The Market Meanness Index is a technical indicator developed by JCL from the Financial Hacker website. In this article we will describe what the indicator is designed to do and provide some code for Amibroker users.
MMI is a statistical algorithm based on the median value of a price series. It’s purpose is to help detect whether the market is in a trending mode or range mode. It therefore provides traders with an excellent filter for trend following strategies. If the market is not trending then you can save yourself some money and skip the signal coming from your trend system.
Understanding The Market Meanness Index
As stated by the author. the Market Meanness Index is based on a statistical fact – a series of random numbers will revert to the median with a probability of 75%. This is because, by definition, half the series of prices will be below the median and half will be above. Therefore, if a market is not moving in a similar pattern, prices are not random and more likely to be trending.
According to its creator, MMI can’t be used to predict price movement, it’s only used to detect whether ‘trend is trending’ and therefore whether a trend signal has a good chance of being profitable or not.
JCL suggests that MMI should be calculated over a minimum of 200 bars and smoothed with a low pass filter or similar low lag moving average.
A perfectly efficient market (with random price sequences) would return an MMI value of around 75%. Therefore, a falling MMI value that is under 75% suggests an inefficient market and is a good indicator of a real trend. Meanwhile, a rising MMI suggests that the market is becoming more efficient and the trend is becoming unsustainable.
In the following chart, you can see a 200-period MMI plotted below Apple stock. You can see that the MMI (orange) begins to decline since July just as Apple starts an upward trend. This is quite a nice example of the MMI in action:
Market Meanness Code And Calculations
With some help from Matt Radtke we have put together some Amibroker code for the Market Meanness Index as below:
To better understand how this indicator works it’s a good idea to consider the following couple of worksheets that were used to create this indicator. You can click the images themselves to get a better view.
In the worksheets, columns H through K contain the calculations used to create MMI for the price series up to July 10, 2018. Similarly, columns L-O calculate MMI for July 9th, and so on.
So in this first worksheet, you will see that we have a price series of random numbers in column F. MMI has been calculated on this random data and the resulting values are shown for six different date ranges. The MMI is shown in columns K, O, S, W, AA and AE. You can see that we are returning MMI values between 68 and 84 based on this random data series.
Now in this next worksheet, we have replaced the random price series with a trending series. You can see now that we have a perfect price trend in column F starting at a value of 100 and ending in 125. And you can see that the MMI value for this perfect trend is 52.63.
In other words, a random market produces a MMI value around 70-80 while a solid trend produces a MMI around 53.
Finally, in the next sheet you can see some calculations for real SPY data. Notice that the MMI fluctuates from 58 to 68.
In other words, SPY during this date range is not showing complete randomness but it isn’t showing a strong trend either.
Market Meanness In Action
The idea of the MMI indicator is to filter out trend signals that have a poor chance of success. It is used to find the best trending market environments.
I have briefly used this indicator on US equity markets on it’s own and by smoothing the indicator with an EMA and also an ALMA (Arnaud Legoux Moving Average) as suggested by the author.
I found that the MMI does improve some simple trend following strategies that I tried. However, I have a feeling it may be better suited to currency markets and higher timeframes.
I need to spend more time with this indicator but so far it does look promising as a trend filter. Take it for a spin and let me know what you think.
Thank You For Reading
Joe Marwood is an independent trader and the founder of Decoding Markets. He worked as a professional futures trader and has a passion for investing and building mechanical trading strategies. If you are interested in more quantitative trading strategies, investing ideas and tutorials make sure to check out our program Marwood Research.
This post expresses the opinions of the writer and is for information, entertainment purposes only. Joe Marwood is not a registered financial advisor or certified analyst. The reader agrees to assume all risk resulting from the application of any of the information provided. Past performance is not a reliable indicator of future returns and financial trading is full of risk. Margin trading can lead to losses more than in your account. Mistakes in backtesting and presenting of analysis regularly occur. Please read the Full disclaimer.