Effective Portfolio Management: How to Choose the Best Strategies for Live Trading

by Andrea Unger | Technical

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Having a large and well-diversified portfolio is essential to properly managing risk.

However, sometimes we can’t afford to trade a large number of strategies, either because our capital is not high enough or for technical reasons related to our infrastructure.

The solution to this problem is, of course, to trade a selection of strategies from time to time. But how do we choose which ones to use in live trading and which ones to "put on hold"? What criteria should be applied?

In this video, we'll show you an example of portfolio management and explain which assessments to make to select which strategies to activate.

By watching it, you'll discover:

  • Why choosing strategies solely based on available capital isn't enough

  • Which criteria should be applied when selecting strategies to trade in the next period (equity, capital, correlation)

  • An example of automated management (via our proprietary Titan software)

Enjoy! 😎

Transcription

Hey everyone and welcome to another brand new video! Today we're going to be bringing you an example of managing a trading system portfolio. Once we've coded our strategies, how do we decide which ones to use?

Filtering the equity lines

Alright, here on the screen, you can see our proprietary software, it's Titan, that helps us every month in choosing which trading system to use in the next month.

The list of systems, in this case, includes more than 300 strategies and unless you have several millions of dollars of capital, there’s no way to use all 300 at the same time.

In addition, there would also be considerable problems due to the IT infrastructure, but that's another matter entirely.

So how do you choose which systems to use? Well, I can already tell you that the secret, which isn't much of a secret, after all, is to reward in some way the trading systems that are performing better and leave out the systems that aren't doing so well.

So the first part of our system selection will be handled by a filter on the equity lines, all out-of-sample of course, of the systems.

Once the best equities have been selected, a check is then made on the available capital, which is what you see here as 'starting capital'. And this step is done simultaneously with a check on the correlation of the various systems.

But let's just go in order. What I often recommend is to go step-by-step. So, in this case, we'll start with unlimited capital. Here we'll use $10 million for simplicity's sake.

We'll assign risks that make little sense in this case on our systems and that consider the drawdown or the worst-case scenario, which could be the worst day of the strategy, the worst week even or whatever.

And let's see, based on the filters placed on the equities, which systems would be used month-by-month according to our program, so according to Titan.

We'll then decide whether to use only those systems that have performed well, namely those that have been positive in the long and short term. And then we can use 9 months or even 12 months, so a year, for the long term, and from 2 to 4 months for a shorter period.

Then we can see that there could be so many other ways to filter the equity lines. Another example might involve standard deviations. Assume that we have a moving average of our equities and we want to filter out systems that are doing either too well or too poorly, so they are above or below a certain number of standard deviations from the mean.

Another filter that I think is very interesting is the peak equity filter. In this case, I'll tell the software to only use the systems that have had a peak in equity in the last year.

I've got to tell you I've found this filter rather effective in my case with this portfolio of systems on all the backtests I've done. So, let's go and see in this case what our systems would do by applying only filters on equity and leaving a very high starting capital.

Let's go and launch the backtest. It'll take a moment to do it. It's done. In the backtest, we can see what strategies are proposed and how many contracts are involved.

Needless to say, we put in so many millions that it suggests strategies with 26 contracts on Crude Oil. I mean, far-fetched stuff! But we don't care because what we need to understand is the worthiness of what we're doing by filtering the equity.

And here look, we see it right away. By filtering the equities we’ll get the performance from the beginning of 2018, the year in which most of the systems that I'm currently using were created, so I'm going to look for the out-of-sample and out-of-optimization performance of the strategies.

And we see how the result of the filtered equities, which is about 12 million, is significantly higher than using a logic that doesn't apply any filtering on the equities, so looking only at money management in this case, but we've already said that this aspect is insignificant.

You can see that the earnings are double. If we then go to look at the equity line, the one in black reflects the use of all strategies indiscriminately with a single contract. You can see that it's more or less similar to the unfiltered one, so money management logic depends on how much capital we have.

What matters most is the difference between the red line, which is the unfiltered line, versus the blue line, namely the line that corresponds to using the systems with the filters on the equities that we previously mentioned.

So, this equity remains a very real equity, because all the costs of commissions and slippage that one encounters on average in real trading are included.

However, the drawdown is very pronounced - even more in absolute value - than for the unfiltered equity. It's also true, however, that it's a much shorter drawdown, as we're currently on an equity peak for the filtered equity, while for the red equity we're well below the peak levels reached last year.

Capital limitations

Let's go on to add the next step, which is the limitation due to the starting capital, namely the capital we have available to spend on our futures trading.

Let's go back and this time let's assume we use $30,000 as starting capital. In this case, we'd have to change our list, which allowed us to use the reference contracts for all futures, namely the Gold contract with the Big Point Value of 100, the Crude Oil contract of 1000, the Mini SP, and so on.

We're therefore going to add a new list that involves the use of micro contracts. What does this mean? We're going to allow Titan to split our strategies created on the main contracts.

So, for example, we may get a backtest that proposes to trade 0.1 contracts on Crude Oil. There, that means trading a Micro Crude Oil instead of a whole contract.

In this case, by putting a capital of 30,000, to give more room for strategies, we'll usually use more risk, because we're already limited in our operations to using only micro contracts.

Thus, the main energy futures, RBOB, Heating Oil, and Natural Gas are excluded. Even metals such as Silver or Copper could be excluded. So, we have to somehow give more leeway to the systems that we can continue using because of the presence of micro contracts.

I wouldn't increase it excessively. We can leave 1.75% as a daily risk and a maximum drawdown of 6 instead of 4. So, we aren't disrupting the metrics at all. We're simply giving the systems a little bit more room, increasing the risk level a little bit.

The filters on the equities will remain the same. Correlation between systems will also be done at this stage, so perhaps the most similar systems will be excluded and we'll be proposed to trade perhaps only one or both with half the contracts.

Okay, so the backtest finished. Let's go and look at the result. So you can see that in this case there's an even clearer difference compared to the unfiltered equity, that is to say, trading by including the equities based only on the capital that we have available and without looking at whether the system is performing well or not. While if we look at the filtered equity line we see that it's a completely different story.

Let's take a look at the performance summary. We would have made $36,800 starting from 2018. The unfiltered result would even be negative, because some of the systems we coded at the end of 2018 didn't do well at all, on the contrary.

So as you can see, in out-of-sample it's important to go and make decisions based on equity performance to figure out whether or not to continue using such systems.

Final output

As I mentioned before, these would be the strategies proposed by Titan for the upcoming month. You can see here, as I mentioned before, Crude Oil 0.1 contracts mean one micro contract.

In this case, 0.04 on DAX would imply trading one DAX micro, which we know is 1/25. Indeed, one contract divided by 25 makes 0.04. So 0.12 would then correspond to three micro contracts. And so on and so forth.

These would be the strategies to use for the month of June. Needless to say, I'm recording during the month of June and therefore using the sixth as the end date. In one week, there will be the next rebalancing for the next month.

Titan is free software for Unger Academy students and is very useful when developing and creating portfolios.

If you want to learn more and start investing in the markets systematically, then I'd also recommend that you click on the link below, which will take you to a page where you can find some very useful resources. From there you can also register for a free presentation by Andrea Unger, get our best-selling book, "The Unger Method," covering only the shipping costs, or you can also book a call with a member of our team to get a free strategic consultation.

And hey, finally, if you haven't already done so by this stage, please go and subscribe to our channel and please make sure you're going to click on the little bell, so you will be promptly notified as soon as we post a new video.

If you're also interested in any particular topic, please also let us know below in the comments section underneath this video.

Alright! And with that, we'll see you soon with the next technical video! Bye-bye!

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Andrea Unger

Andrea Unger

Andrea Unger here and I help retail traders to improve their trading, scientifically. I went from being a cog in the machine in a multinational company to the only 4-Time World Trading Champion in a little more than 10 years.

I've been a professional trader since 2001 and in 2008 I became World Champion using just 4 automated trading systems. 

In 2015 I founded Unger Academy, where I teach my method of developing effecting trading strategies: a scientific, replicable and universal method, based on numbers and statistics, not hunches, which led me and my students to become Champions again and again.

Now I'm here to help you learn how to develop your own strategies, autonomously. This channel will help you improve your trading, know the markets better, and apply the scientific method to financial markets.

Becoming a trader is harder than you think, but if you have passion, will, and sufficient capital, you'll learn how to code and develop effective strategies, manage risk, and diversify a portfolio of trading systems to greatly improve your chances of becoming successful.