# Momentum, Quality, and R Code

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### Momentum Research with R

In a previous post, we covered the steps for implementing a basic momentum investing strategy with R code. We covered quite a bit of code in that post and it’s worth a look if momentum investing or algorithmic (fancy word for if/else) logic is new to you (if R code is brand new, a good place to start is this post on calculating prices and returns with R). Today, we’re going to tackle a slightly different momentum project and focus on the idea of quality.

Visit Alpha Architect website to read more on the two methodologies referenced in this article: Grinblatt and Moskowitz 2004 and the measure of quality covered here.

### Building Grinblatt and Moskowitz Momentum

Let’s get to the R code, starting with methodology 1, wherein we determine whether at least 8 of the past 12 months showed a positive return. We’re going to do things a bit differently from last time where we examined a momentum strategy that used SPY and EFA. Today, we’ll work with SPY, XLF and XLE and try to compare the quality of their momentum signals. That means we’ll need to calculate their monthly returns and their past 12-months’ returns.

We’ll import daily prices, convert to monthly returns, and then create a new column called `skip_mon_return` that is the one month lagged returns. We do that because the most recent previous month might be a bit noisy or volatile, and leaves us at the mercy of the so-called short term reversal effect.

`library(tidyverse) library(highcharter) library(tibbletime) library(tidyquant) library(timetk) library(riingo) riingo_set_token("your token here")`

Then we’ll import price data from tiingo, using the R package called riingo.

`symbols <- c("SPY", "XLF", "XLE")`

`prices_daily <- symbols %>% riingo_prices(., start_date = "2000-01-01", end_date = "2018-12-31") %>% mutate(date = ymd(date)) %>% group_by(ticker)`

Now let’s convert to monthly prices.

`prices_monthly <- prices_daily %>% tq_transmute(select = adjClose, mutate_fun = to.monthly, indexAt = "lastof") `

And finally, convert from monthly prices to monthly returns. We’ll also lag our returns and place the lagged values in a new column called ‘skip_mon_return’.

`prices_monthly %>% mutate(mon_return = ((adjClose / lag(adjClose)) - 1), skip_mon_return = lag(mon_return)) %>% head()`

Have a quick peek at that data and notice how the `skip_mon_return` column is ignoring the previous month. For example, on March 31, 2001, we are not going to consider what happened from the end of February to the end of March, because there could be some weird stuff going on that’s isn’t going to last more than a few days. Instead, we’ll look back to what happened during February as our first data point.

In the next installment Jonathan Regenstein will show us how to calculate the 12-months cumulative return, but he’ll lag that as well with `lag(adjClose) / lag(adjClose, 12) - 1)`.

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