Outline

generate iDs for each tday with indcs as components
iD length is from 1-x and should be descriptive enough to identify w >= .9 given θ,φ, & α values.
syntax = indc1_name.value,indc2_name.value,...,indcx_name.value


backtesting == forward-testing
  for all tdays in range find level 1 iDs generated on that day and forward-test this indication ´til end of global tday range
  what is the % bw/fw?
  is there periodicity?
  
periodicity = auto_fit(w(x.range)).variation
  variation = w(x.range) > w(y.range) __ x<~>y & there is no overlap between x and y
  auto_fit(glb_tdays):
    wpers = find longest consequetive periods w = 1
    lpers = remaining periods
    merge wpers with lpers itemwise maintaining the highest w value for wpers and the lowest w value for lpers
    return boolean mask of timeseries