Most system developers are aware of the Max Favorable Excursion and Max Adverse Excursion measures. These measures track the max profit and loss a trade experiences over its lifetime. However, a very useful metric that I’m unaware of being even tracked in most trading platforms is Max Favorable Consecutive Ticks (MFT) before Max Adverse Consecutive Ticks (MAT). A similar measure might be Initial Favorable Excursion (IFE). This measure could be really useful though for scalpers, scalping systems, and active day traders. This could be a per trade measure and an average measure. I think as a scalper, it is really critical.
This data would be primarily useful for knowing whether scaling out of contracts makes sense. But, it would also be a good metric to optimize for. For example, if you know that a trade with a 12 tick target tends to have 4 ticks of MFT before taking 2 ticks of MAT then you know it would be a good candidate system for adding an extra contract and scaling out the first contract at +4 ticks. Basically, how much does a position move in your favor before it takes a given amount heat?
I am not aware of these metrics being included in any trading programs.
One way to simulate it might be to optimize the stop loss and take profit. You would need to look at the optimization report results to infer IFE. For example, let’s imagine you have a system that has already optimized to work best with an X point stop. If you could test your entries for brackets of 1 point, 2 point, 3 point, etc. You could look at the winning percentage/ratio for each bracket to estimate the MFT before MAT. However, the benefit for having this as a metric would be the ability to analyze it in reports. I imagine one way to do this would might be a box and whisker’s style plot with statistics shown for avg, median, max, min But, that simulation method would still not give you the individual trade stats. The only way to do that would be track the “real-time” position P&L for each individual trade. The individual stats might be extremely useful for looking for filters to improve trading results.
Do you think this is useful metric? Why or why not? And, do you know any other way to track this in Tradestation or Ninjatrader? Please let me know if you’ve built a solution to track this for discretionary or trading systems in either platform in the comments below.
Curtis is passionate about markets. He has developed top ranked futures strategies. His core focus is (1) applying machine learning and developing systematic strategies, and (2) solving the toughest problems of discretionary trading by applying quantitative tools, machine learning, and performance discipline. You can contact him at firstname.lastname@example.org.
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