Latest Resources in Quant Finance

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Accelerating Python for Exotic Option Pricing nvidia.com

Published April 3, 2020 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.

Empyrical: Common financial risk and performance metrics in Python github.io

Published February 11, 2020 under Quant Finance

Lots of quantitative risk metrics for analyzing your backtest and trading performance. Created by Quantopian for their popular Zipline backtesting framework, this library works totally independently.

End To End Python Implementation Of Finding Optimised Efficient Investment Portfolios medium.com

Published January 18, 2020 under Quant Finance

One of the milestones of the investment management application was to implement an end to end solution that starts by fetching company stock prices and builds a set of efficient and optimum portfolios using optimisation routines.

Quant Finance Numerical Methods in Jupyter Notebooks github.com

Published November 28, 2019 under Quant Finance

This is a collection of Jupyter notebooks based on different topics in the area of quantitative finance. Wow!

Stock Market Trend Analysis with Python medium.com

Published September 7, 2019 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.

CVA in the Cloud with NAG nag.com

Published August 15, 2019 under Quant Finance

NAG has developed, in collaboration with Xi-FINTIQ, a CVA demonstration code to show how the NAG Library and NAG Algorithmic Differentiation (AD) tool dco/c++ combined with Origami – a Grid/Cloud Task Execution Framework available through NAG – can work together to solve large scale CVA computations.

Fast Implied Volatilities using Chebyshev Interpolation nag.com

Published July 13, 2019 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.

How to make the most amount of money with the least amount of risk? towardsdatascience.com

Published June 3, 2019 under Quant Finance

In this article, we show one such amazing application of LP using Python programming in the area of economic planning — maximizing the expected profit from a stock market investment portfolio while minimizing the risk associated with it.