risk Archives - Algorithmic and Mechanical Forex Strategies

One line of code makes all the difference

February 9, 2017 by Shaun Overton 4 Comments

I was really excited about my Pilum strategy two months ago. The research looked great and everything was ready to rock and roll. Demo testing began and then… not much happened.

The Quantilator is (mostly) finished, which finally gave me time to circle back and review what happened with Pilum.

Live demo trading of Pilum. Dec 9, 2016 to Feb 7, 2017

The expected outcome was that I would win 75% of the time. Trades were infrequent, so I thought maybe I’m just having bad luck. But then my win rate remained stuck around 50%. Simple statistical tests told me this was unlikely to be bad luck.

I used the research time to pour over my research code and to compare it with live trades. What I found was that a single line of code (AHHHHHHHHHHHHHHH!) was incorrectly calculating my entry price, dramatically overstating the profits.

The flawed code produced this equity curve from a single combination of settings:

When the actual, correct result looks like this with those same settings:

The accurate backtest of Pilum

I’ll be honest… I like the flawed backtest a lot more!

The new, single-setting backtest isn’t as good, but it’s still trade-worthy. There are some characteristics that I dislike and features that I love. Let’s dig into those.

What I dislike

The frequency of trades is very low. Out of 19 months there were a total of 43 trades. 43 trades to comprise a backtest on 40+ instruments is a very small number.

If it weren’t for the statistical pattern backing up the frequency, I would not consider the test. However, there are 20,000 bars each on the 44 instruments. There are 880,000 total bars used to analyze whether my Pilum pattern offers any predictive value.

The most valuable predictions, however, are also exceptionally rare. That’s why I’m not able to get the trading frequency higher, which would potentially smooth the returns.

What I love

My previous systems like QB Pro and Dominari traded actively for relatively small wins. Trading costs exercised a massive impact on the overall performance.

The accurate backtest of Pilum

Now look again at the correct equity curve (the image to the right). Do you see the final profit of roughly 0.14? That’s a 14% unleveraged return over a 19 month period.

Allocating 2:1 or 3:1 leverage on this strategy could average annual returns of 15-25%.

Detecting hidden risk

A key measure of risk is skewness. You may not use that term yourself, but it’s something most of you already understand. The biggest complaint about people trading Dominari was that the average winner relative to the average loser was heavily skewed towards the losers.

Dominari wins on most months, but when it lost in December it was devastating. I implemented what I thought was a portfolio stop after the December 9th aftermath. Then I had a smaller, but still very painful, loss in January. The portfolio level stop loss of 3% should prevent future blowouts now that I know what goes wrong.

I still believe in Dominari. But, I obviously lost the work of most of the year due to those events.

Knowing that skewness is a good measure of blowout risk (even if you’ve never seen it in a backtest, like happened with Dominari), Pilum looks extremely encouraging.

This is a histogram of profit and loss by days. You should notice a few things.

The tallest bar is to the right of 0. That means that the most frequent outcome is winning.

The biggest winning day is dramatically better than the worst losing day. The worst outcome was a loss of 2%. The best outcome is gains near 10% in a single day (unleveraged!).

This is the statistical profile of an idea that’s much more likely to grab an avalanche of profits than it is to get blown out.

It gets even better

Would you say that the blue and red equity curves are highly or loosely correlated? Look closely.

Writing this blog post made me think carefully about the Pilum strategy. I decided that maybe I should see if all of the profits are coming from different settings at the same time. There’s very little risk of overfitting the data as my strategy only has 1 degree of freedom.

The blue bars are the equity curve of Setting 1.

The red bars are for Setting 2.

Do you think these are tightly or loosely correlated?

If you said loosely correlated, then you are correct. Notice how each equity curve shows large jumps of profit. Did you notice how those profit jumps occur on different days?

The blue setting skyrockets on a single day in November 2016. It leaves the red equity curve choking in its dust.

But then, look what happens as I advance into December. The red curve dramatically catches up to the blue curve and even overtakes it.

The correlation between the 2 strategies is only 57%.

Combine multiple settings into 1 portfolio

This is a much nicer equity curve!

Loose correlations are a GIFT. Combining two bumpy equity curves into a single strategy makes the performance much, much smoother.

The percentages of days that are profitable also increases. Setting 1 is profitable on 58.0% of days. Setting 2 is profitable on 53.5% of days.

But… combining them makes Pilum profitable on 68.2% of days. Awesome!

That also provides more data, which puts me in a stronger position to analyze the strategy’s skewness. Look at the frequency histograms below. They’re the same type of histograms that I showed you in the first section of this blog post. As you’ll notice, they look a lot different.

Pilum most probable daily profit and loss

The most probable outcome for any given day is a small winner

The tall green bar is the most probable trading outcome for any given day with filled orders. The average day is a positive return of 0-1%.

The small red bar is the worst trading day of the combined strategy.

The small green bars are the best trading days of the combined strategy.

Look how far to the right the green bars go. The largest winner is more than 3x the biggest loss. And, there are so many more large winners compared to losers.

Giant winners are far more likely than comparable losses.

The Plan

I immediately pushed Pilum into live trading this combination of two strategies. I expect that adding a second degree of freedom and running about 30 different versions of the strategy – all with different settings – will add to the performance and smooth the returns even further.

Dominari hasn’t been working on my FXCM account, which is very difficult to accept because the lacking performance seems to be a buried execution issue. Pilum, however, trades very infrequently. It’s unlikely that execution quality will make a dramatic difference in the long term outcomes.

So, I’m going to convert the FXCM account to trading Pilum exclusively. That will be offered as a strategy on Collective2 within the next few weeks, a company with whom I’ve been working closely. Their users are more investor rather than trading oriented – they’re far more likely to view low trading frequency as a good thing. I suspect that most people here have a different opinion and want to see a lot of market action.

I’ll write an update on Dominari shortly.

Beware of a Trading Strategy That Does Too Well

May 10, 2016 by Lior Alkalay 2 Comments

Can a trading strategy be doing too well? That sounds counter-intuitive, certainly. Like suggesting that someone can be too rich or too successful. You might be thinking there’s no such thing. But when it comes to managing your trading strategy, one that is performing too well is a warning sign. That’s because, quite often, an over-achieving trading strategy can turn into a honey trap.

In this article we will focus on a couple of cases studies where a “too good” strategy is something to watch out for. Hopefully, that’s a lesson one can learn from now rather than, painfully, at some later date.

A Trading Strategy with a High Win Ratio

The first case study is one of a strategy of mid- to short-term timeframes. In this case study, the frequency of executed trades is higher than 100 a month.

Usually, a solid strategy has an average win ratio of 45% to 60%. That ratio might sometimes be lower if the risk reward ratio is very strong. But higher than that is almost always is unsuitable.

In the strategy below we can see that the average win ratio per month runs from as low as 44 to as high as 64. Sometimes it is on the rise in the higher range and sometimes in the lower range. Regardless which, eventually it will all revolve around the average.

For example, in the month of January 2014, the win ratio was 52%. That means that for every 100 trades executed, 52 were profitable.

But in the second part(red), we can see things start to go a bit too well. The win ratio jumped to above 80% and stayed above that level for 3 months. Naturally, at 80%, that was far beyond the norm. What would come next was inevitable.

After 3 months of more than 80% of profitable trades, the win ratio fell to roughly 20% for the following 3 months. Because a long-term win ratio always has a long-term expectancy of maximal 50-60%. That means that a prolonged period of a high win ratio can be followed by a prolonged period of a very low win ratio.

Why is it Risky?

First, if you had 3 months of roughly 20% win ratio means you lost 80% of the trades. That means that, assuming you trade 100 times a month, you lost 80 trades. That’s a pretty hard hit. And if you risk $50 in every trade you lost $6,000 on aggregate (80 losing, 20 profitable) after 3 months. That is a big blow.

The second reason it is risky is more psychological but still very common. That is that many traders fail to realize that an overly high win ratio is very temporary in nature. Having failed to see that, what do they do? They raise their leverage. And when the sweet turns to bitter and they have only a 20% win ratio for 3 months, then what? All they’re left with is an empty account.

What should you do? This is rather simple; you reduce the risk you take per trade by lowering your leverage. It’s true you will gain less but you will also avoid the pitfall that comes after. Of course, if your leverage is already low and your account can sustain a prolonged period of a low win ratio than let statistics reign. But be prepared, psychologically, for choppy times.

Trading Strategy Profits that are Too Much Too Fast

The second case study is common in trading strategies that are low in frequency. The durations may range from a few days to even a few months. Let’s say you enter into a buy trade after your trading strategy indicated a bullish trend. You set the limit at 1,290. On average, you expect the limit to be reached within 3 to 4 days. But suddenly the trade advances in a fraction of the time and reaches relatively close to your trade.

Why is it Risky?

Because many times traders insist on waiting for their limit to be hit so they leave the trade open. But then the pair moves into overbought territory and before you know it the trend has reversed. You either close the position with a much smaller gain or, even worse, you lose the trade. Because you trade at a low frequency, this one outcome can be quite painful. You lost precious time when your position was open and missed the potential profit that you could have made.

What should you do? The solution here is rather simple, as well. You could use a trailing stop loss which is the most obvious tool to avert such cases.

Alternatively, you could use oscillators to identify a potentially hazardous situation. Add a component to your trading strategy to alert you if your pair reaches an overbought (or oversold) level. Make it a practice to exit the trade even if the target was not reached.

Spotting the next FX Swing

February 5, 2016 by Lior Alkalay 11 Comments

When trading in FX, scouting for just the right currency pair can be time consuming. Of course, you’re looking for the pair on the verge of producing the next lucrative trade. Many times it’s hard to decide which pair is the one with the most momentum.

And even if you do invest all your time homing in on the “right” pair, you just might get it wrong. Getting wrong means you’ll ride on a trend that’s barely moving. Luckily, there are some rules of thumb to help guide you to those pairs that will generate the big swing you’re waiting for.

FX Risk On/Off

The first rule of thumb for FX pairs with big swings is to ensure that the two currencies belong to opposite groups. For example, one of the most popular divisions between FX pairs is between risk on and risk off currencies. Risk off currencies tend to rise when investors are “jittery;” prominent members of that group are the USD and JPY. On the other hand, risk on currencies tend to rise when investors’ appetite for risk has been whetted. Risk on currencies include the Aussie, the Kiwi, the Euro and most exotic pairs.

When you pick an FX pair which has currencies from opposing groups it has a greater chance of generating a big swing. That’s because you tend to get a dual movement. It’s not one currency which rises against the other it’s that one is rising while the other is falling. That divergence makes the pair’s fluctuation larger and therefore the potential could likewise be larger.

Let me give you an example as to how to put this particular rule of thumb to good use. Back in May 2014, when fears over the Eurozone surged, it was a classic risk on trade scenario. Selling the EUR/USD was one of the most lucrative FX trends since the Dollar was gaining and the Euro was losing. That created a much bigger fluctuation for the pair than it did for, say, the EUR/GBP or EUR/AUD. In those cases, both sides of the equation had currencies from the same groups that were losing value.

This rule of thumb is rather easy to spot. Is Russia in trouble? Good! The Russian Ruble is a classic risk on and it will be hit hard while the Dollar is most likely to gain. Thus your weapon of choice should be USD/RUB long which would gain the most.

The Broken Sideways

Another rule of thumb you can use is an FX pair with a broken range. One of the most noticeable things about an FX pair that trades sideways is that it stretches like a coil. The longer the pair trades sideways the stronger the burst of momentum will be upon a break. So, if you spot an FX pair that’s been on a sideways trend for long, keep an eye on it. When the break does eventually occur the swing could be lucrative.

Mean Reversion

Our last rule of thumb is basically to look for an FX pair that’s hit its maximal range. We’re talking either a triple top or a double bottom or some such other indicator. Any or all of those indicators could suggest that the latest trend has reached its climax. The reversal which comes in the aftermath usually is rather significant. There are numerous ways to identify a mean reversion, which we elaborated on in past articles. The key here is that once you identify it, as with the other rules of thumb, it raises the chance of generating a bigger swing.

In Conclusion

Of course, even following all of those rules of thumb can’t guarantee a gain. These rules, by their very nature, are meant to simplify things. And as we all know FX trading has many more layers, including risk which should always be taken into account. But there are many FX traders for whom these rules of thumb are especially valuable. For example, for those of you who find it a challenge to spot the right FX pair to focus on. Or for those who, often times, become frustrated by choosing the wrong FX pair to trade.

Have Your Algo Running like a Swiss Watch

January 11, 2016 by Lior Alkalay 6 Comments

Setting up a trading algo that works smoothly, like a well-oiled machine, is not a trivial endeavor. There are many, many parameters that you have to take into account. It begins with the signals your algo is generating to the margins in your account, volatility, gains and, of course, risk. Certainly you’ve used our previous tips to build your algo and to draft your strategy. So how do you incorporate all those elements and optimize your algo for the greatest precision?

How do all the quants make their algos run smoothly like a Swiss watch? You have to treat your algo like a machine, built up with numerous mechanisms. Because of course, that’s what an algo really is. I like to call those mechanisms boxes. Now don’t despair if you think it all sounds a bit too complicated. By the time you reach this article’s conclusion you’ll have realized that it’s all quite simple and logical.

An Algo Made of Boxes

So what is the approach quants use that I like to call boxes? You divide your algo into separate mechanisms, or boxes, if you will. Each box will become a stand-alone mechanism that receives input data and generates an output. There will be one box that we can consider the brain; that box decides if it is a go/no go for the specific trade. The brain box gets all the inputs from all the other boxes.

Algo Boxes

Signal Box- The signal box scans prices and parameters, such as the moving average or any other condition you have written into it. Basically, these inputs are the rules of engagement you had early written for your algo. One simple rule could be, let’s say, 14 days moving average < 30 days moving average = signal to sell. Typically, this box will be running prices in several pairs.

In other words, its input data and its output would normally be three parameters; an entry signal, a recommended stop loss and a limit. Of course, once again, these inputs are according to the parameters you have already defined.

Risk Box- The risk box, as its name implies, is the box in charge of risk monitoring. This one is a bit more complicated. The risk box gets input from several sources. It gets the risk, i.e. stop loss required, from the signal box. Moreover, the risk box constantly reads your available margin.

Its output is a go/no go on each trade that exists, based on the parameters you entered. For example, how much you want to risk in total or the minimal available margin to be left in the account.

Let’s say you have a free margin of 11% and you set up a minimal margin of 10% in the risk box. The signal box will send output of an upcoming trade.

The risk box can calculate that the executed trade would take 2% from your remaining margin. You started with 11% so that would leave your free margin at 9%. Therefore, the output from your risk box will be a no go for this trade.

If you had had 13% free margin rather than the 11%, the output would be a go. Of course, there are many more options to program into this box but this is the simplest one.

Volatility Box- The volatility box might be the most complicated to program. However, volatility charting is something we covered fairly thoroughly in these articles – Volatility & Your Trade, The World of the VIX, Using VIX Alternatives. The box’s task is to chart market volatility and provide an output if volatility is about to change dramatically. A major change to volatility could, of course, warrant a change in your strategy.

Execution Box (aka the Brain Box)- Easily can be considered the most important box of all. This box is in charge of making the finally decision. It gets input from all the other boxes and decides if the trade should be opened or not. It also decides if a strategy needs to be changed.

For example, if the volatility box signals an upcoming surge in volatility the execution box may decide to close some trades. Or it could instruct the signal box to change into a secondary signal model suited to high volatility. There are many other ideas that you can program into it.

How Boxes Help You Win

All of those boxes can help you make your algo run more smoothly and efficiently. How? Quite simply because it lets you optimize your algo to a much higher level. It provides you with flexibility to easily adjust each mechanism. More importantly, it lets you monitor the inputs and outputs of each box and assess which needs fixing.

Algo Box: The Bottom-line

Of course, the partitioning into various boxes is not a new algo concept. It’s also not rigid; there’s no need to do it exactly as I did. If you’re intrigued by the Algo mechanism box concept and want to delve into it a bit farther you’re in luck. I highly recommend the book Inside the Black Box by Rishi K. Narang. I’ve found that it sheds a great deal of light on what some might construe as a complicated strategy.

And for those of you that are short of time? You can use this as a basic guide on how to make your algo run smoothly, without reinventing the wheel.

The Dimension of Time in Trading

July 9, 2015 by Lior Alkalay 6 Comments

The trading blogosphere is full of articles about the importance of timing your trade. Certainly, it’s important; identifying the right trend, knowing when to open or close – all integral to the trade. But here is the thing, there’s another dimension and timing is only half the story. You see, in the real world, it’s not just about the timing of your entry and exit but also the time between. In fact, the duration of your trade matters a lot more than you might think. In this article I’ll discuss the various aspects in which time can affect both short and long term strategies.

Duration and Returns

Let’s consider a theoretical trading situation, looking at two strategies. The first is a high frequency one designed at gaining 1 pip, with every trade taking a single second. We’ll call this Strategy A. The second strategy, Strategy B, is designed to generate a profit of 10 pips every 10 seconds. So which is the better strategy? On paper, both should generate 60 pips per minute hence equal returns. But in the real world? Well, that’s an entirely different story.

In real life, when you aggregate the results of the two strategies, you’re going to be in for a big surprise. Under Strategy A you had a profit of 50 pips after 1 minute. But under Strategy B you had a profit of 66 pips. Now which strategy do you think is better? Of course, you’d pick B. Interestingly, per trade, each strategy profited exactly as planned. Strategy A profited 1 pip and Strategy B profited 10 pips per trade.

So what made the difference? The time. As you can see below in the two charts, strategy A, when executed, took a little longer on average than initially expected. From the measurement of the duration of trades it clustered around 1.2 seconds per trade rather than 1 second. In Strategy B, the measurement of duration per trade, on average, is about 9 seconds. When executed, Strategy B trades took less time than initially expected. Eventually, at the end of the day, Strategy B returned 66 pips or 32% more than Strategy A’s 50 pips. That means Strategy B is the superior choice.

Now, let’s look at this a bit closer still. We’re going to take the duration of each of the trades executed and measure the standard deviation (a simple exercise in Excel). You would discover that in Strategy A, while the average might be 1 second per trade, the standard deviation is high. That suggests the cycles are not equal thus the strategy is less efficient. On the other hand, Strategy B has a low standard deviation, meaning most trades are close to 10 seconds. Therefore, it has more predictable returns and, hence, a more effective strategy. When you measure a strategy, understand that beyond returns and risk, the time each trade takes matters. And in fact, it can matter quite a lot.

Duration and Swing Trading

So now we understand how the nuances of time can affect the eventual returns of each strategy. Yet when it comes to longer duration trades (i.e. weeks or months) there is still another element. In this case, it is the amount of time the trade has been positive.

Let’s look at another theoretical scenario. This time, we have Strategies C and D, each has the identical duration of two weeks. Likewise, both have the same standard deviation of duration and roughly the same returns. But one strategy is riskier than the other. The question is which? The answer is the strategy that has been profitable for the least amount of time. Say you opened two trades; Strategy C was profitable for 12 of the 14 days it took to reach its target. Strategy D had been negative for the first 12 days before producing steep returns in the last 2. Strategy D, then, is clearly more risky because you are risking a loss for a longer time. This also suggests that, perhaps, the entry signal for Strategy D is not very well calibrated. Strategy C is clearly more effective and less risky, on average.

The Bottom Line

Of course, there are many more aspects to measuring time effectiveness in a trading strategy. There are also other dimensions to examine beyond just time. But the lesson here is clear. The next time you measure your own trading strategy remember that time isn’t just about timing.

Measure Yourself as a Hedge Fund

June 29, 2015 by Lior Alkalay 2 Comments

To the average trader, the idea of comparing yourself to a hedge fund may sound a bit absurd. What does a hedge fund, which manages billions upon billions, have in common with your account? Let’s face facts; there are very few of us who have that kind of cash in our trading accounts. Years ago, there was an adage my father taught me, which I believe to this day. That is; “if you can’t measure it, it’s not working.” In business, put another way, if you can’t measure it then it’s probably not worth doing. And really, trading is just like any other business. So if you can’t measure how well your trading strategy is doing, then it’s not working.

Asking the Tough Questions

I’ve been on the receiving end of tons of “advice” on trading and investment – some good, some not so good. My father’s sage advice not only “fits the case” but comes from real world experience. A “measure” forces you to move into the adult world, where you need to ask (and accept the answer to) the tough questions. Do I have a good, sound trading strategy? Am I sticking to the goals I’ve set? Am I taking on too much risk? These are tough questions, and only effective measurements can provide you with the answers.

Why Hedge Funds?

So, why would you measure your trading performance like a hedge fund and not, for instance, an investment portfolio? First, the hedge fund space is the only segment in investment that is the closest to trading. And that is regardless of the hedge fund’s “style.” Furthermore, hedge funds are not limited to a specific strategy. They are usually able to perform and switch positions quickly, just as traders do. In fact, many hedge funds are closer, strategy-wise, to a trader than they are to anything else. As a result, many of the methodologies developed to measure hedge funds are very applicable to trading.

Distribution of Return

The first measure of your strategy should come from distribution of returns. That may sound complicated but it’s really not. Let’s say your strategy is to gain, on average, 100 pips per trade (and no, we’re not holding you to that). If you export your data to a spreadsheet program and measure each, the distribution of your trading profits should stack up fairly close to 100 pips. In order to illustrate we have two examples of profit distribution. In the one on the left you can see Trader A that failed to deliver the target (a gain 100 pips per trade) with widely scattered results. On the right, we can see the trader B has been able to generally earn in most trades very close to 100 pips. That’s an indication that the strategy that was set has indeed worked pretty much as planned.

Sharpe Ratio

The second tool, the Sharpe ratio, is among the most popular ratios around and essentially measures the return per unit of risk. The calculation may sound complicated, but really it’s rather straightforward. It is the average return after deducting the risk free rate divided by the standard deviation of the returns. Now, what does risk free rate mean? When it comes to investments it means the benchmark rate. When it comes to trading, it means the rate you pay on your margin (i.e. your broker’s fee, which could be as high as 7% p.a.).

But here’s the thing; if you trade primarily in the short term, the Sharpe ratio is less relevant. Moreover, since we are talking about many trades per week or month, it would complicate your analysis and would not necessarily yield better results. If you want to compare your trading performance and you trade in the short term, you don’t need to benchmark yourself to interest rates.

So what is a good Sharpe ratio? Well, the text books say a Sharpe ratio above 1 is generally okay while below 1 is considered not that great. From my own experience, however, a Sharpe ratio at 0.8 or above is absolutely fine. For example, if we take the data from the two sample traders above, Trader A would have a -0.4 Sharpe ratio while Trader B would have 1.8.

The Bottom Line

So what’s the bottom line? What can we learn from the two measurements? Two big things. The first; if your strategy is performing, say you planned to gain 100 pips on average and that’s what your distribution shows, then you’re on track. And the Sharpe ratio? If your Sharpe is low your returns are more a reflection of market volatility than your trading strategy. Conversely, the opposite is also true. Of course, there are many more indicators that hedge funds use, but these two will give you the answers to the tough questions every trader needs to ask.

Use Maximum Leverage To Grow Profits And Reduce Risks

January 12, 2015 by Eddie Flower 9 Comments

The gains can accumulate quickly when a prop trader is using a strategy based on maximum leverage with limited account size. In order to preserve and build those gains, it’s important to remove them from the trading account according to a good plan.

As described in previous articles in this series, the high-leverage, low-balance strategies used by leading prop traders can be applied to multiple trading accounts using different systems, with each account capitalized by not more than a couple thousand dollars.

The amount in the account typically ranges between $1,000 to several thousand dollars. That way, there’s no psychological obstacle to using the max leverage on each trade.

Reduce the risks from drawdowns

When you have a winning system, profits pile up. It’s tempting to “let it ride” by using the same system to trade ever-bigger position sizes in the growing account.

However, when the entire capital is available in the trading account, it means that the capital is exposed to the inevitable system “blow up,” which typically causes a steep drawdown. Even if the trader escapes financial catastrophe, he or she may become so risk-averse afterward as to become indecisive and ineffective.

Pull money out each month

The smart way to avoid excessive drawdowns due to trading system “blow ups” is to pull money out of the account at the end of each successful month. That way, when a major drawdown occurs, it won’t take all your money, just the couple thousand dollars that you can afford to lose.

Successful prop traders like Shaun sweep the profits out of each winning trading account monthly and move them into a non-trading account, where they remain safe. So, each month the trading accounts open with their individual capitalization set at a given amount.

Pull out at least enough to cover one “blow up”

Once you’ve launched your forex system, you’ll want to think about earmarking enough money to cover at least one trading system failure. After you’ve secured that amount to be used for a recapitalization of your trading account, every subsequent gain is “free money,” at least in a psychological sense.

The first milestone is to pull enough money out of the trading account to cover at least one catastrophe. If you’ve been enjoying mostly winning months, next you should allocate 50% of your profits for high-risk systems.

You can’t lose what’s not at risk

Keep in mind: When a prop trader is using maximum leverage, the only money that’s safe is the money already pulled out of the trading account. Profits should be pulled from each winning trading account, each month.

When a prop trader wins consistently using high leverage with a limited-size account, the gains from relatively small individual trades may compound quickly. Profits gathered from the overflowing small trading accounts can compound into large sums, and it’s important to manage those profits effectively.

If you’d like to learn more about using maximum leverage to pull profits each month, just contact Shaun.

Deeper Analysis For Comparing Trading Systems

August 22, 2013 by Andrew Selby Leave a Comment

When first introduced to systems trading, a lack of knowledge and experience leaves many traders with limited options on which system to trade. As they expand their knowledge, these traders can soon become overwhelmed by the number of systems that are out there. Deciding which system is the best fit for a trader can require a tremendous amount of analysis, and many traders don’t consider all of the proper variables when making these decisions.

Many novice traders assume that the system with highest overall return is the best system. This is almost never the case, though. Many times, incredibly high returns are the product of a level of risk that most retail traders are not comfortable with. No amount of money is worth losing sleep over. The same case can be made for a system that either trades too often for the trader to keep up with, or not often enough to make any money.

When reviewing different trading systems, we want to consider their returns with respect to profitability, volatility, and risk. We also need to consider the frequency of their trading signals to make sure that all of the system’s components will mesh well with our personality.

Criteria for comparing trading systems

Profitability

The most commonly used profitability metric is Compounded Annual Growth Rate (CAGR). This takes the long term return of your system and averages it out as if it occurred in a straight line. Clearly, the fatal flaw here is that no system is capable of producing returns on a perfectly steady basis. However, CAGR does give us a convenient way to quickly compare systems. You will certainly want to dive deeper before investing real money!

Another interesting profitability metric is the number of winning trades, or the Win Ratio. This is simply a percent that measures how many of a system’s trades are winners versus how many are losers. The interesting thing about Win Ratio is that systems can be profitable overall with incredibly low Win Ratios. They can also be unprofitable despite very high Win Ratios.

For that reason, Win Ratio is very closely tied to Profit Ratio. This is the average return of a winning trade divided by the average return of a losing trade. Breaking down these two components is a good way to find out how a system is achieving its CAGR.

Systems like the 3 Day HIgh/Low Mean Reversion System can be profitable despite a low Profit Ratio of 0.64 because almost 74% of its trades are winners. On the flip side, a system like the SPY 10/100 Long Only System is profitable despite only winning on 41% of its trades because its winners are more than four times the size of its losers.

Volatility

While profitability is the end goal of just about every trading system, it may come at a cost. A prudent trader will identify what that cost is and then make an educated decision regarding whether the value is worth it.

One of those costs is volatility. Some systems, like the Moving Average Crossover with RSI System or the 50 Unit EMA System provide an excellent combination of Win and Profit Ratios at the cost of severe volatility. These systems are known to experience drawdowns of 40-50%. At that steep of a drawdown, even the most seasoned systematic trader will begin to question his system and whether markets have fundamentally changed.

Even systems with less severe drawdowns can cause traders to lose sleep. As a general rule, you should estimate the maximum drawdown you believe you can tolerate, and then cut that number in half.

Risk

Another cost of high returns can be excessive risk. Most trading systems provide the option of dialing up or down returns based on adjusting risk through leverage. Taking on too much risk in order to chase higher profits has been the nail in the coffin of many formerly successful traders.

The amount of risk you expose yourself to is one of the few things that you can actually control when it comes to trading. It is essential to your success that you constantly monitor your exposure and always keep your risk of ruin at an acceptable level.

Frequency

It is also important to consider the frequency of the signals generated by your system. This is a two-fold issue. First, you have to make sure that your backtesting results contain a significant sample size. If you backtest over a ten year period and your system only generates three signals, the odds are pretty good that your results will be skewed.

You also want to make sure that the system trades at a frequency that matches your lifestyle. A profitable system with low volatility and risk won’t help you if it never trades. On the other hand, the same system will be equally as useless if it forces you to monitor trades 24 hours per day.

The bottom line is that there are no right answers, and there are hundreds of different approaches to systems trading. The key is to find a system that works for you and stick to it.

Profit solely through money management

July 12, 2012 by Shaun Overton 7 Comments

Most traders view the idea of profit protection as a concept that applies to an individual trade. Whenever a trade floats a large profit, they protect the unrealized profit by moving the stop loss closer to the market.

I like to view trading more as a collection of outcomes rather than the result of any particular transaction. This article looks out how to protect realized profits after they accrue in the account rather than protecting the unrealized profit from one trade.

The money management project started off by asking the question, “is it possible to make money with a perfectly random strategy solely using money management.” The strategy is not quite to the point where it will work in the real world. Nonetheless, this new method shows substantial progress toward that goal.

I operate on the assumption that a random strategy is one with 50% profitability and an R multiple of 1.0; we earn a dollar for every dollar we risk. The net outcome over hundreds of trials should average out to the starting balance. My earlier videos and blog on money management modeling verifies that this is indeed the case.

Random outcomes tend to fluctuate above and below the average value. At any given time, half of all possible outcomes should be above average. The opposite holds true for outcomes below the average. The idea here is to get away from standard notions of money management like fixed fractional money management. The risk amount should follow not only the account balance, but also factor in the degree of profit accrued.

The fluctuations above the starting balance are totally random. If a trader is lucky enough to obtain a profitable, realized balance, then it makes sense to decrease the risk. Obviously, decreasing the risk limits the potential profits.

Consider an example. We start with a $100,000 account balance risking 1% of the original balance. Note that all future trades base the risk on the original balance and not the accrued profit and loss.

When a random winner rolls in, it brings the account balance to $101,000. The odds of winning on the next flip are still 50-50. Rather than risking giving it all back, I found that you increase your chances of a final, meaningful profit if you decrease the original risk as profits accrue.

Reducing the original risk by 1% for every $1,000 of profit changes the dollar risk from $1,000 to $990. This may not seem like very much. That’s because it is not. The goal is to suck tiny little advantages as they come. A loss occurs half of the time in this situation. When it does, the account balance still shows a $10 realized profit.

If the second trade (we are now at $101,000) is a winner, the account balance increases to $101,990. A loser following the win drops it down to $100,010. The process is slow, but we’re also making money without a trading strategy. We’re just betting.

Importantly, a change to the risk amount as a balance loses money completely destroys the money management strategy. It is critical that the risk amount not vary in any way whatsoever.

Real examples of how the median return increases with trades placed

The median outcome after 200 trades is 0.6%

The median outcome after 500 trades is 1.5%

The result after 1,000 trades is a 2.4% return

The result after 10,000 trades is a 19.7% return

Outsized Trading Risks

June 6, 2012 by Shaun Overton Leave a Comment

A long standing client flew in to our office last August. He wanted to build a scalping system that would churn through as many trades as possible. I tried explaining that gauging risk from only 6 months of data is not enough time and that the markets can be far wilder than imagined.

The Swiss central bank intervened the very next day, sending the EUR/CHF up 1,400 pips to 1.20. The USD/CHF climbed even more in terms of pips. It was a shocking, jaw dropping display of market insanity.

Many traders almost certainly got caught in the wave of destruction. Low leverage positions received almost unthinkable margin calls in the course of several hours. It seems insane to think that an institution would come in and buy 20 billion euros in the span of several hours. Yet, that’s exactly what happened.

Staring at historical data does not tell you anything about future risk. The only thing that historical data tells you is the past sense of normal. That may continue into the future for a time, but eventually, things change.

My personal opinion is that the best you can do is to characterize the personalities of different instruments. The GBP/JPY is notoriously wild due to its speculative nature. The UK and Japan have no real trade relations to speak of. The currency pair is something of a market accident. They are two liquid currencies that happen to be exchangeable, but very few people in normal business need to exchange them directly.

The Swiss franc, on the other hand, is a bastion of conservatism. The currency was historically backed by physical gold. It used to be fully backed. Then it declined to a 40% backing. I don’t remember what the backing is today, but it’s a fraction of what it used to be.

Not incidentally, the conservative behavior of the franc changed in line with the reserves behind the paper. The franc gets more wild as the gold supply securing its value diminishes. Volatility grows increasingly wild with the passage of time.

I encourage traders to look at the rate of change for an instrument when they attempt any kind of profiling. Rather than saying that the CHF is quiet, I tend to say that it’s historically conservative with an increasing tendency to undergo wild episodes. It’s basically the 30,000 foot view. Rather than asking what it’s like today, we rephrase the question to ask what it’s likely to do tomorrow.

Watching the EUR/CHF explode before he had a chance to trade likely saved my client from blowing up. It was a learning experience, but most traders are not lucky enough to learn from the sidelines.

If you have any kind of hyperactive trading idea that depends on range bound markets, I would encourage you to stick it into the “save for later” bin. This summer and fall are ramping up to be quite a wild ride. If anything, now is probably a great time to bring out strategies that only do well when it looks Armageddon is near.