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Resources for developers using Python for scientific computing and quantitative analysis

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Quant Finance

TF Quant Finance: TensorFlow based Quant Finance Library by Google

Published January 19, 2021 under Quant Finance

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.

Python, TensorFlow

How To Create A Fully Automated AI Based Trading System With Python towardsdatascience.com

Published January 13, 2021 under Quant Finance

How To Create A Fully Automated AI Based Trading System With Python

End-to-end project: get the data, train the model, place the order, get notified.

Algorithmic Trading

Algorithmic Trading Using Python youtube.com

Published January 13, 2021 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.

Algorithmic Trading

Fast Implied Volatilities in the NAG Library nag.com

Published November 12, 2020 under Quant Finance

Fast Implied Volatilities in the NAG Library


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.

NAG, Optimization

Building a Neural Network to Predict Loan Risk tymick.me

Published August 19, 2020 under Quant Finance

Building a Neural Network to Predict Loan Risk

LendingClub is the world’s largest peer-to-peer lending platform. Until recently (through the end of 2018), LendingClub published a public dataset of all loans issued since the company’s launch in 2007.

Machine Learning, Neural Network

Time Series Analysis and Forecasting with ARIMA using Python kanoki.org

Published May 3, 2020 under Quant Finance

Time Series Analysis and Forecasting with ARIMA using Python

In this post we will discuss how to do a time series modelling using ARMA and ARIMA models. Here AR stands for Auto-Regressive and MA stands for Moving Average

Python, Time Series Analysis

Accelerating Python for Exotic Option Pricing nvidia.com

Published April 3, 2020 under Quant Finance

Accelerating Python for Exotic Option Pricing

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. 

GPU, Numerical Methods, Nvidia

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.

Finance, Python, Quant Trading

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

Published January 18, 2020 under Quant Finance

End To End Python Implementation Of Finding Optimised Efficient Investment Portfolios

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.

Efficient Frontier, Optimization, Python

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!

Jupyter, Numerical Methods, Python

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.

Algorithmic Trading, Trading

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.

Derivatives, Numerical Methods, Pricing

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. 

NAG, Numerical Methods

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.

CVXPY, Economics, MPT, Optimization

A complete set of volatility estimators in Python github.com

Published June 30, 2018 under Quant Finance

Python, Volatility

Equity Option Implied Volatility Analytics with Python github.com

Published June 30, 2018 under Quant Finance

Options, Python

Option hedging with LSTM Recurrent Neural Networks ipythonquant.wordpress.com

Published June 8, 2018 under Quant Finance

Options, Python, TensorFlow

Pricing Bermudan Options in TensorFlow ipythonquant.wordpress.com

Published June 8, 2018 under Quant Finance

Exotic Options, Python, TensorFlow

TensorFlow meets Quantitative Finance ipythonquant.wordpress.com

Published June 8, 2018 under Quant Finance

Exotic Options, Python, TensorFlow

A complete set of volatility estimators based on Euan Sinclair’s Volatility Trading github.com

Published January 24, 2018 under Quant Finance

Python, Trading, Volatility

How To Model Volatility Smile In Python quantinsti.com

Published December 19, 2017 under Quant Finance

Options, Python, Quant Finance, Volatility

Popular Python Trading Platforms For Algorithmic Trading quantinsti.com

Published November 21, 2017 under Quant Finance

Python, Quant Finance, Trading

How Can An MBA In Finance Become A Quant? quantinsti.com

Published November 20, 2017 under Quant Finance

Quant Finance

Python Parallel Computing (in 60 Seconds or less) dbader.org

Published October 27, 2017 under Quant Finance

Parallel Computing, Python, Quant Finance

Stock Price Dynamics with Python jtsulliv.github.io

Published October 6, 2017 under Quant Finance

Brownian Motion, Python, Quant Finance

Random walks down Wall Street, Stochastic Processes in Python turingfinance.com

Published January 9, 2017 under Quant Finance

Python, Quant Finance

Bayesian Linear Regression Models with PyMC3 quantstart.com

Published April 30, 2016 under Quant Finance

Pymc3, Python, Quant Finance

Hacking the Random Walk Hypothesis turingfinance.com

Published September 17, 2015 under Quant Finance

Python, Quant Finance

Predicting future returns of trading algorithms: Bayesian cone blog.quantopian.com

Published September 11, 2015 under Quant Finance

Quant Finance, Trading

pyfolio – Performance and Risk Analysis for Quantopian/Zipline quantopian.github.io

Published August 28, 2015 under Quant Finance

Python, Quant Finance

Quasi-Monte Carlo methods for the Heston model arxiv.org

Published July 8, 2015 under Quant Finance

Quant Finance

Non-Gaussianity of the Intraday Returns Distribution and its Evolution in Time arxiv.org

Published July 6, 2015 under Quant Finance

Quant Finance

Predicting Financial Markets – Comparing News, Twitter and Search Engine Data arxiv.org

Published July 6, 2015 under Quant Finance

Quant Finance

Forecasting with Option Implied Information papers.ssrn.com

Published July 6, 2015 under Quant Finance

Quant Finance, Trading

Derivatives Analytics with Python ep2012.europython.eu

Published June 23, 2015 under Quant Finance

Derivatives, Python, Quant Finance

Calculate the skewness of a portfolio of assets quant.stackexchange.com

Published June 17, 2015 under Quant Finance

Quant Finance

Lectures on Financial Economics antoniomele.org

Published June 17, 2015 under Quant Finance

Quant Finance

Hedge Fund Performance and Liquidity Risk papers.ssrn.com

Published June 16, 2015 under Quant Finance

Quant Finance

A Quantitative Approach to Tactical Asset Allocation mebanefaber.com

Published June 15, 2015 under Quant Finance

Quant Finance

Implementing a Principal Component Analysis (PCA) in Python step by step sebastianraschka.com

Published June 12, 2015 under Quant Finance

Quant Finance

Trading with Python tradingwithpython.blogspot.com

Published June 4, 2015 under Quant Finance

Python, Quant Finance, Trading

Marcos Lopez de Prado Quant Research quantresearch.info

Published June 3, 2015 under Quant Finance

Quant Finance

Twitter Increasingly Used By Hedge Funds For Trading Clues valuewalk.com

Published June 1, 2015 under Quant Finance

Quant Finance, Trading

How to Use a Decision Tree to Trade Bank of America Stock inovancetech.com

Published May 20, 2015 under Quant Finance

Quant Finance, Trading

Journal of Algorithmic Finance algorithmicfinance.org

Published April 29, 2015 under Quant Finance

Quant Finance

An open API for company SEC filings kimonolabs.com

Published April 28, 2015 under Quant Finance

API, Data, Quant Finance

Lean Algorithmic Trading Engine by QuantConnect lean.quantconnect.com

Published April 27, 2015 under Quant Finance

C++, Quant Finance, Trading

Volatility estimators in Python github.com

Published April 25, 2015 under Quant Finance

Python, Quant Finance, Trading, Volatility

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