ameriopt 0.1.4

This is a package for pricing American options using reinforcement learning

AmeriOpt

A Python Package for Pricing American Option using Reinforcement Learning.

The full documentation of paper can be found in https://www.mdpi.com/1999-4893/17/9/400

To use package, you need to follwo the following steps:

Installation

pip install ameriopt

Import the package

from ameriopt.rl_policy import RLPolicy

Set the parameters of GBM model

• Number of Laguerre polynomials to be used in the RL model

NUM_LAGUERRE = 5

• Number of training iterations for the RL algorithm

TRAINING_ITERS = 3

• Small constant for numerical stability in the RL algorithm

EPSILON = 1e-5

• Strike price of the option

STRIKE_PRICE = 40

• Time to expiration (in years)

EXPIRY_TIME = 1.0

• Risk-free interest rate

INTEREST_RATE = 0.06

• Number of time intervals

NUM_INTERVALS = 50

• Number of simulations for generating training data

NUM_SIMULATIONS_TRAIN = 5000

• Number of simulations for testing the RL policy

NUM_SIMULATIONS_TEST = 10000

• Spot price of the underlying asset at the start of the simulation

SPOT_PRICE = 36.0

• Volatility of the underlying asset (annualized)

VOLATILITY = 0.2

Simulate Training Data using Geometric Brownian Motion (GBM)

training_data = simulate_GBM_training(
    expiry_time=EXPIRY_TIME,
    num_intervals=NUM_INTERVALS,
    num_simulations=NUM_SIMULATIONS_TRAIN,
    spot_price=SPOT_PRICE,
    interest_rate=INTEREST_RATE,
    volatility=VOLATILITY
)

Instantiate the RLPolicy model with defined parameter GBM Price Model

rl_policy = RLPolicy(
    num_laguerre=NUM_LAGUERRE,
    strike_price=STRIKE_PRICE,
    expiry=EXPIRY_TIME,
    interest_rate=INTEREST_RATE,
    num_steps=NUM_INTERVALS,
    training_iters=TRAINING_ITERS,
    epsilon=EPSILON
)

Train the RL Model and Get Weights (Weight for the optimal policy)

weights = rl_policy.get_weights(training_data=training_data)

Generate test data (GBM paths) for option price scoring

paths_test = scoring_sim_data(
    expiry_time=EXPIRY_TIME,
    num_intervals=NUM_INTERVALS,
    num_simulations_test=NUM_SIMULATIONS_TEST,
    spot_price=SPOT_PRICE,
    interest_rate=INTEREST_RATE,
    volatility=VOLATILITY
)

Option price

option_price = rl_policy.calculate_option_price(stock_paths=paths_test)

Print the calculated option price

print("Option Price using RL Method:", option_price)