Calculating Returns
- Q1 Import Facebook for the last five years, and save the data under Stocks.
- Q2 Calculate daily returns. Do not save the result.
- Q3 Calculate monthly returns. This time save the result under returns_monthly.
- Q4 Create a histogram for monthly returns.
- Q5 Create a line chart for monthly returns.
- Q6 Repeat Q3 - Q5 for yearly returns
Conduct the trend analysis for the Tesla stock.
For Q1 - Q3
library(tidyquant)
# Import data
from = today() - years(4)
Stocks <- tq_get("TSLA", get = "stock.prices", from = from)
Stocks
## # A tibble: 1,006 x 7
## date open high low close volume adjusted
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2015-02-26 204 211. 202. 207. 6472900 207.
## 2 2015-02-27 207. 209. 203. 203. 3882100 203.
## 3 2015-03-02 203. 203. 196. 197. 7922100 197.
## 4 2015-03-03 197. 200. 195. 200. 4432300 200.
## 5 2015-03-04 199. 203. 197. 202. 4212000 202.
## 6 2015-03-05 203. 206. 200. 201. 4877000 201.
## 7 2015-03-06 199. 201. 192. 194. 6712400 194.
## 8 2015-03-09 194. 194. 188. 191. 6736700 191.
## 9 2015-03-10 188. 194. 188. 190. 5530900 190.
## 10 2015-03-11 191. 196. 191. 194. 4974900 194.
## # ... with 996 more rows# See the list of different mutate_fun avaiable
tq_transmute_fun_options()
## $zoo
## [1] "rollapply" "rollapplyr" "rollmax"
## [4] "rollmax.default" "rollmaxr" "rollmean"
## [7] "rollmean.default" "rollmeanr" "rollmedian"
## [10] "rollmedian.default" "rollmedianr" "rollsum"
## [13] "rollsum.default" "rollsumr"
##
## $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" "SNR" "stoch"
## [49] "TDI" "TRIX" "ultimateOscillator"
## [52] "VHF" "VMA" "volatility"
## [55] "VWAP" "VWMA" "wilderSum"
## [58] "williamsAD" "WMA" "WPR"
## [61] "ZigZag" "ZLEMA"
##
## $PerformanceAnalytics
## [1] "Return.annualized" "Return.annualized.excess"
## [3] "Return.clean" "Return.cumulative"
## [5] "Return.excess" "Return.Geltner"
## [7] "zerofill"
# Add the 100-day and 200-day simple moving average by passing close prices
Stocks <-
Stocks %>%
tq_mutate(select = close, mutate_fun = SMA, n = 100) %>%
rename(SMA.100 = SMA) %>%
tq_mutate(select = close, mutate_fun = SMA, n = 200) %>%
rename(SMA.200 = SMA)
Stocks
## # A tibble: 1,006 x 9
## date open high low close volume adjusted SMA.100 SMA.200
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2015-02-26 204 211. 202. 207. 6472900 207. NA NA
## 2 2015-02-27 207. 209. 203. 203. 3882100 203. NA NA
## 3 2015-03-02 203. 203. 196. 197. 7922100 197. NA NA
## 4 2015-03-03 197. 200. 195. 200. 4432300 200. NA NA
## 5 2015-03-04 199. 203. 197. 202. 4212000 202. NA NA
## 6 2015-03-05 203. 206. 200. 201. 4877000 201. NA NA
## 7 2015-03-06 199. 201. 192. 194. 6712400 194. NA NA
## 8 2015-03-09 194. 194. 188. 191. 6736700 191. NA NA
## 9 2015-03-10 188. 194. 188. 190. 5530900 190. NA NA
## 10 2015-03-11 191. 196. 191. 194. 4974900 194. NA NA
## # ... with 996 more rowsQ1 Import Facebook for the last five years, and save the data under Stocks.
Hint: Insert the code below (only importing code).
from = today() - years(5)
Stocks <- tq_get("FB", get = "stock.prices", from = from)
Stocks
## # A tibble: 1,258 x 7
## date open high low close volume adjusted
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2014-02-26 70.2 71.2 68.8 69.3 55322700 69.3
## 2 2014-02-27 69.3 70.0 68.9 68.9 41653700 68.9
## 3 2014-02-28 69.5 69.9 67.4 68.5 66783700 68.5
## 4 2014-03-03 67.0 68.1 66.5 67.4 56824100 67.4
## 5 2014-03-04 68.7 68.9 67.6 68.8 42013500 68.8
## 6 2014-03-05 69.7 72.0 69.6 71.6 74567700 71.6
## 7 2014-03-06 71.9 71.9 70.2 70.8 46026500 70.8
## 8 2014-03-07 71.1 71.2 69.5 69.8 38927000 69.8
## 9 2014-03-10 70.8 72.2 70.5 72.0 59871600 72.0
## 10 2014-03-11 72.5 72.6 70.0 70.1 59408300 70.1
## # ... with 1,248 more rowsQ2 Calculate daily returns. Do not save the result.
Hint: Take the adjusted variable from Stocks, and calculate daily returns using tidyquant::tq_mutate. Note that there are a variety of functions available with the mutate_fun argument: See above for the result of tq_transmute_fun_options(). Note that tq_mutate allows to calculate returns using quantmod::periodReturn. Google the quantmod package manual for more information on the periodReturn function.
Stocks %>% tq_mutate(select = adjusted, mutate_fun = periodReturn, period = "daily")
## # A tibble: 1,258 x 8
## date open high low close volume adjusted daily.returns
## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2014-02-26 70.2 71.2 68.8 69.3 55322700 69.3 0
## 2 2014-02-27 69.3 70.0 68.9 68.9 41653700 68.9 -0.00462
## 3 2014-02-28 69.5 69.9 67.4 68.5 66783700 68.5 -0.00696
## 4 2014-03-03 67.0 68.1 66.5 67.4 56824100 67.4 -0.0153
## 5 2014-03-04 68.7 68.9 67.6 68.8 42013500 68.8 0.0206
## 6 2014-03-05 69.7 72.0 69.6 71.6 74567700 71.6 0.0403
## 7 2014-03-06 71.9 71.9 70.2 70.8 46026500 70.8 -0.0102
## 8 2014-03-07 71.1 71.2 69.5 69.8 38927000 69.8 -0.0147
## 9 2014-03-10 70.8 72.2 70.5 72.0 59871600 72.0 0.0319
## 10 2014-03-11 72.5 72.6 70.0 70.1 59408300 70.1 -0.0268
## # ... with 1,248 more rowsQ3 Calculate monthly returns. This time save the result under returns_monthly.
Hint: Note that tq_transmute(), instead of tq_mutate(), is used when periodicity changes. Another difference between the two is that tq_transmute() returns only newly-created columns while tq_mutate() adds new columns to existing variables.
returns_monthly <- Stocks %>%
tq_transmute(select = adjusted, mutate_fun = periodReturn, period = "monthly", col_rename = "returns_monthly")
returns_monthly
## # A tibble: 61 x 2
## date returns_monthly
## <date> <dbl>
## 1 2014-02-28 -0.0116
## 2 2014-03-31 -0.120
## 3 2014-04-30 -0.00764
## 4 2014-05-30 0.0589
## 5 2014-06-30 0.0630
## 6 2014-07-31 0.0797
## 7 2014-08-29 0.0299
## 8 2014-09-30 0.0564
## 9 2014-10-31 -0.0512
## 10 2014-11-28 0.0361
## # ... with 51 more rowsQ4 Create a histogram for monthly returns.
Hint: Refer to the ggplot2 cheatsheet. Google it. See the section for One Variable. Note that there are two different cases: 1) Continuous and 2) Discrete. The type of chart you can use depends on what type of data your variable is.
ggplot(returns_monthly, aes(x = returns_monthly)) +
geom_histogram()
Q5 Create a line chart for monthly returns.
Hint: Refer to the ggplot2 cheatsheet. See the section for Two Variables.
ggplot(returns_monthly, aes(x = date, y = returns_monthly)) +
geom_line()
Q6 Repeat Q3 - Q5 for yearly returns
returns_yearly <- Stocks %>%
tq_transmute(select = adjusted, mutate_fun = periodReturn, period = "yearly", col_rename = "returns_yearly")
returns_yearly
## # A tibble: 6 x 2
## date returns_yearly
## <date> <dbl>
## 1 2014-12-31 0.126
## 2 2015-12-31 0.341
## 3 2016-12-30 0.0993
## 4 2017-12-29 0.534
## 5 2018-12-31 -0.257
## 6 2019-02-25 0.256
ggplot(returns_yearly, aes(x = returns_yearly)) +
geom_histogram()
ggplot(returns_yearly, aes(x = date, y = returns_yearly)) +
geom_line()