All Functions

YFinance.NewsItem — Type

This is an NewsItem

Fields

title: Title of the news article
publisher: Publisher of the news
link: The link to the news article
timestamp: The timestamp of the time when the news was published (DateTime)
symbols: An array of the tickers related to the news item

source

YFinance.YahooNews — Type

This is an YahooNews <: AbstractArray{NewsItem, N}

Basically a custom Array of NewsItems

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YFinance.YahooSearch — Type

This is an YahooSearch <: AbstractArray{YahooSearchItem, N}

Basically a custom Array of YahooSearchItems

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YFinance.YahooSearchItem — Type

This is an YahooSearchItem

Fields

symbol: The Symbol (Ticker)
shortname: The short name of the instrument/company
quoteType: The type of asset (e.g. EQUITY)
sector: The Sector (only if quotetype==EQUITY, otherwise "")
industry: The Industry (only if quotetype==EQUITY, otherwise "")

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YFinance._rand_header — Method

_rand_header()

Chooses a random header.

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YFinance._renew_cookies_and_crumb — Method

_renew_cookies_and_crumb()

Renews both the cookies and the crumb.

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YFinance._set_cookies_and_crumb — Method

_set_cookies_and_crumb()

Checks if the global _COOKIE and _CRUMB variables are set if not it creates them.

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YFinance.clear_proxy_settings — Method

clear_proxy_settings()

Clears the proxy settings by setting them back to their default (no proxy configuration).

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YFinance.create_proxy_settings — Function

create_proxy_settings(p::AbstractString,user=nothing,password=nothing)

Sets the global proxy variable _PROXY_SETTINGS::NamedTuple. This NamedTuple contains a proxy and a auth field. These fields default to nothing and and empty Dict respectively.

Arguments

p::String (Required) of the form: "http://proxy.xyz.com:8080"
user::String Username (optional) only required if proxy requires authentication. Defaults to nothing (no authentication needed)
password::String The password corresponding to the Username. Defaults to nothing (no authentication needed)

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YFinance.get_ESG — Method

get_ESG(symbol::String)

Retrievs ESG Scores from Yahoo Finance stored in a OrderedCollections.OrderedDict 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)
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 OrderedCollections.OrderedDict is returned.

Examples

julia> get_ESG("AAPL")
OrderedDict{String, OrderedDict{String, Any}} with 2 entries:
  "score"      => OrderedDict("symbol"=>"AAPL", "timestamp"=>[DateTime("2014-09-01T00:00:…
  "peer_score" => OrderedDict("symbol"=>"Technology Hardware", "timestamp"=>[DateTime("20…

julia> using DataFrames
julia> get_ESG("AAPL")["score"] |> DataFrame
96×6 DataFrame
 Row │ symbol  timestamp            esgScore    governanceScore  environmentScore  socialScore 
     │ String  DateTime             Real?       Real?            Real?             Real?       
─────┼─────────────────────────────────────────────────────────────────────────────────────────
   1 │ AAPL    2014-09-01T00:00:00       61               62                74           45
   2 │ AAPL    2014-10-01T00:00:00       60               62                74           45
   3 │ AAPL    2014-11-01T00:00:00       61               62                74           45
  ⋮  │   ⋮              ⋮               ⋮              ⋮                ⋮               ⋮
  95 │ AAPL    2022-07-01T00:00:00  missing          missing           missing      missing    
  96 │ AAPL    2022-08-01T00:00:00       16.68             9.18              0.65         6.86
                                                                                91 rows omitted

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YFinance.get_Fundamental — Method

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

Retrievs financial statement information from Yahoo Finance stored in a OrderedCollections.OrderedDict.

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:"income_statement", "valuation", "cash_flow", "balance_sheet". To see valid sub items grouped by financial statement type in a OrderedCollections.OrderedDict call _Fundamental_Types
interval::String can be one of "annual", "quarterly", "monthly"
startdt and enddt take the following types: ::Date,::DateTime, or a String of the following form yyyy-mm-dd
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 OrderedCollections.OrderedDict is returned.

Examples

julia> get_Fundamental("NFLX", "income_statement","quarterly","2000-01-01","2022-12-31")
OrderedDict{String, Any} with 40 entries:
  "timestamp"                       => [DateTime("2021-12-31T00:00:00"), DateTime("2022-0…  "GeneralAndAdministrativeExpense" => Any[397790000, 397928000, 409297000, 373213000]    
  "SellingGeneralAndAdministration" => Any[1190503000, 953906000, 984257000, 941167000]   
  "InterestIncome"                  => Any[108512000, 195645000, 220226000, 261404000]    
  "OperatingRevenue"                => Any[7709318000, 7867767000, 7970141000, 7925589000]  "DilutedNIAvailtoComStockholders" => Any[607429000, 1597447000, 1440951000, 1398242000] 
  "NormalizedIncome"                => Any[607429000, 1597447000, 1440951000, 1398242000] 
  "NetIncomeCommonStockholders"     => Any[607429000, 1597447000, 1440951000, 1398242000] 
  "BasicAverageShares"              => Any[443462000, 444146000, 444557000, 444878000]    
  ⋮                                 => ⋮


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

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YFinance.get_Options — Method

get_Options(symbol::String)

Retrievs options data from Yahoo Finance stored in a OrderedCollections.OrderedDict with two items. One contains Call options the other Put options. These subitems are OrderedCollections.OrderedDict themselves. The call and put options OrderedCollections.OrderedDict 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)
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 OrderedCollections.OrderedDict is returned.

Examples

julia> get_Options("AAPL")
OrderedDict{String, OrderedDict{String, Vector{Any}}} with 2 entries:
  "calls" => OrderedDict("contractSymbol"=>["AAPL221230C00050000", "AAPL221230C00055000",…
  "puts"  => OrderedDict("contractSymbol"=>["AAPL221230P00050000", "AAPL221230P00055000",…

julia> using DataFrames
julia> get_Options("AAPL")["calls"] |> DataFrame
72×16 DataFrame
 Row │ contractSymbol       strike  currency  lastPrice  change  percentChange  volume   ⋯
     │ Any                  Any     Any       Any        Any     Any            Any      ⋯
─────┼────────────────────────────────────────────────────────────────────────────────────
   1 │ AAPL221230C00050000  50      USD       79.85      0       0              1        ⋯
   2 │ AAPL221230C00055000  55      USD       72.85      0       0              1
   3 │ AAPL221230C00060000  60      USD       66.4       0       0              19        
  ⋮  │          ⋮             ⋮        ⋮          ⋮        ⋮           ⋮           ⋮     ⋱
  71 │ AAPL221230C00230000  230     USD       0.02       0       0              missing   
  72 │ AAPL221230C00250000  250     USD       0.01       0       0              2        ⋯
                                                             9 columns and 67 rows omitted

julia> using DataFrames
julia> data  = get_Options("AAPL");
julia> vcat( [DataFrame(i) for i in values(data)]...)
141×16 DataFrame
 Row │ contractSymbol       strike  currency  lastPrice  change  percentChange  volume   ⋯
     │ Any                  Any     Any       Any        Any     Any            Any      ⋯
─────┼────────────────────────────────────────────────────────────────────────────────────
   1 │ AAPL221230C00050000  50      USD       79.85      0       0              1        ⋯
   2 │ AAPL221230C00055000  55      USD       72.85      0       0              1
   3 │ AAPL221230C00060000  60      USD       66.4       0       0              19        
  ⋮  │          ⋮             ⋮        ⋮          ⋮        ⋮           ⋮          ⋮      ⋱
 140 │ AAPL221230P00225000  225     USD       94.65      0       0              1
 141 │ AAPL221230P00230000  230     USD       99.65      0       0              1        ⋯
                                                            9 columns and 136 rows omitted

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YFinance.get_all_symbols — Method

get_all_symbols(market::T)::Vector{T} where {T<:String}

Fetch all the symbols from a given market.

Arguments

market::String: The market to fetch the symbols from.

Currently supported markets are:

AMEX
NASDAQ
NYSE

Uses dumbstockapi.com

Returns

Vector{String}: A vector of strings containing the symbols.

Example

julia> get_all_symbols("NYSE")
3127-element Vector{String}:
 "A"
 "AA"
 "AAC"
 "AAN"
 ⋮
 "ZTR"
 "ZTS"
 "ZUO"
 "ZYME"

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YFinance.get_calendar_events — Method

get_calendar_events(quoteSummary::JSON3.Object)

Retrievs calendar events from the quote summary.

Arguments

Can be either a JSON3.Object returned from get_quoteSummary(symbol::String; item=nothing,throw_error=false) or a ticker symbol of type AbstractString If a ticker symbol is provided get_quoteSummary(symbol::String) is called first.

Examples

julia> get_quoteSummary("AAPL") |> get_calendar_events
OrderedDict{String, Any} with 3 entries:
  "dividend_date"   => DateTime("2022-11-10T00:00:00")
  "earnings_dates"  => [DateTime("2023-01-25T10:59:00"), DateTime("2023-01-30T12:00:00")]
  "exdividend_date" => DateTime("2022-11-04T00:00:00")

julia> get_calendar_events("AAPL")
OrderedDict{String, Any} with 3 entries:
  "dividend_date"   => DateTime("2022-11-10T00:00:00")
  "earnings_dates"  => [DateTime("2023-01-25T10:59:00"), DateTime("2023-01-30T12:00:00")]
  "exdividend_date" => DateTime("2022-11-04T00:00:00")

julia> using DataFrames
julia> get_calendar_events("AAPL") |> DataFrame
2×3 DataFrame
 Row │ dividend_date        earnings_dates       exdividend_date     
     │ DateTime             DateTime             DateTime
─────┼───────────────────────────────────────────────────────────────
   1 │ 2022-11-10T00:00:00  2023-01-25T10:59:00  2022-11-04T00:00:00
   2 │ 2022-11-10T00:00:00  2023-01-30T12:00:00  2022-11-04T00:00:00

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YFinance.get_cookie — Method

get_cookie()

Retrieves cookies from "https://fc.yahoo.com".

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YFinance.get_crumb — Method

get_crumb()

Passess the request header and the cookies to "https://query2.finance.yahoo.com/v1/test/getcrumb" and retrieves the crumb. If the global _HEADER or _COOKIE variables are not defined they are created.

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YFinance.get_dividends — Method

get_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)
startdt and enddt: Optional. Can be of type Date, DateTime, or a String in the format "yyyy-mm-dd". If not provided, startdt defaults to the earliest available data and enddt to the current date.
timeout: Integer, defaults to 10. The timeout for the HTTP request in seconds.
throw_error: Boolean, defaults to false. 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 empty OrderedDict is returned.
exchange_local_time: Boolean, defaults to false. 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    0.1

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YFinance.get_earnings_estimates — Method

get_earnings_estimates(quoteSummary::JSON3.Object)

Retrievs the earnings estimates from the quote summary.

Arguments

Examples

julia> get_quoteSummary("AAPL") |> get_earnings_estimates
OrderedDict{String, Vector} with 3 entries:
  "quarter"  => ["4Q2021", "1Q2022", "2Q2022", "3Q2022", "4Q2022"]
  "estimate" => [1.89, 1.43, 1.16, 1.27, 1.98]
  "actual"   => Union{Missing, Float64}[2.1, 1.52, 1.2, 1.29, missing]

julia> get_earnings_estimates("AAPL")
OrderedDict{String, Vector} with 3 entries:
  "quarter"  => ["4Q2021", "1Q2022", "2Q2022", "3Q2022", "4Q2022"]
  "estimate" => [1.89, 1.43, 1.16, 1.27, 1.98]
  "actual"   => Union{Missing, Float64}[2.1, 1.52, 1.2, 1.29, missing]

julia> using DataFrames
julia> get_earnings_estimates("AAPL") |> DataFrame
5×3 DataFrame
 Row │ quarter  estimate  actual     
     │ String   Float64   Float64?   
─────┼───────────────────────────────
   1 │ 4Q2021       1.89        2.1
   2 │ 1Q2022       1.43        1.52
   3 │ 2Q2022       1.16        1.2
   4 │ 3Q2022       1.27        1.29
   5 │ 4Q2022       1.98  missing

source

YFinance.get_eps — Method

get_eps(quoteSummary::JSON3.Object)

Retrievs the earnings per share from the quote summary.

Arguments

Examples

julia> get_quoteSummary("AAPL") |> get_eps
OrderedDict{String, Vector} with 4 entries:
  "quarter"  => [DateTime("2021-12-31T00:00:00"), DateTime("2022-03-31T00:00:00"), DateTime("2022-06-30T00:00:00"), DateTime("2022-09-30T00:00:00")]
  "estimate" => [1.89, 1.43, 1.16, 1.27]
  "actual"   => [2.1, 1.52, 1.2, 1.29]
  "surprise" => [0.111, 0.063, 0.034, 0.016]

julia> get_eps("AAPL")
OrderedDict{String, Vector} with 4 entries:
  "quarter"  => [DateTime("2021-12-31T00:00:00"), DateTime("2022-03-31T00:00:00"), DateTime("2022-06-30T00:00:00"), DateTime("2022-09-30T00:00:00")]
  "estimate" => [1.89, 1.43, 1.16, 1.27]
  "actual"   => [2.1, 1.52, 1.2, 1.29]
  "surprise" => [0.111, 0.063, 0.034, 0.016]

julia> using DataFrames
julia> get_eps("AAPL") |> DataFrame
4×4 DataFrame
 Row │ quarter              estimate  actual   surprise 
     │ DateTime             Float64   Float64  Float64  
─────┼──────────────────────────────────────────────────
   1 │ 2021-12-31T00:00:00      1.89     2.1      0.111
   2 │ 2022-03-31T00:00:00      1.43     1.52     0.063
   3 │ 2022-06-30T00:00:00      1.16     1.2      0.034
   4 │ 2022-09-30T00:00:00      1.27     1.29     0.016

source

YFinance.get_insider_holders — Method

get_insider_holders(quoteSummary::JSON3.Object)

Retrievs the insiders holdings from the quote summary.

Arguments

Examples

julia> get_quoteSummary("AAPL") |> get_insider_holders
OrderedDict{String, Vector} with 8 entries:
    "name"                 => ["ADAMS KATHERINE L", "BELL JAMES A", "JUNG ANDREA", "KONDO C…
    "relation"             => Union{Missing, String}["General Counsel", "Director", "Direct…  
    "description"          => Union{Missing, String}["Sale", "Stock Gift", "Conversion of E…  
    "lastestTransDate"     => Union{Missing, DateTime}[DateTime("2022-10-03T00:00:00"), Dat…  
    "positionDirect"       => Union{Missing, Int64}[427334, 34990, 139594, 31505, 4588720, …  
    "positionDirectDate"   => Union{Missing, DateTime}[DateTime("2022-10-03T00:00:00"), Dat…  
    "positionIndirect"     => Union{Missing, Int64}[missing, missing, missing, missing, mis…  
    "positionIndirectDate" => Union{Missing, DateTime}[missing, missing, missing, missing, …

julia> get_insider_holders("AAPL")
OrderedDict{String, Vector} with 8 entries:
    "name"                 => ["ADAMS KATHERINE L", "BELL JAMES A", "JUNG ANDREA", "KONDO C…
    "relation"             => Union{Missing, String}["General Counsel", "Director", "Direct…  
    "description"          => Union{Missing, String}["Sale", "Stock Gift", "Conversion of E…  
    "lastestTransDate"     => Union{Missing, DateTime}[DateTime("2022-10-03T00:00:00"), Dat…  
    "positionDirect"       => Union{Missing, Int64}[427334, 34990, 139594, 31505, 4588720, …  
    "positionDirectDate"   => Union{Missing, DateTime}[DateTime("2022-10-03T00:00:00"), Dat…  
    "positionIndirect"     => Union{Missing, Int64}[missing, missing, missing, missing, mis…  
    "positionIndirectDate" => Union{Missing, DateTime}[missing, missing, missing, missing, …


julia> using DataFrames
julia> get_insider_holders("AAPL") |> DataFrame
10×8 DataFrame
 Row │ name                relation                 description                        l ⋯
     │ String              String?                  String?                            D ⋯
─────┼────────────────────────────────────────────────────────────────────────────────────
   1 │ ADAMS KATHERINE L   General Counsel          Sale                               2 ⋯
   2 │ BELL JAMES A        Director                 Stock Gift                         2  
   3 │ JUNG ANDREA         Director                 Conversion of Exercise of deriva…  2  
   4 │ KONDO CHRISTOPHER   Officer                  Sale                               2  
   5 │ LEVINSON ARTHUR D   Director                 Sale                               2 ⋯
   6 │ MAESTRI LUCA        Chief Financial Officer  Sale                               2  
   7 │ O'BRIEN DEIRDRE     Officer                  Sale                               2  
   8 │ SUGAR RONALD D      Director                 Conversion of Exercise of deriva…  2  
   9 │ WAGNER SUSAN L      Director                 Conversion of Exercise of deriva…  2 ⋯
  10 │ WILLIAMS JEFFREY E  Chief Operating Officer  Conversion of Exercise of deriva…  2  
                                                                         5 columns omitted

source

YFinance.get_insider_transactions — Method

get_insider_transactions(quoteSummary::JSON3.Object)

Retrievs the insider transactions from the quote summary.

Arguments

Examples

julia> get_quoteSummary("AAPL") |> get_insider_transactions
OrderedDict{String, Vector} with 7 entries:
  "filerName"       => ["KONDO CHRISTOPHER", "MAESTRI LUCA", "O'BRIEN DEIRDRE", "KONDO CH…  
  "filerRelation"   => Union{Missing, String}["Officer", "Chief Financial Officer", "Offi…  
  "transactionText" => Union{Missing, String}["Sale at price 148.72 per share.", "Sale at…  
  "date"            => Union{Missing, DateTime}[DateTime("2022-11-22T00:00:00"), DateTime…  
  "ownership"       => Union{Missing, String}["D", "D", "D", "D", "D", "D", "D", "D", "I"…  
  "shares"          => Union{Missing, Int64}[20200, 176299, 8053, 13136, 16612, 181139, 1…  
  "value"           => Union{Missing, Int64}[3004144, 27493275, 1147150, missing, missing…

julia> get_insider_transactions("AAPL")
OrderedDict{String, Vector} with 7 entries:
  "filerName"       => ["KONDO CHRISTOPHER", "MAESTRI LUCA", "O'BRIEN DEIRDRE", "KONDO CH…  
  "filerRelation"   => Union{Missing, String}["Officer", "Chief Financial Officer", "Offi…  
  "transactionText" => Union{Missing, String}["Sale at price 148.72 per share.", "Sale at…  
  "date"            => Union{Missing, DateTime}[DateTime("2022-11-22T00:00:00"), DateTime…  
  "ownership"       => Union{Missing, String}["D", "D", "D", "D", "D", "D", "D", "D", "I"…  
  "shares"          => Union{Missing, Int64}[20200, 176299, 8053, 13136, 16612, 181139, 1…  
  "value"           => Union{Missing, Int64}[3004144, 27493275, 1147150, missing, missing…

julia> using DataFrames
julia> get_insider_transactions("AAPL") |> DataFrame
75×7 DataFrame
 Row │ filerName           filerRelation            transactionText                    d ⋯
     │ String              String?                  String?                            D ⋯
─────┼────────────────────────────────────────────────────────────────────────────────────
   1 │ KONDO CHRISTOPHER   Officer                  Sale at price 148.72 per share.    2 ⋯
   2 │ MAESTRI LUCA        Chief Financial Officer  Sale at price 154.70 - 157.20 pe…  2  
   3 │ O'BRIEN DEIRDRE     Officer                  Sale at price 142.45 per share.    2  
   4 │ KONDO CHRISTOPHER   Officer                                                     2  
   5 │ O'BRIEN DEIRDRE     Officer                                                     2 ⋯   
   6 │ ADAMS KATHERINE L   General Counsel          Sale at price 138.44 - 142.93 pe…  2  
   7 │ O'BRIEN DEIRDRE     Officer                  Sale at price 141.09 - 142.83 pe…  2  
  ⋮  │         ⋮                      ⋮                             ⋮                    ⋱
  70 │ WAGNER SUSAN L      Director                                                    2  
  71 │ JUNG ANDREA         Director                                                    2 ⋯
  72 │ BELL JAMES A        Director                                                    2  
  73 │ LOZANO MONICA C.    Director                                                    2  
  74 │ GORE ALBERT A JR    Director                                                    2  
  75 │ ADAMS KATHERINE L   General Counsel          Sale at price 131.79 - 134.56 pe…  2 ⋯
                                                             4 columns and 62 rows omitted

source

YFinance.get_institutional_ownership — Method

get_institutional_ownership(quoteSummary::JSON3.Object)

Retrievs the institutional ownership from the quote summary.

Arguments

Examples

julia> get_quoteSummary("AAPL") |> get_institutional_ownership
OrderedDict{String, Vector} with 6 entries:
  "organization" => ["Vanguard Group, Inc. (The)", "Blackrock Inc.", "Berkshire Hathaway,…  
  "reportDate"   => Union{Missing, DateTime}[DateTime("2022-09-30T00:00:00"), DateTime("2…  
  "pctHeld"      => Union{Missing, Float64}[0.08, 0.0641, 0.0562, 0.0372, 0.0221, 0.0176,…  
  "position"     => Union{Missing, Int64}[1272378901, 1020245185, 894802319, 591543874, 3…  
  "value"        => Union{Missing, Int64}[164913030135, 132233979050, 115975329111, 76670…  
  "pctChange"    => Union{Missing, Float64}[-0.0039, -0.0082, 0.0, -0.0111, 0.0191, 0.005…

julia> get_institutional_ownership("AAPL")
OrderedDict{String, Vector} with 6 entries:
  "organization" => ["Vanguard Group, Inc. (The)", "Blackrock Inc.", "Berkshire Hathaway,…  
  "reportDate"   => Union{Missing, DateTime}[DateTime("2022-09-30T00:00:00"), DateTime("2…  
  "pctHeld"      => Union{Missing, Float64}[0.08, 0.0641, 0.0562, 0.0372, 0.0221, 0.0176,…  
  "position"     => Union{Missing, Int64}[1272378901, 1020245185, 894802319, 591543874, 3…  
  "value"        => Union{Missing, Int64}[164913030135, 132233979050, 115975329111, 76670…  
  "pctChange"    => Union{Missing, Float64}[-0.0039, -0.0082, 0.0, -0.0111, 0.0191, 0.005…

julia> using DataFrames
julia> get_institutional_ownership("AAPL") |> DataFrame
10×6 DataFrame
 Row │ organization                   reportDate           pctHeld   position    value   ⋯
     │ String                         DateTime?            Float64?  Int64?      Int64?  ⋯
─────┼────────────────────────────────────────────────────────────────────────────────────
   1 │ Vanguard Group, Inc. (The)     2022-09-30T00:00:00    0.08    1272378901  1649130 ⋯
   2 │ Blackrock Inc.                 2022-09-30T00:00:00    0.0641  1020245185  1322339  
   3 │ Berkshire Hathaway, Inc        2022-09-30T00:00:00    0.0562   894802319  1159753  
   4 │ State Street Corporation       2022-09-30T00:00:00    0.0372   591543874   766700  
   5 │ FMR, LLC                       2022-09-30T00:00:00    0.0221   350900116   454801 ⋯
   6 │ Geode Capital Management, LLC  2022-09-30T00:00:00    0.0176   279758518   362595  
   7 │ Price (T.Rowe) Associates Inc  2022-09-30T00:00:00    0.0141   224863541   291445  
   8 │ Morgan Stanley                 2022-09-30T00:00:00    0.0115   182728771   236834  
   9 │ Northern Trust Corporation     2022-09-30T00:00:00    0.0111   176084862   228223 ⋯
  10 │ Bank of America Corporation    2022-09-30T00:00:00    0.0089   142260591   184383  
                                                                         2 columns omitted

source

YFinance.get_major_holders_breakdown — Method

get_major_holders_breakdown(quoteSummary::JSON3.Object)

Retrievs the breakdown of the major holders from the quote summary.

Arguments

Examples

julia> get_quoteSummary("AAPL") |> get_major_holders_breakdown
OrderedDict{String, Real} with 4 entries:  
  "insidersPercentHeld"          => 0.00072
  "institutionsPercentHeld"      => 0.60915
  "institutionsFloatPercentHeld" => 0.60959
  "institutionsCount"            => 5526  

julia> get_major_holders_breakdown("AAPL")
OrderedDict{String, Real} with 4 entries:  
  "insidersPercentHeld"          => 0.00072
  "institutionsPercentHeld"      => 0.60915
  "institutionsFloatPercentHeld" => 0.60959
  "institutionsCount"            => 5526

source

YFinance.get_prices — Method

get_prices(symbol::String; range::String="5d", interval::String="1d", startdt::Union{Date,DateTime,AbstractString}="", enddt::Union{Date,DateTime,AbstractString}="", prepost::Bool=false, autoadjust::Bool=true, timeout::Int=10, throw_error::Bool=false, exchange_local_time::Bool=false, divsplits::Bool=false, wait::Float64=0.0)

Retrieves prices from Yahoo Finance.

Arguments

symbol: A ticker (e.g., AAPL for Apple Inc., or ^GSPC for the S&P 500)

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

range: A string specifying the time range. It can be one of the predefined values ("ytd", "max") or a custom range using the following suffixes:
- "m" for minutes (e.g., "30m" for 30 minutes)
- "d" for days (e.g., "7d" for 7 days)
- "mo" for months (e.g., "3mo" for 3 months)
- "y" for years (e.g., "1y" for 1 year)
startdt and enddt: Can be of type Date, DateTime, or a String in the format "yyyy-mm-dd". Both must be provided if one is specified.
interval: The data interval. Valid values are "1m", "2m", "5m", "15m", "30m", "60m", "90m", "1h", "1d", "5d", "1wk", "1mo", "3mo". Defaults to "1d".
prepost: Boolean indicating whether pre and post market data should be included. Defaults to false.
autoadjust: Defaults to true. 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.
timeout: The timeout for the HTTP request in seconds. Defaults to 10.
throw_error: Boolean, defaults to false. 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 empty OrderedDict is returned.
exchange_local_time: Boolean, defaults to false. If set to true, the timestamp corresponds to the exchange local time; otherwise, it's in GMT.
divsplits: Boolean, defaults to false. If set to true, dividends and stock split data are also returned. Split data contains the numerator, denominator, and split ratio. The interval needs to be set to "1d" for this to work.
wait: Float, defaults to 0.0. Specifies the wait time in seconds between consecutive API calls when fetching minute data over extended periods.

Notes

For minute data requests over periods longer than 7 days, the function automatically splits the request into multiple 7-day chunks and combines the results.
When using startdt and enddt, both must be provided.

Returns

An OrderedDict{String, Union{String,Vector{DateTime},Vector{Float64}}} containing the requested data.

Examples

julia> get_prices("AAPL", range="1d", interval="90m")
OrderedDict{String, Union{String,Vector{DateTime},Vector{Float64}}} with 7 entries:
  "ticker"    => "AAPL"
  "timestamp" => [DateTime("2022-12-29T14:30:00"), DateTime("2022-12-29T16:00:00"), DateTime("2022-12-29T17:30:00"), DateTime("2022-12-29T19:00:00"), DateTime("2022-12-29T20:30:00"), DateTime("2022-12-29T21:00:00")]   
  "open"      => [127.99, 129.96, 129.992, 130.035, 129.95, 129.61]
  "high"      => [129.98, 130.481, 130.098, 130.24, 130.22, 129.61]
  "low"       => [127.73, 129.44, 129.325, 129.7, 129.56, 129.61]
  "close"     => [129.954, 129.998, 130.035, 129.95, 129.6, 129.61]
  "vol"       => [2.9101646e7, 1.4058713e7, 9.897737e6, 9.552323e6, 6.308537e6, 0.0]

## Can be easily converted to a DataFrame
julia> using DataFrames
julia> get_prices("AAPL", range="1d", interval="90m") |> DataFrame
6×7 DataFrame
 Row │ ticker  timestamp            open     high     low      close    vol       
     │ String  DateTime             Float64  Float64  Float64  Float64  Float64   
─────┼────────────────────────────────────────────────────────────────────────────
   1 │ AAPL    2022-12-29T14:30:00  127.99   129.98   127.73   129.954  2.9101646e7
   2 │ AAPL    2022-12-29T16:00:00  129.96   130.481  129.44   129.998  1.4058713e7
   3 │ AAPL    2022-12-29T17:30:00  129.992  130.098  129.325  130.035  9.897737e6
   4 │ AAPL    2022-12-29T19:00:00  130.035  130.24   129.7    129.95   9.552323e6
   5 │ AAPL    2022-12-29T20:30:00  129.95   130.22   129.56   129.6    6.308537e6
   6 │ AAPL    2022-12-29T21:00:00  129.61   129.61   129.61   129.61   0.0

## Broadcasting
julia> get_prices.(["AAPL","NFLX"], range="1d", interval="90m")
2-element Vector{OrderedDict{String, Union{String, Vector{DateTime}, Vector{Float64}}}}:
 OrderedDict{String, Union{String, Vector{DateTime}, Vector{Float64}}} with 7 entries:
  "ticker"    => "AAPL"
  "timestamp" => [DateTime("2022-12-29T14:30:00"), DateTime("2022-12-29T16:00:00"), DateTime("2022-12-29T17:30:00"), DateTime("2022-12-29T19:00:00"), DateTime("2022-12-29T20:30:00"), DateTime("2022-12-29T21:00:00")]
  "open"      => [127.98999786376953, 129.9600067138672, 129.99240112304688, 130.03500366210938, 129.9499969482422, 129.61000061035156]
  "high"      => [129.97999572753906, 130.4813995361328, 130.09829711914062, 130.24000549316406, 130.22000122070312, 129.61000061035156]
  "low"       => [127.7300033569336, 129.44000244140625, 129.3249969482422, 129.6999969482422, 129.55999755859375, 129.61000061035156]
  "close"     => [129.95419311523438, 129.99830627441406, 130.03500366210938, 129.9499969482422, 129.60000610351562, 129.61000061035156]
  "vol"       => [2.9101646e7, 1.4058713e7, 9.897737e6, 9.552323e6, 6.308537e6, 0.0]
 OrderedDict{String, Union{String, Vector{DateTime}, Vector{Float64}}} with 7 entries:
  "ticker"    => "NFLX"
  "timestamp" => [DateTime("2022-12-29T14:30:00"), DateTime("2022-12-29T16:00:00"), DateTime("2022-12-29T17:30:00"), DateTime("2022-12-29T19:00:00"), DateTime("2022-12-29T20:30:00"), DateTime("2022-12-29T21:00:00")]
  "open"      => [283.17999267578125, 289.5199890136719, 293.4200134277344, 290.05499267578125, 290.760009765625, 291.1199951171875]
  "high"      => [291.8699951171875, 295.4999084472656, 293.5, 291.32000732421875, 292.3299865722656, 291.1199951171875]
  "low"       => [281.010009765625, 289.489990234375, 289.5400085449219, 288.7699890136719, 290.5400085449219, 291.1199951171875]
  "close"     => [289.5199890136719, 293.46990966796875, 290.04998779296875, 290.82000732421875, 291.1199951171875, 291.1199951171875]
  "vol"       => [2.950791e6, 2.458057e6, 1.362915e6, 1.212217e6, 1.121821e6, 0.0]

## 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]...)
12×7 DataFrame
 Row │ ticker  timestamp            open     high     low      close    vol       
     │ String  DateTime             Float64  Float64  Float64  Float64  Float64   
─────┼────────────────────────────────────────────────────────────────────────────
   1 │ AAPL    2022-12-29T14:30:00  127.99   129.98   127.73   129.954  2.9101646e7
   2 │ AAPL    2022-12-29T16:00:00  129.96   130.481  129.44   129.998  1.4058713e7
   3 │ AAPL    2022-12-29T17:30:00  129.992  130.098  129.325  130.035  9.897737e6
   4 │ AAPL    2022-12-29T19:00:00  130.035  130.24   129.7    129.95   9.552323e6
   5 │ AAPL    2022-12-29T20:30:00  129.95   130.22   129.56   129.6    6.308537e6
   6 │ AAPL    2022-12-29T21:00:00  129.61   129.61   129.61   129.61   0.0
   7 │ NFLX    2022-12-29T14:30:00  283.18   291.87   281.01   289.52   2.950791e6
   8 │ NFLX    2022-12-29T16:00:00  289.52   295.5    289.49   293.47   2.458057e6
   9 │ NFLX    2022-12-29T17:30:00  293.42   293.5    289.54   290.05   1.362915e6
  10 │ NFLX    2022-12-29T19:00:00  290.055  291.32   288.77   290.82   1.212217e6
  11 │ NFLX    2022-12-29T20:30:00  290.76   292.33   290.54   291.12   1.121821e6
  12 │ NFLX    2022-12-29T21:00:00  291.12   291.12   291.12   291.12   0.0

source

YFinance.get_quoteSummary — Method

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)
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 JSON3.Object is returned.

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_recommendation_trend — Method

get_recommendation_trend(quoteSummary::JSON3.Object)

Retrievs the recommendation trend from the quote summary.

Arguments

Examples

julia> get_quoteSummary("AAPL") |> get_recommendation_trend
OrderedDict{String, Vector} with 6 entries:
  "period"     => ["0m", "-1m", "-2m", "-3m"]
  "strongbuy"  => [11, 11, 11, 13]
  "buy"        => [21, 25, 26, 20]
  "hold"       => [6, 6, 5, 8]
  "sell"       => [0, 1, 1, 0]
  "strongsell" => [0, 0, 0, 0]

julia> get_recommendation_trend("AAPL")
OrderedDict{String, Vector} with 6 entries:
  "period"     => ["0m", "-1m", "-2m", "-3m"]
  "strongbuy"  => [11, 11, 11, 13]
  "buy"        => [21, 25, 26, 20]
  "hold"       => [6, 6, 5, 8]
  "sell"       => [0, 1, 1, 0]
  "strongsell" => [0, 0, 0, 0]
  
julia> using DataFrames
julia> get_recommendation_trend("AAPL") |> DataFrame
4×6 DataFrame
 Row │ period  strongbuy  buy    hold   sell   strongsell 
     │ String  Int64      Int64  Int64  Int64  Int64      
─────┼────────────────────────────────────────────────────
   1 │ 0m             11     21      6      0           0
   2 │ -1m            11     25      6      1           0
   3 │ -2m            11     26      5      1           0
   4 │ -3m            13     20      8      0           0

source

YFinance.get_sector_industry — Method

get_sector_industry(quoteSummary::JSON3.Object)

Retrievs the Sector and Industry from the quote summary.

Arguments

Examples

julia> get_quoteSummary("AAPL") |> get_sector_industry
OrderedDict{String, String} with 2 entries:
  "sector"   => "Technology"
  "industry" => "Consumer Electronics"

julia> get_sector_industry("AAPL")
OrderedDict{String, String} with 2 entries:
  "sector"   => "Technology"
  "industry" => "Consumer Electronics"

source

YFinance.get_splits — Method

get_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)
startdt and enddt: Optional. Can be of type Date, DateTime, or a String in the format "yyyy-mm-dd". If not provided, startdt defaults to the earliest available data and enddt to the current date.
timeout: Integer, defaults to 10. The timeout for the HTTP request in seconds.
throw_error: Boolean, defaults to false. 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 empty OrderedDict is returned.
exchange_local_time: Boolean, defaults to false. 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

source

YFinance.get_summary_detail — Method

get_summary_detail(quoteSummary::JSON3.Object)

Retrievs the summaryDetail Item from the quote summary.

Arguments

Examples

julia> get_quoteSummary("AAPL") |> get_summary_detail
OrderedDict{String, Any} with 41 entries:
  "priceHint"                  => 2
  "previousClose"              => 126.04
  "open"                       => 127.99
  "dayLow"                     => 127.815
  "dayHigh"                    => 130.48
  "regularMarketPreviousClose" => 126.04
  "regularMarketOpen"          => 127.99
  "regularMarketDayLow"        => 127.815
  "regularMarketDayHigh"       => 130.48
  "dividendRate"               => 0.92
  "dividendYield"              => 0.0073
  "exDividendDate"             => 1667520000
  "payoutRatio"                => 0.1473
  "fiveYearAvgDividendYield"   => 0.99
  "beta"                       => 1.21947
  "trailingPE"                 => 21.2128
  "forwardPE"                  => 19.1448
  ⋮                            => ⋮

julia> get_summary_detail("AAPL")
OrderedDict{String, Any} with 41 entries:
  "priceHint"                  => 2
  "previousClose"              => 126.04
  "open"                       => 127.99
  "dayLow"                     => 127.815
  "dayHigh"                    => 130.48
  "regularMarketPreviousClose" => 126.04
  "regularMarketOpen"          => 127.99
  "regularMarketDayLow"        => 127.815
  "regularMarketDayHigh"       => 130.48
  "dividendRate"               => 0.92
  "dividendYield"              => 0.0073
  "exDividendDate"             => 1667520000
  "payoutRatio"                => 0.1473
  "fiveYearAvgDividendYield"   => 0.99
  "beta"                       => 1.21947
  "trailingPE"                 => 21.2128
  "forwardPE"                  => 19.1448
  ⋮                            => ⋮

source

YFinance.get_symbols — Method

get_symbols(search_term::String)

Allows searches for specific securities.

Arguments

search_term::String: Typically a company/security name (e.g. microsoft)

Returns

A YahooSearch <: AbstractArray containing YahooSearchItems containing the following fields: symbol::String, shortname::String, exchange::String, quoteType::String, sector::String, industry::String

Example

julia> get_symbols("micro")
7-element YahooSearch{YahooSearchItem, 1}:
 
Symbol:  MGC=F
Name:    Micro Gold Futures,Jun-2023
Type:    FUTURE
Exch.:   New York Commodity Exchange (CMX)


Symbol:  MSFT
Name:    Microsoft Corporation
Type:    EQUITY
Exch.:   NASDAQ (NMS)
Sec.:    Technology
Ind.:    Software—Infrastructure


Symbol:  AMD
Name:    Advanced Micro Devices, Inc.
Type:    EQUITY
Exch.:   NASDAQ (NMS)
Sec.:    Technology
Ind.:    Semiconductors


Symbol:  MU
Name:    Micron Technology, Inc.
Type:    EQUITY
Exch.:   NASDAQ (NMS)
Sec.:    Technology
Ind.:    Semiconductors


Symbol:  MSTR
Name:    MicroStrategy Incorporated
Type:    EQUITY
Exch.:   NASDAQ (NMS)
Sec.:    Technology
Ind.:    Software—Application


Symbol:  SMCI
Name:    Super Micro Computer, Inc.
Type:    EQUITY
Exch.:   NASDAQ (NMS)
Sec.:    Technology
Ind.:    Computer Hardware


Symbol:  FNGU
Name:    MicroSectors FANG  Index 3X Lev
Type:    ETF
Exch.:   NYSEArca (PCX)

source

YFinance.get_upgrade_downgrade_history — Method

get_upgrade_downgrade_history(quoteSummary::JSON3.Object)

Retrievs the upgrade and downgrade history from the quote summary.

Arguments

Examples

julia> get_quoteSummary("AAPL") |> get_upgrade_downgrade_history
OrderedDict{String, Vector} with 5 entries:
  "firm"      => ["JP Morgan", "UBS", "Morgan Stanley", "B of A Securities", "Barclays", …  
  "date"      => Union{Missing, DateTime}[DateTime("2022-12-20T11:47:33"), DateTime("2022…  
  "toGrade"   => Union{Missing, String}["Overweight", "Buy", "Overweight", "Neutral", "Eq…  
  "fromGrade" => Union{Missing, String}["", "", "", "", "", "", "", "", "", ""  …  "", ""…  
  "action"    => Union{Missing, String}["main", "main", "main", "main", "main", "main", "…

julia> get_upgrade_downgrade_history("AAPL")
OrderedDict{String, Vector} with 5 entries:
  "firm"      => ["JP Morgan", "UBS", "Morgan Stanley", "B of A Securities", "Barclays", …  
  "date"      => Union{Missing, DateTime}[DateTime("2022-12-20T11:47:33"), DateTime("2022…  
  "toGrade"   => Union{Missing, String}["Overweight", "Buy", "Overweight", "Neutral", "Eq…  
  "fromGrade" => Union{Missing, String}["", "", "", "", "", "", "", "", "", ""  …  "", ""…  
  "action"    => Union{Missing, String}["main", "main", "main", "main", "main", "main", "…
  
julia> using DataFrames
julia> get_upgrade_downgrade_history("AAPL") |> DataFrame
872×5 DataFrame
 Row │ firm               date                 toGrade       fromGrade  action  
     │ String             DateTime?            String?       String?    String? 
─────┼──────────────────────────────────────────────────────────────────────────
   1 │ JP Morgan          2022-12-20T11:47:33  Overweight               main
   2 │ UBS                2022-11-08T12:17:03  Buy                      main
   3 │ Morgan Stanley     2022-11-08T12:14:23  Overweight               main
   4 │ B of A Securities  2022-11-07T13:08:30  Neutral                  main
   5 │ Barclays           2022-11-07T12:39:27  Equal-Weight             main
   6 │ Wedbush            2022-10-28T13:19:17  Outperform               main
   7 │ Credit Suisse      2022-10-28T11:59:30  Outperform               main
  ⋮  │         ⋮                   ⋮                ⋮            ⋮         ⋮
 867 │ Oxen Group         2012-03-14T15:25:00  Buy                      init
 868 │ Canaccord Genuity  2012-03-14T08:21:00  Buy                      main
 869 │ Morgan Stanley     2012-03-14T06:13:00  Overweight               main
 870 │ Jefferies          2012-03-13T06:08:00  Buy                      main
 871 │ FBN Securities     2012-03-08T07:33:00  Outperform               main
 872 │ Canaccord Genuity  2012-02-09T08:17:00  Buy                      main
                                                                859 rows omitted

source

YFinance.get_valid_symbols — Method

get_valid_symbols(symbol::AbstractString)

Takes a symbol. If the symbol is valid it returns the symbol in a vector if not it returns and empy vector.

Arguments

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

Examples

julia> get_valid_symbols("AAPL")
1-element Vector{String}:
 "AAPL"

julia> get_valid_symbols("asdfs")
 String[]

source

YFinance.get_valid_symbols — Method

get_valid_symbols(symbol::AbstractVector{<:AbstractString})

Takes a AbstractVector of symbols and returns only the valid ones.

Arguments

smybol::AbstractVector{<:AbstractString} is a vector of tickers (e.g. AAPL for Apple Computers, or ^GSPC for the S&P500)

Examples

julia> get_valid_symbols("AAPL","AMD","asdfs")
2-element Vector{String}:
 "AAPL"
 "AMD"

source

YFinance.links — Method

links(x::YahooNews)

Returns the links of all NewsItems in a Vector

Arugments:

x::YahooNews

Returns:

Vector{String}

Example:

julia> x = search_news("MSFT");

julia> links(x)
8-element Vector{String}:
 "https://finance.yahoo.com/news/" ⋯ 20 bytes ⋯ "itter-ad-program-221121298.html"
 "https://finance.yahoo.com/m/06a" ⋯ 37 bytes ⋯ "chatbots-guzzle-water.-how.html"
 "https://finance.yahoo.com/m/65b" ⋯ 36 bytes ⋯ "st-dow-jones-stocks-to-buy.html"
 "https://finance.yahoo.com/m/9c4" ⋯ 35 bytes ⋯ "op-companies-for-financial.html"
 "https://finance.yahoo.com/m/ebf" ⋯ 35 bytes ⋯ "ideo%3A-your-top-questions.html"
 "https://finance.yahoo.com/news/board-oks-microsoft-data-center-163630944.html"
 "https://finance.yahoo.com/news/" ⋯ 20 bytes ⋯ "-workplace-where-155427039.html"
 "https://finance.yahoo.com/news/microsoft-working-space-time-add-150000132.html"

source

YFinance.search_news — Method

search_news(str::String;lang="en-us")

Returns news related to the seach string str.

Arugments:

str::String: The search string. It is usually a symbol.
lang::String: The search language and region. The region is automatically set according to the language. Supported languages are: "en-us", "en-ca", "en-gb", "en-au", "en-nz", "en-SG", "en-in", "de", "es", "fr", "it", "pt-br", "zh", and "zh-tw".

Returns:

YahooNews <: AbstractArray that contains NewsItems with fields: title::String, publisher::String, link::String, timestamp::DateTime, symbols::Array{String,1}

Example:

julia> search_news("MSFT")
8-element YahooNews{NewsItem, 1}:
 Title:          Microsoft Removes Twitter From Ad Program; Musk Threatens Suit
Timestamp:       Apr 19 22:11 PM
Publisher:       Bloomberg
Link:            https://finance.yahoo.com/news/microsoft-removes-twitter-ad-program-221121298.html
Symbols:         MSFT

 Title:          AI ChatBots Guzzle Water. How and Why It’s a Problem.
Timestamp:       Apr 19 20:33 PM
Publisher:       Barrons.com
Link:            https://finance.yahoo.com/m/06a973de-215d-3928-9c99-00867b512966/ai-chatbots-guzzle-water.-how.html
Symbols:         GOOGL, MSFT

 Title:          Best Dow Jones Stocks To Buy And Watch In April: Travelers Surges On Earnings
Timestamp:       Apr 19 18:06 PM
Publisher:       Investor's Business Daily
Link:            https://finance.yahoo.com/m/65b53896-faf4-3a06-9d0d-a63cf3c83192/best-dow-jones-stocks-to-buy.html
Symbols:         ^DJI, MSFT

 Title:          Top Companies for Financial Strength
Timestamp:       Apr 19 18:03 PM
Publisher:       The Wall Street Journal
Link:            https://finance.yahoo.com/m/9c4f6782-7ce7-3e1e-8d0a-ff7f41bc5ef7/top-companies-for-financial.html
Symbols:         XOM, MSFT, AAPL, NUE, MRNA

 Title:          VIDEO: Your Top Questions Answered on the Debt Ceiling and Portfolio Management
Timestamp:       Apr 19 16:46 PM
Publisher:       TheStreet.com
Link:            https://finance.yahoo.com/m/ebf41ba6-6cbc-38ce-93c5-bcee152080e7/video%3A-your-top-questions.html
Symbols:         CHPT, MSFT

 Title:          Microsoft agrees to buy 50m Foxconn parcel in Wisconsin
Timestamp:       Apr 19 16:36 PM
Publisher:       AP Finance
Link:            https://finance.yahoo.com/news/board-oks-microsoft-data-center-163630944.html
Symbols:         MSFT

 Title:          LinkedIn Reveals Top Workplace: Where Amazon and Netflix Rank For Happy Employees
Timestamp:       Apr 19 15:54 PM
Publisher:       Benzinga
Link:            https://finance.yahoo.com/news/linkedin-reveals-top-workplace-where-155427039.html
Symbols:         AMZN, GOOGL, MSFT, NFLX, WFC

 Title:          Microsoft Working With Space and Time to Add Real-Time Blockchain Data for Azure Cloud
Timestamp:       Apr 19 15:00 PM
Publisher:       CoinDesk
Link:            https://finance.yahoo.com/news/microsoft-working-space-time-add-150000132.html
Symbols:         MSFT

source

YFinance.sink_prices_to — Method

sink_prices_to(::Type{OrderedDict},x::OrderedDict{String,Any})

Converts an exisitng OrderedDict output from get_prices to an OrderedDict If TimeSeries.jl or TSFrames.jl are loaded this function is extended to allow sinking into these types.

source

YFinance.timestamps — Method

timestamps(x::YahooNews)

Returns the timestamp of all NewsItems in a Vector

Arugments:

x::YahooNews

Returns:

Vector{DateTime}

Example:

julia> x = search_news("MSFT");

julia> timestamps(x)
8-element Vector{Dates.DateTime}:
 2023-04-19T22:11:21
 2023-04-19T20:33:00
 2023-04-19T18:06:33
 2023-04-19T18:03:00
 2023-04-19T16:46:00
 2023-04-19T16:36:30
 2023-04-19T15:54:27
 2023-04-19T15:00:00

source

YFinance.titles — Method

titles(x::YahooNews)

Returns the titles of all NewsItems in a Vector

Arugments:

x::YahooNews

Returns:

Vector{String}

Example:

julia> x = search_news("MSFT");

julia> titles(x)
8-element Vector{String}:
 "Microsoft Removes Twitter From Ad Program; Musk Threatens Suit"
 "AI ChatBots Guzzle Water. How and Why It s a Problem."
 "Best Dow Jones Stocks To Buy And Watch In April: Travelers Surges On Earnings"
 "Top Companies for Financial Strength"
 "VIDEO: Your Top Questions Answe" ⋯ 17 bytes ⋯ "eiling and Portfolio Management"
 "Microsoft agrees to buy 50m Foxconn parcel in Wisconsin"
 "LinkedIn Reveals Top Workplace:" ⋯ 19 bytes ⋯ "etflix Rank For Happy Employees"
 "Microsoft Working With Space an" ⋯ 24 bytes ⋯ "Blockchain Data for Azure Cloud"

source

YFinance.validate_symbol — Method

validate_symbol(symbol::AbstractString)

Validates a Symbol (Ticker). Returns true if the ticker is valid and false if the ticker is not valid.

Arguments

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

How it works

Checks if the HTTP request works (status 200) or whether the request errors (common status in this case: 404)

source