Bedankt !
Bedankt !
Here's the IWM data ...
Some notes :
- The first few trades are missing because I don't have the initial stop loss data.
- Transaction costs are not taken into account.
- Data is collected from various sources. I cannot garantee there are no mistakes in it.
For each trade I've calculated the R value. R is a measure of risk versus reward. It indicates how much you've gained or lost on the trade compared to how much initial risk you had on the trade. For example a trade with an end result of 2 R made twice as much as you risked on the trade. More info on this concept here : http://www.iitm.com/sm-risk-and-r-multiples.htm
Next I've calculated some KPI's based on these R values that give an overview of the system.
Expectancy shows us the average R value of all trades. In other words what to expect from a new trade based on the history. More info on this concept here : http://www.iitm.com/sm-Expectancy.htm
The standard deviation shows us how much a trade on average deviates from the expectancy. The lower this number the more consistent a sytem is considered to be.
SQN (System Quality Number) is a proprietary measure of the quality of a trading system as developed by Dr. Van Tharp. SQN measures the relationship between the mean (expectancy) and the standard deviation of the R-multiple distribution generated by a trading system. The better the SQN, the easier it is to use various position sizing strategies to meet one’s objectives.
I started this exercise to have a look at what position sizing strategy would be appropriate for the IWM robot. Based on the trade history we have to see the reality that the expectancy is negative which also translates into a negative SQN. Tharp does not advise to trade systems with a SQN below 1 and so there are no position sizing strategies or guidelines.
I sincerely hope the IWM robot evolves into a great system. For the moment I have to consider to stop trading the robot or use a minimal position sizing. I'll continue to monitor the statistics going foreward and will re-evaluate when there is an improvement.
Thank you for this analysis Rembert.Originally Posted by Rembert
Here's the IWM data ...
Some notes :
- The first few trades are missing because I don't have the initial stop loss data.
- Transaction costs are not taken into account.
- Data is collected from various sources. I cannot garantee there are no mistakes in it.
For each trade I've calculated the R value. R is a measure of risk versus reward. It indicates how much you've gained or lost on the trade compared to how much initial risk you had on the trade. For example a trade with an end result of 2 R made twice as much as you risked on the trade. More info on this concept here : http://www.iitm.com/sm-risk-and-r-multiples.htm
Next I've calculated some KPI's based on these R values that give an overview of the system.
Expectancy shows us the average R value of all trades. In other words what to expect from a new trade based on the history. More info on this concept here : http://www.iitm.com/sm-Expectancy.htm
The standard deviation shows us how much a trade on average deviates from the expectancy. The lower this number the more consistent a sytem is considered to be.
SQN (System Quality Number) is a proprietary measure of the quality of a trading system as developed by Dr. Van Tharp. SQN measures the relationship between the mean (expectancy) and the standard deviation of the R-multiple distribution generated by a trading system. The better the SQN, the easier it is to use various position sizing strategies to meet one’s objectives.
I started this exercise to have a look at what position sizing strategy would be appropriate for the IWM robot. Based on the trade history we have to see the reality that the expectancy is negative which also translates into a negative SQN. Tharp does not advise to trade systems with a SQN below 1 and so there are no position sizing strategies or guidelines.
I sincerely hope the IWM robot evolves into a great system. For the moment I have to consider to stop trading the robot or use a minimal position sizing. I'll continue to monitor the statistics going foreward and will re-evaluate when there is an improvement.
We have all noted for some time that the IWM Robot was not performing well.
No need to make a sophisticated analysis.
I have adapted the 20DMF direction model and the LT/ST edges in order to see if this situation can somehow improve.
We will give it a few more months to decide on what to do. By July/August we will have a one year experience and then we will see if it is worthwhile to continue or not. We might switch to the simpler MF RT models, with no specific entries/exists, but nothing has been decided.
Pascal
I'm doing this analysis for all the systems I have in use, including my disc trading to have a better understanding of them in general. And for position sizing.We have all noted for some time that the IWM Robot was not performing well. No need to make a sophisticated analysis.
I appreciate your efforts to improve the robot. Let's hope it makes an impact. No doubt the 20DMF protection so it doesn't get stuck in the wrong mode is an important one.I have adapted the 20DMF direction model and the LT/ST edges in order to see if this situation can somehow improve.
Rembert, thank you for posting, very informative.
I am wondering if you performed a similar analysis for the GDX Robot?
Not yet for GDX. Next week probably.
For your ease, I attach a file with the GDX trade records.
Since inception, the EOD GDX Robot produced a return of 13.98%.
The EOD combined to the RT GDX MF with all the signals produced 25.10%.
The EOD combined to the RT GDX MF with only the strong signals produced 32.33%.
These results are not exceptional, but they are not catastrophic either (Buy and hold produced a return of -28.06%.)
This trading environment is very challenging to say the least.
Pascal
GDX_record.xls
I believe that it is important to see how the model operates in today's environment.
Below is a MF figure for the past 500 days. Please focus on the distance between signals. You can see that on the right of the Figure, we have had many signals issued: the model is whipsawing. This tells us that the model does not react well in this environment when cheaper PM miners attract more money because of a "safe heaven" feeling of gold related investments, but then goes down fast the next day when gold reacts negatively to us US$ bounce.
I believe that the model will work again in a more appropriate environment. However, today's environment is what we are faced with (Tuning the model to use longer time frame will fix the most recent whipsaws, but will destroy past performance.)
Turning to the RT model, we can see even more whipsaws. The model avoids us being on the wrong side of the trade, but the many whipsaws render an execution almost impossible, even for me. We also all know that whipsaws can kill a portfolio, especially when one uses leverage.
When we look at the average returns of each normal "Buy" trades from the GDX MF, we can see that for the first days of trading, the drawdown is important, while the return is almost nil. The figure shown below tells to "give room to the trade." However, the RT model works on the opposite of that principle: it says "change the position because there is a MF directional change". Since December 22, the EOD strategy produced a loss of 4%, while the RT strategy produced a gain a little higher than 9%, but with 2.5 times more trades, whose execution itself might be an issue.
When we now look at the Bought Oversold signals, it is clear that these are better signals, with quicker payback and lower DD. However, we did not have such a signal since Mid of last year. A pretty safe strategy, but hardly a strategy that meets the need of an "active trader" like me.
This is the reason why I am sticking to my strategy explained earlier: at each normal buy signal, I buy a 2014 leap using 10% of a normal position. This allows to "give room to the trade," while limiting the risk to a small position. The danger is that if the market continues to whipsaw, I would be forced to buy new leaps at every (cheaper) buy signal and end up building a losing position which could stay against me if the sector continues to be negative for another 18 months, which I doubt it will.
Yesterday, while I was not at my desk, the RT system issued a buy signal. Maybe time for another leap, if I can it cheap today.
As a last word: you might have noted that most XLX models produce better trades when the signal is "Buy Oversold".
One strategy could be to Buy one position on a buy oversold signal of an XLX ETF and buy two positions when this signal is in the same direction as the 20DMF, but trade short only when the 20DMF issues a short signal.
For such a strategy, an E-mail alert system in RT seems appropriate, with a confirming e-mail 20 minutes before the close.
Anyway, something to think about.
Pascal
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