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# Trend Value
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#https://www.valuesignals.com/Screens/Details/OShaughnessy_Trending_Value
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#fix dplr undef13
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if(!require("pacman")){
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install.packages("pacman")
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}
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rm(list = ls(all = TRUE))
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# library(dplyr)
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# library(quantmod)
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# library(xlsx)
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pacman::p_load(bindrcpp,dplyr, quantmod, xlsx, rio)
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#Momentum will be Regression Relative Strength instead. 126 == 6 month
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# 21,42,63,126,189
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trenddays <- 126
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SIGMA <- 0
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ifelse(dir.exists("C:/Users/msghe/OneDrive/Stocks/R"),
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setwd("C:/Users/msghe/OneDrive/Stocks/R"), setwd("C:/Users/michael/SkyDrive/Stocks/R"))
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stockdata <-
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read.csv("data/WEEKLY.TXT",
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header = FALSE,
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stringsAsFactors = FALSE,
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na.strings = '-99999999.990')
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stocknames <-
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read.csv(
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"data/WEEKLY_KEY.TXT",
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header = FALSE,
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stringsAsFactors = FALSE,
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na.strings = '-99999999.990'
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)
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stocknames[, 1]
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names(stockdata) <- stocknames[, 1]
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#clean ticker name for yahoo finance
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# stockdata$TICKER <- gsub('.', '-', stockdata$TICKER, fixed = TRUE)
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# Remove bad tickers
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#NA bad data
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# stockdata[stockdata == -99999999.990] <- NA
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stockdata <- stockdata[complete.cases(stockdata$TICKER),]
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#Create all stock universe
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# All stocks is 150 million in 1995 $$. It is adjusted to todays dollors. All
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# stocks exclude over the counter.
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# min.mark.cap <- quantile(stockdata$MKTCAP, na.rm = TRUE)[[2]]
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#find inflation Market Cap 150 @ 1995
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getSymbols("CPIAUCSL", src = "FRED")
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deflator <-
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last(Cl(to.yearly(CPIAUCSL)))[[1]] / Cl(to.yearly(CPIAUCSL))['1995'][[1]]
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tvminmarketcap <- 150 * deflator
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allstock <- stockdata[stockdata$MKTCAP >= tvminmarketcap,]
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# stockdata[stockdata$MKTCAP > quantile(stockdata$MKTCAP, na.rm = TRUE)[[4]],]
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#Minimum market cap for later sanity
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# condition <- c("N - New York", "A - American", "M - Nasdaq")
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# allstock <- filter(allstock, EXCHG_DESC %in% condition)
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# min.mark.cap <- median(allstock$MKTCAP, na.rm = TRUE)
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# min.mark.cap <- quantile(allstock$MKTCAP, na.rm = TRUE)[[2]]
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#add market cap requirement
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# allstock <- filter(allstock, MKTCAP > min.mark.cap)
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#Minimum Market Cap
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min(allstock$MKTCAP)
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#Start Ranking the stocks
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#Calculate VC2
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#subtract ntile from 101 to reverse (correct) Order. So Small is big
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allstock$PBVPS.Rank <- 101 - ntile(allstock$PBVPS, 100)
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allstock$PE.Rank <- 101 - ntile(allstock$PE, 100)
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allstock$PSPS.Rank <- 101 - ntile(allstock$PSPS, 100)
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allstock$EVEDA_12M.Rank <- 101 - ntile(allstock$EVEDA_12M, 100)
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allstock$PCFPS.Rank <- 101 - ntile(allstock$PCFPS, 100)
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allstock$SHY.Rank <- ntile(allstock$SHY, 100)
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# allstock$SHY.Rank <- ntile(allstock$YIELD, 100)
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#Stocks with no rank get 50
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allstock$PBVPS.Rank[is.na(allstock$PBVPS.Rank)] <- 50
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allstock$PE.Rank[is.na(allstock$PE.Rank)] <- 50
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allstock$PSPS.Rank[is.na(allstock$PSPS.Rank)] <- 50
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allstock$EVEDA_12M.Rank[is.na(allstock$EVEDA_12M.Rank)] <- 50
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allstock$PCFPS.Rank[is.na(allstock$PCFPS.Rank)] <- 50
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allstock$SHY.Rank[is.na(allstock$SHY.Rank)] <- 50
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# allstock$SHY.Rank[is.na(allstock$YIELD.Rank)] <- 0
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#Sum the Ranks
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allstock$SumRank <-
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allstock$PBVPS.Rank + allstock$PE.Rank + allstock$PSPS.Rank + allstock$EVEDA_12M.Rank + allstock$PCFPS.Rank + allstock$SHY.Rank
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# Calculate VC2
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allstock$VC2 <- ntile(allstock$SumRank, 100)
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#Get top 10% of allstock
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tvstocks <- filter(allstock, VC2 > 90)
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#I like Fscore >= 5, Z score >=3
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# However, just use Fscore >= 7
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#Clean data
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# tvstocks <- tvstocks[complete.cases(tvstocks$FSCORE_12M),]
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# tvstocks <- tvstocks[complete.cases(tvstocks$ZSCORE_Q1),]
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#Zscore Prime
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# tvstocks <- tvstocks[complete.cases(tvstocks$UDEF13),]
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#weed out must sells
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# tvstocks <- filter(tvstocks, FSCORE_12M >= 4, ZSCORE_Q1 >= 1)
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# tvstocks <- filter(tvstocks, ZSCORE_Q1 >= 1)
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# tvstocks <- filter(tvstocks, FSCORE_12M >= 4)
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#My Market Cap Needs
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# tvstocks <- filter(tvstocks, MKTCAP > min.mark.cap)
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# tvstocks <- filter(, YIELD > 0)
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#Final Cleaning
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# tvstocks <- tvstocks[complete.cases(tvstocks$RPS_6M),]
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# tvstocks <- tvstocks[complete.cases(tvstocks$RS_26W),]
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# tvstocks <- filter(tvstocks,RS_26W > 0)
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# tvstocks <- filter(tvstocks,PRCHG_26W >= median(allstock$PRCHG_26W, na.rm = TRUE))
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# tvstocks <- filter(tvstocks,PRCHG_13W >= median(allstock$PRCHG_13W, na.rm = TRUE))
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# head((screen[order(as.numeric(screen$SCORE), decreasing = TRUE),]),25) #[1]
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# head((screen[order(as.numeric(screen$SCORE), decreasing = TRUE),]),25) #[1]
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# names(stockdata) <- stocknames$V1
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# screen.rpt <- left_join(screen, stockdata, by = "TICKER")
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# screen.rpt <- arrange(screen.rpt, desc(as.numeric(SCORE)))
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# screen.rpt$SCORE <- round(as.numeric(screen.rpt$SCORE), 4)
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# # screen.rpt <- arrange(screen.rpt, desc(as.numeric(PRCHG_26W)))
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# nrow(screen.rpt)
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# stockenv <- new.env()
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# getSymbols(tvstocks$TICKER, env = stockenv, adjust=TRUE)
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# getSymbols(tvstocks$TICKER, env = stockenv)
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# rm(screen)
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# Keep it simple, stop using RRS
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# tvstocks[,"krs26"] <- NA
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# for (i in ls(stockenv)) {
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# print(i)
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# # tvstocks[tvstocks$TICKER == i,"krs26"] <- last(Ad(stockenv[[i]]),1)[[1]]/last(SMA(Ad(stockenv[[i]]),126),1)[[1]]
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# tvstocks[tvstocks$TICKER == i,"krs26"] <- last(Ad(stockenv[[i]]),1)[[1]]/ mean(last(Ad(stockenv[[i]]),126))
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# }
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screen.rpt <- arrange(tvstocks,desc(as.numeric(PRCHG_26W)))
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# screen.rpt <- arrange(tvstocks,desc(as.numeric(RPS_6M)))
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# screen.rpt <- arrange(tvstocks,desc(as.numeric(krs26)))
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screen.rpt <-
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select(screen.rpt, TICKER, COMPANY, SMG_DESC, PRCHG_26W, MKTCAP,FSCORE_12M,VC2)
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head(screen.rpt,25)
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write.xlsx(
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head(screen.rpt, 25),
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"MoneyPicks.xlsx",
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sheetName = "Money",
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append = FALSE,
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row.names = FALSE
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)
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rio::export(head(screen.rpt, 25),"MoneyPicks.csv")
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