.com/mcp) and as a Python client (pip install optionsahoy).

Testing

pip install requests pytest
pytest tests                 # offline structure checks
OA_LIVE=1 pytest tests        # also runs each recipe live