From the Julia General Registry:
julia> ] # enters the pkg interface
pkg> add ScrapeSEC
julia> using Pkg; Pkg.add("ScrapeSEC")
From source:
julia> ] # enters the pkg interface
pkg> add https://github.com/tylerjthomas9/ScrapeSEC.jl
Download filing metadata for 2020-2022 from the SEC archives.
using ScrapeSEC
download_metadata_files(2020, 2022)
Download 10-K, 8-K, and 10-Q metadata, filings for 2020-2022
using ScrapeSEC
download_filings(2023, 2024; filing_types=["10-K", "8-K", "10-Q"])
df = DataFrame(CSV.File(metadata_file; delim="|"))
julia> first(df, 5)
5×5 DataFrame
Row │ CIK Company Name Form Type Date Filed Filename
│ Int64 String String31 Dates.Date String
─────┼───────────────────────────────────────────────────────────────────────────────────────────
1 │ 1000045 NICHOLAS FINANCIAL INC 10-Q 2023-02-14 edgar/data/1000045/0000950170-23…
2 │ 1000045 NICHOLAS FINANCIAL INC 4 2023-02-24 edgar/data/1000045/0001000045-23…
3 │ 1000045 NICHOLAS FINANCIAL INC 4 2023-02-28 edgar/data/1000045/0001000045-23…
4 │ 1000045 NICHOLAS FINANCIAL INC 4 2023-03-09 edgar/data/1000045/0001398344-23…
5 │ 1000045 NICHOLAS FINANCIAL INC 8-K 2023-01-24 edgar/data/1000045/0000950170-23…
Download filing metadata for 2020-2022, create a main index file, and download 10-Ks using the combined index file
using ScrapeSEC
download_metadata_files(2020, 2022)
create_main_index()
download_filings("./metadata/main_idx.tsv"; filing_types=["10-K", ])
Download filings from a vector of filenames
using CSV, DataFrames, ScrapeSEC
df = CSV.File("./metadata/main_idx.tsv", delim = "|") |> DataFrame
download_filings(df.FileName)
If you use ScrapeSEC.jl as part of your research, teaching, or other activities, we would be grateful if you could cite our work.
@misc{ScrapeSEC.jlPackage,
author = {Tyler Thomas}
title = {ScrapeSEC.jl}
year = {2023}
url = {https://tylerjthomas9.github.io/ScrapeSEC.jl/}
}
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Not maintained: