The Bull & Bear Power

Creating and the Bull & Bear Power Index in Python

There are many methods to measure who is in control, bulls or bears, and among those methods is Elder Ray’s bull bear power Indicator where it uses simple formulas to approximate this implied technical strength. In this article, we will code the indicator and then judge it from an objective point of view.

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Creating the Bull & Bear Power Index Step-by-Step

The Elder Ray Index measures the amount of buying and selling pressure and is composed of two histograms where one is called the Bull Power and the other the Bear Power. The lines are calculated following these formulas:

The EMA variable refers to the exponential moving average which is a type of a moving average that places more weight on recent values. The exponential moving average can be calculated using this below function:

def adder(data, times):
    
    for i in range(1, times + 1):
    
        new = np.zeros((len(data), 1), dtype = float)
        
        data = np.append(data, new, axis = 1)

     return data

def deleter(data, index, times):
    
    for i in range(1, times + 1):
    
        data = np.delete(data, index, axis = 1)

     return data

   def jump(data, jump):
    
    data = data[jump:, ]
    
    return data

def ma(data, lookback, close, where): 
    
    data = adder(data, 1)
    
    for i in range(len(data)):
           
            try:
                
                data[i, where] = (data[i - lookback + 1:i + 1, close].mean())
            
            except IndexError:
                
                pass
            
    data = jump(data, lookback)
    
    return data

def ema(data, alpha, lookback, what, where):
    
    alpha = alpha / (lookback + 1.0)
    
    beta  = 1 - alpha
    
    data = ma(data, lookback, what, where)

    data[lookback + 1, where] = (data[lookback + 1, what] * alpha) + (data[lookback, where] * beta)

    for i in range(lookback + 2, len(data)):
        
            try:
                
                data[i, where] = (data[i, what] * alpha) + (data[i - 1, where] * beta)
        
            except IndexError:
                
                pass
            
      return data

GBPUSD in the first panel and the 13-period bull bear indicator.

The above shows the GBPUSD hourly data with the 13-period bull bear indicator in the second panel.

def bull_bear_power(data, lookback, high, low, close, where):
    
    # Adding the required columns
    data = adder(data, 3)
    
    # Calculating the exponential moving average
    data = ema(data, 2, lookback, close, where)    
    
    # Calculating the Bull Power
    data[:, where + 1] = data[:, high] - data[:, where]
    
    # Calculating the Bear Power
    data[:, where + 2] = data[:, low] - data[:, where]
    
    # Deleting initial empty rows
    data = jump(data, lookback)
    
    # Deleting EMA Column
    data = deleter(data, where, 1)
    
    return data

EURCAD in the first panel and the 13-period bull bear indicator.

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Personal Critique

The indicator has an interesting way of analysis. Basically, whenever the bear power is increasing while the bull power is making higher highs, we know that we ought to be bullish. Similarly, whenever the bull power is making lower highs while the bear power is making lower lows, we know that we ought to be bearish.

The indicator is vulnerable to whipsaws however and the back-testing results are not impressive. Therefore, I would rank this indicator in the basic section of technical indicators.

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Summary

To sum up, what I am trying to do is to simply contribute to the world of objective technical analysis which is promoting more transparent techniques and strategies that need to be back-tested before being implemented. This way, technical analysis will get rid of the bad reputation of being subjective and scientifically unfounded.

I recommend you always follow the the below steps whenever you come across a trading technique or strategy:

• Have a critical mindset and get rid of any emotions.

• Back-test it using real life simulation and conditions.

• If you find potential, try optimizing it and running a forward test.

• Always include transaction costs and any slippage simulation in your tests.

• Always include risk management and position sizing in your tests.

Finally, even after making sure of the above, stay careful and monitor the strategy because market dynamics may shift and make the strategy unprofitable.

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