Collaborating Authors

Algorithmic Trading Strategies and Modelling Ideas


'Looks can be deceiving,' a wise person once said. The phrase holds true for Algorithmic Trading Strategies. The term'Algorithmic trading strategies' might sound very fancy or too complicated. However, the concept is very simple to understand, once the basics are clear. In this article, We will be telling you about algorithmic trading strategies with some interesting examples. If you look at it from the outside, an algorithm is just a set of instructions or rules. These set of rules are then used on a stock exchange to automate the execution of orders without human intervention. This concept is called Algorithmic Trading. Popular algorithmic trading strategies used in automated trading are covered in this article.

Computing Robust Counter-Strategies

Neural Information Processing Systems

Adaptation to other initially unknown agents often requires computing an effective counter-strategy. In the Bayesian paradigm, one must find a good counter-strategy to the inferred posterior of the other agents' behavior. In the experts paradigm, one may want to choose experts that are good counter-strategies to the other agents' expected behavior. In this paper we introduce a technique for computing robust counter-strategies for adaptation in multiagent scenarios under a variety of paradigms. The strategies can take advantage of a suspected tendency in the decisions of the other agents, while bounding the worst-case performance when the tendency is not observed.

ALDI – A New Paradigm for Integrating Marketing Analytics with Data Science


Owing to the data deluge and the Cambrian explosion of machine learning techniques over the past decade, one might have expected the transformation of marketing strategy into a predominantly quantitative discipline by now. The fact that it hasn't happened yet, and the observation that marketing is still influenced by a lot of qualitative inputs can be ascribed to two reasons, in my opinion. The first and principal reason continues to be institutional inertia. Second, there is a significant communication and knowledge gap between data scientists and marketers, owing to their relative lack of familiarity with the other side's perspectives and paradigms. The successful marketer of the next decade is someone who is conversant with management theories of Kotler[1] as well as machine learning advances by Hinton[2]/LeCun[3]/ Ng[4].

Refuel 2018 Digital Transformations With Artificial Intelligence (AI), Internet of Things (IoT), and Business Intelligence (BI)


We are no longer merely sharing the road with self-driving cars or having a robotic vacuum clean our floors. Augmented technology has quickly changed our business paradigms, even in digital marketing. Is your digital strategy outdated? Want to be an AI, IoT, BI digital disruption terminator? Here is a 2018 digital transformation travel guide to help refuel your digital strategy.

AI paradigms


The unsupervised learning is used for datasets whose items are not labeled and, therefore, our goal now is just to learn some patterns in the dataset. How do we train an unsupervised model? Using the same example as in supervised learning section, the cacti and Ryan Reynolds are now provided to the model without any label nor train or test split. The fact that the data are not labeled, "Unsupervised paradigm" means that we have no test set. If we have no test set we need to validate our model based on other methods like cluster cohesion.