blackdog6621

Ultimate Oscillator Divergence Detector v0.1

I wanted a more dynamic divergence indicator than the stock one so this is what's come of it so far. This is a very early version but I found its detecting normal bullish divergences pretty well so I thought I'd release it.

Right now it only supports normal bullish divergences. I'm working on improving accuracy before I add in normal bearish divergences, and then will be handling hidden divergences.

The spacing parameter will probably need to be tweaked based on the time frame of interest. For small time frames a value of 3 seems to work, for longer ones I go up to 7. Anything below 3 will be extremely sensitive and anything above 10 will hardly notice anything. The spacing for the example here is 3 for the main graph and 7 for the ultimate oscillator graph.
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study("blackdog6621s Ultimate Oscillator Divergence Detector v0.1", shorttitle="UO Div Detector v0.1", overlay=true)

spacing = input(7)
length7 = input(7, minval=1), length14 = input(14, minval=1), length28 = input(28, minval=1)

average(bp, tr_, length) => sum(bp, length) / sum(tr_, length)
high_ = max(high, close[1])
low_ = min(low, close[1])
bp = close - low_
tr_ = high_ - low_
avg7 = average(bp, tr_, length7)
avg14 = average(bp, tr_, length14)
avg28 = average(bp, tr_, length28)
uo = 100 * (4*avg7 + 2*avg14 + avg28)/7

lowest_in_segment(s_close, s_osc, length) =>
    min_bar = lowestbars(s_close, length)
    min_val = lowest(s_close, length)
    next_s_close = offset(s_close, length)
    osc_val = lowest(s_osc, length)
    next_s_osc = offset(s_osc, length)
    [min_bar, min_val, osc_val, next_s_close, next_s_osc]

s_close = close
s_osc = uo
[low_bar_1, low_val_1, low_osc_1, low_s_close_1, low_s_osc_1] = lowest_in_segment(s_close, s_osc, spacing)
[low_bar_2, low_val_2, low_osc_2, low_s_close_2, low_s_osc_2] = lowest_in_segment(low_s_close_1, low_s_osc_1, spacing)
[low_bar_3, low_val_3, low_osc_3, low_s_close_3, low_s_osc_3] = lowest_in_segment(low_s_close_2, low_s_osc_2, spacing)
[low_bar_4, low_val_4, low_osc_4, low_s_close_4, low_s_osc_4] = lowest_in_segment(low_s_close_3, low_s_osc_3, spacing)
[low_bar_5, low_val_5, low_osc_5, low_s_close_5, low_s_osc_5] = lowest_in_segment(low_s_close_4, low_s_osc_4, spacing)
[low_bar_6, low_val_6, low_osc_6, low_s_close_6, low_s_osc_6] = lowest_in_segment(low_s_close_5, low_s_osc_5, spacing)

lowest_padded(series) => lowest(series, spacing * 3)

first_low = 1

second_low_5x() => low_val_5 < low_val_6 ? 5 : 6
second_low_4x() => lowest_padded(low_s_close_2) == low_val_4 ? 4 : second_low_5x()
second_low_34() => lowest_padded(low_s_close_1) == low_val_3 ? 3 : second_low_4x()
second_low_3x() => first_low != 3 ? second_low_34() : second_low_4x()
second_low_23() => lowest_padded(s_close) == low_val_2 ? 2 : second_low_34()
second_low = first_low == 1 ? second_low_23() : second_low_3x()

seg(i, seg_1, seg_2, seg_3, seg_4, seg_5, seg_6) =>
    i == 1 ? seg_1 : (i == 2 ? seg_2 : (i == 3 ? seg_3 : (i == 4 ? seg_4 : (i == 5 ? seg_5 : seg_6))))
first_low_val = seg(first_low, low_val_1, low_val_2, low_val_3, low_val_4, low_val_5, low_val_6)
first_low_bar = (first_low - 1) * spacing +
    seg(first_low, low_bar_1, low_bar_2, low_bar_3, low_bar_4, low_bar_5, low_bar_6)
second_low_val = seg(second_low, low_val_1, low_val_2, low_val_3, low_val_4, low_val_5, low_val_6)
second_low_bar = (second_low - 1) * spacing +
    seg(second_low, low_bar_1, low_bar_2, low_bar_3, low_bar_4, low_bar_5, low_bar_6)

first_osc_val = seg(first_low, low_osc_1, low_osc_2, low_osc_3, low_osc_4, low_osc_5, low_osc_6)
second_osc_val = seg(second_low, low_osc_1, low_osc_2, low_osc_3, low_osc_4, low_osc_5, low_osc_6)

val_lows_direction = sign(first_low_val - second_low_val)
osc_lows_direction = sign(first_osc_val - second_osc_val)

n_bull_diff = val_lows_direction < 0 and osc_lows_direction >= 0
bgcolor(n_bull_diff ? lime : na, transp=85) //, offset=-8)
Great stuff! Any updates on the script featuring bearish normal divergence and hidden divergences? Many thanks!
Reply
Nice and useful as-is. I look forward to its improvements and bearish detections in future releases.
Thanks for making it public!
+2 Reply
Good work!
+3 Reply
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