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Resources for developers using Python for scientific computing and quantitative analysis

Latest Python Resources (check out PyQuant Books)

Finance Database github.com

Published February 17, 2021 under Investing

As a private investor, the sheer amount of information that can be found on the internet is rather daunting. Trying to understand what type of companies or ETFs are available is incredibly challenging with there being millions of companies amd derivatives available on the market. Sure, the most traded companies and ETFs can quickly be found simply because they are known to the public (for example, Microsoft, Tesla, S&P500 ETF or an All-World ETF). However, what else is out there is often unknown.

Sentiment Analysis for Trading with Reddit Text Data medium.com

Published February 17, 2021 under Data Science

Sentiment Analysis for Trading with Reddit Text Data

In this article the author uses Reddit sentiment data to inform trading strategies. He derives market sentiment in two ways using the wallstreetbets subreddit:

  1. Collecting comments from daily discussion submissions then running the VADER sentiment model to assess overall daily positive/negative sentiment.
  2. Collecting all submission titles per day then assessing daily bullish/bearish sentiment using keyword analysis.

Python, Trading

Python Programming And Numerical Methods: A Guide For Engineers And Scientists berkeley.edu

Published February 17, 2021 under Python

Python Programming And Numerical Methods: A Guide For Engineers And Scientists

For the most part, this book follows the standard material taught at the University of California, Berkeley, in the class E7: Introduction to computer programming for scientists and engineers. This class is taken by most science and engineering freshmen in the College of Engineering, and by undergraduate students from other disciplines, including physics, biology, Earth, and cognitive sciences. The course was originally taught in Matlab, but with the recent trend of the data science movement at Berkeley, the Division of Data Sciences agreed on and supported the transform of this course into a Pythonoriented course to prepare students from different fields for further data science courses. 

Numerical Methods

How to Analyze Volume Profiles With Python medium.com

Published January 21, 2021 under Trading

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.

Python

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

Pandas DataFrame Visualization Tools pbpython.com

Published January 14, 2021 under Python

Pandas DataFrame Visualization Tools

One of Excel’s benefits is that it offers an intuitive and powerful graphical interface for viewing your data. In contrast, pandas + a Jupyter notebook offers a lot of programmatic power but limited abilities to graphically display and manipulate a DataFrame view.

There are several tools in the Python ecosystem that are designed to fill this gap. They range in complexity from simple JavaScript libraries to complex, full-featured data analysis engines. The one common denominator is that they all provide a way to view and selectively filter your data in a graphical format. From this point of commonality they diverge quite a bit in design and functionality.

This article will review several of these DataFrame visualization options in order to give you an idea of the landscape and evaluate which ones might be useful for your analysis process.

Excel, Pandas

Build a Real Time Bitcoin Price Notification Project using Python thecodingpie.com

Published January 14, 2021 under Trading

In this project, we are going to build a python script that will keep track of the latest bitcoin price. And it will send you a telegram message every 30 minutes (you can tweak that) with the latest 6 bitcoin prices (again you can tweak that too). You can set a minimum threshold value so that if the BTC price goes below that threshold, then the script will send an immediate alert message showing the price.

Cryptocurrency

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

NumPy Illustrated: The Visual Guide to NumPy medium.com

Published January 13, 2021 under Python

NumPy Illustrated: The Visual Guide to NumPy

NumPy is a fundamental library that most of the widely used Python data processing libraries are built upon (pandas, OpenCV), inspired by (PyTorch), or can efficiently share data with (TensorFlow, Keras, etc). Understanding how NumPy works gives a boost to your skills in those libraries as well. It is also possible to run NumPy code with no or minimal changes on GPU.

Numpy

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

Build a fully production ready machine learning app with Python Django, React, and Docker towardsdatascience.com

Published October 1, 2020 under Web Development

Build a fully production ready machine learning app with Python Django, React, and Docker

A complete, step by step guide to building a production-grade machine learning app with Django, PostgreSQL, React, Redux and Docker

Machine Learning, ReactJS

Automate Excel with Python Tutorial youtube.com

Published October 1, 2020 under Python

Excel, Pandas

Introducing PyTorch Forecasting towardsdatascience.com

Published October 1, 2020 under Data Science

Introducing PyTorch Forecasting

Forecasting time series is important in many contexts and highly relevant to machine learning practitioners. Take, for example, demand forecasting from which many use cases derive. Almost every manufacturer would benefit from better understanding demand for their products in order to optimise produced quantities. Underproduce and you will lose revenues, overproduce and you will be forced to sell excess produce at a discount. Very related is pricing, which is essentially a demand forecast with a specific focus on price elasticity. Pricing is relevant to virtually all companies.

Forecasting, PyTorch

A Low-Cost Introduction to Machine Learning Training on Microsoft Azure nag.com

Published October 1, 2020 under Machine Learning

This tutorial demonstrates porting an existing machine learning model to a virtual machine on the Microsoft Azure cloud platform. We will train a small movie recommendation model using a single GPU to give personalised recommendations. The total cost of performing this training should be no more than $5 using any of the single GPU instances currently available on Azure.

Azure, NAG

How to build a custom production-ready Deep Learning Training loop in Tensorflow from scratch theaisummer.com

Published October 1, 2020 under Data Science

How to build a custom production-ready Deep Learning Training loop in Tensorflow from scratch

Training is without a doubt the most important part of developing a machine learning application. It’s when you start realizing whether or not your model is worth it, how your hyperparameters should look like and what do you need to change in your architecture. In general, most machine learning engineers spend quite some time on training, experimenting with different models, tuning their architecture and discovering the best metrics and losses for their problem.

Deep Learning, TensorFlow

Scientific Computing in Python: Introduction to NumPy and Matplotlib sebastianraschka.com

Published October 1, 2020 under Data Science

Scientific Computing in Python: Introduction to NumPy and Matplotlib

Numerical Methods, Numpy

Making Concurrent HTTP requests with Python AsyncIO laac.dev

Published October 1, 2020 under Web Development

Making Concurrent HTTP requests with Python AsyncIO

We’ll demonstrate the usage of concurrent HTTP requests by fetching prices for stock tickers. The only third party package we’ll use is httpx. Httpx is very similar to the popular requests package, but httpx supports asyncio.

Investing, Trading

Machine Learning from Scratch github.io

Published September 20, 2020 under Data Science

Machine Learning from Scratch

This book covers the building blocks of the most common methods in machine learning. This set of methods is like a toolbox for machine learning engineers. Those entering the field of machine learning should feel comfortable with this toolbox so they have the right tool for a variety of tasks. Each chapter in this book corresponds to a single machine learning method or group of methods. In other words, each chapter focuses on a single tool within the ML toolbox.

Ebook, Free, Machine Learning

Algorithmic Trading Using Logistic Regression handsoffinvesting.com

Published September 20, 2020 under Machine Learning

Algorithmic Trading Using Logistic Regression

In order to implement an algorithmic trading strategy though, you have to first narrow down a list of stocks that you want to analyze. This walk-through provides an automated process (using python and logistic regression) for determining the best stocks to algo-trade.

I will dive deeper into the logic and code below, but here is a high-level overview of the process:

  1. Import the historical data of every stock using yahoo finance.
  2. Pull in over 32 technical indicators for each stock using the technical analysis library.
  3. Perform a logistic regression on each stock using 5, 30, and 60 day observation time periods.
  4. Interpret the results.

Algorithmic Trading, Quant Trading

Array programming with NumPy nature.com

Published September 20, 2020 under Python

Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. 

Numerical Methods, Numpy

Time-Series Forecasting with TensorFlow 2.0 theclickreader.com

Published September 7, 2020 under Machine Learning

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.

Python, Time Series Analysis

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

NumPy Fundamentals for Data Science and Machine Learning github.io

Published July 28, 2020 under Python

NumPy Fundamentals for Data Science and Machine Learning

This post reviews NumPy main components and functionality, with attention to the needs of Data Science and Machine Learning practitioners, and people who aspire to become a data professional. 

Data Science, Machine Learning, Numpy

Building AI Trading Systems dennybritz.com

Published July 13, 2020 under Machine Learning

Lessons learned building a profitable algorithmic trading system using Reinforcement Learning techniques.

Algorithmic Trading, Cryptocurrency, Reinforcement Learning

Plotting in Pandas Just Got Prettier towardsdatascience.com

Published July 13, 2020 under Data Science

Plotting in Pandas Just Got Prettier

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.

Data Visualization, Python

Stock Analysis in Python deepnote.com

Published July 13, 2020 under Trading

Stock Analysis in Python

It’s easy to get carried away with the wealth of data and free open-source tools available for data science. After spending a little bit of time with the quandl financial library and the prophet modeling library, I decided to try some simple stock data exploration. Several days and 1000 lines of Python later, I ended up with a complete stock analysis and prediction tool. Although I am not confident (or foolish) enough to use it to invest in individual stocks, I learned a ton of Python in the process and in the spirit of open-source, want to share my results and code so others can benefit.

Quant Trading, Time Series Analysis

Machine Learning Financial Laboratory (mlfinlab) readthedocs.io

Published May 12, 2020 under Machine Learning

Machine Learning Financial Laboratory (mlfinlab)

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.

Algorithmic Trading, Python

Designing an energy arbitrage strategy with linear programming https://www.steveklosterman.com

Published May 12, 2020 under Python

Designing an energy arbitrage strategy with linear programming

The price of energy changes hourly, which opens up the possibility of temporal arbitrage: buying energy at a low price, storing it, and selling it later at a higher price. To successfully execute any temporal arbitrage strategy, some amount of confidence in future prices is required, to be able to expect to make a profit. In the case of energy arbitrage, the constraints of the energy storage system must also be considered. For example, batteries have limited capacity, limited rate of charging, and are not 100% efficient in that not all of the energy used to charge a battery will be available later for discharge.

Quant Trading, Trading

Machine Learning & Deep Learning Fundamentals https://deeplizard.com/learn/playlist/PLZbbT5o_s2xq7LwI2y8_QtvuXZedL6tQU

Published May 12, 2020 under Machine Learning

This series explains concepts that are fundamental to deep learning and artificial neural networks for beginners. In addition to covering these concepts, we also show how to implement some of the concepts in code using Keras, a neural network API written in Python. We will learn about layers in an artificial neural network, activation functions, backpropagation, convolutional neural networks (CNNs), data augmentation, transfer learning and much more!

Deep Learning

How to Download Stock Fundamentals Data with Python theautomatic.net

Published May 6, 2020 under Python

How to Download Stock Fundamentals Data with Python

In this post we will explore how to download fundamentals data with Python. We’ll be extracting fundamentals data from Yahoo Finance using the yahoo_fin package. For more on yahoo_fin, including installation instructions, check out its full documentation here.

Investing, Trading

Free foreign exchange rates API exchangerate.host

Published May 3, 2020 under Python

Exchange rates API is a simple and lightweight free service for current and historical foreign exchange rates.

API

Coding a Python Stock Trading bot with Alpaca youtube.com

Published May 3, 2020 under Trading

Algorithmic Trading, Python

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

Analyzing the Impact of Coronavirus on the Stock Market using Python, Google Sheets and Google Finance adilmoujahid.com

Published April 18, 2020 under Investing

Analyzing the Impact of Coronavirus on the Stock Market using Python, Google Sheets and Google Finance

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.

Data Science, Python

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

How to use deep learning for data extraction from financial documents nanonets.com

Published March 24, 2020 under Neural Networks

How to use deep learning for data extraction from financial documents

If you have a relative working in the banking industry, ask the person what annoys him/her most about the job. You will surely receive an answer that is related to the task of data entry i.e. the practice of manually entering serial numbers and names from financial documents into the bank’s database.

Computer Vision, Deep Learning, Neural Network

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

PyKrylov: Accelerating Machine Learning Research at eBay ebayinc.com

Published February 11, 2020 under Machine Learning

PyKrylov: Accelerating Machine Learning Research at eBay

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.

Open Source, Python

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

Stock Trend Prediction with Technical Indicators medium.com

Published January 18, 2020 under Data Science

Stock Trend Prediction with Technical Indicators

Predictive model to correctly forecast future trend is crucial for investment management and algorithmic trading. The use of technical indicators for financial forecasting is quite common among the traders. Input window length is a time frame parameter required to be set when calculating many technical indicators.

Classification, Factor Analysis, Trading

Automating an Insider Trading Dashboard with Python and Tableau | Part 2: Collecting Live Stock Data youtube.com

Published January 8, 2020 under Python

Algorithmic Trading

Automating an Insider Trading Dashboard with Python and Tableau: Part 1 youtube.com

Published January 8, 2020 under Python

Algorithmic Trading

Building & Deploying End-to-end Fake News Classifier hatem-hassan.com

Published January 8, 2020 under Data Science

Building & Deploying End-to-end Fake News Classifier

Our problem here is to define whether or not a certain news article is fake news. The dataset is comprised of 3997 news articles each includes a title, text, and the target label as a REAL/FAKE binary label. Part of the course was also testing the model on a test dataset but I never received target for this dataset. The accuracy score of cross validation testing within the training dataset was 94%.

NLP

Open Registration for Loominus Data Science Platform – Use it Free loominus.ai

Published December 15, 2019 under Machine Learning

Loominus has opened up registration and is offering free accounts for a limited time. There’s a whole slew of new features including private data repos, data pipeline cloning, automated data pipelines, enhanced column type detection, UI/UX improvements, detailed information for active tasks, model stream updates and updated API documentation.

Loominus is an end-to-end platform that helps teams ingest and stage data, build advanced machine learning models with no code and deploy them into production. Loominus makes it easy for individuals and teams without experience building machine learning pipelines to take advantage of machine learning faster. Loominus is equally great for experienced data scientists that need to focus on model selection and tuning.

Data Science, Loominus

Forecasting in Python with Facebook Prophet towardsdatascience.com

Published December 8, 2019 under Data Science

Facebook, Prophet, Time Series Analysis

Using Convolutional Neural Networks to Classify Street Signs medium.com

Published December 8, 2019 under Machine Learning

Since the invention of the automobile, manufacturers have steadily added more safety features and improved car design over time with the goal of keeping drivers safer on the road. Automotive manufacturers have spent millions of dollars researching safety improvements for seatbelts, tires, and pretty much every car piece or part imaginable. Despite all of this investment, driving remains substantially more fatal than alternatives such as air travel in 2019. According to the National Safety Council, approximately 40,000 people died in automotive accidents in the United States alone in 2018. In fact, there were a total of ~500 deaths resulting from plane crashes recorded globally in 2018 — that’s 80 times fewer deaths when compared to car crash fatalities in the US only.

Computer Vision, Convolutional NN, Neural Network

Lessons learned building an ML trading system that turned $5k into $200k tradientblog.com

Published December 8, 2019 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.

Algorithmic Trading, Cryptocurrency

Tips for Selecting Columns in a DataFrame pbpython.com

Published December 8, 2019 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.

Data Science, Pandas

End to End Machine Learning: From Data Collection to Deployment ahmedbesbes.com

Published December 8, 2019 under Machine Learning

Python

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