Download price, fundamental, option, currency, futures, ETF, mutual fund, and various other data from Yahoo Finance
Yahoo!, Y!Finance, and Yahoo! finance are registered trademarks of Yahoo, Inc.
YFinance.jl is not endorsed or in anyway affiliated with Yahoo, Inc. The data retreived can only be used for personal use. Please see Yahoo's terms of use to ensure that you can use the data:
The implementation of YFinance.jl is similar to the python package yahooquery in that it accesses data through API endpoints. Therefore, YFinance.jl does not experience the same decryption issues that python’s yfinance faces at the moment.
The package is registered in the General registry and so can be installed at the REPL with ] add YFinance or by running:
using Pkg
Pkg.add("YFinance")Precompilation of the get_prices function. This has caused precompilation hangs for Julia 1.10 and 1.11 - package typically still precompiles but takes long and gives warnings. Note precompilation also does not work if you require to set a proxy to access the internet.
To Install this version enter the package mode by entering ] in the REPL and run the following command:
(@v1.10) pkg> add YFinance @v0.1.8Does not precompile.
To Install this version enter the package mode by entering ] in the REPL and run the following command:
(@v1.10) pkg> add YFinance @v0.1.9Precompiles only the response processing part of the get_prices and not the HTTP request. Most of the compilation time lies unfortunately with the request itself.
To Install this version enter the package mode by entering ] in the REPL and run the following command:
(@v1.10) pkg> add YFinanceThere are more examples in the Docs.
Apple monthly stock price over the past 5 years, with dividend and stock split information in local user time.
using YFinance
get_prices("AAPL",range="5y",interval="1mo",divsplits=true,exchange_local_time=false)OrderedCollections.OrderedDict{String, Any} with 8 entries:
"ticker" => "AAPL"
"timestamp" => [DateTime("2019-09-01T04:00:00"), DateTime("2019-10-01T04:00:00"), DateTime("2019-11-01T04:00:00"), DateT…
"open" => [49.9955, 54.5099, 60.4363, 64.9247, 71.9621, 73.92, 68.7337, 60.0214, 69.7003, 77.5799 … 189.83, 186.6…
"high" => [54.8369, 60.4872, 64.9072, 71.4106, 79.6407, 79.4877, 74.0224, 71.7165, 78.9507, 90.918 … 199.096, 195…
"low" => [49.4603, 52.1026, 60.3443, 62.2575, 71.1143, 62.2769, 51.7694, 57.6839, 69.6029, 77.448 … 186.958, 179…
"close" => [55.9925, 62.19, 66.8125, 73.4125, 77.3775, 68.34, 63.5725, 73.45, 79.485, 91.2 … 192.53, 184.4, 180.75…
"adjclose" => [54.2435, 60.2474, 64.7255, 71.3329, 75.1856, 66.4041, 61.9183, 71.5387, 77.4167, 89.0673 … 192.024, 18…
"vol" => [2.10248e9, 2.35721e9, 1.73731e9, 2.32113e9, 2.85125e9, 2.93375e9, 6.11666e9, 3.18033e9, 2.73292e9, 3.167…Bitcoin USD, 5 minute data points for an entire day.
DataFrame(get_prices("BTC-USD",range="1d",interval="5m",exchange_local_time=true))147×7 DataFrame
Row │ ticker timestamp open high low close vol
│ String DateTime Float64 Float64 Float64 Float64 Float64
─────┼─────────────────────────────────────────────────────────────────────────────
1 │ BTC-USD 2024-08-08T00:00:00 55139.5 55254.3 55139.5 55254.3 7.3769e6
2 │ BTC-USD 2024-08-08T00:05:00 55229.6 55355.9 55229.6 55355.9 0.0
3 │ BTC-USD 2024-08-08T00:10:00 55273.2 55273.2 55121.3 55121.3 1.9198e7
4 │ BTC-USD 2024-08-08T00:15:00 55099.8 55272.9 55099.8 55232.8 0.0
⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮
145 │ BTC-USD 2024-08-08T12:00:00 57346.1 57406.9 57346.1 57406.9 0.0
146 │ BTC-USD 2024-08-08T12:05:00 57384.4 57384.4 57364.8 57364.8 0.0
147 │ BTC-USD 2024-08-08T12:08:00 57330.2 57330.2 57330.2 57330.2 0.0
140 rows omittedRolls-Royce daily stock price between 2020-01-01 and 2020-05-30
using TimeSeries
get_prices(TimeArray,"RR.L",startdt="2020-01-01",enddt="2020-05-30")103×6 TimeArray{Float64, 2, DateTime, Matrix{Float64}} 2020-01-02T08:00:00 to 2020-05-29T07:00:00
┌─────────────────────┬─────────┬─────────┬─────────┬─────────┬──────────┬───────────┐
│ │ open │ high │ low │ close │ adjclose │ vol │
├─────────────────────┼─────────┼─────────┼─────────┼─────────┼──────────┼───────────┤
│ 2020-01-02T08:00:00 │ 236.374 │ 238.845 │ 232.874 │ 233.766 │ 233.766 │ 1.12197e7 │
│ 2020-01-03T08:00:00 │ 232.668 │ 233.629 │ 229.03 │ 232.599 │ 232.599 │ 1.2363e7 │
│ 2020-01-06T08:00:00 │ 231.295 │ 233.766 │ 228.932 │ 232.325 │ 232.325 │ 1.13855e7 │
│ 2020-01-07T08:00:00 │ 234.041 │ 234.26 │ 227.658 │ 229.305 │ 229.305 │ 9.56679e6 │
│ ⋮ │ ⋮ │ ⋮ │ ⋮ │ ⋮ │ ⋮ │ ⋮ │
│ 2020-05-27T07:00:00 │ 110.5 │ 126.904 │ 110.06 │ 118.771 │ 118.771 │ 7.7212e7 │
│ 2020-05-28T07:00:00 │ 109.814 │ 114.035 │ 104.049 │ 109.471 │ 109.471 │ 1.33343e8 │
│ 2020-05-29T07:00:00 │ 103.294 │ 103.946 │ 93.2044 │ 93.2044 │ 93.2044 │ 1.60135e8 │
└─────────────────────┴─────────┴─────────┴─────────┴─────────┴──────────┴───────────┘
96 rows omittedEURO-USD (EURUSD=X) daily price since start of data
using TSFrames
tsf = get_prices(TSFrame,"EURUSD=X",startdt=Date(1900,1,1),enddt=today())5399×7 TSFrame with DateTime Index
Index open high low close adjclose vol ticker
DateTime Float64 Float64 Float64 Float64 Float64 Float64 String
──────────────────────────────────────────────────────────────────────────────────────
2003-12-01T00:00:00 1.2034 1.20401 1.1944 1.1965 1.1965 0.0 EURUSD=X
2003-12-02T00:00:00 1.1961 1.2109 1.1946 1.2089 1.2089 0.0 EURUSD=X
2003-12-03T00:00:00 1.209 1.213 1.2077 1.2123 1.2123 0.0 EURUSD=X
2003-12-04T00:00:00 1.212 1.2144 1.2044 1.20809 1.20809 0.0 EURUSD=X
⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮
2024-08-05T23:00:00 1.0951 1.09631 1.09046 1.0951 1.0951 0.0 EURUSD=X
2024-08-06T23:00:00 1.09262 1.09364 1.09069 1.09262 1.09262 0.0 EURUSD=X
2024-08-08T12:41:04 1.09266 1.09481 1.09039 1.09039 1.09039 0.0 EURUSD=X
5392 rows omittedusing Plots
plot(tsf[:,[:Index,:adjclose]])
get_ESG("NFLX")["score"] |> DataFrame |> dropmissing79×6 DataFrame
Row │ symbol timestamp esgScore governanceScore environmentScore socialScore
│ String DateTime Real Real Real Real
─────┼───────────────────────────────────────────────────────────────────────────────────────
1 │ NFLX 2014-09-01T00:00:00 43 57 37 39
2 │ NFLX 2014-10-01T00:00:00 43 57 37 39
3 │ NFLX 2014-11-01T00:00:00 43 57 37 39
4 │ NFLX 2014-12-01T00:00:00 43 57 37 39
⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮
77 │ NFLX 2022-05-01T00:00:00 15.78 8.87 0.09 6.83
78 │ NFLX 2022-08-01T00:00:00 16.25 9.34 0.09 6.83
79 │ NFLX 2023-09-01T00:00:00 16.41 9.01 0.09 7.31
72 rows omittedget_Fundamental("NFLX", "valuation","annual",today()-Year(3) , today()) |> DataFrame3×10 DataFrame
Row │ timestamp PegRatio ForwardPeRatio MarketCap PeRatio EnterprisesValueRevenueRatio PbRatio EnterprisesValueEBITDARatio PsRatio EnterpriseValue
│ DateTime Any Any Any Any Any Any Any Any Any
─────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
1 │ 2021-12-31T00:00:00 1.4751 46.5116 267461134181 54.3228 9.6192 17.4644 14.914 9.57841 275427617181
2 │ 2022-12-31T00:00:00 2.1982 27.027 131227643270 26.3993 4.4166 6.39726 6.7377 4.238 139002027270
3 │ 2023-12-31T00:00:00 1.951 30.6748 213097477242 48.5908 6.7047 9.53074 10.686 6.71235 219530399242is=get_Fundamental("AMD", "income_statement","annual",today()-Year(1) , today()) |> DataFrame
stack(is, Not(:timestamp))49×3 DataFrame
Row │ timestamp variable value
│ DateTime String Any
─────┼─────────────────────────────────────────────────────────────────────
1 │ 2023-12-31T00:00:00 InterestExpenseNonOperating 106000000
2 │ 2023-12-31T00:00:00 CostOfRevenue 12220000000
3 │ 2023-12-31T00:00:00 TotalRevenue 22680000000
4 │ 2023-12-31T00:00:00 EarningsFromEquityInterestNetOfT… 16000000
⋮ │ ⋮ ⋮ ⋮
47 │ 2023-12-31T00:00:00 NormalizedIncome 854000000
48 │ 2023-12-31T00:00:00 EBIT 598000000
49 │ 2023-12-31T00:00:00 TotalUnusualItems 0
42 rows omittedop = get_Options("AMD")OrderedCollections.OrderedDict{String, OrderedCollections.OrderedDict{String, Vector{Any}}} with 2 entries:
"calls" => OrderedDict("contractSymbol"=>["AMD240809C00075000", "AMD240809C00080000", "AMD240809C00085000", "AMD240809C0…
"puts" => OrderedDict("contractSymbol"=>["AMD240809P00075000", "AMD240809P00080000", "AMD240809P00085000", "AMD240809P0…DataFrame(op["calls"])65×16 DataFrame
Row │ contractSymbol strike currency lastPrice change percentChange volume openInterest bid ask contractSi ⋯
│ Any Any Any Any Any Any Any Any Any Any Any ⋯
─────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
1 │ AMD240809C00075000 75 USD 59.65 0 0 1 26 0 0 REGULAR ⋯
2 │ AMD240809C00080000 80 USD 52.61 0 0 1 11 0 0 REGULAR
3 │ AMD240809C00085000 85 USD 49.69 0 0 2 0 0 0 REGULAR
4 │ AMD240809C00090000 90 USD 44.54 0 0 3 12 0 0 REGULAR
⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋱
63 │ AMD240809C00285000 285 USD 0.01 0 0 15 16 0 0 REGULAR ⋯
64 │ AMD240809C00290000 290 USD 0.08 0 0 7 10 0 0 REGULAR
65 │ AMD240809C00295000 295 USD 0.01 0 0 1 19 0 0 REGULAR
6 columns and 58 rows omitted