# S&P 500 Returns

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Let’s continue with a plot of the 200-day running mean of logarithmic returns. If you bought the index on a given day, what average logarithmic returns would you have seen across the next 200 trading days? Look below:

Here’s my code; suggestions and comments welcome.

# Yahoo finance url for S&P 500 data str <- sprintf("%s?s=^GSPC&d=7&e=4&f=2011&g=d&a=0&b=3&c=1950", "http://ichart.finance.yahoo.com/table.csv") df <- tryCatch(read.csv(url(str)), error = function(e) NA) names(df) <- tolower(names(df)) df$date <- as.Date(df$date) df <- df[order(df$date), ] # Plot prices, daily log returns & running mean of log returns since 1990 start.date <- "1990-01-01" dev.new(width=12, height=6) plot(subset(df, date >= start.date)[ , c("date", "close")], type="l", main="S&P 500", xlab="", col="tomato") mtext(sprintf("Closing prices since %s", start.date)) mtext(sprintf("Created on %s", Sys.Date()), side=1, line=3, cex=0.60) savePlot("s&p_500_prices.png") # Logarithmic returns: p0 * e^r = p1 ; r = ln(p1) - ln(p0) df$return <- c(diff(log(df$close)), NA) dev.new(width=12, height=6) plot(subset(df, date >= start.date)[ , c("date", "return")], type="p", main="S&P 500", xlab="", col=rgb(0, 75, 0, 75, maxColorValue=255)) mtext(sprintf("Logarithmic close-to-close returns since %s", start.date)) mtext(sprintf("Created on %s", Sys.Date()), side=1, line=3, cex=0.6) savePlot("s&p_500_daily_returns.png") # Running average of logarithmic returns lag <- 200 df$mean.return <- c(diff(c(0, cumsum(df$return)), lag=lag), rep(NA, (lag - 1))) * (1 / lag) dev.new(width=12, height=6) plot(subset(df, date >= start.date)[ , c("date", "mean.return")], type="l", main="S&P 500", xlab="", col="darkred", ylab=sprintf("%s-day mean return", lag)) mtext(sprintf("%s-day mean of logarithmic returns since %s", lag, start.date)) mtext(sprintf("Created on %s", Sys.Date()), side=1, line=3, cex=0.6) abline(0, 0, lty=2) savePlot(sprintf("s&p_500_%s_day_mean_returns.png", lag))

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