YFinance.jl

Download price, fundamental, and option data from Yahoo Finance This is a side project and my first package so do not expect too much.

* LEGAL DISCLAIMER *

Yahoo!, Y!Finance, and Yahoo! finance are registered trademarks of Yahoo, Inc.

YFinance.jl is not affiliated with Yahoo, Inc. in any way. The data retreived can only be used for personal use. Please see Yahoo's terms of use:

What you can download

Price data (including intraday)
Fundamental data
Option Data
ESG Data
quoteSummary data (this is a JSON3.object that contains a multitude of different information)

Function Documentation

YFinance.get_prices — Function

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

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 the with the ratios between the close and the adjusted close prices - only available for intervals of 1d and up.

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> 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> 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

YFinance.get_quoteSummary — Function

get_quoteSummary(symbol::String; item=nothing)

Retrievs general information from Yahoo Finance stored in a JSON3 object.

Arguments

smybol::String is a ticker (e.g. AAPL for Apple Computers, or ^GSPC for the S&P500)
item can either be a string or multiple items as a Vector of Strings. To see valid items call _QuoteSummary_Items (not all items are available for all types of securities)

Examples

julia> get_quoteSummary("AAPL")

JSON3.Object{Vector{UInt8}, SubArray{UInt64, 1, Vector{UInt64}, Tuple{UnitRange{Int64}}, true}} with 31 entries:
:assetProfile             => {…
:recommendationTrend      => {…
:cashflowStatementHistory => {…

⋮                         => ⋮
julia> get_quoteSummary("AAPL",item = "quoteType")
JSON3.Object{Vector{UInt8}, SubArray{UInt64, 1, Vector{UInt64}, Tuple{UnitRange{Int64}}, true}} with 13 entries:
:exchange               => "NMS"
:quoteType              => "EQUITY"
:symbol                 => "AAPL"
⋮                       => ⋮

source

YFinance.get_Fundamental — Function

get_Fundamental(symbol::AbstractString, item::AbstractString,interval::AbstractString, startdt, enddt)

Retrievs financial statement information from Yahoo Finance stored in a Dictionary.

Arguments

smybol::String is a ticker (e.g. AAPL for Apple Computers, or ^GSPC for the S&P500)
item::String can either be an entire financial statement or a subitem. Entire financial statements:"incomestatement", "valuation", "cashflow", "balancesheet". To see valid sub items grouped by financial statement type in a Dictionary call `Fundamental_Types`
interval::String can be one of "annual", "quarterly", "monthly"
startdtandenddttake the following types:::Date,::DateTime, or aStringof the following formyyyy-mm-dd`

Examples

julia> get_Fundamental("NFLX", "income_statement","quarterly","2000-01-01","2022-12-31")

Dict{String, Any} with 39 entries:
"NetNonOperatingInterestIncomeExpense" => Any[-94294000, -80917000, 8066000, 44771000, 88829000]
"NetInterestIncome"                    => Any[-94294000, -80917000, 8066000, 44771000, 88829000]
"InterestExpense"                      => Any[190429000, 189429000, 187579000, 175455000, 172575000]
⋮                                      => ⋮

julia> get_Fundamental("AAPL", "InterestExpense","quarterly","2000-01-01","2022-12-31") |> DataFrame
5×2 DataFrame
Row │ InterestExpense  timestamp
    │ Any              DateTime
────┼──────────────────────────────────────
  1 │ 672000000        2021-09-30T00:00:00 
  2 │ 694000000        2021-12-31T00:00:00
  3 │ 691000000        2022-03-31T00:00:00
  4 │ 719000000        2022-06-30T00:00:00
  5 │ 827000000        2022-09-30T00:00:00

source

YFinance.get_Options — Function

get_Options(symbol::String)

Retrievs options data from Yahoo Finance stored in a Dictionary with two items. One contains Call options the other Put options. These subitems are dictionaries themselves. The call and put options Dictionaries can readily be transformed to a DataFrame.

Arguments

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

Examples

julia> get_Options("AAPL")
Dict{String, Dict{String, Vector{Any}}} with 2 entries:
"calls" => Dict("percentChange"=>[ -2.90804  …   0], "expiration"=>[DateTime("2022-12-09T00:…  
"puts"  => Dict("percentChange"=>[0,  …   0], "expiration"=>[DateTime("2022-12-09T00:00:00"), DateTime("20…

 
julia> get_Options("AAPL")["calls"] |> DataFrame
65×16 DataFrame
Row │ ask    bid    change     contractSize  contractSymbol       currency  exp ⋯
    │ Any    Any    Any        Any           Any                  Any       Any ⋯
────┼────────────────────────────────────────────────────────────────────────────
  1 │ 94.3   94.1   0          REGULAR       AAPL221209C00050000  USD       202 ⋯
  2 │ 84.3   84.15  0          REGULAR       AAPL221209C00060000  USD       202  
 ⋮  │   ⋮      ⋮        ⋮           ⋮                 ⋮              ⋮          ⋱  
 64 │ 0.01   0      0          REGULAR       AAPL221209C00240000  USD       202  
 65 │ 0      0      0          REGULAR       AAPL221209C00250000  USD       202  
                                                   10 columns and 61 rows omitted


julia> data  = get_Options("AAPL")
julia> vcat( [DataFrame(i) for i in values(data)]...)
124×16 DataFrame
Row │ ask    bid    change     contractSize  contractSymbol       cur ⋯
    │ Any    Any    Any        Any           Any                  Any ⋯
────┼──────────────────────────────────────────────────────────────────
  1 │ 94.3   94.1   0          REGULAR       AAPL221209C00050000  USD ⋯
  2 │ 84.55  84.35  0          REGULAR       AAPL221209C00060000  USD  
 ⋮  │   ⋮      ⋮        ⋮           ⋮                 ⋮               ⋱ 
123 │ 75.85  75.15  0          REGULAR       AAPL221209P00220000  USD  
124 │ 85.85  85.15  0          REGULAR       AAPL221209P00230000  USD  
                                        11 columns and 120 rows omitted

source

YFinance.get_ESG — Function

get_ESG(symbol::String)

Retrievs ESG Scores from Yahoo Finance stored in a Dictionary with two items. One, score, contains the companies ESG scores and individal Overall, Environment, Social and Goverance Scores as well as a timestamp of type DateTime. The other, peer_score, contains the peer group's scores. The subdictionaries can be transformed to DataFrames

Arguments

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

Examples

julia> get_ESG("AAPL")
Dict{String, Dict{String, Any}} with 2 entries:
"peer_score" => Dict("governanceScore"=>Union{Missing, Float64}[63.2545, 63.454…  
"score"      => Dict("governanceScore"=>Union{Missing, Real}[62, 62, 62, 62, 62… 
   
julia> get_ESG("AAPL")["score"] |> DataFrame
96×6 DataFrame
Row │ environmentScore  esgScore    governanceScore  socialScore  symbol  times ⋯
    │ Real?             Real?       Real?            Real?        String  DateT ⋯
────┼────────────────────────────────────────────────────────────────────────────
  1 │            74          61               62           45     AAPL    2014- ⋯
  2 │            74          60               62           45     AAPL    2014-  
 ⋮  │        ⋮              ⋮              ⋮              ⋮         ⋮           ⋱
 95 │       missing     missing          missing      missing     AAPL    2022-  
 96 │             0.65       16.68             9.18         6.86  AAPL    2022-  
                                                     1 column and 92 rows omitted

source

YFinance.jl

*** LEGAL DISCLAIMER ***

What you can download

Function Documentation

* LEGAL DISCLAIMER *