Download Price Data

YFinance.get_prices — Function

get_prices(symbol::AbstractString; range::AbstractString="1mo", interval::AbstractString="1d",startdt="", enddt="",prepost=false,autoadjust=true,timeout = 10,throw_error=false)

Retrievs prices from Yahoo Finance.

Arguments

Smybol is a ticker (e.g. AAPL for Apple Computers, or ^GSPC for the S&P500)

You can either provide a range or a startdt and an enddt.

range takes the following values: "1d","5d","1mo","3mo","6mo","1y","2y","5y","10y","ytd","max"
startdt and enddt take the following types: ::Date,::DateTime, or a String of the following form yyyy-mm-dd
prepost is a boolean indicating whether pre and post periods should be included. Defaults to false
autoadjust defaults to true. It adjusts open, high, low, close prices, and volume by multiplying by the ratio between the close and the adjusted close prices - only available for intervals of 1d and up.
throw_error::Bool defaults to false. If set to true the function errors when the ticker is not valid. Else a warning is given and an empty dictionary is returned.
exchangelocaltime::Bool defaults to false. If set to true the timestamp corresponds to the exchange local time else to GMT.

Examples

julia> get_prices("AAPL",range="1d",interval="90m")
Dict{String, Any} with 7 entries:
"vol"    => [10452686, 0]
"ticker" => "AAPL"
"high"   => [142.55, 142.045]
"open"   => [142.34, 142.045]
"timestamp"     => [DateTime("2022-12-09T14:30:00"), DateTime("2022-12-09T15:08:33")]
"low"    => [140.9, 142.045]
"close"  => [142.28, 142.045]

Can be easily converted to a DataFrame

julia> using DataFrames
julia> get_prices("AAPL",range="1d",interval="90m") |> DataFrame
2×7 DataFrame
Row │ close    timestamp            high     low      open     ticker  vol      
    │ Float64  DateTime             Float64  Float64  Float64  String  Int64    
────┼───────────────────────────────────────────────────────────────────────────
    1 │  142.28  2022-12-09T14:30:00   142.55   140.9    142.34  AAPL    10452686
    2 │  142.19  2022-12-09T15:08:03   142.19   142.19   142.19  AAPL           0

Broadcasting

julia> get_prices.(["AAPL","NFLX"],range="1d",interval="90m")
2-element Vector{Dict{String, Any}}:
Dict(
    "vol" => [11085386, 0], 
    "ticker" => "AAPL", 
    "high" => [142.5500030517578, 142.2949981689453], 
    "open" => [142.33999633789062, 142.2949981689453], 
    "timestamp" => [DateTime("2022-12-09T14:30:00"), DateTime("2022-12-09T15:15:34")], 
    "low" => [140.89999389648438, 142.2949981689453], 
    "close" => [142.27000427246094, 142.2949981689453])
Dict(
    "vol" => [4435651, 0], 
    "ticker" => "NFLX", 
    "high" => [326.29998779296875, 325.30999755859375], 
    "open" => [321.45001220703125, 325.30999755859375], 
    "timestamp" => [DateTime("2022-12-09T14:30:00"), DateTime("2022-12-09T15:15:35")], 
    "low" => [319.5199890136719, 325.30999755859375], 
    "close" => [325.79998779296875, 325.30999755859375])

Converting it to a DataFrame:

julia> using DataFrames
julia> data = get_prices.(["AAPL","NFLX"],range="1d",interval="90m");
julia> vcat([DataFrame(i) for i in data]...)
4×7 DataFrame
Row │ close    timestamp            high     low      open     ticker  vol      
    │ Float64  DateTime             Float64  Float64  Float64  String  Int64    
────┼───────────────────────────────────────────────────────────────────────────
    1 │  142.21  2022-12-09T14:30:00   142.55   140.9    142.34  AAPL    11111223
    2 │  142.16  2022-12-09T15:12:20   142.16   142.16   142.16  AAPL           0
    3 │  324.51  2022-12-09T14:30:00   326.3    319.52   321.45  NFLX     4407336
    4 │  324.65  2022-12-09T15:12:20   324.65   324.65   324.65  NFLX           0

source