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)

TimeArray from TimeSeries

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)

TSFrame from TSFrames.jl

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)