Artificial Intelligence News & Topics

Artificial Intelligence (AI) takes many forms for the trading industry including electronic trading, quantitative trading strategies, algorithmic trading development and research, risk, compliance, and management. AI refers to simulated intelligence using computer programs. These programs are designed to “think” for the purposes of achieving some tasks. For CloudQuant this task is typically determining a trading signal to initiate an investment or to close out an investment.

Forms of AI include rules-based programming, Machine Learning, and Deep Learning.

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Quantitative Strategies and Capital for Trading

Quantitative Trading and Data Science in the News August 14 2017

Topics include: GeoLocation Alternative Data, robotic revolution, buy side, sell side, hot jobs, financial crime, …
World Market Access

2017 – The Year of Artificial Intelligence

2017 is the year of artificial intelligence. Here’s why

World Economic Forum published that Artificial Intelligence (AI) is a rapidly growing discussion point in corporations and governments. This is driven by: 1. Everything is now becoming a connected device

The internet of things is collecting data in ways never before possible.

2. Computing is becoming free

The cost of computing continues to drop, especially with crowdsourced research platforms like CloudQuant.

3. Data is becoming the new oil

“The amounts and types of data available digitally have proliferated exponentially over the last decade, as everything has moved online, been made mobile with smartphones, and tracked via sensors. New sources of data emerged through things like social media, digital images and video.” 

4. Machine learning is becoming the new combustion engine

“new machine learning models have emerged recently that seem to be able to take better advantage of all the new data. For example, deep learning enables computers to ‘see’ or distinguish objects and text in images and videos much better than before.”

At CloudQuant our crowd researchers are finding that access to markets, and to data is allowing them to research and develop profitable algos in ways never before conceived. Access to new data sets, like social sentiment, allow new dimensions of quantitative strategies that were not conceived even five years ago. We anticipate that the new data “oil” and machine learning “engines” will continue to grow our world of trading.   See the full article on World Economic Forum’s web site by Sandhya Venkatachalam (24 May 2017).
Battle of the Quants June 2017

Battle of The Quants – Discusses Crowd Researching in NY

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Crowdsourcing in fund management and trading is the move to utilize anyone with an internet connection to participate in the research with the goal of finding new and better ways of trading. During the discussion the differing approaches being taken with the business models, and the technology, and the challenges each are facing.
Sample python code from the CloudQuant trading strategy backtesting and trade simulation platform

Code-Dependent: Pros and Cons of the Algorithm Age

Algorithms are aimed at optimizing everything. They can save lives, make things easier and conquer chaos. Still, experts worry they can also put too much control in the hands of corporations and governments, perpetuate bias, create filter bubbles, cut choices, creativity and serendipity, and could result in greater unemployment.

Algorithms are instructions for solving a problem or completing a task. Recipes are algorithms, as are math equations. Computer code is algorithmic. The internet runs on algorithms and all online searching is accomplished through them. Email knows where to go thanks to algorithms. Smartphone apps are nothing but algorithms. Computer and video games are algorithmic storytelling. Online dating and book-recommendation and travel websites would not function without algorithms. GPS mapping systems get people from point A to point B via algorithms. Artificial intelligence (AI) is naught but algorithms. The material people see on social media is brought to them by algorithms. In fact, everything people see and do on the web is a product of algorithms. Every time someone sorts a column in a spreadsheet, algorithms are at play, and most financial transactions today are accomplished by algorithms. Algorithms help gadgets respond to voice commands, recognize faces, sort photos and build and drive cars. Hacking, cyberattacks and cryptographic code-breaking exploit algorithms. Self-learning and self-programming algorithms are now emerging, so it is possible that in the future algorithms will write many if not most algorithms.

Algorithms are often elegant and incredibly useful tools used to accomplish tasks. They are mostly invisible aids, augmenting human lives in increasingly incredible ways. However, sometimes the application of algorithms created with good intentions leads to unintended consequences. Recent news items tie to these concerns:

Read the full article on Pew Research Center
Stock Market, Quantitative Strategy, Trading, and Algo Development Industry News

Social Sentiment in Trading Algorithms

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Bloomberg recently wrote that “It’s no secret that hedge fund managers are always looking for new sources of data that will help them in their never-ending quest to beat the market.” (1) One of the most interesting new sources of data is social sentiment. We have found that the incorporation of social sentiment data is definitely improving the quality of algorithms as shown in our backtesting on CloudQuant. Over the next couple of weeks an intern from the University of Chicago who is mastering in Financial Mathematics is working on incorporating social signals into a DeMark Indicators script that is available for all registered users to see in the CloudQuant base working scripts. I look forward to seeing how this improves. And I look forward to seeing her quantitative reasons for why social sentiment and other changes to the TD Sequential script improves. (1) Finding Novel Ways to Trade on Sentiment Data | Tech At Bloomberg
Quantitative Strategy, Trading, and Algo Development Industry News

Discretionary Managers Seek Alpha in Alternative Data

Alternative data providers see huge potential in providing their data to discretionary asset managers who are losing assets to quantitative and systematic funds.

As active managers trail the performance of passive index funds and exchange-traded funds (ETFs), discretionary fund managers are scrambling to consume big data analytics into their decision making process.
While early movers in the big data analytics industry have mainly been quant hedge funds and systematic fund managers, the next wave is going to be discretionary fund managers, according to panelists at an event sponsored by Wall Street Horizon, EstimizeOTAS Technologies and FlexTrade Systems.
Read the full story on Traders Magazine Online
Stock Market, Quantitative Strategy, Trading, and Algo Development Industry News

Morgan Stanley’s 16,000 Human Brokers Get Algorithmic Makeover

Call them cyborgs. Morgan Stanley is about to augment its 16,000 financial advisers with machine-learning algorithms that suggest trades, take over routine tasks and send reminders when your birthday is near. The project, known internally as “next best action,” shows how one of the world’s biggest brokerages aims to upgrade its workforce while a growing number of firms roll out fully automated platforms called robo-advisers. The thinking is that humans with algorithmic assistants will be a better solution for wealthy families than mere software allocating assets for the masses. At Morgan Stanley, algorithms will send employees multiple-choice recommendations based on things like market changes and events in a client’s life, according to Jeff McMillan, chief analytics and data officer for the bank’s wealth-management division. Phone, email and website interactions will be cataloged so machine-learning programs can track and improve their suggestions over time to generate more business with customers, he said. … Read the full story on Bloomberg
Stock Market, Quantitative Strategy, Trading, and Algo Development Industry News

Machine learning set to shake up equity hedge funds – Financial Times

AI seen becoming powerful enough to forecast market moves better than humans

Financial Times May 25, 2017 by: Lindsay Fortado and Robin Wigglesworth
Machine learning poses a threat to equity hedge funds within the next decade as the technique becomes powerful enough to forecast market moves better than humans, one of the earliest investors in the industry is forecasting. Jeff Tarrant, the founder of Protégé Partners, says that the model of hedge funds charging “2 and 20” — a 2 per cent management fee and 20 per cent performance fee — for investing in large-cap stocks rising and falling “doesn’t work any more” and is ripe for disruption. He pointed to the overhaul of other industries in the past decade at the hands of engineers and scientists. “Jeff Bezos picked off the bookstore business. Apple totally picked off the music business and Netflix totally changed television. Now [machine learning] is going to pick off the hedge funds.”
Read the full story on Financial Times
Python Scripts in CloudQuant's Algorithmic Trading and Quantitative Strategy Backtesting Application

Algo Developers – Entry Level

CloudQuant is THE trading strategy incubator. We’re building a free python data research tool for ordinary people with extraordinary trading ideas. We license and fund the best trading strategies and pay our users a share of the profits. Our group is a FINTECH startup housed under the umbrella of a trading firm with existing infrastructure and financial resources.