Information-driven bars for financial machine learning: imbalance bars

In previous articles we talked about tick bars, volume bars and dollar bars, alternative types of bars which allow market activity-dependent sampling based on the number of ticks, volume or dollar value exchanged. Additionally, we saw how these bars display better statistical properties such as lower serial correlation when compared to traditional time-based bars. In this article we will talk about information-driven bars and specifically about imbalance bars. These bars aim to extract information encoded in the observed sequence of trades and notify us of a change in the imbalance of trades. The early detection of an imbalance change will allow us to anticipate a potential change of trend before reaching a new equilibrium.


recorder: Validate Predictors in New Data

recorder 0.8.1 is now available on CRAN. recorder is a lightweight toolkit to validate new observations before computing their corresponding predictions with a predictive model.
With recorder the validation process consists of two steps:
• record relevant statistics and meta data of the variables in the original training data for the predictive model
• use these data to run a set of basic validation tests on the new set of observations.
Now we will take a deeper look into, what recorder has to offer.


Becoming a machine learning company means investing in foundational technologies

In this post, I share slides and notes from a keynote I gave at the Strata Data Conference in London earlier this year. I will highlight the results of a recent survey on machine learning adoption, and along the way describe recent trends in data and machine learning (ML) within companies. This is a good time to assess enterprise activities, as there are many indications a number of companies are already beginning to use machine learning. For example, in a July 2018 survey that drew more than 11,000 respondents, we found strong engagement among companies: 51% stated they already had machine learning models in production.


Microsoft wants to apply AI ‘to the entire application developer lifecycle’

At its Build 2018 developer conference a year ago, Microsoft previewed Visual Studio IntelliCode, which uses AI to offer intelligent suggestions that improve code quality and productivity. In April, Microsoft launched Visual Studio 2019 for Windows and Mac. At that point, IntelliCode was still an optional extension that Microsoft was openly offering as a preview. But at Build 2019 earlier this month, Microsoft shared that IntelliCode’s capabilities are now generally available for C# and XAML in Visual Studio 2019 and for Java, JavaScript, TypeScript, and Python in Visual Studio Code. Microsoft also now includes IntelliCode by default in Visual Studio 2019.


The human problem of AI

When it comes to most things business, AI is making its mark as the must-have technology. Whether we are talking about customer-facing chatbots to help with engagement and conversion or AI working in the background to help make critical business decisions, AI is everywhere. And the expectations of what it can and should be able to do is often sky-high. When those expectations aren’t met, however, it’s not always the tech that’s to blame. More likely, it’s the humans who brought it on board. Here are some of the most common human errors when it comes to implementing AI.
Mistake #1: Confusing automation with AI
Mistake #2: Not determining success factors
Mistake #3: Not getting organizational buy-in
Mistake #4: Not considering the impact on the entire customer journey
Mistake #5: Not understanding the cause of the problems you’re trying to solve


Computational Socioeconomics

Uncovering the structure of socioeconomic systems and timely estimation of socioeconomic status are significant for economic development. The understanding of socioeconomic processes provides foundations to quantify global economic development, to map regional industrial structure, and to infer individual socioeconomic status. In this review, we will make a brief manifesto about a new interdisciplinary research field named Computational Socioeconomics, followed by detailed introduction about data resources, computational tools, data-driven methods, theoretical models and novel applications at multiple resolutions, including the quantification of global economic inequality and complexity, the map of regional industrial structure and urban perception, the estimation of individual socioeconomic status and demographic, and the real-time monitoring of emergent events. This review, together with pioneering works we have highlighted, will draw increasing interdisciplinary attentions and induce a methodological shift in future socioeconomic studies.


Practical Strategies to Handle Missing Values

One of the major challenges in most data science projects is to figure out a way to get clean data. 60 to 80 percent of the total time is spent on cleaning the data before you can make any meaningful sense of it. This is true for both BI and Predictive Analytics projects. To improve the effectiveness of the data cleaning process, the current trend is to migrate from the manual data cleaning to more intelligent machine learning-based processes.


Writing Quotes like Aristotle with Recurrent Neural Networks

It’s all thanks to a machine learning architecture called known as the Recurrent Neural Network (RNN).


Stock Market Analysis Using ARIMA

Time Series is a big component of our everyday lives. They are in fact used in medicine (EEG analysis), finance (Stock Prices) and electronics (Sensor Data Analysis). Many Machine Learning models have been created in order to tackle these types of tasks, two examples are ARIMA (AutoRegressive Integrated Moving Average) models and RNNs (Recurrent Neural Networks).


A New Way to look at GANs

A Generative Adversarial Network is an extremely interesting deep neural network architecture able to generate new data (often images) that resembles the data given during training (or in mathematical terms, matches the same distribution). Immediately after discovering GANs and how they work, I got intrigued. There is something special, maybe magical, about generating realistic looking images in an unsupervised manner. One area of GAN research that really caught my attention has been image-to-image translation: the ability to turn an image into another image keeping some sort of correspondence (for example turning a horse into a zebra or an apple into an orange). Academic papers like the one introducing CycleGAN (a particular architecture which uses two GANs ‘helping’ each other to perform image to image translation) showed me a powerful and captivating deep learning application that I immediately wanted to try and implement myself.


Enabling Cognitive Visual Question Answering

Exploring a hybrid approach to visual question answering through deeper integration of OpenCog and a Vision Subsystem.


Creative Artificial Intelligence… Towards New Horizons

In the creative world, an evolution is happening thanks to the innovations of machine learning and of deep learning. This change gives a glimpse into unpublished creative horizons in terms of design, cybernetic art, music, writing, imaging and video. This is an unprecedented revolution, leading to an unstoppable change, as well as, a major return to creativity. There is still a question on the lips of many creative people, artists, producers, directors and creative agencies: ‘Are robots will steal our jobs?’ The answer is NO. On the contrary, the emergence of this creative artificial intelligence is going to improve their everyday life. Before understanding the 3 keys of this major return to creativity (creativity, jobs and ethic), understanding the past and the founding events is necessary.
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