system Archives - Algorithmic and Mechanical Forex Strategies

Coppock Curves : A Straight Line To Trading Success

April 15, 2014 by Eddie Flower Leave a Comment

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.

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.

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

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.

Backtesting Biases and Variations

October 3, 2013 by Andrew Selby 4 Comments

Last week, I wrote a post discussing how altering the timeframe of a system can change its results. That got me thinking about other ways that backtesting results could be skewed in one way or another based on user defined data such as the date range and market used. These simple differences can have a tremendous influence on the overall returns of any system, so it is important to pay them their proper respect.

When running backtests, it can be very easy to gloss over the down periods and cherry-pick the big return years. The problem is that you won’t have that opportunity when actually trading a system live. You will need to prepare yourself for the possibility that you select the wrong time or the wrong market to trade a given system. Otherwise, you run the risk of letting these backtesting biases adjust your expected return to values the system cannot possibly deliver.

Adjusting the Date Range

Let’s use our 10/100 Moving Average Crossover System from last week as a base. We tested it from January 1, 2001 through December 31, 2010 on the Vanguard Total Stock Market ETF (VTI). All of our tests last week used a starting portfolio value of $10,000, a 10% trailing stop, and a $7 commission.

MA crosses on VTI returned almost 90% over the last decade.

Based on those settings, our 10/100 MA Crossover System returned 89.8% over ten years. This works out to be an annualized return of 12% with a maximum drawdown of 16.2%.

If we would have started trading this system on January 1, 2003, we would have registered a total return of 39% in the three years of trading until the end of 2005. This would have been good for a 16.4% annualized return with a maximum drawdown of only 6%. As you can see, if we based our strategy on these results, we would be expecting the system to continue to produce these extremely high returns.

On the other hand, if we would have started trading this system on January 1, 2006, we would have seen a total return of only 2.5% in the first three years. We also would have had to sit through a 14.2% drawdown.

It is also worth noting that while the ten year track record of this system from 2001 through 2010 is very respectable, we wouldn’t have known that when we started in 2001. If we actually started trading this system in 2001, we would show a total return of -6.2% at the end of 2002. After two full years trading this system, we would not have had a single thing to show for it. The system didn’t find its first big winner until April 15, 2003.

As you can see, the time you chose to begin trading the 10/100 Moving Average Crossover System could have made all the difference over the course of what was a net-profitable decade. It is very important to keep this in mind when you are struggling through drawdowns.

Adjusting the Markets Traded

The market you choose to trade can have the same affect on your trading as the date you start trading. Let’s look at how the exact same system would have performed over the exact same decade if we chose to trade it on different ETFs.

Trading the 10/100 Moving Average Crossover System on the XLF, which represents financials, would have provided a total return of -9.4% for the decade with a maximum drawdown of 30%. It is obvious to us at this point that financials had a rough time during this period, but we would have had no clue about that when we started in 2001.

The XLY, which represents consumer discretionary stocks, also would have underperformed the VTI. Trading the system on the XLY would have returned a total of 39.4, or 6.4% annually, with a maximum drawdown of 21.3%.

XLY shows a 39.4% return over the same decade

If we would have been fortunate enough to trade the XLK, which represents the technology sector, we would have seen a tremendous total return of 95.7%. This works out to be an annual return of 14.2% with a maximum drawdown of 22.3%.

Once again, we see that decisions like what markets to trade and when to start can have a tremendous influence on our results. This is why it is so important to thoroughly backtest any strategy across many different combinations of date ranges and markets.

Comparing Exit Strategies for the Cumulative RSI System

September 19, 2013 by Andrew Selby Leave a Comment

This is the third post in a series covering the work Larry Connors and Cesar Alvarez have done using the 2-period RSI as an entry signal. In the first post, we discussed their evidence that shows how accurate the indicator can be in identifying short term oversold situations. Then, we reviewed how they took that entry signal and built the Cumulative RSI System around it.

In the second post, I noted that Connors and Alvarez had suggested that there were a number of different exit strategies that could be implemented. In a later chapter of their book, Short Term Trading Strategies That Work, they discussed five different types of exits and then provided data from backtesting some of those signals.

Five Different Types of Exit Strategies

Much like using the 2-Period RSI as an oversold indicator, many of these exit strategies go against what has become my natural preference towards long-term trend following strategies. Most long-term trend following strategies look to hold on to positions that are closing up, making new highs, and closing above their moving averages.

It is important to remember that we are looking at these strategies from a very short-term viewpoint. That explains why they can be almost exactly opposite from some of the long-term trend following strategies that I prefer and still be profitable.

Fixed Time Exit Strategies

Fixed Time Exit Strategies are exactly what the name implies. They commit to exiting a position a certain amount of time after the entry. If you recall, the average holding time for a position using the Cumulative RSI Strategy was between three and four days. Based on that, it is reasonable to assume that if a position is going to produce a positive return, it will do so sooner rather than later.

First Up Close Exit Strategies

First Up Close Exit Strategies look to exit a position on the first positive close made after a position is entered. Obviously, this only works when used with a short-term system that is looking to take quick, small profits out of the market with a very high win rate. In those situations, it can be surprisingly profitable.

New High Exit Strategies

New High Exit Strategies exit positions after they close at a new high. As I said, this concept runs counter to the long-term trend following approach, but can be very profitable in short-term situations. These strategies wouldn’t work if you were buying at new highs, but since the Cumulative RSI System looks to enter markets that have become oversold during uptrends, a bounce back up to new highs would represent a profitable situation.

Close Above the Moving Average Exit Strategies

Close Above the Moving Average Exit Strategies provide exit signals when a market closes above a specified moving average. The logic here is very similar to the New High Exit Strategies. When entering a position, an oversold market in a long-term uptrend will likely be below its moving averages, so a bounce back above those moving averages would represent a profitable trade.

2-Period RSI Exit Strategies

This is the exit strategy that was used in backtesting the Cumulative RSI System. It looks to exit a position when the 2-Period RSI closes above a certain number. Connors and Alvarez suggest values of 65, 70, or 75 for this number. The concept behind these strategies is that once the 2-Period RSI value has risen to one of those values, the market is no longer oversold and may actually have become overbought.

Backtesting These Exit Strategies

While they could have simply stopped after identifying all of these different strategies, what I like about Connors and Alarez’s work is that they went a step further and actually tested three of these strategies. In order to do that, they looked at every stock from 1995 through 2007 that traded above its 200-day moving average and had closed at a 10-day low. This provided them with 63,101 entry signals, so this was certainly not a small sample size.

Fixed Time Exit Strategies

On those entry signals, Fixed Time Exit Strategies performed the worst of the three strategies tested. However, they still performed much better than I expected. Exiting after holding for one day produced an average trade return of 0.61%. Increasing the hold time to just three days jumped that return number to 1.76%. Continuing that trend, increasing the hold time to 5 days provided a return of 1.97%, and holding the position for 7 days produced an average return of 2.05%.

Close Above the Moving Average Exit Strategies

While the Fixed Time Exit Strategies produced impressive return numbers, the exit strategies based on moving averages performed even better. Exiting on a close above the 5-day moving average produced an average return of 2.65%. Using the 10-day moving average increased the average return to 2.80%.

2-Period RSI Exit Strategies

Much like we saw with using the 2-Period RSI as an entry signal, the higher RSI values returned more profitable trades on average. Using a 2-Period RSI value of 65 produced an average return of 2.76%. Increasing the RSI value to 70 gave us an average return of 2.83%, and increasing the RSI value even higher to 75 gave us an average return of 2.93%.

Choosing an Exit Strategy

While I was not surprised that the dynamic exit strategies outperformed the Fixed Time Exit Strategies, I was surprised at how well those fixed time strategies performed to begin with. It appears that choosing an exit strategy for your system has more to do with your comfort level with a given strategy than its actual performance.

While using a value of 75 for your 2-Period RSI Exit may return a higher average profit than using a value of 65, if you lose sleep worrying about positions that don’t make it to that higher value then you might be better off using the lower value.

Why You Must Design Your Own Trading System

May 20, 2013 by Andrew Selby Leave a Comment

After deciding to explore system trading, many traders are tempted to expedite the process by purchasing someone else’s system. These traders are often met with disastrous results.

In order to successfully trade a system, you must have unshakable confidence in that system and the system must fit your personality. The only way to achieve this is to build your own system.

Confidence In Your System

No trading system is perfect. All trading systems experience drawdowns. The difficult part comes when you try to determine if a drawdown is normal or if markets have fundamentally changed in a way that erases your system’s edge. The only way that you will be able to tell the difference is if you know your system inside and out. That only happens when you build it yourself.

There are many trading systems that you can purchase for a wide range of prices. Some are based on solid strategies. Some are over fitted to a specific style of market. The problem is that if you jump into using someone else’s system, you won’t know how it was designed to handle different markets. When the system experiences a drawdown – it’s going to happen – you won’t know whether or not that drawdown is normal.

Shaun wrote a post earlier this year discussing drawdowns. He referenced how Dustin Pedroia struggled when the Boston Red Sox first called him up to the big leagues. The Red Sox stuck with their young second baseman because they were able to identify that his low average was not sustainable because of his high contact rate.

Shaun compared the Red Sox sticking with Pedroia to a trader sticking with his system during a drawdown. The Red Sox were able to understand Pedroia’s slump because they were able to look deeper into his performance statistics. A system trader can do the same thing if he knows his system well enough to look deeper into its performance statistics.

Pedroia stays in the lineup during a drawdown

The Red Sox stuck with Pedroia during a slump because they knew how good he was

By taking the time to backtest a trading system through different market periods, you will gain an understanding of how the system reacts to different types of markets. For example, if your system creates most of its profits during a trending market, then you won’t have any reason to panic if it experiences a drawdown during a non-trending period. That would be expected. However if the same system was struggling to produce in a trending market, you would be more concerned.

Building A System The Fits Your Personality

Another reason that you must construct your own trading system is that the system needs to fit your personality. The system used by The Turtles is probably the most famous system of all time, and you can purchase software that trades that exact system for less than $1,000. The problem with that approach is that you might not be a good fit to trade the way the Turtles traded.

The amount of trades a system makes, the time frame that those trades are held, and amount of capital risked on each trade are all factors that affect how a system suits your personality. While the Turtle’s system worked for some traders, if that system exposes your capital to more risk than you are comfortable with, then you won’t be able to sleep at night. While each of these factors can be adjusted during the process of building a system, many black box systems do not allow for adjustments. Also, without backtesting data, you won’t be able to determine how these adjustments will affect the system’s performance.

Going back to Shaun’s Dustin Pedroia reference, the Boston Red Sox were able to have confidence in the numbers of their short, odd-looking prospect because he fit their personality. They also had great success when they acquired corner infielder Kevin Youkilis, who was not an outstanding hitter, but had an exceptional ability to draw walks. If either of these players made the front office feel uncomfortable, they never would have been successful.

Every element of a trading system must reflect your personality

Youkilis is a player that makes the Red Sox comforable. Are you comfortable with each element of your trading system?

Acquiring players like Pedroia and Youkilis fit the personality of the Boston Red Sox, who were obsessed with crunching numbers and didn’t mind looking foolish if they were wrong. I would compare this to trading a system that focused on absolute return that also expects steep drawdowns.

On the opposite end of the spectrum, the New York Yankees are known for acquiring players that are already proven commodities later in their careers for much higher salaries. This approach is more in line with a trading system that trades for smaller profits with much less risk.

OneStepRemoved.com is dedicated to helping traders find a system appropriate for their personalities. Email info@onestepremoved.com and let us know how we can help with your trading.

Trading System Drawdown and Emotion

January 28, 2013 by Shaun Overton 13 Comments

What do baseball and trading system drawdowns have in common? A lot more than I ever realized.

If you haven’t read the book or seen the movie Moneyball (which I highly recommend), professional baseball adopted a statistical approach to recruitment and gameplay around 2000.

The Oakland Athletics adopted the approach before any team. They experienced tremendous success in their first system-driven season, despite countless expert predictions of imminent failure.

I started reading The Signal and the Noise by Nate Silver last night. The baseball chapter picks up where Moneyball left off. Dustin Pedroia, a star second baseman for the Boston Red Sox, enters the scene as an unlikely success story.

Most scouts overlooked him. He is short. He has a paunch and bowed legs. Nobody looks at the guy and thinks “professional athlete”.

Dustin Pedroia held strong through a trading system drawdown of his own – an unexpectedly low batting average.

Sticking with the system

Pedroia batted a lackluster .198 when the Red Sox brought him to the major leagues in August of 2006. The first month of the 2007 season started off even worse at .172.

A team like the Cubs, who until recently were notorious for their haphazard decision-making process, might have cut Pedroia at this point. For many clubs, every action is met by an equal and opposite overreaction. The Red Sox, on the other hand, are disciplined by their more systematic approach. And when the Red Sox looked at Pedroia at that point in the season, James told me, they actually saw a lot to like. Pedroia was making plenty of contact with the baseball – it just hadn’t been falling for hits. The numbers, most likely, would start to trend his way.

“We all have moments of losing confidence in the data,” James told me. “You probably know this, but if you look back at the previous year, when Dustin hit .180 or something, if you go back and look at his swing-and-miss percentage, it was maybe about 8 percent, maybe 9 percent. It was the same during that period in the spring when he was struggling. It was always logically apparent – when you swing as hard as he does, there’s no way in the world that you make that much contact and keep hitting .180.”¹

Trading System Drawdown

Every trader goes through a slump – even the best of them. When your trading system drawdown reaches uncomfortable levels, how do you cope?

I had the opportunity to lead managed fund sales at one of the largest forex brokerages in the world. The product was the Sentiment Fund and it was AWESOME. When the fund took off and upper management suddenly got involved, the fund had $40 million under management and nearly 1,000 investors.

If there’s one thing that you can count on with forex traders, it’s that they are going to lose. All the time. No doubt about it.

The firm received real time information on the positions of all clients. Whenever too many traders held positions long or short, the fund did the exact opposite.

That’s it. The automated strategy was stupid simple, but the returns were amazing. The aggressive fund kept the leverage at 2:1, yet was on track to earn 40% annually.

Bad luck

Two months before the hand off, the fund experienced a sharp drawdown of -8%. Like most people, investors thought of 40% returns as 40/12 = 3.3% profit per month. They don’t think about the natural slumps that happen – just like the one Pedroia experienced.

That “surprise” drawdown caused a mass herding effect. It didn’t matter that the fundamentals of the strategy were unchanged. Forex traders didn’t wake up smart last month and know how to game the system. Bad luck happens to the best strategies. Nonetheless, investors ran for the exits from a world class strategy.

Were the guys on the systems desk worried about the strategy that they built? No way! When Pedroia went through his slump, he could have overreacted and start messing with his batting stance. He could have adjusted his distance to the plate or any number of things.

Pedroia knew that his system was good. The systems guys knew their strategy was phenomenal. In spite of all the pressure, they held firm and blew past the drawdown by the time I handed off the sales effort.

What element of trading matches the “swing-and-miss percentage”? The Red Sox clearly used it to justify sticking with their system. In Sentiment Fund’s case, it was knowledge of the underlying system.

How can quantitative traders with less obvious fundamental theories know that their system is sound? I’d love to hear your comments.

1. Excerpted from The Signal and the Noise by Nate Silver, page 104.

System Expectations

September 19, 2012 by Shaun Overton Leave a Comment

I found myself outside today looking for an excuse to make a video. It’s 80°F / 27°C, sunny skies and a soft breeze in Dallas. The last place anyone wants to be is in front of the computer when the weather is this nice.

No doubt that some active daytraders or people that hate their jobs are thinking the same thing. I suspect that the motivation for most people making automated expert advisors is the dream of making money without doing anything. Turn on the software and wait for the trading profits to roll in. That was certainly the case with the company Forex Made Sleazy… I mean, Forex Made Easy several years ago.

We do have a handful of customers that trade profitably, but even then, it takes a long time for an automated system to get to the point where it’s largely hands off. The best conceived ideas, which I would define as plausibly worthy of my own investment funds, takes a bare minimum of several months to execute from start to finish. This also presumes the unlikely notion that the idea has genuine potential to start with.

Even the most simple, valid concepts encounter substantial setbacks before the system can truly run hands-free. It’s usually not some kind of epic programming disaster where the client wants black and the programmer makes white. Don’t get me wrong; communication is critical. The smoothest projects are always the ones where both parties understand one another readily.

Nonetheless, even the most well-oiled team experiences countless hiccups in the process of morphing from idea to reality. Simple ideas often fall the most vulnerable to real world problems. Trade execution stands out as the most common obstacle. If anything goes remotely unexpected, a potentially profitable scenario may lead to unexpected losses.

I worked with one client that came up with a simple idea that mathematically showed a heavy positive expectation. Yet when we launched the idea in the real world, the prices that the system absolutely required in order to function never came through. Slippage occurred precisely when it was the most damaging.

We had to go back to the drawing board looking for ways to re-engineer the expert advisor where the importance of execution declined. That setback alone took several months to overcome in any meaningful sense.

The take away here is that it’s totally unreasonable to expect to hire a forex programmer and expect a dramatic shift in profits and life style. The best ideas take several months before they are worthy of running their full account balance. Unfortunately, most of the ideas out there are not good to begin with. That’s why making an EA that is profitable over the long run is so incredibly difficult.