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I am wondering if anyone has used NoSQL ... to store and analyze data. Yes. Have a look at arctic on github. This is an open-source API built on top of MongoDB, that is in production use by one of the largest hedge funds in the world, for storing time-series data. I would imagine that NoSQL would be much faster. In the github wiki you'll find links ...


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CSI: Expensive, but the data is not bad (quality wise) SIX Financial (former Telekurs): Middle tier price-wise, OK data CRB: Terrible customer service, but reasonable pricing CQG (don't know about their pricing)


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In regards to the above answers, for tick data or time series , you could probably use a combination of redis (in memory data-store) & mongodb, or use Hbase with bus events.


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First, if you are using python for this, I would recommend that you take a look at pandas in general and the pandas data reader package If data reliability is a concern, I recommend that you self-verify by randomly selecting dates and assets, pulling data from multiple sources (like say yahoo, google, and Quandl) and checking them against each other and ...


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Use DVD_HIST_GROSS_WITH_AMT_STAT. It includes normal and abnormal dividends,stock dividends, stock splits, rights, and spinoffs.


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XBRL became mandatory for US filers on June 15th, 2011. The SEC requires XBRL data for: Quarterly and annual reports and transition reports Form 8-K revisions Limited Securities Act registration statements XBRL instances for quarterly and annual reports would typically contain the usual items found on Income statements, Balance Sheets, and Cash Flows.



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