Hello. Can you more say over "Portfolio-Collator"?
When you compile a large collection of strategies across a broad range of markets and timeframes you will find that each strategy possesses a unique risk-weighting. For example Strategy A may have a compound annual growth rate of say 6% and a maximum drawdown of 12%. Strategy B may have a compound annual growth rate of 2% and a Maximum drawdown of 3% etc etc etc.
The variation between the return/risk profile will be significant and this is attributed to the standard positon sizing applied of say 0.1 lot as opposed to the risk characteristics of the strategy itself.
If you simply compile these strategies into a portfolio with no further global treatment then some strategies will dominate the overall performance of the portfolio and some strategies will unduly bias the under-performance as well. You will find that you will be very limited in your ability to scale this up to make the most of your finite capital.
To remove this bias associated with standard position sizing, you need to standardise the risk weighting of each strategy in the portfolio.
To do this you adopt a standard drawdown for each strategy in the collection and apply a multiplier to the CAGR which is then used to as the multiplier for your position sizing.
For example we have 2 separate strategies in a hypothetical portfolio.
Strategy A - Position Size 0.1 Lots CAGR 6% Max Draw 12%
Strategy B - Position Size 0.1 Lots CAGR 2% Max Draw 3%
Assume a 10% Max Draw for both strategies
Strategy A = 10/12 * 0.1 Lots = 0.08 Lots CAGR 5% Max Draw 10%
Strategy B = 10/3 x 0.1 Lots = 0.33 Lots CAGR 6.7% Max Draw 10%
Adjust the position size accordingly so now you have equal risk weighted strategies where any original bias from the prior position sizing impact is removed.
This is the first level of treatment.
I have attached a graphic of the difference between a raw portfolio of 50 strategies and a risk weighted one. Have a look at the overall portfolio results of CAGR and Max Draw to see this principle in action.
Thereafter for further portfolio treatment you look at methods to reduce the adverse correlated impacts of the collection. Not all correlation is bad. You only need to take care of unfavourable correlation. It is a lot like leverage. There can be favorable leverage and unfavourable leverage. You need to compare the equity curves of your different strategies to eliminate the weaknesses at the portfolio level.
Once this is all done, then you can scale up with position sizing at the 'global' portfolio level to bring home the bacon.
Now refer back to the process we just took. You will note that as the drawdowns of different strategies get larger, the position sizing applied to that strategy decreases. This is how you manage a portfolio of strategies without having to make a decision to drop a strategy if it is under-performing. The risk weighting treatment will do that itself. I risk weight every 6 months or so and continuously add strategies to the collection. In a way it is very much like applying the 'theory of natural selection' to your data mined strategies without having to allow for discretionary decision making in portfolio adjustment. Your portfolio progressively builds and its composition favorably adjusts to emerging market conditions. provided that you continuously data mine to include the new data sets.
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Diversification and risk-weighted returns is what this game is about