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It depends on how you define “crashing and burning.” If you define crashing and burning as a 50% drawdown, based on my calculations from the volatility of the system’s trade data on C2 (which is optimistic because it presumes trade losses no bigger than you’ve already had), it would take around 23 months on average to run into a 50% drawdown. A 40% drawdown would take on average about 7 months.
Note that this is just from the current volatility of your system and presumes it keeps performing as well as it currently has. This is what anyone trading your system should expect. However what is also likely is the trade data on C2 isn’t yet representative, you’ll get a few bad trades and hit a worse drawdown than 50% even sooner than the current data predicts. That’s just the reality of trading something that can gain/lose so much so much so quickly.
A few times… try about every year. You are crazy if you trade that system IMO. The odds of the system hitting a 50% drawdown while you are in losses are about 50% by my calculations. Odds on the system hitting a 20% DD while you are in losses are 75% or so. Do you think you’d quit if the system took a 20% DD and you were in losses? Because odds are 75% for that happening by my calcs. As a result most people are likely to give up on that system with losses before they bank anything significant IMO. But feel free to try–it certainly can make a lot of money quickly.
I did not take into account your stop losses… however in general stop losses make drawdowns more common and worse in my experience. I can’t speak to your system in particular though.
Good point. I just wanted to note that my system doesn’t bias, or fit itself like most. I haven’t counted them (and I didn’t see a C2 statistic for it) but I suspect my trades during this “bull” period will be near 50/50 Long and Short. Correct me if I’m wrong.
CkNN Algo doesn’t ‘relax’ during trends. It trades hard in trends and in chop.
I would like to say that MachineLearningTradr’s system doesn’t seem to be martingale like some other high return systems on C2, so if I was going to risk some small amount of capital on a high risk system (banking gains as it went along against future DD), I’d consider his as a good candidate. (Though I’d probably want to see more data come through… see how it does when gold is not doing as well).
Imagine two bad systems. The same. Except one is scaled down, and the other is not. Obviously, the bad system that is scaled down will outlast the same bad system that is not.
As a vendor here is another problem I have seen constantly: You have a robust tested strategy with a ~30% drawdown in history in exchange for a strong return overall. You mention these stats in the description. Subscribers join in heavy numbers as the strategy is zipping to new highs. As the inevitable drawdown occurs, they begin to exit. The day the last subscriber punts the system, it experiences the equity low for the move. It takes a week or so after this event, but then the “free trial” subscribers start to watch again.
C2 subscribers want high returns without drawdowns. Perhaps there is some guru who can post such performance, but they are unknown on C2 as of yet. The ideal systems that post a few basis points better than stock indexes + dividends and avoid the bear periods are simple…yet are ignored. Who cares about a few basis points when 300% can be made with only 87% drawdown
Frankly it is a bit of a bummer for the subscriber. I think there are quite a number of viable strategies on C2. Many have poor money management. Quite a few probably using a faulty strategy. But if the subsriber cannot sustain the drawdown, then they are allocating too much of their capital to the strategy. So high flyers will probably crash, but it is the subscriber who lacks the ability to do the homework and due diligence before subscribing that is the true problem.
I completely agree and can confirm that most subscribers jump off at the first sign of a drawdown and rejump on as soon as it goes up again. Investors do the same on stocks or any asset class so to speak.
Additionally many subscribers are much more concerned about the subscription fee instead of the potential loss in their trading account. This point I yet need to understand.
If you don’t mind, how specifically did you calculate this without knowing how/why my system trades?
For example, old trades may indicate that my system is in nugt 50% of the time; but without knowing why it is in either nugt or dust on a particular day would make it purely guess-work to speculate whether it be wrong enough, fast enough, to result in a 50% draw down. More on that later.
I ask for obvious reasons; but also because I will be responding to your and some of the other interesting posts relating to systems with high gains.
To evalute potential drawdowns, returns, and other aspects of systems on C2 I use daily system equity values available from Collective2 and monte carlo simulation. Using the daily changes in system equity I can see what any trading system has done on a daily basis without regard for what and how it actually trades. Putting these daily equity changes into a distribution curve allows me to model how the system has performed, and presuming the distribution is representitive of system trading, allows me to make predictions into the future using monte carlo simulation.
After a distribution of daily returns is created for a system, the software randomly samples the distribution to create thousands of simulated years of trading each with the common property of having resulted from the the same distribution of daily returns as the original system. The result is thousands of simulated potential “trading years” which can then be sorted and examined to draw predictions about possible behavior of the trading system.
Thanks. That’s what I figured, but didn’t want to assume.
The flaw in such an exercise is that it does not actually trade as the system would actually trade. Each simulation assumes random trades, but with the same distribution. However, my system doesn’t make such random trades. It would trade the same, over the same data, during the same time in history.
I realize this methodology has significant limitations, however it’s objective and I find it useful, especailly in getting an idea for potential drawdowns from systems that don’t have drawdowns representative of their volatility yet.
For some systems, it can take a few years before a max DD representative of its volatility is hit. I don’t want to wait that long. Monte carlo simulation gives me an idea of drawdowns that are completely reasonable and expected for a system given its volatility, even remaining at the same levels of profitability.
My experience is a system will eventually make drawdowns representative of its volatility. I’ve never run into an exception yet.
My software suggests to not be surprised by a -55% DD on your system. That’s probably conservative given how young the system is. If you look at how much your system can make it seems reasonable. High returns have risks.
Lol…There’s always a couple in every internet forum.
Don’t hold your breath.
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