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Getting Started: A Walkthrough#

This guide is a hands-on tutorial that will walk you through the library's core features, from pricing a single option to running a full model calibration, using the command-line interface (CLI).

1. Run the Built-In Demo#

The quickest way to see QuantFin in action is with the included benchmark script.

Prerequisite: install the library if you haven’t already:

pip install optPricing

Then execute:

quantfin demo

This will price a standard option across multiple models and techniques and print a formatted comparison table.

2. Pricing an Option Programmatically#

First, import and configure the objects you need:

from quantfin.atoms import Option, Stock, Rate, OptionType, ZeroCouponBond
from quantfin.models import BSMModel, VasicekModel, CIRModel
from quantfin.techniques import ClosedFormTechnique

# 1. Define an option, underlying and rate
option = Option(strike=105, maturity=1.0, option_type=OptionType.CALL)
stock  = Stock(spot=100, dividend=0.01)
rate   = Rate(rate=0.05)

# 2. Choose a model and technique
bsm_model     = BSMModel(params={"sigma": 0.20})
cf_technique  = ClosedFormTechnique()

2.1 Compute the Price#

result = cf_technique.price(option, stock, bsm_model, rate)
print(f"The option price is: {result.price:.4f}")
>>> The option price is: 7.4917

2.2 Compute the Greeks#

delta = cf_technique.delta(option, stock, bsm_model, rate)
gamma = cf_technique.gamma(option, stock, bsm_model, rate)
vega  = cf_technique.vega(option, stock, bsm_model, rate)

print(f"Delta: {delta:.4f}")
print(f"Gamma: {gamma:.4f}")
print(f"Vega:  {vega:.4f}")

>>> Delta: 0.5172
>>> Gamma: 0.0197
>>> Vega:  39.4353

2.3 Find Implied Volatility#

target_price = 7.50
iv = cf_technique.implied_volatility(
    option, stock, bsm_model, rate, target_price=target_price
)
print(f"Implied volatility for price ${target_price:.2f}: {iv:.4%}")
>>> Implied volatility for price $7.50: 20.0211%

3. Pricing Interest‐Rate Instruments#

QuantFin reuses the same pricing interfaces for interest‐rate models:

# Zero‐coupon bond maturing in 1 year
bond      = ZeroCouponBond(maturity=1.0)
r0_stock  = Stock(spot=0.05)    # initial short rate
dummy_rate = Rate(rate=0.0)     # ignored by rate models

vasicek = VasicekModel(params={"kappa":0.86, "theta":0.09, "sigma":0.02})
cir     = CIRModel(params={"kappa":0.86, "theta":0.09, "sigma":0.02})

p_vasi = cf_technique.price(bond, r0_stock, vasicek, dummy_rate).price
p_cir  = cf_technique.price(bond, r0_stock, cir,     dummy_rate).price

print(f"Vasicek ZCB Price: {p_vasi:.4f}")
print(f"CIR ZCB Price:     {p_cir:.4f}")

>>> Vasicek ZCB Price: 0.9388
>>> CIR ZCB Price:     0.9388

4. Live Pricing and Analysis via CLI#

4.1 Price an Option#

quantfin price \
  --ticker SPY \
  --strike 500 \
  --maturity 2025-12-19 \
  --type call \
  --model Heston \
  --param "v0=0.04" \
  --param "kappa=2.0" \
  --param "theta=0.05" \
  --param "rho=-0.7" \
  --param "vol_of_vol=0.5"

4.2 Implied Rate from Put-Call Parity#

The tools implied-rate command fetches live prices for a call-put pair and calculates the risk-free rate implied by put-call parity.

quantfin tools implied-rate --ticker SPY --strike 500 --maturity 2025-12-19

5. Model Calibration with the CLI#

Download historical returns (for initial guesses and jump paramaters):

quantfin data download --ticker SPY

Download a snapshot of the market-data

# For the 25 benchmark stocks use --all
quantfin data snapshot --all

# If just a particular ticker use e.g.
quantfin data snapshot --ticker SPY --ticker AAPL

A simple calibration for the Merton Jump model. The workflow will find the latest market data for SPY and solve for the implied volatility that best fits the front-month options.

The --verbose flag provides detailed logs from the workflow.

quantfin calibrate --ticker SPY --model Merton --verbose

The final calibrated parameters are printed to the console and saved to a JSON file in the artifacts/calibrated_params/ directory.

6. Managing Data#

  • Download specific tickers:
quantfin data download --ticker AAPL --ticker TSLA
  • Download all defaults (from config.yaml):
quantfin data download --all
  • Snapshot the live option chain:
quantfin data snapshot --ticker NVDA

7. Running Tests#

If you have installed dev dependencies:

pip install -e .[dev]
pytest

8. Launching the Dashboard#

Make sure you have the [app] extras installed:

pip install optPricing[app]

Then run:

quantfin dashboard

This will open the Streamlit application in your browser for interactive exploration.