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

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.
Quantitative Strategy, Trading, and Algo Development Industry News

Finding Novel Ways to Trade on Sentiment Data

“Everyone is looking into alternative data sets, sometimes without really understanding their value,” says Dr. Arun Verma, Ph.D.,
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.
Quantitative Strategies and Capital for Trading

Funds Face ‘Alt’ Data Challenge

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.