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High-performance TensorFlow library for quantitative finance.-

time/embed

tensorflow

github pypi

How to install-

pip install inaccel-tf-quant-finance

FPGA Platforms-

Get the available accelerators for your platform.

intel 38d782e3b6125343b9342433e348ac4c

inaccel bitstream install https://store.inaccel.com/artifactory/bitstreams/intel/pac_a10/38d782e3b6125343b9342433e348ac4c/com/inaccel/quantitativeFinance/blackScholes/1.0/1binary-price
inaccel bitstream install https://store.inaccel.com/artifactory/bitstreams/intel/pac_a10/38d782e3b6125343b9342433e348ac4c/com/inaccel/quantitativeFinance/blackScholes/1.0/1option-price

Examples-

Black Scholes pricing-

Here we see how to price vanilla options in the Black Scholes framework using the library.

import numpy as np

from inaccel.tf_quant_finance.black_scholes import option_price

# Calculate discount factors (e^-rT)
rate = 0.05
expiries = np.array([0.5, 1.0, 2.0, 1.3])
discount_factors = np.exp(-rate * expiries)

# Current value of assets.
spots = np.array([0.9, 1.0, 1.1, 0.9])

# Forward value of assets at expiry.
forwards = spots / discount_factors

# Strike prices given by:
strikes = np.array([1.0, 2.0, 1.0, 0.5])

# Indicate whether options are call (True) or put (False)
is_call_options = np.array([True, True, False, False])

# The volatilites at which the options are to be priced.
volatilities = np.array([0.7, 1.1, 2.0, 0.5])

# Calculate the prices given the volatilities and term structure.
prices = option_price(
    volatilities=volatilities,
    strikes=strikes,
    expiries=expiries,
    forwards=forwards,
    discount_factors=discount_factors,
    is_call_options=is_call_options)