Authentication

To make full use of the integration we recommend you set your api key. To do this create or sign into your Quandl account and go to your account api key page.

quandl_api_key("<your-api-key>")

Search

Searching Quandl from within the R console is possible with quandl_search(), a wrapper for Quandl::Quandl.search(). An example search is shown below. The only required argument is query. You can also visit the Quandl Search webpage to search for available database codes.

quandl_search(query = "Oil", database_code = "NSE", per_page = 3)

Getting Quandl Data

Getting data is integrated into tq_get(). Two get options exist to retrieve Quandl data:

1. get = "quandl": Get’s Quandl time series data. A wrapper for Quandl().
2. get = "quandl.datatable": Gets Quandl datatables (larger data sets that may not be time series). A wrapper for Quandl.datatable().

Getting data from Quandl can be achieved in much the same way as the other “get” options. Just pass the “codes” for the data along with desired arguments for the underlying function.

The following uses get = "quandl" and the “WIKI” database to download daily stock prices for AAPL in 2016. The output is a tidy data frame.

c("WIKI/AAPL") %>%
    tq_get(get  = "quandl",
           from = "2016-01-01",
           to   = "2016-12-31")

The following time series options are available to be passed to the underlying Quandl() function:

• start_date (from) = “yyyy-mm-dd” | end_date (to) = “yyyy-mm-dd”
• column_index = numeric column number (e.g. 1)
• rows = numeric row number indicating first n rows (e.g. 100)
• collapse = “none”, “daily”, “weekly”, “monthly”, “quarterly”, “annual”
• transform = “none”, “diff”, “rdiff”, “cumul”, “normalize”

Here’s an example to get period returns of the adj.close (column index 11) using the column_index, collapse and transform arguments.

"WIKI/AAPL" %>%
    tq_get(get          = "quandl",
           from         = "2007-01-01",
           to           = "2016-12-31",
           column_index = 11, 
           collapse     = "annual",      
           transform    = "rdiff")       

Datatables are larger data sets. These can be downloaded using get = "quandl.datatable". Note that the time series arguments do not work with data tables.

Here’s several examples of Zacks Fundamentals Collection B

# Zacks Fundamentals Collection B (DOW 30 Available to non subscribers)
tq_get("ZACKS/FC", get = "quandl.datatable")   # Zacks Fundamentals Condensed
tq_get("ZACKS/FR", get = "quandl.datatable")   # Zacks Fundamental Ratios
tq_get("ZACKS/MT", get = "quandl.datatable")   # Zacks Master Table
tq_get("ZACKS/MKTV", get = "quandl.datatable") # Zacks Market Value Supplement
tq_get("ZACKS/SHRS", get = "quandl.datatable") # Zacks Shares Out Supplement

Authentication

To make full use of the integration you need to get an API key and then set your api key. If you don’t have one already, go to Tiingo account and get your FREE API key. You can then set it as follows:

tiingo_api_key('<your-api-key>')

Getting Tiingo Data

The tidyquant package provides convenient wrappers to the riingo package (R interface to Tiingo). Here’s how tq_get() maps to riingo:

• Tiingo Prices: tq_get(get = "tiingo") = riingo::riingo_prices()
• Tiingo IEX Data: tq_get(get = "tiingo.iex") = riingo::riingo_iex_prices()
• Tiingo Crypto Data: tq_get(get = "tiingo.crypto") = riingo::riingo_crypto_prices()

# Tiingo Prices (Free alternative to Yahoo Finance!)
tq_get(c("AAPL", "GOOG"), get = "tiingo", from = "2010-01-01")

# Sub-daily prices from IEX ----
tq_get(c("AAPL", "GOOG"),
       get = "tiingo.iex",
       from   = "2020-01-01",
       to     = "2020-01-15",
       resample_frequency = "5min")

# Tiingo Bitcoin in USD ----
tq_get(c("btcusd"),
       get    = "tiingo.crypto",
       from   = "2020-01-01",
       to     = "2020-01-15",
       resample_frequency = "5min")

Authentication

To make full use of the integration you need to get an API key and then set your api key. If you don’t have one already, go to Alpha Vantage account and get your FREE API key. You can then set it as follows:

# install.packages("alphavantager")
av_api_key("<your-api-key>")

Getting Alpha Vantage Data

Getting data is simple as the structure follows the Alpha Vantage API documentation. For example, if you wish to retrieve intraday data at 5 minute intervals for META and MSFT, you can build the parameters x = c("META", "MSFT"), get = "alphavantager", av_fun = "TIME_SERIES_INTRADAY", interval = "5min". The familiar x and get are the same as you always use. The av_fun argument comes from alphavantager::av_get() and the Alpha Vantage documentation. The interval argument comes from the docs as well.

# Scaling is as simple as supplying multiple symbols
c("META", "MSFT") %>%
    tq_get(get = "alphavantage", av_fun = "TIME_SERIES_INTRADAY", interval = "5min")

Authentication

To make full use of the integration you need to have a Bloomberg Terminal account (Note this is not a free service). If you have Bloomberg Terminal running on your machine, you can connect as follows:

# install.packages("Rblpapi")
Rblpapi::blpConnect()

Getting Bloomberg Data

Getting data is simple as the structure follows the Rblpapi API documentation. For example, if you wish to retrieve monthly data for SPX Index and AGTHX Equity, you can build the tq_get parameters as follows:

• x = c('SPX Index','ODMAX Equity')
• get = "rblpapi"
• rblpapi_fun = "bdh" Note that “bdh” is the default, and options include “bdh” (Bloomberg Data History), “bds” (Bloomberg Data Set), and “bdp” (Bloomberg Data Point)
• from / to These get passed to start.date and end.date and can be provided in “YYYY-MM-DD” character format. Note that start.date and end.date from Rblpapi can be used but must be converted to date or datetime.
• Other arguments: These are options that depend on the rblpapi_fun. See Rblpapi documentation.

# Get Bloomberg data in a tidy data frame
my_bloomberg_data <- c('SPX Index','ODMAX Equity') %>%
    tq_get(get         = "Rblpapi",
           rblpapi_fun = "bdh",
           fields      = c("PX_LAST"),
           options     = c("periodicitySelection" = "WEEKLY"),
           from        = "2016-01-01",
           to          = "2016-12-31")

Working with non-OHLC data

Returns from FRED, Oanda, and other sources do not have open, high, low, close (OHLC) format. However, this is not a problem with select. The following example shows how to transmute WTI Crude daily prices to monthly prices. Since we only have a single column to pass, we can leave the select argument as NULL which selects all columns by default. This sends the price column to the to.period mutate function.

wti_prices <- tq_get("DCOILWTICO", get = "economic.data") 

wti_prices %>%    
    tq_transmute(mutate_fun = to.period,
                 period     = "months", 
                 col_rename = "WTI Price")

## # A tibble: 130 × 2
##    date       `WTI Price`
##    <date>           <dbl>
##  1 2014-01-31        97.6
##  2 2014-02-28       103. 
##  3 2014-03-31       102. 
##  4 2014-04-30       100. 
##  5 2014-05-30       103. 
##  6 2014-06-30       106. 
##  7 2014-07-31        98.2
##  8 2014-08-29        97.9
##  9 2014-09-30        91.2
## 10 2014-10-31        80.5
## # ℹ 120 more rows

Mutate rolling regressions with rollapply

A very powerful example is applying custom functions across a rolling window using rollapply. A specific example is using the rollapply function to compute a rolling regression. This example is slightly more complicated so it will be broken down into three steps:

1. Get returns
2. Create a custom function
3. Apply the custom function across a rolling window using tq_mutate(mutate_fun = rollapply)

Step 1: Get Returns

First, get combined returns. The asset and baseline returns should be in wide format, which is needed for the lm function in the next step.

fb_returns <- tq_get("META", get  = "stock.prices", from = "2016-01-01", to   = "2016-12-31") %>%
    tq_transmute(adjusted, periodReturn, period = "weekly", col_rename = "fb.returns")

xlk_returns <- tq_get("XLK", from = "2016-01-01", to = "2016-12-31") %>%
    tq_transmute(adjusted, periodReturn, period = "weekly", col_rename = "xlk.returns")

returns_combined <- left_join(fb_returns, xlk_returns, by = "date")
returns_combined

## # A tibble: 52 × 3
##    date       fb.returns xlk.returns
##    <date>          <dbl>       <dbl>
##  1 2016-01-08   -0.0478     -0.0516 
##  2 2016-01-15   -0.0242     -0.0187 
##  3 2016-01-22    0.0313      0.0264 
##  4 2016-01-29    0.146       0.0213 
##  5 2016-02-05   -0.0725     -0.0422 
##  6 2016-02-12   -0.0198     -0.00582
##  7 2016-02-19    0.0251      0.0354 
##  8 2016-02-26    0.0320      0.0148 
##  9 2016-03-04    0.00436     0.0281 
## 10 2016-03-11    0.00941     0.0106 
## # ℹ 42 more rows

Step 2: Create a custom function

Next, create a custom regression function, which will be used to apply over the rolling window in Step 3. An important point is that the “data” will be passed to the regression function as an xts object. The timetk::tk_tbl function takes care of converting to a data frame for the lm function to work properly with the columns “fb.returns” and “xlk.returns”.

regr_fun <- function(data) {
    coef(lm(fb.returns ~ xlk.returns, data = timetk::tk_tbl(data, silent = TRUE)))
}

Step 3: Apply the custom function

Now we can use tq_mutate() to apply the custom regression function over a rolling window using rollapply from the zoo package. Internally, since we left select = NULL, the returns_combined data frame is being passed automatically to the data argument of the rollapply function. All you need to specify is the mutate_fun = rollapply and any additional arguments necessary to apply the rollapply function. We’ll specify a 12 week window via width = 12. The FUN argument is our custom regression function, regr_fun. It’s extremely important to specify by.column = FALSE, which tells rollapply to perform the computation using the data as a whole rather than apply the function to each column independently. The col_rename argument is used to rename the added columns.

returns_combined %>%
    tq_mutate(mutate_fun = rollapply,
              width      = 12,
              FUN        = regr_fun,
              by.column  = FALSE,
              col_rename = c("coef.0", "coef.1"))

## # A tibble: 52 × 5
##    date       fb.returns xlk.returns coef.0 coef.1
##    <date>          <dbl>       <dbl>  <dbl>  <dbl>
##  1 2016-01-08   -0.0478     -0.0516      NA     NA
##  2 2016-01-15   -0.0242     -0.0187      NA     NA
##  3 2016-01-22    0.0313      0.0264      NA     NA
##  4 2016-01-29    0.146       0.0213      NA     NA
##  5 2016-02-05   -0.0725     -0.0422      NA     NA
##  6 2016-02-12   -0.0198     -0.00582     NA     NA
##  7 2016-02-19    0.0251      0.0354      NA     NA
##  8 2016-02-26    0.0320      0.0148      NA     NA
##  9 2016-03-04    0.00436     0.0281      NA     NA
## 10 2016-03-11    0.00941     0.0106      NA     NA
## # ℹ 42 more rows

returns_combined

## # A tibble: 52 × 3
##    date       fb.returns xlk.returns
##    <date>          <dbl>       <dbl>
##  1 2016-01-08   -0.0478     -0.0516 
##  2 2016-01-15   -0.0242     -0.0187 
##  3 2016-01-22    0.0313      0.0264 
##  4 2016-01-29    0.146       0.0213 
##  5 2016-02-05   -0.0725     -0.0422 
##  6 2016-02-12   -0.0198     -0.00582
##  7 2016-02-19    0.0251      0.0354 
##  8 2016-02-26    0.0320      0.0148 
##  9 2016-03-04    0.00436     0.0281 
## 10 2016-03-11    0.00941     0.0106 
## # ℹ 42 more rows

As shown above, the rolling regression coefficients were added to the data frame.

Mutate with two primary inputs

EVWMA (exponential volume-weighted moving average) requires two inputs, price and volume. To work with these columns, we can switch to the xy variants, tq_transmute_xy() and tq_mutate_xy(). The only difference is instead of the select argument, you use x and y arguments to pass the columns needed based on the mutate_fun documentation.

FANG %>%
    group_by(symbol) %>%
    tq_mutate_xy(x = close, y = volume, 
                 mutate_fun = EVWMA, col_rename = "EVWMA")

## # A tibble: 4,032 × 9
## # Groups:   symbol [4]
##    symbol date        open  high   low close    volume adjusted EVWMA
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>     <dbl>    <dbl> <dbl>
##  1 META   2013-01-02  27.4  28.2  27.4  28    69846400     28    NA  
##  2 META   2013-01-03  27.9  28.5  27.6  27.8  63140600     27.8  NA  
##  3 META   2013-01-04  28.0  28.9  27.8  28.8  72715400     28.8  NA  
##  4 META   2013-01-07  28.7  29.8  28.6  29.4  83781800     29.4  NA  
##  5 META   2013-01-08  29.5  29.6  28.9  29.1  45871300     29.1  NA  
##  6 META   2013-01-09  29.7  30.6  29.5  30.6 104787700     30.6  NA  
##  7 META   2013-01-10  30.6  31.5  30.3  31.3  95316400     31.3  NA  
##  8 META   2013-01-11  31.3  32.0  31.1  31.7  89598000     31.7  NA  
##  9 META   2013-01-14  32.1  32.2  30.6  31.0  98892800     31.0  NA  
## 10 META   2013-01-15  30.6  31.7  29.9  30.1 173242600     30.1  30.1
## # ℹ 4,022 more rows