Predicting volatility

Predicting volatility is a very old topic. Every finance student has been taught to use the GARCH model for that. But like most things we learned in school, we don't necessarily expect them to be useful in practice, or to work well out-of-sample. (When was the last time you need to use calculus in your job?) But out of curiosity, I did a quick investigation of its power on predicting the volatility of SPY daily close-to-close returns. I estimated the parameters of a GARCH model on training data from December 21, 2005 to December 5, 2011 using Matlab's Econometric toolbox, and tested how often the sign of the predicted 1-day change in volatility agree with reality on the test set from December 6, 2011 to November 25, 2015. (One-day change in realized volatility is defined as the change in the absolute value of the 1-day return.) A pleasant surprise: the agreement is 58% of the days.

If this were the accuracy for predicting the sign of the SPY return itself, we should prepare to retire in luxury. Volatility is easier to predict than signed returns, as every finance student has also been taught. But what good is a good volatility prediction? Would that be useful to options traders, who can trade implied volatilities instead of directional returns? The answer is yes, realized volatility prediction is useful for implied volatility prediction, but not in the way you would expect.

If GARCH tells us that the realized volatility will increase tomorrow, most of us would instinctively go out and buy ourselves some options (i.e. implied volatility). In the case of SPY, we would probably go buy some VXX. But that would be a terrible mistake. Remember that the volatility we predicted is an unsigned return: a prediction of increased volatility may mean a very bullish day tomorrow. A high positive return in SPY is usually accompanied by a steep drop in VXX. In other words, an increase in realized volatility is usually accompanied by a decrease in implied volatility in this case. But what is really strange is that this anti-correlation between change in realized volatility and change in implied volatility also holds when the return is negative (57% of the days with negative returns). A very negative return in SPY is indeed usually accompanied by an increase in implied volatility or VXX, inducing positive correlation. But on average, an increase in realized volatility due to negative returns is still accompanied by a decrease in implied volatility.

The upshot of all these is that if you predict the volatility of SPY will increase tomorrow, you should short VXX instead.

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Industry Update

• Quantiacs.com just launched a trading system competition with guaranteed investments of $2.25M for the best three trading systems. (Quantiacs helps Quants get investments for their trading algorithms and helps investors find the right trading system.)
• A new book called "Momo Traders - Tips, Tricks, and Strategies from Ten Top Traders" features extensive interviews with ten top day and swing traders who find stocks that move and capitalize on that momentum. 
• Another new book called "Algorithmic and High-Frequency Trading" by 3 mathematical finance professors describes the sophisticated mathematical tools that are being applied to high frequency trading and optimal execution. Yes, calculus is required here.

My Upcoming Workshop

January 27-28: Algorithmic Options Strategies

This is a new online course that is different from most other options workshops offered elsewhere. It will cover how one can backtest intraday option strategies and portfolio option strategies.

March 7-11: Statistical Arbitrage, Quantitative Momentum, and Artificial Intelligence for Traders.

These courses are highly intensive training sessions held in London for a full week. I typically need to walk for an hour along the Thames to rejuvenate after each day's class.

The AI course is new, and to my amazement, some of the improved techniques actually work.

My Upcoming Talk

I will be speaking at QuantCon 2016 on April 9 in New York. The topic will be "The Peculiarities of Volatility". I pointed out one peculiarity above, but there are others.

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QTS Partners, L.P. has a net return of +1.56% in October (YTD: +11.50%). Details available to Qualified Eligible Persons as defined in CFTC Rule 4.7.

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Follow me on Twitter: @chanep

An open-source genetic algorithm software (Guest post)

By Lukasz Wojtow

Mechanical traders never stop researching for the next market edge. Not only to get better results but also to have more than one system. The best trading results can be achieved with multiple non-correlated systems traded simultaneously. Unfortunately, most traders use similar market inefficiency: some traders specialize in trend following, some in mean reversion and so on. That's because learning to exploit one kind of edge is hard enough, mastering all of them – impossible. It would be beneficial to have a software that creates many non-related systems.

Recently I released Genotick - an open source software that can create and manage a group of trading systems. At the Genotick's core lies an epiphany: if it's possible to create any software with just a handful of assembler instructions, it should be possible to create any trading systems with a handful of similarly simple instructions. These simple and meaningless-on-its-own instructions become extremely powerful when combined together. Right instructions in the right order can create any type of mechanical system: trend following, mean reverting or even based on fundamental data.

The driving engine behind Genotick's power is a genetic algorithm. Current implementation is quite basic, but with some extra quirks. For example, if any of the systems is really bad – it stays in the population but its predictions are reversed. Another trick is used to help recognize biased trading systems: a system can be removed if it doesn't give mirrored prediction on mirrored data. So for example, position on GBP/USD must be opposite to the one on USD/GBP. Genotick also supports optional elitism (where the best systems always stay in the population, while others are retired due to old age), protection for new systems (to avoid removing systems that didn't yet have a chance to prove themselves) and inheriting initial system's weight from parents. These options give users plenty of room for experimentation.

When Genotick is run for the first time - there are no systems. They are created at the start using randomly chosen instructions. Then, a genetic algorithm takes over: each system is executed to check its prediction on historical data. Systems that predicted correctly gain weight for future predictions, systems that predicted incorrectly – lose weight. Gradually, day after day, population of systems grows. Bad systems are removed and good systems breed. Prediction for each day is calculated by adding predictions of all systems available at the time. Genotick doesn't iterate over the same historical data more than once – training process looks exactly as if it was executed in real life: one day at a time. In fact, there is no separate “training” phase, program learns a little bit as each day passes by.

Interestingly, Genotick doesn't check for rationale behind created systems. As each system is created out of random instructions, it's possible (and actually very likely) that some systems use ridiculous logic. For example, it's possible that a system will give a “Buy” signal if Volume was positive 42 days ago. Another system may want to go short each time the third digit in yesterday's High is the same as second digit in today's Open. Of course, such systems would never survive in real world and also they wouldn't survive for long in Genotick's population. Because each system's initial weight is zero, they never gain any significant weight and therefore don't spoil cumulative prediction given by the program. It may seem a little silly to allow such systems in the first place, but it enables Genotick to test algorithms that are free from traders' believes, misguided opinions and personal limitations. The sad fact is, the market doesn't care about what system you use and how much sweat and tears you put into it. Market is going to do what it wants to do – no questions asked, not taking prisoners. Market doesn't even care if you use any sort of intelligence, artificial or not. And so, the only rationale behind every trading system should be very simple: “Does it work?”. Nothing more, nothing less. This is the only metric Genotick uses to gauge systems.

Each program's run will be a little bit different. Equity chart below shows one possible performance. Years shown are 2007 until 2015 with actual training starting in 2000. There is nothing special about year 2007, remember – Genotick learns as it goes along. However, I felt it's important to look how it performed during financial crisis. Markets traded were:

USD/CHF, USD/JPY, 10 Year US Bond Yield, SPX, EUR/USD, GBP/USD and Gold.

(In some cases, I tested the system on a market index such as SPX instead of an instrument that tracks the index such as SPY, but the difference should be minor.)  All markets were mirrored to allow removing biased systems. Some vital numbers:

CAGR: 9.88%
Maxim drawdown: -21.6%
Longest drawdown: 287 trading days
Profitable days: 53.3 %
CALMAR ratio: 0.644
Sharpe ratio: 1.06
Mean annual gain: 24.1%
Losing year: 2013 (-12%)

(Click the cumulative returns in % chart below to enlarge.)

Cumulative Returns (%) since 2007

These numbers represent only “directional edge” offered by the software. There were no stop-losses, no leverage and no position sizing, which could greatly improve real life results. The performance assumes that at the end of each day, the positions are rebalanced so that each instrument starts with equal dollar value. (I.e. this is a constant rebalanced portfolio.)

Artificial Intelligence is a hot topic. Self driving cars that drive better than an average human and chess algorithms that beat an average player are facts. The difference is that using AI for trading is perfectly legal and opponents may never know. Unlike chess and driving, there is a lot of randomness in financial markets and it may take us longer to notice when AI starts winning. Best hedge funds can be still run by humans but if any trading method is really superior, AI will figure it out as well.

At the moment Genotick is more of a proof-of-concept rather than production-ready.

It is very limited in usability, it doesn't forgive mistakes and it's best to ask before using it for real trading. You will need Java 7 to run it. It's tested on both Linux and Windows 10. Example historical data is included. Any questions or comments are welcomed.

Genotick website: http://genotick.com

Source code: http://github.com/alphatica/genotick

Forum: https://groups.google.com/forum/#!forum/genotick

For a general reference on genetic algorithms, see "How to Solve It: Modern Heuristics". 

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My Upcoming Workshop

October 28-29: Quantitative Momentum Strategies. 

Momentum strategies have performed superbly in the recent market turmoil, since they are long volatility. This course will cover momentum strategies on a variety of asset classes and with a range of trading horizons.

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Follow me on Twitter: @chanep

Interview with Euan Sinclair

I have been a big fan of options trader and author Euan Sinclair for a long time. I have cited his highly readable and influential book Option Trading in my own work, and it is always within easy reach from my desk. His more recent book Volatility Trading is another must-read. I ran into him at the Chicago Trading Show a few months ago where he was a panelist on volatility trading, and he graciously agreed to be interviewed by me.

What is your educational background, and how did you start your trading career?

I got a Ph.D. in theoretical physics, studying the transition from quantum to classical mechanics. I always had intended to become a professor but the idea became less appealing once I saw what they did all day. At this time Nick Leeson was making news by blowing up Barings Bank and I thought I could do that. I mean trade derivatives not blowing up a bank (although I could probably manage that as well).

Do you recommend a new graduate with a similar educational background as yours to pursue finance or trading as a career today?

I don't think I would for a few reasons.

The world of derivatives and trading in general is now so much more visible than it was and there are now far better ways to prepare. When I started, physics Ph.D.s were hired only because they were smart and numerate and could pick things up on their own. My first trading firm had no training program. You just had to figure stuff out on your own. Now there are many good MFE courses or you could do a financial economics Ph.D.

Further, it would very much depend on exactly what kind of physics had been studied. I did a lot of classical mechanics which is really geometry. This kind of "pure" theory isn't nearly as useful as a background heavy with stats or simulation.

I think I could still make the transition, but it is no longer close to the ideal background.

You have been a well-known options trader with a long track record: what do you think is the biggest obstacle to success for a retail options trader?

Trading costs. Most option trading ideas are still built on the Black-Scholes-Merton framework and the idea of dynamic hedging (albeit heavily modified). Most pro firms have stat arb like execution methods to reduce the effective bid-ask they pay in the underlying. They also pay practically no ticket charges and probably get rebates. Even then, their average profit per option trade is very small and has been steadily decreasing.

Further, a lot of positional option trading relies on a large universe of possible trades to consider. This means a trader needs good scanning software to find trades, and a decent risk system because she will tend to have hundreds of positions on at one time. This is all expensive as well. 

Retail traders can't play this game at all. They have to look for situations that require little or no rebalancing and that can be limited to a much smaller universe. I would recommend the VIX complex or equity earnings events.

As an options trader, do you tend to short or long volatility?

I am short about 95% of the time, but about 35% of my profits come from the long trades.

Do you find it possible to fully automate options trading in the same way as that stocks, futures, and FX trading have been automated?

I see no reason why not. 

You have recently started a new website called FactorWave.com. Can you tell us about it? What prompted the transition of your focus from options to stocks?

FactorWave is a set of stock and portfolio tools that do analysis in terms of factors such as value, size, quality and momentum. There is a lot of research by both academics and investors that shows that these (and other) factors can give market beating returns and lower volatility.

I've been interested in stocks for a long time. Most of my option experience has been with stock options and some of my best research was on how these factors affected volatility trading returns.Also, equity markets are a great place to build wealth over the long term. They are a far more suitable vehicle for retirement planning than options!

I actually think the distinction between trading and investing is fairly meaningless. The only difference seems to be the time scale and this is very dependent on the person involved as well, with long-term meaning anything form months to inter-generational. All I've ever done as a trader is to look for meaningful edges and I found a lot of these in options. But I've never found anything as persistent as the stock factors. There is over a hundred years of statistical evidence, studies in many countries and economic and behavioral reasons for their existence. They present some of the best edges I have ever found. That should be appealing to any trader or investor.

Thank you! These are really valuable insights.

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My Upcoming Workshop

October 28-29: Quantitative Momentum Strategies. 

Momentum strategies have performed superbly in the recent market turmoil, since they are long volatility. This course will cover momentum strategies on a variety of asset classes and with a range of trading horizons.

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QTS Partners, L.P. has a net return of 1.25% in August (YTD: 10.44%).

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Reader Burak B. has converted some of the Matlab codes from my book Algorithmic Trading into Python codes and made them open-source: https://github.com/burakbayramli/quant_at.

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Follow me on Twitter: @chanep

Time series analysis and data gaps

Most time series techniques such as the ADF test for stationarity, Johansen test for cointegration, or ARIMA model for returns prediction, assume that our data points are collected at regular intervals. In traders' parlance, it assumes bar data with fixed bar length. It is easy to see that this mundane requirement immediately presents a problem even if we were just to analyze daily bars: how are we do deal with weekends and holidays?

You can see that the statistics of return bars over weekdays can differ significantly from those over weekends and holidays. Here is a table of comparison for SPY daily returns from 2005/05/04-2015/04/09:

SPY daily returns	Number of bars	Mean Returns (bps)	Mean Absolute Returns (bps)	Kurtosis (3 is “normal”)
Weekdays only	1,958	3.9	80.9	13.0
Weekends/holidays only	542	0.3	82.9	23.7

Though the absolute magnitude of the returns over a weekday is similar to that over a weekend, the mean returns are much more positive on the weekdays. Note also that the kurtosis of returns is almost doubled on the weekends. (Much higher tail risks on weekends with much less expected returns: why would anyone hold a position over weekends?) So if we run any sort of time series analysis on daily data, we are force-fitting a model on data with heterogeneous statistics that won't work well.

The problem is, of course, much worse if we attempt time series analysis on intraday bars. Not only are we faced with the weekend gap, in the case of stocks or ETFs we are faced with the overnight gap as well. Here is a table of comparison for AUDCAD 15-min returns vs weekend returns from 2009/01/01-2015/06/16:

AUDCAD 15-min returns	Number of bars	Mean Returns (bps)	Mean Absolute Returns (bps)	Kurtosis (3 is “normal”)
Weekdays only	158,640	0.01	4.5	18.8
Weekends/holidays only	343	-2.06	15.3	4.6

In this case, every important statistic is different (and it is noteworthy that kurtosis is actually lower on the weekends here, illustrating the mean-reverting character of this time series.)

So how should we predict intraday returns with data that has weekend gaps? (The same solution should apply to overnight gaps for stocks, and so omitted in the following discussion.) Let's consider several proposals:

1) Just delete the weekend returns, or set them as NaN in Matlab, or missing values NA in R. 

This won't work because the first few bars of a week isn't properly predicted by the last few bars of the previous week. We shouldn't use any linear model built with daily or intraday data to predict the returns of the first few bars of a week, whether or not that model contains data with weekend gaps. As for how many bars constitute the "first few bars", it depends on the lookback of the model. (Notice I emphasize linear model here because some nonlinear models can deal with large jumps during the weekends appropriately.)

2) Just pretend the weekend returns are no different from the daily or intraday returns when building/training the time series model, but do not use the model for predicting weekend returns. I.e. do not hold positions over the weekends.

This has been the default, and perhaps simplest (naive?) way of handling this issue for many traders, and it isn't too bad. The predictions for the first few bars in a week will again be suspect, as in 1), so one may want to refrain from trading then. The model built this way isn't the best possible one, but then we don't have to be purists.

3) Use only the most recent period without a gap to train the model. So for an intraday FX model, we would be using the bars in the previous week, sans the weekends, to train the model. Do not use the model for predicting weekend returns nor the first few bars of a week.

This sounds fine, except that there is usually not enough data in just a week to build a robust model, and the resulting model typically suffers from severe data snooping bias.

You might think that it should be possible to concatenate data from multiple gapless periods to form a larger training set. This "concatenation" does not mean just piecing together multiple weeks' time series into one long time series - that would be equivalent to 2) and wrong. Concatenation just means that we maximize the total log likelihood of a model over multiple independent time series, which in theory can be done without much fuss since log likelihood (i.e. log probability) of independent data are additive. But in practice, most pre-packaged time series model programs do not have this facility. (Do add a comment if anyone knows of such a package in Matlab, R, or Python!) Instead of modifying the guts of a likelihood-maximization routine of a time series fitting package, we will examine a short cut in the next proposal.

4) Rather than using a pre-packaged time series model with maximum likelihood estimation, just use an equivalent multiple linear regression (LR) model. Then just fit the training data with this LR model with all the data in the training set except the weekend bars, and use it for predicting all future bars except the weekend bars and the first few bars of a week.

This conversion of a time series model into a LR model is fairly easy for an autoregressive model AR(p), but may not be possible for an autoregressive moving average model ARMA(p, q). This is because the latter involves a moving average of the residuals, creating a dependency which I don't know how to incorporate into a LR. But I have found that AR(p) model, due to its simplicity, often works better out-of-sample than ARMA models anyway. It is of course, very easy to just omit certain data points from a LR fit, as each data point is presumed independent. 

Here is a plot of the out-of-sample cumulative returns of one such AR model built for predicting 15-minute returns of NOKSEK, assuming midpoint executions and no transaction costs (click to enlarge.)

Whether or not one decides to use this or the other techniques for handling data gaps, it is always a good idea to pay some attention to whether a model will work over these special bars.

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My Upcoming Workshop

July 29-30: Artificial Intelligence Techniques for Traders. 

This is a new online workshop focusing on the practical use of AI techniques for identifying predictive indicators for asset returns.

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Managed Accounts Update

Our FX Managed Account program is 6.02% in June (YTD: 31.33%).

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Industry Update

• I previously reported on a fundamental stock model proposed by Lyle and Wang using a linear combination of just two firm fundamentals ― book-to-market ratio and return on equity. Professor Lyle has posted a new version of this model.

• Charles-Albert Lehalle, Jean-Philippe Bouchaud, and Paul Besson reported that "intraday price is more aligned to signed limit orders (cumulative order replenishment) rather than signed market orders (cumulative order imbalance), even if order imbalance is able to forecast short term price movements." Hat tip: Mattia Manzoni. (I don't have a link to the original paper: please ask Mattia for that!)

• A new investment competition to help you raise capital is available at hedgefol.io.

• Enjoy an Outdoor Summer Party with fellow quants benefiting the New York Firefighters Burn Center Foundation on Tuesday, July 14th with great food and cool drinks on a terrace overlooking Manhattan. Please RSVP to join quant fund managers, systematic traders, algorithmic traders, quants and high frequency sharks for a great evening. This is a complimentary event (donations are welcomed). 

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Follow me on Twitter: @chanep

Beware of Low Frequency Data

(This post is based on the talk of the same title I gave at Quantopian's NYC conference which commenced at 3.14.15 9:26:54. Do these numbers remind you of something?)

A correct backtest of a trading strategy requires accurate historical data. This isn't controversial. Historical data that is full of errors will generate fictitious profits for mean-reverting strategies, since noise in prices is mean-reverting. However, what is lesser known is how perfectly accurate capture of historical prices, if done in a sub-optimal way, can still lead to dangerously inflated backtest results. I will illustrate this with three simple strategies.

CEF Premum Reversion

Patro et al published a paper on trading the mean reversion of closed-end funds’ (CEF) premium. Based on rational analysis, the market value of a CEF should be the same as the net asset value (NAV) of its holdings. So the strategy to exploit any differences is both reasonable and simple: rank all the CEF's by their % difference ("premium") between market value and NAV, and short the quintile with the highest premium and buy the quintile with the lowest (maybe negative) premium. Hold them for a month, and repeat. (You can try this on a daily basis too, since Bloomberg provides daily NAV data.) The Sharpe ratio of this strategy from 1998-2011 is 1.5. Transaction costs are ignored, but shouldn't be significant for a monthly rebalance strategy.

The authors are irreproachable for their use of high quality price data provided by CRSP and monthly fund NAV data from Bloomberg for their backtest. So I was quite confident that I can reproduce their results with the same data from CRSP, and with historical NAV data from Compustat instead. Indeed, here is the cumulative returns chart from my own backtest (click to enlarge):

However, I also know that there is one detail that many traders and academic researchers neglect when they backtest daily strategies for stocks, ETFs, or CEFs. They often use the "consolidated" closing price as the execution price, instead of the "official" (also called "auction" or "primary") closing price. To understand the difference, one has to remember that the US stock market is a network of over 60 "market centers" (see the teaching notes of Prof. Joel Hasbrouck for an excellent review of the US stock market structure). The exact price at which one's order will be executed is highly dependent on the exact market center to which it has been routed. A natural way to execute this CEF strategy is to send a market-on-close (MOC) or limit-on-close (LOC) order near the close, since this is the way we can participate in the closing auction and avoid paying the bid-ask spread. Such orders will be routed to the primary exchange for each stock, ETF, or CEF, and the price it is filled at will be the official/auction/primary price at that exchange. On the other hand, the price that most free data service (such as Yahoo Finance) provides is the consolidated price, which is merely that of the last transaction received by the Securities Information Processor (SIP) from any one of these market centers on or before 4pm ET. There is no reason to believe that one's order will be routed to that particular market center and was executed at that price at all. Unfortunately, the CEF strategy was tested on this consolidated price. So I decide to backtest it again with the official closing price.

Where can we find historical official closing price? Bloomberg provides that, but it is an expensive subscription. CRSP data has conveniently included the last bid and ask that can be used to compute the mid price at 4pm which is a good estimate of the official closing price. This mid price is what I used for a revised backtest. But the CRSP data also doesn't come cheap - I only used it because my academic affiliation allowed me free access. There is, however, an unexpected source that does provide the official closing price at a reasonable rate: QuantGo.com will rent us tick data that has a Cross flag for the closing auction trade. How ironic: the cheapest way to properly backtest a strategy that trades only once a month requires tick data time-stamped at 1 millisecond, with special tags for each trade!

So what is the cumulative returns using the mid price for our backtest?

Opening Gap Reversion

Readers of my book will be familiar with this strategy (Example 4.1): start with the SPX universe, buy the 10 stocks that gapped down most at the open, and short the 10 that gapped up most. Liquidate everything at the close. We can apply various technical or fundamental filters to make this strategy more robust, but the essential driver of the returns is mean-reversion of the overnight gap (i.e. reversion of the return from the previous close to today's open).

We have backtested this strategy using the closing mid price as I recommended above, and including a further 5 bps transaction cost each for the entry and exit trade. The backtest looked wonderful, so we traded it live. Here is the comparison of the backtest vs live cumulative P&L:

Yes, it is still mildly profitable, but nowhere near the profitability of the backtest, or more precisely, walk-forward test. What went wrong? Two things:

• Just like the closing price, we should have used the official/auction/primary open price. Unfortunately CRSP does not provide the opening bid-ask, so we couldn't have estimated the open price from the mid price. QuantGo, though, does provide a Cross flag for the opening auction trade as well.
• To generate the limit on open (LOO) or market on open (MOO) orders suitable for executing this strategy, we need to submit the order using the pre-market quotes before 9:28am ET, based on Nasdaq's rules.

Once again, a strategy that is seemingly low frequency, with just an entry at the open and an exit at the close, actually requires TAQ (ticks and quotes) data to backtest properly.

Futures Momentum

Lest you think that this requirement for TAQ data for backtesting only applies to mean reversion strategies, we can consider the following futures momentum strategy that can be applied to the gasoline (RB), gold (GC), or various other contracts trading on the NYMEX.

At the end of a trading session (defined as the previous day's open outcry close to today's open outcry close), rank all the trades or quotes in that session. We buy a contract in the next session if the last price is above the 95th percentile, sell it if it drops below the 60th (this serves as a stop loss). Similarly, we short a contract if the last price is below the 5th percentile, and buy cover if it goes above the 40th.

Despite being an intraday strategy, it typically trades only 1 roundtrip a day - a low frequency strategy. We backtested it two ways: with 1-min trade bars (prices are from back-adjusted continuous contracts provided by eSignal), and with best bid-offer (BBO) quotes with 1 ms time stamps (from QuantGo's actual contract prices, not backadjusted). 

For all the contracts that we have tested, the 1-ms data produced much worse returns than the 1-min data. The reason is interesting: 1-ms data shows that the strategy exhibits high frequency flip-flops. These are sudden changes in the order book (in particular, BBO quotes) that quickly revert. Some observers have called these flip-flops "mini flash crashes", and they happen as frequently in the futures as in the stock market, and occasionally in the spot Forex market as well. Some people have blamed it on high frequency traders. But I think flip-flop describe the situation better than flash crash, since flash crash implies the sudden disappearance of quotes or liquidity from the order book, while in a flip-flopping situation, new quotes/liquidity above the BBO can suddenly appear and disappear in a few milliseconds, simultaneous with the disappearance and re-appearance of quotes on the opposite side of the order book. Since ours is a momentum strategy, such reversals of course create losses. These losses are very real, and we experienced it in live trading. But these losses are also undetectable if we backtest using 1-min bar data.

Some readers may object: if the 1-min bar backtest shows good profits, why not just trade this live with 1-min bar data and preserve its profit? Let's consider why this doesn't actually allow us to avoid using TAQ data. Note that we were able to avoid the flip-flops using 1-min data only because we were lucky in our backtest - it wasn't because we had some trading rule that prevented our entering or exiting a position when the flip-flops occurred. How then are we to ensure that our luck will continue with live market data? At the very least, we have to test this strategy with many sets of 1-min bar data, and choose the set that shows the worst returns as part of our stress testing. For example, one set may be [9:00:00, 9:01:00, 9:02:00, ...,] and the second set may be [9:00:00.001, 9:01:00.001, 9:02:00.001, ...], etc. This backtest, however, still requires TAQ data, since no historical data vendor I know of provides such multiple sets of time-shifted bars!

As I mentioned above, these  flip-flops are omnipresent in the stock market as well. This shouldn't be surprising considering that 50% of the stock transaction volume is due to high frequency trading. It is particularly damaging when we are trading spreads, such as the ETF pair EWA vs EWC. A small change in the BBO of a leg may represent a big percentage change in the spread, which itself may be just a few ticks wide. So such flip-flops can frequently trigger orders which are filled at much worse prices than expected. 

Conclusion

The three example strategies above illustrate that even when a strategy trades at low frequency, maybe as low as once a month, we often still require high frequency TAQ data to backtest it properly, or even economically. If the strategy trades intraday, even if just once a day, then this requirement becomes all the more important due to the flip-flopping of the order book in the millisecond time frame.

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My Upcoming  Talks and Workshops

5/13-14: "Mean Reversion Strategies", "AI techniques in Trading" and "Portfolio Optimization" at Q-Trade Bootcamp 2015, Milan, Italy. 

5/18-22: "Statistical Arbitrage", 'Quantitative Momentum", and "Millisecond Frequency Trading", London, UK.

6/17-19: "Mean Reversion Strategies" live online workshop.

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Managed Account Program Update

Our FX Managed Account program has a net return of +4.29% in March (YTD: +12.7%).

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Follow me on Twitter: @chanep

Commitments of Traders (COT) strategy on soybean futures

In our drive to extract alphas from a variety of non-price data, we came across this old-fashioned source: Commitments of Traders (COT) on futures. This indicator is well-known to futures traders since 1923 (see www.cmegroup.com/education/files/COT_FBD_Update_2012-4-26.pdf), but there are often persistent patterns (risk factors?) in the markets that refuse to be arbitraged away. It is worth another look, especially since the data has become richer over the years.

First, some facts about COT:
1) CFTC collects the reports of the number of long and short futures and options contracts ("open interest") held by different types of firms by Tuesdays, and reports them every Friday by 4:30 CT.
2) Options positions are added to COT as if they were futures but adjusted by their deltas.
3) COT are then broken down into contracts held by different types of firms. The most familiar types are "Commercial" (e.g. an ethanol plant) and "Non-Commercial" (i.e. speculators).
4) Other types are "Spreaders" who hold calendar spreads, "Index traders", "Money Managers", etc. There are 9 mutually exclusive types in total.

Since we only have historical COT data from csidata.com, and they do not collect data on all these types, we have to restrict our present analysis only to Commercial and Non-Commercial. Also, beware that csidata tags a COT report by its Tuesday data collection date. As noted above, that information is unactionable until the following Sunday evening when the market re-opens.

A simple strategy would be to compute the ratio of long vs short COT for Non-Commercial traders. We buy the front contract when this ratio is equal to or greater than 3, exiting when the ratio drops to or below 1. We short the front contract when this ratio is equal to or less than 1/3, exiting when the ratio rises to or above 1. Hence this is a momentum strategy: we trade in the same direction as the speculators did. As most profitable futures traders are momentum traders, it would not be surprising this strategy could be profitable.

Over the period from 1999 to 2014, applying this strategy on CME soybean futures returns about 9% per annum, though its best period seems to be behind us already. I have plotted the cumulative returns below (click to enlarge).

I have applied this strategy to a few other agricultural commodities, but it doesn't seem to work on them. It is therefore quite possible that the positive result on soybeans is a fluke. Also, it is very unsatisfactory that we do not have data on the Money Managers (which include the all important CPOs and CTAs), since they would likely to be an important source of alpha. Of course, we can go directly to the cftc.gov, download all the historical reports in .xls format, and compile the data ourselves. But that is a project for another day.

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My Upcoming  Talks and Workshops

3/14: "Beware of Low Frequency Data" at QuantCon 2015, New York.

3/22-∞: "Algorithmic Trading of Bitcoins" pre-recorded online workshop.

3/24-25: "Millisecond Frequency Trading" live online workshop.

5/13-14: "Mean Reversion Strategies", "AI techniques in Trading" and "Portfolio Optimization" at Q-Trade Bootcamp 2015, Milan, Italy. 

5/18-22: "Statistical Arbitrage", 'Quantitative Momentum", and "Millisecond Frequency Trading", London, UK.

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Managed Account Program Update

Our FX Managed Account program has a net return of +7.68% in February (YTD: +8.06%).

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Follow me on Twitter: @chanep

Trading with Estimize and I/B/E/S earnings estimates data

By Yang Gao

Estimize is an online-community utilizing 'wisdom of crowds' to offer intelligence about market. It contains a wide range of crowd-sourced estimates from over 4,500 buy-side, sell-side and individual analysts. Studies (from Deustche Bank and Rice University among others) show estimates from Estimize are more accurate than estimates from traditional sell-side analysts.

The first strategy we tested is a mean reversion strategy developed by the quantitative research team from Deltix using Estimize’s data. This strategy is based on the idea that post-earning-announcement prices typically revert from the short-term trend driven by the more recent Estimize estimates just before the announcement. We backtested this strategy with S&P100 over the period between 2012/01/01 and 2013/12/31. (Even though Estimize has 2014 data, we do not have the corresponding survivorship-bias-free price data from the Center for Research in Securities Prices that includes the closing bid and ask prices.) With 5bp one-way transaction cost, we found that the backtest shows a Sharpe ratio of 0.8 and an average annual return of 6%.  The following figure is the cumulative P&L of the strategy based on $1 per stock position.

Cumulative P&L of Deltix Mean Reversion Strategy with Estimize 

It surprised us that a mean-reverting instead of a momentum strategy was used in conjunction with Estimize data, since earnings estimates and announcements typically generate price momentum. In order to show that this return is really driven by the information in Estimize and not simply due to price reversal, we provide a benchmark mean-reverting strategy that uses prices alone to generate signal:

1. Find long period T and short period T_s, where T is average period of the reporting of all the quarterly estimates and T_s is average period of the reporting of the latest 20% of all estimates.
2. Calculate stock return R over T and Rs over T_s, and let delta = R - Rs
3. Buy stocks with delta > 0 at close before an earnings announcement and exit the positions next morning at the open after the announcement.
4. Sell stocks with delta < 0 at close before an earnings announcement and exit the positions next morning at the open after the announcement.
5. Hedge net exposure with SPY during the entire holding period.

This benchmark shows no significant positive return and so it does seem that there is useful information in the Estimize data captured by Deltix’s mean-reversion strategy.

Next, we compare the traditional earnings estimates from I/B/E/S gathered from sell-side Wall Street analysts to the crowd-sourced Estimize estimates. Backtest showed the same Deltix mean reversion strategy described above but using I/B/E/S estimates gave negative return over the same S&P100 universe and over the same 2012-2013 period, again supporting the thesis that Estimize estimates may be superior.

Since Deltix's mean reversion strategy gives negative returns on I/B/E/S data, it is natural to see if a momentum strategy would work instead: if the short-term average estimate is higher than the long-term average estimate (i.e. analogous to delta < 0 above), we expect the price to move up and vice verse.

The backtest result of this momentum strategy over the same universe and time period is quite promising: with 5bp transaction cost, the Sharpe ratio = 1.5 and average annual return = 11%. The following figure is the daily P&L of the strategy based on $1 per stock position

 Cumulative P&L of momentum Strategy with I/B/E/S

We tried the same momentum strategy using Estimize data over 2012-2013, and it generated negative returns this time. This is not surprising since we found earlier that the mean reversion strategy using Estimize data generated positive returns.

We proceeded to backtest this momentum strategy over the S&P100 using out-of-sample I/B/E/S data between 2010 and 2012, and unfortunately the strategy failed there too. The following figure is the daily P&L of the strategy from 2010-2014.

Cumulative P&L of momentum Strategy with I/B/E/S 

So how would Deltix’s mean-reversion strategy with Estimize data work over this out-of-sample period? Unfortunately, we won’t know because Estimize didn't start collecting data until the end of 2011. The following table is a summary on the annual returns comparing different strategies using different data sets and periods.

Strategies	Mean-Reversion	Momentum
Estimize (2012.01-2013.12)	6%	-9%
I/B/E/S (2012.01-2013.12)	-17%	11%
I/B/E/S (2010.01-2011.12)	1.8%	-6.4%

As a result, we cannot conclude that Estimize data is consistently better than I/B/E/S data in terms of generating alpha: it depends on the strategy deployed. We also cannot decide which strategy – mean-reversion or momentum – is consistently better: it depends on the time period and the data used. The only conclusion we can reach is that the short duration of the Estimize data coupled with our lack of proper price data in 2014 means that we cannot have a statistically significant backtest. This state of inconclusiveness will of course be cured in time.

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Yang Gao, Ph.D., is a research intern at QTS Capital Management, LLC.

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Industry Update

(No endorsement of companies or products is implied by our mention.)

• There is a good discussion comparing Quantconnect to Quantopian here.
• For FX traders, Rizm offers a comparable service as Quantconnect and Quantopian as it is directly connected to FXCM.
• Quantopian now offers free fundamental data from MorningStar. Also, check out their Quantopian Managers Program where you can compete to manage real money.

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Workshop Update

Our next online workshop will be Millisecond Frequency Trading on March 25-26. It is for traders who are interested in intraday trading (even if not at millisecond frequency) and who want to defend against certain HFT tactics.

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Managed Account Program Update

Our FX Managed Account program had a strong finish in 2014, with annual net return of 69.86%.

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Follow me on Twitter: @chanep