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)

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

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

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

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

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)

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)

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)

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)

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)

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)

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)