The Equilibrium Indicator — Profiting from Mean-Reversion.

The above plot shows an example on the EURUSD hourly data plotted with the Equilibrium Indicator in blue. The full code can be find below:

def ema(Data, alpha, lookback, what, where):    # alpha is the smoothing factor
# window is the lookback period
# what is the column that needs to have its average calculated
# where is where to put the exponential moving average
alpha = alpha / (lookback + 1.0)
beta  = 1 - alpha
    # First value is a simple SMA
Data = ma(Data, lookback, what, where)
    # Calculating first EMA
Data[lookback + 1, where] = (Data[lookback + 1, what] * alpha) + (Data[lookback, where] * beta)    # Calculating the rest of EMA
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 Datadef deleter(Data, index, times):for i in range(1, times + 1):
Data = np.delete(Data, index, axis = 1)return Datadef jump(Data, jump):
Data = Data[jump:, ]
return Datadef equilibrium(Data, lookback, close, where):    # Calculating the moving average
Data = ma(Data, lookback, close, where)
Data = jump(Data, lookback)
    # Calculating the distance from the moving average
Data[:, where + 1] = Data[:, close] - Data[:, where]
    # Calculating the Exponential Moving Average of the Equilibrium
Data = ema(Data, 2, lookback, where + 1, where + 2)
Data = jump(Data, lookback)
Data = deleter(Data, where, 2)
return Data# Using the function
my_data = equilibrium(Data, 5, 3, 4)

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