I discovered the hard way that reversion to mean trading is not simply about how far price has traveled from the mean. Selling an OB oscillator or thinking, “it can’t go any further” is a recipe for pain. Lots of it.
I found it was critical to understand the interplay of several non-correlated calculations and cross reference them over a significant time period to gain greater insight to high probability mean reversion set ups. The above calculation would allow me to create comparative probability calculations at the time of placing a trade.
Factors to consider in any Mean Reversion set up:
1) Distance price is from the mean in pips
2) Time price has spent away from mean (bars / hours)
3) Rate of change of the mean
4) Translation of above into probability numbers
5) Trading skill (Not a calculation however critical, includes the need for applying a proven trading strategy to effectively trade the set up once identified using comparative probability calculations)
Furthermore, this data needs to be current and relevant to the trader at the time of placing a trade. It is not ideal to use last weeks probability data on this week’s trade set ups. Live streaming probability data is the answer. I however could find no software available on the market which considered all the above criteria, calculated it over a large sample base (say 350 000 bars of data) and provided live streaming probability data across multiple instruments on a single screen.
As a result, I began working on the design of the ChartSmart Trading Scanner and the software you see today is a result of that project.
So what might a reversion to mean formula look like? If I tried to simplify the formula for calculating Reversion To Mean probability data it might look something like this:
Time (spent away from the mean) X Distance (travelled away from the mean) / Rate of change (of the mean), cross referenced against as much historical data of the above calculation as possible.
The above then needs to be applied to the market using a systematic consistent trading approach / system with correct risk controls in place.
If you are a reversion to mean trader and want to discuss further, please drop me a line, I’m always keen for a (Skype) coffee and some trading talk. The above approach has made all the difference in my trading and I am very happy to share my findings with you.
Reversion To Mean trading can be extremely rewarding, however if we want to find consistent success we need to consider several factors, not simply how far price has traveled from the mean. Namely the interplay between Distance, Time, Mean rate of change and the relationship between these factors at any moment in time compared with the same relationship historically.
This comparative probability data, provided in live streaming format, is extremely powerful and provides the trader with the means to consistently identify high probability mean reversion trade set ups.
Wishing you every success in your trading.
Justin Orwin
ChartSmart Trading
www.chartsmart.co.za
I found it was critical to understand the interplay of several non-correlated calculations and cross reference them over a significant time period to gain greater insight to high probability mean reversion set ups. The above calculation would allow me to create comparative probability calculations at the time of placing a trade.
Factors to consider in any Mean Reversion set up:
1) Distance price is from the mean in pips
2) Time price has spent away from mean (bars / hours)
3) Rate of change of the mean
4) Translation of above into probability numbers
5) Trading skill (Not a calculation however critical, includes the need for applying a proven trading strategy to effectively trade the set up once identified using comparative probability calculations)
Furthermore, this data needs to be current and relevant to the trader at the time of placing a trade. It is not ideal to use last weeks probability data on this week’s trade set ups. Live streaming probability data is the answer. I however could find no software available on the market which considered all the above criteria, calculated it over a large sample base (say 350 000 bars of data) and provided live streaming probability data across multiple instruments on a single screen.
As a result, I began working on the design of the ChartSmart Trading Scanner and the software you see today is a result of that project.
So what might a reversion to mean formula look like? If I tried to simplify the formula for calculating Reversion To Mean probability data it might look something like this:
Time (spent away from the mean) X Distance (travelled away from the mean) / Rate of change (of the mean), cross referenced against as much historical data of the above calculation as possible.
The above then needs to be applied to the market using a systematic consistent trading approach / system with correct risk controls in place.
If you are a reversion to mean trader and want to discuss further, please drop me a line, I’m always keen for a (Skype) coffee and some trading talk. The above approach has made all the difference in my trading and I am very happy to share my findings with you.
Reversion To Mean trading can be extremely rewarding, however if we want to find consistent success we need to consider several factors, not simply how far price has traveled from the mean. Namely the interplay between Distance, Time, Mean rate of change and the relationship between these factors at any moment in time compared with the same relationship historically.
This comparative probability data, provided in live streaming format, is extremely powerful and provides the trader with the means to consistently identify high probability mean reversion trade set ups.
Wishing you every success in your trading.
Justin Orwin
ChartSmart Trading
www.chartsmart.co.za
