Converting To Tables
DataFrames (DataFrames.jl)
The OrderedDicts can readily be converted to DataFrames by simply calling the DataFrames function on them.
using DataFrames
prices = get_prices("AAPL")
DataFrame(prices)Broadcasting to get one DataFrame (stacked)
tickers=["AAPL","TSLA","F"]
prices = get_prices.(tickers,startdt="2024-01-01",enddt="2024-08-01") |> (x-> DataFrame.(x)) |> (x->vcat(x...))438×8 DataFrame
Row │ ticker timestamp open high low close adjclose vol
│ String DateTime Float64 Float64 Float64 Float64 Float64 Float64
─────┼─────────────────────────────────────────────────────────────────────────────────────────
1 │ AAPL 2024-01-02T14:30:00 186.658 187.945 183.407 185.64 185.152 8.2272e7
2 │ AAPL 2024-01-03T14:30:00 183.736 185.392 182.948 184.25 183.766 5.8261e7
3 │ AAPL 2024-01-04T14:30:00 181.671 182.609 180.405 181.91 181.432 7.17945e7
4 │ AAPL 2024-01-05T14:30:00 181.512 182.28 179.697 181.18 180.704 6.21396e7
⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮
436 │ F 2024-07-29T13:30:00 11.0085 11.0085 10.6344 11.01 10.8411 9.02616e7
437 │ F 2024-07-30T13:30:00 10.8608 10.9396 10.6147 10.84 10.6737 6.82735e7
438 │ F 2024-07-31T13:30:00 10.6934 10.8411 10.5556 10.82 10.654 7.43204e7
431 rows omittedTimeArray from TimeSeries
If you use Julia 1.9 or newer you can just use a sink argument in get_prices instead.
using TimeSeries, YFinance
get_prices(TimeArray,"AAPL")or alternatively
using TimeSeries, YFinance
prices = get_prices("AAPL")
sink_prices_to(TimeArray,prices)If you use an older version you can run the below code:
The TimeArray takes a Vector with the timestamp, a matrix with the price data, column names, and some metadata.
Below is a simple function showing how one may convert the dictionaries containing the price information into a TimeArray - this is most likely not the fastest or most elegant way.
using TimeSeries
prices = get_prices("AAPL")
function stock_price_to_time_array(d)
coln = collect(keys(x))[3:end] # only get the keys that are not ticker or datetime
m = hcat([x[k] for k in coln]...) #Convert the dictionary into a matrix
return TimeArray(x["timestamp"],m,Symbol.(coln),x["ticker"])
end
stock_price_to_time_array(prices)Broadcast and create one TimeArray for the adjclose prices
tickers = ["AAPL","TSLA","F"]
prices = get_prices.((TimeArray,),tickers)3-element Vector{TimeArray{Float64, 2, DateTime, Matrix{Float64}}}:
5×6 TimeArray{Float64, 2, DateTime, Matrix{Float64}} 2024-08-01T13:30:00 to 2024-08-07T13:30:00
5×6 TimeArray{Float64, 2, DateTime, Matrix{Float64}} 2024-08-01T13:30:00 to 2024-08-07T13:30:00
5×6 TimeArray{Float64, 2, DateTime, Matrix{Float64}} 2024-08-01T13:30:00 to 2024-08-07T13:30:00Combine into one TimeArray:
prices=hcat(prices...)5×18 TimeArray{Float64, 2, DateTime, Matrix{Float64}} 2024-08-01T13:30:00 to 2024-08-07T13:30:00
┌─────────────────────┬────────┬────────┬────────┬────────┬──────────┬───────────┬────────┬────────┬────────┬─────────┬────────────┬───────────┬─────────┬─────────┬─────────┬─────────
│ │ open │ high │ low │ close │ adjclose │ vol │ open_1 │ high_1 │ low_1 │ close_1 │ adjclose_1 │ vol_1 │ open_2 │ high_2 │ low_2 │ close_ ⋯
├─────────────────────┼────────┼────────┼────────┼────────┼──────────┼───────────┼────────┼────────┼────────┼─────────┼────────────┼───────────┼─────────┼─────────┼─────────┼─────────
│ 2024-08-01T13:30:00 │ 224.37 │ 224.48 │ 217.02 │ 218.36 │ 218.36 │ 6.2501e7 │ 227.69 │ 231.87 │ 214.33 │ 216.86 │ 216.86 │ 8.38619e7 │ 10.6934 │ 10.7525 │ 10.4473 │ 10.6 ⋯
│ 2024-08-02T13:30:00 │ 219.15 │ 225.6 │ 217.71 │ 219.86 │ 219.86 │ 1.05569e8 │ 214.88 │ 216.13 │ 205.78 │ 207.67 │ 207.67 │ 8.28801e7 │ 10.3882 │ 10.3882 │ 9.84663 │ 10.0 ⋯
│ 2024-08-05T13:30:00 │ 199.09 │ 213.5 │ 196.0 │ 209.27 │ 209.27 │ 1.19549e8 │ 185.22 │ 203.88 │ 182.0 │ 198.88 │ 198.88 │ 1.00309e8 │ 9.41338 │ 9.74816 │ 9.34445 │ 9.7 ⋯
│ 2024-08-06T13:30:00 │ 205.3 │ 209.99 │ 201.07 │ 207.23 │ 207.23 │ 6.96605e7 │ 200.75 │ 202.9 │ 192.67 │ 200.64 │ 200.64 │ 7.37839e7 │ 9.63985 │ 9.70877 │ 9.53153 │ 9.7 ⋯
│ 2024-08-07T13:30:00 │ 206.9 │ 213.64 │ 206.39 │ 209.82 │ 209.82 │ 6.34023e7 │ 200.77 │ 203.49 │ 191.48 │ 191.76 │ 191.76 │ 7.07614e7 │ 9.85 │ 9.98 │ 9.75 │ 9.7 ⋯
└─────────────────────┴────────┴────────┴────────┴────────┴──────────┴───────────┴────────┴────────┴────────┴─────────┴────────────┴───────────┴─────────┴─────────┴─────────┴─────────
3 columns omittedGet the relevant column names (all columns that are called adjclose)
cidx = colnames(prices)[occursin.(r"adj",string.(colnames(prices)))]3-element Vector{Symbol}:
:adjclose
:adjclose_1
:adjclose_2Select these columns
prices = prices[cidx]5×3 TimeArray{Float64, 2, DateTime, Matrix{Float64}} 2024-08-01T13:30:00 to 2024-08-07T13:30:00
┌─────────────────────┬──────────┬────────────┬────────────┐
│ │ adjclose │ adjclose_1 │ adjclose_2 │
├─────────────────────┼──────────┼────────────┼────────────┤
│ 2024-08-01T13:30:00 │ 218.36 │ 216.86 │ 10.526 │
│ 2024-08-02T13:30:00 │ 219.86 │ 207.67 │ 9.87617 │
│ 2024-08-05T13:30:00 │ 209.27 │ 198.88 │ 9.56107 │
│ 2024-08-06T13:30:00 │ 207.23 │ 200.64 │ 9.63 │
│ 2024-08-07T13:30:00 │ 209.82 │ 191.76 │ 9.77 │
└─────────────────────┴──────────┴────────────┴────────────┘Rename them back to the tickers:
TimeSeries.rename!(prices,Symbol.(tickers))5×3 TimeArray{Float64, 2, DateTime, Matrix{Float64}} 2024-08-01T13:30:00 to 2024-08-07T13:30:00
┌─────────────────────┬────────┬────────┬─────────┐
│ │ AAPL │ TSLA │ F │
├─────────────────────┼────────┼────────┼─────────┤
│ 2024-08-01T13:30:00 │ 218.36 │ 216.86 │ 10.526 │
│ 2024-08-02T13:30:00 │ 219.86 │ 207.67 │ 9.87617 │
│ 2024-08-05T13:30:00 │ 209.27 │ 198.88 │ 9.56107 │
│ 2024-08-06T13:30:00 │ 207.23 │ 200.64 │ 9.63 │
│ 2024-08-07T13:30:00 │ 209.82 │ 191.76 │ 9.77 │
└─────────────────────┴────────┴────────┴─────────┘Or in one function:
function get_adj_close_TA(tickers,startdt,enddt,interval)
prices = get_prices.((TimeArray,),tickers,startdt=startdt,enddt=enddt,interval=interval) |> (x->hcat(x...))
prices = prices[colnames(prices)[occursin.(r"adj",string.(colnames(prices)))]]
TimeSeries.rename!(prices,Symbol.(tickers))
return prices
end;
get_adj_close_TA(["AAPL","TSLA","F","AMD","NFLX","WBA"],Date(2023-01-01),Date(2024-01-01),"1d")252×6 TimeArray{Float64, 2, DateTime, Matrix{Float64}} 2021-01-04T14:30:00 to 2021-12-31T14:30:00
┌─────────────────────┬─────────┬─────────┬─────────┬────────┬────────┬─────────┐
│ │ AAPL │ TSLA │ F │ AMD │ NFLX │ WBA │
├─────────────────────┼─────────┼─────────┼─────────┼────────┼────────┼─────────┤
│ 2021-01-04T14:30:00 │ 126.83 │ 243.257 │ 7.03792 │ 92.3 │ 522.86 │ 34.6737 │
│ 2021-01-05T14:30:00 │ 128.398 │ 245.037 │ 7.1453 │ 92.77 │ 520.8 │ 34.4727 │
│ 2021-01-06T14:30:00 │ 124.076 │ 251.993 │ 7.30225 │ 90.33 │ 500.49 │ 36.0389 │
│ 2021-01-07T14:30:00 │ 128.31 │ 272.013 │ 7.48398 │ 95.16 │ 508.89 │ 37.9066 │
│ ⋮ │ ⋮ │ ⋮ │ ⋮ │ ⋮ │ ⋮ │ ⋮ │
│ 2021-12-29T14:30:00 │ 176.888 │ 362.063 │ 17.069 │ 148.26 │ 610.54 │ 45.4322 │
│ 2021-12-30T14:30:00 │ 175.724 │ 356.78 │ 16.9943 │ 145.15 │ 612.09 │ 45.2061 │
│ 2021-12-31T14:30:00 │ 175.103 │ 352.26 │ 17.2434 │ 143.9 │ 602.44 │ 45.3539 │
└─────────────────────┴─────────┴─────────┴─────────┴────────┴────────┴─────────┘
245 rows omittedTSFrame from TSFrames.jl
If you use Julia 1.9 or newer you can just use a sink argument in get_prices instead.
using TSFrames, YFinance
get_prices(TSFrame,"AAPL")or alternatively
using TSFrames, YFinance
prices = get_prices("AAPL")
sink_prices_to(TSFrame,prices)If you use an older version you can run the below code:
The TSFrame takes a matrix, a DateTime index, and a Vector of column names as arguments.
Below is an example of a function converting the price data to a TSFrame - this is most likely not the fastest or most elegant way.
using TSFrames
prices = get_prices("AAPL")
function stock_price_to_TSFrames(x)
coln = collect(keys(x))[3:end] # only get the keys that are not ticker or datetime
m = hcat([x[k] for k in coln]...) #Convert the dictionary into a matrix
tsf = TSFrame(m,x["timestamp"],colnames = Symbol.(coln)) # create the timeseries array
return tsf
end
stock_price_to_TSFrames(prices)Broadcast and create one TSFrame for the adjclose prices
tickers = ["AAPL","TSLA","F"]
prices = get_prices.((TSFrame,),tickers);We now want to combine them by taking the adjclose of each tsframe.
prices = join(getindex.(prices,:,([:Index,:adjclose],))...)5×3 TSFrame with DateTime Index
Index adjclose adjclose_1 adjclose_2
DateTime Float64? Float64? Float64?
───────────────────────────────────────────────────────
2024-08-01T13:30:00 218.36 216.86 10.526
2024-08-02T13:30:00 219.86 207.67 9.87617
2024-08-05T13:30:00 209.27 198.88 9.56107
2024-08-06T13:30:00 207.23 200.64 9.63
2024-08-07T13:30:00 209.82 191.76 9.77Rename:
TSFrames.rename!(prices, tickers)5×3 TSFrame with DateTime Index
Index AAPL TSLA F
DateTime Float64? Float64? Float64?
───────────────────────────────────────────────────
2024-08-01T13:30:00 218.36 216.86 10.526
2024-08-02T13:30:00 219.86 207.67 9.87617
2024-08-05T13:30:00 209.27 198.88 9.56107
2024-08-06T13:30:00 207.23 200.64 9.63
2024-08-07T13:30:00 209.82 191.76 9.77Or in one function:
function get_adj_close_TSF(tickers,startdt,enddt,interval)
prices = get_prices.((TSFrame,),tickers,startdt=startdt,enddt=enddt,interval=interval)
prices = join(getindex.(prices,:,([:Index,:adjclose],))...)
TSFrames.rename!(prices, tickers)
return prices
end;
get_adj_close_TSF(["AAPL","TSLA","F","AMD","NFLX","WBA"],Date(2023-01-01),Date(2024-01-01),"1d")252×6 TSFrame with DateTime Index
Index AAPL TSLA F AMD NFLX WBA
DateTime Float64? Float64? Float64? Float64? Float64? Float64?
─────────────────────────────────────────────────────────────────────────────────
2021-01-04T14:30:00 126.83 243.257 7.03792 92.3 522.86 34.6737
2021-01-05T14:30:00 128.398 245.037 7.1453 92.77 520.8 34.4727
2021-01-06T14:30:00 124.076 251.993 7.30225 90.33 500.49 36.0389
2021-01-07T14:30:00 128.31 272.013 7.48398 95.16 508.89 37.9066
⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮
2021-12-29T14:30:00 176.888 362.063 17.069 148.26 610.54 45.4322
2021-12-30T14:30:00 175.724 356.78 16.9943 145.15 612.09 45.2061
2021-12-31T14:30:00 175.103 352.26 17.2434 143.9 602.44 45.3539
245 rows omitted