Backtest vs. Trading Reality

Kris Sidial, whose Twitter posts are often interesting, recently posted about the reality of trading profitability vs backtest performance, as follows:

While I certainly agree that the latter example is more representative of a typical trader’s P&L, I don’t concur that the first P&L curve is necessarily “99.9% garbage”. There are many strategies that have equity curves that are smoother and more monotonic than those of Kris’s Skeleton Case V2 strategy. Admittedly, most of these lie in the area of high frequency, which is not Kris’s domain expertise. But there are also lower frequency strategies that produce results which are not dissimilar to those shown the first chart.

As a case in point, consider the following strategy for the S&P 500 E-Mini futures contract, described in more detail below. The strategy was developed using 15-minute bar data from 1999 to 2012, and traded live thereafter. The live and backtest performance characteristics are almost indistinguishable, not only in terms of rate of profit, but also in regard to strategy characteristics such as the no. of trades, % win rate and profit factor.

Just in case you think the picture is a little too rosy, I would point out that the average profit factor is 1.25, which means that the strategy is generating only 25% more in profits than losses. There will be big losing trades from time to time and long sequences of losses during which the strategy appears to have broken down. It takes discipline to resist the temptation to “fix” the strategy during extended drawdowns and instead rely on reversion to the mean rate of performance over the long haul. One source of comfort to the trader through such periods is that the 60% win rate means that the majority of trades are profitable.

As you read through the replies to Kris’s post, you will see that several of his readers make the point that strategies with highly attractive equity curves and performance characteristics are typically capital constrained. This is true in the case of this strategy, which I trade with a very modest amount of (my own) capital. Even trading one-lots in the E-Mini futures I occasionally experience missed trades, either on entry or exit, due to limit orders not being filled at the high or low of a bar. In scaling the strategy up to something more meaningful such as a 10-lot, there would be multiple partial fills to deal with. But I think it would be a mistake to abandon a high performing strategy such as this just because of an apparent capacity constraint. There are several approaches one can explore to address the issue, which may be enough to make the strategy scalable.

Where (as here) the issue of scalability relates to the strategy fill rate on limit orders, a good starting point is to compute the extreme hit rate, which is the proportion of trades that take place at the high or low of the bar. As a rule of thumb, for strategies running on typical low frequency infrastructure an extreme hit rate of 10% or less is manageable; anything above that level quickly becomes problematic. If the extreme hit rate is very high, e.g. 25% or more, then you are going to have to pay a great deal of attention to the issues of latency and order priority to make the strategy viable in practise. Ultimately, for a high frequency market making strategy, most orders are filled at the extreme of each “bar”, so almost all of the focus in on minimizing latency and maintaining a high queue priority, with all of the attendant concerns regarding trading hardware, software and infrastructure.

Next, you need a strategy for handling missed trades. You could, for example, decide to skip any entry trades that are missed, while manually entering unfilled exit trades at the market. Or you could post market orders for both entry and exit trades if they are not filled. An extreme solution would be to substitute market-if-touched orders for limit orders in your strategy code. But this would affect all orders generated by the system, not just the 10% at the high or low of the bar and is likely to have a very adverse affect on overall profitability, especially if the average trade is low (because you are paying an extra tick on entry and exit of every trade).

The above suggests that you are monitoring the strategy manually, running simulation and live versions side by side, so that you can pick up any trades that the strategy should have taken, but which have been missed. This may be practical for a strategy that trades during regular market hours, but not for one that also trades the overnight session.

An alternative approach, one that is commonly applied by systematic traders, is to automate the handling of missed trades. Typically the trader will set a parameter that converts a limit order to a market order X seconds after a limit price has been traded but not filled. Of course, this will result in paying up an extra tick (or more) to enter trades that perhaps would have been filled if one had waited longer than X seconds. It will have some negative impact on strategy profitability, but not too much if the extreme hit rate is low. I tend to use this method for exit trades, preferring to skip any entry trades that don’t get filled at the limit price.

Beyond these simple measures, there are several other ways to extend the capacity of the strategy. An obvious place to start is by evaluating strategy performance on different session times and bar lengths. So, in this case, we might look at deploying the strategy on both the day and night sessions. We can also evaluate performance on bars of different length. This will give different entry and exit points for individual trades and trades that are at the extreme of a bar on one timeframe may not be at the high or low of a bar on the other timescale. For example, here is the (simulated) performance of the strategy on 13 minute bars:

There is a reason for choosing a bar interval such as 13 minutes, rather than the more commonplace 5- or 10 minutes, as explained in this post:

Finally, it is worth exploring whether the strategy can be applied to other related markets such as NQ futures, for example. Typically this will entail some change to the strategy code to reflect the difference in price levels, but the thrust of the strategy logic will be similar. Another approach is to use the signals from the current strategy as inputs – i.e. alpha generators – for a derivative strategy, such as trading the SPY ETF based on signals from the ES strategy. The performance of the derived strategy may not be as good, but in a product like SPY the capacity might be larger.

Long/Short Stock Trading Strategy

The Long-Short Stock Trader strategy uses a quantitative model to introduce market orders, both entry and exits. The model looks for divergencies between stock price and its current volatility, closing the position when the Price-volatility gap is closed.  The strategy is designed to obtain a better return on risk than S&P500 index and the risk management is focused on obtaining a lower drawdown and volatility than index.
The model trades only Large Cap stocks, with high liquidity and without scalability problems. Thanks to the high liquidity, market orders are filled without market impact and at the best market prices.

For more information and back-test results go here.

The Long-Short Trader is the first strategy launched on the Systematic Algotrading Platform under our new Strategy Manager Program.

Performance Summary


 

Monthly Returns

 

Value of $100,000 Portfolio

 

 

 

 

Career Opportunity for Quant Traders

Career Opportunity for Quant Traders as Strategy Managers

We are looking for 3-4 traders (or trading teams) to showcase as Strategy Managers on our Algorithmic Trading Platform.  Ideally these would be systematic quant traders, since that is the focus of our fund (although they don’t have to be).  So far the platform offers a total of 10 strategies in equities, options, futures and f/x.  Five of these are run by external Strategy Managers and five are run internally.

The goal is to help Strategy Managers build a track record and gain traction with a potential audience of over 100,000 members.  After a period of 6-12 months we will offer successful managers a position as a PM at Systematic Strategies and offer their strategies in our quantitative hedge fund.  Alternatively, we will assist the manager is raising external capital in order to establish their own fund.

If you are interested in the possibility (or know a talented rising star who might be), details are given below.

Manager Platform

Volatility Trading Styles

The VIX Surge of Feb 2018

Volatility trading has become a popular niche in investing circles over the last several years.  It is easy to understand why:  with yields at record lows it has been challenging to find an alternative to equities that offers a respectable return.  Volatility, however, continues to be volatile (which is a good thing in this context) and the steepness of the volatility curve has offered investors attractive returns by means of the volatility carry trade.  In this type of volatility trading the long end of the vol curve is sold, often using longer dated futures in the CBOE VIX Index, for example.  The idea is that profits are generated as the contract moves towards expiration, “riding down” the volatility curve as it does so.  This is a variant of the ever-popular “riding down the yield curve” strategy, a staple of fixed income traders for many decades.  The only question here is what to use to hedge the short volatility exposure – highly correlated S&P500 futures are a popular choice, but the resulting portfolio is exposed to significant basis risk.  Besides, when the volatility curve flatten and inverts, as it did in spectacular fashion in February, the transition tends to happen very quickly, producing a substantial losses on the portfolio.  These may be temporary, if the volatility spike is small or short-lived, but as traders and investors discovered in the February drama, neither of these two desirable outcomes is guaranteed.  Indeed as I pointed out in an earlier post this turned out to be the largest ever two-day volatility surge in history.  The results for many hedge funds, especially in the quant sector were devastating, with several showing high single digit or double-digit losses for the month.

VIX_Spike_1

 

Over time, investors have become more familiar with the volatility space and have learned to be wary of strategies like volatility carry or option selling, where the returns look superficially attractive, until a market event occurs.  So what alternative approaches are available?

An Aggressive Approach to Volatility Trading

In my blog post Riders on the Storm  I described one such approach:  the Option Trader strategy on our Algo Trading Platform made a massive gain of 27% for the month of February and as a result strategy performance is now running at over 55% for 2018 YTD, while maintaining a Sharpe Ratio of 2.23.

Option Trader

 

The challenge with this style of volatility trading is that it requires a trader (or trading system) with a very strong stomach and an investor astute enough to realize that sizable drawdowns are in a sense “baked in” for this trading strategy and should be expected from time to time.  But traders are often temperamentally unsuited to this style of trading – many react by heading for the hills and liquidating positions at the first sign of trouble; and the great majority of investors are likewise unable to withstand substantial drawdowns, even if the eventual outcome is beneficial.

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The Market Timing Approach

So what alternatives are there?  One way of dealing with the problem of volatility spikes is simply to try to avoid them.  That means developing a strategy logic that step aside altogether when there is a serious risk of an impending volatility surge.  Market timing is easy to describe, but very hard to implement successfully in practice.  The VIX Swing Trader strategy on the Systematic Algotrading platform attempts to do just that, only trading when it judges it safe to do so. So, for example, it completely side-stepped the volatility debacle in August 2015, ending the month up +0.74%.  The strategy managed to do the same in February this year, finishing ahead +1.90%, a pretty creditable performance given how volatility funds performed in general.  One helpful characteristic of the strategy is that it trades the less-volatile mid-section of the volatility curve, in the form of the VelocityShares Daily Inverse VIX MT ETN (ZIV).  This ensures that the P&L swings are much less dramatic than for strategies exposed to the front end of the curve, as most volatility strategies are.

VIX Swing Trader1 VIX Swing Trader2

A potential weakness of the strategy is that it will often miss great profit opportunities altogether, since its primary focus is to keep investors out of trouble. Allied to this, the system may trade only a handful of times each month.  Indeed, if you look at the track record above you find find months in which the strategy made no trades at all. From experience, investors are almost as bad at sitting on their hands as they are at taking losses:  patience is not a highly regarded virtue in the investing community these days.  But if you are a cautious, patient investor looking for a source of uncorrelated alpha, this strategy may be a good choice. On the other hand, if you are looking for high returns and are willing to take the associated risks, there are choices better suited to your goals.

The Hedging Approach to Volatility Trading

A “middle ground” is taken in our Hedged Volatility strategy. Like the VIX Swing Trader this strategy trades VIX ETFs/ETNs, but it does so across the maturity table. What distinguishes this strategy from the others is its use of long call options in volatility products like the iPath S&P 500 VIX ST Futures ETN (VXX) to hedge the short volatility exposure in other ETFs in the portfolio.  This enables the strategy to trade much more frequently, across a wider range of ETF products and maturities, with the security of knowing that the tail risk in the portfolio is protected.  Consequently, since live trading began in 2016, the strategy has chalked up returns of over 53% per year, with a Sharpe Ratio of 2 and Sortino Ratio above 3.  Don’t be confused by the low % of trades that are profitable:  the great majority of these loss-making “trades” are in fact hedges, which one would expect to be losers, as most long options trades are.  What matters is the overall performance of the strategy.

Hedged Volatility

All of these strategies are available on our Systematic Algotrading Platform, which offers investors the opportunity to trade the strategies in their own brokerage account for a monthly subscription fee.

The Multi-Strategy Approach

The approach taken by the Systematic Volatility Strategy in our Systematic Strategies hedge fund again seeks to steer a middle course between risk and return.  It does so by using a meta-strategy approach that dynamically adjusts the style of strategy deployed as market conditions change.  Rather than using options (the strategy’s mandate includes only ETFs) the strategy uses leveraged ETFs to provide tail risk protection in the portfolio. The strategy has produced an average annual compound return of 38.54% since live trading began in 2015, with a Sharpe Ratio of 3.15:

Systematic Volatility Strategy 1 Page Tear Sheet June 2018

 

A more detailed explanation of how leveraged ETFs can be used in volatility trading strategies is given in an earlier post:

http://jonathankinlay.com/2015/05/investing-leveraged-etfs-theory-practice/

 

Conclusion:  Choosing the Investment Style that’s Right for You

There are different styles of volatility trading and the investor should consider carefully which best suits his own investment temperament.  For the “high risk” investor seeking the greatest profit the Option Trader strategy in an excellent choice, producing returns of +176% per year since live trading began in 2016.   At the other end of the spectrum, the VIX Swing trader is suitable for an investor with a cautious trading style, who is willing to wait for the right opportunities, i.e. ones that are most likely to be profitable.  For investors seeking to capitalize on opportunities in the volatility space, but who are concerned about the tail risk arising from major market corrections, the Hedge Volatility strategy offers a better choice.  Finally, for investors able to invest $250,000 or more, a hedge fund investment in our Systematic Volatility strategy offers the highest risk-adjusted rate of return.

New Algotrading Platform

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Systematic Strategies is pleased to announce the launch of its new Algo Trading Platform.  This will allow subscribers to follow a selection of our best strategies in equities, futures and options, for a low monthly subscription fee.

There is no minimum account size, and accounts of up to $250,000 can be traded on the platform.

The strategies are fully systematic and trades are executed automatically in your existing brokerage account, or you can open an account at one of our supported brokers, which include  Interactive Brokers, NinjaTrader, CQG, Gain Capital, AMP, Garwood, and many others.

 

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SYSTEMATIC STRATEGIES LLC

Systematic Strategies is an alternative investments firm utilizing quantitative modeling techniques to develop profitable trading strategies for deployment into global markets. Systematic Strategies seeks qualified investors as defined in Regulation D of the Securities Act of 1933. For information please contact us at info@ systematic-strategies.com or visit www.systematic-strategies.com.

 

RISK DISCLOSURE

This web site and the information contained herein is not and must not be construed as an offer to sell securities. Certain statements included in this web site, including, without limitation, statements regarding investment goals, strategies, and statements as to the manager’s expectations or opinions are forward-looking statements within the meaning of Section 27A of the Securities Act of 1933 (the “Securities Act”) and Section 21E of the Securities Exchange Act of 1944 (the “Exchange Act”) and are subject to risks and uncertainties. The factors discussed herein could cause actual results and development to be materially different from those expressed in or implied by such forward-looking statements. Accordingly, the information in this web site cannot be construed as to be guaranteed.

Systematic Futures Trading

In its proprietary trading, Systematic Strategies primary focus in on equity and volatility strategies, both low and high frequency. In futures, the emphasis is on high frequency trading, although we also run one or two lower frequency strategies that have higher capacity, such as the Futures WealthBuilder. The version of WealthBuilder running on the Collective 2 site has performed very well in 2017, with net returns of 30% and a Sharpe Ratio of 3.4:

Futures C2 oct 2017

 

In the high frequency space, our focus is on strategies with very high Sharpe Ratios and low drawdowns. We trade a range of futures products, including equity, fixed income, metals and energy markets. Despite the current low levels of market volatility, these strategies have performed well in 2017:

HFT Futures Oct 2017 (NFA)

Building high frequency strategies with double-digit Sharpe Ratios requires a synergy of computational capability and modeling know-how. The microstructure of futures markets is, of course, substantially different to that of equity or forex markets and the components of the model that include microstructure effects vary widely from one product to another. There can be substantial variations too in the way that time is handled in the model – whether as discrete or continuous “wall time”, in trade time, or some other measure. But some of the simple technical indicators we use – moving averages, for example – are common to many models across different products and markets. Machine learning plays a role in most of our trading strategies, including high frequency.

Here are some relevant blog posts that you may find interesting:

http://jonathankinlay.com/2016/04/high-frequency-trading-equities-vs-futures/

 

http://jonathankinlay.com/2015/05/designing-scalable-futures-strategy/

 

http://jonathankinlay.com/2014/10/day-trading-system-in-vix-futures/

Reflections on Careers in Quantitative Finance

CMU’s MSCF Program

Carnegie Mellon’s Steve Shreve is out with an interesting post on careers in quantitative finance, with his commentary on the changing landscape in quantitative research and the implications for financial education.

I taught at Carnegie Mellon in the late 1990’s, including its excellent Master’s program in quantitative finance that Steve co-founded, with Sanjay Srivastava.  The program was revolutionary in many ways and was immediately successful and rapidly copied by rival graduate schools (I help to spread the word a little, at Cambridge).

Fig1The core of the program remains largely unchanged over the last 20 years, featuring Steve’s excellent foundation course in stochastic calculus;  but I am happy to see that the school has added many, new and highly relevant topics to the second year syllabus, including market microstructure, machine learning, algorithmic trading and statistical arbitrage.  This has moved the program in terms of its primary focus, which was originally financial engineering, to include coverage of subjects that are highly relevant to quantitative investment research and trading.

It was this combination of sound theoretical grounding with practitioner-oriented training that made the program so successful.  As I recall, every single graduate was successful in finding a job on Wall Street, often at salaries in excess of $200,000, a considerable sum in those days.  One of the key features of the program was that it combined theoretical concepts with practical training, using a simulated trading floor gifted by Thomson Reuters (a model later adopted btrading-floor-1y the ICMA centre at the University of Reading in the UK).  This enabled us to test students’ understanding of what they had been taught, using market simulation models that relied upon key theoretical ideas covered in the program.  The constant reinforcement of the theoretical with the practical made for a much deeper learning experience for most students and greatly facilitated their transition to Wall Street.

Masters in High Frequency Finance

While CMU’s program has certainly evolved and remains highly relevant to the recruitment needs of Wall Street firms, I still believe there is an opportunity for a program focused exclusively on high frequency finance, as previously described in this post.  The MHFF program would be more computer science oriented, with less emphasis placed on financial engineering topics.  So, for instance, students would learn about trading hardware and infrastructure, the principles of efficient algorithm design, as well as HFT trading techniques such as order layering and priority management.  The program would also cover HFT strategies such as latency arbitrage, market making, and statistical arbitrage.  Students would learn both lower level (C++, Java) and higher level (Matlab, R) programming languages and there is  a good case for a mandatory machine code programming course also.  Other core courses might include stochastic calculus and market microstructure.

Who would run such a program?  The ideal school would have a reputation for excellent in both finance and computer science. CMU is an obvious candidate, as is MIT, but there are many other excellent possibilities.

Careers

I’ve been involved in quantitative finance since the beginning:  I recall programming one of the first 68000 Assembler microcomputers in the 1980s, which was ultimately used for an F/X system at a major UK bank. The ensuing rapid proliferation of quantitative techniques in finance has been fueled by the ubiquity of cheap computing power, facilitating the deployment of quantitate techniques that would previously been impractical to implement due to their complexity.  A good example is the machine learning techniques that now pervade large swathes of the finance arena, from credit scoring to HFT trading.  When I first began working in that field in the early 2000’s it was necessary to assemble a fairly sizable cluster of cpus to handle the computation load. These days you can access comparable levels of computational power on a single server and, if you need more, you can easily scale up via Azure or EC2.

fig3It is this explosive growth in computing power  that has driven the development of quantitative finance in both the financial engineering and quantitative investment disciplines. As the same time, the huge reduction in the cost of computing power has leveled the playing field and lowered barriers to entry.  What was once the exclusive preserve of the sell-side has now become readily available to many buy-side firms.  As a consequence, much of the growth in employment opportunities in quantitative finance over the last 20 years has been on the buy-side, with the arrival of quantitative hedge funds and proprietary trading firms, including my own, Systematic Strategies.  This trend has a long way to play out so that, when also taking into consideration the increasing restrictions that sell-side firms face in terms of their proprietary trading activity, I am inclined to believe that the buy-side will offer the best employment opportunities for quantitative financiers over the next decade.

It was often said that hedge fund managers are typically in their 30’s or 40’s when they make the move to the buy-side. That has changed in the last 15 years, again driven by the developments in technology.  These days you are more likely to find the critically important technical skills in younger candidates, in their late 20’s or early 30’s.  My advice to those looking for a career in quantitative finance, who are unable to find the right job opportunity, would be: do what every other young person in Silicon Valley is doing:  join a startup, or start one yourself.

 

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