tidyquant
tidyquant integrates the best quantitative resources for collecting and analyzing quantitative data, xts, quantmod and TTR, with the tidy data infrastructure of the tidyverse allowing for seamless interaction between each and working within the tidyverse.
Benefits
The tidyquant philosophy:
- A few core functions with a lot of power, that
- leverage the quantitative analysis power of
xts,quantmodandTTR, and are - designed to be used and scaled with the
tidyverse.
Installation
To install from GitHub:
# install.packages("devtools")
devtools::install_github("mdancho84/tidyquant")Examples
# Loads tidyquant, tidyverse, lubridate, quantmod, TTR, and xts
library(tidyquant) Getting Data in Tibble Format:
tq_get() is the one-stop shop for retrieving data. The full list of get options are:
tq_get_options()
#> [1] "stock.prices" "stock.index" "dividends" "splits"
#> [5] "financials" "economic.data" "exchange.rates" "metal.prices"Set get = "stock.prices" to get stock prices. Notice the output is always a tibble.
aapl_prices <- tq_get("AAPL", get = "stock.prices")
aapl_prices
#> # A tibble: 2,769 × 7
#> date open high low close volume adjusted
#> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2006-01-03 72.38 74.75 72.25 74.75 201808600 9.726565
#> 2 2006-01-04 75.13 75.98 74.50 74.97 154900900 9.755191
#> 3 2006-01-05 74.83 74.90 73.75 74.38 112355600 9.678420
#> 4 2006-01-06 75.25 76.70 74.55 76.30 176114400 9.928252
#> 5 2006-01-09 76.73 77.20 75.74 76.05 168760200 9.895722
#> 6 2006-01-10 76.25 81.89 75.83 80.86 569967300 10.521606
#> 7 2006-01-11 83.84 84.80 82.59 83.90 373448600 10.917174
#> 8 2006-01-12 84.97 86.40 83.62 84.29 320202400 10.967921
#> 9 2006-01-13 84.99 86.01 84.60 85.59 194076400 11.137079
#> 10 2006-01-17 85.70 86.38 83.87 84.71 208905900 11.022573
#> # ... with 2,759 more rowsSet get = "financials" to get financial statements. The statements are returned as nested tibbles, that can be unnested and analyzed together.
tq_get("AAPL", get = "financials")
#> # A tibble: 3 × 3
#> type annual quarter
#> * <chr> <list> <list>
#> 1 BS <tibble [168 × 4]> <tibble [210 × 4]>
#> 2 CF <tibble [76 × 4]> <tibble [76 × 4]>
#> 3 IS <tibble [196 × 4]> <tibble [245 × 4]>There are many other get options including stock indexes, dividends, splits, economic data from the FRED, and exchange rates and metals from Oanda.
Working in the tidyverse
You probably already know and love tidyverse packages like dplyr, tidyr, purrr, readr, and tibble along with lubridate for working with date and datetime. tidyquant works solely in tibbles, so all of the tidyverse functionality is intact.
A simple example inspired by Kan Nishida's blog shows the dplyr and lubridate capability: Say we want the growth in the stock over the past year. We can do this with dplyr operations.
Getting the last year is simple with dplyr and lubridate. We first select the date and adjusted price (adjusted for stock splits). We then filter using lubridate date functions. We can also get a baseline price using the first function. Growth and growth percent versus baseline columns can be added now. We tack on a final select statement to remove unnecessary columns. The final workflow looks like this:
aapl_prices %>%
select(date, adjusted) %>%
filter(date >= today() - years(1)) %>%
mutate(baseline = first(adjusted),
growth = adjusted - baseline,
growth_pct = growth / baseline * 100) %>%
select(-(baseline:growth))
#> # A tibble: 253 × 3
#> date adjusted growth_pct
#> <date> <dbl> <dbl>
#> 1 2015-12-31 102.96903 0.0000000
#> 2 2016-01-04 103.05706 0.0854995
#> 3 2016-01-05 100.47452 -2.4225751
#> 4 2016-01-06 98.50827 -4.3321348
#> 5 2016-01-07 94.35077 -8.3697559
#> 6 2016-01-08 94.84967 -7.8852393
#> 7 2016-01-11 96.38550 -6.3936946
#> 8 2016-01-12 97.78438 -5.0351540
#> 9 2016-01-13 95.27031 -7.4767271
#> 10 2016-01-14 97.35395 -5.4531690
#> # ... with 243 more rowsTransforming & Mutating Data with xts, quantmod, and TTR Functions
You may already know and love xts, quantmod, and TTR, which is why the core functionality is fully integrated. The workhorse functions are tq_transform() and tq_mutate(). These functions leverage the power of xts, quantmod, and TTR. The full list of xts, quantmod, and TTR functions that can be used are:
tq_transform_fun_options()
#> $xts
#> [1] "apply.daily" "apply.monthly" "apply.quarterly"
#> [4] "apply.weekly" "apply.yearly" "diff.xts"
#> [7] "lag.xts" "period.apply" "period.max"
#> [10] "period.min" "period.prod" "period.sum"
#> [13] "periodicity" "to.daily" "to.hourly"
#> [16] "to.minutes" "to.minutes10" "to.minutes15"
#> [19] "to.minutes3" "to.minutes30" "to.minutes5"
#> [22] "to.monthly" "to.period" "to.quarterly"
#> [25] "to.weekly" "to.yearly" "to_period"
#>
#> $quantmod
#> [1] "allReturns" "annualReturn" "ClCl"
#> [4] "dailyReturn" "Delt" "HiCl"
#> [7] "Lag" "LoCl" "LoHi"
#> [10] "monthlyReturn" "Next" "OpCl"
#> [13] "OpHi" "OpLo" "OpOp"
#> [16] "periodReturn" "quarterlyReturn" "seriesAccel"
#> [19] "seriesDecel" "seriesDecr" "seriesHi"
#> [22] "seriesIncr" "seriesLo" "weeklyReturn"
#> [25] "yearlyReturn"
#>
#> $TTR
#> [1] "adjRatios" "ADX" "ALMA"
#> [4] "aroon" "ATR" "BBands"
#> [7] "CCI" "chaikinAD" "chaikinVolatility"
#> [10] "CLV" "CMF" "CMO"
#> [13] "DEMA" "DonchianChannel" "DPO"
#> [16] "DVI" "EMA" "EMV"
#> [19] "EVWMA" "GMMA" "growth"
#> [22] "HMA" "KST" "lags"
#> [25] "MACD" "MFI" "momentum"
#> [28] "OBV" "PBands" "ROC"
#> [31] "rollSFM" "RSI" "runCor"
#> [34] "runCov" "runMAD" "runMax"
#> [37] "runMean" "runMedian" "runMin"
#> [40] "runPercentRank" "runSD" "runSum"
#> [43] "runVar" "SAR" "SMA"
#> [46] "SMI" "stoch" "TDI"
#> [49] "TRIX" "ultimateOscillator" "VHF"
#> [52] "VMA" "volatility" "VWAP"
#> [55] "VWMA" "wilderSum" "williamsAD"
#> [58] "WMA" "WPR" "ZigZag"
#> [61] "ZLEMA"tq_transform
tq_transform() returns a new data set that can either be in the same periodicity or a different periodicity as the original data set. Let's use tq_transform to transform the periodicity of the aapl_prices. The quantmod OHLC codes are used to select the open, high, low, close, and volume (OHLCV) columns, which are then sent to the transformation function, xts::to.period, for transformation to monthly periodicity. We now have a much smaller data set containing the monthly prices.
aapl_prices %>%
tq_transform(x_fun = OHLCV, transform_fun = to.period, period = "months")
#> # A tibble: 132 × 6
#> date open high low close volume
#> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2006-01-31 75.50 76.34 73.75 75.51 228385500
#> 2 2006-02-28 71.58 72.40 68.10 68.49 316745100
#> 3 2006-03-31 63.25 63.61 62.24 62.72 203839300
#> 4 2006-04-28 69.38 71.30 69.20 70.39 190009400
#> 5 2006-05-31 61.76 61.79 58.69 59.77 320244400
#> 6 2006-06-30 57.59 57.75 56.50 57.27 184923900
#> 7 2006-07-31 66.83 68.63 66.28 67.96 223210400
#> 8 2006-08-31 67.28 68.30 66.66 67.85 143674300
#> 9 2006-09-29 77.11 77.52 76.68 76.98 101453100
#> 10 2006-10-31 81.45 81.68 80.23 81.08 125368600
#> # ... with 122 more rowstq_mutate
The cousin of tq_transform() is tq_mutate(). While tq_transform() produces a new, transformed data set, tq_mutate() modifies the existing data set. This is very useful for applying TTR functions like BBands, MACD, Moving Averages, etc. There is one caveat: the mutation must be in the same periodicity as the original data set (otherwise you can't add columns because the rows will not match up). Let's use tq_mutate() to add some Bollinger Bands and MACD using the closing prices (Cl in OHLC notation).
aapl_prices %>%
tq_mutate(x_fun = Cl, mutate_fun = MACD) %>%
tq_mutate(x_fun = HLC, mutate_fun = BBands)
#> # A tibble: 2,769 × 13
#> date open high low close volume adjusted macd signal
#> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2006-01-03 72.38 74.75 72.25 74.75 201808600 9.726565 NA NA
#> 2 2006-01-04 75.13 75.98 74.50 74.97 154900900 9.755191 NA NA
#> 3 2006-01-05 74.83 74.90 73.75 74.38 112355600 9.678420 NA NA
#> 4 2006-01-06 75.25 76.70 74.55 76.30 176114400 9.928252 NA NA
#> 5 2006-01-09 76.73 77.20 75.74 76.05 168760200 9.895722 NA NA
#> 6 2006-01-10 76.25 81.89 75.83 80.86 569967300 10.521606 NA NA
#> 7 2006-01-11 83.84 84.80 82.59 83.90 373448600 10.917174 NA NA
#> 8 2006-01-12 84.97 86.40 83.62 84.29 320202400 10.967921 NA NA
#> 9 2006-01-13 84.99 86.01 84.60 85.59 194076400 11.137079 NA NA
#> 10 2006-01-17 85.70 86.38 83.87 84.71 208905900 11.022573 NA NA
#> # ... with 2,759 more rows, and 4 more variables: dn <dbl>, mavg <dbl>,
#> # up <dbl>, pctB <dbl>Scaling with the tidyverse
All functions return data sets as tibbles, which allows for interaction within the tidyverse. This means we can:
- Use
dplyrandtidyrto select, filter, nest/unnest, etc. - Use the pipe (
%>%) for chaining operations. - Seamlessly scale data retrieval and transformations/mutations using
purrrto map functions.
A very basic example is retrieving the stock prices for multiple stocks. We can do this by piping a tibble of stock symbols to a mutation that maps the tq_get(get = "stock.prices") function.
tibble(symbol = c("AAPL", "GOOG", "AMZN", "FB", "AVGO", "SWKS","NVDA")) %>%
mutate(stock.prices = map(.x = symbol, ~ tq_get(.x, get = "stock.prices")))
#> # A tibble: 7 × 2
#> symbol stock.prices
#> <chr> <list>
#> 1 AAPL <tibble [2,769 × 7]>
#> 2 GOOG <tibble [2,769 × 7]>
#> 3 AMZN <tibble [2,769 × 7]>
#> 4 FB <tibble [1,163 × 7]>
#> 5 AVGO <tibble [1,865 × 7]>
#> 6 SWKS <tibble [2,769 × 7]>
#> 7 NVDA <tibble [2,769 × 7]>Further Information
This just scratches the surface of the features. See the tidyquant vignette for further details on the package.