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This library provides high-performance components leveraging the hardware acceleration support and automatic differentiation of TensorFlow. The library will provide TensorFlow support for foundational mathematical methods, mid-level methods, and specific pricing models. The coverage is being expanded over the next few months.

The library is structured along three tiers:

  1. Foundational methods. Core mathematical methods – optimisation, interpolation, root finders, linear algebra, random and quasi-random number generation, etc.
  2. Mid-level methods. ODE & PDE solvers, Ito process framework, Diffusion Path Generators, Copula samplers etc.
  3. Pricing methods and other quant finance specific utilities. Specific Pricing models (e.g Local Vol (LV), Stochastic Vol (SV), Stochastic Local Vol (SLV), Hull-White (HW)) and their calibration. Rate curve building, payoff descriptions and schedule generation.

We aim for the library components to be easily accessible at each level. Each layer will be accompanied by many examples which can be run independently of higher level components.

under Quant Finance

Learn how to perform algorithmic trading using Python in this complete course. Algorithmic trading means using computers to make investment decisions. Computer algorithms can make trades at a speed and frequency that is not possible by a human.

under Quant Finance


Mark 27.1 of the NAG Library contains a new routine, s30acf, for computing the implied volatility of a European option contract for arrays of input data.

This routine gives the user a choice of two algorithms. The first is the method of Jäckel (2015), which uses a third order Householder method to achieve close to machine accuracy for all but the most extreme inputs. This method is fast for short vectors of input data.

The second algorithm is based on that of Glau et al. (2018), with additional performance enhancements developed in a collaboration between NAG and mathematicians at Queen Mary University of London. This method uses Chebyshev interpolation and is designed for long vectors of input data, where vector instructions can be exploited. For applications in which accuracy to machine precision is not required, the algorithm can also be instructed to aim for accuracy to roughly single precision (approximately seven decimal places), giving even further performance improvements.

under Quant Finance

In finance, computation efficiency can be directly converted to trading profits sometimes. Quants are facing the challenges of trading off research efficiency with computation efficiency. Using Python can produce succinct research codes, which improves research efficiency. However, vanilla Python code is known to be slow and not suitable for production. In this post, I explore how to use Python GPU libraries to achieve the state-of-the-art performance in the domain of exotic option pricing. 

under Quant Finance

The purpose of this article is to introduce the reader to some of the tools used to spot stock market trends.

We will utilize a data set consisting of five years of daily stock market data for Analog Devices. The time period we consider starts on January 1, 2013 and ends on December 31, 2017. We will start analyzing the data using line plots, then introduce candlestick charts. Patterns that can be seen in the candlestick chart will be introduced which can be used to spot changes in the market. We add another of level analysis by overlaying moving averages and discussing how these can help confirm trend changes. Finally, we construct a figure that concisely summarizes the stock price data for any company.

under Quant Finance

Calculating Black-Scholes implied volatilities is a key part of financial modelling, and is not easy to do efficiently.

The benchmark in this field is the iterative method due to Peter Jaeckel (2015), though some banks have their own methods. NAG have teamed up with Dr Kathrin Glau and her colleagues from Queen Mary University of London to see whether their research in Chebyshev interpolation could be combined with NAG’s expertise in efficient computing to provide a faster way of obtaining implied volatilities.