Alternative Data (AltData)

CloudQuant Showcases AltData

Alternative data, sometimes called AltData, provides algorithmic trading developers potential to improve quantitative and systematic trading strategies by providing a new source of trading signals.

CloudQuant has multiple sources of alternative data that includes News Sentiment and social sentiment from StockTwits and Twitter.

Users are also encouraged to upload their own alternative data into their private data directories for additional research.

If you have other AltData that you would like to have in CloudQuant please let us by contacting customer_success@cloudquant.com

See the CloudQuant Alternative Data Set Catalog/Library.

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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
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