**Timely Portfolio**, and kindly contributed to R-bloggers)

I owe someone at the Fed a beer for all the recent additions at http://research.stlouisfed.org/fred2/. I have covered some in Spreads and Stress and Gifts from BAC ML and the Federal Reserve. The newest addition 8 Chicago Fed Indexes Added to FRED contains a nice weekly series on US National Financial Conditions that I’m sure we can use.

From TimelyPortfolio |

How does the S&P 500 look against the Chicago Fed Adjusted National Financial Conditions (ANFCI) index? I did not look at the nonadjusted FCI, but I’m sure it offers potentially useful information also.

From TimelyPortfolio |

From TimelyPortfolio |

Can we build a silly system with the S&P 500 and ANFCI? **THIS IS MERELY AN ILLUSTRATION AND DOES NOT CONSTITUTE FINANCIAL ADVICE. YOUR GAINS AND LOSSES ARE YOUR OWN RESPONSIBILITY.**

From TimelyPortfolio |

Nothing particularly special system-wise, but 1998-2004 is a very abnormal circumstance.

R code:

#explore how SP500 behaves in different ranges of Financial Conditions require(quantmod)

require(PerformanceAnalytics)

require(ggplot2) #get data from St. Louis Federal Reserve (FRED)

getSymbols("SP500",src="FRED") #load SP500

getSymbols("ANFCI",src="FRED") #load Adjusted Chicago Financial #show a chart of ANFCI

chartSeries(ANFCI,theme="white",name="Adjusted National Financial Conditions (ANFCI)") #do a little manipulation to get the data lined up on weekly basis

SP500 <- to.weekly(SP500)[,4]

ANFCI <- to.weekly(ANFCI)[,4]

#get weekly format to yyyy-mm-dd with the first day of the week

index(SP500) <- as.Date(index(SP500))

index(ANFCI) <- as.Date(index(ANFCI)) #use ceiling to get ranges for ANFCI so we can run boxlplots

ANFCI <- floor(ANFCI)

#lag ANFCI signal

ANFCI <- lag(ANFCI,k=1) #merge sp500 returns and ANFCI

SP500_ANFCI <- na.omit(merge(ROC(SP500,n=1,type="discrete"),ANFCI))

colnames(SP500_ANFCI) <- c("SP500","ANFCI") #convert xts to data frame for ggplot boxplot exploration

df <- as.data.frame(cbind(index(SP500_ANFCI),

coredata(SP500_ANFCI[,1:2])))

ggplot(df,aes(factor(ANFCI),SP500)) +

geom_boxplot(aes(colour = factor(ANFCI))) +

opts(title="Box Plot of SP500 Weekly Change by Adjusted Financial Conditions") #show linked returns based on Adjusted Financial Conditions

ret_eq_neg3 <- ifelse(SP500_ANFCI[,2] == -3, SP500_ANFCI[,1], 0)

ret_eq_neg2 <- ifelse(SP500_ANFCI[,2] == -2, SP500_ANFCI[,1], 0)

ret_eq_neg1 <- ifelse(SP500_ANFCI[,2] == -1, SP500_ANFCI[,1], 0)

ret_eq_0 <- ifelse(SP500_ANFCI[,2] == 0, SP500_ANFCI[,1], 0)

ret_eq_pos1 <- ifelse(SP500_ANFCI[,2] == 1, SP500_ANFCI[,1], 0)

ret_eq_pos2 <- ifelse(SP500_ANFCI[,2] == 2, SP500_ANFCI[,1], 0)

ret_eq_pos3 <- ifelse(SP500_ANFCI[,2] == 3, SP500_ANFCI[,1], 0)

ret_eq_pos4 <- ifelse(SP500_ANFCI[,2] == 4, SP500_ANFCI[,1], 0)

ret_eq_pos5 <- ifelse(SP500_ANFCI[,2] == 5, SP500_ANFCI[,1], 0) #merge series for PerformanceSummary chart

ret_comp <- merge(ret_eq_neg3, ret_eq_neg2, ret_eq_neg1, ret_eq_0,

ret_eq_pos1,ret_eq_pos2,ret_eq_pos3,ret_eq_pos4,ret_eq_pos5)

#name columns for the legend

colnames(ret_comp) <- c("ANFCI=-3","ANFCI=-2","ANFCI=-1","ANFCI=0",

"ANFCI=1","ANFCI=2","ANFCI=3","ANFCI=4","ANFCI=5") charts.PerformanceSummary(ret_comp, main="SP500 Linked Returns by Financial Conditions") #and just for fun a very basic system

signal <- runMax(SP500_ANFCI[,2] , n=10)

#go long if Max of ANFCI over last ten weeks is 0; already lagged earlier

ret <- ifelse(signal <= 0, 1, 0) * SP500_ANFCI[,1]

ret <- merge(ret, SP500_ANFCI[,1])

colnames(ret) <- c("ANFCI_LongOnlySystem","SP500")

charts.PerformanceSummary(ret, ylog=TRUE, main="Very Simple ANFCI S&P 500 System",

colorset=c("cadetblue","darkolivegreen3"))

Created by Pretty R at inside-R.org

**leave a comment**for the author, please follow the link and comment on their blog:

**Timely Portfolio**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...