STOPS AND TRADING SYSTEMS
It is common to use stops to avoid big losses and to avoid the psychological danger a trader can have when he is uncertain to close his positions at time.
Still sometimes no stop is proposed in trading. We all now that too small a stop the will punch out too often, sometimes leaving us behind with a potential winner while too big a stop can have a big influence on our overall results.
Suppose someone trades 100 times a year of which 80% are small winners and mall losers. This is not an unreal situation for a discretionary trader. His results are determined by the last 20% which we suppose to be big winners and big losers.
The influence of one of such a trade is substantial. A big loser or big winner can make a substantial difference to the final results of the trader.
But the question remains if how losers and winners effect each ohter.
We know the concept of Expectancy:
E = P / NP - L / NL = Average Win – Average Loss (1)
In words: The expectation of a trade is the total win divided by the total amount of losses minus the total loss divided by the total amount of losers.
The above formula has four variables, P the total win, L, the total loss, NP the amount of winners and NL the amount of losers. Each of them can be changed, e.g. decreasing the the total loss L, decreases the average loss and so increases the sum which is the expectation E.
But changing one variable in formula (1), changing one may affect the other ones. The variables are said to be dependant from each other. For a given system, changing the average loss may result in changing the average win too!
An example: a system produces 100 trades a year, 60 losers and 40 winners. When 5 potential losses could eventually turned into 5 winners by taking a bigger stop the average win will increase due to this, but you may also expect that the average loss will increase due to the bigger stop of the remaining 35 losses. We also could go for bigger winners just to see an increase in the amount of losses.
We don’t know at forehand what happens and the problem stays. Suppose there is a function G with parameters NP, NL, P, L so that :
G (NP,NL,P,L).
G produces some results in terms of expectation E:
G (NP,NL,P,L) = f (E) (2)
I did some effort to investigate some general characteristics of formula (2). Though not being very being successfully it helped me I a better understanding the concept of a trading system, in fact a way of producing losers and winners which outcomes are dependant to each other.
One of the strangest things I found was that the outcome of a system is changing over time and not seldom worsening, not necessarily in terms of expectation E or E per dollar risk but more in terms of other characteristics as drawdown, amount of losses and winners, runs of losses and winners and so on.
This is a fundamental (stochastic) principle everywhere probability plays such an important role.
Still sometimes no stop is proposed in trading. We all now that too small a stop the will punch out too often, sometimes leaving us behind with a potential winner while too big a stop can have a big influence on our overall results.
Suppose someone trades 100 times a year of which 80% are small winners and mall losers. This is not an unreal situation for a discretionary trader. His results are determined by the last 20% which we suppose to be big winners and big losers.
The influence of one of such a trade is substantial. A big loser or big winner can make a substantial difference to the final results of the trader.
But the question remains if how losers and winners effect each ohter.
We know the concept of Expectancy:
E = P / NP - L / NL = Average Win – Average Loss (1)
In words: The expectation of a trade is the total win divided by the total amount of losses minus the total loss divided by the total amount of losers.
The above formula has four variables, P the total win, L, the total loss, NP the amount of winners and NL the amount of losers. Each of them can be changed, e.g. decreasing the the total loss L, decreases the average loss and so increases the sum which is the expectation E.
But changing one variable in formula (1), changing one may affect the other ones. The variables are said to be dependant from each other. For a given system, changing the average loss may result in changing the average win too!
An example: a system produces 100 trades a year, 60 losers and 40 winners. When 5 potential losses could eventually turned into 5 winners by taking a bigger stop the average win will increase due to this, but you may also expect that the average loss will increase due to the bigger stop of the remaining 35 losses. We also could go for bigger winners just to see an increase in the amount of losses.
We don’t know at forehand what happens and the problem stays. Suppose there is a function G with parameters NP, NL, P, L so that :
G (NP,NL,P,L).
G produces some results in terms of expectation E:
G (NP,NL,P,L) = f (E) (2)
I did some effort to investigate some general characteristics of formula (2). Though not being very being successfully it helped me I a better understanding the concept of a trading system, in fact a way of producing losers and winners which outcomes are dependant to each other.
One of the strangest things I found was that the outcome of a system is changing over time and not seldom worsening, not necessarily in terms of expectation E or E per dollar risk but more in terms of other characteristics as drawdown, amount of losses and winners, runs of losses and winners and so on.
This is a fundamental (stochastic) principle everywhere probability plays such an important role.
posted by Carlo Giuntoni at 4:17 PM
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