Should System Developers Have Multiple Systems

Hello,

From my research and reading, I have read that one trading systems do not work for all market conditions.

My primary focus is day trading systems (typical one trade per day less then 60 min timeframes). I am not interested in swing trading systems where hold trades for X period of days.

So far I have been developing one system what the thinking that the system will last forever and be profitable forever. From my research, I may be headed down the road path.

Questions:
1. Should a system trader have multiple trading systems for different market conditions?
2. If yes, do you recommend system traders keep all their systems running in sim mode to constantly evaluate the performance and turn on best performing system as needed?
3. Should a system trader develop skill for accessing when market conditions are changing and create one system adaptive for all market conditions.

The questions are from readings that no one system stay consistent profitable forever. Unless the systems is highly adaptive to market conditions.
I hope I make sense.

Thanks for your comments.

Check out this blokes views---very different approach http://www.longtermforex.com/

Thank you Iamfx for responding to me. I will read the link now.

1) yes, develop three systems and trade with the same big picture of the market.

2) yes, follow one simple trading methodology but trade that in different timeframes - day trading, swing trading and position trading.

3) yes, most important is to keep up to date with the changing market conditions which define the market type to trade.

this old article may help,

https://www.axitrader.com/uk/market-...x-market-types

there is a trend in every timeframe we see on our charts, know the differences in each timeframe and know the entries and exits...

Hi goodoboy

Great questions mate.

System diversification is a great way to manage the correlation between return streams. There are many different ways you can diversify to achieve improved risk-weighted returns including:
a) instrument diversification.....ideally across asset classes whereby you look for uncorrelated instruments that allow you to achieve higher risk-weighted returns than what is achievable with a single instrument;
b) timeframe diversification which also allows you to deploy uncorrelated return streams to optimise risk-return relationships; and
c) system diversification in which you deliberately construct your return streams to offer uncorrelated relationships.

Of the different methods described above system diversification offers additional benefits to that delivered by a) and b) in that you can actually design your uncorrelated return distributions to remain constant as opposed to leaving matters to prevailing market conditions. Where a) and b) become more problematic is that given the non-stationery conditions of a non-gaussian market, there is no guarantee that the correlation between return streams remain constant. What may be uncorrelated at one point in time, may become correlated in other periods when market conditions change.

The way I look at systems designed to attack particular market conditions is that "No single system can be profitable across all market conditions; however a blend of uncorrelated systems has the ability to address a suite of market conditions".

Now either you can deploy market condition filters to turn 'off' or 'on' your different systems to capitalise on conditions that respond to the strengths of each system....or alternatively you can develop very robust systems that outperform when conditions are favorable and manage capital stringently when conditions are unfavorable....and therefore keep each system turned on all the time. I prefer the latter technique as the deployment of market condition filters are inevitably lagging in nature and when you turn on your system, you may find that market conditions quickly turn unfavorable and there is a lag before you turn them off.

If you prefer the former 'market condition filter' approach, then one way to achieve this is to run live walk forward systems in demo mode in tandem with your live systems and when the demo accounts are entering drawdown, then turn off your live system trader until we see a reversal of drawdown fortunes where you then turn the live system on again.....but despite the theory you may be a bit disappointed with the outcome (given the lags described above).

In designing your portfolio of systems it can be helpful to think in terms of the following:
1. Classify your systems into two broad categories....whether they are divergent systems (capitalise on trending periods), or whether they are mean reverting systems. Obviously these two broad system types are uncorrelated or anti correlated to each other. When one system is performing well the other is floundering and vice versa.
2. Determine which system you will use as your major bread-winner. This should be the system with the best risk-weighted performance metrics and have positive expectation. I tend to find the safest bet is to ensure the major bread winner is a divergent system with high positive skew (eg. many small losses with occasional large wins). This ensures that your total portfolio when constructed also is dominated by positive skew which assists in robustness tests.
3. Select a system from the alternative category which also has positive expectancy which is un-correlated or anti-correlated with the primary system and adopt a lower allocation weighting to ensure that this additional system supplements as opposed to dominates the mix.

This post gives an idea of what I am on about.

Cheers

C

Thank you emmanuel7788 for the response.

I update my initial post with more comments on my view of how I see the markets and trade.

I only develop systems for day trading. Does your comments still apply for different systems even if developer on seek edges in day trading?


Just to make sure I understand: 1) its important to keep up with and define market conditions even if day trading 2) equally important to match a trading system with the right market condition?

Otherwise, trade one system and suffer drawdown when the edge of the system does not match the market condition that best fit the system?

Thank you for response.

Thank you Copernicus for responding to me.

My initial plan was to develop a trading system (1) that would produce income forever. But as I read and study more, develop and back test more, I see, there is times when the edge produces money in back test and times when it does not. And overall, program and back many (20 so far) intra-day systems from 2006-2011 (in sample) and none of them are profitable unless really over optimize all the parameters. I am just thinking, is it something I am missing or not thinking clearly.

I will respond to your comments below and appreciate your response.

What is ununcorrelated systems mean?

Is an example of uncorrelated systems is System A that runs seeking to trade market trading in a range (counter trend). System B that runs seeking to trade trending (instrument trends in one direction throughout the day) market days. System C that runs seeking to trade momentum setups.


I prefer these type of systems as I believe a system should run perform similar how it performed in back test. And the odds in my favor if I take every trade. From the back test, I should know when my system will perform good and when it will perform bad. I rather take every trade.

Could an example of two broad categories be: Systems for trending market conditions and systems for range bound market conditions?

Thank you

Yep mate I hear you. What is essential is the relationship between how your system performs over a broad variety of different market conditions.

Any type of trending strategy is likely to perform well when the market is trending....but likely to flounder and enter drawdown phase when market conditions are non-trending.

Non trending markets can be broadly categorised into markets that do not diverge away from a mean but rather converge towards a mean. For example what some call counter-trend strategies may fall into this category or those strategies that operate on the principle that an overextended market will tend to converge back to a non-stationery mean (eg. sideways congesting markets etc.)

In backtesting....contrary to what many might think.....it is not actually the sample size that is the most important factor as market conditions can remain in place for indeterminate periods of time. What is the most important consideration in your testing is the degree to which your system performs over the greatest variety of different market conditions. This is a better statement of system robustness that using a defined sample size. Of course sample size is a proxy for this as the greater the number, the more chance you encounter a greater variety of different market conditions but it is no guarantee....so using a fixed number like a backtest of 200 trades etc to be representative of a more robust performer is a bit of a misnomer. If your system is a high frequency trader then that sample size can potentially arise in a single market condition. In testing your system under an array of different market conditions.....then the longer the test the better. There is no fixed number of assuredness.

The way to assess the strengths and weakness of your system is to plot how your equity curve (or system return distribution) performs under different market conditions. I do this visually by superimposing the equity curve generated by the system over market price for the same period and then identifying in what market conditions the system flourished and in what market conditions it didn't. Having this understanding of the relationship between your system performance and the market provides powerful info that you can capitalise on. For example if you can define in what conditions it doesn't perform, then this gives clues in system design how to develop a complementary strategy that outperforms during these poor conditions for your primary strategy.....and hence under a blended arrangement, the drawdown of your primary system will be reduced by the draw-up of your secondary system.


Yep mate. That's it in principle....but as a really good guide, start closely looking at equity curves and how they blend when you merge them together. Correlation statistics give you a little bit of info...but equity curve comparisons tell you so much more in relation to correlation. In portfolio management, correlation of return streams (equity curves) is perhaps the most important thing to get your head around.

For example have a look at the equity curves and statistics of the two different systems below.



Let's focus on EDTT first
The EDTT is a breakout trader (a divergent system) that performs very well when markets are divergent in nature (eg. trending). The strong equity growth between Jan 2013 to June 2013......and between Sep 2014 to Mar 2015 were when markets were volatile and had extended periods of trending behaviour where things took off. During these periods, drawdowns were eliminated (refer to drawdown profile of the charts). Outside of these trending periods, the EDTT was quite robust as despite unfavourable non-trending market conditions, drawdowns did not become pronounced and were restrained below 10%.

Now have a look at what this system achieves in isolation over the 7 year testing period. The compound annual growth rate (CAGR) was 28.34%. Some years produced returns of >50% while others produced low returns of around 4-8%. The variation can be attributed to different market conditions over the period as we always methodologically apply the same strategy irrespective of the nature of market condition.

Also look at the maximum drawdown of the system of 10.71% which arose in August 2014 immediately prior to the system taking off into the stratosphere in Sep 2014.

Now look at the CAGR/DRAW ratio of 2.65. This is an important risk-weighted metric that you use for portfolio blending purposes. Also look at the skew of 1.00. Such high positive skew demonstrates that the strategy is designed to take many small losses but has the potential to achieve a few very large gains. This is attributed to its strict risk management policy while it let's profits run to capture potentially unlimited upside. This positive skew is a great weapon for robustness in protecting yourself against black swan events. In fact, the open ended profit nature of this strategy allows you to participate in exceptional white swan events (that would be a black swan for other systems).

The EDTT produces these results with a 1% trade risk taken on each trade. If I doubled this risk% to 2% by doubling the position sizing of each trade taken, I would generate a CAGR of approximately 56% over the period with a Max draw of approximately 21%. The MAR ratio would stay constant. Notice how I have scaled down my position sizing through my trade risk % to achieve a max draw I am comfortable with. The bottom line is that you can scale up or down any risk metrics of any system using position sizing....but with an isolated return stream you cannot avoid the relationship between increased return and increased risk no matter how you cut it.....but under portfolio management, where you blend return streams you can actually do something about this relationship.....and there lies the beauty of diversification.

Overall over the given period, the strategy has positive expectation. This is an important criteria as you can do things with positive expectancy under portfolio management utilising the free lunch principle of blending uncorrelated or anti correlated return streams. If two different return streams have high positive correlation then when in unfavourable market conditions, both systems perform poorly and vice versa for favourable market conditions. Trading two strongly positive correlated return streams therefore makes the combined return stream of both systems far more volatile....which is a bad thing.

If two different return streams are anti-correlated (opposite), then when one system is in drawdown, the other system is in drawup. This is a great thing as the overall return stream of the combined systems is almost linear in nature. When this occurs you can magnify your position sizes to achieve far higher total return than what can be achieved by a single system with far lower drawdowns. This allows you to more efficiently utilise your finite capital to get far higher bang for buck. Achieving this holy grail of portfolio management (namely trading two perfectly anti-correlated systems) is a difficult endeavor to achieve as you not only need two different return streams in which both have positive expectancy but also correlations do change over time. Note however that through a knowledge of system design, you can manage this outcome with far greater precision than what can be achieved by instrument diversification of timeframe diversification.

So if you cannot muster an anti-correlated relationship.....then the next best thing is an uncorrelated relationship. What this means is that the two systems have no causal connection between each other and their individual return streams are effectively independent. What this allows for then is the ability to merge the return streams with the confidence that it is very unlikely that both systems will be in drawdown at the same time. The result therefore will be a composite return stream whereby the overall drawdown is diluted (given that each systems drawdowns are likely to occur at different times), yet you can summate the overall returns of both systems to achieve a far higher risk-weighted return.

Look at the MAR ratio (CAGR/DRAW) of EDTT. It is 2.65. Remember this result as we move on to the Mean reverting System.

Now let's focus on the supplementary system.
The Boll MR Long is a mean reverting long only trader that operates well as a convergent trading style.It uses a Bollinger band to identify when price appears oversold and will take a long position on the assumption that price will revert back to the median regression of the Bollinger band.

Go through the steps we just took with the EDTT understand when it performs well and when it doesn't. Also look at the key performance metrics:
CAGR - 7.63%
Max Draw 10.96%
CAGR/DRAW 0.70
Skew -0.56

On their own these stats would not be very appealing but what is critical is it's positive expectancy and how the equity curve is uncorrelated to the EDTT. Look at the equity curve itself and cross compare this to EDTT. Refer to the areas circled in the equity curve chart. A yellow circle reflects an uncorrelated relationship which is good and the red circle represents a positive correlation relationship which is bad and leads to building drawdowns during unfavourable times) By visual comparison of equity curves you can see that it is very uncorrelated without even having to refer to the correl statistic which demonstrates an almost perfectly uncorrelated nature of -0.003. Remember that a positive correlation of 1.00 is bad and an anti correlated relationship of -1.0 is fantastic. Well in this instance the relationship is close to 0 which represents an uncorrelated relationship.


Now let's blend both systems


Look what is achieved with the blend and compare this against that stats of the single systems in isolation.

CAGR 40.13%
Max Draw 10.58%
CAGR/Max Draw 3.79
Skew 0.99

Look at what you have achieved through this system blend under a portfolio. Almost double the return of the EDTT in isolation with the same level of drawdown plus the retention of the very favorable positive skew. Also now look at the MAR ratio and see how much higher it is compared to the individual system results. Mate....if you can understand these principles...you seriously will turn your fortunes around in trading. It takes you to a new place where you can focus on very simply systems that in isolation do not appear attractive....but under a portfolio can be very powerful beasts.....and it allows you to avoid the propensity to look for curve fit solutions that do not stand the test of time.

Spot on mate. When you get into this space you realise that a failure is not measured by the trades you take.....but rather in the trades you missed that turn out to be windfalls. Having your systems turned on all the time ensure that you can participate in the action when the market decides it is time to do so.....otherwise you find that you tend to overtrade to make things happen as opposed to allowing the market to decide when it will happen.

Yep mate....but any strategy can be categorised into either of these two buckets and there is almost an infinite variety of strategies you could muster up. The way to categorize them is to think in these broad terms. Does the strategy harvest opportunities when markets are taking a journey away from the historical mean of price action (divergent) or does the strategy harvest opportunities when markets are converging back to the historical mean of price action (convergent).

I hope this helps G. Sorry about being so long winded....but there are a few important messages to relay to assist you if you decide to take this wonderful journey into portfolio management and system design. :-)

Cheers

C

Thank you so much Copernicus for detailed response,

I will respond tomorrow after work. Thank you

Copernicus,

Thank you so much for your effort and response and time and patience. Your comments really helped me.

I am still studying your post in detail and comprehending the full extent of the post as it rare such thoughtful knowledge is exposed online. Thank you. I will respond with more questions or comments.

I have a burning question cycling in my head for the past days, now that I know 1 system doesn't work for all market conditions and the key is know when your system is profitable and what market condition fit your system.

My experience is only with Intraday (or day trading or positions closed by end of the day) trading. I would like to only build systems for intraday trading. I study 30 to 5 minutes charts.

With day trading their is a market condition changing frequently.

Does your writings apply to designing and testing day or intraday systems as well for different market conditions? For instance, there are days when price breaks out, trade in range, trends in one direction, or reversals.

Thank you,

No probs G

I have a thread devoted to your line of enquiry that gives info on how to test and plan for a systematic portfolio that handles the gamut of different potential market conditions...timeframes and systems deployed are not critical to the subject, yet I do use a preferred divergent and convergent system in the explanation. The 'secret sauce' is in how to blend your systems....not in their individual performance. The thread hopefully does justice to this notion...yet it is called EDTT (which many think is the critical ingredient). If you are patient and have a read....hopefully you will get the gist.

Don't worry about the long winded read in the beginning. Start looking at posts about mid-way through the thread and beyond that start discussing portfolio management and creation. If you can handle general nonsense such as music clips....then there is lot's of info in their and also articles from the professional fund management space on the topic.

If you want any clarification, then just leave a post in this thread and I will do my best to explain.

Cheers

C

Hello Copernicus and thank you for invite to other thread.

I will leave this thread now and go join the other thread for review and reading. I will ask questions there after doing some reading if needed.

Thank you kindly.

Hi good boy you can make a plan system that will work forever forget about it good luck
My initial plan was to develop a trading system (1) that would produce income forever.

I dont know how long you are ngaged in these GAME trading but dont expect positivity before 4 years expereince.