RS Matrix Pseudocode

rs_matrix_pseudocode

Set the parameter values:

	boxSize = 0.035
	reversalThreshold = 3
	reversalAmount = boxSize x reversalThreshold = 0.105

	historyDays = 180
	lagTime = 0 days
	endDate = today - lagTime
	startDate = today - historyDays

Upload the ticker list

Create an empty dataframe for the plot symbols (Xs and Os)

Create an empty dataframe for the trend signals (BUY vs SELL)

Get the EOD close price history for each ticker symbol (I use Intrinio's API)

For each ticker on the ticker list as Ticker1:

	Iterate through every other ticker on the list as Ticker 2 to:

		Calculate the relative strength ratio of Ticker1 / Ticker2 for each date in the time series
		Calculate the PnF stats, plot_symbol (Xs vs Os) and signal_name (BUYs vs SELLs) for each date for each RS ratio pair

		# The algorithm for calculating the PnF stats is as follows:

		    Set the plot_symbol for the first day as X
		    Set the signal_name for the fist day as BUY

		    Set high_point = current ratio value
		    Set low_point = current ratio value
		    Set last_high_point = current ratio value
		    Set last_low_point = current ratio value
		    Set prev_high_point = current ratio value
		    Set prev_low_point = current ratio value

		    For each subsequent date:

		    If previous day’s plot_symbol = "X", then:

		      If current value >= previous value, then:
			  Today's plot_symbol = "X".
			  and copy yesterday's signal_name to today.

			  And if today's value is higher than the last high_point:
			      then make today's value the high_point,
			      and copy yesterday's signal_name to today.

			  And if today's value is higher than the high point from the last X column:
			      then today's signal_name = "BUY"

		      Else if today's value is less than the previous high_point times 1 - reversalAmount:
			  the plot_symbol reverses to "O",
			  and the low_point is today's value,
			  and reversal = 1,
			  and prev_high_point = last_high_point, saving this value to use in the Target value calc below
			  and last_high_point = most recent high_point

			  And if today's value is lower than the last_low_point from the last O column:
			      then today's signal = "SELL".
			  Else:
			      copy yesterday's signal to today

		      Else:
			  plot_symbol = "X" (value is down but not enough to trigger a reversal)
			  and copy yesterday's signal to today.

		    If previous plot_symbol = "O", then:

		    If current value <= previous value, then:
		      Today's plot_symbol = "O".
		      and copy yesterday's signal_name to today.

		      And if today's value is lower than the most recent low_point, 
			  then make today's value the low_point.
			  and copy yesterday's signal_name to today.

			  And if today's value is lower than the last_low_point from the last O column,
			      then today's signal = "SELL".

		      Else if today's value is greater than the last_high_point, times 1 + reversalAmount:
			  the plot_symbol reverses to "X",
			  and the  high_point is today's value,
			  and reversal = 1,
			  and prev_low_point = last_low_point, saving this value to use in the Target value calc below
			  and last_low_point = low_point
			  and if today's value is higher than the high point from the last X column:
			      then today's signal_name = "BUY".

			  Else copy yesterday's signal_name to today.

		      Else:
			  Plot Symbol = "O" (value is up but not enough to trigger a reversal)
			  and copy yesterday's signal_name to today.

		    Set high_point = current "high_point"
		    Set low_point = current "low_point"
		    Set last_high_point = current "last_high_point"
		    Set last_low_point = current "last_low_point"
		    Set prev_high_point = current "prev_high_point"
		    Set prev_low_point = current "prev_low_point"

		    Capture the most recent signal_name and save it to the trend signals dataframe for the RS pair
		    Capture the most recent plot_ymbol and save it to the plot symbol dataframe for the RS pair

		Count the total number of BUYs for each ticker symbol
		Count the total number of Xs for each ticker symbol

Create a data frame with the following columns: Ticker Symbol, Asset Name, Count of BUYs, Count of Xs, Total Count