USING QUANTITATIVE DATA TO DERIVE PROBABILITY BASED TARGETS AND STOPS

Many of us deal with trading systems or trading strategies , be it manual or automated. One of the most common questions that traders have is to where to place a stoploss in each trade and where to place targets? Typically most of us keep a fixed point based or percentage targets. The problem with such targets/stops is that they don’t have anything to do with the actual entry/exit signal performances

Here we present a different perspectives This is  a method of deciding on how much can stop or target is based on data from the trades. Every trade that one individual or system takes has two sets of data called as MFE and MAE. These data sets are mostly available readily in trading softwares like amibroker, metastock or metatrader.

So, what are these?

MAE: max adverse excursion -- max movement against my trade

MFE: Max Favorable excursion-- max movement in favor of my trade

Let us illustrate this with and example say we went long in BANKNIFTY FUTURES  at 12000 levels with initial stoploss 11800 that is 200 points.

Trade went down to 11940 then went pulled back to make a high of 12240 and finally close at 12120

MAE plot-sample system

we see that most of the MAE data is within -1%. hence that can be mostly used as a hard stop. That essentially means that most of the trade negative movement in this system is within 1% after that stage the chances of an adverse movement in the trading resulting in loss becomes significantly higher. So in this particular system a 1% stop should serve fairly well in most cases

MFE plot-sample system

With respect to the MFE data now, We also see that 80% of trades are within 2% of profit. so that means markets will mostly reverse within 2 % of movement and above that there are chances of a true break out. Hence it would be ideal to book gains within 2% movements and  re enter trades above it as the probability of a significant move drastically increases for the system.

There are also numerous other ways of using this data in Trading system design and system comparison. We will explore some of that in future posts.

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