PyQuant News

Resources for developers using Python for scientific computing and quantitative analysis

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. 

under Computer Vision

This article describes how to to use Microsoft Azure’s Cognitive Services Face API and python to identify, count and classify people in a picture. In addition, it will show how to use the service to compare two face images and tell if they are the same person. We will try it out with several celebrity look-alikes to see if the algorithm can tell the difference between two similar Hollywood actors. By the end of the article, you should be able to use these examples to further explore Azure’s Cognitive Services with python and incorporate them in your own projects.

under Data Science

Machine learning is pretty undeniably the hottest topic in data science right now. It’s also the basic concept that underpins some of the most exciting areas in technology, like self-driving cars and predictive analytics. Searches for Machine Learning on Google hit an all-time-high in April of 2019, and they interest hasn’t declined much since.

under Computer Vision

But like in most cities, finding a parking space here is always frustrating. Spots get snapped up quickly and even if you have a dedicated parking space for yourself, it’s hard for friends to drop by since they can’t find a place to park.

My solution was to point a camera out the window and use deep learning to have my computer text me when a new parking spot opens up.

Keras Mask R-CNN pyimagesearch.com

under Machine Learning

In this tutorial, you will learn how to use Keras and Mask R-CNN to perform instance segmentation (both with and without a GPU).

Using Mask R-CNN we can perform both: Object detection, giving us the (x, y)-bounding box coordinates of for each object in an image; Instance segmentation, enabling us to obtain a pixel-wise mask for each individual object in an image.

under Machine Learning

In this tutorial, you will learn how to perform fine-tuning with Keras and Deep Learning.

We will take a CNN pre-trained on the ImageNet dataset and fine-tune it to perform image classification and recognize classes it was never trained on.

Today is the final post in our three-part series on fine-tuning:

  1. Part #1: Transfer learning with Keras and Deep Learning
  2. Part #2: Feature extraction with on large datasets with Keras and Deep Learning
  3. Part #3: Fine-tuning with Keras and Deep Learning (today’s post)

under Machine Learning

People often complain about important subjects being covered too little in the news. One such subject is climate change. The scientific consensus is that this is an important problem, and it stands to reason that the more people are aware of it, the better our chances may be of solving it. But how can we assess how widely covered climate change is by various media outlets? We can use Python to do some text analysis!

under Data Science

The pandas library is a powerful tool for multiple phases of the data science workflow, including data cleaning, visualization, and exploratory data analysis. However, the size and complexity of the pandas library makes it challenging to discover the best way to accomplish any given task.

under Neural Networks

There’s been a lot of buzz about Convolution Neural Networks (CNNs) in the past few years, especially because of how they’ve revolutionized the field of Computer Vision. In this post, we’ll build on a basic background knowledge of neural networks and explore what CNNs are, understand how they work, and build a real one from scratch (using only numpy) in Python.

under Data Science

The Pattern library is a multipurpose library capable of handling the following tasks:

  • Natural Language Processing: Performing tasks such as tokenization, stemming, POS tagging, sentiment analysis, etc.
  • Data Mining: It contains APIs to mine data from sites like Twitter, Facebook, Wikipedia, etc.
  • Machine Learning: Contains machine learning models such as SVM, KNN, and perceptron, which can be used for classification, regression, and clustering tasks.

In this article, we will see the first two applications of the Pattern library from the above list. We will explore the use of the Pattern Library for NLP by performing tasks such as tokenization, stemming and sentiment analysis. We will also see how the Pattern library can be used for web mining.

Python at Netflix medium.com

under Python

As many of us prepare to go to PyCon, we wanted to share a sampling of how Python is used at Netflix. We use Python through the full content lifecycle, from deciding which content to fund all the way to operating the CDN that serves the final video to 148 million members. We use and contribute to many open-source Python packages, some of which are mentioned below. If any of this interests you, check out the jobs site or find us at PyCon. We have donated a few Netflix Originals posters to the PyLadies Auction and look forward to seeing you all there.

End-To-End Topic Modeling in Python towardsdatascience.com

under Data Science

Topic Model: In a nutshell, it is a type of statistical model used for tagging abstract “topics” that occur in a collection of documents that best represents the information in them.

Many techniques are used to obtain topic models. This post aims to demonstrate the implementation of LDA: a widely used topic modeling technique.

When to ‘Buy the Dip’ towardsdatascience.com

under Trading

“Buy the dip” — it’s a frustratingly simple piece of advice. Like most pieces of advice, it’s easier said than done and the giver of such advice has probably not attempted to practice what they preach. It induces FOMO, which leads to the “hope trade”, when the “hope trade” goes awry you’re stuck as the “long term investor” who “really believes in the company’s mission”.

under Trading

The bot is written in Python and relies on two core libraries for the majority of its functionality: robin-stocks and ta. robin-stocks is a library that interacts with the Robinhood API and allows one to execute buy and sell orders, get real time ticker information, and more. ta is a technical analysis library that also incorporates the Python Pandas library to generate indicators from stock data.

under Data Science

In this post, we’ll learn about Pandas, a high-performance open-source package for doing data analysis in Python.

We’ll cover:

  • What Pandas is and why should you use it.
  • What a Pandas DataFrame is.
  • Creating and viewing a DataFrame.
  • Manipulating data in a DataFrame.

under Python

A curated list of awesome resources for practicing data science using Python, including not only libraries, but also links to tutorials, code snippets, blog posts and talks. So. Much. Python.

Vaex: A DataFrame with super strings towardsdatascience.com

under Data Science

String manipulations are an essential part of Data Science. The latest release of Vaex adds incredibly fast and memory efficient support for all common string manipulations. Compared to Pandas, the most popular DataFrame library in the Python ecosystem, string operations are up to ~30–100x faster on your quadcore laptop, and up to a 1000 times faster on a 32 core machine.

under Python

The very basic idea of anomalies is really centered around two values – extremely high values and extremely low values. Then why are they given importance? In this article, we will try to investigate questions like this. We will see how they are created/generated, why they are important to consider while developing machine learning models, how they can be detected.

under Data Science

In this article, we will explore TextBlob, which is another extremely powerful NLP library for Python. TextBlob is built upon NLTK and provides an easy to use interface to the NLTK library. We will see how TextBlob can be used to perform a variety of NLP tasks ranging from parts-of-speech tagging to sentiment analysis, and language translation to text classification.