Working on your position sizing algorithm is an easy way to pimp an existing trading strategy. Today we have a look at an energy trading strategy and how the position sizing can influence the performance of the strategy.
The screenshot shows you the returns of the same trading strategy, trading the same markets, the same time frames and using the same parameters. The returns on the left side look nice, making money every year. The returns on the right side are somehow shaky, and you would have to love volatility of returns if you would think about trading this basket. The only difference between the basket on the right and on the left side is the position sizing.
The energy basket:
The basket trades German power, base and peak (yearly, quarterly, monthly), coal, gas, emissions. All instruments are traded on a daily and weekly time frame chart, using the same parameters. If the daily trading uses a 10-period parameter, the weekly trading would use a 10-week parameter. This limits the degrees of freedom I have when doing the strategy-time frame-parameter merge, thus minimizing the curve fitting trap.
The strategy which generates the buy and sell signals is quite simple, a classic trend following strategy described in my book and similar to the bitcoin trading strategy described within this blog. Go with the trend, buy the high, short the low. Not rocket science but a solid trend following approach.
The position sizing:
The basket on the right shows you the performance of the strategy when you would invest the same amount of money in every trade. This usually is a nice approach when trading stocks which have a somehow similar volatility, but it seems to cause problems when trading commodities. Emissions have a higher volatility than API2…
The basket on the right will not invest the same amount of money in every trade, it will risk the same amount of money with every trade. This basically eliminates the influence of volatility, as commodities with high volatility just will invest less. At this stage it is important NOT to use value at risk or any other time-series based risk measurement. You will have to calculate the actual risk for the trade, meaning the difference between the entry point and the nearest exit point. If you just use VAR, which basically is just a deviation of a standard deviation calculation, the risk information will always be not up to date and brng you all the problems which delayed information causes in trading.
Don`t try to find the perfect entry and exit points, work on your risk and how you weight the different markets. As long as you are long when the market goes up and do not invest all you got in one trade, you should survive. If you use a wise combination of strategy – position sizing and market selection, the gods will sprinkle gold on your path.