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.

Fig 6

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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Signal Processing and Sample Frequency

The Importance of Sample Frequency

Too often we apply a default time horizon for our trading, whether it below (daily, weekly) or higher (hourly, 5 minute) frequency.  Sometimes the choice is dictated by practical considerations, such as a desire to avoid overnight risk, or the (lack of0 availability of low-latency execution platform.

But there is an alternative approach to the trade frequency decision that often yields superior results in terms of trading performance.    The methodology derives from signal processing and the idea essentially is to use Fourier transforms to help identify the cyclical behavior of the strategy alpha and hence determine the best time-frames for sampling and trading.  I wrote about this is a previous blog post, in which I described how to use principal components analysis to investigate the factors driving the returns in various pairs trading strategies.  Here I want to take a simpler approach, in which we use Fourier analysis to select suitable sample frequencies.  The idea is simply to select sample frequencies where the signal strength appears strongest, in the hope that it will lead to superior performance characteristics in what strategy we are trying to develop.

Signal Decomposition for S&P500 eMini Futures

Let’s take as an example the S&P 500 emini futures contract. The chart below shows the continuous ES futures contract plotted at 1-minute intervals from 1998. At the bottom of the chart I have represented the signal analysis as a bar chart (in blue), with each bar representing the amplitude at each frequency. The white dots on the chart identify frequencies that are spaced 10 minutes apart.  It is immediately evident that local maxima in the spectrum occur around 40 mins, 60 mins and 120 mins.  So a starting point for our strategy research might be to look at emini data sampled at these frequencies.  Incidentally, it is worth pointing out that I have restricted the session times to 7AM – 4PM EST, which is where the bulk of the daily volume and liquidity tend to occur.  You may get different results if you include data from the Globex session.

Emini Signal

This is all very intuitive and unsurprising: the clearest signals occur at frequencies that most traders typically tend to trade, using hourly data, for example. Any strategy developer is already quite likely to consider these and other common frequencies as part of their regular research process.  There are many instances of successful trading strategies built on emini data sampled at 60 minute intervals.

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Signal Decomposition for US Bond Futures

Let’s look at a rather more interesting example:  US (30 year) Bond futures. Unlike the emini contract, the spectral analysis of the US futures contract indicates that the strongest signal by far occurs at a frequency of around 47 minutes.  This is decidedly an unintuitive outcome – I can’t think of any reason why such a strong signal should appear at this cycle length, but, statistically it does. 

US Bond futures

Does it work?  Readers can judge for themselves:  below is an example of an equity curve for a strategy on US futures sampled at 47 minute frequency over the period from 2002.  The strategy has performed very consistently, producing around $25,000 per contract per year, after commissions and slippage.

US futures EC

Conclusion

While I have had similar success with products as diverse as Corn and VIX futures, the frequency domain approach is by no means a panacea:  there are plenty of examples where I have been unable to construct profitable strategies for data sampled at the frequencies with very strong signals. Conversely, I have developed successful strategies using data at frequencies that hardly registered at all on the spectrum, but which I selected for other reasons.  Nonetheless, spectral analysis (and signal processing in general) can be recommended as a useful tool in the arsenal of any quantitative analyst.

Making Money with High Frequency Trading

There is no standard definition of high frequency trading, nor a single type of strategy associated with it. Some strategies generate returns, not by taking any kind of view on market direction, but simply by earning Exchange rebates. In other cases the strategy might try to trade ahead of the news as it flows through the market, from stock to stock (or market to market).  Perhaps the most common and successful approach to HFT is market making, where one tries to earn (some fraction of) the spread by constantly quoting both sides of the market.  In the latter approach, which involves processing vast numbers of order messages and other market data in order to decide whether to quote (or pull a quote), latency is of utmost importance.  I would tend to argue that HFT market making owes its success as much, or more, to computer science than it does to trading or microstructure theory.

By contrast, Systematic Strategies’s approach to HFT has always been model-driven.  We are unable to outgun firms like Citadel or Getco in terms of their speed of execution; so, instead, we focus on developing theoretical models of market behavior, on the assumption that we are more likely to identify a source of true competitive advantage that way.  This leads to slower, less latency-sensitive strategies (the models have to be re-estimated or recomputed in real time), but which may nonetheless trade hundreds of times a day.

A good example is provided by our high frequency scalping strategy in Corn futures, which trades around 100-200 times a day, with a win rate of over 80%.

Corn Monthly PNL EC

 

One of the most important considerations in engineering a HFT strategy of this kind is to identify a suitable bar frequency.  We find that our approach works best using data at frequencies of 1-5 minutes, trading at latencies of around 1 millisec, whereas other firms are reacting to data tick-by-tick, with latencies measured in microseconds.

Often strategies are built using only data derived from with a single market, based on indicators involving price action, pattern trading rules, volume or volatility signals.  In other cases, however, signals are derived from other, related markets: the VXX-ES-TY complex would be a typical example of this kind of inter-market approach.

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When we build strategies we often start by using a simple retail platform like TradeStation or MultiCharts.  We know that if the strategy can make money on a platform with retail levels of order and market data latency (and commission rates), then it should perform well when we transfer it to a production environment, with much lower latencies and costs.  We might be able to trade only 1-2 contracts in TradeStation, but in production we might aim to scale that up to 10-15 contract per trade, or more, depending on liquidity.  For that reason we prefer to trade only intraday, when market liquidity is deepest; but we often find sufficient levels of liquidity to make trading worthwhile 1-2 hours before the open of the day session.

Generally, while we look for outside money for our lower frequency hedge fund strategies, we tend not to do so for our HFT strategies.  After all, what’s the point?  Each strategy has limited capacity and typically requires no more than a $100,000 account, at most.  And besides, with Sharpe Ratios that are typically in double-digits, it’s usually in our economic interest to use all of the capacity ourselves.  Nor do we tend to license strategies to other trading firms.  Again, why would we?  If the strategies work, we can earn far more from trading rather than licensing them.

We have, occasionally, developed strategies for other firms for markets in which we have no interest (the KOSPI springs to mind).  But these cases tend to be the exception, rather than the rule.

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