Steven,

I have a relatively simple strategy that I've been using that I can share. I wanted to find a way to ease in and out of bonds depending on how much the stock market is "humming." I had trouble finding momentum indicators based on the S&P 500 alone though because they were binary signals. So instead I started looking at sector trends to help give an indicator of the underlying strength of the overall market.

Here's the basis of the strategy: calculate the 20-to-200 day simple moving average ratio of all 10 sectors (don't include telecom, but include REITs). Per sector that has their 20 day below the 200 day increase the bond allocation until it hits a user defined max bond holding. In the backtest attached, I have a minimum bond holding of 10%, maximum of 90% and a dynamic allocation step of 16% per "bearish" sector.

Within bonds I also look at momentum to help define which bond asset to hold. It looks at short term treasuries, long term treasuries, aggregate bonds, and TIPS. The 20-to-60 day moving average ratio is determined per bond and we hold the bond class that has the most momentum.

Rebalance this monthly, daily it looks to put cash to work in the appropriate asset class. The backtest only holds SPY for stocks, but you can add more ETFs in there if you'd like.

I'm happy sharing the above strategy, but I've been working on a "dynamic efficient asset allocation" model that's a bit more sophisticated...

It looks at the trailing 3 months to compute the most efficient combination of a large universe of passive index ETFs that stretch across many asset classes. Then it buys 12.5% to each asset (total of 8) each month, recomputes and rebalances the following month.

I wanted to see how effective my strategy was compared to some simple asset allocation models based off Vanguard's target date funds. So I created a few benchmarks that bought just that one asset (so it includes dividends), imported the performance of that algorithm, and compared different static allocations to the dynamic asset allocation model. The attached notebook goes through that comparison and also includes a full tear sheet of my strategy.

Let me know if you're interested in this model, I'm still trying to figure out the logistics of allowing others to use it without directly sharing the code - instead use fetcher() and make a publicly available CSV file. Some of the research I did to develop this strategy and universe of assets is detailed on my blog, with much more explanatory posts to come! https://engineeredportfolio.com/