Reproducible Finance With R
Ch1: Introduction
Provide major takeaways from Chapter 1 in 50 words:
Reproducible finance is described by the textbook as a philosophy about how to do quantitative analysis. It is essentially data that can be interpreted an understandable and efficient way. There are three universes of portfolio analysis with R: xts, tidyverse, and tidyquant. Data visualization is about turning numbers into shapes and colors, which supports the original theory.
Stock Index
tq_index_options()## [1] "DOW" "DOWGLOBAL" "SP400" "SP500" "SP600"data <- tq_index("SP400")
data## # A tibble: 401 × 8
## symbol company identifier sedol weight sector shares_held local_currency
## <chr> <chr> <chr> <chr> <dbl> <chr> <int> <chr>
## 1 IBKR Interactiv… 45841N107 B1WT… 0.00794 Finan… 847624 USD
## 2 EME EMCOR Grou… 29084Q100 2474… 0.00754 Indus… 358132 USD
## 3 OKTA Okta Inc. … 679295105 BDFZ… 0.00712 Infor… 1273676 USD
## 4 RBA RB Global … 74935Q107 BMWG… 0.00697 Indus… 1435672 USD
## 5 DUOL Duolingo I… 26603R106 BMCM… 0.00691 Consu… 295030 USD
## 6 DOCU DocuSign I… 256163106 BFYT… 0.00629 Infor… 1572757 USD
## 7 GWRE Guidewire … 40171V100 B7JY… 0.00623 Infor… 650194 USD
## 8 USFD US Foods H… 912008109 BYVF… 0.00622 Consu… 1793506 USD
## 9 CSL Carlisle C… 142339100 2176… 0.00622 Indus… 346106 USD
## 10 PSTG Pure Stora… 74624M102 BYZ6… 0.00608 Infor… 2412778 USD
## # ℹ 391 more rowsStock Exchanges
tq_exchange_options() data <- tq_exchange(“NYSE”) data
tq_get
Stock prices from Yahoo Finance
stock <- tq_get("TSLA")
stock## # A tibble: 2,611 × 8
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 TSLA 2015-01-02 14.9 14.9 14.2 14.6 71466000 14.6
## 2 TSLA 2015-01-05 14.3 14.4 13.8 14.0 80527500 14.0
## 3 TSLA 2015-01-06 14.0 14.3 13.6 14.1 93928500 14.1
## 4 TSLA 2015-01-07 14.2 14.3 14.0 14.1 44526000 14.1
## 5 TSLA 2015-01-08 14.2 14.3 14.0 14.0 51637500 14.0
## 6 TSLA 2015-01-09 13.9 14.0 13.7 13.8 70024500 13.8
## 7 TSLA 2015-01-12 13.5 13.6 13.3 13.5 89254500 13.5
## 8 TSLA 2015-01-13 13.6 13.8 13.4 13.6 67159500 13.6
## 9 TSLA 2015-01-14 12.4 13.0 12.3 12.8 173278500 12.8
## 10 TSLA 2015-01-15 13.0 13.1 12.7 12.8 78247500 12.8
## # ℹ 2,601 more rowsEconomic Data From FRED
unemployment_nh <- tq_get ("NHUR", get = "economic.data")
unemployment_nh## # A tibble: 123 × 3
## symbol date price
## <chr> <date> <dbl>
## 1 NHUR 2015-01-01 3.8
## 2 NHUR 2015-02-01 3.8
## 3 NHUR 2015-03-01 3.7
## 4 NHUR 2015-04-01 3.6
## 5 NHUR 2015-05-01 3.5
## 6 NHUR 2015-06-01 3.4
## 7 NHUR 2015-07-01 3.3
## 8 NHUR 2015-08-01 3.3
## 9 NHUR 2015-09-01 3.2
## 10 NHUR 2015-10-01 3.1
## # ℹ 113 more rowsImport Data
AAPL <- tq_get("AAPL")
AAPL## # A tibble: 2,611 × 8
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 AAPL 2015-01-02 27.8 27.9 26.8 27.3 212818400 24.3
## 2 AAPL 2015-01-05 27.1 27.2 26.4 26.6 257142000 23.6
## 3 AAPL 2015-01-06 26.6 26.9 26.2 26.6 263188400 23.6
## 4 AAPL 2015-01-07 26.8 27.0 26.7 26.9 160423600 23.9
## 5 AAPL 2015-01-08 27.3 28.0 27.2 28.0 237458000 24.9
## 6 AAPL 2015-01-09 28.2 28.3 27.6 28.0 214798000 24.9
## 7 AAPL 2015-01-12 28.1 28.2 27.2 27.3 198603200 24.3
## 8 AAPL 2015-01-13 27.9 28.2 27.2 27.6 268367600 24.5
## 9 AAPL 2015-01-14 27.3 27.6 27.1 27.5 195826400 24.4
## 10 AAPL 2015-01-15 27.5 27.5 26.7 26.7 240056000 23.7
## # ℹ 2,601 more rowsLine Chart
AAPL %>%
ggplot(aes(x = date, y = close)) +
geom_line() +
labs(title = "AAPL Line Chart", y = "Closing Price", x = "") +
theme_tq()
Bar Chart
AAPL %>%
ggplot(aes(x = date, y = close)) +
geom_barchart(aes(open = open, high = high, low = low, close = close)) +
labs(title = "AAPL Bar Chart", y = "Closing Price", x = "") +
theme_tq()
Candlesticks
AAPL %>%
tail(30) %>%
ggplot(aes(x = date, y = close)) +
geom_candlestick(aes(open = open, high = high, low = low, close = close)) +
labs(title = "AAPL Candlestick Chart", y = "Closing Price", x = "") +
theme_tq()
1 Get Stock Prices And Convert To Returns
Ra <- c("AAPL", "GOOG", "NFLX") %>%
tq_get(get = "stock.prices",
from = "2010-01-01",
to = "2015-12-31") %>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = "monthly",
col_rename = "Ra")
Ra## # A tibble: 72 × 2
## date Ra
## <date> <dbl>
## 1 2010-01-29 0.383
## 2 2010-02-26 0.0610
## 3 2010-03-31 0.116
## 4 2010-04-30 0.341
## 5 2010-05-28 0.124
## 6 2010-06-30 -0.0225
## 7 2010-07-30 -0.0561
## 8 2010-08-31 0.224
## 9 2010-09-30 0.292
## 10 2010-10-29 0.0704
## # ℹ 62 more rows2 Get baseline and convert to returns
Rb <- "XLK" %>%
tq_get(get = "stock.prices",
from = "2010-01-01",
to = "2015-12-31") %>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = "monthly",
col_rename = "Rb")
Rb## # A tibble: 72 × 2
## date Rb
## <date> <dbl>
## 1 2010-01-29 -0.0993
## 2 2010-02-26 0.0348
## 3 2010-03-31 0.0684
## 4 2010-04-30 0.0126
## 5 2010-05-28 -0.0748
## 6 2010-06-30 -0.0540
## 7 2010-07-30 0.0745
## 8 2010-08-31 -0.0561
## 9 2010-09-30 0.117
## 10 2010-10-29 0.0578
## # ℹ 62 more rows3 Join the two tables
RaRb <- left_join(Ra, Rb, by = c("date" = "date"))
RaRb## # A tibble: 72 × 3
## date Ra Rb
## <date> <dbl> <dbl>
## 1 2010-01-29 0.383 -0.0993
## 2 2010-02-26 0.0610 0.0348
## 3 2010-03-31 0.116 0.0684
## 4 2010-04-30 0.341 0.0126
## 5 2010-05-28 0.124 -0.0748
## 6 2010-06-30 -0.0225 -0.0540
## 7 2010-07-30 -0.0561 0.0745
## 8 2010-08-31 0.224 -0.0561
## 9 2010-09-30 0.292 0.117
## 10 2010-10-29 0.0704 0.0578
## # ℹ 62 more rows4 Calculate CAPM
RaRb_capm <- RaRb %>%
tq_performance(Ra = Ra,
Rb = Rb,
performance_fun = table.CAPM)
RaRb_capm## # A tibble: 1 × 17
## ActivePremium Alpha AlphaRobust AnnualizedAlpha Beta `Beta-` `Beta-Robust`
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.492 0.0581 0.0501 0.969 0.199 -2.42 -2.36
## # ℹ 10 more variables: `Beta+` <dbl>, `Beta+Robust` <dbl>, BetaRobust <dbl>,
## # Correlation <dbl>, `Correlationp-value` <dbl>, InformationRatio <dbl>,
## # `R-squared` <dbl>, `R-squaredRobust` <dbl>, TrackingError <dbl>,
## # TreynorRatio <dbl>