ATTN: This scan is an extension of an earlier scan (refer to Example 22: Define a time frame and classify the daily % fluctuations in closing prices within that timeframe into ‘n’ distinct categories), with the exception of extracting a few more data points in this scan.
List data from "01-Sep-2023"
Add col MARKET_COUNT(chg%<=-4) as Chg <-4% format 0
Add col MARKET_COUNT(chg%<0 and chg%>-4) as Chg 0 To -4% format 0
Add col MARKET_COUNT(chg%>0 and chg%<4) as Chg 0 to 4% format 0
Add col MARKET_COUNT(chg%>=4) as Chg >4% format 0
Add col MARKET_COUNT(chg%(cl, cl a month ago) >= 25) as MChg >25% format 0
Add col MARKET_COUNT(chg%(cl, cl a month ago)<= -25) as MChg <-25% format 0
Add col MARKET_COUNT(chg%(cl, cl a month ago) >= 50) as MChg >50% format 0
Add col MARKET_COUNT(chg%(cl, cl a month ago)<= -50) as MChg <-50% format 0
Add col MARKET_COUNT(chg%(cl, cl 34 bars ago) >= 13) as 34BChg >13% format 0
Add col MARKET_COUNT(chg%(cl, cl 34 bars ago)<= -13) as 34BChg <-13% format 0
Add col MARKET_COUNT(chg%(cl, cl a quarter ago) >= 25) as QChg >25% format 0
Add col MARKET_COUNT(chg%(cl, cl a quarter ago)<= -25) as QChg <-25% format 0
Add Column chg%
Add Column chg%(cl, cl a month ago) as MChg
Add Column chg%(cl, cl 34 bars ago) as 34BChg
Add Column chg%(cl, cl a quarter ago) as QChg
Apply to niftymic250
Set report type to SUMMARY-DATEWISE
Below is a breakdown of the scan presented in a step-by-step manner.
Continue reading “Stock Screener (‘Sentiment’) – Example 23: Define a time frame and classify daily, monthly and quarterly % fluctuations in closing prices within that timeframe into several distinct categories”