The McGinley Dynamic — A Superior Moving Average?
Coding the McGinley Dynamic in Python.
Moving averages come in all shapes and forms and each type has its advantages and limitations. In this article, we will discuss a less common type called the McGinley dynamic, an unusual form of smoothing.
I have just released a new book after the success of my previous one “The Book of Trading Strategies”. It features advanced trend-following indicators and strategies with a GitHub page dedicated to the continuously updated code. Also, this book features the original colors after having optimized for printing costs. If you feel that this interests you, feel free to visit the below Amazon link, or if you prefer to buy the PDF version, you could contact me on LinkedIn.
The Concept of Moving Averages
Moving averages help us confirm and ride the trend. They are the most known technical indicator and this is because of their simplicity and their proven track record of adding value to the analyses. We can use them to find support and resistance levels, stops and targets, and to understand the underlying trend. This versatility makes them an indispensable tool in our trading arsenal.
As the name suggests, this is your plain simple mean that is used everywhere in statistics and basically any other part in our lives. It is simply the total values of the observations divided by the number of observations. Mathematically speaking, it can be written down as:
We can see that the moving average is providing decent dynamic support and resistance levels from where we can place our orders in case the market goes down there. The code for the moving average can be written down as the following:
# The function to add a number of columns inside an array
def adder(Data, times):
for i in range(1, times + 1):
new_col = np.zeros((len(Data), 1), dtype = float)
Data = np.append(Data, new_col, axis = 1)
return Data# The function to delete a number of columns starting from an index
def deleter(Data, index, times):
for i in range(1, times + 1):
Data = np.delete(Data, index, axis = 1)
return Data
# The function to delete a number of rows from the beginning
def jump(Data, jump):
Data = Data[jump:, ]
return Data# Example of adding 3 empty columns to an array
my_ohlc_array = adder(my_ohlc_array, 3)# Example of deleting the 2 columns after the column indexed at 3
my_ohlc_array = deleter(my_ohlc_array, 3, 2)# Example of deleting the first 20 rows
my_ohlc_array = jump(my_ohlc_array, 20)# Remember, OHLC is an abbreviation of Open, High, Low, and Close and it refers to the standard historical data fileThe below states that the moving average function will be called on the array named my_data for a lookback period of 200, on the column indexed at 3 (closing prices in an OHLC array). The moving average values will then be put in the column indexed at 4 which is the one we have added using the adder function.
my_data = ma(my_data, 200, 3, 4)The McGinley Dynamic
The McGinley dynamic is not very known but is highly underrated. It was invented by John R. McGinley as a lag reduction technique similar to moving averages. The concept is based on self-adjustment with regards to the speed of the market. It is calculated using this formula:
The first value of the McGinley dynamic is the price itself where afterwards, we apply the formula above. The letter k refers to a constant that equals 0.60 and the N is the lookback variable. Just as we tweak the lookback periods on moving average, we can do the same thing with the McGinley dynamic.
def mc_ginley_dynamic_average(Data, lookback, close, where):
# Adding a column
Data = adder(Data, 1)
# Calculating the McGinley Dynamic
for i in range(len(Data)):
if Data[i - 1, where] == 0:
Data[i, where] = Data[i, close]
elif Data[i - 1, where] > 0:
Data[i, where] = Data[i - 1, where] + ((Data[i, close] - Data[i - 1, where]) / (0.6 * lookback * (Data[i, close] / Data[i - 1, where])**4))
return Data
The above chart shows the McGinley dynamic in action. The indicator tries to avoid whipsaws and follows the market while adjusting itself to its speed.
It even has an innate psychological element embedded in the formula. The line becomes faster in down markets and slower in up markets. This is due to a long-only mentality where people panicking are likely to sell faster and therefore, we need an indicator that confirms the drop as fast as possible.
The above figure shows the difference between the 20-period simple moving average and the 20-period McGinley dynamic.
my_data = mc_ginley_dynamic_average(my_data, 20, 3, 4)
my_data = ma(my_data, 20, 3, 5)I recommend tweaking the parameters to find a good balance and quick signals that minimize whipsaws. The dynamic shows good potential.
If you are also interested by more technical indicators and strategies, then my book might interest you:
Conclusion
Remember to always do your back-tests. You should always believe that other people are wrong. My indicators and style of trading may work for me but maybe not for you.
I am a firm believer of not spoon-feeding. I have learnt by doing and not by copying. You should get the idea, the function, the intuition, the conditions of the strategy, and then elaborate (an even better) one yourself so that you back-test and improve it before deciding to take it live or to eliminate it. My choice of not providing specific Back-testing results should lead the reader to explore more herself the strategy and work on it more.
To sum up, are the strategies I provide realistic? Yes, but only by optimizing the environment (robust algorithm, low costs, honest broker, proper risk management, and order management). Are the strategies provided only for the sole use of trading? No, it is to stimulate brainstorming and getting more trading ideas as we are all sick of hearing about an oversold RSI as a reason to go short or a resistance being surpassed as a reason to go long. I am trying to introduce a new field called Objective Technical Analysis where we use hard data to judge our techniques rather than rely on outdated classical methods.