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On the face of it, with a few compromises, it sounds like what you want isn't terribly difficult to acheive. I have cut a few corners and adapted the basic template algorithm.

If this is any help perhaps you can explain the underlying assertions of the strategy. How are the risk allocations arrived at? What is the preference of holding t-bills?

Thanks. As you say, this is not risk adjusted, (and without a stop loss) which means it's a fairly simple strategy. I believe there are methods exposed to the algorithm to calculate correlations on the fly, but when you're talking about long timescales, this is not particularly useful. What approach would you take if you wanted to convert this into a full risk parity strategy? Would you require an additional hedging strategy or are the risks already adequately hedged? I suppose if you hard code the risk allocations you would want to hedge against the correlations falling out of expectation.

As you’ll see I’ve diverged in a few areas from your description, and am fairly sure I’ve transposed your risk allocations. You should be able to tweak the main parameters you mentioned with the months and leverage variables. If you need to rebalance at a frequency less than annually or more than monthly, wider changes will be needed.

I'm intrigued by the consistent performance up until the middle of last year and am wondering what steps could be taken to avoid the drawdown.

I’ve been working on a project that allows genetic optimization of algorithm parameters. I ran this strategy in the optimizer and as it is low resolution with only two parameters, it decided an optimum fairly quickly:

• start: 2017-01-26 11:56:34Z
• end: 2017-01-26 12:14:54Z
• leverage: 2.0
• monthKey: 5 (rebalance every 12 months)
• sharpe: 1.007

I then extended the parameters to include asset allocations for the ETF’s. This was still a fairly simple solution which resulted in the following optimum:

• start: 2017-01-26 12:58:19Z
• end: 2017-01-26 13:14:35Z
• leverage: 2.1
• monthKey: 2 (rebalance every 2 months)
• tlt: 0.4
• spy: 0.6
• sharpe: 1.191

These results aren’t of great practical significance given the timescale of the test data (and some other caveats). You’ll notice that the GLD event last year contributes to an optimum excluding this asset. I’m wondering how these findings compare to your expectations based on macroeconomic or other factors?

I have rejigged this slightly for a longer timescale and required a minimum 10% of each etf. Rebalancing can now occur every n>7 days.  Slippage and fees will likely preclude rebalancing too frequently on IB, and this will be accounted for by the backtests.

When run through the optimizer, there was no way to survive 2008 without a margin call event. This means the trading engine may detect an imminent margin call and offload the culprit to bring the portfolio back within limit. Whether this would work in (freefall) live trading is anyone’s guess. The eventual optimum was found to be:

start: 2017-01-27 14:44:11Z

end: 2017-01-27 16:17:35Z

leverage: 2

days: 330

tlt: 0.3

spy: 0.4

sharpe: 0.618

To investigate the effect of higher leverage, I changed the default IB leverage to a maximum of x4. I also changed the start date to late 2004 as the Yahoo data does not extend any earlier for the GLD ETF. As results were nearing the 365 days rebalancing ceiling I increased the maximum, to encourage the optimizer to explore higher values. This is still using the default (optimistic) slippage, which will bias towards more frequent rebalancing. If you’d like to suggest a slippage estimation, this can be included in the model.

start: 2017-01-30 09:58:05Z

end: 2017-01-30 10:14:27Z

leverage: 3.8

days: 391

tlt: 0.3

spy: 0.5

sharpe: 0.84

Sure.