The Correlation-Adjusted Reversal Indicator.
Creating a Correlation-Adjusted Mean-Reversion Indicator in Python.
Combining statistical metrics with technical indicators is a powerful tool that allows us to take advantage of more market properties. In this article, we will try to combine mean-reversion through a simple differencing with the linear correlation metric and how it performs when used in a normal contrarian trading strategy.
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Introduction to the Concept of Correlation
Correlation is the degree of linear relationship between two or more variables. It is bounded between -1 and 1 with one being a perfectly positive correlation, -1 being a perfectly negative correlation, and 0 as an indication of no linear relationship between the variables (they relatively go in random directions). The measure is not perfect and can be biased by outliers and non-linear relationships, it does however provide quick glances to statistical properties.
Before coding the function, we need the following three primal functions that allow us to manipulate the data easily:
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 datadef 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
We can code the correlation function between two variables in Python using the below. Note that it has to be an array and not a data frame:
def rolling_correlation(Data, first_data, second_data, lookback, where):
for i in range(len(Data)):
try:
Data[i, where] = pearsonr(Data[i - lookback + 1:i + 1, first_data], Data[i - lookback + 1:i + 1, second_data])[0]
except ValueError:
pass
Data = jump(Data, lookback)
return DataCreating the Correlation-Adjusted Reversal Indicator
The idea for the correlation-adjusted reversal indicator is to detect average extremes where the correlation between returns and prices is high enough to justify a possible market inflection. The steps required to calculate the indicator are as follows:
Calculate the difference between the current price and the price 1 period ago.
Calculate the difference between the current price and the price 2 periods ago.
Calculate the difference between the current price and the price 3 periods ago.
Calculate the difference between the current price and the price 4 periods ago.
Calculate the difference between the current price and the price 5 periods ago.
Calculate the difference between the current price and the price 6 periods ago.
Calculate the average of all the calculations above for each row.
Calculate the 10-period correlation between the price and the average from the previous step.
Select a threshold such as 0.75. Then, loop around the data with a condition that if the correlation is greater than 0.75, keep the current average, otherwise, input zero as the average.
The full code of the indicator can be found as below, considering an OHLC data array.
def correlation_adjusted_reversal_indicator(Data, lookback, close, where, threshold = 0.75):
# Adding a few columns
Data = adder(Data, 8)
# Average of current close minus the previous period
for i in range(len(Data)):
Data[i, where] = Data[i, close] - Data[i - 1, close] # Average of current close minus n then
for i in range(len(Data)):
Data[i, where + 1] = Data[i, close] - Data[i - 2, close] # Average of current close minus the close 2 periods ago
for i in range(len(Data)):
Data[i, where + 2] = Data[i, close] - Data[i - 3, close] # Average of current close minus the close 3 periods ago
for i in range(len(Data)):
Data[i, where + 3] = Data[i, close] - Data[i - 4, close] # Average of current close minus close 4 periods ago
for i in range(len(Data)):
Data[i, where + 4] = Data[i, close] - Data[i - 5, close] # Average of current close minus close 5 periods ago
for i in range(len(Data)):
Data[i, where + 5] = Data[i, close] - Data[i - 6, close] # Calculating the average mean-reversion
Data[:, where + 6] = (Data[:, where] + Data[:, where + 1] + Data[:, where + 2] + Data[:, where + 3] + Data[:, where + 4] + Data[:, where + 5]) / 6
# Cleaning
Data = deleter(Data, where, 6)
# Adjusting for correlation
Data = rolling_correlation(Data, close, where, lookback, where + 1)
for i in range(len(Data)):
if Data[i, where + 1] > threshold:
Data[i, where] = Data[i, where]
elif Data[i, where + 1] < threshold:
Data[i, where] = 0
# Cleaning
Data = deleter(Data, where + 1, 1)
return Data# Calling the function
my_data = correlation_adjusted_reversal_indicator(my_data, 10, 3, 4)
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Using the Indicator
As with any proper research method, the aim is to test the indicator and to be able to see for ourselves whether it is worth having as an add-on to our pre-existing trading framework. The conditions for the back-test are as follows:
Long (Buy) whenever the CARI reaches -0.002 with the two previous readings above -0.002.
Short (Sell) whenever the CARI reaches 0.002 with the two previous readings below 0.002.Â
def signal(Data, what, buy, sell):
Data = adder(Data, 10)
Data = rounding(Data, 5)
for i in range(len(Data)):
if Data[i, what] < lower_barrier and Data[i - 1, what] > lower_barrier and Data[i - 2, what] > lower_barrier :
Data[i, buy] = 1
if Data[i, what] > upper_barrier and Data[i - 1, what] < upper_barrier and Data[i - 2, what] < upper_barrier :
Data[i, sell] = -1
return Data
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