{
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    "exact_name": "Historical-Financials-Data-for-3000-stocks",
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    "description": "Context\nGetting access to high-quality historical stock market data can be very expensive and\/or complicated; parsing SEC 10-Q filings direct from the SEC EDGAR is difficult due to the varying structures of filings and SEC filing data from providers such as Quandl charge hundreds or thousands of dollars in yearly fees to get access to them. Here, I provide an easy-to-use, straight from the source database of parsed financials information from SEC 10-Q filings for more than 3000 stocks.\nContent\nThe quarterly financials are provided in a single .csv file, quarterly_financials.csv\n50 of the data is NaN either because the field wasn't detected by my XBRL parsing system or the field wasn't addressed in the SEC filing.\nAcknowledgements\nAll the data is scraped from the SEC from the XBRL files.",
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    "uploader": "Elif Ceren Gok",
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    "contributor": null,
    "date": "2022-03-24 15:32:09",
    "update_comment": null,
    "last_update": "2022-03-24 15:32:09",
    "licence": "CC0: Public Domain",
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            "Historical-Financials-Data-for-3000-stocks",
            "Context Getting access to high-quality historical stock market data can be very expensive and\/or complicated; parsing SEC 10-Q filings direct from the SEC EDGAR is difficult due to the varying structures of filings and SEC filing data from providers such as Quandl charge hundreds or thousands of dollars in yearly fees to get access to them. Here, I provide an easy-to-use, straight from the source database of parsed financials information from SEC 10-Q filings for more than 3000 stocks. Content T "
        ],
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        {
            "uploader": "38960",
            "tag": "Machine Learning"
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