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One of the characteristics that deter traders from using Trend Following is the typical lower winning percentage rate i. Psychologically, it is harder to trade a system that produces more losing trades. Despite this, Trend Following is a profitable strategy. An aspect of robust systems is the volatility in the system results and its equity curve. Here is a quote from David Druz — a recent addition to the Trend Following Wizards report — which explains the link between robustness and volatility: The robustness of a trading system is proportional to its volatility.
This is the no-free-lunch part. A robust system is one which works and is stable over many types of market conditions and over many timeframes.
It works in German Bund futures and it works in Wheat. It works when tested over or over Robust systems tend to be designed around successful trading tactics, classical money management techniques, and universal principles of market behavior.
These systems are not designed around specific types of markets or market action. And here is the amazing thing about robust systems: The more robust a system, the more volatile it tends to be!
This is because robust systems are not optimized to particular markets or market conditions. The converse is also true. You can design systems with excellent returns and low volatility on historical testing, but which work only for given periods in given markets.
These systems tend to be curve-fit or market-fit and are not robust. For a system to have the highest odds of profitability over time and markets, the inescapable tradeoff is volatility. Diversification can be used of course, but it will only dampen the volatility so much.
Money Management is a pivotal part of a trading system. It can make or break any system, however good it is. Over-trading a really good system will still lose you money. This is probably a hyperbole, but the impact of money management should not be understated. Depending on preferences, there exists an optimal f which maximizes growth and which can take risk factors into account. The optimum approach is obviously to be as close as possible to the optimal f. However, this is not simple: It is not a fixed value.
From the start, we know that optimal f is bounded between 0 and 1 and that the further we are from it, the less optimal performance will be. Vince had an amusing analogy, comparing the optimal f to a roaming tiger on a football field. The tiger could be anywhere on the field; the hunting trader needs to find its location to place the tiger cage. Any error in locating the tiger would result in sub-optimal performance: However, there is a corollary from the Optimal f calculations: From a trading point of view, this means that there is less room for error in the location of the optimal leverage and therefore less impact of a sub-optimal leverage.
In terms of robustness, this means that as markets change, so will the optimal f. A trading system with a low winning percentage will reduce the possible variation in the optimal f e. Reducing the error should also reduce its negative impact on the system performance and make the system less sensitive to underlying market changes.
This is just a thought. I do not have any hard evidence or theory for it. As a young kid I always dreamt of having a tiger and riding it to school. Restricting the search space should help the optimization converge faster. You know what Josh? The problem Jez is not optimal f or whatever optimization used but the fact that for any win rate, there is a finite probability of ruin.
The real danger in using optimal f is that finite probability of ruin. Now, the LTCM story was that they changed their leverage and although correct about market direction they could not sustain a small adverse move and they were ruined. Thus, the graph you have must be adjusted for changing leverage in order to reflect the real story. It is like trading forex using That is the reason — the finite probability of ruin — that forces most traders to use a small fixed percent risk knowing that they are way sub-optimal.
The trade -off to consider is optimal growth versus risk of ruin. Only the Holy Grail has zero probability of ruin. Michael is spot on, you have to take into account leverage and LTCM was running something in the neighborhood of No money management scheme can make a system with negative expectancy profitable! With all due respect to Vince, optimal-f is a sure fire way to ruin.
I interpret this as, extremely high performance is due to luck not skill and please ignore all the traders who went broke.
You have to look at the entire distribution! Those who trade real money know how easy it is to get 4 to 5 consecutive losers during periods of uncertainty or turmoil. Using optimal methods just for allocation is a different story but proper use depends on objectives. In trend-following, for example, which was my first and very successful style of trading, the objective should be to open positions with the smallest possible amount of money and then accumulate slowly as price goes in your direction.
Trends are preceded and followed by choppy periods where drawdown increases. There is no way anyone in the right state of mind would allocate significant amounts of money to a trading system that is experiencing a choppy market period for the sake of future optimal growth. Before the future comes, the present will kill your system. This is what my experience of trading the markets for the last 25 years has shown. I am sure Jez knows of these issues and will do some more of his impressive math studies to investigate them.
Note that optimal f is not necessarily the leverage level that maximizes equity growth with the risk of a higher risk of ruin. You can check my recent post on this, where I explained this concept. Anyway, I just like to poke fun at them as they were a big failure despite all their Nobel credentials and EMH academic background. Directional accuracy is not as important as it is touted.
It is a daily MR like method. It was very successful in the last 5 years you probably know. I thought tell you that a good MR system like DV2 accuracy is not high either. To me, the directional accuracy is not really important. IMO a low winning percentage of a trend-following system may eventually lead to ruin. This is because the probability of getting a number of sequences of trades — not necessarily all losing — that can cause a large drawdown increases non-linearly as the win rate decreases.
Even worse, the possibility of no trend during an extended period of time or even an early exit makes things much worse. You can see that from the simple equation I have derived in this paper:.
Implications for trend-following systems are discussed on page 5. It is clear that a given winning rate guarantees profitability only if future trends have at least the same magnitude as past trends.
Many overlook this simple realization and face ruin. As for LTCM there were several factors that led to the demise. One problem was they decided to return money to investors b. Problem was as Keynes said the market can stay irregular for much longer than you can stay solvent.
Another problem was the banks, Goldman etc. Knew their positions after getting to take a peek at them with the help of the Fed. I assert, without offering a shred of proof, that the robustness of a trading system is proportional to is volatility.
I assert, without offering a shred of proof, that for a system to have the highest odds of profitability over time and markets, the inescapable tradeoff is volatility. In fact it may be quite enlightening to ruminate about HOW one might either prove these assertions, or at least amass a weighty body of confirmatory evidence.
Your introspections may force you to decide that the assertions are ultimately not provable, and must simply be accepted as articles of faith, rather than rational conclusions based upon analysis of experimental measurements.
A small and insignificant side-branch of the ruminations might be: For example if we assume we are trading a trend following strategy with 2 static parameters, then we could assert that this system does not have enough degrees of freedom to closely follow a market for any long length of time. Therefore if close market following in terms of predictive power is observed then based on the assumptions we have to conclude that in other market environments there will have to be little or no market following power.
If we want to consider complex trading systems that change behavior dynamically based on how indicators are performing across multiple markets, then the assumption about fewer degrees of freedom than the under-lying may not hold any longer. An example of over-prescribed system might be if there are many more non-linear parameters than market participants or number of shares traded in the market.
In economics, robustness defines the ability of a financial trading system to remain effective under different markets and different market conditions, or the ability of an economic model to remain valid under different assumptions, parameters and initial conditions.
As you and Jousha pointet out this could possibly be used to speed up the optimization. Maybe they read your post the wrong way? And if I misunderstood the mood in their comments, pardon me! Trey — most professionals I know have robustness well defined mathematically. Actually it can only be observed after the fact.
Robustness is also like performance, defining which CTA is the best is not that straight forward.