In this example, we’ll use a simple linear model to identify the
trend in annual returns to determine if the stock returns are decreasing
or increasing over time.
Analyze a Single Stock
First, let’s collect stock data with tq_get()
AAPL <- tq_get("AAPL", from = "2007-01-01", to = "2016-12-31")
AAPL
## # A tibble: 2,518 × 8
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 AAPL 2007-01-03 3.08 3.09 2.92 2.99 1238319600 2.53
## 2 AAPL 2007-01-04 3.00 3.07 2.99 3.06 847260400 2.58
## 3 AAPL 2007-01-05 3.06 3.08 3.01 3.04 834741600 2.57
## 4 AAPL 2007-01-08 3.07 3.09 3.05 3.05 797106800 2.58
## 5 AAPL 2007-01-09 3.09 3.32 3.04 3.31 3349298400 2.79
## 6 AAPL 2007-01-10 3.38 3.49 3.34 3.46 2952880000 2.93
## 7 AAPL 2007-01-11 3.43 3.46 3.40 3.42 1440252800 2.89
## 8 AAPL 2007-01-12 3.38 3.39 3.33 3.38 1312690400 2.85
## 9 AAPL 2007-01-16 3.42 3.47 3.41 3.47 1244076400 2.93
## 10 AAPL 2007-01-17 3.48 3.49 3.39 3.39 1646260000 2.86
## # ℹ 2,508 more rows
TQ03-scaling-and-modeling-with-tidyquant.R
Next, come up with a function to help us collect annual log returns.
The function below mutates the stock prices to period returns using
tq_transmute(). We add the type = "log" and
period = "monthly" arguments to ensure we retrieve a tibble
of monthly log returns. Last, we take the mean of the monthly returns to
get MMLR.
get_annual_returns <- function(stock.returns) {
stock.returns %>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
type = "log",
period = "yearly")
}
TQ03-scaling-and-modeling-with-tidyquant.R
Let’s test get_annual_returns out. We now have the
annual log returns over the past ten years.
AAPL_annual_log_returns <- get_annual_returns(AAPL)
AAPL_annual_log_returns
## # A tibble: 10 × 2
## date yearly.returns
## <date> <dbl>
## 1 2007-12-31 0.860
## 2 2008-12-31 -0.842
## 3 2009-12-31 0.904
## 4 2010-12-31 0.426
## 5 2011-12-30 0.228
## 6 2012-12-31 0.282
## 7 2013-12-31 0.0776
## 8 2014-12-31 0.341
## 9 2015-12-31 -0.0306
## 10 2016-12-30 0.118
TQ03-scaling-and-modeling-with-tidyquant.R
Let’s visualize to identify trends. We can see from the linear trend
line that AAPL’s stock returns are declining.
AAPL_annual_log_returns %>%
ggplot(aes(x = year(date), y = yearly.returns)) +
geom_hline(yintercept = 0, color = palette_light()[[1]]) +
geom_point(size = 2, color = palette_light()[[3]]) +
geom_line(linewidth = 1, color = palette_light()[[3]]) +
geom_smooth(method = "lm", se = FALSE) +
labs(title = "AAPL: Visualizing Trends in Annual Returns",
x = "", y = "Annual Returns", color = "") +
theme_tq()
TQ03-scaling-and-modeling-with-tidyquant.R
Now, we can get the linear model using the lm()
function. However, there is one problem: the output is not “tidy”.
mod <- lm(yearly.returns ~ year(date), data = AAPL_annual_log_returns)
mod
##
## Call:
## lm(formula = yearly.returns ~ year(date), data = AAPL_annual_log_returns)
##
## Coefficients:
## (Intercept) year(date)
## 58.86282 -0.02915
TQ03-scaling-and-modeling-with-tidyquant.R
We can utilize the broom package to get “tidy” data from
the model. There’s three primary functions:
augment: adds columns to the original data such as
predictions, residuals and cluster assignmentsglance: provides a one-row summary of model-level
statisticstidy: summarizes a model’s statistical findings such as
coefficients of a regression
We’ll use tidy to retrieve the model coefficients.
## # A tibble: 2 × 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 58.9 113. 0.520 0.617
## 2 year(date) -0.0291 0.0562 -0.518 0.618
TQ03-scaling-and-modeling-with-tidyquant.R
Adding to our workflow, we have the following:
get_model <- function(stock_data) {
annual_returns <- get_annual_returns(stock_data)
mod <- lm(yearly.returns ~ year(date), data = annual_returns)
tidy(mod)
}
TQ03-scaling-and-modeling-with-tidyquant.R
Testing it out on a single stock. We can see that the “term” that
contains the direction of the trend (the slope) is “year(date)”. The
interpretation is that as year increases one unit, the annual returns
decrease by 3%.
## # A tibble: 2 × 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 58.9 113. 0.520 0.617
## 2 year(date) -0.0291 0.0562 -0.518 0.618
TQ03-scaling-and-modeling-with-tidyquant.R
Now that we have identified the trend direction, it looks like we are
ready to scale.
Scale to Many Stocks
Once the analysis for one stock is done scale to many stocks is
simple. For brevity, we’ll randomly sample ten stocks from the
S&P500 with a call to dplyr::sample_n().
set.seed(10)
stocks_tbl <- tq_index("SP500") %>%
sample_n(5)
stocks_tbl
## # A tibble: 5 × 8
## symbol company identifier sedol weight sector shares_held local_currency
## <chr> <chr> <chr> <chr> <dbl> <chr> <dbl> <chr>
## 1 MHK MOHAWK INDU… 608190104 2598… 1.68e-4 - 612264 USD
## 2 TGT TARGET CORP 87612E106 2259… 1.51e-3 - 5500695 USD
## 3 WST WEST PHARMA… 955306105 2950… 4.95e-4 - 881367 USD
## 4 BAX BAXTER INTE… 071813109 2085… 4.08e-4 - 6041921 USD
## 5 NWSA NEWS CORP … 65249B109 BBGV… 2.26e-4 - 4549095 USD
TQ03-scaling-and-modeling-with-tidyquant.R
We can now apply our analysis function to the stocks using
dplyr::mutate() and purrr::map(). The
mutate() function adds a column to our tibble, and the
map() function maps our custom get_model
function to our tibble of stocks using the symbol column.
The tidyr::unnest() function unrolls the nested data frame
so all of the model statistics are accessible in the top data frame
level. The filter, arrange and
select steps just manipulate the data frame to isolate and
arrange the data for our viewing.
stocks_model_stats <- stocks_tbl %>%
select(symbol, company) %>%
tq_get(from = "2007-01-01", to = "2016-12-31") %>%
# Nest
group_by(symbol, company) %>%
nest() %>%
# Apply the get_model() function to the new "nested" data column
mutate(model = map(data, get_model)) %>%
# Unnest and collect slope
unnest(model) %>%
filter(term == "year(date)") %>%
arrange(desc(estimate)) %>%
select(-term)
stocks_model_stats
## # A tibble: 5 × 7
## # Groups: symbol, company [5]
## symbol company data estimate std.error statistic p.value
## <chr> <chr> <list> <dbl> <dbl> <dbl> <dbl>
## 1 WST WEST PHARMACEUTICAL SERV… <tibble> 0.0524 0.0216 2.43 0.0412
## 2 MHK MOHAWK INDUSTRIES INC <tibble> 0.0420 0.0294 1.43 0.190
## 3 TGT TARGET CORP <tibble> 0.0178 0.0240 0.741 0.480
## 4 BAX BAXTER INTERNATIONAL INC <tibble> 0.00242 0.0160 0.151 0.884
## 5 NWSA NEWS CORP CLASS A <tibble> -0.0819 0.0480 -1.71 0.230
TQ03-scaling-and-modeling-with-tidyquant.R
We’re done! We now have the coefficient of the linear regression that
tracks the direction of the trend line. We can easily extend this type
of analysis to larger lists or stock indexes. For example, the entire
S&P500 could be analyzed removing the sample_n()
following the call to tq_index("SP500").
TQ03-scaling-and-modeling-with-tidyquant.R