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10/16/2014 - 1:38 PM

Turning Point Oscillator

Turning Point Oscillator

TPOscillator.py
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def create_tposcillator(s, peaks, troughs):

    tps = sorted(peaks + troughs)
    TPOscillator = []
    index = []

    for i in range(len(s)):
        if i < min(min(peaks), min(troughs)) or i > max(max(peaks), max(troughs)):   
            TPOsc = 999     # TPOsc undefined
        elif i in troughs:
            TPOsc = 0.
        elif i in peaks:
            TPOsc = 1.
        else:
            idx_left = max([idx for idx in tps  if idx < i])
            idx_right = min([idx for idx in tps  if idx > i])
            if idx_left in peaks:
                Pt = s[idx_left]
                Tt = s[idx_right]
            else :
                Tt = s[idx_left]
                Pt = s[idx_right]
          
            TPOsc = (s[i] - Tt) / (Pt - Tt)
            
            if TPOsc < 0. or TPOsc > 1.:
                print (s.index[i],'TPOsc=',TPOsc,'s[i]=',s[i],'idx_left=',idx_left, 'idx_right=',idx_right, 'Pt=',Pt, 'Tt=',Tt)

        if TPOsc != 999:
            #print (i, TPOsc)
            TPOscillator.append(TPOsc)
            index.append(s.index[i])
            
    return pd.Series(TPOscillator, index=index)
TPOscillator.md
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Definition 4: Turning point oscillator(TPOscillator) Let $X$ be a price sequence and let $A(X)$ be its alternating pivots sequence(that is, $A(x)$ is the list of turning point times in $X$). The TPOscillator is a mapping $Γ: N → [0, 1]$, such that: $Γ(t)= 0$ if $t$ is a trough, $1$ if it is a peak and $x_t − P_t / P_t − T_t$ otherwise, where $P_t$ and $T_t$ are the values of the timeseries at the nearest peak and trough located on opposite sides of time $t$.