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Simplify Your Trading to Improve Your Performance

Active trading, whether it’s day trading or swing trading, can be very stressful, but it doesn’t have to be that way. The difficult part is deciding what to focus on, when the reality is most of us resort to the one thing we know we shouldn’t do, and that’s stare at the screen and overanalyze what we should do or how and when we should act, while we ignore a small number of things we should be doing.

So, what am I talking about? I’m talking about hanging hopes on some kind of market wisdom, like letting winners run. How in the hell do you know when a winner is gonna run, and when to step aside and watch it happen? Or, take quick losses, which seems antithetical to letting things run…the reality is that some of the biggest winners are trades that drew down significantly, only to rebound to explosive heights.

If this is the type of stuff consuming your mind, then I have two things to point out…first, if you’re stressing on your trading, then you’re trading size is probably too big. and second, you’re likely fixated on individual trades, when you should be concentrating on your execution, so whether it’s one or ten positions to manage, you’ve got it down. It’s simple. Trading smaller makes the second thing a lot easier.

Reduce the Mind Clutter

When trading, your only purpose should be trading your strategy. You get a signal that triggers an action, you execute that action. If you have distractions you may miss that trigger and miss an opportunity. And the more strategy triggers you miss, the more difficult it will become to evaluate your strategy and your individual performance. These are important metrics that you need to measure. Otherwise how do you know what works and what doesn’t work?

A cluttered mind is a trader’s nightmare. Your trading will be affected, and not in a good way. So, turn off CNBC or Bloomberg, put your phone on silent. Clear your inbox before you start trading, and focus on the job at hand.

One surefire way to reduce the number of moving parts is to trade using a signal generator. It relieves you of the analysis and decision making, so you can focus on the execution. A signal generator that isn’t time dependent is best, like our PatternCast, it provides signals for the next session, which you can enter ahead of time, go to sleep and wake up and manage.

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Life Cycle of a Trading Strategy in an Automated Portfolio

Creating automated trading strategies is being presented as super hard work, that takes incredible discipline, lots of brain power and testing, testing and more testing. And only if you follow the prescribe thousands of steps will you achieve success.

Well I say that’s a bunch of bunk!

Now let’s be clear, I’m not saying it’s easy as pie, what I am saying is that the emphasis and effort is all in the wrong place to make a trading system a profitable system. The emphasis should not be on creating the perfect rock star strategy, this is HUGELY wasted effort.

The emphasis should be on how well one strategy plays with the other strategies. Because a successful trading system isn’t about one strategy, it’s about a community of strategies that work well together. And the mind blowing thing here is that a bunch of average strategies working together the right way, that don’t take gargantuan amounts of effort to develop, will perform way better than one or two rock star strategies.

And the total effort to build these average strategies is orders of magnitude less to create than the rock star strategy or strategies. The rock stars can consume years or even a life time of effort. Simpler strategies can be churned out weekly. The difference is the playground in which you place them and how well they play together.

The key phrase here is, you should be running multiple non-correlated strategies.

So here’s the end-to-end process, the cradle-to-grave life cycle of a strategy. Don’t be intimidated, it’s not that complex. It’s all a matter of moving the strategies from left to right as they grow up and become better citizens.

Strategy Life Cycle

The concept is pretty simple. We want to produce a bunch of average strategies that have a low degree of correlation with each other. That simply means that they generate trading signals in ways that are not related. For example, one strategy might take signals based on seasonality events, another might wait for breakouts after an economic report, another might work on lunar cycles (not kidding), while another takes signals on significant pullbacks. And we can measure the degree of non-correlation with a simple Excel function called CORREL.

There are also fantastic tools that let you organize your portfolio of strategies into one consolidated performance report, that will show the correlation as well as the combined statistical measures. TradeStation has such a tool called Portfolio Maestro, and there are third party tools like PortfolioMerge.

Ok, so let’s look at the picture above. Our goal is to move strategies from the idea phase on the left, to the live campaign on the right. About one in twenty strategies will make it to the campaign. Not a big deal when you get my course as I provide you with a bunch of campaign ready strategies right out of the box.

Ideas to Development

Ideas can come from anywhere. I talked about this in previous emails, it’s not that hard once you get in the swing of generating ideas, as a matter of fact, this is the fun part of this process, the part where you’ll spend most of your time, perhaps 75% in total. This is where the magic happens. There’s a definite process and you build upon things that are known to work, and hopefully discover more things that work from time to time.

If an idea is a flop, then you scrap it. If it makes it through you initial evaluation, and shows some promise, it can go into the curation process. Your main job is to think of this process like a factory, where you need to develop lots of small ideas of reasonably high quality.

Strategy Curation

This is where your strategy goes through both in sample and out of sample testing. That simply means historic and live market data. The testing process is the key here, and the goal is to weed out the good from the not so good, so that after time a strategy shows its true colors and proves that it’s ready.

This is the boring part of the process, but perhaps the most important. You spend about 15% of your time here. Once a strategy has proven it works under lots of conditions and is robust and shows a low degree of correlation to other strategies in the live campaign, it sits and waits for the opportunity to join the game.

Live Campaign

Campaigns are like serial projects. This is where all the live trading takes place. A new campaign starts periodically, let’s say every three months. Thats enough time to evaluate how well the strategies are doing, and decide whether they deserve to stay in the game, or get replaced by a curated strategy waiting on the bench.

Not all strategies live forever. Sooner or later they loose their mojo, and with have to be retired permanently or put back into the curation process. Every once in a while, they can be recycled;ed into new strategies, which is not depicted on the graphic.


So that’s the entire process from end-to-end. There’s obviously a lot of detail left out for the sake of brevity, but in essence that is it. The key take aways here is that you don’t have to sweat over perfect strategies. Ok is good enough, so long as it’s non correlated with other strategies.

You scale your system simply by getting more strategies into the live campaign, and spreading the risk between them.

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Do More Of What Works – Finding An Edge In Systems Trading

There’s so much information out there for traders. You stick your finger in the air and get bombarded with advice, strategies, stock picks, analysts projections, and that’s all good for traders. The more information you have the better. So, the process of learning how to trade should be easy, right?

Oh, that is so wrong! The problem is everyone has an opinion, and shows like CNBC have at least 100 people everyday telling you what to buy. Fox business, Bloomberg TV, and all those Internet sites all offer you an opinion, which often sounds like statements of fact. Often you’ll here something like…oil is up due to the weak dollar, then within the same minute you’ll hear that oil is up due to the strengthening dollar. It happens all the time.

You can go on the Internet and look up trading strategies, and how to trade. But few if any of these bits of information are quantifiable. In other words there’s no backing study that shows statistical evidence of what works and what doesn’t. And so, you are left to figure it out for yourself. But most of us simply don’t have the time, so we rely on what we think are authoritative sources.

The problem with authoritative sources is that besides the fact that they may be peddling a bunch of bull, but let’s assume they aren’t…a lot of the data may be good for now, but a few months down the line, a year or two from now, it may all be worthless. Things change, for the most part. However, there are some things that seem to endure, basic principles that work. And so I’m going to list them for you here. Future articles will cover each of these points in detail.

  • Choose to trade short term (overnight to days, or weeks) vs intraday.
  • Buy pullbacks over breakouts, learn about hidden divergences
  • Buy after the market has dropped, not after it has risen (kind of like #2).
  • Stops hurt stock performance, the tighter the stop, the worse the performance.
  • Futures are more stop friendly, but dynamic exits are almost always better
  • Buy when stocks, ETFs and futures are above their 200-day moving average.
  • Buy when the VIX is 5% above its 10-day moving average.
  • Lock in gains when the VIX is 5% below its 10-day moving average.
  • Act on intraday highs and lows to increase your edge.

When you apply these broad principles to your trading, you will do better than had you not applied them. As a general rule, strategies that I have developed or are in the process of development, which lead to entries into the market, and my exit components, all follow these general rules. There are other very specific tools I use, that have held up to the test of time as well.

There are two specific indicators I use to help filter out noise and produce clearer signals. One is the 2-period RSI, the other is the rate of change of volume. These two oscillators are as close as one comes to the holy grail of indicators. Neither are standards, for example the RSI comes out of the box with most charting programs with a 14-period, and the ROC Volume doesn’t even exist in most platforms, the Chaikin Money Flow indicator is closest. But from a statistical point of view, these two clearly outshine everything else.

The 2-period RSI does the best job I’ve seen at identifying markets that are overbought and oversold, and the ROC Volume is the best leading indicator prior to a market move.

There is one other thing that I find curious, and that’s the need for traders to find a good shorting strategy. And while I’m sure there are some excellent ones out there, that probably work wonders in specific stocks, futures or markets. I can’t for the life of me find a universal principle to lay my hat dow on, that makes shorting a bit easier. So, as a general rule, I avoid shorting unless it’s handed to me on a silver platter.

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How To Come Up With 200 Profitable Trading Ideas

Sounds a bit crazy, that anyone could think up that many tradable ideas, develop, test and then put them into action. But it’s not so crazy if you have a process, and you’re motivated.

So, what’s the motivation? The motivation is to build an automated trading system that has some very desirable characteristics, like very small draw downs, a smooth equity curve, and distributed risk. But to achieve this, you need a process and a framework.

Most retail algorithmic traders are on a perpetual hunt for the killer strategy. They believe there is a holy grail, and if they push hard enough, if they optimize enough, they’ll eventually stumble upon it through the brute force of determination, endless research and long hours of testing.

The Holy Grail Strategy

Does the holy grail strategy exist? Maybe, but if it does, someone has already discovered it, and they’re either keeping it close to their chest or it no longer works. Rock star strategies will emerge from time to time, depends on market conditions. But these strategies are not available to you, unless you stumble upon one, or through endless testing you find the right combination of parameters…highly unlikely.

Below is a strategy that has made money for 20 years, maybe longer, I don’t know, because that’s as far back as I tested it. There are very few parameters to optimize. It’s super simple, based on a 2-period RSI using daily bars, it works across a number of markets and asset types. I found it in a book by Larry Connors and Cesar Alvarez called, Short Term Strategies That Work. Here are the rules…

  1. The asset being traded is above its 200-day moving average (I used the e-mini S&P 500 futures).
  2. Buy if the 2-period RSI drops below a level of 5.
  3. Exit if the asset crosses above its 5-period moving average.

I coded it up in TradeStation EasyLanguage and I’m testing it to see if it really works. It appears to be super robust. Stays in trades on average for a little over 10 days, has a very high profit factor well over 4.0, and a percent profitable that’s greater than 80%. Is this the holy grail?

The strategy isn’t perfect, in that the average winning trade versus the average losing trade is good, a such and with a really high winning percentage, but it’s a Long-Only Trade and doesn’t trade if the asset is under its 200-day moving average, so there are times I’d be sitting on my hands waiting for something to happen…extended times.

So, is this strategy the holy grail? Hardly. But that’s where other strategies might fill the holes. If I wanted to add another strategy, what should it do, how should it take trades. Is it sensible to add another long-only trader that works on daily bars? Probably not, because the returns are likely to be similar, meaning that when it does good, they both do well, and conversely when one is losing, the other is likely to be losing.

Multiple Non-Correlated Strategies

What I need to do is find another strategy to run along side this one, that has a low degree of correlation of its returns to my 2-period RSI strategy. But here’s the big questions. Do I need to find another super duper strategy? It turns out that you don’t. In fact, you are much better off adding an okay strategy, a strategy that is profitable but simple. But not just one, it’s better to add several, the more the merrier. Each one with a low degree of correlation to the others.

Low Correlation means that the strategies don’t come up with trade signals in the same way. So that when one strategy might be down, the others might be profitable. This has the effect of minimizing the overall drawdown of your portfolio of strategies, and making the profits additive.

Okay, so what does that mean? It means you need to become an idea factory. You need to come up with multiple, okay strategies and run them simultaneously. But why?

Agile Methodology

The answer has a parallel to Agile software development. And while you may not have any experience with agile development, let me describe it a bit for you, and why it works.

Agile development is about breaking up big problems into small ones, then putting together a team of generalists that can do a lot of different things, and then time boxing the project into short iterations, purposed with getting a small piece of functionality completely done with high quality results.

The key here is the iterative process, and okay experienced developers working as a team. They spread the risk, and actually do better work than a few super star developers. They are a lot cheaper too than the super stars.

Become a Strategy Making Machine

So, it turns out that if you add several non-correlated simple strategies to your portfolio, they do a much better job than one or two Rock Star strategies, for essentially the same reason that agile development teams do better. And this is where the motivation comes into play for creating lots of strategies.

Imagine if you could trade like a super star, and all you had to do was assemble several easy to code, simple, not particularly great performing strategies. Of course the strategies would have to be profitable, but there’s a lot of leeway here, the key is the low level of correlated returns, this is much more important than a killer strategy. And the more strategies you can add the better.

The thing is, strategies come and go, For periods of time they work great, then other periods they don’t. Sometimes they come to their end of life and need to be replaced. This is why you want to create as many as you can in a steady stream, and keep a number of them on the bench to test and curate, let the good ones rise to the top, and take the place of old and tired ones.

It’s not so hard to come up with new and interesting strategies, especially after you get into the swing of things. There are trading ideas everywhere, and can usually be modeled with a few simple lines of codes. Granted some may take a bit more coding expertise than you have, but those strategies are probably not critical to your success as an algorithmic trader.

Types of Strategies

There are many types or category of strategies possible. In the world of trading most people focus on price action, but the real interesting strategies are those that have some story behind them, with a solid premiss and hypothesis.

Like the effect of bonds after an anticipated Fed rate hike. The rate hike might happen, it might not, one thing is for sure, there’s a high likelihood that bonds are going to move. If you know the day and time of the rate hike announcement, or when a Fed governor might be speaking, you could create a strategy that waits for that event, then goes long or short once the market starts to move. These types of events generally have an effect on bonds for a few days. And detecting a discernible move a short period after the announcement is relatively easy to do in code.

Most strategies fall under one of the following effects on the market, they start a trend or continuation of a trend, or are counter trend makers. Some are mean reverting, or seasonally effected, others are technical, or relationships.

All you have to do is identify some thing that happens, something that interests you, or not, then apply one of these strategy types to try and capture the anticipated moves. Generally the coding is super simple. What you need to learn is how to back test your hypothesis and past events to see how your creation would have handled them. This is where process and testing skills come into play.


So is it possible to come up with 200 profitable strategies? Absolutely. But it takes a process, and an active imagination, and the motivation knowing the good things that will come from it. I’ve created or stole (borrowed) at least three strategies this past week. I’m on pace for generating almost 200 in a single year. They aren’t all winners, in fact a large number of them are complete flops, but that doesn’t stop the factory, rejects are part of the process.

The reality is that you’ll come up with a fairly good strategy that is ready to join your other live portfolio strategies at a rate of one in 20. The rest might have future potential, but after a while they simply need to be scrapped if they don’t meet some minimal criteria. It could be that their time isn’t right, as I’ve opened loser strategies I worked on a couple years ago, and now they perform brilliantly.

So, if your creating 200 strategies a year and 5 percent of them are winners, that means your potentially adding 10 winning, non-correlated strategies a year to your portfolio. Keep in mind, they all won’t stay winners forever, that’s why you need to keep the process going.

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Where Do You Get Trading Ideas?

Trading ideas are everywhere, you just have to look and recognize them. The best ideas are intuitive and easy to explain. A trading idea doesn’t have to be mind blowing to work, it only needs a sound premise. Cobbling together a strategy with a bunch of indicators and tweaking the money management isn’t an idea, even if it does produce a great equity curve after several optimizations, what you’ve done is created a strategy that works perfectly in the past, via curve fitting, but has little hope of working on markets it’s never seen.

Types of Trading Ideas

When you’ve been doing this long enough you start to catalog ideas, here’s some:

  • Trends based on some identifiable factor like the monetary policy of the Federal reserve. Trends are by far the most followed pattern.
  • You could exploit the relationship between two similar stocks or futures in a statistical arbitrage (pairs trade). Perhaps even an inter market relationship like the auto industry and platinum group metals (primary use is catalytic converters for gas powered engines), or soybean vs soy meal.
  • There are seasonal patterns, speaking of soy beans, in agriculture, heating oil; did you know 40% of heating oil price movement is influenced by supply-demand issues in the northeastern United States, and weather is a big factor. Airlines and resorts have seasonal patterns that are easy to sort out.
  • Selling premium up to a big announcement or earnings report, or buying volatility cheap after the earnings are announced and volatility has crashed.
  • How about turnaround Tuesday, or over the weekend patterns, or same time next month when funds redeem and add to their position.

I could go on, as there are an unlimited number of ideas out there that are exploitable and profitable, if it’s a sound premise and you see it, then figure out how to trade it. Until you put up the money, your ideas are just that, ethereal moments in time, but ain’t worth a dime.

The best way to get in tune with the market and the economy is to get yourself involved, trade geopolitical events, wait for zinger economic reports and fade the move, be early on the spot by monitoring juicy breaking news from web news sites like The Fly on the Wall. When you have your money on the line, working for you, your attention is attenuated and focused. This is when ideas are born.

Trading Ideas Matter

If you think you’ll do well in the trading business with your idea de jour, think again. Someday that idea isn’t going to work. A constant stream of ideas is what you need, in fact it is the very life blood of a successful trader to stay ahead of, and take advantage of opportunities in these constantly changing markets. So, how does a trader come up with new ideas, and not just rehash the tired old ones?

“I begin with an idea, then it becomes something else.” -Pablo Picasso

The big mistake traders make looking for ideas is looking in the same old tired places. The market is reflective of what’s happening in the world, so doesn’t it make sense to expand your notion of a trade to the real world? Maybe, maybe not, perhaps you have to jump to the non-real world. The point is, trade idea generation is exactly like any creative endeavor, there are lots of ways to do it, just google “how to generate ideas.” That’ll get you going.

Most creatives use a process for generating ideas, let’s apply that to trading.

Research is first, you should focus on a particular market, like Oil for example. Start by looking at key reports, such as the petroleum status report, which comes out every Wednesday, and the weekly Natural gas report on Thursdays…ask yourself what happens to the market after these reports, how about before the reports…do you see patterns. Study why investors care, perhaps that will lead to research around OPEC and what they are currently up to, both business wise and political.

Most events experience a half-life, that’s where the maximum intensity of the report will last a certain number of days, then fade away until the next report. Ask yourself, what’s the half life of oil inventory reports? Is there a reversion to the mean that’s noticeable, or are these the beginning of momentum plays. Look for secondary markets that confirm a change in price of crude, like gasoline, maybe there’s a trade there, or maybe just a confirming indicator. Are there seasonal influencers, or weather anomalies at play. How many oil rigs might be affected by a nasty tropical storm, what about just the fear of a big storm coming.

Do you see how each question leads to another?

Once you’ve identified a pattern, think about how you can turn it into a trade. What are the setups, the ranges in time and price, what’s the average duration of the trade, what can go wrong, what can intensify the effect…on and on, one idea begets the next.

Let’s Get Real

It’s all and good to throw hypotheticals at you, but how would you actually turn an idea into a real trade? Well, let’s do that with crude oil. Perhaps we can trade some behavior we noticed crude takes after an impactful report. Let’s first develop the strategy.

After a big move does crude oil tend to fall back in line or does it continue down a path. In other words, is this a reversion to the mean or momentum play? Will the path be a circle (reversion) or momentum (line). From my research, crude is definitely a momentum play, so we’ll want to look at the strategy as a trend follower, but relatively short in duration due to the weekly reports. So we’ll make the following assumptions:


  • 10-day loopback covers 2 weekly status reports and smoothes out noise.
  • A move of 4 to 5 standard deviations over 10 days is big enough to matter, and small enough to generate enough trades.
  • Giving price at least two days of pullback will allow us to stay in the trade, while providing the room to discover a trend.
  • Holding the trade for 20 days allows the strategy to catch bigger market moves

Here are the Rules

  • Enter Long when the close > close 10 days ago + 4 standard deviations
  • Enter Short when the close < close 10 days ago + 4 standard deviations
  • Reverse the trade after two days if there’s a new signal
  • Exit the trade after 20 days

Results and Next Steps

I have executed this trade 4 times so far this year with excellent results, all four trades were big winners. I did 2 trades last year that has similar setups, but this year’s trade represents a refinement. How did I refine the trade you might ask? I varied some of the assumptions by trying different loopback periods, and varying the standard deviation threshold. I also incorporated a confirming market, using Heating Oil and looked for divergences.

I noticed a profound inverse relationship between the difference in price between crude oil and heating oil, and the price of crude oil alone. I also noticed this same inverse relationship of the Crude-Heating pair versus the S&P 500. With major changes in the market usually having a 2-3 day lagging effect on my trade.

I will continue to study this trade, and see if there are further refinements that can be made, or other triggers that can help it, confirm it, or turn it into something completely different. That’s the nature of trade idea generation.

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Statistical Arbitrage Basic Strategy

Statistical Arbitrage is a pairs or spread trading strategy, predominately used by hedge funds, investment banks, and professional traders. The strategy involves tracking the difference in notional value between two highly correlated instruments, like Silver and Gold futures, or the NoB spread, which is a trade between the 10 year and 30 year treasury futures contracts. The notional value is the actual cash value of a futures contract. Statistical Arbitrageurs trade the notional difference of the pair. Here is how you calculate the notional value of a futures contract, and then the notional difference between a trading pair:

Notional Value = Current Price x Big Point Value

Notional Difference A & B = Notional A - Notional B

There are three key features in this strategy. The first is that when the difference in value between a trading pair changes in a statistically significant manner, perhaps due to some market shock, there is a high probability that that change will regress back to an average or statistical mean value. There are mathematical proofs that show the probability of regressing back to the mean is 75%. Second, futures have tremendous leverage, presenting an opportunity for high return on capital. And third, most futures brokers provide a substantial discount on the total margin of a pair due to perceived reduced risk, this means pairs trades use much smaller amounts of trading capital, so more flexible risk management can be employed.[/vc_column_text]

This is a classical regression to the mean strategy, where the difference between prices is tracked, rather than just a single price. The pairs must be highly correlated assets. So, if ABC is positively correlated with CBA, and suddenly ABC is up 20 points, while CBA is down 20 points, we can assume this price dislocation is an unusual and a temporary condition, that will eventually revert back to a mean. Profit is derived from taking a position during that regression by going long the under performing asset, and short the over performing one. As they regress, profit is realized.

When selecting pairs to trade, it can be very important to draw on fundamentals, as well as statistics, to help identify relationships between two instruments. Start by pairing one instrument in a particular sector or industry with an equal dollar value and correlated instrument, typically in the same sector or industry. Look for instruments that are not only highly correlated, but also trade with good liquidity, can be easily shorted, and with minimal slippage. Example pairs might include futures contracts for Gold and Silver, Crude Oil and Gasoline, Treasury Notes and Bonds.

When selecting pairs to trade, it can be very important to draw on fundamentals, as well as statistics, to help identify relationships between two instruments. Start by pairing one instrument in a particular sector or industry with an equal dollar value and correlated instrument, typically in the same sector or industry. Look for instruments that are not only highly correlated, but also trade with good liquidity, can be easily shorted, and with minimal slippage. Example pairs might include futures contracts for Gold and Silver, Crude Oil and Gasoline, Treasury Notes and Bonds.

Determining how well a pair of assets are correlated is important to determining the viability of pair.  Remember, pairs with a high degree of historical correlation have strong regressive tendencies (75% or higher). This presents an incredible edge to traders.

A correlation coefficient is a statistical method that measures how well the price of a pair of assets moves relative to each other tick by tick. The more they move together, the higher the correlation coefficient. Values of the correlation coefficient range from -1 to +1; with a value of +1 representing a perfect positive correlation (two instruments move in the same direction every tick), a value of 0 representing no correlation, and a value of -1 meaning perfect negative correlation (When two instruments move in perfect inverse to one another).

Correlations of 0.75 or above are often used as a benchmark for Statistical Arbitrage traders. Correlation less than 0.5 is generally looked upon as a weak correlation. Factors that can weaken correlation between a pair over time include supply and demand factors, politics, interest rates, economic growth, environmental factors, etc.

To tell whether a divergence is worth placing a trade, we need to measure the move using a statistical tool. The Z-Score is often used for this, it is a measure of a price movement relative to its mean or average price. Specifically, the Z-Score is calculated by taking the the difference between the current price and the average price, and then dividing that by the standard deviation of the current price over a specific period of time. We calculate the Z-Score this way:

Z-Score = (price - Avg( price, length)) / StdDev(price, length)

A common trading strategy is to watch over bought and over sold conditions on the Z-Score when it exceeds plus or minus 1.5 to 2 standard deviations. For example; one would short the pair if the Z-Score moved above +2 standard deviations, and go long should it fall below -2 standard deviations.

Standard deviation is a statistical concept that shows how a specific set of prices are spread around an average value. Statistically, in a normal bell curve distribution of prices; 68% of prices should fall within +/- one standard deviation of the mean, 95% of prices should fall within +/- two standard deviations of the mean, and 99.75% of prices should fall within +/- three standard deviations of the mean.

Through back testing and optimization strategies, trading opportunities can be found when the notional value diverges “X” number of standard deviations from the mean. You may further find that adding filters or price sizing strategies will further improve the probability of a successful trade.

Technical analysis, fundamental analysis, or a combination of the two can be used to find trading opportunities. Fundamental factors could include major economic events, long-term trends, monetary policy, growing seasons, etc. Technical analysis might involve one or more of the following; statistical measures, analyzing chart patterns, moving averages, stochastic, RSI’s, commercial indicators, etc.

Pairs trading with statistical arbitrage is a great market-neutral strategy for high probability returns with reduced risk, but it’s necessary to have access to quality tools to model your opinions and execute the trades accurately and consistently. Also, finding a rock solid premise for trading a pair is important. In fact, the best pairs trades are ones that adhere to a fundamental condition of the pairs, or the market they are in. These conditions might determine exclusive trade direction, execution times, seasonality, or any number of domain-specific reasons that can make trading that pair extraordinary.