PyQuant News

Resources for developers using Python for scientific computing and quantitative analysis

under Trading

A common misconception is that the market cannot be predicted and that hedge fund managers are no better than dart-throwing monkeys. Many academic research papers back up this claim with data. This is an overly simplistic view. Just because some markets cannot be predicted under some experimental settings, such as equities traded on a daily basis, this does not mean no market can be predicted in any setting. Let us try to get an intuitive understanding of what it means to predict the market.

under Python

This article will discuss several tips and shortcuts for using iloc to work with a data set that has a large number of columns. Even if you have some experience with using iloc you should learn a couple of helpful tricks to speed up your own analysis and avoid typing lots of column names in your code.

under Data Science

It’s hard to imagine a modern, tech-literate business that doesn’t use data analysis, data science, machine learning, or artificial intelligence in some form. NumPy is at the core of all of those fields.

under Machine Learning

From GPS navigation to network-layer link-state routing, Dijkstra’s Algorithm powers some of the most taken-for-granted modern services. Utilizing some basic data structures, let’s get an understanding of what it does, how it accomplishes its goal, and how to implement it in Python (first naively, and then with good asymptotic runtime!)

under Machine Learning

This April a 1.5 billion dollar medicare scheme took advantage of hundreds of thousands of seniors in the US. In reality, this is just a small sliver of the billions of dollars healthcare fraud costs both consumers and insurance providers annually.

Healthcare fraud can come from many different directions. Some people might think of the patient who pretends to be injured, but actually, much of fraud is caused by providers(as in the NYT article).

Providers often have financial incentives for increasing performing unnecessary surgeries or claiming work they never even did. This leads to many different flavors of fraud that can all be difficult to detect on a claim by claim basis.

under Python

Investing was always associated with large amounts of money, both in terms of the invested amount as well as costs associated with it. Here at BUX, we want to make investing accessible to everyone. That is why we recently launched BUX Zero in the Netherlands and other European countries will follow soon! BUX Zero is a zero-commission stock trading app, which makes investing not only accessible but also easy to do directly from your phone.

under Machine Learning

Bayesian Optimization provides a principled technique based on Bayes Theorem to direct a search of a global optimization problem that is efficient and effective. It works by building a probabilistic model of the objective function, called the surrogate function, that is then searched efficiently with an acquisition function before candidate samples are chosen for evaluation on the real objective function.

under Machine Learning

A large amount of data that is generated today is unstructured, which requires processing to generate insights. Some examples of unstructured data are news articles, posts on social media, and search history. The process of analyzing natural language and making sense out of it falls under the field of Natural Language Processing (NLP). Sentiment analysis is a common NLP task, which involves classifying texts or parts of texts into a pre-defined sentiment. You will use the Natural Language Toolkit (NLTK), a commonly used NLP library in Python, to analyze textual data.

under Data Science

In this post, we are going to work with Pandas iloc, and loc. More specifically, we are going to learn slicing and indexing by iloc and loc examples. 

Once we have a dataset loaded as a Pandas dataframe, we often want to start accessing specific parts of the data based on some criteria. For instance, if our dataset contains the result of an experiment comparing different experimental groups, we may want to calculate descriptive statistics for each experimental group separately.

under Python

In this tutorial, we’re going to dig into how to transform data using Python scripts and the command line.

But first, it’s worth asking the question you may be thinking: “How does Python fit into the command line and why would I ever want to interact with Python using the command line when I know I can do all my data science work using IPython notebooks or Jupyter lab?”

Notebooks are great for quick data visualization and exploration, but Python scripts are the way to put anything we learn into production. Let’s say you want to make a website to help people make Hacker News posts with ideal headlines and submission times. To do this, you’ll need scripts.

Logistic Regression Models in Scikit-Learn https://towardsdatascience.com/dont-sweat-the-solver-stuff-aea7cddc3451

under Machine Learning

Logistic regression is the bread-and-butter algorithm for machine learning classification. If you’re a practicing or aspiring data scientist, you’ll want to know the ins and outs of how to use it. Also, Scikit-learn’s LogisticRegression is spitting out warnings about changing the default solver, so this is a great time to learn when to use which solver. 😀

under Machine Learning

TensorFlow 2 is now live! This tutorial walks you through the process of building a simple CIFAR-10 image classifier using deep learning. In this tutorial, we will:

  • Define a model
  • Set up a data pipeline
  • Train the model
  • Accelerate training speed with multiple GPUs
  • Add callbacks for monitoring progress/updating learning schedules

The code in this tutorial is available here.

Open Source Onesies opensourceonesies.com

under Programming

Onesies with logos of open source software. Your favorite open source software for your favorite munchkin.

under Python

Although there are an increasing number of commercial AutoML products, the open-source ecosystem has been innovating here as well. In the early days of the AutoML movement, the focus was on those looking to leverage the power of ML models without a background in data science – citizen data scientists. Today, however, AutoML tools have a lot to offer experts too. 

under Machine Learning

In this article, we’ll use some basic machine learning methods to train a bot to play cards against me. The card game that I’m interested in is called Literature, a game similar to Go Fish.

The version of Literature that we implemented is roughly similar to the rules I linked above. Literature is played in two teams, and the teams compete to collect “sets.” A set is a collection of either A – 6 of a suit or 8 – K of a suit (7’s are not included in the game).

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 Python

An introduction to running parallel tasks with Celery, plus how and why we built an API on top of Celery’s Canvas task primitives.

One of the technology goals of Zymergen is to empower biologists to explore genetic edits of microbes in a high throughput and highly automated manner. The Computational Biology team at Zymergen is responsible for building software to help scientists design and execute these genetic edits. (For a brief overview, see our Zymergen 101 tutorial).

under Machine Learning

The Machine Learning team at commercetools is excited to release the beta version of our new Image Search API.

Image search (sometimes called reverse image search) is a tool, where given an image as a query, a duplicate or similar image is returned as a response. The technology driving this search engine is called computer vision, and advancements in this field are giving way to some compelling product features.

under Data Science

What is Pyjanitor? Before we continue learning on how to use Pandas and Pyjanitor to clean our datasets, we will learn about this package. The python package Pyjanitor extends Pandas with a verb-based API. This easy to use API is providing us with convenient data cleaning techniques. Apparently, it started out as a port of the R package janitor. Furthermore, it is inspired by the ease-of-use and expressiveness of the r-package dplyr. Note, there are some different ways how to work with the methods and this post will not cover all of them (see the documentation).

under Machine Learning

In this post, which can be read as a follow up to our ultimate web scraping guide, we will cover almost all the tools Python offers you to web scrape. We will go from the more basic to the most advanced one and will cover the pros and cons of each. Of course, we won’t be able to cover all aspect of every tool we discuss, but this post should be enough to have a good idea of which tools does what, and when to use which.

under Data Science

One of the most common mistakes data scientists make when training machine learning models is incorrectly splitting data for training and testing. The train/test split involves splitting data during the model training and evaluation process.

Learner makes this simple with a single parameter selection during the model building process. It’s also simple to set the percentage split between training and testing data for each model trained. 

under Books

Systematic trading allows you to test and evaluate your trading ideas before risking your money. By formulating trading ideas as concrete rules, you can evaluate past performance and draw conclusions about the viability of your trading plan. 
Following systematic rules provides a consistent approach where you will have some degree of predictability of returns, and perhaps more importantly, it takes emotions and second guessing out of the equation.
From the onset, getting started with professional grade development and backtesting of systematic strategies can seem daunting. Many resort to simplified software which will limit your potential. 

under Neural Networks

Transfer learning is a powerful technique for training deep neural networks that allows one to take knowledge learned about one deep learning problem and apply it to a different, yet similar learning problem.

Using transfer learning can dramatically speed up the rate of deployment for an app you are designing, making both the training and implementation of your deep neural network simpler and easier.

under Data Science

This article introduces how to build a Python and Flask based web application for performing text analytics on internet resources such as blog pages. To perform text analytics I will utilizing Requests for fetching web pages, BeautifulSoup for parsing html and extracting the viewable text and, apply the TextBlob package to calculate a few sentiment scores.  The code for this article is hosted on GitHub so please fork and experiment with it.

under Data Science

Data pipelines are where most of the time is spent for those working with data because the bulk of a machine learning project involves data collection and cleaning. Loominus gives everyone the power to build the data pipelines critical to any machine learning project. 

Teraport is a powerful tool within the Loominus product suite that ingests and stages data. In another post, we’ll discuss the data ingestion APIs. For now we’ll focus on building a powerful data pipeline for feature engineering.

under Data Science

Hugging Face, the NLP startup behind several social AI apps and open source libraries such as PyTorch BERT, just released a new python library called PyTorch Transformers.

Transformers are a new set of techniques used to train highly performing and efficient models for performing natural language processing (NLP) and natural language understanding (NLU) tasks such as questions answering and sentiment analysis. Several of the recent techniques used to improve and advance the performance of NLP models, such as XLNet and BERT, are all based on a variation of Transformer.