In the same vein as yesterday's FAS analysis, here is a low drawdown trading strategy for TNA, with daily intervention. Prasad had asked me to search for low drawdown strategies for a few of the triple leveraged ETFs, this being one of them. As with FAS, the algorithm was found by optimizing the SignalSolver backtest engine for drawdown (100% weighting), with a min QA return weight of 50% thrown in to get rid of all the zero trades-zero return hits. Period of the analysis was 1.9 years.

Again, the result had a low time in the market (18%), low drawdown (4.9%) and an annualized return which, while modest (38%), was better than the underlying ETF.

Andrew

This is one of the triple leveraged funds which Prasad had asked me to come up with a low drawdown trading strategy for,  I'll be looking at the others soon. You can set up SignalSolver to optimize for low drawdown. The way you do that is by setting up the Figure of Merit parameters to look for low drawdown, however you need to fold return into the picture, otherwise it simply comes up with algorithms that don't trade and have zero drawdown. In this instance I set the FOM parameters to be Total Return (25%), drawdown (100%) and Min Quartus Return of (25%). The data is divided into four quartus's, so including Min Quartus Return gives more consistent returns than just including Total Return. 

A few things to note about this result. The drawdown (the worst case loss you would have suffered if you had entered and exited the strategy once) was 4.5% compared with the underlying stock's drawdown of 24%, and the total return beat the return of the underlying stock (56.7% annualized vs. 41.7%).  Its a long-only strategy. If you had shorted instead of exiting, the drawdown was close to 30%. The trading strategy was also very efficient, that is, the returns were good given the small amount of time (20%) you were in the market. If you could have invested at the same rate of return during the 'out' periods, you would be talking about a 288% annualized return (a figure I call Efficiency).

The trading strategy didn't trade frequently, nor was it particularly consistent in its results, as you can see from the last two graphs below. Also, its not particularly stable with respect to the buy and sell point percentages as you can see from the surface plot. None of these factors suggest a strategy worth pursuing--its really just a curiosity, a demonstration that you can find low drawdown strategies that worked if you look for them.  

One last point of clarification, the buy signal is a negative percentage (-3.74%) with respect to the reference (the 50 day simple MA). If you were waiting for a buy signal and the 50 day SMA at the previous daily close was at $100, then the buy signal would occur if the stock price failed to rise above $96.26, assuming no sell signal. Buying was done at the subsequent open, selling at the close on the day of the sell signal.

Andrew

Post has been corrected 1-2-15 for an error in short-hold calculation.

The algorithm peaked at the end of Aug 2015:

Since it peaked at the end of Aug 2015 the algorithm has not recovered:

NUGT Trading Strategy (Daily) Update 10-20-2016

This Palo Alto Networks PANW trading strategy would have given a 119% annualized return. Here, I chose to show only the long side of the algorithm because its more impressive than the short side (which only gave 27% annualized return). To appreciate the algorithm more, notice that the efficiency of the algorithm was close to 200%. Efficiency (as we define it) is the annualized return divided by the percentage of time you were in the market. Its the return you would have received if you had realized the same return all the time as you had got while you were in the market. The theory is that since you are not in the market all the time, you could have invested the money elsewhere.

Efficiency only applies to long or short style algorithms, since if you were running both long and short (L&S) you are in the market 100% and efficiency equals annualized return.

Updated 1-2-2016 to correct an error in the short-hold returns.

Update Aug 28th 2015:

Algorithm has had better return, efficiency and drawdown than Buy/Hold, but not consistently. Optimum buy point dropped to 1.25% (which would have given a return of $4,316)

Algorithm continues to have better return, efficiency and drawdown than Buy/Hold. Buy-hold lost 12% annualized, this strategy made 28.7% annualized. Efficiency (55%) is what the strategy would have made annualized, had the money been invested at the same rate while the strategy was out of the market.

PANW Trading Strategy Performance since publication as of Oct 10th 2016

Here is a view of the performance since original publication in March 2015:

PANW Trading Strategy, performance since initial publication, 60% gain vs 12.3% for buy-hold.

This algorithm was not a stellar performer. Since publication it has been essentially flat, let down badly by the short-side performance

To be fair, the long side performance was reasonable, but buy-hold was on a roll.

Optimum parameters for this period were different than the original posting, buy point -0.98%, sell point 10.33% for this result:

Again, long-side was much more interesting than short-side performance.

This algorithm continues to perform badly

MSFT Trading Strategy (Daily) update Oct 19th 2016