In just the same way that prior to Markowitz investment advisers performed this charade of matching the riskiness of a set of single names with the risk appetite of the customer, safe stocks to widows, racy high growth stocks to adventurous greedy investors with long time horizons, so to does Darst expect his potential customers to be willing to accept this cumbersome and sub-optimal selection process to satisfy. It doesn't. Just as with CAPM, one can satisfy the customer's risk profile simply by leveraging up (or down) on the market portfolio, likewise one ought to be able to sample strategies in the same way - having the full universe and weighting them with leverage according to their risk/return profile. This is indeed what the population of managers of 'fund of funds' do. They take investor capital, then, depending on how risk hungry they are, they avail themselves of more or less PB leverage on their collection of strategies, and reap a hedge fund average return. Strategies, like companies, can be born, can run to exhaustion, can be merged, delist, go bust. And like stocks, the space of strategies can be partitioned into its own sector or industry - just as GICS partitions the universe of stocks. And occasionally new 'strategy sectors' can be born (just as, eg real estate can become a new GICS sector).
How would this index of all strategies arrive at weightings? Probably by capital invested in the strategy. If this drove your allocation sizing, then you'd always get 'the market'. And just like with ETF providers who model the whole market by targeting a subset only of representative stocks, accepting a degree of tracking error, so too in theory with strategies. You could select representative institutions or canonical implementations of the main strategies, and this would serve you, to a sufficient degree of error, as a proxy for the market of strategies. These days, factor models allow you to get a handle on the factor exposure, so I think it ought to be possible to apply the same technology, given the right data, to the issue of constructing a portfolio of names which, in toto, sufficiently closely tracks the full investible market.
By sheer AUM allocated (the equivalent of market capital), I would suspect that long equities and long credit would simply dominate the weightings. Also each strategy has its own internal (eg asset based) leverage, so the concept of a singular leverage value which can be tweaked up and down needs to be revised. Some strategies are inherently more capital intensive, some less, and the meaning of 'increase leverage to get more risk' needs to be transformed into a series of leverage adjustments for each of the strategies. A further point is that one can also have in the equity world the concept of an equal weighted index, which typically gives you more risk and more return.
In practical terms, find the set of n ETFs which best represent the universe of all strategies. Find the weightings by invested market capital. Buy the basket. Lever up or down, depending on your risk profile. Re-balance on a very slow timescale. Unless of course you have a cycle based model for investment weightings. Perhaps cyclicity would be one of the 'factors' in a strategy factor analysis. What else might take the place of, say, fundamental factors?
I wouldn't put the strategy equivalent of market cap in there, since this is expressed in the weightings. Perhaps sensitivity to volatility, to equity, to credit, and to interest rates. Perhaps asset class exposure, country exposure, sector exposure and inherent leverage of strategy (gross exposure over capital allocated) and liquidity. These would be the areas I'd be looking to get strategy factor models out of. They also provide more or less well known hooks into modelling cycles (credit conditions predict economic contractions, the debt/equity relationship of corporates could see itself recapitulated in the relative allocation weights for debt and equity.
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