Building Systematic Strategies – A New Approach

Anyone active in the quantitative space will tell you that it has become a great deal more competitive in recent years.  Many quantitative trades and strategies are a lot more crowded than they used to be and returns from existing  strategies are on the decline.

THE CHALLENGE

The Challenge

Meanwhile, costs have been steadily rising, as the technology arms race has accelerated, with more money being spent on hardware, communications and software than ever before.  As lead times to develop new strategies have risen, the cost of acquiring and maintaining expensive development resources have spiraled upwards.  It is getting harder to find new, profitable strategies, due in part to the over-grazing of existing methodologies and data sets (like the E-Mini futures, for example). There has, too, been a change in the direction of quantitative research in recent years.  Where once it was simply a matter of acquiring the fastest pipe to as many relevant locations as possible, the marginal benefit of each extra $ spent on infrastructure has since fallen rapidly.  New strategy research and development is now more model-driven than technology driven.

 

 

 

THE OPPORTUNITY

The Opportunity

What is needed at this point is a new approach:  one that accelerates the process of identifying new alpha signals, prototyping and testing new strategies and bringing them into production, leveraging existing battle-tested technologies and trading platforms.

 

 

 

 

GENETIC PROGRAMMING

Genetic programming, which has been around since the 1990’s when its use was pioneered in proteomics, enjoys significant advantages over traditional research and development methodologies.

GP

GP is an evolutionary-based algorithmic methodology in which a system is given a set of simple rules, some data, and a fitness function that produces desired outcomes from combining the rules and applying them to the data.   The idea is that, by testing large numbers of possible combinations of rules, typically in the  millions, and allowing the most successful rules to propagate, eventually we will arrive at a strategy solution that offers the required characteristics.

ADVANTAGES OF GENETIC PROGRAMMING

AdvantagesThe potential benefits of the GP approach are considerable:  not only are strategies developed much more quickly and cost effectively (the price of some software and a single CPU vs. a small army of developers), the process is much more flexible. The inflexibility of the traditional approach to R&D is one of its principle shortcomings.  The researcher produces a piece of research that is subsequently passed on to the development team.  Developers are usually extremely rigid in their approach: when asked to deliver X, they will deliver X, not some variation on X.  Unfortunately research is not an exact science: what looks good in a back-test environment may not pass muster when implemented in live trading.  So researchers need to “iterate around” the idea, trying different combinations of entry and exit logic, for example, until they find a variant that works.  Developers are lousy at this;  GP systems excel at it.

CHALLENGES FOR THE GENETIC PROGRAMMING APPROACH

So enticing are the potential benefits of GP that it begs the question as to why the approach hasn’t been adopted more widely.  One reason is the strong preference amongst researchers for an understandable – and testable – investment thesis.  Researchers – and, more importantly, investors –  are much more comfortable if they can articulate the premise behind a strategy.  Even if a trade turns out to be a loser, we are generally more comfortable buying a stock on the supposition of, say,  a positive outcome of a pending drug trial, than we are if required to trust the judgment of a black box, whose criteria are inherently unobservable.

GP Challenges

Added to this, the GP approach suffers from three key drawbacks:  data sufficiency, data mining and over-fitting.  These are so well known that they hardly require further rehearsal.  There have been many adverse outcomes resulting from poorly designed mechanical systems curve fitted to the data. Anyone who was active in the space in the 1990s will recall the hype over neural networks and the over-exaggerated claims made for their efficacy in trading system design.  Genetic Programming, a far more general and powerful concept,  suffered unfairly from the ensuing adverse publicity, although it does face many of the same challenges.

A NEW APPROACH

I began working in the field of genetic programming in the 1990’s, with my former colleague Haftan Eckholdt, at that time head of neuroscience at Yeshiva University, and we founded a hedge fund, Proteom Capital, based on that approach (large due to Haftan’s research).  I and my colleagues at Systematic Strategies have continued to work on GP related ideas over the last twenty years, and during that period we have developed a methodology that address the weaknesses that have held back genetic programming from widespread adoption.

Advances

Firstly, we have evolved methods for transforming original data series that enables us to avoid over-using the same old data-sets and, more importantly, allows new patterns to be revealed in the underlying market structure.   This effectively eliminates the data mining bias that has plagued the GP approach. At the same time, because our process produces a stronger signal relative to the background noise, we consume far less data – typically no more than a couple of years worth.

Secondly, we have found we can enhance the robustness of prototype strategies by using double-blind testing: i.e. data sets on which the performance of the model remains unknown to the machine, or the researcher, prior to the final model selection.

Finally, we are able to test not only the alpha signal, but also multiple variations of the trade expression, including different types of entry and exit logic, as well as profit targets and stop loss constraints.

OUTCOMES:  ROBUST, PROFITABLE STRATEGIES

outcomes

Taken together, these measures enable our GP system to produce strategies that not only have very high performance characteristics, but are also extremely robust.  So, for example, having constructed a model using data only from the continuing bull market in equities in 2012 and 2013, the system is nonetheless capable of producing strategies that perform extremely well when tested out of sample over the highly volatility bear market conditions of 2008/09.

So stable are the results produced by many of the strategies, and so well risk-controlled, that it is possible to deploy leveraged money-managed techniques, such as Vince’s fixed fractional approach.  Money management schemes take advantage of the high level of consistency in performance to increase the capital allocation to the strategy in a way that boosts returns without incurring a high risk of catastrophic loss.  You can judge the benefits of applying these kinds of techniques in some of the strategies we have developed in equity, fixed income, commodity and energy futures which are described below.

CONCLUSION

After 20-30 years of incubation, the Genetic Programming approach to strategy research and development has come of age. It is now entirely feasible to develop trading systems that far outperform the overwhelming majority of strategies produced by human researchers, in a fraction of the time and for a fraction of the cost.

SAMPLE GP SYSTEMS

Sample

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emini    emini MM

NG  NG MM

SI MMSI

US US MM

 

 

Developing High Performing Trading Strategies with Genetic Programming

One of the frustrating aspects of research and development of trading systems is that there is never enough time to investigate all of the interesting trading ideas one would like to explore. In the early 1970’s, when a moving average crossover system was considered state of the art, it was relatively easy to develop profitable strategies using simple technical indicators. Indeed, research has shown that the profitability of simple trading rules persisted in foreign exchange and other markets for a period of decades. But, coincident with the advent of the PC in the late 1980’s, such simple strategies began to fail. The widespread availability of data, analytical tools and computing power has, arguably, contributed to the increased efficiency of financial markets and complicated the search for profitable trading ideas. We are now at a stage where is can take a team of 5-6 researchers/developers, using advanced research techniques and computing technologies, as long as 12-18 months, and hundreds of thousands of dollars, to develop a prototype strategy. And there is no guarantee that the end result will produce the required investment returns.

The lengthening lead times and rising cost and risk of strategy research has obliged trading firms to explore possibilities for accelerating the R&D process. One such approach is Genetic Programming.

Early Experiences with Genetic Programming
I first came across the GP approach to investment strategy in the late 1990s, when I began to work with Haftan Eckholdt, then head of neuroscience at Yeshiva University in New York. Haftan had proposed creating trading strategies by applying the kind of techniques widely used to analyze voluminous and highly complex data sets in genetic research. I was extremely skeptical of the idea and spent the next 18 months kicking the tires very hard indeed, of behalf of an interested investor. Although Haftan’s results seemed promising, I was fairly sure that they were the product of random chance and set about devising tests that would demonstrate that.

SSALGOTRADING AD

One of the challenges I devised was to create data sets in which real and synthetic stock series were mixed together and given to the system evaluate. To the human eye (or analyst’s spreadsheet), the synthetic series were indistinguishable from the real thing. But, in fact, I had “planted” some patterns within the processes of the synthetic stocks that made them perform differently from their real-life counterparts. Some of the patterns I created were quite simple, such as introducing a drift component. But other patterns were more nuanced, for example, using a fractal Brownian motion generator to induce long memory in the stock volatility process.

It was when I saw the system detect and exploit the patterns buried deep within the synthetic series to create sensible, profitable strategies that I began to pay attention. A short time thereafter Haftan and I joined forces to create what became the Proteom Fund.

That Proteom succeeded at all was a testament not only to Haftan’s ingenuity as a researcher, but also to his abilities as a programmer and technician. Processing such large volumes of data was a tremendous challenge at that time and required a cluster of 50 cpu’s networked together and maintained with a fair amount of patch cable and glue. We housed the cluster in a rat-infested warehouse in Brooklyn that had a very pleasant view of Manhattan, but no a/c. The heat thrown off from the cluster was immense, and when combined with very loud rap music blasted through the walls by the neighboring music studios, the effect was debilitating. As you might imagine, meetings with investors were a highly unpredictable experience. Fortunately, Haftan’s intellect was matched by his immense reserves of fortitude and patience and we were able to attract investments from several leading institutional investors.

The Genetic Programming Approach to Building Trading Models

Genetic programming is an evolutionary-based algorithmic methodology which can be used in a very general way to identify patterns or rules within data structures. The GP system is given a set of instructions (typically simple operators like addition and subtraction), some data observations and a fitness function to assess how well the system is able to combine the functions and data to achieve a specified goal.

In the trading strategy context the data observations might include not only price data, but also price volatility, moving averages and a variety of other technical indicators. The fitness function could be something as simple as net profit, but might represent alternative measures of profitability or risk, with factors such as PL per trade, win rate, or maximum drawdown. In order to reduce the danger of over-fitting, it is customary to limit the types of functions that the system can use to simple operators (+,-,/,*), exponents, and trig functions. The length of the program might also be constrained in terms of the maximum permitted lines of code.

We can represent what is going on using a tree graph:

Tree

In this example the GP system is combining several simple operators with the Sin and Cos trig functions to create a signal comprising an expression in two variables, X and Y, which may be, for example, stock prices, moving averages, or technical indicators of momentum or mean reversion.
The “evolutionary” aspect of the GP process derives from the idea that an existing signal or model can be mutated by replacing nodes in a branch of a tree, or even an entire branch by another. System performance is re-evaluated using the fitness function and the most profitable mutations are retained for further generation.
The resulting models are often highly non-linear and can be very general in form.

A GP Daytrading Strategy
The last fifteen years has seen tremendous advances in the field of genetic programming, in terms of the theory as well as practice. Using a single hyper-threaded CPU, it is now possible for a GP system to generate signals at a far faster rate than was possible on Proteom’s cluster of 50 networked CPUs. A researcher can develop and evaluate tens of millions of possible trading algorithms with the space of a few hours. Implementing a thoroughly researched and tested strategy is now feasible in a matter of weeks. There can be no doubt of GP’s potential to produce dramatic reductions in R&D lead times and costs. But does it work?

To address that question I have summarized below the performance results from a GP-developed daytrading system that trades nine different futures markets: Crude Oil (CL), Euro (EC), E-Mini (ES), Gold (GC), Heating Oil (HO), Coffee (KC), Natural gas (NG), Ten Year Notes (TY) and Bonds (US). The system trades a single contract in each market individually, going long and short several times a day. Only the most liquid period in each market is traded, which typically coincides with the open-outcry session, with any open positions being exited at the end of the session using market orders. With the exception of the NG and HO markets, which are entered using stop orders, all of the markets are entered and exited using standard limit orders, at prices determined by the system

The system was constructed using 15-minute bar data from Jan 2006 to Dec 2011 and tested out-of-sample of data from Jan 2012 to May 2014. The in-sample span of data was chosen to cover periods of extreme market stress, as well as less volatile market conditions. A lengthy out-of-sample period, almost half the span of the in-sample period, was chosen in order to evaluate the robustness of the system.
Out-of-sample testing was “double-blind”, meaning that the data was not used in the construction of the models, nor was out-of-sample performance evaluated by the system before any model was selected.

Performance results are net of trading commissions of $6 per round turn and, in the case of HO and NG, additional slippage of 2 ticks per round turn.

Ann Returns Risk

Value 1000 Sharpe

Performance

(click on the table for a higher definition view)

The most striking feature of the strategy is the high rate of risk-adjusted returns, as measured by the Sharpe ratio, which exceeds 5 in both in-sample and out-of-sample periods. This consistency is a reflection of the fact that, while net returns fall from an annual average of over 29% in sample to around 20% in the period from 2012, so, too, does the strategy volatility decline from 5.35% to 3.86% in the respective periods. The reduction in risk in the out-of-sample period is also reflected in lower Value-at-Risk and Drawdown levels.

A decline in the average PL per trade from $25 to $16 in offset to some degree by a slight increase in the rate of trading, from 42 to 44 trades per day, on average, while daily win rate and percentage profitable trades remain consistent at around 65% and 56%, respectively.

Overall, the system appears to be not only highly profitable, but also extremely robust. This is impressive, given that the models were not updated with data after 2011, remaining static over a period almost half as long as the span of data used in their construction. It is reasonable to expect that out-of-sample performance might be improved by allowing the models to be updated with more recent data.

Benefits and Risks of the GP Approach to Trading System Development
The potential benefits of the GP approach to trading system development include speed of development, flexibility of design, generality of application across markets and rapid testing and deployment.

What about the downside? The most obvious concern is the risk of over-fitting. By allowing the system to develop and test millions of models, there is a distinct risk that the resulting systems may be too closely conditioned on the in-sample data, and will fail to maintain performance when faced with new market conditions. That is why, of course, we retain a substantial span of out-of-sample data, in order to evaluate the robustness of the trading system. Even so, given the enormous number of models evaluated, there remains a significant risk of over-fitting.

Another drawback is that, due to the nature of the modelling process, it can be very difficult to understand, or explain to potential investors, the “market hypothesis” underpinning any specific model. “We tested it and it works” is not a particularly enlightening explanation for investors, who are accustomed to being presented with a more articulate theoretical framework, or investment thesis. Not being able to explain precisely how a system makes money is troubling enough in good times; but in bad times, during an extended drawdown, investors are likely to become agitated very quickly indeed if no explanation is forthcoming. Unfortunately, evaluating the question of whether a period of poor performance is temporary, or the result of a breakdown in the model, can be a complicated process.

Finally, in comparison with other modeling techniques, GP models suffer from an inability to easily update the model parameters based on new data as it become available. Typically, as GP model will be to rebuilt from scratch, often producing very different results each time.

Conclusion
Despite the many limitations of the GP approach, the advantages in terms of the speed and cost of researching and developing original trading signals and strategies have become increasingly compelling.

Given the several well-documented successes of the GP approach in fields as diverse as genetics and physics, I think an appropriate position to take with respect to applications within financial market research would be one of cautious optimism.

How to Spot a Fake

One of the issues that comes up regularly is how, as an investor or other interested party, one can protect oneself from unscrupulous scam artists posing as professional traders or money managers. This is a particular problem on web sites featuring trader forums, where individuals with unverified track records claiming stellar trading histories use their purported trading “prowess” to try to impress and intimidate other participants, usually impressionable newbies. The purpose of this post is to provide some guidance to help investors, traders and other fellow travelers sort the wheat from the chaff. We’ll be doing some forensic analysis on the track record for a strategy in NG futures that one such character recently posted in one of these forums, as a classic example of the kind of fakery I am describing.

One thing you should understand about scam artists operating on forums, is that they don’t work alone: usually they have a bunch of groupies who will shill for them at every opportunity and who will try to shout down any investigative questioning. Don’t be deterred. These know-it-alls are usually just ignorant dupes, who understand no more about trading than the scam artist. They may just as easily be fellow-scam artists themselves.

THE FIRST BIG RED FLAG: UNWILLINGNESS TO PRODUCE A TRACK RECORD
Anyone claiming to be a CTA or professional money manager (or whose shills claim he is one) has to have a track record that is freely available in the public domain. So how does a scam artist overcome a challenge to produce it? He will claim that he “can’t advertise”, or make some other, similar excuse. Don’t accept that at face value. Ask him to PM it to you. If he won’t, there’s already a high probability he’s a con artist.

THE SECOND BIG RED FLAG: CURVE FITTING
Let’s say our suspect meets the challenge and produces a track record. Ideally this will be an audited P&L statement, but let’s assume for the purposes of this discussion that he produces something along the lines of the Performance Reports produced by a product like Tradestation or MultiCharts, i.e. we are dealing with a simulated back-test.

If your suspect produces a back-test, you can be pretty sure it’s going to look good – otherwise he wouldn’t produce it. The task now is to dig into those reports to spot the red flags that give clues as to whether it might be fake.
Now of course any trading system is going to make assumptions – about fill rates, slippage, commissions, capacity etc. All that is fine, as long as the assumptions are clearly stated. You might want to challenge any or all of the assumptions, and the trader may disagree with you about some or all of them. That’s perfectly ok – it’s an honest, open discussion about a set of investment assumptions that have been revealed at the outset.

But here is what is NOT ok: any opacity about which data was used to build the trading model and which data was used to test it. The former, the in-sample (IS) data set, used to construct the model, must be entirely separate and distinct from the out-of-sample (OOS) data set. It is trivially easy using a tool like Tradestation to produce a trading system that shows stellar results in-sample, but which will immediately crash and burn when it is used in live trading. This is known as curve-fitting. And it’s by far the most common method by which scam artists try to dupe investors.

In order to demonstrate the robustness of the system prior to risking real money, a genuine trader will test his system OOS and show you the results. What you are looking for ideally is congruity between the IS and OOS results. Now by congruity, I don’t mean that they should be identical. Far from it – markets evolve and strategy performance will vary over time. But what you are hoping is that the key performance metrics in the OOS and IS periods, such as annual returns, Sharpe ratio, PNL per contract, profit ratio and win rate, will be comparable. At the very least, you would like to be able to identify some portion of the IS data set for which the strategy performance characteristics are similar to those in the OOS period.

Any – I mean ANY – ambiguity or lack of clarity about which data was used to build the model and which was used for OOS testing is a HUGE red flag. Chances are, your scam artist is already trying to fudge the issue that he curve-fitted the system.
This was the case in the recent forum post we are using as a test case. The trader made no attempt whatsoever to clarify which data was used for model development and which for testing. Immediately, I was suspicious and began looking for other evidence of curve fitting. It didn’t take me long to find it.

THE THIRD BIG RED FLAG: THE EQUITY CURVE
The first item I turned to in the performance reports was the equity curve and I immediately spotted two rather large clues that I was dealing with a fake.

The first clue was the large sign on the chart labelled “live start date”. What does this mean? This is a back-test, so all of the results are theoretical, including those after the supposed “live start date” sometime in 2013. What the faker is trying to do is imply the part of the equity curve shown after that date indicate actual performance results. He doesn’t actually claim this, so he has plausible deniability if you call him on it (“I said it was just a back test”). But he hopes that you won’t, and that, by default, you’ll accept these results are real. But they aren’t.

The second clue of fakery is much more important: the equity curve itself. When someone shows you and equity curve like the one reported by this trader, rising in a straight line from the lower left to upper right quadrants, you can be 99% confident that you are dealing with a fake.
You see, in finance there are almost never any straight lines. They are as rare as unicorns. Especially when it comes to strategy performance. They only time you will EVER see an equity curve like this is when you are looking at the equity curve of (i) a high frequency market making trading system or (ii) a fake, produced by curve fitting a strategy to the ENTIRE data set.
And this strategy was not high frequency – as we shall see, it operated on 15 minute bars, holding positions overnight.

EC Chart

THE FOURTH BIG RED FLAG: GOD’s EQUITY CURVE
I said that straight line equity curve were extremely rare. In fact, even God’s equity curve isn’t often a straight line. What does that mean?

Suppose you had a strategy that could predict with 100% accuracy whether the market would go up or down over the next bar (whether you are using daily bars, or 15 minute bars, as in our example). The system would buy (or hold) when the market was forecast to rise, and sell when the market was predicted to fall. What would the performance of such a perfect system look like? Pretty stellar, obviously. And most people would guess that the system’s equity curve would be a straight line, or maybe even exponential in shape. In fact that’s typically not the case. God’s equity curve will be sloped and kinked, just like any other equity curve. And if your suspect’s equity curve is real, it should show some commonality with God’s equity curve, by which I mean it should show changes in slope and level that reflect those seen in the perfect equity curve.

What does God’s Equity Curve look like in NG futures?

Gods EC

As you can see it’s not straight. In fact it’s concave. So a REAL equity curve should have similar characteristics, like this one, for example:

NG EC

As you can see, the equity curve of the real trading system track’s God’s Equity Curve, albeit at a much lower level. It’s concave, with an upswing during the final few months of trading, just like God’s. That’s a good sign that the strategy back-test is very likely genuine (which it is – I produced it).

Why is Gods’ Equity Curve the shape it is? The answer will vary from market to market. In the case of NG, the suggestion is that the market is becoming more efficient: simple trading strategies based on technical indicators work less well than they did five years ago. We have seen something very similar in F/X markets. During the 1970’s and 1980’s when Soros was active in the field, simple strategies like moving average crossovers made great returns, but these entirely dissipated in the 1990’s, with the advent of widely available computing power.

THE FIFTH BIG RED FLAG: THE SHILL SHOUTDOWN
When I posted my analysis, which clearly indicated fakery by this well known forum participant, I was immediately flamed by one of his supporters who shouted something to the effect that (i) everyone knows that the downward slope of God’s Equity Curve was caused by volatility and (ii) the star trader, unlike God, or me, knows about position sizing.

This attempt at misdirection in the face of awkward facts is a classic sign of fakery. What distinguishes the shill post is:

(i) Immediacy – clearly no attempt has been made to evaluate the argument or analysis. The shill simply attempts to drown out the critic with a lot of noise, as quickly as possible.

(ii) Plausibility – shills will throw around terms that lend plausibility to their objection, but which after a moment’s reflection are entirely irrelevant or, as in this case, detrimental to their own cause.

(iii) Invective – the more intemperate the post, the more likely the shill is simply trying to provide cover for the faker.

So let’s take a moment to dispose of the plausible sounding objections posted by the shill in this example.
I am going to take it as read that everyone understands that trading profitability is positively correlated with volatility. There is a huge amount of empirical research supporting that finding, but to keep it simple we can appeal to one of the cornerstones of modern finance: risk and return. The higher the volatility, i.e. the greater the risk, the greater the return traders and investors in the markets will require on their capital. This is a principle of modern financial theory that even a graduate of the Scranton college of fine art should be expected to appreciate.

So what’s the story with NG volatility? You can see the time series of NG volatility in the chart below. One feature stands out above all others: the upward slope of the curve. NG volatility has RISEN over the sample period from 2008 to 2014. Consequently, returns from trading NG futures should also have RISEN rather than fallen. One thing we can say for sure, whatever caused the concave shape in God’s Equity Curve in NG futures, it was NOT volatility!

NG Volatility

Turning to the shill’s next, plausible sounding, but dubious “explanation”, position sizing: this really is completely irrelevant. Because, as we shall see from an examination of the performance report, the track record was created by trading a constant one-lot! So this was just an attempt to sound “sophisticated” by someone trying to misdirect the reader away from the increasingly obvious evidence of fakery.

THE SIXTH BIG RED FLAG: LOW DRAWDOWNS AND OVERNIGHT GAP RISK
One of the highly unusual features of our faker’s equity curve is it’s exceptional smoothness. Low volatility in the equity curve is, in and of itself, an indicator the track record results from curve fitting. But we can get even more insight by digging into the performance report, shown below.

Perf 1
Perf 2

As you can see from the second page of the report, the strategy holds positions for an average of 57 15-minute bars, equivalent to slightly over 14 hours. So this is a low frequency strategy that takes overnight risk. Now, as any trader will know, overnight gap risk in a product like NG can be very significant and likely to be produce much larger drawdowns over a 5 year period than the $8,470 reported here.

The only other possible explanation is that the strategy is traded continuously through both day and night sessions. But this is not only itself improbable, it gives rise to another implausibility: liquidity in the overnight session is so poor that the strategy is unlikely to be able to trade more than 1-2 contracts, at most. This would be of little value to a CTA, or its customers, whatever the star trader’s protestations that his “clients are happy”.

There is no plausible way to resolve the disconnection between the low drawdown, overnight gap risk and market illiquidity. The most plausible explanation: the back-test is a curve fitting exercise.

THE SEVENTH AN FINAL BIG RED FLAG: INCONSISTENCY BETWEEN PERFORMANCE METRICS
As any experienced strategy developer knows, you can get some of the things you want, but you can never achieve all of them. Amongst the desirable features to be maximized are
• Profit factor
• Average PNL per contract
• Percentage win rate

There is a trade-off between the features. A high PNL per contract typically means you are trading less frequently, with longer hold periods, and consequently the percentage win rate tends to be lower. Alternatively, you can increase the win rate, at the cost of lowering the average PNL per contract and/or the profit factor. And so on.

This strategy purports to have it all: a high average PNL per contract resulting from low frequency trading, coupled with good percentage win rate of over 50% and profit factor. A win rate of much over 40% is highly unusual for a momentum strategy entering and exiting with market or stop orders – and its almost inconceivable for a strategy with a PNL per contract and profit factor as large as suggested here.

CONCLUSION
This back-test fails the sniff test on so many levels, I would rate the chance of it being real as less than 1 in 1000.
The final, conclusive proof of fakery is that the “star trader” responsible for producing the report was unable and/or unwilling to attempt to answer even a single one of the criticisms.

So, be warned. If you see forum members banding about track records like this one, you can be sure that they and their strategies are likely to be fake, and not to be trusted.