We’ve got some good stuff cooking over at Business Science. Yesterday, we had the fifth official release (0.5.0) of tidyquant to CRAN. The release includes some great new features. First, the Quandl integration is complete, which now enables getting Quandl data in “tidy” format. Second, we have a new mechanism to handle selecting which columns get sent to the mutation functions. The new argument name is… select, and it provides increased flexibility which we show off in a rollapply example. Finally, we have added several PerformanceAnalytics functions that deal with modifying returns to the mutation functions. In this post, we’ll go over a few of the new features in version 5.

Quandl integration

We couldn’t wait to talk about the new Quandl integration for tidyquant! Two weeks ago, we went ahead and released a Quandl blog post using the GitHub development version. However, it is worth mentioning again now that it is officially being released. Definitely go check out that post to learn more!

We would like to highlight one feature that that post didn’t cover: data tables. These are data sets from Quandl that aren’t in a time series format, but are more of a treasure trove of other information. Many of them are premium datasets, meaning they cost money, but a few are free or have free samples. One of them is the Fundamentals Condensed datatable from Zacks. According to Quandl,

“Updated daily, this database contains over 70 fundamental indicators, including income statement, balance sheet, cash flow line items and precalculated ratios, for over 17,000 US and Canadian Equities, including 9,000+ delisted stocks.”

Without a premium Quandl account, we won’t be able to access all of it, but Quandl still offers a sample of the data. Let’s see how to grab this. First, we will want to set the Quandl API key linked to our account so that we have more than 50 calls per day.

library(tidyquant)
quandl_api_key("api-key-goes-here")

Now, we can use tq_get(get = "quandl.datatable") with the ZACKS/FC Quandl code. There’s a lot of data here, including mailing address, company officers, and numerous fundamentals stats!

FC <- tq_get("ZACKS/FC", get = "quandl.datatable")
FC %>%
    slice(1:3) %>%
    knitr::kable()
m_ticker ticker comp_name comp_name_2 exchange currency_code per_end_date per_type per_code per_fisc_year per_fisc_qtr per_cal_year per_cal_qtr data_type_ind filing_type qtr_nbr zacks_sector_code zacks_x_ind_code zacks_metrics_ind_code fye_month comp_cik per_len sic_code filing_date last_changed_date state_incorp_name bus_address_line_1 bus_city bus_state_name bus_post_code bus_phone_nbr bus_fax_nbr mail_address_line_1 mail_city mail_state_name mail_post_code country_name country_code home_exchange_name emp_cnt emp_pt_cnt emp_ft_cnt emp_other_cnt comm_share_holder auditor auditor_opinion comp_url email_addr nbr_shares_out shares_out_date officer_name_1 officer_title_1 officer_name_2 officer_title_2 officer_name_3 officer_title_3 officer_name_4 officer_title_4 officer_name_5 officer_title_5 rpt_0_date tot_revnu cost_good_sold gross_profit tot_deprec_amort int_exp_oper int_invst_income_oper res_dev_exp in_proc_res_dev_exp_aggr tot_sell_gen_admin_exp rental_exp_ind_broker pension_post_retire_exp other_oper_income_exp tot_oper_exp oper_income non_oper_int_exp int_cap asset_wdown_impair_aggr restruct_charge merger_acq_income_aggr rental_income spcl_unusual_charge impair_goodwill litig_aggr gain_loss_sale_asset_aggr gain_loss_sale_invst_aggr stock_div_subsid income_loss_equity_invst_other pre_tax_minority_int int_invst_income other_non_oper_income_exp tot_non_oper_income_exp pre_tax_income tot_provsn_income_tax income_aft_tax minority_int equity_earn_subsid invst_gain_loss_other other_income income_cont_oper income_discont_oper income_bef_exord_acct_change exord_income_loss cumul_eff_acct_change consol_net_income_loss non_ctl_int net_income_parent_comp pref_stock_div_other_adj net_income_loss_share_holder eps_basic_cont_oper eps_basic_discont_oper eps_basic_acct_change eps_basic_extra eps_basic_consol eps_basic_parent_comp basic_net_eps eps_diluted_cont_oper eps_diluted_discont_oper eps_diluted_acct_change eps_diluted_extra eps_diluted_consol eps_diluted_parent_comp diluted_net_eps dilution_factor avg_d_shares avg_b_shares norm_pre_tax_income norm_aft_tax_income ebitda ebit rpt_1_date cash_sterm_invst note_loan_rcv rcv_est_doubt rcv_tot invty prepaid_expense def_charge_curr def_tax_asset_curr asset_discont_oper_curr other_curr_asset tot_curr_asset gross_prop_plant_equip tot_accum_deprec net_prop_plant_equip net_real_estate_misc_prop cap_software lterm_invst adv_dep lterm_rcv invty_lterm goodwill_intang_asset_tot def_charge_non_curr def_tax_asset_lterm asset_discont_oper_lterm pension_post_retire_asset other_lterm_asset tot_lterm_asset tot_asset note_pay acct_pay div_pay other_pay accrued_exp other_accrued_exp curr_portion_debt curr_portion_cap_lease curr_portion_tax_pay defer_revnu_curr defer_tax_liab_curr liab_discont_oper_curr other_curr_liab tot_curr_liab tot_lterm_debt defer_revnu_non_curr pension_post_retire_liab defer_tax_liab_lterm mand_redeem_pref_sec_subsid pref_stock_liab min_int liab_disc_oper_lterm other_non_curr_liab tot_lterm_liab tot_liab tot_pref_stock comm_stock_net addtl_paid_in_cap retain_earn_accum_deficit equity_equiv treas_stock compr_income def_compsn other_share_holder_equity tot_comm_equity tot_share_holder_equity tot_liab_share_holder_equity comm_shares_out pref_stock_shares_out tang_stock_holder_equity rpt_2_date net_income_loss tot_deprec_amort_cash_flow other_non_cash_item tot_non_cash_item change_acct_rcv change_invty change_acct_pay change_acct_pay_accrued_liab change_income_tax change_asset_liab tot_change_asset_liab oper_activity_other cash_flow_oper_activity net_change_prop_plant_equip net_change_intang_asset net_acq_divst net_change_sterm_invst net_change_lterm_invst net_change_invst_tot invst_activity_other cash_flow_invst_activity net_lterm_debt net_curr_debt debt_issue_retire_net_tot net_comm_equity_issued_repurch net_pref_equity_issued_repurch net_tot_equity_issued_repurch tot_comm_pref_stock_div_paid fin_activity_other cash_flow_fin_activity fgn_exchange_rate_adj disc_oper_misc_cash_flow_adj incr_decr_cash beg_cash end_cash stock_based_compsn comm_stock_div_paid pref_stock_div_paid tot_deprec_amort_qd stock_based_compsn_qd cash_flow_oper_activity_qd net_change_prop_plant_equip_qd comm_stock_div_paid_qd pref_stock_div_paid_qd tot_comm_pref_stock_div_qd wavg_shares_out wavg_shares_out_diluted eps_basic_net eps_diluted_net
AAPL AAPL APPLE INC Apple Inc. NSDQ USD 2011-09-30 A NA 2011 4 2011 3 F 10-K 2011 10 199 9 9 0000320193 12 3571 2011-10-26 2011-10-26 CA ONE INFINITE LOOP CUPERTINO CA 95014 408-996-1010 408-996-0275 ONE INFINITE LOOP CUPERTINO CA 95014 UNITED STATES X1 NA 63300 2900 60400 NA 28543 Ernst & Young LLP Fair www.apple.com investor_relations@apple.com 6505863000 2011-10-14 Timothy D. Cook Chief Executive Officer and Director Peter Oppenheimer Senior Vice President and Chief Financial Officer Betsy Rafael Vice President and Corporate Controller William V. Campbell Director Millard S. Drexler Director 2013-10-30 108249 64431 43818 NA NA NA 2429 NA 7599 NA NA NA 74459 33790 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 415 415 34205 8283 25922 NA NA NA NA 25922 NA 25922 NA NA 25922 NA 25922 NA 25922 4.0066 NA NA NA 4.0066 4.0066 4.0071 3.9536 NA NA NA 3.9536 3.9536 3.9543 4.3340 6556.515 6469.806 34205 25922 35604 33790 2012-10-31 25952 NA NA 11717 776 NA NA 2014 NA 4529 44988 11768 3991 7777 NA NA 55618 NA NA NA 4432 NA NA NA NA 3556 71383 116371 NA 14632 NA NA 9247 NA NA NA NA 4091 NA NA NA 27970 NA 1686 NA NA NA NA NA NA 10100 11786 39756 NA 13331 NA 62841 NA NA 443 NA NA 76615 76615 116371 6504.939 NA 72183 2013-10-30 25922 1814 4036 5850 143 275 2515 NA NA 2824 5757 NA 37529 -4260 -3192 -244 -32464 NA -32464 -259 -40419 NA NA NA 831 NA 831 NA 613 1444 NA NA -1446 11261 9815 1168 0 NA NA NA NA NA NA NA NA 6469.806 6556.515 4.0071 3.9543
AAPL AAPL APPLE INC Apple Inc. NSDQ USD 2012-09-30 A NA 2012 4 2012 3 F 10-K 2012 10 199 9 9 0000320193 12 3571 2012-10-31 2012-10-31 CA ONE INFINITE LOOP CUPERTINO CA 95014 408-996-1010 408-996-0275 ONE INFINITE LOOP CUPERTINO CA 95014 UNITED STATES X1 NA 76100 3300 72800 NA 27696 Ernst & Young LLP Fair www.apple.com investor_relations@apple.com 6584844000 2012-10-19 Timothy D. Cook Chief Executive Officer and Director Peter Oppenheimer Senior Vice President and Chief Financial Officer William V. Campbell Director Millard S. Drexler Director Al Gore Director 2014-10-27 156508 87846 68662 NA NA NA 3381 NA 10040 NA NA NA 101267 55241 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 522 522 55763 14030 41733 NA NA NA NA 41733 NA 41733 NA NA 41733 NA 41733 NA 41733 6.3776 NA NA NA 6.3776 6.3776 6.3771 6.3065 NA NA NA 6.3065 6.3065 6.3071 23.3164 6617.485 6543.726 55763 41733 58518 55241 2013-10-30 29129 NA NA 18692 791 NA NA 2583 NA 6458 57653 21887 6435 15452 NA NA 92122 NA NA NA 5359 NA NA NA NA 5478 118411 176064 NA 21175 NA NA 11414 NA NA NA NA 5953 NA NA NA 38542 NA 2648 NA NA NA NA NA NA 16664 19312 57854 NA 16422 NA 101289 NA NA 499 NA NA 118210 118210 176064 6574.456 NA 112851 2014-10-27 41733 3277 6145 9422 -5551 -15 4467 NA NA 800 -299 NA 50856 -8295 -1107 -350 -38427 NA -38427 -48 -48227 NA NA NA 665 NA 665 -2488 125 -1698 NA NA 931 9815 10746 1740 -2488 NA NA NA NA NA NA NA NA 6543.726 6617.483 6.3800 6.3100
AAPL AAPL APPLE INC Apple Inc. NSDQ USD 2013-09-30 A NA 2013 4 2013 3 F 10-K 2013 10 199 9 9 0000320193 12 3571 2013-10-30 2013-10-29 CA ONE INFINITE LOOP CUPERTINO CA 95014 408-996-1010 408-974-2483 ONE INFINITE LOOP CUPERTINO CA 95014 UNITED STATES X1 NA 84400 4100 80300 NA 24710 Ernst & Young LLP Fair www.apple.com investor_relations@apple.com 6298166000 2013-10-18 Timothy D. Cook Chief Executive Officer and Director Peter Oppenheimer Senior Vice President and Chief Financial Officer Luca Maestri Vice President and Corporate Controller William V. Campbell Director Millard S. Drexler Director 2015-10-28 170910 106606 64304 NA NA NA 4475 NA 10830 NA NA NA 121911 48999 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 1156 1156 50155 13118 37037 NA NA NA NA 37037 NA 37037 NA NA 37037 NA 37037 NA 37037 5.7180 NA NA NA 5.7180 5.7180 5.7186 5.6791 NA NA NA 5.6791 5.6791 5.6786 5.8789 6521.634 6477.317 50155 37037 55756 48999 2014-10-27 40546 NA NA 20641 1764 NA NA 3453 NA 6882 73286 28519 11922 16597 NA NA 106215 NA NA NA 5756 NA NA NA NA 5146 133714 207000 NA 22367 NA NA 13856 NA NA NA NA 7435 NA NA NA 43658 16960 2625 NA NA NA NA NA NA 20208 39793 83451 NA 19764 NA 104256 NA NA -471 NA NA 123549 123549 207000 6294.494 NA 117793 2015-10-28 37037 6757 3394 10151 -2172 -973 2340 NA NA 7283 6478 NA 53666 -8165 -911 -496 -24042 NA -24042 -160 -33774 16896 NA 16896 -22330 NA -22330 -10564 -381 -16379 NA NA 3513 10746 14259 2253 -10564 NA NA NA NA NA NA NA NA 6477.320 6521.634 5.7200 5.6800

Quandl is a very extensive data source. Finding the right series can sometimes be a challenge, but their documentation and search function has greatly improved from a few years ago. In addition, with quandl_search() users can even find data sets from within the R console. Let’s search for the WTI crude prices within the FRED database.

quandl_search(query = "WTI", database_code = "FRED", silent = TRUE) %>%
    select(-description) %>%
    slice(1:3) %>%
    knitr::kable()
id dataset_code database_code name refreshed_at newest_available_date oldest_available_date column_names frequency type premium database_id
34399442 WTISPLC FRED Spot Crude Oil Price: West Texas Intermediate (WTI) 2017-03-18T04:39:31.083Z 2017-02-01 1946-01-01 DATE, VALUE monthly Time Series FALSE 118
120621 ACOILWTICO FRED Crude Oil Prices: West Texas Intermediate (WTI) - Cushing, Oklahoma 2016-12-11T19:35:20.310Z 2015-01-01 1986-01-01 DATE, VALUE annual Time Series FALSE 118
120619 MCOILWTICO FRED Crude Oil Prices: West Texas Intermediate (WTI) - Cushing, Oklahoma 2017-03-17T15:46:22.490Z 2017-02-01 1986-01-01 DATE, VALUE monthly Time Series FALSE 118

selecting columns: A flexible approach

Previously, when selecting columns with tq_mutate() and tq_transmute(), we would use the ohlc_fun argument to select the columns to mutate on. ohlc_fun leveraged the numerous functions from quantmod that extract specific columns from OHLC (Open High Low Close) price data such as Ad() (adjusted) or Cl() (close). While this was great for OHLC data, it was not ideal for datasets not in the OHLC form. It also kept users from performing rolling regressions using custom functions with rollapply(). To fix this, we’ve come up with a new approach that replaces ohlc_fun through a new argument, select. This new approach uses dplyr for selecting data frame columns instead of relying on quantmod, and as a result it has a “tidyverse” feel. While this seems like a small change, in the following examples you will see just how much more flexibility this brings.

select VS ohlc_fun

Here’s a quick example demonstrating the difference in the approaches. Let’s first grab the stock price data for IBM.

ibm <- tq_get("IBM")
ibm
## # A tibble: 2,581 × 7
##          date   open   high   low  close   volume adjusted
##        <date>  <dbl>  <dbl> <dbl>  <dbl>    <dbl>    <dbl>
## 1  2007-01-03  97.18  98.40 96.26  97.27  9196800 77.73997
## 2  2007-01-04  97.25  98.79 96.88  98.31 10524500 78.57116
## 3  2007-01-05  97.60  97.95 96.91  97.42  7221300 77.85985
## 4  2007-01-08  98.50  99.50 98.35  98.90 10340000 79.04270
## 5  2007-01-09  99.08 100.33 99.07 100.07 11108200 79.97778
## 6  2007-01-10  98.50  99.05 97.93  98.89  8744800 79.03470
## 7  2007-01-11  99.00  99.90 98.50  98.65  8000700 78.84289
## 8  2007-01-12  98.99  99.69 98.50  99.34  6636500 79.39435
## 9  2007-01-16  99.40 100.84 99.30 100.82  9602200 80.57719
## 10 2007-01-17 100.69 100.90 99.90 100.02  8200700 79.93782
## # ... with 2,571 more rows

What if you wanted to calculate the daily returns of the adjusted stock price? Previously, you might have done something like this…

tq_mutate(ibm, ohlc_fun = Ad, mutate_fun = dailyReturn) %>%
    slice(1:5) %>%
    knitr::kable()
## Warning: Argument ohlc_fun is deprecated; please use select instead.
date open high low close volume adjusted daily.returns
2007-01-03 97.18 98.40 96.26 97.27 9196800 77.73997 0.0000000
2007-01-04 97.25 98.79 96.88 98.31 10524500 78.57116 0.0106919
2007-01-05 97.60 97.95 96.91 97.42 7221300 77.85985 -0.0090530
2007-01-08 98.50 99.50 98.35 98.90 10340000 79.04270 0.0151920
2007-01-09 99.08 100.33 99.07 100.07 11108200 79.97778 0.0118301

At this point, ohlc_fun will still work, but you’ll notice that a warning appears notifying you that we’ve deprecated ohlc_fun. In version 6, we will remove the argument completely. Let’s see the new approach with select.

tq_mutate(ibm, select = adjusted, mutate_fun = dailyReturn) %>%
    slice(1:5) %>%
    knitr::kable()
date open high low close volume adjusted daily.returns
2007-01-03 97.18 98.40 96.26 97.27 9196800 77.73997 0.0000000
2007-01-04 97.25 98.79 96.88 98.31 10524500 78.57116 0.0106919
2007-01-05 97.60 97.95 96.91 97.42 7221300 77.85985 -0.0090530
2007-01-08 98.50 99.50 98.35 98.90 10340000 79.04270 0.0151920
2007-01-09 99.08 100.33 99.07 100.07 11108200 79.97778 0.0118301

As you can see, the new approach is similar to dplyr::mutate in that you simply specify the name of the columns that get mutated. You can even select multiple columns with the dplyr shorthand. Here we succinctly select the columns open, high, low, and close and change to a weekly periodicity. Since we change periodicity, we have to use tq_transmute() to drop the other columns.

tq_transmute(ibm, select = open:close, mutate_fun = to.weekly) %>%
    slice(1:5) %>%
    knitr::kable()
date open high low close
2007-01-05 97.60 97.95 96.91 97.42
2007-01-12 98.99 99.69 98.50 99.34
2007-01-19 95.00 96.85 94.55 96.17
2007-01-26 97.52 97.83 96.84 97.45
2007-02-02 99.10 99.73 98.88 99.17

One final note. If you want to mutate the entire dataset, you can leave off the select argument so that it keeps its default value of NULL which passes the entire data frame to the mutate function.

Working with non-OHLC data

The select argument is more than a surface level change. The change from the OHLC functions allows us to work with non-OHLC data straight from tq_mutate(). This used to be a little awkward. Since the columns of non-OHLC data could not be selected with a quantmod function, we had to use tq_mutate_xy() to find them. The intended use of the *_xy() functions is for working with functions that require mutating both x and y in parallel, so this definitely wasn’t best practice. Check out this old example of working with some crude oil prices from FRED, and applying a 1 period lag.

oil_prices <- tq_get("DCOILWTICO", get = "economic.data")
oil_prices
## # A tibble: 2,671 × 2
##          date price
##        <date> <dbl>
## 1  2007-01-01    NA
## 2  2007-01-02 60.77
## 3  2007-01-03 58.31
## 4  2007-01-04 55.65
## 5  2007-01-05 56.29
## 6  2007-01-08 56.08
## 7  2007-01-09 55.65
## 8  2007-01-10 53.95
## 9  2007-01-11 51.91
## 10 2007-01-12 52.96
## # ... with 2,661 more rows

tq_mutate_xy() was the only thing that worked in the old version, but it just felt wrong!

tq_mutate_xy(oil_prices, x = price, y = NULL, mutate_fun = lag.xts, k = 1)
## # A tibble: 2,671 × 3
##          date price lag.xts
##        <date> <dbl>   <dbl>
## 1  2007-01-01    NA      NA
## 2  2007-01-02 60.77      NA
## 3  2007-01-03 58.31   60.77
## 4  2007-01-04 55.65   58.31
## 5  2007-01-05 56.29   55.65
## 6  2007-01-08 56.08   56.29
## 7  2007-01-09 55.65   56.08
## 8  2007-01-10 53.95   55.65
## 9  2007-01-11 51.91   53.95
## 10 2007-01-12 52.96   51.91
## # ... with 2,661 more rows

Enter, select. This works much better!

tq_mutate(oil_prices, select = price, mutate_fun = lag.xts, k = 1)
## # A tibble: 2,671 × 3
##          date price lag.xts
##        <date> <dbl>   <dbl>
## 1  2007-01-01    NA      NA
## 2  2007-01-02 60.77      NA
## 3  2007-01-03 58.31   60.77
## 4  2007-01-04 55.65   58.31
## 5  2007-01-05 56.29   55.65
## 6  2007-01-08 56.08   56.29
## 7  2007-01-09 55.65   56.08
## 8  2007-01-10 53.95   55.65
## 9  2007-01-11 51.91   53.95
## 10 2007-01-12 52.96   51.91
## # ... with 2,661 more rows

Getting things rolling with rollapply

Something that we are very excited about with this new version is the ability to perform custom rolling calculations with rollapply. This is a byproduct of the new select argument, and has some pretty cool use cases. In the example below, we will walk through a rolling CAPM analysis of Apple stock.

CAPM stands for Capital Asset Pricing Model, and is a way to determine the theoretically appropriate expected stock return, based on exposure to market risk. Essentially, the CAPM model performs a linear regression with Apple’s excess return as the dependent variable, and the “market’s” excess return as the independent variable. We will pull from the Fama French website for the risk free rate and the market return. Here is the regression formula for CAPM:

Where r_f is the risk free return, and epsilon is the normally distributed error term. The key terms of interest are alpha and beta, the coefficients estimated from the regression. Alpha is the excess return of a stock relative to a market index, and beta is a measure of the volatility of a stock in relation to the market. It would be interesting to evaluate the stability of the model, and one way of doing this is by performing a rolling regression to see how alpha and beta change throughout time. First, we will need two datasets: Fama French factors and Apple stock returns.

Fama French factors

Quandl actually has a cleaned up series for the Fama French factors going back to 1926! As of writing this, the most recent data point in their weekly series is 02/24/2017, so for reproducibility purposes we will stop the series there.

fama_french <- tq_get("KFRENCH/FACTORS_W", get = "quandl", to = "2017-02-24")
fama_french
## # A tibble: 5,849 × 5
##          date mkt.rf   smb   hml    rf
##        <date>  <dbl> <dbl> <dbl> <dbl>
## 1  2017-02-24   0.48 -1.09 -0.46 0.009
## 2  2017-02-17   1.53 -0.54 -0.39 0.009
## 3  2017-02-10   0.88  0.14 -1.20 0.009
## 4  2017-02-03   0.15  0.41 -0.67 0.009
## 5  2017-01-27   1.15  0.22  0.59 0.009
## 6  2017-01-20  -0.31 -1.13 -0.15 0.009
## 7  2017-01-13   0.09  0.47 -1.04 0.009
## 8  2017-01-06   1.69 -0.58 -1.25 0.009
## 9  2016-12-30  -1.16 -0.11  0.13 0.006
## 10 2016-12-23   0.32  0.17  0.71 0.006
## # ... with 5,839 more rows

We only need two columns for the CAPM analysis, mkt.rf and rf, so let’s select them.

fama_french <- fama_french %>%
    select(date, mkt.rf, rf)
fama_french
## # A tibble: 5,849 × 3
##          date mkt.rf    rf
##        <date>  <dbl> <dbl>
## 1  2017-02-24   0.48 0.009
## 2  2017-02-17   1.53 0.009
## 3  2017-02-10   0.88 0.009
## 4  2017-02-03   0.15 0.009
## 5  2017-01-27   1.15 0.009
## 6  2017-01-20  -0.31 0.009
## 7  2017-01-13   0.09 0.009
## 8  2017-01-06   1.69 0.009
## 9  2016-12-30  -1.16 0.006
## 10 2016-12-23   0.32 0.006
## # ... with 5,839 more rows

Apple weekly stock returns

This one is easy, we will use tq_get() to pull the stock prices, and then use tq_transmute() for the returns. For this analysis, we will pull 15 years of prices.

apple_prices <- tq_get("AAPL", 
                       get  = "stock.prices", 
                       from = "2002-02-24", 
                       to   = "2017-02-24")
apple_prices
## # A tibble: 3,778 × 7
##          date  open  high   low close    volume adjusted
##        <date> <dbl> <dbl> <dbl> <dbl>     <dbl>    <dbl>
## 1  2002-02-25 22.85 24.72 22.36 23.81 106712200 1.542406
## 2  2002-02-26 23.91 24.37 23.25 23.67  65032800 1.533337
## 3  2002-02-27 23.94 24.25 20.94 21.96 257539800 1.422564
## 4  2002-02-28 22.15 22.59 21.35 21.70 114234400 1.405721
## 5  2002-03-01 21.93 23.50 21.82 23.45  87248000 1.519086
## 6  2002-03-04 23.26 24.58 22.76 24.29  87064600 1.573501
## 7  2002-03-05 24.15 24.43 23.40 23.53  68675600 1.524268
## 8  2002-03-06 23.48 24.34 22.93 24.07  56551600 1.559249
## 9  2002-03-07 24.06 24.53 23.61 24.38  64562400 1.579331
## 10 2002-03-08 24.74 25.09 24.30 24.66  67443600 1.597469
## # ... with 3,768 more rows

Now, let’s get the weekly returns. We have to use tq_transmute() because the periodicity changes, and we will use the periodReturn() function from quantmod as our mutate_fun. Lastly, we change the return to a percentage because that is the form that the Fama French factors are in.

apple_return <- tq_transmute(apple_prices, 
                             select     = adjusted, 
                             col_rename = "weekly_return", 
                             mutate_fun = periodReturn, 
                             period     = "weekly") %>%
    mutate(weekly_return = weekly_return * 100)
apple_return
## # A tibble: 783 × 2
##          date weekly_return
##        <date>         <dbl>
## 1  2002-03-01    -1.5119236
## 2  2002-03-08     5.1598790
## 3  2002-03-15     1.1759853
## 4  2002-03-22    -3.4468571
## 5  2002-03-28    -1.7434935
## 6  2002-04-05     4.5205327
## 7  2002-04-12     1.2934187
## 8  2002-04-19    -0.3192103
## 9  2002-04-26    -7.8862983
## 10 2002-05-03     2.1729753
## # ... with 773 more rows

Merge the two datasets

Alright, now that we have the two data sets, let’s join them together. We want to keep all of apple_return, and just match the fama_french data to it. A left_join will serve that purpose.

joined_data <- left_join(apple_return, fama_french)
joined_data
## # A tibble: 783 × 4
##          date weekly_return mkt.rf    rf
##        <date>         <dbl>  <dbl> <dbl>
## 1  2002-03-01    -1.5119236   3.79 0.033
## 2  2002-03-08     5.1598790   3.15 0.033
## 3  2002-03-15     1.1759853   0.09 0.033
## 4  2002-03-22    -3.4468571  -1.19 0.033
## 5  2002-03-28    -1.7434935  -0.04 0.033
## 6  2002-04-05     4.5205327  -2.15 0.038
## 7  2002-04-12     1.2934187  -0.51 0.038
## 8  2002-04-19    -0.3192103   1.26 0.038
## 9  2002-04-26    -7.8862983  -4.13 0.038
## 10 2002-05-03     2.1729753  -0.13 0.036
## # ... with 773 more rows

Remembering that the left side of the CAPM formula is apple return minus the risk free rate, we calculate that as well.

joined_data <- mutate(joined_data, 
       weekly_ret_rf = weekly_return - rf)
joined_data
## # A tibble: 783 × 5
##          date weekly_return mkt.rf    rf weekly_ret_rf
##        <date>         <dbl>  <dbl> <dbl>         <dbl>
## 1  2002-03-01    -1.5119236   3.79 0.033    -1.5449236
## 2  2002-03-08     5.1598790   3.15 0.033     5.1268790
## 3  2002-03-15     1.1759853   0.09 0.033     1.1429853
## 4  2002-03-22    -3.4468571  -1.19 0.033    -3.4798571
## 5  2002-03-28    -1.7434935  -0.04 0.033    -1.7764935
## 6  2002-04-05     4.5205327  -2.15 0.038     4.4825327
## 7  2002-04-12     1.2934187  -0.51 0.038     1.2554187
## 8  2002-04-19    -0.3192103   1.26 0.038    -0.3572103
## 9  2002-04-26    -7.8862983  -4.13 0.038    -7.9242983
## 10 2002-05-03     2.1729753  -0.13 0.036     2.1369753
## # ... with 773 more rows

Rolling CAPM

We are almost ready for the rolling CAPM. The last thing we need is a function that is applied at each iteration of our roll, which here will be a regression. We are really only interested in the coefficients from the regression, so that is what we will return. Note that the incoming data_xts will be an xts object, so we need to convert to a data.frame in the lm() function. The output should be a numeric vector. tq_mutate will take care of adding the appropriate columns provided the custom function (in our case regr_fun()) is handled in this manner.

regr_fun <- function(data_xts) {
    lm(weekly_ret_rf ~ mkt.rf, data = as_data_frame(data_xts)) %>%
        coef()
}

Now let’s work some magic. We will leave select out of this call to tq_mutate() since we can just pass the entire dataset into rollapply. To have enough data for a good analysis, we will set width equal to 260 for a 5 year rolling window of data per call. The final argument, by.column, is especially important so that rollapply doesn’t try and apply our regression to each column individually.

joined_data <- joined_data %>% 
    tq_mutate(mutate_fun = rollapply,
              width      = 260,
              FUN        = regr_fun,
              by.column  = FALSE,
              col_rename = c("alpha", "beta"))
joined_data %>%
    slice(255:265)
## # A tibble: 11 × 7
##          date weekly_return mkt.rf    rf weekly_ret_rf     alpha
##        <date>         <dbl>  <dbl> <dbl>         <dbl>     <dbl>
## 1  2007-01-12    11.2522154   1.73 0.111    11.1412154        NA
## 2  2007-01-19    -6.4679733  -0.31 0.111    -6.5789733        NA
## 3  2007-01-26    -3.5254340  -0.57 0.111    -3.6364340        NA
## 4  2007-02-02    -0.7378731   1.94 0.096    -0.8338731        NA
## 5  2007-02-09    -1.7463114  -0.65 0.096    -1.8423114        NA
## 6  2007-02-16     1.8734247   1.22 0.096     1.7774247 0.6905407
## 7  2007-02-23     4.9982362  -0.05 0.096     4.9022362 0.7304938
## 8  2007-03-02    -4.1091364  -4.62 0.106    -4.2151364 0.7331868
## 9  2007-03-09     2.9973031   1.00 0.106     2.8913031 0.7353908
## 10 2007-03-16     1.8415416  -1.18 0.106     1.7355416 0.7560794
## 11 2007-03-23     4.3866501   3.48 0.106     4.2806501 0.7626701
## # ... with 1 more variables: beta <dbl>

We had to scroll down to row 260 before we find any values for alpha and beta, but it’s more useful to create a plot. Looking at the rolling coefficient for alpha, it seems to be steadily trending downward over time.

filter(joined_data, !is.na(alpha)) %>%
ggplot(aes(x = date, y = alpha)) + 
    geom_line(size = 1, color = palette_light()[[1]]) + 
    geom_smooth() +
    labs(title = "AAPL: 5 year rolling CAPM - alpha", x = "",
         subtitle = "Rolling alpha is steadily trending downward") +
    theme_tq(base_size = 18)

plot of chunk unnamed-chunk-20

The rolling coefficient for beta is just as interesting, with a huge drop around 2008 likely having to deal with the financial crisis, but consistently over or near 1.

filter(joined_data, !is.na(alpha)) %>%
ggplot(aes(x = date, y = beta))  + 
    geom_rect(xmin = as.numeric(ymd("2008-01-01")), 
              xmax = as.numeric(ymd("2009-03-01")), 
              ymin = 0, ymax = 2.0, 
              fill = palette_light()[[4]], alpha = 0.01) + 
    geom_line(size = 1, color = palette_light()[[1]]) +
    labs(title = "AAPL: 5 year rolling CAPM - beta", x = "",
         subtitle = "Huge drop in 2008 following financial crisis") + 
    theme_tq(base_size = 18)

plot of chunk unnamed-chunk-21

Hopefully you can see the power that tidyquant gives to the user by allowing them to perform these rolling calculations with data frames!

As an aside, there are some improvements to this model that could be of interest to some users:

  1. This says nothing to how well the CAPM model fits Apple’s return data. It only monitors the stability of the coefficients.

  2. The mkt.rf data pulled from the Fama-French data set was only one part of a more detailed extension of CAPM, the Fama French 3 Factor Model. It would be interesting the stability of this model over time as well.

PerformanceAnalytics Mutation Functions

We have added a few new functions from the PerformanceAnalytics package to tq_mutate() and tq_transmute(), which can be reviewed using tq_mutate_fun_options()$PerformanceAnalytics. These differ from the PerformanceAnalytics functions available for use with tq_performance() (refer to tq_performance_fun_options()) in that the deal with transforming the returns data rather than calculating performance metrics.

tq_mutate_fun_options()$PerformanceAnalytics
## [1] "Return.annualized"        "Return.annualized.excess"
## [3] "Return.clean"             "Return.cumulative"       
## [5] "Return.excess"            "Return.Geltner"          
## [7] "zerofill"

We can use the functions to clean and format returns for the FANG data set. The example below uses three progressive applications of tq_transmute() to apply various quant functions to the grouped stock prices from the FANG data set. First, we calculate daily returns using periodReturn from the quantmod package. Next, we use Return.clean to clean outliers from the return data. The alpha parameter is the percentage of outliers to be cleaned. Finally, Return.excess is used to calculate the excess returns using a risk-free rate of 3% (divided by 252 for 252 trade days in one year). Finally, we calculate general statistics on the returns by passing to tq_performance(performance_fun = table.Stats).

FANG %>%
    group_by(symbol) %>%
    tq_transmute(adjusted, periodReturn, period = "daily") %>%
    tq_transmute(daily.returns, Return.clean, alpha = 0.05) %>%
    tq_transmute(daily.returns, Return.excess, Rf = 0.03 / 252,
                 col_rename = "formatted.returns") %>%
    tq_performance(Ra = formatted.returns, 
                   performance_fun = table.Stats) %>%
    knitr::kable()
symbol ArithmeticMean GeometricMean Kurtosis LCLMean(0.95) Maximum Median Minimum NAs Observations Quartile1 Quartile3 SEMean Skewness Stdev UCLMean(0.95) Variance
FB 0.0015 0.0013 35.2725 2e-04 0.2960 1e-03 -0.0695 0 1008 -0.0098 0.0119 7e-04 2.9098 0.0220 0.0029 0.0005
AMZN 0.0011 0.0009 9.6476 -1e-04 0.1412 6e-04 -0.1101 0 1008 -0.0082 0.0111 6e-04 0.4804 0.0194 0.0023 0.0004
NFLX 0.0026 0.0021 35.9090 6e-04 0.4221 -1e-04 -0.1938 0 1008 -0.0119 0.0149 1e-03 3.0745 0.0324 0.0046 0.0011
GOOG 0.0007 0.0006 22.1306 -2e-04 0.1604 0e+00 -0.0533 0 1008 -0.0067 0.0078 5e-04 2.2165 0.0148 0.0017 0.0002

Getting, cleaning and calculating excess returns is that easy with the additional PerformanceAnalytics mutation functions.

Further Reading

That’s all for now. We hope you enjoy the new release, and be on the lookout for more to come!

  1. Tidyquant Vignettes: The Core Functions in tidyquant vignette has a nice section on the features related to the Quandl integration! The R Quantitative Analysis Package Integrations in tidyquant vignette includes another example of working with rollapply.

  2. R for Data Science: A free book that thoroughly covers the “tidyverse”. A prerequisite for maximizing your abilities with tidyquant.