Data Sets

Data sets drive the quantitative researcher. Standard data sets, like historical market data, and alternative data sets, like social sentiment, allow the quant to search for trading signals in the data.

Posts

Newsweek AI Data Science for Capital Markets

Newsweek Event: Artificial Intelligence and Data Science (December 5th to 7th, 2017)

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CloudQuant will be participating in the Newsweek conference on Artificial Intelligence and Data Science for the Capital Markets Industry on December 5th to December 7th, 2017 in New York.
Quantamental alternative data

The Rise of Quants in Trading and Financial Markets

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Cloud computing and access to industrial grade investment and data science tools are changing the playing field for quantitative trading firms. CloudQuant’s CEO Morgan Slade participated in a panel at Stocktoberfest West in October 2017. This has raised the discussion of quantamental investment and data science techniques. This is the merger of technology, investment management, and data science.
World Market Access

Futures Radio Show from FIA Expo 2017

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An interview by Anthony Crudele of Futures Radio Show discussing the success of the Trading Strategy Incubator, crowd researching, and algorithmic trading.
Crowdsourcing Algorithmic Research

CloudQuant at FIA Expo in Chicago: perfect storm for open source revolution in quant trading

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The impact of machine learning and open source resources on quant trading could be described as explosive. At FIA Expo in Chicago, CloudQuant’s CEO Morgan Slade will be discussing how that’s translating into opportunity for a wider variety of participants.
Morgan Slade, Python Data Scientist and Trader

Quant Trading and Superpowers: Morgan Slade speaks on Opportunity

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“You have a chance to try and change an industry” said Slade, CEO of CloudQuant at the MarketsWiki Education’s World of Opportunity event in New York.

CloudQuant Launches with Unprecedented Risk Capital Allocation to Crowd Researcher

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CloudQuant, the trading strategy incubator, has launched its crowd research platform by licensing and allocating risk capital to a trading algorithm. The algorithm licensor will receive a direct share of the strategy’s monthly net trading profits.
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

Finding Novel Ways to Trade on Sentiment Data

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. Quantitative researchers at Bloomberg have been developing innovative methods to help reveal embedded signals in one of the more popular sources of unconventional financial data: sentiment analysis of news stories and social posts. “Everyone is looking into alternative data sets, sometimes without really understanding their value,” says Dr. Arun Verma, Ph.D., a researcher who leads the Quant solutions team within Bloomberg’s Quantitative Research group, which is headed by Bruno Dupire. “They are looking at data like sentiment, supply chain relationships, and even things like satellite imagery. Often Machine Learning methods are applied to optimize alpha from such data, but a lack of scientific rigor can lead to poor out of sample performance. We avoid the trap of extreme data mining by using robust statistics.” Read the full story on Tech at Bloomberg, June 14 2017 Alternative Data sets from Bloomberg for social sentiment making its way into algorithmic trading. At Cloudquant have been using it in our backtesting with the intention of improving quantitative trading strategies.     
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
Quantitative Strategies and Capital for Trading

Funds Face ‘Alt’ Data Challenge

MarketsMedia by By Rob Daly on 5/18/2017
Although alternative data sets are helping funds with systematic investment strategies, those funds that employ discretionary strategies are finding it harder to separate the new trading signals from the noise.

Much of it comes from the structure of the discretionary funds, which have separated their data science/quant research teams away from their portfolio managers, according to Leigh Drogen, CEO of Estimize and who participated in an alternative data panel hosted by Wall Street Horizon.

“They are left sending reports and Excel spreadsheets to the portfolio managers and asking them to buy in with P&L,” he said. “It’s almost like they were a sell-side shop.”

Even if a managing partner and head quant are convinced that new data sets can capture further alpha, the portfolio managers have to buy into it.

Read the full story on MarketsMedia