Download Dividend and Stock Split Data
YFinance.get_dividends — Functionget_dividends(symbol::String; startdt::Union{Date,DateTime,AbstractString}="", enddt::Union{Date,DateTime,AbstractString}="", timeout::Int=10, throw_error::Bool=false, exchange_local_time::Bool=false)Retrieves dividend data from Yahoo Finance.
Arguments
symbol: A ticker (e.g., AAPL for Apple Inc., or ^GSPC for the S&P 500)startdtandenddt: Optional. Can be of typeDate,DateTime, or aStringin the format "yyyy-mm-dd". If not provided,startdtdefaults to the earliest available data andenddtto the current date.timeout: Integer, defaults to 10. The timeout for the HTTP request in seconds.throw_error: Boolean, defaults tofalse. If set to true, the function raises an error when the ticker is not valid or other issues occur. If false, a warning is given and an emptyOrderedDictis returned.exchange_local_time: Boolean, defaults tofalse. If set to true, the timestamp corresponds to the exchange local time; otherwise, it's in GMT.
Returns
An OrderedDict{String, Union{String,Vector{DateTime},Vector{Float64}}} containing the following keys:
- ticker
- timestamp
- div
Examples
julia> get_dividends("AAPL", startdt = "2021-01-01", enddt="2022-01-01")
OrderedDict{String, Union{String,Vector{DateTime},Vector{Float64}}} with 3 entries:
"ticker" => "AAPL"
"timestamp" => [DateTime("2021-02-05T14:30:00"), DateTime("2021-05-07T13:30:00"), DateTime("2021-08-06T13:30:00"), DateTime("2021-11-05T13:30:00")]
"div" => [0.205, 0.22, 0.22, 0.22]
## Can be easily converted to a DataFrame
julia> using DataFrames
julia> get_dividends("AAPL", startdt = "2021-01-01", enddt="2022-01-01") |> DataFrame
4×3 DataFrame
Row │ ticker timestamp div
│ String DateTime Float64
─────┼───────────────────────────────────────
1 │ AAPL 2021-02-05T14:30:00 0.205
2 │ AAPL 2021-05-07T13:30:00 0.22
3 │ AAPL 2021-08-06T13:30:00 0.22
4 │ AAPL 2021-11-05T13:30:00 0.22
## Broadcasting
julia> get_dividends.(["AAPL", "F"], startdt = "2021-01-01", enddt="2022-01-01")
2-element Vector{OrderedDict{String, Union{String,Vector{DateTime},Vector{Float64}}}}:
OrderedDict("ticker" => "AAPL", "timestamp" => [DateTime("2021-02-05T14:30:00"), DateTime("2021-05-07T13:30:00"), DateTime("2021-08-06T13:30:00"), DateTime("2021-11-05T13:30:00")], "div" => [0.205, 0.22, 0.22, 0.22])
OrderedDict("ticker" => "F", "timestamp" => [DateTime("2021-11-18T14:30:00")], "div" => [0.1])
## Converting it to a DataFrame:
julia> using DataFrames
julia> data = get_dividends.(["AAPL", "F"], startdt = "2021-01-01", enddt="2022-01-01");
julia> vcat([DataFrame(i) for i in data]...)
5×3 DataFrame
Row │ ticker timestamp div
│ String DateTime Float64
─────┼───────────────────────────────────────
1 │ AAPL 2021-02-05T14:30:00 0.205
2 │ AAPL 2021-05-07T13:30:00 0.22
3 │ AAPL 2021-08-06T13:30:00 0.22
4 │ AAPL 2021-11-05T13:30:00 0.22
5 │ F 2021-11-18T14:30:00 0.1YFinance.get_splits — Functionget_splits(symbol::String; startdt::Union{Date,DateTime,AbstractString}="", enddt::Union{Date,DateTime,AbstractString}="", timeout::Int=10, throw_error::Bool=false, exchange_local_time::Bool=false)Retrieves stock split data from Yahoo Finance.
Arguments
symbol: A ticker (e.g., AAPL for Apple Inc., or ^GSPC for the S&P 500)startdtandenddt: Optional. Can be of typeDate,DateTime, or aStringin the format "yyyy-mm-dd". If not provided,startdtdefaults to the earliest available data andenddtto the current date.timeout: Integer, defaults to 10. The timeout for the HTTP request in seconds.throw_error: Boolean, defaults tofalse. If set to true, the function raises an error when the ticker is not valid or other issues occur. If false, a warning is given and an emptyOrderedDictis returned.exchange_local_time: Boolean, defaults tofalse. If set to true, the timestamp corresponds to the exchange local time; otherwise, it's in GMT.
Returns
An OrderedDict{String, Union{String,Vector{DateTime},Vector{Int},Vector{Float64}}} containing the requested split data.
Examples
julia> get_splits("AAPL", startdt = "2000-01-01", enddt = "2020-01-01")
OrderedDict{String, Union{String,Vector{DateTime},Vector{Int},Vector{Float64}}} with 5 entries:
"ticker" => "AAPL"
"timestamp" => [DateTime("2000-06-21T13:30:00"), DateTime("2005-02-28T14:30:00"), DateTime("2014-06-09T13:30:00")]
"numerator" => [2, 2, 7]
"denominator" => [1, 1, 1]
"ratio" => [2.0, 2.0, 7.0]
## Can be easily converted to a DataFrame
julia> using DataFrames
julia> get_splits("AAPL", startdt = "2000-01-01", enddt = "2020-01-01") |> DataFrame
3×5 DataFrame
Row │ ticker timestamp numerator denominator ratio
│ String DateTime Int64 Int64 Float64
─────┼──────────────────────────────────────────────────────────────
1 │ AAPL 2000-06-21T13:30:00 2 1 2.0
2 │ AAPL 2005-02-28T14:30:00 2 1 2.0
3 │ AAPL 2014-06-09T13:30:00 7 1 7.0
## Broadcasting
julia> get_splits.(["AAPL", "F"], startdt = "2000-01-01", enddt = "2020-01-01")
2-element Vector{OrderedDict{String, Union{String,Vector{DateTime},Vector{Int},Vector{Float64}}}}:
OrderedDict("ticker" => "AAPL", "timestamp" => [DateTime("2000-06-21T13:30:00"), DateTime("2005-02-28T14:30:00"), DateTime("2014-06-09T13:30:00")], "numerator" => [2, 2, 7], "denominator" => [1, 1, 1], "ratio" => [2.0, 2.0, 7.0])
OrderedDict("ticker" => "F", "timestamp" => [DateTime("2000-06-29T13:30:00"), DateTime("2000-08-03T13:30:00")], "numerator" => [10000, 1748175], "denominator" => [9607, 1000000], "ratio" => [1.0409076714895389, 1.748175])
## Converting it to a DataFrame:
julia> using DataFrames
julia> data = get_splits.(["AAPL", "F"], startdt = "2000-01-01", enddt = "2020-01-01");
julia> vcat([DataFrame(i) for i in data]...)
5×5 DataFrame
Row │ ticker timestamp numerator denominator ratio
│ String DateTime Int64 Int64 Float64
─────┼──────────────────────────────────────────────────────────────
1 │ AAPL 2000-06-21T13:30:00 2 1 2.0
2 │ AAPL 2005-02-28T14:30:00 2 1 2.0
3 │ AAPL 2014-06-09T13:30:00 7 1 7.0
4 │ F 2000-06-29T13:30:00 10000 9607 1.04091
5 │ F 2000-08-03T13:30:00 1748175 1000000 1.74818