tq_index_options()

tq_index("SP500")

tq_exchange("NASDAQ")

aapl_prices<-tq_get("AAPL", get="stock.prices", from=" 1990-01-01")aapl_prices

stocks<-c("AAPL","META","NFLX")%>%tq_get(from="2013-01-01",           to="2017-01-01")stocks

x8411T<-tq_get("8411.T", get="stock.prices.japan", from="2016-01-01", to="2016-12-31")

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

install.packages("Quandl")

Authentication

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

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

Search

Searching Quandl from within the R console is possible withquandl_search(), a wrapper forQuandl::Quandl.search(). An example search is shown below.The only required argument isquery. You can also visit theQuandl Search webpage tosearch for available database codes.

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

Getting Quandl Data

Getting data is integrated intotq_get(). Two getoptions exist to retrieve Quandl data:

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

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

The following usesget = "quandl" and the “WIKI”database to download daily stock prices for AAPL in 2016. The output isa 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 theunderlyingQuandl() 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 (columnindex 11) using thecolumn_index,collapse andtransform 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 usingget = "quandl.datatable". Note that the time seriesarguments do not work with data tables.

Here’s several examples ofZacksFundamentals Collection B

# Zacks Fundamentals Collection B (DOW 30 Available to non subscribers)tq_get("ZACKS/FC", get="quandl.datatable")# Zacks Fundamentals Condensedtq_get("ZACKS/FR", get="quandl.datatable")# Zacks Fundamental Ratiostq_get("ZACKS/MT", get="quandl.datatable")# Zacks Master Tabletq_get("ZACKS/MKTV", get="quandl.datatable")# Zacks Market Value Supplementtq_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 andthen set your api key. If you don’t have one already, go to Tiingoaccount and get your FREE API key. You can then set it as follows:

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

Getting Tiingo Data

Thetidyquant package provides convenient wrappers totheriingo package (R interface to Tiingo). Here’s howtq_get() maps toriingo:

• 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")

install.packages("alphavantager")

Authentication

To make full use of the integration you need to get an API key andthen set your api key. If you don’t have one already, go toAlpha Vantage account and getyour 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 theAlpha Vantage APIdocumentation. For example, if you wish to retrieve intraday data at5 minute intervals for META and MSFT, you can build the parametersx = c("META", "MSFT"), get = "alphavantager", av_fun = "TIME_SERIES_INTRADAY", interval = "5min".The familiarx andget are the same as youalways use. Theav_fun argument comes fromalphavantager::av_get() and the Alpha Vantagedocumentation. Theinterval argument comes from the docs aswell.

# Scaling is as simple as supplying multiple symbolsc("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 BloombergTerminal account (Note this is not a free service). If you haveBloomberg Terminal running on your machine, you can connect asfollows:

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

Getting Bloomberg Data

Getting data is simple as the structure follows theRblpapi APIdocumentation. For example, if you wish to retrieve monthly data forSPX Index and AGTHX Equity, you can build thetq_getparameters as follows:

• x = c('SPX Index','ODMAX Equity')
• get = "rblpapi"
• rblpapi_fun = "bdh" Note that “bdh” is the default, andoptions include “bdh” (Bloomberg Data History), “bds” (Bloomberg DataSet), and “bdp” (Bloomberg Data Point)
• from / to These get passed tostart.dateandend.date and can be provided in “YYYY-MM-DD” characterformat. Note thatstart.date andend.date fromRblpapi can be used but must be converted to date ordatetime.
• Other arguments: These are options that depend on therblpapi_fun. SeeRblpapi documentation.

# Get Bloomberg data in a tidy data framemy_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")

FANG%>%group_by(symbol)%>%tq_transmute(select=adjusted, mutate_fun=to.monthly, indexAt="lastof")

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 withselect. The following example shows how to transmute WTICrude daily prices to monthly prices. Since we only have a single columnto pass, we can leave theselect argument asNULL which selects all columns by default. This sends theprice column to theto.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 2015-01-30        47.8## 2 2015-02-27        49.8## 3 2015-03-31        47.7## 4 2015-04-30        59.6## 5 2015-05-29        60.2## 6 2015-06-30        59.5## 7 2015-07-31        47.1## 8 2015-08-31        49.2## 9 2015-09-30        45.1##10 2015-10-30        46.6### ℹ 120 more rows

FANG%>%group_by(symbol)%>%tq_mutate(select=close,              mutate_fun=MACD,              col_rename=c("MACD","Signal"))

Mutate rolling regressions with rollapply

A very powerful example is applyingcustom functionsacross a rolling window usingrollapply. A specific exampleis using therollapply function to compute a rollingregression. This example is slightly more complicated so it will bebroken down into three steps:

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

Step 1: Get Returns

First, get combined returns. The asset and baseline returns should bein wide format, which is needed for thelm function in thenext 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 toapply over the rolling window in Step 3. An important point is that the“data” will be passed to the regression function as anxtsobject. Thetimetk::tk_tbl function takes care ofconverting to a data frame for thelm function to workproperly 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 usetq_mutate() to apply the customregression function over a rolling window usingrollapplyfrom thezoo package. Internally, since we leftselect = NULL, thereturns_combined data frameis being passed automatically to thedata argument of therollapply function. All you need to specify is themutate_fun = rollapply and any additional argumentsnecessary to apply therollapply function. We’ll specify a12 week window viawidth = 12. TheFUNargument is our custom regression function,regr_fun. It’sextremely important to specifyby.column = FALSE, whichtellsrollapply to perform the computation using the dataas a whole rather than apply the function to each column independently.Thecol_rename argument is used to rename the addedcolumns.

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     -0.0516NANA## 2 2016-01-15   -0.0242     -0.0187NANA## 3 2016-01-22    0.0313      0.0264NANA## 4 2016-01-29    0.146       0.0213NANA## 5 2016-02-05   -0.0725     -0.0422NANA## 6 2016-02-12   -0.0198     -0.00582NANA## 7 2016-02-19    0.0251      0.0354NANA## 8 2016-02-26    0.0320      0.0148NANA## 9 2016-03-04    0.00436     0.0281NANA##10 2016-03-11    0.00941     0.0106NANA### 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 thedata frame.

Mutate with two primary inputs

EVWMA (exponential volume-weighted moving average) requires twoinputs, price and volume. To work with these columns, we can switch tothe xy variants,tq_transmute_xy() andtq_mutate_xy(). The only difference is instead of theselect argument, you usex andyarguments to pass the columns needed based on themutate_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     28NA## 2 META   2013-01-03  27.9  28.5  27.6  27.8  63140600     27.8NA## 3 META   2013-01-04  28.0  28.9  27.8  28.8  72715400     28.8NA## 4 META   2013-01-07  28.7  29.8  28.6  29.4  83781800     29.4NA## 5 META   2013-01-08  29.5  29.6  28.9  29.1  45871300     29.1NA## 6 META   2013-01-09  29.7  30.6  29.5  30.6 104787700     30.6NA## 7 META   2013-01-10  30.6  31.5  30.3  31.3  95316400     31.3NA## 8 META   2013-01-11  31.3  32.0  31.1  31.7  89598000     31.7NA## 9 META   2013-01-14  32.1  32.2  30.6  31.0  98892800     31.0NA##10 META   2013-01-15  30.6  31.7  29.9  30.1 173242600     30.1  30.1### ℹ 4,022 more rows