I have re-read this thread and have some final comments. To me position/risk management can be grouped into:
1. Growth optimal position sizing under risk constraints and the assumption that your trading systems work (i.e. have an edge) and that future returns will be drawn from a distribution similar to the historical.
2. Scaling out of systems that stop working, i.e. where the edge disappears. The assumption is that the system fails slowly compared to the time it takes to scale down leverage (the reaction time of leverage control).
3. Worst case/catastrophe scenarios. These are not really related to if the trading system works or not, but more to having bad luck. Even trading systems that works well can take on positions that result in big losses (news events, disasters, etc)
Whereas LSP covers the first two it does not address the third (at least not directly). This was discussed in the beginning of the thread but no systematic approach was presented. We have a test, called âthe unlucky bastard testâ, that we run before deciding on limits on leverage levels. It tries to estimate tail risks for our stock trading systems. Input data is the historical daily close-to-close (or close-to-open) returns and the leverage/position sizes (these are kept unchanged during the period).
From these the result of the most adverse selection of stocks is calculated for each day. Moreover it calculates the proportion of the possible combinations of stocks that would violate a daily âruinâ condition. In the end we get a value for the largest daily loss during the period (e.g. 10 years) and the probability of avoiding ruin during the period given our leverage. This type of test will reduce the risk of using too high leverage due to instability of the returns distributions.
Hugin
1. Growth optimal position sizing under risk constraints and the assumption that your trading systems work (i.e. have an edge) and that future returns will be drawn from a distribution similar to the historical.
2. Scaling out of systems that stop working, i.e. where the edge disappears. The assumption is that the system fails slowly compared to the time it takes to scale down leverage (the reaction time of leverage control).
3. Worst case/catastrophe scenarios. These are not really related to if the trading system works or not, but more to having bad luck. Even trading systems that works well can take on positions that result in big losses (news events, disasters, etc)
Whereas LSP covers the first two it does not address the third (at least not directly). This was discussed in the beginning of the thread but no systematic approach was presented. We have a test, called âthe unlucky bastard testâ, that we run before deciding on limits on leverage levels. It tries to estimate tail risks for our stock trading systems. Input data is the historical daily close-to-close (or close-to-open) returns and the leverage/position sizes (these are kept unchanged during the period).
From these the result of the most adverse selection of stocks is calculated for each day. Moreover it calculates the proportion of the possible combinations of stocks that would violate a daily âruinâ condition. In the end we get a value for the largest daily loss during the period (e.g. 10 years) and the probability of avoiding ruin during the period given our leverage. This type of test will reduce the risk of using too high leverage due to instability of the returns distributions.
Hugin
