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/

Futures WealthBuilder – June 2017: +4.4%

The Futures WealthBuilder product is an algorithmic CTA strategy that trades several highly liquid futures contracts using machine learning algorithms.  More details about the strategy are given in this blog post.

We offer a version of the strategy on the Collective 2 site (see here for details) that the user can subscribe to for a very modest fee of only $149 per month.  The Collective 2 version of the strategy is unlikely to perform as well as the product we offer in our Systematic Strategies Fund, which trades a much wider range of futures products.  But the strategy is off to an excellent start, making +4.4% in June and is now up 6.7% since inception in May.  In June the strategy made profitable trades in US Bonds, Euro F/X and VIX futures, and the last seven trades in a row have been winners.

You can find full details of the strategy, including a listing of all of the trades, on the Collective 2 site.

Subscribers can sign up for a free, seven day trial and thereafter they can choose to trade the strategy automatically in their own brokerage account, using the Collective 2 api.

Futures WealthBuilder June 2017

Futures WealthBuilder

We are launching a new product, the Futures WealthBuilder,  a CTA system that trades futures contracts in several highly liquid financial and commodity markets, including SP500 EMinis, Euros, VIX, Gold, US Bonds, 10-year and five-year notes, Corn, Natural Gas and Crude Oil.  Each  component strategy uses a variety of machine learning algorithms to detect trends, seasonal effects and mean-reversion.  We develop several different types of model for each market, and deploy them according to their suitability for current market conditions.

Performance of the strategy (net of fees) since 2013 is detailed in the charts and tables below.  Notable features include a Sharpe Ratio of just over 2, an annual rate of return of 190% on an account size of $50,000, and a maximum drawdown of around 8% over the last three years.  It is worth mentioning, too, that the strategy produces approximately equal rates of return on both long and short trades, with an overall profit factor above 2.

 

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Low Correlation

Despite a high level of correlation between several of the underlying markets, the correlation between the component strategies of Futures WealthBuilder are, in the majority of cases, negligibly small (with a few exceptions, such as the high correlation between the 10-year and 5-year note strategies).  This accounts for the relative high level of return in relation to portfolio risk, as measured by the Sharpe Ratio.   We offer strategies in both products chiefly as a mean of providing additional liquidity, rather than for their diversification benefit.

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Strategy Robustness

Strategy robustness is a key consideration in the design stage.  We use Monte Carlo simulation to evaluate scenarios not seen in historical price data in order to ensure consistent performance across the widest possible range of market conditions.  Our methodology introduces random fluctuations to historical prices, increasing or decreasing them by as much as 30%.  We allow similar random fluctuations in that value strategy parameters, to ensure that our models perform consistently without being overly-sensitive to the specific parameter values we have specified.  Finally, we allow the start date of each sub-system to vary randomly by up to a year.

The effect of these variations is to produce a wide range of outcomes in terms of strategy performance.  We focus on the 5% worst outcomes, ranked by profitability, and select only those strategies whose performance is acceptable under these adverse scenarios.  In this way we reduce the risk of overfitting the models while providing more realistic expectations of model performance going forward.  This procedure also has the effect of reducing portfolio tail risk, and the maximum peak-to-valley drawdown likely to be produced by the strategy in future.

GC Daily Stress Test

Futures WealthBuilder on Collective 2

We will be running a variant of the Futures WealthBuilder strategy on the Collective 2 site, using a subset of the strategy models in several futures markets(see this page for details).  Subscribers will be able to link and auto-trade the strategy in their own account, assuming they make use of one of the approved brokerages which include Interactive Brokers, MB Trading and several others.

Obviously the performance is unlikely to be as good as the complete strategy, since several component sub-strategies will not be traded on Collective 2.  However, this does give the subscriber the option to trial the strategy in simulation before plunging in with real money.

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Alpha Extraction and Trading Under Different Market Regimes

Market Noise and Alpha Signals

One of the perennial problems in designing trading systems is noise in the data, which can often drown out an alpha signal.  This is turn creates difficulties for a trading system that relies on reading the signal, resulting in greater uncertainty about the trading outcome (i.e. greater volatility in system performance).  According to academic research, a great deal of market noise is caused by trading itself.  There is apparently not much that can be done about that problem:  sure, you can trade after hours or overnight, but the benefit of lower signal contamination from noise traders is offset by the disadvantage of poor liquidity.  Hence the thrust of most of the analysis in this area lies in the direction of trying to amplify the signal, often using techniques borrowed from signal processing and related engineering disciplines.

There is, however, one trick that I wanted to share with readers that is worth considering.  It allows you to trade during normal market hours, when liquidity is greatest, but at the same time limits the impact of market noise.

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Quantifying Market Noise

How do you measure market noise?  One simple approach is to start by measuring market volatility, making the not-unreasonable assumption that higher levels of volatility are associated with greater amounts of random movement (i.e noise). Conversely, when markets are relatively calm, a greater proportion of the variation is caused by alpha factors.  During the latter periods, there is a greater information content in market data – the signal:noise ratio is larger and hence the alpha signal can be quantified and captured more accurately.

For a market like the E-Mini futures, the variation in daily volatility is considerable, as illustrated in the chart below.  The median daily volatility is 1.2%, while the maximum value (in 2008) was 14.7%!

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The extremely long tail of the distribution stands out clearly in the following histogram plot.

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Obviously there are times when the noise in the process is going to drown out almost any alpha signal. What if we could avoid such periods?

Noise Reduction and Model Fitting

Let’s divide our data into two subsets of equal size, comprising days on which volatility was lower, or higher, than the median value.  Then let’s go ahead and use our alpha signal(s) to fit a trading model, using only data drawn from the lower volatility segment.

This is actually a little tricky to achieve in practice:  most software packages for time series analysis or charting are geared towards data occurring at equally spaced points in time.  One useful trick here is to replace the actual date and time values of the observations with sequential date and time values, in order to fool the software into accepting the data, since there are no longer any gaps in the timestamps.  Of course, the dates on our time series plot or chart will be incorrect. But that doesn’t matter:  as long as we know what the correct timestamps are.

An example of such a system is illustrated below.  The model was fitted  to  3-Min bar data in EMini futures, but only on days with market volatility below the median value, in the period from 2004 to 2015.  The strategy equity curve is exceptionally smooth, as might be expected, and the performance characteristics of the strategy are highly attractive, with a 27% annual rate of return, profit factor of 1.58 and Sharpe Ratio approaching double-digits.

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Dealing with the Noisy Trading Days

Let’s say you have developed a trading system that works well on quiet days.  What next?  There are a couple of ways to go:

(i) Deploy the model only on quiet trading days; stay out of the market on volatile days; or

(ii) Develop a separate trading system to handle volatile market conditions.

Which approach is better?  It is likely that the system you develop for trading quiet days will outperform any system you manage to develop for volatile market conditions.  So, arguably, you should simply trade your best model when volatility is muted and avoid trading at other times.  Any other solution may reduce the overall risk-adjusted return.  But that isn’t guaranteed to be the case – and, in fact, I will give an example of systems that, when combined, will in practice yield a higher information ratio than any of the component systems.

Deploying the Trading Systems

The astute reader is likely to have noticed that I have “cheated” by using forward information in the model development process.  In building a trading system based only on data drawn from low-volatility days, I have assumed that I can somehow know in advance whether the market is going to be volatile or not, on any given day.  Of course, I don’t know for sure whether the upcoming session is going to be volatile and hence whether to deploy my trading system, or stand aside.  So is this just a purely theoretical exercise?  No, it’s not, for the following reasons.

The first reason is that, unlike the underlying asset market, the market volatility process is, by comparison, highly predictable.  This is due to a phenomenon known as “long memory”, i.e. very slow decay in the serial autocorrelations of the volatility process.  What that means is that the history of the volatility process contains useful information about its likely future behavior.  [There are several posts on this topic in this blog – just search for “long memory”].  So, in principle, one can develop an effective system to forecast market volatility in advance and hence make an informed decision about whether or not to deploy a specific model.

But let’s say you are unpersuaded by this argument and take the view that market volatility is intrinsically unpredictable.  Does that make this approach impractical?  Not at all.  You have a couple of options:

You can test the model built for quiet days on all the market data, including volatile days.  It may perform acceptably well across both market regimes.

For example, here are the results of a backtest of the model described above on all the market data, including volatile and quiet periods, from 2004-2015.  While the performance characteristics are not quite as good, overall the strategy remains very attractive.

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Another approach is to develop a second model for volatile days and deploy both low- and high-volatility regime models simultaneously.  The trading systems will interact (if you allow them to) in a highly nonlinear and unpredictable way.  It might turn out badly – but on the other hand, it might not!  Here, for instance, is the result of combining low- and high-volatility models simultaneously for the Emini futures and running them in parallel.  The result is an improvement (relative to the low volatility model alone), not only in the annual rate of return (21% vs 17.8%), but also in the risk-adjusted performance, profit factor and average trade.

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CONCLUSION

Separating the data into multiple subsets representing different market regimes allows the system developer to amplify the signal:noise ratio, increasing the effectiveness of his alpha factors. Potentially, this allows important features of the underlying market dynamics to be captured in the model more easily, which can lead to improved trading performance.

Models developed for different market regimes can be tested across all market conditions and deployed on an everyday basis if shown to be sufficiently robust.  Alternatively, a meta-strategy can be developed to forecast the market regime and select the appropriate trading system accordingly.

Finally, it is possible to achieve acceptable, or even very good results, by deploying several different models simultaneously and allowing them to interact, as the market moves from regime to regime.

 

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