1. Collecting price data

Collecting price data is simple in R. In order to keep this post as a demo I’ll use the Nasdaq-100 stocks but you could collect prices of more stocks. For example the Russel-1000 or S&P 500 constituents. In this example I’ll use quantmod that provides a simple way to access to yahoo-finance data and data.table to aggregate the result of the screen and query it.

# Load libraries
library(data.table)
library(magrittr)
library(quantmod)

options("getSymbols.warning4.0"=FALSE)
options("getSymbols.yahoo.warning"=FALSE)

# Read data from ishares site to get stock tickers
ticker_lkp = suppressWarnings(fread("https://www.ishares.com/uk/individual/en/products/253741/ishares-nasdaq-100-ucits-etf/1506575576011.ajax?fileType=csv&fileName=CNDX_holdings&dataType=fund", showProgress=F))

# get stock tickers
tickers = ticker_lkp[nchar(ISIN) == 12, `Issuer Ticker`]

I’ll collect the price data in a list in order to be able to access each ticker by it’s name

# Get prices as xts
data = list()
for (i in seq_along(tickers)) {
  Sys.sleep(0.005) # not to get rate limited
  ticker = tickers[i]
  from = "2015-01-01"
  to = as.character(Sys.Date())
  data[[ticker]] = try(getSymbols(ticker, from=from, to=to, auto.assign = FALSE, src='yahoo'), silent=TRUE)
}
names(data) = tickers

First I’ll just check if all the stocks were downloaded. It looks like we have 100 stocks plus 2 that have A shares. All objects are of class xts so it seems the data was collected properly.

# Remove empty data
table(sapply(data, function(x) class(x)[1]))

## 
## xts 
## 102

Below I show a few examples of how to access the price and volume data of each stock.

tail(data[['MSFT']], 3)

##            MSFT.Open MSFT.High MSFT.Low MSFT.Close MSFT.Volume MSFT.Adjusted
## 2021-01-20    217.70    225.79   217.29     224.34    37777300        224.34
## 2021-01-21    224.70    226.30   222.42     224.97    30749600        224.97
## 2021-01-22    227.08    230.07   225.80     225.95    30124900        225.95

tail(data[['COST']], 3)

##            COST.Open COST.High COST.Low COST.Close COST.Volume COST.Adjusted
## 2021-01-20    354.39    361.90   353.41      361.3     2767200         361.3
## 2021-01-21    361.30    363.99   359.94      362.8     2122600         362.8
## 2021-01-22    363.20    364.63   359.85      362.3     1959100         362.3

2. Develop the screener

In this case I’ll show how to find stocks in a long term up-trend that seem have some short term weakness.

First of all I’ll adjust the OHLC values.

data = lapply(data, adjustOHLC, use.Adjusted=TRUE)

In order to make the code reproducible I’ll keep the data up to today. You can remove this like if you want to get the most recent results

data = lapply(data, function(d) d['/20210115'])

Screener parameters

• Long term uptrend: Close > SMA-150 (long term uptrend)
• High volatility: ATR t10 > 4% (high volatility stocks)
• Short term oversold: RSI t3 < 30
• Ranked by lower RSI-3

Some definitions: - ATR: Average true range - RSI: Relative strenght indicator - SMA-150: Simple moving average of the past 150 days

Of course this is an example, you can try different parameters.

Here is the main function I used, the code is commented.

mean_reversion <- function(data, ticker){
  
  # Read the data
  df = data[[ticker]]

  # Compute indicators
  HLC = HLC(df)
  sma150 = SMA(Cl(df), n=150)
  atr10 = ATR(HLC, n=10, maType='EMA')
  rsi3 = RSI(Cl(df), n=3)
  
  # join them to the main data
  df = merge(df, sma150)
  df = merge(df, atr10$atr)
  df = merge(df, rsi3)
  df$atr = df$atr / Cl(df)
  df$close = Cl(df)
  
  # Select relevant columns
  keep_cols = c("SMA", "atr", "rsi", "close")
  df = df[, keep_cols]
  names(df)[1:3] = c("sma150", "atr10", "rsi3")
  
  # Get the most recent observation
  df = tail(df, 1)
  
  # Convert the output as a data table
  dt = as.data.table(df)
  dt[, ticker:=ticker]
  setnames(dt, "index", "date")
  
  dt
}

Execute the screener and evaluate results

indicators = list()
for(ticker in tickers){
  indicators[[ticker]] = mean_reversion(data, ticker)
}

# Rbind the indicators list
indicators = rbindlist(indicators)
head(indicators)

##          date    sma150      atr10     rsi3   close ticker
## 1: 2021-01-15  113.4679 0.02581628 27.30745  127.14   AAPL
## 2: 2021-01-15  210.9870 0.01823743 24.45446  212.65   MSFT
## 3: 2021-01-15 3132.2195 0.02003178 29.26385 3104.25   AMZN
## 4: 2021-01-15  439.5304 0.05079055 43.58718  826.16   TSLA
## 5: 2021-01-15  262.9280 0.03270333 43.37887  251.36     FB
## 6: 2021-01-15 1602.1517 0.02265435 28.77536 1736.19   GOOG

Keep stocks with close higher than the SMA-150

indicators = indicators[close >= sma150]   
nrow(indicators)

## [1] 87

indicators = indicators[atr10 >= 0.04]  
nrow(indicators)

## [1] 15

indicators = indicators[rsi3 <= 30]
nrow(indicators)

## [1] 3

indicators[, rk:=frank(rsi3)]
indicators[order(rk)]

##          date    sma150      atr10     rsi3  close ticker rk
## 1: 2021-01-15 116.08595 0.04108247 14.94451 136.60   XLNX  1
## 2: 2021-01-15  78.94353 0.04119751 15.14981  88.21    AMD  2
## 3: 2021-01-15 105.78900 0.06850013 21.49396 161.20    PDD  3

df = data[['PDD']]["2020/"]
{plot(Cl(df))
lines(SMA(Cl(df), n = 20), col="blue")
lines(SMA(Cl(df), n = 50), col="red", lty=2)
# add legend to panel 1
addLegend("topleft", legend.names = c("Close", "SMA(20)", "SMA(50)"), 
          lty=c(1, 1, 2), lwd=c(2, 1, 1),
          col=c("black", "blue", "red"))}