Options pricing is of central importance to financial institutions and is widely applied across risk management, product design, trading profitability, and regulatory compliance. Financial institutions rely on options to hedge market risks, structure structured products, and generate returns through market-making or arbitrage. Accurate options pricing helps control risk exposure, enhance product competitiveness, and meet regulatory requirements such as capital adequacy ratios. For standard European options, well-established analytical pricing models exist, such as the Black-Scholes formula. However, in real-world business scenarios, many complex options—including Asian options, barrier options, snowball options, and others—do not have closed-form solutions, posing significant pricing challenges.
Among these, path-dependent options (such as Asian options and barrier options) are particularly difficult to price and typically require numerical methods such as Monte Carlo simulation. While the Monte Carlo method offers great flexibility and is applicable to a wide range of complex path structures, it suffers from low computational efficiency and slow convergence, especially when dealing with complex path dependencies or high-dimensional problems. Furthermore, products such as barrier options are highly sensitive to path discretization points, and overly large time steps during simulation can easily lead to misjudgment of barrier triggering conditions, thereby affecting pricing accuracy. To improve both precision and efficiency, we have proposed a quantum-inspired Monte Carlo method, which further increases implementation complexity and computational resource consumption.
We provide pricing calculation interfaces for a total of 11 option types. Below is example of option price calculations.
from qoption import *
time=1 * 365
simulation_num=100000
initial_price=100.
strike_price=120.
rate=0.04
vol=0.3
call=True
discounting=True
p1=european_option(simulation_num, time, initial_price, strike_price, rate, vol, call, discounting=True, mode='Q')
p2=european_option(simulation_num, time, initial_price, strike_price, rate, vol, call, discounting=True, mode='C')
print(p1, p2)
