Resources for developers using Python for scientific computing and quantitative analysis
Maybe you’re not a software architecture wonk aficionado. That’s fair: I’d speculate that’s the exception rather than the rule for folks using pandas. This is not a deep dive into how the extension mechanisms are implemented. Interesting in their own right but out of the intended scope.
By the end of this article, you will be able to write an information extraction NLP pipeline using spaCy’s Matcher. It will extract dividend information from news headlines and articles.
You should be interested in this article if you like algorithmic trading and stock screening using Python. Mining unstructured text data for insights is what could set your algorithm apart.
GS Quant is a Python toolkit for quantitative finance, created on top of one of the world’s most powerful risk transfer platforms. Designed to accelerate development of quantitative trading strategies and risk management solutions, crafted over 25 years of experience navigating global markets. Designed and used by the quants at Goldman Sachs.
In this lecture, we review the theory behind Metropolis Monte Carlo modeling and apply these concepts to the simulations of atomic systems.
The Shapley value is a method used in game theory that involves fairly distributing both gains and costs to actors working in a coalition.
Since each actor contributes differently to the coalition, the Shapley value makes sure that each actor gets a fair share depending on how much they contribute.
This tutorial demonstrates how to apply clustering algorithms with Python to a dataset with two concrete use cases. The first example uses clustering to identify meaningful groups of Greco-Roman authors based on their publications and their reception. The second use case applies clustering algorithms to textual data in order to discover thematic groups. After finishing this tutorial, you will be able to use clustering in Python with Scikit-learn applied to your own data, adding an invaluable method to your toolbox for exploratory data analysis.
The Alpaca brokerage service is very useful for algorithmic traders that comes with an API to retrieve data and execute trades in a paper or live environment. While you can also check the status and returns of your positions through the API, Alpaca has spent some time creating a frontend where users can visually check their live and paper accounts. Seeing that Alpaca is more focused on building out robust API’s for its users, the frontend is very simple and provides the bare necessities.
In this article the author uses Reddit sentiment data to inform trading strategies. He derives market sentiment in two ways using the wallstreetbets subreddit:
When trading in markets such as equities or currencies it is important to identify value areas to inform our trading decisions. One way to do this is by looking at the volume profile.
In this post, we explore quantitative methods for examining the distribution of volume over a period of time.
More specifically, we’ll be using Python and statistical and signal processing tools in SciPy’s suite of modules. Data plots are rendered with Plotly.
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:
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.
In this tutorial, you will be learning how to build powerful time-series forecasting model of your own using various kinds of deep learning algorithms such as Dense Neural Networks (DNN), Convolutional Neural Network (CNN) and Recurrent Neural Networks (RNN). Also, this course is an elaboration of the time-series forecasting tutorial by TensorFlow.
The plotting functionality in the popular Python data analysis library Pandas has always been one of my go-to methods for super quick charts. However, the available visualisations have always been fairly basic and not particularly pretty.
MlFinlab is a python package which helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools. Adding MlFinLab to your companies pipeline is like adding a department of PhD researchers to your team.
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
Since its emergence in Asia late 2019, the coronavirus COVID-19 pandemic has been devastating. The virus spread to most countries causing severe respiratory infections and many human casualties. The virus also put half of the world population in lockdown which resulted in a slowdown of the world economy and a fall in stock prices.
The goal of this tutorial is to introduce the steps for collecting and analyzing stock data in the context of the coronavirus pandemic. To do this, we will use Python, Google Sheets and Google 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.
The experience while accessing the AI platform and running machine learning (ML) training code on the platform must be smooth and easy for the researchers. Migrating any ML code from a local environment to the platform should not require any refactoring of the code at all. Infrastructure configuration overhead should be minimal. Our mission while developing PyKrylov was to abstract the ML logic from the infrastructure and Krylov core components as much as possible in order to achieve the best experience for the platform users.
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.
This is a collection of Jupyter notebooks based on different topics in the area of quantitative finance. Wow!
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.
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:
The code in this tutorial is available here.
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.
In this tutorial you will learn how to use OpenCV to stream video from a webcam to a web browser/HTML page using Flask and Python.
Python’s pandas library is one of the things that makes Python a great programming language for data analysis. Pandas makes importing, analyzing, and visualizing data much easier. It builds on packages like NumPy and matplotlib to give you a single, convenient, place to do most of your data analysis and visualization work.
With Python code to scrape, extract, transform and load it into a HDF5 data store to please your future self.
If you think Bayes’ theorem is counter-intuitive and Bayesian statistics, which builds upon Baye’s theorem, can be very hard to understand. I am with you.
There are countless reasons why we should learn Bayesian statistics, in particular, Bayesian statistics is emerging as a powerful framework to express and understand next-generation deep neural networks.
What it sounds like 🙂
In today’s tutorial, you will learn how to use Keras’ ImageDataGenerator class to perform data augmentation. I’ll also dispel common confusions surrounding what data augmentation is, why we use data augmentation, and what it does/does not do.
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.
For roughly $100 USD, you can add deep learning to an embedded system or your next internet-of-things project.
Are you just getting started with machine/deep learning, TensorFlow, or Raspberry Pi? Perfect, this blog series is for you!
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.
A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python
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!
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.
This post will go through the process of gathering and cleaning this data followed by an exploratory analysis examining price trends and the impact of events on prices using data from the IEX API and scraped events from financial news sites.
PyTorch is the fastest growing Deep Learning framework and it is also used by Fast.ai in its MOOC, Deep Learning for Coders and its library. PyTorch is also very pythonic, meaning, it feels more natural to use it if you already are a Python developer.
“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”.
Cropping, Resizing, Rotating, Thresholding, Blurring, Drawing & Writing on an image, Face Detection & Contouring to detect objects. All Explained.
A tutorial on how to build a GitHub App that predicts and applies issue labels using Tensorflow and public datasets.
Tutorials covering how to do sentiment analysis using PyTorch 1.0 and TorchText 0.3 using Python 3.7.
The first 2 tutorials will cover getting started with the de facto approach to sentiment analysis: recurrent neural networks (RNNs). The third notebook covers the FastText model and the final covers a convolutional neural network (CNN) model.
Get a comprehensive, in-depth introduction to the core Python language with this hands-on book. Based on author Mark Lutz’s popular training course, this updated fifth edition will help you quickly write efficient, high-quality code with Python. It’s an ideal way to begin, whether you’re new to programming or a professional developer versed in other languages.
