Billy or Pascal,
For a fixed amount dedicated to robot trades, would you split this amount equally between the robots, and stick to that allocation, trading all primary signals? Alternatively and diversification benefits aside would you rotate out of positions that indicate Strong primary entries for a given robot, in an effort to seek better returns, for the robot account.
Dave
Dave, we are currently researching these optimal allocations scenarios. Pascal is doing intensive backtesting on this and he'll come soon with his conclusions.Originally Posted by davidallison@shaw.ca
In the meantime, there is no way to be sure that any choice is superior to another.
Billy
Hello Dave,
This is a very good question.
As Billy wrote it, I am now busy with the research, whihc could still last a few days, as more results bring more questions.
As of now, the basic findings are the following:
1. If you trade one instrument with high leverage (for example TNA/TZA) and do not mind the drawdown risk, then adding GDX will not help create better returns. In fact, GDX would even hurt the TNA/TZA returns.
2. If you use TWM/RWM (double leveraged), then a combination with GDX improves the returns.
3. If you use IWM and GDX then staying with only GDX is better. But then GDX is more volatile than IWM, which means larger drawdowns.
The principle of this test was to invest 100% of the capital in the first robot that issues a signal that is not neutral (Buy, strong buy, sell, strong sell). You then keep the trade unless one of the following event occurs:
- You hit a stop loss or you have a signal change
- The other robot issues a signal that is not neutral. In such a case, you sell 50% of the first position and invest 50% in the second robot.
The idea is to be 100% invested whenever there is a non neutral signal in one of the robot and to split between robots when the two robots have produced a signal.
I did not test the idea of switching 100% to the second robot from the first if the second robot signal is very strong, while the first turned to neutral. I believe that this could encourage over trading and would deny some benefits of trading two instruments that are lightly correlated. However, this is just an opinion. Testing might reveal that this opinion is groundless.
To be honest, what I like best in the case number two (TWM/RWM and GDX) is that the equity curve is very very smooth. I will not post it now, because I need to check some things and then do more tests, but as I am much a "risk averse" person, that rings a bell somewhere in my trading guts.
Pascal
Last edited by Pascal; 06-20-2011 at 04:27 AM.
Pascal,
Do you have any significant indication yet about which allocation is providing the best long term risk-adjusted returns between 100% of the time only in TNA/TZA (option 1) and a combination of TWM/RWM + GDX (option2)?
Billy
Not yet, but when you have an instrument with such strong returns as TNA/TZA, switching 50% out of that instrument into an instrument that produces only half of the return cannot help the performance (drawdowns/risks put aside). Also, the definition of a good risk/reward balance is different for each investor.
So, as I wrote, new research results bring more questions, which lead to more research and so on.
I plan to wrap what I did up to now, write a report and then do more work on the pending issues.
Pascal
Yet another option may be to try achieving better returns w/ the GDX robot by using the leveraged ETFs NUGT and DUST. That said, GDX is already quite jumpy, so the result may be too volatile for most investors. Also, they haven't been around much, so any backtest is going to be hard. Thanks,
Max
Last edited by Maxime A.; 06-20-2011 at 07:14 AM.
Just my oppinion but I think it is best to initially take leveraged ETF's out of the equation when searching for the optimal use for a given amount of capital.
Otherwise the test will probably indicate it's best go with TNA/TZA only for maximum gain.
The problem I see with that is that this is based on historical data. The maximum drawdown of any system lies in the future and is unknown.
Risk of ruin is probably quite high going all in on TNA/TZA even tough historical tests show it maximizes profit.
Instead of trying to maximize gain it might be better to maximze the MAR ratio. (MAR = CAGR / Max DD) This way, risk is taken into account.
My guess is diversification in systems leads to a higher MAR ratio and to a smoother equity curve. I believe that it is safer to use leverage on a diverse portfolio of systems (either by margin or leveraged ETF's) than it is to do so on a single system, no matter how great historical backtest says that system is.
GGT Forum Manager
Completely concur with Ray's comment. If I may add some color ...
Risk adjusted positioning increases the work we must put into our trading activities. It basically throws out the concept of "I'll own a maximum of 10 positions and my portfolio is Y, so each position is Y/10". While this approach certainly works, it is far less than optimum as the number of positions gets less and less. As M&K, O'Neil, et al. advise, fewer positions is best. If you believe this, then the impact of MDD is crucial as the drawdown contains more of your capital. Unfortunately, most individuals who counsel a low number of holdings rarely address risk-adjusted position sizing, because it's not a Finance 101 concept.
The obvious question is how to correctly position size. The not-so-obvious answer is dependent upon:
1) the expected gain of the securities in the entire portfolio
2) the correlation of the securities in the entire portfolio
3) the variance of the individual securities in the entire portfolio
4) the standard deviation of the prices of the individual securities
5) the allocation of capital between the different components of the individual securities
A very good starting point on understanding these concepts can be found in the following PowerPoint presentation. I've zipped it because ".ppt" is not an available upload option.
mathematicsofDiversification-ch05.zip
The key concept here, which directly supports Ray's assertions, are that we start the optimization process by constructing a portfolio with a minimization of variance in mind. Here, variance is equated to drawdown. Hence, for a given set of equities with quantified gains as well as standard deviation of the price series, we can form a portfolio which reduces variance while increasing gain.
The PowerPoint shows how to do it for a two-security portfolio -- doing it for more (which obviously is practical) requires more work and further requires that you know how to use Excel. The outline of how to do this for N-portfolios is also in the PowerPoint, and relies heavily on the work of Simon Benninga. The book has a disk with macros and worksheets ready to go, but of course, you have to understand some of the basic concepts shown in the PowerPoint in order to get anything meaningful out of the book.
Further supporting Ray's assertion that we should not use leveraged instruments in our optimization process is this paper:
1st_Place_Tony_Cooper_abstract.pdf
This is a heavier read, but pay attention to Figures 1 and 2, which give you some framework around the "optimum" leverage levels for the general market over various periods in the past. This paper is relevant because leveraged instruments multiply BOTH volatility as well as gains, resulting in the same behavior as their non-leveraged counterparts during the optimization process (I ignore the impact of the daily rebalancing of leveraged daily ETFs, which is adverse over the longer term).
Finally, the approach in the book and the included paper above do not address in a concise manner that volatility changes with the time frame being measured; they use standard lookback periods and take the volatility as equal-weighted over the lookback period. This is not a good approach in real life, so this last paper is a (heavy) introduction on using a moving average concept to have a better volatility estimate:
TD4ePt_2-StatisticsOfFinancialMarketReturns.pdf
Riskmetrics pioneered research on risk management, and I've not found a better reference.
If you've made it this far and you have a basic understanding of the concepts of volatility, jump to chapter 5, specifically Table 5.7 onward, to get a view on how the exponentially weighted moving average (EWMA) model is used in the forecast of returns and variances.
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Disclaimer: This is all a work in progress, and I'm still learning, learning, learning. Portfolio/position sizing is HARD, which is why most people (including me) simply do the Y/10 per position and are done with it. The concept of minimal-variance portfolio construction is hard to achieve, yet maintain in a practical fashion, but this shouldn't make you throw your hands up and say "it's not worth it". It's clear it *is* worth it, especially if you are happy with your holdings and have the knowledge that you're closer to maximum efficiency in gains and reduction in drawdown than you'd ever be with a Y/10 approach.
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