Performance Analysis with tidyquant
Tidy analysis of stock and portfolio return performance with
PerformanceAnalytics
Overview
Financial asset (individual stocks, securities, etc) and portfolio
(groups of stocks, securities, etc) performance analysis is a deep field
with a wide range of theories and methods for analyzing risk versus
reward. The PerformanceAnalytics package consolidates
functions to compute many of the most widely used performance metrics.
tidyquant integrates this functionality so it can be used
at scale using the split, apply, combine framework within the
tidyverse. Two primary functions integrate the performance
analysis functionality:
tq_performanceimplements the performance analysis functions in a tidy way, enabling scaling analysis using the split, apply, combine framework.tq_portfolioprovides a useful tool set for aggregating a group of individual asset returns into one or many portfolios.
This vignette aims to cover three aspects of performance analysis:
The general workflow to go from start to finish on both an asset and a portfolio level
Some of the available techniques to implement once the workflow is implemented
How to customize
tq_portfolioandtq_performanceusing the...parameter
1.0 Key Concepts
An important concept is that performance analysis is based on the
statistical properties of returns (not prices). As a
result, this package uses inputs of time-based returns as
opposed to stock prices. The arguments change to
Ra for the asset returns and Rb for the
baseline returns. We’ll go over how to get returns in the Workflow section.
Another important concept is the baseline. The
baseline is what you are measuring performance against. A baseline can
be anything, but in many cases it’s a representative average of how an
investment might perform with little or no effort. Often indexes such as
the S&P500 are used for general market performance. Other times more
specific Exchange Traded Funds (ETFs) are used such as the SPDR
Technology ETF (XLK). The important concept here is that you measure the
asset performance (Ra) against the baseline
(Rb).
Now for a quick tutorial to show off the
PerformanceAnalytics package integration.
2.0 Quick Example
One of the most widely used risk to return metrics is the Capital Asset Pricing Model (CAPM). According to Investopedia:
The capital asset pricing model (CAPM) is a model that describes the relationship between systematic risk and expected return for assets, particularly stocks. CAPM is widely used throughout finance for the pricing of risky securities, generating expected returns for assets given the risk of those assets and calculating costs of capital.
We’ll use the PerformanceAnalytics function,
table.CAPM, to evaluate the returns of several technology
stocks against the SPDR Technology ETF (XLK).
First, load the tidyquant package.
Second, get the stock returns for the stocks we wish to evaluate. We
use tq_get to get stock prices from Yahoo Finance,
group_by to group the stock prices related to each symbol,
and tq_transmute to retrieve period returns in a monthly
periodicity using the “adjusted” stock prices (adjusted for stock
splits, which can throw off returns, affecting the performance
analysis). Review the output and see that there are three groups of
symbols indicating the data has been grouped appropriately.
Ra <- c("AAPL", "GOOG", "NFLX") %>%
tq_get(get = "stock.prices",
from = "2010-01-01",
to = "2015-12-31") %>%
group_by(symbol) %>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = "monthly",
col_rename = "Ra")
Ra## # A tibble: 216 × 3
## # Groups: symbol [3]
## symbol date Ra
## <chr> <date> <dbl>
## 1 AAPL 2010-01-29 -0.103
## 2 AAPL 2010-02-26 0.0654
## 3 AAPL 2010-03-31 0.148
## 4 AAPL 2010-04-30 0.111
## 5 AAPL 2010-05-28 -0.0161
## 6 AAPL 2010-06-30 -0.0208
## 7 AAPL 2010-07-30 0.0227
## 8 AAPL 2010-08-31 -0.0550
## 9 AAPL 2010-09-30 0.167
## 10 AAPL 2010-10-29 0.0607
## # ℹ 206 more rowsNext, we get the baseline prices. We’ll use the XLK. Note that there is no need to group because we are just getting one data set.
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 rowsNow, we combine the two data sets using the “date” field using
left_join from the dplyr package. Review the
results and see that we still have three groups of returns, and columns
“Ra” and “Rb” are side-by-side.
## # A tibble: 216 × 4
## # Groups: symbol [3]
## symbol date Ra Rb
## <chr> <date> <dbl> <dbl>
## 1 AAPL 2010-01-29 -0.103 -0.0993
## 2 AAPL 2010-02-26 0.0654 0.0348
## 3 AAPL 2010-03-31 0.148 0.0684
## 4 AAPL 2010-04-30 0.111 0.0126
## 5 AAPL 2010-05-28 -0.0161 -0.0748
## 6 AAPL 2010-06-30 -0.0208 -0.0540
## 7 AAPL 2010-07-30 0.0227 0.0745
## 8 AAPL 2010-08-31 -0.0550 -0.0561
## 9 AAPL 2010-09-30 0.167 0.117
## 10 AAPL 2010-10-29 0.0607 0.0578
## # ℹ 206 more rowsFinally, we can retrieve the performance metrics using
tq_performance(). You can use
tq_performance_fun_options() to see the full list of
compatible performance functions.
## # A tibble: 3 × 13
## # Groups: symbol [3]
## symbol ActivePremium Alpha AnnualizedAlpha Beta `Beta+` `Beta-` Correlation
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 AAPL 0.119 0.0089 0.112 1.11 1.04 0.578 0.659
## 2 GOOG 0.034 0.0028 0.034 1.14 1.16 1.39 0.644
## 3 NFLX 0.447 0.053 0.859 0.384 0.0045 -1.52 0.0817
## # ℹ 5 more variables: `Correlationp-value` <dbl>, InformationRatio <dbl>,
## # `R-squared` <dbl>, TrackingError <dbl>, TreynorRatio <dbl>We can quickly isolate attributes, such as alpha, the measure of growth, and beta, the measure of risk.
## # A tibble: 3 × 3
## # Groups: symbol [3]
## symbol Alpha Beta
## <chr> <dbl> <dbl>
## 1 AAPL 0.0089 1.11
## 2 GOOG 0.0028 1.14
## 3 NFLX 0.053 0.384With tidyquant it’s efficient and easy to get the CAPM
information! And, that’s just one of 129 available functions to analyze
stock and portfolio return performance. Just use
tq_performance_fun_options() to see the full list.
3.0 Workflow
The general workflow is shown in the diagram below. We’ll step through the workflow first with a group of individual assets (stocks) and then with portfolios of stocks.
3.1 Individual Assets
Individual assets are the simplest form of analysis because there is no portfolio aggregation (Step 3A). We’ll re-do the “Quick Example” this time getting the Sharpe Ratio, a measure of reward-to-risk.
Before we get started let’s find the performance function we want to
use from PerformanceAnalytics. Searching
tq_performance_fun_options, we can see that
SharpeRatio is available. Type ?SharpeRatio,
and we can see that the arguments are:
## function (R, Rf = 0, p = 0.95, FUN = c("StdDev", "VaR", "ES"),
## weights = NULL, annualize = FALSE, SE = FALSE, SE.control = NULL,
## ...)
## NULLWe can actually skip the baseline path because the function does not
require Rb. The function takes R, which is
passed using Ra in
tq_performance(Ra, Rb, performance_fun, ...). A little bit
of foresight saves us some work.
Step 1A: Get stock prices
Use tq_get() to get stock prices.
stock_prices <- c("AAPL", "GOOG", "NFLX") %>%
tq_get(get = "stock.prices",
from = "2010-01-01",
to = "2015-12-31")
stock_prices## # A tibble: 4,527 × 8
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 AAPL 2010-01-04 7.62 7.66 7.59 7.64 493729600 6.45
## 2 AAPL 2010-01-05 7.66 7.70 7.62 7.66 601904800 6.47
## 3 AAPL 2010-01-06 7.66 7.69 7.53 7.53 552160000 6.36
## 4 AAPL 2010-01-07 7.56 7.57 7.47 7.52 477131200 6.35
## 5 AAPL 2010-01-08 7.51 7.57 7.47 7.57 447610800 6.39
## 6 AAPL 2010-01-11 7.60 7.61 7.44 7.50 462229600 6.34
## 7 AAPL 2010-01-12 7.47 7.49 7.37 7.42 594459600 6.26
## 8 AAPL 2010-01-13 7.42 7.53 7.29 7.52 605892000 6.35
## 9 AAPL 2010-01-14 7.50 7.52 7.47 7.48 432894000 6.32
## 10 AAPL 2010-01-15 7.53 7.56 7.35 7.35 594067600 6.21
## # ℹ 4,517 more rowsStep 2A: Mutate to returns
Using the tidyverse split, apply, combine framework, we
can mutate groups of stocks by first “grouping” with
group_by and then applying a mutating function using
tq_transmute. We use the quantmod function
periodReturn as the mutating function. We pass along the
arguments period = "monthly" to return the results in
monthly periodicity. Last, we use the col_rename argument
to rename the output column.
stock_returns_monthly <- stock_prices %>%
group_by(symbol) %>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = "monthly",
col_rename = "Ra")
stock_returns_monthly## # A tibble: 216 × 3
## # Groups: symbol [3]
## symbol date Ra
## <chr> <date> <dbl>
## 1 AAPL 2010-01-29 -0.103
## 2 AAPL 2010-02-26 0.0654
## 3 AAPL 2010-03-31 0.148
## 4 AAPL 2010-04-30 0.111
## 5 AAPL 2010-05-28 -0.0161
## 6 AAPL 2010-06-30 -0.0208
## 7 AAPL 2010-07-30 0.0227
## 8 AAPL 2010-08-31 -0.0550
## 9 AAPL 2010-09-30 0.167
## 10 AAPL 2010-10-29 0.0607
## # ℹ 206 more rowsStep 3A: Aggregate to Portfolio Returns (Skipped)
Step 3A can be skipped because we are only interested in the Sharpe Ratio for individual stocks (not a portfolio).
Step 3B can also be skipped because the SharpeRatio
function from PerformanceAnalytics does not require a
baseline.
Step 4: Analyze Performance
The last step is to apply the SharpeRatio function to
our groups of stock returns. We do this using
tq_performance() with the arguments Ra = Ra,
Rb = NULL (not required), and
performance_fun = SharpeRatio. We can also pass other
arguments of the SharpeRatio function such as
Rf, p, FUN, and
annualize. We will just use the defaults for this
example.
## # A tibble: 3 × 4
## # Groups: symbol [3]
## symbol `ESSharpe(Rf=0%,p=95%)` StdDevSharpe(Rf=0%,p=9…¹ VaRSharpe(Rf=0%,p=95…²
## <chr> <dbl> <dbl> <dbl>
## 1 AAPL 0.173 0.292 0.218
## 2 GOOG 0.129 0.203 0.157
## 3 NFLX 0.237 0.284 0.272
## # ℹ abbreviated names: ¹`StdDevSharpe(Rf=0%,p=95%)`, ²`VaRSharpe(Rf=0%,p=95%)`Now we have the Sharpe Ratio for each of the three stocks. What if we want to adjust the parameters of the function? We can just add on the arguments of the underlying function.
stock_returns_monthly %>%
tq_performance(
Ra = Ra,
Rb = NULL,
performance_fun = SharpeRatio,
Rf = 0.03 / 12,
p = 0.99
)## # A tibble: 3 × 4
## # Groups: symbol [3]
## symbol `ESSharpe(Rf=0.2%,p=99%)` StdDevSharpe(Rf=0.2%…¹ VaRSharpe(Rf=0.2%,p=…²
## <chr> <dbl> <dbl> <dbl>
## 1 AAPL 0.116 0.258 0.134
## 2 GOOG 0.0826 0.170 0.0998
## 3 NFLX 0.115 0.272 0.142
## # ℹ abbreviated names: ¹`StdDevSharpe(Rf=0.2%,p=99%)`,
## # ²`VaRSharpe(Rf=0.2%,p=99%)`3.2 Portfolios (Asset Groups)
Portfolios are slightly more complicated because we are now dealing
with groups of assets versus individual stocks, and we need to aggregate
weighted returns. Fortunately, this is only one extra step with
tidyquant using tq_portfolio().
Single Portfolio
Let’s recreate the CAPM analysis in the “Quick Example” this time comparing a portfolio of technology stocks to the SPDR Technology ETF (XLK).
Steps 1A and 2A: Asset Period Returns
This is the same as what we did previously to get the monthly returns
for groups of individual stock prices. We use the split, apply, combine
framework using the workflow of tq_get,
group_by, and tq_transmute.
stock_returns_monthly <- c("AAPL", "GOOG", "NFLX") %>%
tq_get(get = "stock.prices",
from = "2010-01-01",
to = "2015-12-31") %>%
group_by(symbol) %>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = "monthly",
col_rename = "Ra")
stock_returns_monthly## # A tibble: 216 × 3
## # Groups: symbol [3]
## symbol date Ra
## <chr> <date> <dbl>
## 1 AAPL 2010-01-29 -0.103
## 2 AAPL 2010-02-26 0.0654
## 3 AAPL 2010-03-31 0.148
## 4 AAPL 2010-04-30 0.111
## 5 AAPL 2010-05-28 -0.0161
## 6 AAPL 2010-06-30 -0.0208
## 7 AAPL 2010-07-30 0.0227
## 8 AAPL 2010-08-31 -0.0550
## 9 AAPL 2010-09-30 0.167
## 10 AAPL 2010-10-29 0.0607
## # ℹ 206 more rowsSteps 1B and 2B: Baseline Period Returns
This was also done previously.
baseline_returns_monthly <- "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")
baseline_returns_monthly## # 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 rowsStep 3A: Aggregate to Portfolio Period Returns
The tidyquant function, tq_portfolio()
aggregates a group of individual assets into a single return using a
weighted composition of the underlying assets. To do this we need to
first develop portfolio weights. There are two ways to do this for a
single portfolio:
- Supplying a vector of weights
- Supplying a two column tidy data frame (tibble) with stock symbols in the first column and weights to map in the second.
Suppose we want to split our portfolio evenly between AAPL and NFLX. We’ll show this using both methods.
Method 1: Aggregating a Portfolio using Vector of Weights
We’ll use the weight vector, c(0.5, 0, 0.5). Two
important aspects to supplying a numeric vector of weights: First,
notice that the length (3) is equal to the number of assets (3). This is
a requirement. Second, notice that the sum of the weighting vector is
equal to 1. This is not “required”, but is best practice. If the sum is
not 1, the weights will be distributed accordingly by scaling the vector
to 1, and a warning message will appear.
wts <- c(0.5, 0.0, 0.5)
portfolio_returns_monthly <- stock_returns_monthly %>%
tq_portfolio(assets_col = symbol,
returns_col = Ra,
weights = wts,
col_rename = "Ra")
portfolio_returns_monthly## # A tibble: 72 × 2
## date Ra
## <date> <dbl>
## 1 2010-01-29 0.0307
## 2 2010-02-26 0.0629
## 3 2010-03-31 0.130
## 4 2010-04-30 0.239
## 5 2010-05-28 0.0682
## 6 2010-06-30 -0.0219
## 7 2010-07-30 -0.0272
## 8 2010-08-31 0.116
## 9 2010-09-30 0.251
## 10 2010-10-29 0.0674
## # ℹ 62 more rowsWe now have an aggregated portfolio that is a 50/50 blend of AAPL and NFLX.
You may be asking why didn’t we use GOOG? The important thing to understand is that all of the assets from the asset returns don’t need to be used when creating the portfolio! This enables us to scale individual stock returns and then vary weights to optimize the portfolio (this will be a further subject that we address in the future!)
Method 2: Aggregating a Portfolio using Two Column tibble with Symbols and Weights
A possibly more useful method of aggregating returns is using a tibble of symbols and weights that are mapped to the portfolio. We’ll recreate the previous portfolio example using mapped weights.
## # A tibble: 2 × 2
## symbols weights
## <chr> <dbl>
## 1 AAPL 0.5
## 2 NFLX 0.5Next, supply this two column tibble, with symbols in the first column
and weights in the second, to the weights argument in
tq_performance().
stock_returns_monthly %>%
tq_portfolio(assets_col = symbol,
returns_col = Ra,
weights = wts_map,
col_rename = "Ra_using_wts_map")## # A tibble: 72 × 2
## date Ra_using_wts_map
## <date> <dbl>
## 1 2010-01-29 0.0307
## 2 2010-02-26 0.0629
## 3 2010-03-31 0.130
## 4 2010-04-30 0.239
## 5 2010-05-28 0.0682
## 6 2010-06-30 -0.0219
## 7 2010-07-30 -0.0272
## 8 2010-08-31 0.116
## 9 2010-09-30 0.251
## 10 2010-10-29 0.0674
## # ℹ 62 more rowsThe aggregated returns are exactly the same. The advantage with this method is that not all symbols need to be specified. Any symbol not specified by default gets a weight of zero.
Now, imagine if you had an entire index, such as the Russell 2000, of 2000 individual stock returns in a nice tidy data frame. It would be very easy to adjust portfolios and compute blended returns, and you only need to supply the symbols that you want to blend. All other symbols default to zero!
Step 3B: Merging Ra and Rb
Now that we have the aggregated portfolio returns (“Ra”) from Step 3A and the baseline returns (“Rb”) from Step 2B, we can merge to get our consolidated table of asset and baseline returns. Nothing new here.
RaRb_single_portfolio <- left_join(portfolio_returns_monthly,
baseline_returns_monthly,
by = "date")
RaRb_single_portfolio## # A tibble: 72 × 3
## date Ra Rb
## <date> <dbl> <dbl>
## 1 2010-01-29 0.0307 -0.0993
## 2 2010-02-26 0.0629 0.0348
## 3 2010-03-31 0.130 0.0684
## 4 2010-04-30 0.239 0.0126
## 5 2010-05-28 0.0682 -0.0748
## 6 2010-06-30 -0.0219 -0.0540
## 7 2010-07-30 -0.0272 0.0745
## 8 2010-08-31 0.116 -0.0561
## 9 2010-09-30 0.251 0.117
## 10 2010-10-29 0.0674 0.0578
## # ℹ 62 more rowsStep 4: Computing the CAPM Table
The CAPM table is computed with the function table.CAPM
from PerformanceAnalytics. We just perform the same task
that we performed in the “Quick Example”.
## # A tibble: 1 × 12
## ActivePremium Alpha AnnualizedAlpha Beta `Beta+` `Beta-` Correlation
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.327 0.0299 0.425 0.754 0.503 -0.243 0.283
## # ℹ 5 more variables: `Correlationp-value` <dbl>, InformationRatio <dbl>,
## # `R-squared` <dbl>, TrackingError <dbl>, TreynorRatio <dbl>Now we have the CAPM performance metrics for a portfolio! While this is cool, it’s cooler to do multiple portfolios. Let’s see how.
Multiple Portfolios
Once you understand the process for a single portfolio using Step 3A, Method 2 (aggregating weights by mapping), scaling to multiple portfolios is just building on this concept. Let’s recreate the same example from the “Single Portfolio” Example this time with three portfolios:
- 50% AAPL, 25% GOOG, 25% NFLX
- 25% AAPL, 50% GOOG, 25% NFLX
- 25% AAPL, 25% GOOG, 50% NFLX
Steps 1 and 2 are the Exact Same as the Single Portfolio Example
First, get individual asset returns grouped by asset, which is the exact same as Steps 1A and 1B from the Single Portfolio example.
stock_returns_monthly <- c("AAPL", "GOOG", "NFLX") %>%
tq_get(get = "stock.prices",
from = "2010-01-01",
to = "2015-12-31") %>%
group_by(symbol) %>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = "monthly",
col_rename = "Ra")Second, get baseline asset returns, which is the exact same as Steps 1B and 2B from the Single Portfolio example.
Step 3A: Aggregate Portfolio Returns for Multiple Portfolios
This is where it gets fun. If you picked up on Single Portfolio, Step3A, Method 2 (mapping weights), this is just an extension for multiple portfolios.
First, we need to grow our portfolios. tidyquant has a
handy, albeit simple, function, tq_repeat_df(), for scaling
a single portfolio to many. It takes a data frame, and the number of
repeats, n, and the index_col_name, which adds
a sequential index. Let’s see how it works for our example. We need
three portfolios:
stock_returns_monthly_multi <- stock_returns_monthly %>%
tq_repeat_df(n = 3)
stock_returns_monthly_multi## # A tibble: 648 × 4
## # Groups: portfolio [3]
## portfolio symbol date Ra
## <int> <chr> <date> <dbl>
## 1 1 AAPL 2010-01-29 -0.103
## 2 1 AAPL 2010-02-26 0.0654
## 3 1 AAPL 2010-03-31 0.148
## 4 1 AAPL 2010-04-30 0.111
## 5 1 AAPL 2010-05-28 -0.0161
## 6 1 AAPL 2010-06-30 -0.0208
## 7 1 AAPL 2010-07-30 0.0227
## 8 1 AAPL 2010-08-31 -0.0550
## 9 1 AAPL 2010-09-30 0.167
## 10 1 AAPL 2010-10-29 0.0607
## # ℹ 638 more rowsExamining the results, we can see that a few things happened:
- The length (number of rows) has tripled. This is the essence of
tq_repeat_df: it grows the data frame length-wise, repeating the data framentimes. In our case,n = 3. - Our data frame, which was grouped by symbol, was ungrouped. This is
needed to prevent
tq_portfoliofrom blending on the individual stocks.tq_portfolioonly works on groups of stocks. - We have a new column, named “portfolio”. The “portfolio” column name
is a key that tells
tq_portfoliothat multiple groups exist to analyze. Just note that for multiple portfolio analysis, the “portfolio” column name is required. - We have three groups of portfolios. This is what
tq_portfoliowill split, apply (aggregate), then combine on.
Now the tricky part: We need a new table of weights to map on. There’s a few requirements:
- We must supply a three column tibble with the following columns: “portfolio”, asset, and weight in that order.
- The “portfolio” column must be named “portfolio” since this is a key name for mapping.
- The tibble must be grouped by the portfolio column.
Here’s what the weights table should look like for our example:
weights <- c(
0.50, 0.25, 0.25,
0.25, 0.50, 0.25,
0.25, 0.25, 0.50
)
stocks <- c("AAPL", "GOOG", "NFLX")
weights_table <- tibble(stocks) %>%
tq_repeat_df(n = 3) %>%
bind_cols(tibble(weights)) %>%
group_by(portfolio)
weights_table## # A tibble: 9 × 3
## # Groups: portfolio [3]
## portfolio stocks weights
## <int> <chr> <dbl>
## 1 1 AAPL 0.5
## 2 1 GOOG 0.25
## 3 1 NFLX 0.25
## 4 2 AAPL 0.25
## 5 2 GOOG 0.5
## 6 2 NFLX 0.25
## 7 3 AAPL 0.25
## 8 3 GOOG 0.25
## 9 3 NFLX 0.5Now just pass the expanded stock_returns_monthly_multi
and the weights_table to tq_portfolio for
portfolio aggregation.
portfolio_returns_monthly_multi <- stock_returns_monthly_multi %>%
tq_portfolio(assets_col = symbol,
returns_col = Ra,
weights = weights_table,
col_rename = "Ra")
portfolio_returns_monthly_multi## # A tibble: 216 × 3
## # Groups: portfolio [3]
## portfolio date Ra
## <int> <date> <dbl>
## 1 1 2010-01-29 -0.0489
## 2 1 2010-02-26 0.0482
## 3 1 2010-03-31 0.123
## 4 1 2010-04-30 0.145
## 5 1 2010-05-28 0.0245
## 6 1 2010-06-30 -0.0308
## 7 1 2010-07-30 0.000600
## 8 1 2010-08-31 0.0474
## 9 1 2010-09-30 0.222
## 10 1 2010-10-29 0.0789
## # ℹ 206 more rowsLet’s assess the output. We now have a single, “long” format data
frame of portfolio returns. It has three groups with the aggregated
portfolios blended by mapping the weights_table.
Steps 3B and 4: Merging and Assessing Performance
These steps are the exact same as the Single Portfolio example.
First, we merge with the baseline using “date” as the key.
RaRb_multiple_portfolio <- left_join(portfolio_returns_monthly_multi,
baseline_returns_monthly,
by = "date")
RaRb_multiple_portfolio## # A tibble: 216 × 4
## # Groups: portfolio [3]
## portfolio date Ra Rb
## <int> <date> <dbl> <dbl>
## 1 1 2010-01-29 -0.0489 -0.0993
## 2 1 2010-02-26 0.0482 0.0348
## 3 1 2010-03-31 0.123 0.0684
## 4 1 2010-04-30 0.145 0.0126
## 5 1 2010-05-28 0.0245 -0.0748
## 6 1 2010-06-30 -0.0308 -0.0540
## 7 1 2010-07-30 0.000600 0.0745
## 8 1 2010-08-31 0.0474 -0.0561
## 9 1 2010-09-30 0.222 0.117
## 10 1 2010-10-29 0.0789 0.0578
## # ℹ 206 more rowsFinally, we calculate the performance of each of the portfolios using
tq_performance. Make sure the data frame is grouped on
“portfolio”.
## # A tibble: 3 × 13
## # Groups: portfolio [3]
## portfolio ActivePremium Alpha AnnualizedAlpha Beta `Beta+` `Beta-`
## <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0.231 0.0193 0.258 0.908 0.741 0.312
## 2 2 0.219 0.0192 0.256 0.886 0.660 0.436
## 3 3 0.319 0.0308 0.439 0.721 0.394 -0.179
## # ℹ 6 more variables: Correlation <dbl>, `Correlationp-value` <dbl>,
## # InformationRatio <dbl>, `R-squared` <dbl>, TrackingError <dbl>,
## # TreynorRatio <dbl>Inspecting the results, we now have a multiple portfolio comparison
of the CAPM table from PerformanceAnalytics. We can do the
same thing with SharpeRatio as well.
## # A tibble: 3 × 4
## # Groups: portfolio [3]
## portfolio ESSharpe(Rf=0%,p=95%…¹ StdDevSharpe(Rf=0%,p…² VaRSharpe(Rf=0%,p=95…³
## <int> <dbl> <dbl> <dbl>
## 1 1 0.172 0.355 0.263
## 2 2 0.146 0.334 0.236
## 3 3 0.150 0.317 0.238
## # ℹ abbreviated names: ¹`ESSharpe(Rf=0%,p=95%)`, ²`StdDevSharpe(Rf=0%,p=95%)`,
## # ³`VaRSharpe(Rf=0%,p=95%)`4.0 Available Functions
We’ve only scratched the surface of the analysis functions available
through PerformanceAnalytics. The list below includes all
of the compatible functions grouped by function type. The table
functions are the most useful to get a cross section of metrics. We’ll
touch on a few. We’ll also go over VaR and
SharpeRatio as these are very commonly used as performance
measures.
## $table.funs
## [1] "table.AnnualizedReturns" "table.Arbitrary"
## [3] "table.Autocorrelation" "table.CAPM"
## [5] "table.CaptureRatios" "table.Correlation"
## [7] "table.Distributions" "table.DownsideRisk"
## [9] "table.DownsideRiskRatio" "table.DrawdownsRatio"
## [11] "table.HigherMoments" "table.InformationRatio"
## [13] "table.RollingPeriods" "table.SFM"
## [15] "table.SpecificRisk" "table.Stats"
## [17] "table.TrailingPeriods" "table.UpDownRatios"
## [19] "table.Variability"
##
## $CAPM.funs
## [1] "CAPM.alpha" "CAPM.beta" "CAPM.beta.bear" "CAPM.beta.bull"
## [5] "CAPM.CML" "CAPM.CML.slope" "CAPM.dynamic" "CAPM.epsilon"
## [9] "CAPM.jensenAlpha" "CAPM.RiskPremium" "CAPM.SML.slope" "TimingRatio"
## [13] "MarketTiming"
##
## $SFM.funs
## [1] "SFM.alpha" "SFM.beta" "SFM.CML" "SFM.CML.slope"
## [5] "SFM.dynamic" "SFM.epsilon" "SFM.jensenAlpha"
##
## $descriptive.funs
## [1] "mean" "sd" "min" "max"
## [5] "cor" "mean.geometric" "mean.stderr" "mean.LCL"
## [9] "mean.UCL"
##
## $annualized.funs
## [1] "Return.annualized" "Return.annualized.excess"
## [3] "sd.annualized" "SharpeRatio.annualized"
##
## $VaR.funs
## [1] "VaR" "ES" "ETL" "CDD" "CVaR"
##
## $moment.funs
## [1] "var" "cov" "skewness" "kurtosis"
## [5] "CoVariance" "CoSkewness" "CoSkewnessMatrix" "CoKurtosis"
## [9] "CoKurtosisMatrix" "M3.MM" "M4.MM" "BetaCoVariance"
## [13] "BetaCoSkewness" "BetaCoKurtosis"
##
## $drawdown.funs
## [1] "AverageDrawdown" "AverageLength" "AverageRecovery"
## [4] "DrawdownDeviation" "DrawdownPeak" "maxDrawdown"
##
## $Bacon.risk.funs
## [1] "MeanAbsoluteDeviation" "Frequency" "SharpeRatio"
## [4] "MSquared" "MSquaredExcess" "HurstIndex"
##
## $Bacon.regression.funs
## [1] "CAPM.alpha" "CAPM.beta" "CAPM.epsilon" "CAPM.jensenAlpha"
## [5] "SystematicRisk" "SpecificRisk" "TotalRisk" "TreynorRatio"
## [9] "AppraisalRatio" "FamaBeta" "Selectivity" "NetSelectivity"
##
## $Bacon.relative.risk.funs
## [1] "ActivePremium" "ActiveReturn" "TrackingError" "InformationRatio"
##
## $Bacon.drawdown.funs
## [1] "PainIndex" "PainRatio" "CalmarRatio" "SterlingRatio"
## [5] "BurkeRatio" "MartinRatio" "UlcerIndex"
##
## $Bacon.downside.risk.funs
## [1] "DownsideDeviation" "DownsidePotential" "DownsideFrequency"
## [4] "SemiDeviation" "SemiVariance" "UpsideRisk"
## [7] "UpsidePotentialRatio" "UpsideFrequency" "BernardoLedoitRatio"
## [10] "DRatio" "Omega" "OmegaSharpeRatio"
## [13] "OmegaExcessReturn" "SortinoRatio" "M2Sortino"
## [16] "Kappa" "VolatilitySkewness" "AdjustedSharpeRatio"
## [19] "SkewnessKurtosisRatio" "ProspectRatio"
##
## $misc.funs
## [1] "KellyRatio" "Modigliani" "UpDownRatios"4.1 table.Stats
Returns a basic set of statistics that match the period of the data passed in (e.g., monthly returns will get monthly statistics, daily will be daily stats, and so on).
## # A tibble: 3 × 17
## # Groups: portfolio [3]
## portfolio ArithmeticMean GeometricMean Kurtosis `LCLMean(0.95)` Maximum Median
## <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0.0293 0.0259 1.14 0.0099 0.222 0.0307
## 2 2 0.029 0.0252 1.65 0.0086 0.227 0.037
## 3 3 0.0388 0.0313 1.81 0.01 0.370 0.046
## # ℹ 10 more variables: Minimum <dbl>, NAs <dbl>, Observations <dbl>,
## # Quartile1 <dbl>, Quartile3 <dbl>, SEMean <dbl>, Skewness <dbl>,
## # Stdev <dbl>, `UCLMean(0.95)` <dbl>, Variance <dbl>4.2 table.CAPM
Takes a set of returns and relates them to a benchmark return. Provides a set of measures related to an excess return single factor model, or CAPM.
## # A tibble: 3 × 13
## # Groups: portfolio [3]
## portfolio ActivePremium Alpha AnnualizedAlpha Beta `Beta+` `Beta-`
## <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0.231 0.0193 0.258 0.908 0.741 0.312
## 2 2 0.219 0.0192 0.256 0.886 0.660 0.436
## 3 3 0.319 0.0308 0.439 0.721 0.394 -0.179
## # ℹ 6 more variables: Correlation <dbl>, `Correlationp-value` <dbl>,
## # InformationRatio <dbl>, `R-squared` <dbl>, TrackingError <dbl>,
## # TreynorRatio <dbl>4.3 table.AnnualizedReturns
Table of Annualized Return, Annualized Std Dev, and Annualized Sharpe.
RaRb_multiple_portfolio %>%
tq_performance(Ra = Ra, Rb = NULL, performance_fun = table.AnnualizedReturns)## # A tibble: 3 × 4
## # Groups: portfolio [3]
## portfolio AnnualizedReturn `AnnualizedSharpe(Rf=0%)` AnnualizedStdDev
## <int> <dbl> <dbl> <dbl>
## 1 1 0.360 1.26 0.286
## 2 2 0.348 1.16 0.301
## 3 3 0.448 1.06 0.4244.4 table.Correlation
This is a wrapper for calculating correlation and significance against each column of the data provided.
## # A tibble: 3 × 5
## # Groups: portfolio [3]
## portfolio `p-value` `Lower CI` `Upper CI` to.Rb
## <int> <dbl> <dbl> <dbl> <dbl>
## 1 1 0.0000284 0.270 0.634 0.472
## 2 2 0.000122 0.229 0.608 0.438
## 3 3 0.0325 0.0220 0.457 0.2524.5 table.DownsideRisk
Creates a table of estimates of downside risk measures for comparison across multiple instruments or funds.
RaRb_multiple_portfolio %>%
tq_performance(Ra = Ra, Rb = NULL, performance_fun = table.DownsideRisk)## # A tibble: 3 × 12
## # Groups: portfolio [3]
## portfolio DownsideDeviation(0%…¹ DownsideDeviation(MA…² DownsideDeviation(Rf…³
## <int> <dbl> <dbl> <dbl>
## 1 1 0.045 0.0488 0.045
## 2 2 0.0501 0.0538 0.0501
## 3 3 0.0684 0.0721 0.0684
## # ℹ abbreviated names: ¹`DownsideDeviation(0%)`, ²`DownsideDeviation(MAR=10%)`,
## # ³`DownsideDeviation(Rf=0%)`
## # ℹ 8 more variables: GainDeviation <dbl>, `HistoricalES(95%)` <dbl>,
## # `HistoricalVaR(95%)` <dbl>, LossDeviation <dbl>, MaximumDrawdown <dbl>,
## # `ModifiedES(95%)` <dbl>, `ModifiedVaR(95%)` <dbl>, SemiDeviation <dbl>4.6 table.DownsideRiskRatio
Table of Monthly downside risk, Annualized downside risk, Downside potential, Omega, Sortino ratio, Upside potential, Upside potential ratio and Omega-Sharpe ratio.
RaRb_multiple_portfolio %>%
tq_performance(Ra = Ra, Rb = NULL, performance_fun = table.DownsideRiskRatio)## # A tibble: 3 × 9
## # Groups: portfolio [3]
## portfolio Annualiseddownsiderisk Downsidepotential Omega `Omega-sharperatio`
## <int> <dbl> <dbl> <dbl> <dbl>
## 1 1 0.156 0.0198 2.48 1.48
## 2 2 0.173 0.0217 2.34 1.34
## 3 3 0.237 0.0294 2.32 1.32
## # ℹ 4 more variables: Sortinoratio <dbl>, Upsidepotential <dbl>,
## # Upsidepotentialratio <dbl>, monthlydownsiderisk <dbl>4.7 table.HigherMoments
Summary of the higher moments and Co-Moments of the return distribution. Used to determine diversification potential. Also called “systematic” moments by several papers.
## # A tibble: 3 × 6
## # Groups: portfolio [3]
## portfolio BetaCoKurtosis BetaCoSkewness BetaCoVariance CoKurtosis CoSkewness
## <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0.756 0.196 0.908 0 0
## 2 2 0.772 1.71 0.886 0 0
## 3 3 0.455 0.369 0.721 0 04.8 table.InformationRatio
Table of Tracking error, Annualized tracking error and Information ratio.
RaRb_multiple_portfolio %>%
tq_performance(Ra = Ra, Rb = Rb, performance_fun = table.InformationRatio)## # A tibble: 3 × 4
## # Groups: portfolio [3]
## portfolio AnnualisedTrackingError InformationRatio TrackingError
## <int> <dbl> <dbl> <dbl>
## 1 1 0.252 0.917 0.0728
## 2 2 0.271 0.809 0.0782
## 3 3 0.412 0.774 0.1194.9 table.Variability
Table of Mean absolute difference, Monthly standard deviation and annualized standard deviation.
## # A tibble: 3 × 4
## # Groups: portfolio [3]
## portfolio AnnualizedStdDev MeanAbsolutedeviation monthlyStdDev
## <int> <dbl> <dbl> <dbl>
## 1 1 0.286 0.0658 0.0825
## 2 2 0.301 0.0679 0.0868
## 3 3 0.424 0.091 0.1224.10 VaR
Calculates Value-at-Risk (VaR) for univariate, component, and marginal cases using a variety of analytical methods.
## # A tibble: 3 × 2
## # Groups: portfolio [3]
## portfolio VaR
## <int> <dbl>
## 1 1 -0.111
## 2 2 -0.123
## 3 3 -0.1635.0 Customizing using the …
One of the best features of tq_portfolio and
tq_performance is to be able to pass features through to
the underlying functions. After all, these are just wrappers for
PerformanceAnalytics, so you probably want to be able
to make full use of the underlying functions. Passing
through parameters using the ... can be incredibly useful,
so let’s see how.
5.1 Customizing tq_portfolio
The tq_portfolio function is a wrapper for
Return.portfolio. This means that during the portfolio
aggregation process, we can make use of most of the
Return.portfolio arguments such as
wealth.index, contribution,
geometric, rebalance_on, and
value. Here’s the arguments of the underlying function:
## function (R, weights = NULL, wealth.index = FALSE, contribution = FALSE,
## geometric = TRUE, rebalance_on = c(NA, "years", "quarters",
## "months", "weeks", "days"), value = 1, verbose = FALSE,
## ...)
## NULLLet’s see an example of passing parameters to the ....
Suppose we want to instead see how our money is grows for a $10,000
investment. We’ll use the “Single Portfolio” example, where our
portfolio mix was 50% AAPL, 0% GOOG, and 50% NFLX.
Method 3A, Aggregating Portfolio Returns, showed us two methods to aggregate for a single portfolio. Either will work for this example. For simplicity, we’ll examine the first.
Here’s the original output, without adjusting parameters.
wts <- c(0.5, 0.0, 0.5)
portfolio_returns_monthly <- stock_returns_monthly %>%
tq_portfolio(assets_col = symbol,
returns_col = Ra,
weights = wts,
col_rename = "Ra")portfolio_returns_monthly %>%
ggplot(aes(x = date, y = Ra)) +
geom_bar(stat = "identity", fill = palette_light()[[1]]) +
labs(title = "Portfolio Returns",
subtitle = "50% AAPL, 0% GOOG, and 50% NFLX",
caption = "Shows an above-zero trend meaning positive returns",
x = "", y = "Monthly Returns") +
geom_smooth(method = "lm") +
theme_tq() +
scale_color_tq() +
scale_y_continuous(labels = scales::percent)
This is good, but we want to see how our $10,000 initial investment
is growing. This is simple with the underlying
Return.portfolio argument,
wealth.index = TRUE. All we need to do is add these as
additional parameters to tq_portfolio!
wts <- c(0.5, 0, 0.5)
portfolio_growth_monthly <- stock_returns_monthly %>%
tq_portfolio(assets_col = symbol,
returns_col = Ra,
weights = wts,
col_rename = "investment.growth",
wealth.index = TRUE) %>%
mutate(investment.growth = investment.growth * 10000)portfolio_growth_monthly %>%
ggplot(aes(x = date, y = investment.growth)) +
geom_line(linewidth = 2, color = palette_light()[[1]]) +
labs(title = "Portfolio Growth",
subtitle = "50% AAPL, 0% GOOG, and 50% NFLX",
caption = "Now we can really visualize performance!",
x = "", y = "Portfolio Value") +
geom_smooth(method = "loess") +
theme_tq() +
scale_color_tq() +
scale_y_continuous(labels = scales::dollar)
Finally, taking this one step further, we apply the same process to the “Multiple Portfolio” example:
- 50% AAPL, 25% GOOG, 25% NFLX
- 25% AAPL, 50% GOOG, 25% NFLX
- 25% AAPL, 25% GOOG, 50% NFLX
portfolio_growth_monthly_multi <- stock_returns_monthly_multi %>%
tq_portfolio(assets_col = symbol,
returns_col = Ra,
weights = weights_table,
col_rename = "investment.growth",
wealth.index = TRUE) %>%
mutate(investment.growth = investment.growth * 10000)portfolio_growth_monthly_multi %>%
ggplot(aes(x = date, y = investment.growth, color = factor(portfolio))) +
geom_line(linewidth = 2) +
labs(title = "Portfolio Growth",
subtitle = "Comparing Multiple Portfolios",
caption = "Portfolio 3 is a Standout!",
x = "", y = "Portfolio Value",
color = "Portfolio") +
geom_smooth(method = "loess") +
theme_tq() +
scale_color_tq() +
scale_y_continuous(labels = scales::dollar)
5.2 Customizing tq_performance
Finally, the same concept of passing arguments works with all the
tidyquant functions that are wrappers including
tq_transmute, tq_mutate,
tq_performance, etc. Let’s use a final example with the
SharpeRatio, which has the following arguments.
## function (R, Rf = 0, p = 0.95, FUN = c("StdDev", "VaR", "ES"),
## weights = NULL, annualize = FALSE, SE = FALSE, SE.control = NULL,
## ...)
## NULLWe can see that the parameters Rf allows us to apply a
risk-free rate and p allows us to vary the confidence
interval. Let’s compare the Sharpe ratio with an annualized risk-free
rate of 3% and a confidence interval of 0.99.
Default:
## # A tibble: 3 × 4
## # Groups: portfolio [3]
## portfolio ESSharpe(Rf=0%,p=95%…¹ StdDevSharpe(Rf=0%,p…² VaRSharpe(Rf=0%,p=95…³
## <int> <dbl> <dbl> <dbl>
## 1 1 0.172 0.355 0.263
## 2 2 0.146 0.334 0.236
## 3 3 0.150 0.317 0.238
## # ℹ abbreviated names: ¹`ESSharpe(Rf=0%,p=95%)`, ²`StdDevSharpe(Rf=0%,p=95%)`,
## # ³`VaRSharpe(Rf=0%,p=95%)`With Rf = 0.03 / 12 (adjusted for monthly
periodicity):
## # A tibble: 3 × 4
## # Groups: portfolio [3]
## portfolio ESSharpe(Rf=0.2%,p=9…¹ StdDevSharpe(Rf=0.2%…² VaRSharpe(Rf=0.2%,p=…³
## <int> <dbl> <dbl> <dbl>
## 1 1 0.157 0.325 0.241
## 2 2 0.134 0.305 0.216
## 3 3 0.141 0.296 0.222
## # ℹ abbreviated names: ¹`ESSharpe(Rf=0.2%,p=95%)`,
## # ²`StdDevSharpe(Rf=0.2%,p=95%)`, ³`VaRSharpe(Rf=0.2%,p=95%)`And, with both Rf = 0.03 / 12 (adjusted for monthly
periodicity) and p = 0.99:
RaRb_multiple_portfolio %>%
tq_performance(Ra = Ra,
performance_fun = SharpeRatio,
Rf = 0.03 / 12,
p = 0.99)## # A tibble: 3 × 4
## # Groups: portfolio [3]
## portfolio ESSharpe(Rf=0.2%,p=9…¹ StdDevSharpe(Rf=0.2%…² VaRSharpe(Rf=0.2%,p=…³
## <int> <dbl> <dbl> <dbl>
## 1 1 0.105 0.325 0.134
## 2 2 0.0952 0.305 0.115
## 3 3 0.0915 0.296 0.117
## # ℹ abbreviated names: ¹`ESSharpe(Rf=0.2%,p=99%)`,
## # ²`StdDevSharpe(Rf=0.2%,p=99%)`, ³`VaRSharpe(Rf=0.2%,p=99%)`