@Moses M., You'll find that such a question doesn't get much activity primarily due to the general disdain for technical indicators most statistical traders and quants retain. You would best be served for such a question by researching common trading techniques. The Encyclopedia of Technical Market Indicators comes to mind. However you'll find that the simple oscillators (RSI, STO, CCI, MFI) all have momentum exhaustion uses. Note that they lie. Often. As they've principally done over the last 5 years. Anytime you have unrelenting momentum these indicators will go into false trigger mode and stay there. Having cautioned you, the recent regime transition into volatility may allow such indicators to work on reduced periodicities. The broad market A/D can assist in momentum exhaustion, as can the ADX indicator. One that I hold a fondness for is called the Choppiness Indicator, which can help detect when momentum energy peters out.

in case you want it....

def ChoppinessIndex(context,data,sid, timeperiod=14):   

# 100 * LOG10( SUM(ATR(1), x) / ( MaxHi(x) - MinLo(x) ) ) / LOG10(x)

    highs = history(timeperiod*2, '1d', 'high')[sid]  
    lows = history(timeperiod*2, '1d', 'low')[sid]  
    closes = history(timeperiod*2, '1d', 'close_price')[sid]  
    ATR = ta.ATR(highs, lows, closes,timeperiod)  
    # ATR returns an array, you want the last value  
    lastATR = ATR[-1]  
    total = sum(ATR[-timeperiod:])  
    lowest = min(lows[-timeperiod:])  
    highest = max(highs[-timeperiod:])  
    diff = highest - lowest  
    temp = (total/diff)  
    chop = 100 * math.log10(temp) / math.log10(timeperiod)  
    return chop