Algorithmic and Mechanical Forex Strategies | OneStepRemoved

Cointegration in Forex Pairs Trading

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Cointegration in forex pairs trading is a valuable tool. For me, cointegration is the foundation for an excellent market-neutral mechanical trading strategy that allows me to profit in any economic environment. Whether a market is in an uptrend, downtrend or simply moving sideways, forex pairs trading allows me to harvest gains year-round.

A forex pairs trading strategy that utilizes cointegration is classified as a form of convergence trading based on statistical arbitrage and reversion to mean. This type of strategy was first popularized by a quantitative trading team at Morgan Stanley in the 1980s using stock pairs, although I and other traders have found it also works very well for forex pairs trading, too.

Forex pairs trading based on cointegration

Forex pairs trading based on cointegration is essentially a reversion-to-mean strategy. Stated simply, when two or more forex pairs are cointegrated, it means the price spread between the separate forex pairs tends to revert to its mean value consistently over time.

It’s important to understand that cointegration is not correlation. Correlation is a short-term relationship regarding co-movements of prices. Correlation means that individual prices move together. Although correlation is relied upon by some traders, by itself it’s an untrustworthy tool.

On the other hand, cointegration is a longer-term relationship with co-movements of prices, in which the prices move together yet within certain ranges or spreads, as if tethered together. I’ve found cointegration to be a very useful tool in forex pairs trading.

During my forex pairs trading, when the spread widens to a threshold value calculated by my mechanical trading algorithms, I “short” the spread between the pairs’ prices. In other words, I’m betting the spread will revert back toward zero due to their cointegration.

Basic forex pairs trading strategies are very simple, especially when using mechanical trading systems: I choose two different currency pairs which tend to move similarly. I buy the under-performing currency pair and sell the out-performing pair. When the spread between the two pairs converges, I close my position for a profit.

Forex pairs trading based on cointegration is a fairly market-neutral strategy. As an example, if a currency pair plummets, then the trade will probably result in a loss on the long side and an offsetting gain on the short side. So, unless all currencies and underlying instruments suddenly lose value, the net trade should be near zero in a worst-case scenario.

By the same token, pairs trading in many markets is a self-funding trading strategy, since the proceeds from short sales can sometimes be used to open the long position. Even without this benefit, cointegration-fueled forex pairs trading still works very well.

Understanding cointegration for forex pairs trading

Cointegration is helpful for me in forex pairs trading because it lets me program my mechanical trading system based on both short-term deviations from equilibrium prices as well as long-term price expectations, by which I mean corrections and returning to equilibrium.

To understand how cointegration-driven forex pairs trading works, it’s important to first define cointegration then describe how it functions in mechanical trading systems.

As I’ve said above, cointegration refers to the equilibrium relationship between sets of time series, such as prices of separate forex pairs that by themselves aren’t in equilibrium. Stated in mathematical jargon, cointegration is a technique for measuring the relationship between non-stationary variables in a time series.

If any two or more time series each have a root value equal to 1, but their linear combination is stationary, then they are said to be cointegrated.

As a simple example, consider the prices of a stock-market index and its related futures contract: Although the prices of each of these two instruments may wander randomly over brief periods of time, ultimately they will return to equilibrium, and their deviations will be stationary.

Here’s another illustration, stated in terms of the classic “random walk” example: Let’s say there are two individual drunks walking homeward after a night of carousing. Let’s further assume these two drunks don’t know each other, so there’s no predictable relationship between their individual pathways. Therefore, there is no cointegration between their movements.

In contrast, consider the idea that an individual drunk is wandering homeward while accompanied by his dog on a leash. In this case, there is a definite connection between the pathways of these two poor creatures.

Although each of the two is still on an individual pathway over a short period of time, and even though either one of the pair may randomly lead or lag the other at any given point in time, still, they will always be found close together. The distance between them is fairly predictable, thus the pair are said to be cointegrated.

Returning now to technical terms, if there are two non-stationary time series, such as a hypothetical set of currency pairs AB and XY, that become stationary when the difference between them is calculated, these pairs are called an integrated first-order series – also call an I(1) series.

Even though neither of these series stays at a constant value, if there is a linear combination of AB and XY that is stationary (described as I(0)), then AB and XY are cointegrated.

The above simple example consists of only two time series of hypothetical forex pairs. Yet, the concept of cointegration also applies to multiple time series, using higher integration orders… Think in terms of a wandering drunk accompanied by several dogs, each on a different-length leash.

In real-world economics, it’s easy to find examples showing cointegration of pairs: Income and spending, or harshness of criminal laws and size of prison population. In forex pairs trading, my focus is on capitalizing on the quantitative and predictable relationship between cointegrated pairs of currencies.

For example, let’s assume that I’m watching those two cointegrated hypothetical currency pairs, AB and XY, and the cointegrated relationship between them is AB – XY = Z, where Z equals a stationary series with a mean of zero, that is I(0).

This would seem to suggest a simple trading strategy: When AB – XY > V, and V is my threshold trigger price, then the forex pairs trading system would sell AB and buy XY, since the expectation would be for AB to decrease in price and XY to increase. Or, when AB – XY < -V, I would expect to buy AB and sell XY.

Avoid spurious regression in forex pairs trading

Yet, it’s not as simple as the above example would suggest. In practice, a mechanical trading system for forex pairs trading needs to calculate cointegration instead of just relying on the R-squared value between AB and XY.

That’s because ordinary regression analysis falls short when dealing with non-stationary variables. It causes so-called spurious regression, which suggests relationships between variables even when there aren’t any.

Suppose, for example, that I regress 2 separate “random walk” time series against each other. When I test to see if there’s a linear relationship, very often I will find high values for R-squared as well as low p-values. Still, there’s no relationship between these 2 random walks.

Formulas and testing for cointegration in forex pairs trading

The simplest test for cointegration is the Engle-Granger test, which works like this:

  • Verify that ABt  and XYt are both I(1)
  • Calculate the cointegration relationship [XYt = aABt + et] by using the least-squares method
  • Verify that the cointegration residuals et are stationary by using a unit-root test like the Augmented Dickey-Fuller (ADF) test

A detailed Granger equation:

ΔABt = α1(XYt-1 − βABt-1) +ut and ΔXYt = α2(XYt-1 − βABt-1) + vt

When XYt-1 − βABt-1 ~ I(0) describes the cointegration relationship.

XYt-1 − βABt-1 describes the extent of the disequilibrium away from the long-run, while αi is both the speed and direction at which the currency pair’s time series corrects itself from the disequilibrium.

When using the Engle-Granger method in forex pairs trading, the beta values of the regression are used to calculate the trade sizes for the pairs.

When using the Engle-Granger method in forex pairs trading, the beta values of the regression are used to calculate the trade sizes for the pairs.

Error correction for cointegration in forex pairs trading:

When using cointegration for forex pairs trading, it’s also helpful to account for how cointegrated variables adjust and return to the long-run equilibrium. So, for example, here are the two sample forex pairs’ time series shown autoregressively:

ABt  = aABt-1 + bXYt-1 + ut  and XYt  = cABt-1 + dXYt-1 + vt

Forex pairs trading based on cointegration

When I use my mechanical trading system for forex pairs trading, the setup and execution are fairly simple. First, I find two currency pairs which seem like they may be cointegrated, such as EUR/USD and GBP/USD.

Then, I calculate the estimated spreads between the two pairs. Next, I check for stationarity using a unit-root test or another common method.

I make sure that my inbound data feed is working appropriately, and I let my mechanical trading algorithms create the trading signals. Assuming I’ve run adequate back-tests to confirm the parameters, I’m finally ready to use cointegration in my forex pairs trading.

I’ve found a MetaTrader indicator which offers an excellent starting point to build a cointegration forex pairs trading system. It looks like a Bollinger Band indicator, yet in fact the oscillator shows the price differential between the two different currency pairs.

When this oscillator moves toward either the high or low extreme, it indicates that the pairs are decoupling, which signals the trades.

Still, to be sure of success I rely on my well-built mechanical trading system to filter the signals with the Augmented Dickey-Fuller test before executing the appropriate trades.

Of course, anyone who wants to use cointegration for his or her forex pairs trading, yet lacks the requisite algo programming skills, can rely on an experienced programmer to create a winning expert advisor.

Through the magic of algorithmic trading, I program my mechanical trading system to define the price spreads based on data analysis. My algorithm monitors for price deviations, then automatically buys and sells currency pairs in order to harvest market inefficiencies.

Risks to be aware of when using cointegration with forex pairs trading

Forex pairs trading is not entirely risk-free. Above all, I keep in mind that forex pairs trading using cointegration is a mean-reversion strategy, which is based on the assumption that the mean values will be the same in the future as they were in the past.

Although the Augmented Dickey-Fuller test mentioned previously is helpful in validating the cointegrated relationships for forex pairs trading, it doesn’t mean that the spreads will continue to be cointegrated in the future.

I rely on strong risk management rules, which means that my mechanical trading system exits from unprofitable trades if or when the calculated reversion-to-mean is invalidated.

When the mean values change, it’s called drift. I try to detect drift as soon as possible. In other words, if the prices of previously-cointegrated forex pairs begin to move in a trend instead of reverting to the previously-calculated mean, it’s time for the algorithms of my mechanical trading system to recalculate the values.

When I use my mechanical trading system for forex pairs trading, I use the autoregressive formula mentioned earlier in this article in order to calculate a moving average to forecast the spread. Then, I exit the trade at my calculated error bounds.

Cointegration is a valuable tool for my forex pairs trading

Using cointegration in forex pairs trading is a market-neutral mechanical trading strategy that lets me trade in any market environment. It’s a smart strategy that’s based on reversion to mean, yet it helps me avoid the pitfalls of some of the other reversion-to-mean forex trading strategies.

Because of its potential use in profitable mechanical trading systems, cointegration for forex pairs trading has attracted interest from both professional traders as well as academic researchers.

There are plenty of recently-published articles, such as this quant-focused blog article, or this scholarly discussion of the subject, as well as plenty of discussion among traders.

Cointegration is a valuable tool in my forex pairs trading, and I highly recommend that you look into it for yourself.

 

Coppock Curves : A Straight Line To Trading Success

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Coppock Curves, sometimes called Coppock indicators or Trendex indicators, are a type of indicator which offers quant traders a solid foundation upon which to build a simple yet successful mechanical trading system.

As described in more detail below, I use Coppock Curves in my mechanical trading system to generate trading signals in the S&P 500 or any other highly-liquid index. Coppock Curves also work well for trading iShares and ETFs.

What is a Coppock Curve?

Coppock Curves are a momentum indicator. Over time, they oscillate over and under zero. The Coppock Curve indicator was first described in 1962 by the economist and trader Edwin Coppock. In fact, it works so well that the Market Technicians Association (MTA) recognized Dr. Coppock with a lifetime achievement award in 1989.

Spiraling staircase

Its value lies in showing the beginning of long-term changes in price trends of stocks and indexes, particularly at the beginning of upward trends. This indicator can also signal the bottoms of futures and forex markets, yet I’ve found it less reliable there.

Although you can program your mechanical trading algorithms to generate trading signals based on this indicator over any time frame, I typically use it with monthly charts across a wide range of stock and index markets. Still, active traders can certainly use Coppock Curves with daily or even hourly time periods.

Specifically, I use Coppock Curves to generate “buy” signals at the bottom of bear markets. This indicator is especially good for distinguishing between bear rallies and actual market bottoms.

This is a trend-following indicator, so it doesn’t precisely show a market bottom. Instead, it shows me when a strong, bullish rally has become safely established enough to trade confidently.

Best of all, in my experience trades from signals based on Coppocks Curves are fairly resistant to shakeouts and whipsaws. Coppock Curves are slow, but they’re safe.

Coppock Curves signal the end of a “mourning period”

As background, it’s worthwhile to note that the original idea which led Dr. Coppock to develop his indicator was based on the natural cycle of life, death, and mourning before returning to new life again.

He thought that the normal upward march of stock markets (and therefore stock indexes) was like the “life” part of the cycle, which of course was followed by “death” that is, the period of falling prices during a bear market.

Dr. Coppock was particularly interested in calculating the length of a stock market’s “mourning” period, after which it would be safe to re-enter the market “long” again. Logically, this entry point at the end of the mourning period would represent the beginning of the next long-term uptrend.

The apocryphal story says that he asked the bishops at a local Episcopal Church, one of his investment clients, how long people usually spent in mourning after bereavements. He was told that human mourning typically requires between 11 to 14 months, so those were the values he adopted in his original equation to determine when stock prices would begin to rise again.

Coppock Curves were first used as long-term indicators based on monthly charts. Of course, the signals generated with monthly time frames are fairly infrequent. Still, because I use Coppock Curves to trade a variety of markets, I receive plenty of trading signals.

In particular, the monthly time frame is very reliable for stock and index trading. Studies have shown that, since 1920 in the U.S. stock markets, Coppock Curves have generated winning signals with about 80% frequency.

Nowadays, with the rapid turnover in modern markets, it seems that trading cycles have become faster. In addition to monthly time frames, some traders have found that daily time frames work very well in generating successful Coppock Curve signals.

A trader can program a mechanical trading system to recognize and respond to signals based on a daily or hourly time frame, although additional algo trading parameters should be added to reduce the chance of overtrading.

If you want to use Coppock Curves to generate signals on shorter time frames, you could experiment with your mechanical trading system using a variety of make-sense “mourning periods” for your particular marketplace.

How to calculate Coppock Curves

The Coppock indicator is based on three variables: A shorter-term rate of change (abbreviated as ROC), and a somewhat longer-term ROC.  Coppock Curves are developed by using the weighted moving average (WMA) derived from the chosen time periods of a given market index.

The classic equation stated in words:

Coppock Curve = The 10-period WMA of a 14-period ROC plus an 11-period ROC

Or, as a formula for programming:

Coppock Curve = WMA[10] of (ROC[14] + ROC[11])

When ROC = [(Close – Close n periods ago) / (Close n periods ago)] * 100

Where n is the number of time periods.

In the classic scenario, 11 and 14 time periods. Be sure to make separate ROC calculations.

As you can see, the basic setup is very simple – On a moving basis, I program my mechanical trading system to calculate the percent of change in a given index (say the S&P or DJIA) from fourteen months ago.

Then, my mechanical trading program calculates the percentage change in the same index from eleven months ago. Next, the mechanical trading system adds together the two different percent changes. Then, it calculates a 10-period weighted moving average of the above total.

It’s important to note that you can use different time periods for the ROC calculations and the WMA calculations. I sometimes program my mechanical trading system to use the classic 11- and 14-month time periods for ROC while using time periods for the WMA which are shorter than the classic 10-month period.

So, I often use using a 2-month or 3-month WMA (instead of 10 months) while the ROC is calculated using the 11- and 14-month prices.

Or, you can modify your mechanical trading system to employ shorter time periods for some or all of the calculations, i.e. use daily or hourly prices instead of monthly price charts. It generates more signals, but in my experience they’re less reliable unless you add additional filters, as discussed below.

As well, you can add additional embellishments to suit your own needs. In any event, the general method remains the same. When charting the basic inputs, you’ll see that the output is a fairly smooth arc, hence the name of this indicator.

In any event, the classic Coppock Curve equation for programming a mechanical trading system can be stated as: The sum of the 14-month rate of change and the 11-month rate of change, with smoothing by applying a 10-month weighted moving average.

The Coppock Curve “buy” signal

On Coppock Curves, the zero line is the trigger. When the price line rises from below the 0 line it signals a low-risk buying opportunity. My mechanical trading system executes a buy when the Coppock indicator is first below 0, then heads upward from the trough.

Since this is most effective as a bullish indicator, I ignore the opposite (“sell”) signals. Still, some traders, especially those using short time frames, use Coppock Curves with algo trading systems to generate sell signals and execute trades that close out long positions. Active traders can both close long trades and open shorts when the Coppock Curve crosses below the zero line.

The figure below shows the classic Coppock Curve trading strategy using monthly time periods. The buy signal came in 1991. The sell signal came ten years later, in 2001. Note that this long time frame helped me avoid the slump in late 2001 and 2002.

The next buy signal came in 2003 and the sell signal was in 2008. This helped me escape the slump in 2008 and into 2009. Note, also that the current “buy” position, signaled in early 2010, continues to remain open, at least through the date of this chart.

Coppock Curve on S&P 500 monthly chart

The Coppock Curve on an S&P 500 monthly chart

Next, for more-active traders here’s a screenshot showing the strategy applied with shorter time periods, as shown on a daily S&P 500 chart. Of course, many more signals are generated, although in general they are less likely to be winners.

Coppock Curve on a daily S&P 500 chart

Coppock Curve on a daily S&P 500 chart

Importantly, the longer the time period, the safer the buy signal. Since my mechanical trading system based on Coppock indicators is a trend-following system, I don’t necessarily capture the immediate gains from the exact moment of a trend reversal. Instead, my mechanical trading system gets me “long” just before the beginning of a profitable advance in a bull market.

Adjusting and filtering signals from Coppock Curves

I’ve found Coppock Curves to be highly reliable when used for monthly time periods. In my experience, using weekly, daily or hourly time periods usually means that my entries and exits aren’t as “tight” as I would like, meaning that I don’t capture all the gains I had hoped for, and I also have more losses.

However, active traders can decrease the ROC variables, which has the effect of increasing the speed of fluctuation in Coppock Curves and will therefore generate more trading signals. Of course, even though monthly time periods are my favorite, an ultra-long-term trader could also increase the ROC time periods to slow fluctuations even more, thus generating fewer signals.

As I’ve said above, in order to receive earlier entry signals, I usually decrease the WMA downward from 10 months, sometimes to 6 months, and often to as little as 2 months. By programming my mechanical trading system carefully with just the right WMA period, and filtering the signals, I maximize my profitability in a given market.

If you want to use Coppock indicators for active trading, I recommend that you filter the trade signals generated by your mechanical trading system so that you only accept trades which are in the same direction as the current dominant trend. You’ll find this mechanical trading strategy to be the most profitable, since you can avoid many losing trades by filtering the signals.

Which markets show reliable Coppock Curves?

I use my Coppock curve-powered mechanical trading system to trade a range of indexes, especially those based directly on stocks, such as:

  • Dow Jones Industrial Average
  • S&P 500
  • NASDAQ Composite
  • EURO STOXX 50
  • FTSE 100
  • Nikkei 225
  • Hang Seng

As well, if you’re focused on ETFs you’ll find that a mechanical trading system using Coppock Curves will allow you to catch the beginning of trends in specific market niches, such as biotechnology, energy, and international or regional equities niches.

The key is to make sure you trade only the liquid indexes. Otherwise, you may run the risk of being shaken out during “fake” trend changes.

Trading Coppock Curves in non-equity indexes

As well, for the sake of diversification and to avoid issues with correlation, I also program my mechanical trading system to spot and trade Coppock Curves in non-equity indexes as well. Again, I focus on markets which have sufficient liquidity.

There are some profitable non-equity indexes, including iShares and ETFs, which can be traded using Coppock indicators:

  • Bloomberg US Treasury Bond Index
  • Bloomberg Canada Sovereign Bond Index
  • Bloomberg U.K. Sovereign Bond Index
  • Bloomberg US Corporate Bond Index
  • Bloomberg GBP Investment Grade European Corporate Bond Index
  • Bloomberg EUR Investment Grade European Corporate Bond Index
  • Bloomberg JPY Investment Grade Corporate Bond Index
  • iShares Barclays 7-10 Year Treasury Bond Fund
  • iShares Barclays 20 Year Treasury Bond Fund
  • Schwab Short-Term U.S. Treasury ETF
  • Vanguard Short-Term Government Bond ETF
  • PIMCO 1-3 Year U.S. Treasury Index ETF

I’ve seen reliable signals from Coppock Curves when trading all the above-listed non-equity indexes. As always, the key is to use a mechanical trading system in only those markets which are highly liquid, so that the algorithms are reasonably sure that a confirmed signal is legitimate before trading it.

Coppock Curves show a straight line to success

In recent years, Coppock Curves have been drawing renewed interest from traders who are turning once again to this tried-and-true trading tool. See, for example, these recent mentions of Coppock indicators in the financial press: Jay On The Markets, and the follow up article, as well as in various trader musings.

In summary, I can say that Coppock Curves can lead you straight to success, as long as you have the patience to let your mechanical trading system do the work for you. If you use the length of variables’ time periods which are most appropriate for your chosen markets, you should do very well with Coppock Curves.

Gap Trading Made Easy

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Gap trading with a mechanical trading system offers independent traders a relatively easy method to capitalize on sudden market moves.

Gaps are often seen in the stock and fund markets. They are somewhat less common in the forex markets, which are usually more liquid and trade overnight.

In gap trading stocks, funds, futures and forex, a price “gap” refers to the open space seen on a chart when the price moves sharply up or down with no appreciable trading in between price points.

In its simplest form, gap trading involves buying based on the rise of a gap-up, and selling on the fall of a gap-down. Put differently, gap trading means buying when a price moves beyond the high of the previous time period without trading through that high. Likewise, gap trading means selling/shorting when the price moves below the previous time period’s low without touching it.

I use mechanical trading systems to program my way toward gap trading success by following some basic gap-trading rules and algo trading strategies.

Why do trading gaps occur?

Gaps can occur for a variety of reasons, such as sudden buying or selling pressure, especially by large players. And, they may result from earnings announcements or fundamental news. In fact, gaps can follow any sudden change in investors’ perceptions about a stock, fund, future or currency.

A gap can happen for either fundamental or technical reasons. For example, if a particular company announces higher earnings than expected, its stock price may gap up during the next trading session, if not overnight. Likewise, unfavorable corporate news can spark a gap down.

Gap trading

Or, a stock, fund or future setting a new all-time or long-term high may gap up for technical reasons. That is, a price move past a certain point may trigger institutions’ buying programs, which sense those new highs and spur even more buying.

Likewise, a stock or fund whose price moves below a threshold point may trigger investors’ rush for the exits and thus push its price further downward. Down gaps tend to accelerate more sharply than upward gaps.

And, in the forex markets, any report or other news may greatly widen the bid-ask spread. This creates a tradable gap either up or down.

In any case, you can program a mechanical trading system to recognize and respond profitably for gap trading.

Classification of trading gaps

For study purposes, gaps are usually classified as one of the four types listed below. It’s important to remember that these classifications will only be confirmed in retrospect, after the gap has occurred.

Once I’ve seen the follow-up movement, these labels are useful for categorizing which type of gap has occurred. These classifications help me to better understand how a given stock, fund or currency reacts under certain market conditions.

Fortunately, when using a mechanical trading system for gap trading I don’t need to know what will happen after the gap, only the circumstances which arise just before the gap.

Common gaps are defined as gaps which cannot be otherwise classified in terms of ending one trend and beginning another. They’re very “common,” hence the name. Usually, they’re uneventful and they simply represent an unexplained price gap from one day to the next. The volume during a common gap is often low and this type of gap is generally “filled” quickly. (See below.)

Exhaustion gaps happen at or near the end of a long or strong price run-up or price drop. They signal a final push toward new highs or new lows before the price movement reverses or begins moving sideways. For me, they’re a warning that the recent move is at its end.

Exhaustion gaps are characterized by higher volume and wide price difference between the price at the previous day’s close and the next day’s opening price. Using my gap trading strategies, it’s fairly easy for me to trade and profit from this type of gap. Higher volume is the key to recognizing them.

Continuation gaps can arise in the middle of any price trend. They are sometimes also referred to as “measuring gaps” or “runaway gaps.” Continuation gaps often happen around the midway point of a strong trend.

I interpret them as showing that on a particular day an exceptionally large number of buyers or sellers chose to move into or out of their positions. Perhaps they represent buyers who didn’t get aboard the trend earlier, but who are now piling in.

As well, this type of gap shows higher-than-average volume both during and immediately after the gap. Still, the volume typically isn’t even as high as it would be with an exhaustion gap.

Breakaway gaps occur at the completion of one trend or chart pattern. They mark the start of a new trend. Breakaway gaps offer great gap trading opportunities for me. From a technical viewpoint, they occur when a price manages to break out of its mid-term trading range – say a period of several weeks — or an area of congestion.

A congestion area represents a zone between resistance and support. To break out from a congestion area, a stock, fund, future or currency must receive a significant amount of new buyer interest (on the upside) or negative attention (on the downside).

A true breakaway gap will show a very large increase in volume, whether on the upside or downside. The volume should be larger than with any other type of gap.

In any case, this represents a major turning point in price direction and in my experience the move is likely to continue for the mid-term or long-term.

I account the breakaway point as the new resistance or new support level. With my mechanical gap trading system, I look to harvest substantial gains from this type of breakout.

Most importantly, I avoid falling into the trap of assuming that a breakaway gap will retrace. Once my position is secured, I stay aboard for the ride. I’m confident that the new trend will continue for a reasonable period of time, at least until the next reversal on very high volume.

Gap fills

One other term-of-art often used to describe trading gaps is the word filled. When a gap is “filled,” it means the price has quickly returned to its pre-gap level.

Gap fills typically happen because buyers decide they were over-optimistic or over-pessimistic. Or, the news which triggered the gap is quickly proven false or overblown.

When prices move above or below technical resistance or support levels, I rely on my mechanical gap trading system to determine whether the gap is likely to be filled, and proceed accordingly.

For example, since exhaustion gaps show the end of a trend, they are likely to be filled:  The price gaps, then retraces. So, my gap trading system uses data from the previous trend to determine the appropriate entry and exit points to take advantage of both sides of this fairly predictable move.

Gap fading

Gap fading described when a price gap is filled during the same trading period when it occurs. The gap movement “fades away.” It occurs when investors’ exuberance or despair is quickly proven unfounded. This scenario often arises during earnings season.

I and other short-term traders use mechanical gap trading systems to recognize and harvest gains from these quickly-reversed moves in stocks, futures and forex markets.

General gap trading strategies

In markets that I’ve been watching closely, I use several gap trading strategies. In order to profitably trade gaps, I need to keep several things in mind.

First, it’s important to realize that when the price begins to fill its gap, it usually continues until the fill is complete. This is because a gap doesn’t have any nearby support or resistance. Otherwise, that gap wouldn’t have occurred in the first place.

Second, I keep in mind that continuation gaps and exhaustion gaps are predictors of price movements in opposite directions. So, I make sure that my mechanical trading system considers gaps in relation to the recent trend which precedes them. If not, I might trade in the wrong direction.

Also, I ensure that my gap trading system makes decisions based on volume as well as price. To help classify a trading gap for purposes of programming my algo trading system, I make a distinction between high volume, medium volume and low volume.

For a successful breakaway gap, very high volume must be present. And, exhaustion gaps are characterized by somewhat lower volumes, although still higher than usual.

I often watch stocks and funds that trade mostly during daytime sessions. Then, I program my mechanical trading system to buy their shares in after-hours trading when positive earnings are unexpectedly released.

If I’ve done my homework correctly, at the beginning of the next daytime trading session there will usually be a gap up when institutional investors crowd into the stock. As well, I use my mechanical trading system to spot and act on technical factors that signal a likely gap the next day.

This gap trading strategy also works well with currencies. My mechanical trading system tells me whether to buy or sell when a currency gaps up on low liquidity and there is no nearby overhead technical resistance. This works especially well during geopolitical events which seem likely to continue for more than one or two days.

Likewise, I use gap trading tools to profit by fading the gaps in the opposite direction. For example, if the price gaps up based on speculation when there’s nearby resistance, I fade the gap with a short order. So, I ride the price from its failed gap-up level while it goes back down to its normal price range.

Gap trading in the forex markets

The forex markets are open twenty-four hourly except for a weekend closing. So, for charting purposes forex gaps are visible as large candles when the market reopens.

Here’s the gap trading strategy that I use in forex markets. By programming my mechanical trading system with the following basic rules, I’m able to harvest satisfactory gains.

First, the direction of my trade must always be in the same direction as the current hourly price. My mechanical trading system watches for the currency to gap above or below its calculated resistance level according to a thirty-minute price chart.

Next, my system looks for a price retracement back to the calculated resistance or support level. This shows the gap is being filled — The price is returning to its previous resistance-turned-support or support-turned-resistance level.

From a charting perspective, I look for a candle showing price continuation in the same direction as the gap. My gap trading system takes this as a confirmation of continuing support or resistance at the indicated level, and trades accordingly.

Double check volume before trading

With help from my mechanical trading system including the appropriate algorithms, I’ve been enjoying good results in trading gaps in the prices of stocks, ETFs, futures and currencies.

I’ve found that volume is the most important qualification when determining the type of gap and assessing the likelihood that the price move will continue.

To avoid becoming emotionally caught up in any price move, I rely on my gap trading system to quantify and verify the volume before sending any buy or sell orders. I set my algo trading parameters to check a variety of pricing sources before generating orders.

If my mechanical trading tools detect high-volume resistance that is preventing the marketplace from filling a gap, then my system double checks the volume and price data to ensure that my trading premise is correct before proceeding.

Of course, I always program my gap trading system with appropriate stop-loss orders. Some traders believe that gap trading is risky. Yet, with the right mechanical trading tools it offers plenty of opportunities for fat gains.

In fact, gap trading by using mechanical trading systems is currently a hot topic:  A book regarding ETF gap trading has recently been published, and there are many academic reports and quant-focused articles about how to trade gaps.

I recommend that you explore the possibilities of gap trading with a good mechanical trading system. With the right tools, gap trading can be both predictable and profitable.

Comprehensive Guide to the Turtle Trading Strategy

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Turtle trading is the name given to a family of trend-following strategies. It’s based on simple mechanical rules to enter trades when prices break out of short-term channels. The goal is to ride long-term trends from the beginning.

Turtle trading was born from an experiment in the 1980s by two pioneering futures traders who were debating whether good traders were born with innate talent, or whether anyone could be trained to trade successfully. The turtles developed a simple, winning mechanical trading system that could be used by any disciplined trader, regardless of previous experience.

The “turtle trading” name has been attributed to several possible origins. For me, it epitomizes the “slow but sure” results from this system. In contrast to complex black box systems, turtle trading rules are simple and easy enough for you to build your own system — I highly recommend it.

The earliest forms of turtle trading were manual. And, they required laborious calculations of moving averages and risk limits. Yet, today’s mechanical traders can use algorithms based on turtle parameters to guide them to successful trading. As always, the key to trading success lies in consistent discipline.

Which markets are best for turtle trading?

Your first decision is which markets to trade. Turtle trading is based on spotting and jumping aboard at the start of long-term trends in highly-liquid markets, usually futures. Since long-term trend changes are rare, you’ll need to choose liquid markets where you can find enough trading opportunities.

turtle trading strategy

My favorites are on the CME:  For Agriculturals, I like Corn, Soybeans, and Soft Red Winter Wheat. The best equities indexes are E-mini S&P 500, E-mini NASDAQ100, and the E-mini Dow. In the Energy group, I like E-mini Crude (CL), E-mini natgas and Heating Oil.

And, in Forex, it’s AUD/USD, CAD/USD, EUR/USD, GPB/USD, and JPY/USD. From the Interest Rate group of derivatives, I like Eurodollar, T-Bonds, and the 5-Year Treasury Note. Finally, in Metals the best candidates are always Gold, Silver and Copper.

Since turtle trading is a long-term undertaking with a limited number of successful entry signals, you should pick a fairly broad group of futures. Those above are my favorites, although others will work as well if they’re highly liquid. For example, in the past I’ve traded Euro-Stoxx 50 futures with excellent results.

Also, I only trade the nearest-month contracts, unless they’re within a few weeks of expiration. Above all, it’s critically important to be consistent. I always watch and trade the same futures.

Position size

With turtle trading, you’ll strike out many times for each home run you hit. That is, you’ll receive many signals to enter trades from which you’ll be quickly stopped out. However, on those few occasions when you’re right, you’ll be entering a winning trade at precisely the right moment – the beginning of a long-term change in trend.

So, for risk management your survival depends on choosing the right position size. You’ll need to program your mechanical trading system to make sure you don’t run out of money on stop-outs before hitting a home run.

Constant-percentage risk based on volatility

The key in turtle trading is to use a volatility-based risk position which remains constant. Program your position-size algorithm so that it will smooth out the dollar volatility by adjusting the size of your position according to the dollar value of each respective type of contract.

This works very well. The turtle trader enters positions which consist of either fewer, more-costly contracts, or else more, less-costly contracts, regardless of the underlying volatility in a particular market.

For example, when turtle trading a mini contract requiring, say, $3000 in margin, I buy/sell only one such contract, whereas when I enter a position with futures contracts requiring $1500 in margin, I buy/sell 2 contracts.

This method ensures that trades in different markets have similar chances for a particular dollar loss or gain. Even when the volatility in a given market is lower, through successful turtle trading in that market you’ll still win big because you’re holding more contracts of that less-volatile future.

How to calculate and capitalize on volatility – The concept of “N”

The early turtles used the letter N to designate a market’s underlying volatility. N is calculated as the exponential moving 20-day True Range (TR).

Described simply, N is the average single-day price movement in a particular market, including opening gaps. N is stated in the same units as the futures contract.

You should program your turtle trading system to calculate true range like this:

True Range = The greater of: Today’s high minus today’s low, or today’s high minus the previous day’s close, or the previous day’s close minus today’s low. In shorthand:

TR = (Maximum of) TH – TL; or TH – PDC; or PDC – TL

Daily N is calculated as: [(19 x PDN) + TR]  / 20 (Where PDN is the previous day’s N and TR is the current day’s True Range.)

Since the formula needs a previous day’s value for N, you’ll start with the first calculation just being a simple 20-day average.

Limiting risk by adjusting for volatility

To determine the size of the position, program your turtle trading system to calculate the dollar volatility of the underlying market in terms of its N value. It’s easy:

Dollar volatility = [Dollars per point of contract value] x N

During times when I feel “normal” risk-aversion, I set 1 N as equal to 1% of my account equity. And, during times when I feel more risk-averse than normal, or when my account is more drawn-down than normal, I set 1 N as equal to 0.5% of my account equity.

The units for position size in a given market are calculated as follows:

1 unit = 1% of account equity / Market’s dollar volatility

Which is the same as:

1 unit = 1% of account equity / ([Dollars per point of contract value] x N)

Here’s an example for Heating Oil (HO):

Day    High             Low              Close TR                N

1        3.7220          3.7124          3.7124          0.0096          0.0134

2        3.7170          3.7073          3.7073          0.0097          0.0132

3        3.7099          3.6923          3.6923          0.0176          0.0134

4        3.6930          3.6800          3.6838          0.0130          0.0134

5        3.6960          3.6736          3.6736          0.0224          0.0139

6        3.6820          3.6706          3.6706          0.0114          0.0137

7        3.6820          3.6710          3.6710          0.0114          0.0136

8        3.6795          3.6720          3.6744          0.0085          0.0134

9        3.6760          3.6550          3.6616          0.0210          0.0138

10      3.6650          3.6585          3.6627          0.0065          0.0134

11      3.6701          3.6620          3.6701          0.0081          0.0131

12      3.6965          3.6750          3.6965          0.0264          0.0138

13      3.7065          3.6944          3.6944          0.0121          0.0137

14      3.7115          3.6944          3.7087          0.0171          0.0139

15      3.7168          3.7100          3.7124          0.0081          0.0136

16      3.7265          3.7120          3.7265          0.0145          0.0136

17      3.7265          3.7098          3.7098          0.0167          0.0138

18      3.7184          3.7110          3.7184          0.0086          0.0135

19      3.7280          3.7200          3.7228          0.0096          0.0133

20      3.7375          3.7227          3.7359          0.0148          0.0134

21      3.7447          3.7310          3.7389          0.0137          0.0134

22      3.7420          3.7140          3.7162          0.0280          0.0141

For HO the dollars-per-point is $42,000 because the contract size is 42,000 gallons and the contract is quoted in dollars.

Assuming a turtle trading account size of $1 million, the unit size for the next trading day (Day 23 in the above series) as calculated using the value of N = .0141 for Day 22 is:

Unit size = [.001 x $1 million] / [.0141 x 42,000] = 16.80

Because partial contracts can’t be traded, in this example the position size is rounded downward to 16 contracts. You can program your algorithms to perform N-size and unit calculations weekly or even daily.

Position sizing helps you build positions with constant volatility risk across all the markets you trade. It’s important to turtle-trade using the largest account possible, even when you’re trading only minis.

You must ensure that the fractions of position size will allow you to trade at least one contract in each market. Small accounts will fall prey to granularity.

The beauty of turtle trading is that N serves to manage your position size as well as position risk and total portfolio risk.

The risk-management rules of turtle trading dictate that you must program your mechanical trading system to limit exposure in any single market to 4 units, your exposure in correlated markets to a total of 8 units, and your total “direction exposure” (i.e. long or short) in all markets to a maximum total of 12 units in each direction.

Entry timing when turtle trading

The N calculations above give you the appropriate position size. And, a mechanical turtle trading system will generate clear signals, so automated entries are easy.

You’ll enter your chosen markets when prices break out from Donchian channels. Breakouts are signaled when the price moves beyond the high or low of the previous 20-day period.

In spite of the round-the-clock availability of e-mini trading, I only enter during the daytime trading session. If there’s a price gap on open, I enter the trade if the price is moving in my target direction on open.

I enter the trade when the price moves one tick past the high or low of the previous 20 days.

However, here’s an important caveat: If the last breakout, whether long or short, would have resulted in a winning trade, I do not enter the current trade.

It doesn’t matter whether that last breakout wasn’t traded because it was skipped for any reason, or whether that last breakout was actually traded and was a loser.

And, if traded, I consider a breakout a loser if the price after the breakout subsequently moves 2N against me before a profitable exit at a minimum 10 days, as described below.

To repeat:  I only enter trades after a previous losing breakout. As a fallback to avoid missing out on major market moves, I can the trade at the end of a 55-day “failsafe breakout” period.

By adhering to this caveat, you will greatly increase your chance of being in the market at the beginning of a long-term move. That’s because the previous direction of the move has been proven false by that (hypothetical) losing trade.

Some turtle traders use an alternative method which involves taking all breakout trades even if the previous breakout trade lost or would have lost. But, for turtle trading personal accounts I have found that my drawdowns are less when adhering to the rule of only trading if the previous breakout trade was or would have been a loser.

Order size

When I receive an entry signal from a breakout, my mechanical trading system automatically enters with an order size of 1 unit. The only exception is when, as mentioned above, I’m in a period of deeper-than-usual drawdown. In that case, I enter ½ unit size.

Next, if the price continues in the hoped-for direction, my system automatically adds to the position in increments of 1 unit at each additional ½ N price movement while the price continues in the desired direction.

The mechanical trading system keeps adding to my holding until the position limit is reached, say at 4N as discussed earlier. I prefer limit orders, although you can also program the system to favor market orders if you wish.

Here’s an example entry into Gold (GC) futures:

N = 12.50, and the long breakout is at $1310

I buy the first unit at 1310. I buy the second unit at the price [1310 + (½ x 12.50) = 1316.25] rounded to 1316.30.

Then, if the price move continues, I buy the third unit at [1316.30 + (½ x 12.50 = 1322.55] rounded to 1322.60.

Finally, if gold keeps advancing I buy the fourth, last unit at [1322.60 + (½ x 12.50) = 1328.85] rounded to 1328.90.

In this example the price progress continues in such a short time period that the N value hasn’t changed. In any event, it’s easy to program your mechanical trading system to keep track of everything on-the-fly, including changes in N, position sizes, and entry points.

Turtle trading stops

Turtle trading involves taking numerous small losses while waiting to catch the occasional long-term changes in trend which are big winners. Preserving equity is critically important.

My automatic turtle trading system helps my confidence and discipline by removing the emotional component of trading, so I’m automatically entered in the winners.

Stops are based on N values, and no single trade represents more than 2% risk to my account. The stops are set at 2N since each N of price movement equals 1% of my account equity.

So, for long positions I set the stop-loss at 2N below my actual entry point (order fill price), and for short positions the stop is at 2N above my entry point.

To balance the risk when I add additional units to a position which has been moving in the desired direction, I raise the stops for the previous entries by ½ N.

This usually means that I will place all my stops for the total position at 2 N from the unit which I added most recently. Yet, in case of gaps-on-open, or fast-moving markets, the stops will be different.

The advantages of using N-based stops are obvious – The stops are based on market volatility, which balances the risk across all my entry points.

Exiting a trade

Since turtle trading means I must suffer many small “strike-outs” to enjoy relatively few “home-runs,” I’m careful not to exit winning trades too early.

My mechanical trading system is programmed to exit at a 10-day low on my long entries, and at a 10-day high for short positions. If the 10-day threshold is breached, my system exits from the entire position.

The mechanical trading system helps overcome my greed and emotional tendency to close out a profitable trade too early. I exit using standard stop orders, and I don’t play any “wait and see” games…. I let my mechanical trading system make those decisions for me.

It can be gut-wrenching to watch my account fatten dramatically during a major market move with a winning trade, then give back significant “paper gains” before I’m stopped out. Still, my pet mechanical trading system works very well.

Turtle trading algorithms offer a quick way to build your own do-it-yourself mechanical trading system which is simple, easy-to-understand and effective.

If you have the discipline to keep your hands off and let your mechanical trading system do its job, turtle trading may be your best choice.

After all, turtles are slow, but they usually win the race……

 

 

Great News for Traders: Russia’s Takeover of Crimea

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Russia’s takeover of Crimea is great news for traders — This geopolitical event has created profitable opportunities for stock and forex traders, especially those who use mechanical trading systems to filter out the emotional headlines which have been appearing daily as the drama unfolds.

For the past few weeks, we’ve been seeing the same pictures in the news media: Squads of Russian soldiers standing watch at Ukrainian military installations, and hearing the same stories about Ukrainian soldiers leaving their bases without firing a shot. The Russians have now succeeded in taking back a peninsula which was formerly theirs anyway.

A troubled stepchild has returned home

The fresh Ukrainian leadership in Kiev is pushing political buttons regarding the prospect of war with Russia, while both European and U.S. allies have talked loudly about Ukraine’s right to sovereignty. Yet, the allies have thus far placed only relatively-minor sanctions on Russian government and business leaders.

Casual readers of news headlines might fear World War III. However, the reality is far different. In contrast to the angry demonstrations in Ukraine’s heartland leading to the recent ouster of the Russian-leaning former leadership, ordinary Ukrainians don’t appear to be truly upset by recent events in Crimea. Instead of fighting for the return of Crimea, nationalistic fervor appears to be focused on preventing Russian incursions into Ukraine proper.

When considered apart from the strong nationalistic sentiments stirred up by Russia’s actions, most Ukrainians have felt little affinity for Crimea, and consider it an economic burden. And, Crimea is historically Russian, so there is little ethnic upset in returning it to Russia.

The Ukrainian government has maintained the moral high ground by protesting the aggressions of their powerful neighbor, but they refuse to physically defend Crimea.

Speak loudly and carry a tiny stick

The Obama administration has already said it isn’t willing to send troops to defend Poland, Lithuania and Latvia, the NATO allies which border Ukraine. Western sanctions thus far have amounted to symbolic pinpricks against a handful of Russian cronies who have very little exposure to the U.S.-centered financial system.

Why are Ukraine and its allies merely talking, instead of acting to defend Crimea?

From a long-term economic standpoint, Crimea is a loser because it doesn’t have any natural resources. Consider the difference in U.S. response when a hostile foreign country invades an oil-rich neighbor, especially when that country supplies U.S. needs. Then, all hell breaks loose, and a broad coalition of concerned (read oil-importing) nations sends troops to help the resource-rich weakling remain “free.”

That hasn’t happened in Crimea, and it seems unlikely to occur. This generally anemic response highlights the fact that the U.S. and Europe have little economic stake in Crimea or Ukraine.

Dumping Crimea is a good move for Ukraine, and possibly for Europe

Still, Europe does indeed have a political stake riding on the outcome of Russian ambitions in the region, and this divergence between economic and political realities creates opportunities for traders.

The Russian takeover of Crimea is a godsend for savvy traders who can see past the front-page clutter and understand the economic implications of Russia’s annexation of a resource-poor territory which was its own until about 60 years ago.

The right conclusions for the wrong reasons

Recently, the well-known market watcher Mark Hulbert penned an article about markets’ seeming indifference to geopolitical events, in which he pointed to a landmark academic study showing that non-economic news has little lasting impact on the markets.

Mr. Hulbert concluded that, after initial volatility caused by the Crimean headlines, U.S. and other stock markets have quickly refocused on economic factors. And, since stock prices were already rising at the time of Russia’s takeover, they should continue to rise in spite of this regional conflict.

Yet, I believe that he is drawing the “right conclusion for the wrong reasons.” From my own perspective, I view the stock markets’ rise as a resumption of the current rally after a brief disruption due to geopolitical news, as well as a sign of longer-term relief that a financially-troubled country (Ukraine), has now found a deep-pocketed patron (Russia) to foot the bill for a costly dependent territory (Crimea).

Even better for Ukraine (but not necessarily for Europe), Russia’s expansionism is pushing Europe to make bigger financial commitments to Ukraine on faster, looser terms than would otherwise have occurred. Stock markets everywhere love easy capital inflows.

A few days ago, I spoke with a Ukrainian friend living in Kiev. Although born in Ukraine, he is a successful international businessman who has spent plenty of time in both the U.S. and Russia, and he sees things from a multicultural perspective. Instead of Ukrainian nationalism, he believes in economic viability.

When I asked his opinion about the Crimea situation, he remarked that he is glad the Russians have annexed Crimea, and he hopes they’ll snatch a few other “welfare provinces” such as Donetsk, Zaporizhzhya and Kherson while they’re at it. He told me that he fears Russian aggression less than he fears the weak Ukrainian economy.

One man’s money pit is another man’s money maker

In the same way that stock market investors often bid up the share prices of ailing companies once they’ve shed some of their liabilities and are able to move forward with less baggage, the loss of Crimea makes Ukraine stronger and more attractive.

Russia Crimea Trading

The problems in Crimea are not what they seem.

U.S. and European investors should see the transfer of Crimea to Russia as a very positive development. For the same reasons that the U.S. stock markets took the Crimean “crisis” in stride, I believe this takeover has created a new array of rich opportunities for savvy traders who can look past the scary news headlines.

As another observer pointed out in a recent CNN Money article, Ukraine and Crimea are a “money pit.” Now that Russia has stepped in to become the new “sucker” by taking Crimea’s financial weight off the shoulders of Ukraine and, by extension, the EU nations which are cozy with Ukraine, there are plenty of winning plays to be made with regard to currencies and stocks.

How traders can profit from the conflict in Crimea and Ukraine

Conventional crisis-focused trading wisdom might suggest that traders go long U.S. Treasuries and gold. And, swing trading and reverse trading strategies seem tempting during crisis periods. Yet, I recommend a more region-specific approach.

It’s important to understand that the Crimean peninsula, in spite of its perceived geopolitical and strategic importance, has no natural resources of its own. Until the Russian takeover it depended on mainland Ukraine for its energy supply and financial support.

Since it has no appreciable oil or gas deposits, Ukraine’s chief economic resource is its rich farmland. Wheat and corn are its main exports. Mechanical trading systems focused on corn and wheat futures can capitalize on the spreads between U.S. wheat and Black Sea wheat futures, and there are probably some arbitrage plays between the cash and futures markets available as well.

Go long Ukraine, short Russia

An even larger set of trading opportunities arise from Europe’s continuing sympathetic over-reaction toward Ukraine, and its increased negativity toward Russia. Even though Ukraine is an economic weakling, Europe is now offering plenty of economic assistance in order to earn political points.

Although Ukraine has long suffered from fundamental issues of governance and financial viability, both the U.S. and Europe are now likely to pump far more money into Ukraine than would be prudent from a purely business standpoint.

I believe that this largesse will certainly boost Ukrainian asset values, at least in the short- and mid-term. Traders should be happy to harvest the inefficiencies from a marketplace which reacts to political headlines before quickly settling back down into economic realities.

From a technical perspective, the Ukrainian stock exchange’s main index (UX) appears to be working its way toward the buying tip of a bullish flag pattern. Mechanical traders who access this market, or make synthetic plays based on it, can harvest rich gains while the Russians take the administrative and financial burdens of Crimea off the shoulders of the Ukrainian economy.

Likewise, I believe that the inevitable downward slide of U.S. investments in Russia caused by political tensions will bring even more opportunities. Smart traders should be able to craft some good long-Ukrainian/short-Russian mechanical trading strategies with funds such as LETRX and others.

Of course, short-Russia plays are also fueled by economic stagnation within Russia, as politicians and citizens become bogged down and preoccupied by the prospects of a war with Ukraine along their lengthy common border. Shorting Russia looks like a winning plan.

Mid-term and long-term infrastructure plays

If Ukrainians are wiser in a business sense than in a nationalistic sense, they’ll quickly cede Crimea to the Russians. That will make it easier for the Russians to spend the billions of dollars necessary to shore up the aging Crimean infrastructure and administer it. As well, the Ruskies will need to invest plenty of money to build new infrastructure such as roads and bridges between this costly stepchild and the over-extended parent country.

Traders will find opportunities to ride along with this coming infrastructure expansion. Even traders who lack direct access to Russian and Ukrainian stock markets can still create systems for trading U.S.-based ETFs and depositary receipts to synthetically take advantage of these opportunities.

And, by using mechanical trading systems with strategies focused on international stocks, bonds and funds, traders will be able to distinguish between the emotional “noise” of the media and the economic realities on the ground.

The energy consequences of the Crimea takeover

There are many natural gas and oil pipelines crossing Ukraine. And, Europe relies on Russia for about 40% of its gas needs. Still, unless there’s a Russian takeover of mainland Ukraine, chilly relations between the two countries shouldn’t adversely affect gas prices. That’s because Russia is already shipping about half its gas to Europe through non-Ukrainian pipelines.

Some pundits have suggested that the Ukrainian leadership might attempt to pressure Russia by cutting off the Ukrainian-hosted portion of the gas flow to Europe. I believe that scenario is highly unlikely – Russia has already shown its resistance to economic threats regarding Crimea, and the Europeans would more likely blame Ukraine than Russia for cutting off the gas.

The energy-trading opportunities created by the geopolitical events in Crimea seem obvious, yet the high volatility means that it may be difficult for independent traders to consistently win through short-term trading alone. A less-direct trading approach will probably be more successful. Instead of trading energy futures or forex, I suggest oil-development stocks.

Ukrainian shale oil

Ukrainian oil shale is a bullish focus for investors and traders: The Crimean takeover will certainly increase Ukraine’s urgency in developing its oil shale industry. Although Ukraine lost most of its potential offshore oil and gas prospects in the Black Sea, it still has its shale deposits to be developed, especially in the western part of the country.

The annexation of Crimea along with the political issues regarding the existing pipelines carrying Russian oil across Ukraine toward Europe should make the Ukrainian government more eager to ensure its energy independence, and shale holds the key.

Traders should discover that both Shell and Chevron will benefit, since they already have early shale-development operations in Ukraine, and the government will now push much harder for quick expansion.

Traders may profit from focusing on stock- or option-plays involving companies that supply equipment and know-how for shale-oil extraction, and it seems likely that American specialty companies operating in this niche will enjoy stock price gains.

At the same time, short-side traders may profit from the turnabout in Black Sea exploration projects now that Russia controls Crimea. Russia will almost certainly nationalize Chornomornaftogaz, the Ukrainian-owned gas company in Crimea, leaving the mostly-U.S.-aligned exploration and development companies at a disadvantage when they resume negotiations.

Traders can build strategies to take advantage of price movements of the underlying petroleum products or the stocks of the companies involved when the negotiations begin with the new owners of the subject development blocks.

In particular, ExxonMobil (XOM) is facing the downside of a long-delayed agreement with Ukraine regarding Black Sea development.

What about currencies?

During geopolitical crises, money typically flows toward “safer” currencies such as the U.S. dollar and the Japanese yen. And, we hear the usual warnings about keeping money on the sidelines until the smoke clears.

Still, I believe traders who look beyond the scary headlines about Crimea, and see the relatively nonchalant attitude of businesspeople on the ground in Ukraine, will soon begin venturing out of cash in search of hot markets.

For reasons indicated above, I believe the Russian ruble will continue to be a good “short” play well into the future. Although there are fewer liquid platforms for trading EUR/RUB than USD/RUB, still, I think the best forex plays in view of the Crimea takeover all involve shorting the ruble, especially EUR/RUB.

The best forex strategies will be spreads involving a steadily-falling ruble and a steadily-rising euro, with the performance of the dollar as a wildcard—rising, but not rising as predictably as the euro. I believe the euro has plenty of upside. In fact, even if Russia backs down from its territorial claims, the euro should still rise.

The Russian central bank has indicated that it will increase its involvement in currency markets in order to reduce the ruble’s slide in favor of the dollar and euro. However, the long, grinding financial drain that Crimea promises to create against the Russian economy means the ruble’s downward trend will become even worse.

At the same time, many Russian businesspeople with access to international banking are voting in favor of Europe by moving their money into the euro. Likewise, since those same Russians may fear asset freezes from U.S. sanctions, the smartest move is to favor EUR over USD during that flight.

Beyond Crimea

Of course, other Eastern European countries like Latvia, Lithuania and Poland are also feeling the pressure of Russia’s territorial expansionism, and are likewise moving into EUR.

Finally, if Russia decides to invade or annex other parts of Ukraine, forex traders who are short RUB and long EUR will earn even more profits, in my view.

How To Win With Mechanical Trading Systems

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Much ink has been devoted to pinpointing the causes of mechanical trading systems failures, especially after the fact. Although it may seem oxymoronic (or, to some traders, simply moronic), the main reason why these trading systems fail is because they rely too much on the hands-free, fire-and-forget nature of mechanical trading. Algorithms themselves lack the objective human oversight and intervention necessary to help systems evolve in step with changing market conditions.

Mechanical trading systems failure, or trader failure?

Instead of bemoaning a trading-system failure, it’s more constructive to consider the ways in which traders can have the best of both worlds:  That is, traders can enjoy the benefits of algorithm-managed mechanical trading systems, such as rapid-fire automatic executions and emotion-free trading decisions, while still leveraging their innate human capacity for objective thinking about failure and success.

The most important element of any trader is the human capability to evolve. Traders can change and adapt their trading systems in order to continue winning before losses become financially or emotionally devastating.

Choose the right type and amount of market data for testing

Successful traders use a system of repetitive rules to harvest gains from short-term inefficiencies in the market. For small, independent traders in the big world of securities and derivatives trading, where spreads are thin and competition fierce, the best opportunities for gains come from spotting market inefficiencies based on simple, easy-to-quantify data, then taking action as quickly as possible.

When a trader develops and operates mechanical trading systems based on historical data, he or she is hoping for future gains based on the idea that current marketplace inefficiencies will continue. If a trader chooses the wrong data set or uses the wrong parameters to qualify the data, precious opportunities may be lost. At the same time, once the inefficiency detected in historical data no longer exists, then the trading system fails. The reasons why it vanished are unimportant to the mechanical trader. Only the results matter.

mechanical trading rules

Pick the most pertinent data sets when choosing the data set from which to create and test mechanical trading systems. And, in order to test a sample large enough to confirm whether a trading rule works consistently under a wide range of market conditions, a trader must use the longest practical period of test data.

So, it seems appropriate to build mechanical trading systems based on both the longest-possible historical data set as well as the simplest set of design parameters. Robustness is generally considered the ability to withstand many types of market conditions. Robustness should be inherent in any system tested across a long time range of historical data and simple rules. Lengthy testing and basic rules should reflect the widest array of potential market conditions in the future.

All mechanical trading systems will eventually fail because historical data obviously does not contain all future events. Any system built on historical data will eventually encounter ahistorical conditions. Human insight and intervention prevents automated strategies from running off the rails. The folks at Knight Capital know something about live trading snafus.

Simplicity wins by its adaptability

Successful mechanical trading systems are like living, breathing organisms. The world’s geologic strata are filled with fossils of organisms which, although ideally suited for short-term success during their own historical periods, were too specialized for long-term survival and adaptation. Simple algorithmic mechanical trading systems with human guidance are best because they can undergo quick, easy evolution and adaptation to the changing conditions in the environment (read marketplace).

Simple trading rules reduce the potential impact of data-mining bias. Bias from data mining is problematic because it may overstate how well a historical rule will apply under future conditions, especially when mechanical trading systems are focused on short time frames. Simple and robust mechanical trading systems shouldn’t by affected by the time frames used for testing purposes. – The number of test points found within a given range of historical data should still be large enough to prove or disprove the validity of the trading rules being tested. Stated differently, simple, robust mechanical trading systems will outshine data-mining bias.

If a trader uses a system with simple design parameters, such as the QuantBar system, and tests it by using the longest appropriate historical time period, then the only other important tasks will be to stick to the discipline of trading the system and monitoring its results going forward. Observation enables evolution.

On the other hand, traders who use mechanical trading systems built from a complex set of multiple parameters run the risk of “pre-evolving” their systems into early extinction.

Build a robust system that leverages the best of mechanical trading, without falling prey to its weaknesses

It’s important not to confuse the robustness of mechanical trading systems with their adaptability. Systems developed based on a multitude of parameters led to winning trades during historical periods – and even during current observed periods – are often described as ‘robust.’ That is no a guarantee that such systems can be successfully tweaked once they have been trade past their “honeymoon period.” That is an initial trading period during which conditions happen to coincide with a certain historical period upon which the system was based.

Simple mechanical trading systems are easily adapted to new conditions, even when the root causes of marketplace change remain unclear, and complex systems fall short. When market conditions change, as they continually do, the trading systems which are most likely to continue to win are those which are simple and most-easily adaptable to new conditions; a truly robust system is one which has longevity above all.

Simple algorithmic mechanical trading systems with human guidance are best because they can undergo quick, easy evolution and adaptation to the changing conditions in the environment (read marketplace).

Unfortunately, after experiencing an initial period of gains when using overly-complex mechanical trading systems, many traders fall into the trap of attempting to tweak those systems back to success. The market’s unknown, yet changing, conditions may have already doomed that entire species of mechanical trading systems to extinction. Again, simplicity and adaptability to changing conditions offer the best hope for survival of any trading system.

Use an objective measurement to distinguish between success and failure

A trader’s most-common downfall is a psychological attachment to his or her trading system. When trading-system failures occur, it’s usually because traders have adopted a subjective rather than objective viewpoint, especially with regard to stop-losses during particular trades.

Human nature often drives a trader to develop an emotional attachment to a particular system, especially when the trader has invested a significant amount of time and money into mechanical trading systems with many complex parts which are difficult to understand. However, it’s critically important for a trader to step outside the system in order to consider it objectively.

In some cases, the trader becomes delusional about the expected success of a system, even to the point of continuing to trade an obviously-losing system far longer than a subjective analysis would have allowed. Or, after a period of fat wins, a trader may become “married” to a formerly-winning system even while its beauty fades under the pressure of losses. Worse, a trader may fall into the trap of selectively choosing the testing periods or statistical parameters for an already-losing system, in order to maintain false hope for the system’s continuing value.

An objective yardstick, such as using standard deviation methods to assess the probability of current failure, is the only winning method for determining whether mechanical trading systems have truly failed. Through an objective eye, it’s easy for a trader to quickly spot failure or potential failure in mechanical trading systems, and a simple system may be quickly and easily adapted to create a freshly-winning system once again.

Failure of mechanical trading systems is often quantified based on a comparison of the current losses when measured against the historical losses or drawdowns. Such an analysis may lead to a subjective, incorrect conclusion. Maximum drawdown is often used as the threshold metric by which a trader will abandon a system. Without considering the manner by which the system reached that drawdown level, or the length of time required to reach that level, a trader should not conclude that the system is a loser based on drawdown alone.

Standard deviation versus drawdown as a metric of failure

In fact, the best method to avoid discarding a winning system is to use an objective measurement standard to determine the current or recent distribution of returns from the system obtained while actually trading it. Compare that measurement against the historical distribution of returns calculated from back-testing, while assigning a fixed threshold value according to the certainty that the current “losing” distribution of mechanical trading systems is indeed beyond normal, to-be-expected losses, and should therefore be discarded as failed.

So, for example, assume that a trader ignores the current drawdown level which has signaled a problem and triggered his investigation. Instead, compare the current losing streak against the historical losses which would have occurred while trading that system during historical test periods. Depending upon how conservative a trader is, he or she may discover that the current or recent loss is beyond, say, the 95% certainty level implied by two standard deviations from the “normal” historical loss level. This would certainly be a strong statistical sign that the system is performing poorly, and has therefore failed. In contrast, a different trader with greater appetite for risk may objectively decide that three standard deviations from the norm (i.e. 99.7%) is the appropriate certainty level for judging a trading system as “failed.”

The most important factor for any trading systems’ success, whether manual or mechanical, is always the human decision-making ability. The value of good mechanical trading systems is that, like all good machines, they minimize human weaknesses and empower achievements far beyond those attainable through manual methods. Yet, when properly built, they still allow firm control according to the trader’s judgment and allow him or her to steer clear of obstacles and potential failures.

Although a trader can use math in the form of a statistical calculation of standard distribution to assess whether a loss is normal and acceptable according to historical records, he or she is still relying on human judgment instead of making purely-mechanical, math-based decisions based on algorithms alone.

Traders can enjoy the best of both worlds. The power of algorithms and mechanical trading minimizes the effects of human emotion and tardiness on order placement and execution, especially with regard to maintaining stop-loss discipline. It still uses the objective assessment of standard deviation in order to retain human control over the trading system.

Be prepared for change, and be prepared to change the trading system

Along with the objectivity to detect when mechanical trading systems change from winners into losers, a trader must also have the discipline and foresight to evolve and change the systems so they can continue to win during new market conditions. In any environment filled with change, the simpler the system, the quicker and easier its evolution will be. If a complex strategy fails, it may be easier to replace than to modify it, while some of the simplest and most-intuitive systems, such as the QuantBar system, are relatively easy to modify on-the-fly in order to adapt to future market conditions.

In summary, it can be said properly-built mechanical trading systems should be simple and adaptable, and tested according to the right type and amount of data so that they will be robust enough to produce gains under a wide variety of market conditions. And, a winning system must be judged by the appropriate metric of success. Instead of merely relying on algorithmic trading rules or maximum drawdown levels, any decision about whether a system has failed should be made according to the trader’s human judgment, and based on an assessment of the number of standard deviations of the system’s current performance when measured against its historic-test losses. If mechanical trading systems are failing to perform, the trader should make the necessary changes instead of clinging to a losing system.

A New Look At Adaptive Asset Allocation

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Adaptive Asset Allocation (AAA) was born as one of several sibling strategies for applying Modern Portfolio Theory (MPT), which was first proposed in 1967 as a way to optimize portfolio gains. Yet, many traders and financial strategists who truly believe in the math of MPT are disillusioned because the real-world results while using AAA haven’t met their calculated expectations for gains, and the volatility of such portfolios has been higher than expected.

Recent studies of this topic have suggested that this mismatch between expectations and reality may be primarily due to the length of the time periods used for input averages and portfolio rebalancing: Apparently, when calculations are based on input data using averages obtained over much shorter periods of time, the portfolio returns are better than when those averages are calculated based on long-term numbers. And, when the portfolio rebalancing intervals are shorter, performance is better and volatility and risk are reduced.

To recap, MPT relies on 3 parameters to create ideal portfolios, typically involving a set of asset classes including stocks in the U.S., European, Japanese and emerging markets, plus U.S. and international REITs, U.S. long-term and intermediate Treasuries, as well as gold and other commodities. The parameters are:

  • Expected volatility
  • Expected returns
  • Expected correlation

It seems that using shorter-term averages for MPT scenarios leads to more accurate results. One shortcoming of the previous-generation allocation model, Strategic Asset Allocation (SAA), becomes apparent because that model applies MPT based on long-term averages regarding the above parameters. As detailed in the recent new work on this topic, using long-term averages leads to significant errors in calculated returns.

In practice, long-term averages over a 5-to-20-year time horizon are poor predictors of volatility, returns and correlation. The statistical gap between calculations using 20-year averages and those using 3-or-4-year averages with regard to stocks’ annualized returns is huge, ranging from negative returns to nearly 14%. Given the relatively short investment time horizons of most investors nowadays, it seems clear that using shorter-term parameters in the calculations will yield more realistic results.

Adaptive portfolio

Portfolios offer better risk adjusted returns when they adapt to short term market conditions.

To acknowledge reality without disavowing longer-term calculations entirely, some investors choose to tweak their calculations by applying a long-term value approach instead of a long-term average approach, which tends to weight portfolios in favor of equities when stock prices fall, and conversely to reduce weighting in equities as their prices become more expensive.

Yet, with advancing technology there are some new alternatives to using long-term valuation for “handicapping” the calculated returns. At the extreme end of the short-term horizon lie the high frequency traders, who take advantage of short-term trends, correlations and reversions-to-mean in order to generate more-realistic estimates of returns. There is currently much excitement in the trading community based on the success of traders who use HFT systems. Still, as more traders crowd into this niche, it’s possible that the spreads will thin or perhaps vanish altogether.

The predictive value of momentum

Momentum is an excellent way for investors to estimate performance over the short term. According to the old adage: The best predictor of short-term future price is the current price. And, as the investment horizon is extended from intraday or daily trading outward toward weekly periods, the effect of momentum becomes more noticeable. Perhaps due to larger, slower-moving investors, prices tend to keep moving in the same direction for several weeks. Given this probability, it’s logical to account for momentum when building a portfolio, regardless of the long-term averages already observed.

Volatility

Volatility, too, has been misapplied with regard to MPT. For example, although average long-term annualized volatility is about 20% for stock prices and about 7% for 10-year Treasuries, actual volatility measured during the shorter time horizons of most investors fluctuates much more wildly, and is therefore much less accurate for projecting future conditions. So, actual volatility can have a far more adverse impact on a portfolio than the calculated volatility implies.

And, although many investors attempt to roughly balance the difference in volatility between stocks and bonds by weighting portfolios with 60% stocks and 40% bonds, still, the actual volatilities experienced can far override such a crude balancing method. Therefore, with regard to volatility assumptions it seems safest to rely on the adage mentioned above, that is, the least-biased guess of tomorrow’s price is based on today’s price. Likewise, the least-biased guess of tomorrow’s price range is the price range during the recent past, which of course represents the recent volatility.

Since recent volatility seems to offer the best guess about near-term future volatility, and most investors have a short-term horizon, it seems logical to use short-term volatility as the parameter for MPT instead of long-term volatility. As a takeaway regarding volatility, a savvy investor rebalancing a portfolio can calculate its volatility and, in order to maintain the volatility risk at a stable level over time, could reduce exposure by partly moving into cash when volatility exceeds the targeted level.

Correlation & returns

Even though long-term correlations between the prices of asset classes such as stocks and Treasuries, or stocks and gold, are low or negative, over shorter time periods the actual correlations vary greatly. So, for example, the volatility of a 50-50 stock-and-bond portfolio may decrease by 50% as the correlation decreases.  

Similarly, although many traders intuitively understand that a portfolio’s risk is reduced by apportioning the volatility of its components, a less-intuitive observation from the recent studies has been that returns from risk-managed portfolios were also improved by as much as 25%. Finally, since the human nature of investors makes it difficult to focus on returns alone while disregarding risks, especially over a longer term when drawdowns may accrue, it’s also prudent to consider maximum drawdown along with volatility when seeking maximum returns.

Summary

If MPT scenarios based on near-term average values give more accurate estimates than those based on long-term values, then it seems best for HFT traders and other short-horizon investors to use current observed values for portfolio optimization. In the recent studies cited herein, the authors have advocated the monthly rebalancing of portfolios by using a true Adaptive Asset Allocation based on returns in the near term in view of their momentum, along with the appropriate short-term volatility and correlation averages.

One algorithmic approach might be to create fresh portfolios at the time of monthly rebalancing based on the top few assets according to six-month or even shorter momentum, and to allocate assets according to an algorithm specifying minimal variance in volatility, instead of apportioning each asset according to its individual volatility. This approach would account for the volatility and correlations among the top few assets in order to create a momentum portfolio with the least expected portfolio volatility, along with a palatable risk profile.

 

How To Create A Winning Trading System

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Many traders are attracted to forex because of the opportunities for fat gains, especially when compared with stocks. Yet, when trading forex the inherent leverage can affect traders’ emotions, leading to over-trading, loss-chasing and second-guessing. A mechanical trading system can provide the winning solution.

Why build a trading system?

Manual trading works well for many stock traders, especially those using buy-and-hold strategies for a limited number of favorite picks, yet forex traders need better tools and stronger discipline in order to be profitable.

In any industry, a well-built machine is more efficient than any human

A well-built mechanical trading system offers a trader the best of both worlds: technology and math give the trader the ability to spot and take advantage of market inefficiencies and harvest gains in a busy, cluttered environment, while freeing him or her from the emotional roller-coaster ride of trading.

Find your own niche

There are plenty of trading systems available nowadays; the key to forex trading success lies in finding or adapting the “right” system for your own needs and style. Once you’ve decided the parameters for success, including your overall goals and objectives for trading, personal tolerance for risk, and the amount of capital to be devoted to trading, a system can be built to fit you like a glove.

When building a system, there’s plenty of room for specialization and individualization – If everyone were trading the same way, spreads would soon disappear. Like fast-moving mosquitoes buzzing around a lumbering elephant, many traders earn an excellent living by capitalizing on opportunities inevitably created by the movements of much-larger players in the marketplace; the key is to gather an actionable set of patterns and indicators that fits your personal style.

If a pattern is noticeable, then it’s probably actionable

The first step is to search through past trading data in order to identify patterns and conditions which appear to consistently offer profitable trading opportunities. Historical price and volume charts often show patterns which appear to signal upcoming price moves, and technical indicators will help clarify an otherwise-fuzzy picture.

Try looking at different combinations of indicators over different historical time periods to see if they may give predictive power in spotting market turns or changes in trend. A “caveman-style” approach to quickly testing your hunches can be as simple as finding a noticeable pattern on a printed chart, then holding a sheet of paper over the upcoming section and “guessing” what will happen next; when you’re right, you may have found a winning pattern.

Testing & optimization

Once you’ve identified a fairly-predictable pattern by looking at charts, it’s time to think about how to trade it profitably. You should consider how it fits with your personal trading style, including risk management. The patterns and indicators upon which your system is based can be simple or complex, as long as they work in the marketplace and fit your circumstances.

How to create a winning trading system

The next step is to translate these patterns and scenarios into mathematical coding, to form a set of trading rules which can be fully tested. You can do this yourself, or you can rely on the services of a coding expert to help accomplish this. After you’ve created the foundation for a system, it can be tested objectively by changing the inputs to find the optimal conditions for trading, such as the best combinations of currency pairs, stops, and other variables.

You can use software to quickly test multiple combinations of indicators. The key is to identify predictable patterns which will give you the confidence to trade when you see them appear, whether long or short, then fine-tune them to maximize your gains. It’s important to realize that more complexity isn’t necessarily better – A super-complex system probably won’t fatten your wallet if it only signals a trade once every ten years and your computer happens to be offline when that finally occurs.

Don’t become married to your system

Most importantly, if your indicators aren’t working out during testing as you had hoped, don’t become emotionally invested in “proving” that they work. Instead, step back and take a broader look – Perhaps it’s time to use a different combination of indicators, or change your approach altogether.

During testing and optimization, it’s important to leave untouched some of your historical market data as untested “out-of-sample” data while you work through testing your system using in-sample data. For statistical purposes during testing, you can only use data once before modifying your system; then of course it becomes part of your in-sample data. If you contaminate your test data, that is, if you rely on a certain date range of data to first develop and test your system, then later re-test your modified system with the same data, the results may be skewed. So, use your out-of-sample data only for final testing and tweaking after you’ve built your system, so you can be sure that such data is “pure” and not already accounted for in the system.

Be sure to back-test any prospective new system over reasonably long periods, so you’ll have an idea how it performs long-term. And, check the results when using different lengths for your moving averages. Also, it’s worthwhile to test your system widely across different forex pairs, even those you don’t typically trade – You may be surprised to find that your system does especially well in a market that you haven’t tried before.

Implementation

Even though testing and minor tweaking should be thought of as an evolutionary process that continues during the life of your trading, at this point you’re ready to implement your system by using it to trade with real money. If you’ve done your homework well, and you stick to the rules that your testing has proved will work under specific conditions, then you’ll be confident in proceeding forward.

Stick to the proven rules and you’ll be successful

Societies rely on laws to govern the behavior of their citizens because they’ve learned over time (tested and optimized) what works. Likewise, in order to be successful with forex you should adhere to the consistent trading rules that you’ve established in a scientific manner. If you stick to the rules, your mechanical trading system can help you win the forex game.