pip install qfin

from qfin.options import BlackScholesCall
from qfin.options import BlackScholesPut
# 100 - initial underlying asset price
# .3 - asset underlying volatility
# 100 - option strike price
# 1 - time to maturity (annum)
# .01 - risk free rate of interest
euro_call = BlackScholesCall(100, .3, 100, 1, .01)
euro_put = BlackScholesPut(100, .3, 100, 1, .01)

print('Call price: ', euro_call.price)
print('Put price: ', euro_put.price)

Call price:  12.361726191532611
Put price:  11.366709566449416

print('Call delta: ', euro_call.delta)
print('Put delta: ', euro_put.delta)

Call delta:  0.5596176923702425
Put delta:  -0.4403823076297575

print('Call gamma: ', euro_call.gamma)
print('Put gamma: ', euro_put.gamma)

Call gamma:  0.018653923079008084
Put gamma:  0.018653923079008084

print('Call vega: ', euro_call.vega)
print('Put vega: ', euro_put.vega)

Call vega:  39.447933090788894
Put vega:  39.447933090788894

print('Call theta: ', euro_call.theta)
print('Put theta: ', euro_put.theta)

Call theta:  -6.35319039407325
Put theta:  -5.363140560324083

from qfin.simulations import GeometricBrownianMotion
# 100 - initial underlying asset price
# 0 - underlying asset drift (mu)
# .3 - underlying asset volatility
# 1/52 - time steps (dt)
# 1 - time to maturity (annum)
gbm = GeometricBrownianMotion(100, 0, .3, 1/52, 1)

print(gbm.simulated_path)

[107.0025048205179, 104.82320056538235, 102.53591127422398, 100.20213816642244, 102.04283245358256, 97.75115579923988, 95.19613943526382, 96.9876745495834, 97.46055174410736, 103.93032659279226, 107.36331603194304, 108.95104494118915, 112.42823319947456, 109.06981862825943, 109.10124426285238, 114.71465058375804, 120.00234814086286, 116.91730159923688, 118.67452601825876, 117.89233466917202, 118.93541257993591, 124.36106523035058, 121.26088015675688, 120.53641952983601, 113.73881043255554, 114.91724168548876, 112.94192281337791, 113.55773877160591, 107.49491796151044, 108.0715118831013, 113.01893111071472, 110.39204535739405, 108.63917240906524, 105.8520395233433, 116.2907247951675, 114.07340779267213, 111.06821275009212, 109.65530380775077, 105.78971667172465, 97.75385009989282, 97.84501925249452, 101.90695475825825, 106.0493833583297, 105.48266575656817, 106.62375752876223, 112.39829297429974, 111.22855058562658, 109.89796974828265, 112.78068777325248, 117.80550869036715, 118.4680557054793, 114.33258212280838]

from qfin.simulations import StochasticVarianceModel
# 100 - initial underlying asset price
# 0 - underlying asset drift (mu)
# .01 - risk free rate of interest
# .05 - continuous dividend
# 2 - rate in which variance reverts to the implied long run variance
# .25 - implied long run variance as time tends to infinity
# -.7 - correlation of motion generated
# .3 - Variance's volatility
# 1/52 - time steps (dt)
# 1 - time to maturity (annum)
svm = StochasticVarianceModel(100, 0, .01, .05, 2, .25, -.7, .3, .09, 1/52, 1)

print(svm.simulated_path)

[98.21311553503577, 100.4491317019877, 89.78475515902066, 89.0169762497475, 90.70468848525869, 86.00821802256675, 80.74984494892573, 89.05033807013137, 88.51410029337134, 78.69736798230346, 81.90948751054125, 83.02502248913251, 83.46375102829755, 85.39018282900138, 78.97401642238059, 78.93505221741903, 81.33268688455111, 85.12156706038515, 79.6351983987908, 84.2375291273571, 82.80206517176038, 89.63659376223292, 89.22438477640516, 89.13899271995662, 94.60123239511816, 91.200165507022, 96.0578905115345, 87.45399399599378, 97.908745925816, 97.93068975065052, 103.32091104292813, 110.58066464778392, 105.21520242908348, 99.4655106985056, 106.74882010453683, 112.0058519886151, 110.20930861932342, 105.11835510815085, 113.59852610881678, 107.13315204738092, 108.36549026977205, 113.49809943785571, 122.67910031073885, 137.70966794451425, 146.13877267735612, 132.9973784430374, 129.75750117504984, 128.7467891695649, 127.13115959080305, 130.47967713110302, 129.84273088908265, 129.6411527208744]

from qfin.simulations import MonteCarloCall
from qfin.simulations import MonteCarloPut
# 100 - strike price
# 1000 - number of simulated price paths
# .01 - risk free rate of interest
# 100 - initial underlying asset price
# 0 - underlying asset drift (mu)
# .3 - underlying asset volatility
# 1/52 - time steps (dt)
# 1 - time to maturity (annum)
call_option = MonteCarloCall(100, 1000, .01, 100, 0, .3, 1/52, 1)
# These additional parameters will generate a Monte Carlo price based on a stochastic volatility process
# 2 - rate in which variance reverts to the implied long run variance
# .25 - implied long run variance as time tends to infinity
# -.5 - correlation of motion generated
# .02 - continuous dividend
# .3 - Variance's volatility
put_option = MonteCarloPut(100, 1000, .01, 100, 0, .3, 1/52, 1, 2, .25, -.5, .02, .3)

print(call_option.price)
print(put_option.price)

12.73812121792851
23.195814963576286

from qfin.simulations import MonteCarloBinaryCall
from qfin.simulations import MonteCarloBinaryPut
# 100 - strike price
# 50 - binary option payout
# 1000 - number of simulated price paths
# .01 - risk free rate of interest
# 100 - initial underlying asset price
# 0 - underlying asset drift (mu)
# .3 - underlying asset volatility 
# 1/52 - time steps (dt)
# 1 - time to maturity (annum)
binary_call = MonteCarloBinaryCall(100, 50, 1000, .01, 100, 0, .3, 1/52, 1)
binary_put = MonteCarloBinaryPut(100, 50, 1000, .01, 100, 0, .3, 1/52, 1)

print(binary_call.price)
print(binary_put.price)

22.42462873441866
27.869902820039087

from qfin.simulations import MonteCarloBarrierCall
from qfin.simulations import MonteCarloBarrierPut
# 100 - strike price
# 50 - binary option payout
# 1000 - number of simulated price paths
# .01 - risk free rate of interest
# 100 - initial underlying asset price
# 0 - underlying asset drift (mu)
# .3 - underlying asset volatility
# 1/52 - time steps (dt)
# 1 - time to maturity (annum)
# True/False - Barrier is Up or Down
# True/False - Barrier is In or Out
barrier_call = MonteCarloBarrierCall(100, 1000, 150, .01, 100, 0, .3, 1/52, 1, up=True, out=True)
barrier_put = MonteCarloBarrierCall(100, 1000, 95, .01, 100, 0, .3, 1/52, 1, up=False, out=False)

print(binary_call.price)
print(binary_put.price)

4.895841997908933
5.565856754630819

from qfin.simulations import MonteCarloAsianCall
from qfin.simulations import MonteCarloAsianPut
# 100 - strike price
# 1000 - number of simulated price paths
# .01 - risk free rate of interest
# 100 - initial underlying asset price
# 0 - underlying asset drift (mu)
# .3 - underlying asset volatility
# 1/52 - time steps (dt)
# 1 - time to maturity (annum)
asian_call = MonteCarloAsianCall(100, 1000, .01, 100, 0, .3, 1/52, 1)
asian_put = MonteCarloAsianPut(100, 1000, .01, 100, 0, .3, 1/52, 1)

print(asian_call.price)
print(asian_put.price)

6.688201154529573
7.123274528125894

from qfin.simulations import MonteCarloExtendibleCall
from qfin.simulations import MontecarloExtendiblePut
# 100 - strike price
# 1000 - number of simulated price paths
# .01 - risk free rate of interest
# 100 - initial underlying asset price
# 0 - underlying asset drift (mu)
# .3 - underlying asset volatility
# 1/52 - time steps (dt)
# 1 - time to maturity (annum)
# .5 - extension if out of the money at expiration
extendible_call = MonteCarloExtendibleCall(100, 1000, .01, 100, 0, .3, 1/52, 1, .5)
extendible_put = MonteCarloExtendiblePut(100, 1000, .01, 100, 0, .3, 1/52, 1, .5)

print(extendible_call.price)
print(extendible_put.price)

13.60274931789973
13.20330578685724