Many commodities, and even some individual stocks or stock groups, have recurring fundamental factors that affect their prices. These forcescan be seen by analyzing a market by day of week, day of month, or day of year. This is called seasonal trading.

Almost everyone who did seasonal analysis back in the 1990’s made a big mistake. That is, you can’t use backtest results which include the period used to discover the seasonal patterns and expect good real-world results.

The problem with this type of seasonal study is the assumptions that needs to be made in the analysis. Say a market rises in price in the selected time frame in 21 of the 23 years from 1980 through 2002. Would a trader have known to make these seasonal trades based on the information that he or she had at the time when a trade would have been made?

For example in 1985, five years into the carefully selected period, would you know the market had appreciated during this particular time frame in just six of the 10 years from 1975 through 1984? Few traders would have taken the trade then, although in the original analysis 1985 may have been the most profitable year. Even assuming that the same trades would have been made, using a static seasonal relationship in a walk-forward simulation is a flawed approach. The proper way to study seasonality is a pure walk-forward dynamic seasonal.

It is necessary to use either all the previous years or a window of previous years to trade the current year and then to shift the window forward. It is also important to use the same rules for defining all seasonal periods and making all trading decisions across all markets.

Predictive Seasonal

One approach to seasonal analysis focuses on creating a predictive seasonal. For example, when looking at the 20th trading day of the year and trying to create a five-day price prediction, it would involve taking the difference between the 24th trading day of the year and the 20th. In this way, a seasonal based on how prices will move over the next n days is created. The key is that we only use data through the previous year to calculate the seasonal for this year and we walk it forward. Most seasonal references talk about performance during an in-sample period. This approach is not statistically valid. The only way to do a seasonal that can be used in trading is to develop a rolling window seasonal that holds up walking forward. Seasonality does evolve over time. You might ask why that would happen? Well, for example, corn, was strongly corollated with a seasonal based on the US market, but over time we had two seasonal signals overlaying each other, one from the US market and the other from Argentina, which is the other big corn producer.

Some seasonal calculation methods use raw prices and average the price for each seasonal period, for example daily, weekly or monthly. These types of seasonal calculations require individual contracts or calculations from cash prices. Using the price differences between days allows the calculation of seasonal tendencies from back-adjusted contracts.

We could use average returns for this type of predictive seasonal difference.

The problem is this type of simple classic seasonal can be distorted by a large move on one day. During the mid-1990’s, the solution came to me; use the percentage the market rises or falls to develop seasonal trades. However, you still have the problem of normalizing the seasonal so that it can be traded using the same relative numbers across all markets.

This concept became the seasonal indicator called the Ruggiero/Barna Seasonal Index that integrated both of these factors.

The Ruggiero/Barna Seasonal Index

The Ruggiero/Barna Seasonal Index was developed by myself and Michael Barna.

I would like to make one point about the RuggierolBarna Seasonal Index: It is calculated rolling forward. This means that all resulting trades are not based on hindsight. Past data are used only to calculate the sea-sonal index for tomorrow’s trading. This allows development of a more re- alistic historical backtest on a seasonal trading strategy.

The Ruggiero/Barna Seasonal Index is calculated as follows:

• Develop your seasonal and update it as you walk forward in your data.
• For each trading day of the year, record the next n-day returns and the percentage of time the market moved up or down.
• Multiply this n-day return by the proper percentage.
• Scale the numbers calculated in step 3 between -1.00 and 1.00 over the trading year. An output of 1.00 means the market will be the most bullish of the year over the next five days and an output of -1.00 is the most bearish.

The Ruggiero Barna Seasonal is so powerful it can be used as a system all by itself. Look at these results for coffee. Using an 8-year lookback to calculate a walk forward seasonal the Ruggiero/Barna Seasonal produced the following amazing results, no slippage, and commissions deducted since we wanted to look at the seasonal bias. First trade in 2002, 15 years ago.

Let’s look at the yearly breakdown for Coffee using the Ruggiero/Barna Seasonal Index.

Now let’s look at the equity curve for this system.

This approach works across many markets. Remember, this is a true walk-forward analysis!

The only way to develop seasonal-based strategies that can reliability help you trade is using walk-forward seasonality -- that is, identifying the seasonal bias using historical data only and rolling this forward to generate results. Even well-known seasonal companies make the mistake of showing results on in-sample data, and they charge a lot of money for their flawed approach.

The Ruggiero/Barna Seasonal let's you uncover seasonal patterns for any market, and has been traded for over 20 years. It has continued to hold up and produce amazing results over that time.

How To Do This In TradeStation or MultiCharts

In 1996, I released my first version of the Universal Seasonal for TradeStation. This was one of my most popular products. This product had open code but the engine of it was done in C DLL  This meant that when TradeStation changed the API, this product needed to be totally reworked. 

Now, 21 years later, I have redone this product and it  now in 100% easy language and will work in both TradeStation and Multi-charts. The key to the universal seasonal is that it uses a walk-forward methodology.

It's called Universal Seasonal.

Universal Seasonal has all you need to develop your own seasonal strategies and includes the Ruggiero/Barna seasonal index strategy, developed over 20 years ago based on walk forward analysis and still works today on a wide basket of markets. 

If you're interested in creating seasonality based trading systems, Universal Seasonal is a must-have. 

-- Murray Ruggiero

Practically every week, I get an e-mail or two from an aspiring trader. “I want to make some extra money trading”, the story usually goes. Sometimes it is from young adults, already scrambling to get out of the rat race. Other times it is from a retired folk who suddenly realize that money is tight, and working at a fast food restaurant is a loathsome option.

For some reason, $50,000 per year after taxes seems to be the amount most frequently requested. I guess it makes sense – who wouldn’t have a more comfortable life with an extra $1,000 spending money each week in their pocket?

Most want this amount by part time trading. They have obviously seen all the trading “gurus” out there bragging about their alleged profits (spoiler alert: most gurus do not even trade, and the alleged profits are fairy dust!), and the people who e-mail me want just a little taste of that but do not want to be full time traders.

Let’s forget about the sheer audacity of the request – seriously, what kind of part time job pays someone with zero or little experience $50-75 per hour?

And let’s forget about the difficulty in making ANY money in trading – the statistics tell us more than 90% of people lose at trading – which is absolutely true.

Casting the sheer difficulty of the task aside (becoming a profitable trader is a whole book in itself!), let’s just look at the math. How can someone net $50,000 per year from part time trading?

First, how much capital would you need? Assuming a 30% tax rate, to net $50,000 per year, you’d need to make around $71,000 in a year. Not a trivial amount. If you only had a $10,000 account to start, you have to have over 700% annual return! WOW!!!

Unfortunately, it is even worse for most part time traders, who might only have $5,000 to start with. They would need over 1400% return per year, for every year they trade, since they are withdrawing all profits.

Of course, some gaudy returns are possible, but those returns are possible only with EXTREME risk – basically losing it all versus a slim chance at an extraordinary gain. I call it a “lottery ticket” approach, since it has about as much chance of occurring as winning the big prize in the lottery.

Obviously, as the amount of starting capital goes up, the annual return goes down. But even with $180,000 starting capital, you’d still need 40% annual return to meet the profit goal. Most professionals do not hit that kind of annual percentage return, and even if they do, it is hard to do multiple consecutive years.

So, what about an alternative: take some money out and slowly build the account? The good news is you’ll eventually be able to take out $50,000 after taxes, every year. The bad news is that even with 50% annual returns (VERY difficult), and taking out only 60% of net profits, it will take 26 years of this kind of performance to hit the $50,000 withdrawal level. In other words, not very palatable for most part timers.

Even for someone willing to forego current income to reach the $50,000 goal has it tough. Assuming 50% per year return, 30% tax rate, and reinvesting 100% of net profits (taking out 0%), it still takes 10 years before one can withdraw $50K per year without dropping the account balance.

As daunting as even this scenario sounds, remember two things:

•  This assumes 50% annual returns, each year, for 10 years! In other words, you will be in VERY rare air to reach this return level.
• This ignores the inevitable drawdowns that will accompany such returns (assume 25-50% annual drawdowns, as a likely minimum).

Download

I have included a spreadsheet for you to tailor to your own situation. Just change the numbers highlighted in blue for your circumstances.

Conclusion:

You can definitely become a part time trader - that is not an issue. But make sure your goals and objectives are realistic before you start trading. For most people, the math simply does not work out. Unrealistic expected returns, small starting capital, and pie-in-the-sky income requirements make part time trading very difficult for most.

Remember, trading is a marathon, not a sprint. Keep your requirements for return and income small at first, and focus just on growing the account and trading well. If you do so, profits will eventually follow. 

Get in touch to Download files, resources, and some materials to take the advantage.

-- Kevin Davey of KJ Trading Systems

A short signal for the S&P500? Believe it or not, some do exist. Although these can often be hard to find they can also contribute greatly to your success – so tons of traders search endlessly for a complimentary short system or two for their portfolios.

I want to talk about a simple short edge that I have recently been tweeting about for the past year or so as it has been an interesting signal as of late. It is nothing more than a short signal – not a complete system! The sample size is not great, but it is still noteworthy.

Sigma Score

The raw signal itself is just a +2 sigma move based on rolling 20 day (historical) volatility. A sigma score or “z score” is simply a way to express something in terms of standard deviation units. For example, a sigma of 1.5 signifies a value that would allow us to fit 1.5 standard deviations between the mean and the current value. Note: That mean is zero in this case due to standardization.

The trading idea being that the market has moved up too much too fast and some short-term pause or price retraction could be expected.

So how do we calculate sigma score? Below is simple python code using Build Alpha’s python environment for creating custom signals. (Another example: buildalpha.com/python)

Step 1. Calculate the natural log of one bar price changes
Step 2. Calculate the rolling average of the price changes
Step 3.  Calculate the rolling standard deviation of the price changes
Step 4. Calculate the Z score or sigma score which is defined as z = (value – average) / standard deviation.
Step 5.Create python list named Signal that contains 1 for true signal or 0 for false signal.

There is also an ELD with TradeStation code attached that contains a simple function for sigma_score(lookback) that will do the same calculation.

Performance

Below you can see the equity curve from 2002 to 2018 if one were to have sold short (1 contract) all +2 sigma score days and hold exactly for one trading day (entry and exit both executed next bar open). No stop or profit target included.

It is not an excellent edge by any means, but a short signal to persist while the market has gone straight up is always worth keeping tabs on. Below you can see the e-ratio for this event. (Learn more about eratio here: buildalpha.com/eratio).

You can see it peaks at 1 bar hold but maintains almost 0.30 units of volatility over normal (1.00) so longer hold times might not be out of the question for this edge – but that will be left for further research. A better idea might be to use this daily signal as a multi-timeframe filter for some intraday short systems as the peak is clearly at x = 1.

I am a quantitative trader with a high frequency,  market-making firm but also recently started developing the Build Alpha trading software which has the ability to create, stress test, and produce tradeable code for hundreds of systematic trading strategies. All of this can be achieved with NO programming.  However, I recently added a Python development environment to allow sophisticated users a bit more flexibility. I can be reached at David@buildalpha.com or on twitter @dburgh.

Downloads

Sigma Function, Strategy and Indicator ( TradeStation ELD )
Sigma Strategy WorkSpace ( TradeStation WorkSpace )

-- Dave Bergstrom from Build Alpha

Figure: The green lines show historical performance of stocks that were members of the S&P 500 in 2012. Note that all of these stocks came out of the 2008/2009 downturn very nicely.

Figure: What really happened: If, instead we use the members of the S&P 500 starting in 2008, we find that more than 10% of the listed companies failed.

In our work at Lucena Research, we see an annual 3% to 5% performance “improvement” with strategies using survivor-biased data.

Solution: Find datasets that include historical members of indices, then use those lists to sample from for your strategies.

3. Observing the close & other forms of lookahead bias

In this failure mode, the quant assumes he can observe market closing prices in order to compute an indicator, and then also trade at the close. As an example, one might use closing price/volume to calculate a technical factor used in the strategy, then trade based on that information.

This is a specific example of lookahead bias in which the strategy is allowed to peek a little bit into the future. In my work I have seen time and again that even a slight lookahead bias can provide fantastic (and false) returns.

Other examples of lookahead bias have to do with incorrect registration of data such as earnings reports or news. Assuming for instance that one can trade on the same day earnings are announced even though earnings are usually announced after the close.

Solution: Don’t trade until the open of the next day after information becomes available.

4. Ignoring market impact

The very act of trading affects price. Historical pricing data does not include your trades and is therefore not an accurate representation of the price you would get if you were trading.

Consider the chart below that describes the performance of a real strategy I helped develop. Consider the region A, the first part of the upwardly sloping orange line. This region was the performance of our backtest. The strategy had a Sharpe Ratio over 7.0! Based on the information we had up until that time (the end of A), it looked great so we started trading it.

When we began live trading we saw the real performance illustrated with the green “live” line in region B– essentially flat. The strategy was not working, so we halted trading it after a few weeks. After we stopped trading it, the strategy started performing well again in paper trading (Region C, Arg!).

How can this be? We thought perhaps that the error was in our predictive model, so we backtested again over the “live” area and the backtest showed that same flat area. The only difference between the nice 7.0 Sharpe Ratio sections and the flat section was that we were engaged in the market in the flat region.

What was going on? The answer, very simply, is that by participating in the market we were changing the prices to our disadvantage. We were not modeling market impact in our market simulation. Once we added that feature more accurately, our backtest appropriately showed a flat, no-return result for region A. If we had had that in the first place we probably would never have traded the strategy.

Solution: Be sure to anticipate that price will move against you at every trade. For trades that are a small part of overall volume, a rule of thumb is about 5 bps for S&P 500 stocks and up to 50 bps for more thinly traded stocks. It depends of course on how much of the market your strategy is seeking to trade.

5. Buy $10M of a $1M company

Naïve backtesters will allow a strategy to buy or sell as much of an asset as it likes. This may provide a misleadingly optimistic backtest because large allocations to small companies are allowed.

There often is real alpha in thinly traded stocks, and data mining approaches are likely to find it. Consider for a moment why it seems there is alpha there. The reason is that the big hedge funds aren’t playing there because they can’t execute their strategy with illiquid assets. There are perhaps scraps of alpha to be collected by the little guy, but check to be sure you’re not assuming you can buy $10M of a $1M company.

Solution: Have your backtester limit the strategy’s trading to a percentage of the daily dollar volume of the equity. Another alternative is to filter potential assets to a minimum daily dollar volume.

6. Overfit the model

An overfit model is one that models in-sample data very well. It predicts the data so well that it is likely modeling noise rather than the underlying principle or relationship in the data that you are hoping it will discover.

Here’s a more formal definition of overfitting: As the degrees of freedom of the model increase, overfitting occurs when in-sample prediction error decreases and out-of-sample prediction error increases.

What do we mean by “degrees of freedom?” Degrees of freedom can take many forms, depending on the type of model being created: Number of factors used, number of parameters in a parameterized model and so on.

Solution: Don’t repeatedly “tweak” and “refine” your model using in-sample data. And always compare in-sample error versus out-of-sample error.

7. Trust complex models

Complex models are often overfit models. Simple approaches that arise from a basic idea that makes intuitive sense lead to the best models. A strategy built from a handful of factors combined with simple rules is more likely to be robust and less sensitive to overfitting than a complex model with lots of factors.

Solution: Limit the number of factors considered by a model, use simple logic in combining them.

8. Trusting stateful strategy luck

A stateful strategy is one whose holdings over time depend on which day in history it was started. As an example, if the strategy rapidly accrues assets, it may be quickly fully invested and therefore miss later buying opportunities. If the strategy had started one day later, it’s holdings might be completely different.

Sometimes such strategies’ success vary widely if they are started on a different day. I’ve seen, for instance, a difference in 50% return for the same strategy started on two days in the same week.

Solution: If your strategy is stateful, be sure to test it starting on many difference days. Evaluate the variance of the results across those days. If is large you should be concerned.

9. Data mining fallacy

Even if you avoid all of the pitfalls listed above, if you generate and test enough strategies you’ll eventually find one that works very well in a backtest. However, the quality of the strategy cannot be distinguished from a lucky random stock picker.

How can this pitfall be avoided? It can’t be avoided. However, you can and should forward test before committing significant capital.

Solution: Forward test (paper trade) a strategy before committing capital.

Summary

It is best to view backtesting as a method for rejecting strategies, than as a method for validating strategies. One thing is for sure: If it doesn’t work in a backtest, it won’t work in real life. The converse is not true: Just because it works in a backtest does not mean you can expect it to work in live trading.

However, if you avoid the pitfalls listed above, your backtests stand a better chance of more accurately representing real life performance.

-- Tucker Balch from Augmented Trader

Many people begin their trading endeavor by trading a single system. This is a great stepping stone to becoming a successful trader, but you can’t rely on a single system. Why? Trading systems naturally ebb and flow much like the market. Sometimes they are hot, other times they lose money for months while on a few occasions, they crash completely.

The goal is to build a portfolio of trading systems. By trading several different strategies you can experience profound impact on the trading performance.

My three intermarket systems have been very popular, with the two index systems being the most popular. Most of the customers who own the S&P Intermarket System also own the Nasdaq Intermarket System, and that's great. The problem is many of them are not trading my bond system which costs them performance and increases risk.

Let’s take a closer look at combining several strategies resulting in missing out on the uncorrelated returns of the bond system. It's called, U.S. Bond Intermarket System​.

Most traders have not seen studies of testing multiple systems because it’s not easy to combine results in TradeStation. It can be done with Portfolio Maestro but that's not easy to use and the money management options you have are limited. Because of these reasons, we will use TradersStudio-for-TradeStation.

TradersStudio-for-TradeStation uses TradeStation data and is currently offline so it can give you orders for tomorrow on daily systems you can manually enter. It can also do intra-day backtesting and if you use longer bars (90 minutes) you can update data, generate orders, and kludge intra-day trading. A true real-time version will be available later this spring. TradersStudio for TradeStation uses an EasyLanguage-friendly language and also has a migrations tool to move your EasyLanguage over without having to reprogram your strategies. I think this software will provide a real benefit to TradeStation users by providing true portfolio level backtesting. But I digress…

In reading this article you may discover issues about TradeStation that are disconcerting. For example, how TradeStation handles holidays. If a day ends 1:00 pm EST or earlier it seems that TradeStation just throws that data way at least in the stock indexes. This creates problems since when you export the data, even back to TradeStation as third-party data, these holiday-shorten days will not be included, and really they should be because that price action should affect indicators and moving average skipping them is wrong.

This holiday problem is ​an issue on why results between TradeStation and TradersStudio-for-TradeStation do not match. Another one is if a market order to buy and sell short occur on the same bar in a system TradeStation handles it poorly. TradeStation generates both orders, it gives a reverse first and then a new signal in the original direction. TradersStudio will either reverse on these type of orders or stay in the original direction, which gives you the same results as TradeStation without the bogus trade.

Now that we have introduced the tool and the issues, let’s look at trading two different sets of systems. First we'll look at the S&P Intermarket System alone. Then we'll add the Nasdaq Intermarket System thus, trading both strategies together. Finally, we'll add the U.S. Bond Intermarket  System to trade all three.

We will start by Trading a 1 contract with the S&P Intermarket System from 4/11/2007 to 3/14/2018. We will also use $37.50 slippage and commission round trip. Let’s look at the S&P Intermarket System by itself in TradersStudio-for-TradeStation (see note above on trade differences).

Let’s now look at the Nasdaq Intermarket System. This system has different results in TradersStudio because both 4th of July holiday in 2009 and Good Friday 2013 create issues since major trades were affected around those periods.

Now let’s look at what happens if we trade a one lot of each.

We make the money on both systems, which is good. However, we also take on the drawdown of both systems. Trading them together does nothing extra for us.

Let’s now look at my U.S. Bond Intermarket System again from 4/11/2007 to 3/13/2018 with $37.50 deducted for slippage and commission.

Now let’s see what happens when we combine the three. I think you'll see some "magic".

If we trade just the two index systems our maximum drawdown is $29,172. Trading all three our drawdown is $24,185. This is a reduction of 18% while increasing profit to 67%. It's clear that combining non-correlated strategies is one of the important keys to long-term success as a trader.

We will see that our $50K becomes over 1.8 million dollars, but dollars are not how professional trades look at things. Let’s take a quick look at some other measures. Our compound annual growth rate is over 57%, maximum drawdown around 33%, the Sharpe ratio is over 1.8, and MAR is 1.73. We also have 74% winning months by combining these

Conclusion

In closing, we can see that combining certain strategies can work wonders for the performance of a trading portfolio. Adding a position sizing method is the next step. There is no holy grail when it comes to trading but, building portfolios and applying a position sizing method to that portfolio get pretty close. 

You can learn more about the U.S. Bond System by clicking the button below.

Downloads

Download the performance reports (excel format) used in this article.

“The journey, Not the destination matters...” ― T.S. Eliot

You might be wondering what the Nobel Prize winning poet, T.S. Eliot has to do with trading. No, he did not leave a secret trading manual in his attic for his grandchildren to stumble upon. And I’m guessing he did not even trade – he was too busy creating immortal prose.

Yet, his quote, “The journey, Not the destination matters...” has profound implications for us as traders. Let me explain…

What if I told you of a Soybean Oil strategy that earned nearly $40,000 in single contract profits in five years? That is $8,000 profit per contract each year. Would you want to trade it?

Right now, margin on Soybean Oil is $660, so you might determine you could trade 1 contract of this strategy with $2,500. So, in five years, your $2,500 would turn into $42,500, which is a compounded annual growth rate of over 76%. Per year!

At first blush, who would not want that?  

The problem is that it's where most traders stop their analysis. They see the start point, and they see the end point five years later, with bags of cash awaiting them. They jump for joy at their performance, and rush to trade the strategy live.

Of course, our friend T.S. Eliot would disagree. He would argue that the journey during those five years, not just the end point, is what really matters.

So what does that journey look like? Here it is:

The end profit is there, sure enough. But look at what we had to endure to get there! A maximum drawdown of $9,000, numerous quick and steep equity collapses, extended flat periods – UGH!

The final profit says this is a nice strategy, but the path to get there is brutal. In other words, the journey is really important. Unfortunately, too many traders ignore that.

Psychologically, this strategy would be practically impossible to trade. Pretend you are trading this strategy, and just imagine yourself at the following point on the equity curve:

In other words, we’re trying to find a predictor that can positively influence or inform our trading decisions. We can create as many classes or states as we desire, i.e. we can have multiple optimal values for our indicator. However, one word of caution, if we create too many classes then we run the risk of overfitting.  We can guard against the possibility of overfitting in a few ways. We can limit the number of classes that we create, and we can enforce a minimum number of trades per class; like we would with an individual system. Finally, we can use “force logic” such that the optimizer is not able to optimize each class individually but rather is only able to optimize some delta. We don’t consider such advanced possibilities any further in this example.

For this example, we will look at a long only version of  Connor’s RSI(2) system applied against the ES futures contract, and will seek to classify the optimal buy and sell thresholds based on the longer term RSI. We observe that the intermediate RSI tends to stay in the overbought or oversold during extended periods of strength or weakness. We want to make it easier to buy and sell during strong periods, and we want to make it more difficult to buy and sell during weak periods. In other words, we want to become more selective when the market is persistently weak which makes sense because the risk is higher.

In this example, we’ll use an RSI(14) for our longer term RSI. We observe the RSI(14) tends to stay between 15 to 70.

However, to be sure that we capture the lower and upper extents then we’ll define our classes as: -infinity to 30, 30 to 50, and 50 to infinity. If the intermediate RSI is in the lower region then that indicates a more bearish market that should dictate more trade selectivity; on the other hand if the market is strong then we need to be more aggressive to capture the dips. We could use the optimizer to find the optimal values for these classes. However, in this example, we simply force the logic we desire and optimize by hand.

Before we get to the results, it might be illustrative to see the most pertinent EasyLanguage code. We can actually make the class assignment code rather short and elegant as long as we pay attention to the ordering of the statements:

BuyThresholdV = 30;
BuyThresholdV = IFF( RsiClassifier >= -10, 10, BuyThresholdV);
BuyThresholdV = IFF( RsiClassifier >= 30, 20, BuyThresholdV);
BuyThresholdV = IFF( RsiClassifier >= 50, 30, BuyThresholdV);

SellThresholdV = 70;
SellThresholdV = IFF( RsiClassifier >= -10, 60, SellThresholdV);
SellThresholdV = IFF( RsiClassifier >= 30, 70, SellThresholdV);
SellThresholdV = IFF( RsiClassifier >= 50, 80, SellThresholdV);
BuyCondition = RsiSignal < BuyThresholdV; SellCondition = RsiSignal > SellThresholdV;

Finally, we compare the simple RSI(2) strategy to our hand optimized multi-state classifier.

We introduced the concept of multi-state (or multi-class) classification by using the RSI(2) strategy as an example. We quantitatively improved the results of the RSI(2) system using only hand optimization and forced logic. Our multi-state classifier improved the net-profit and significantly improved the average profit per trade. The winning percentage was basically unchanged.

Unfortunately, we were not able to significantly reduce the drawdown with this method. This suggest that our profits may be deriving more from trading more aggressively in bull markets than avoiding the losses in bear markets. However, the fact our total trades is lower and our profits higher suggest the quality of trades overall we took were higher. That would suggest on average we did better but both systems took a single or a few big hits sometime over the entire history.

One suggestion as to why this may be the case is that the RSI itself tends to make large movements on a bar-by-bar basis which limits the degree to which we can use its value to inform our decision. Fortunately, one is not limited to using RSI, and there are many more types of market conditions that one can classify that may yield even better performance.

–by Curtis White from blog Beyond Backtesting

I want to discuss three trading “truths” that I often hear but when I finally got into testing ideas found them to be myths. These discoveries were instrumental in turning my trading around.

For those that know my story, it was not all roses and rainbows – what trading story is?!? I actually “learned” about a lot of traders from online sources, chat rooms, webinars, and eventually found the right circles to roam in after a LOT of trial and error. I was then lucky enough to land a job with a high frequency trading firm. I was quickly made to realize that much of what I thought trading “was” was most certainly false.

In an attempt to show me the light, a few of the quick trading axioms they wanted to disprove to me were simple “well known” trading ideas that have been around for years but were in fact large falsehoods. I quickly realized beyond this list of three examples that there must have been many, many more “trading truths” that were causing harm to my account and doing my trading aspirations a disservice.

I then realized the value in testing everything and quantifying my entire approach. It wasn’t until this point in my journey did I get out of the “rat race” of trading… the ups and downs, the barely consistent, always doing slightly better but not really getting anywhere trading most of the traders I meet today are going through.

I cannot stress the importance of testing everything and quantifying your edge. Investigate these trading axioms. Otherwise you will be stuck in randomness until your account walks to 0. For more on my journey check out this podcast I did, chatwithtraders.com/103. Below you will find my three favorite trading “truths” quantified.

The truth is that this pattern has been one of the most bullish (not bearish) 1-day signals for the SP500 over the past 15+ years. Did you know that the day following a bearish engulfing candle actually closes higher than 61.72% of the time in the SP500 futures and 65.33% of the time in SPY ETF? The day following a bullish engulfing candle only closes higher than 54.05% of the time in the SP500 futures and 51.70% of the time in SPY. You tell me why they’re named how they are!

Most of the time this is true but it depends on the moving average, moving average length, and the market. I recently came across a few blogs that mentioned using a short term moving average as a sign to exit long market exposure and wait for sunnier days. In reality, this sounds great; however, analyzing the data this can be an extremely misleading “truth”.

Below is a chart plotting the equity curve if you would buy every close when the SP500 is BELOW the 8-period simple moving average (8SMA) and sell the next bar (repeating until above 8SMA). The second chart is if you were to buy every close when the SP500 is ABOVE the 8SMA and exit the next bar (repeating until below 8SMA). Yes, being below this moving average is actually better for long returns.

Of course this is not true for all markets and moving averages, as mentioned. I pointed this out in another post on SeeItMarket where I dissected popular stocks and different moving average lengths here: https://www.seeitmarket.com/testing-moving-averages-popular-stocks-etfs-16809.

The point is that these trading truths like “above a moving average is bullish” and “below a moving average is bearish” need to be quantified and tested. It is important to stop thinking trading truths can be generalized to every market, timeframe, indicator value, etc. and just verify them yourself and you’ll be much better off!

Sure earnings announcements and large unexpected news announcements typically happen after market close. Does this mean we should avoid trading overnight, if possible? So many want to day trade and be flat on the close that they miss a lot of gains from the overnight session (sometimes all of them).

Below you will see 30 top stocks where I breakdown their returns in the day session compared to the overnight session. The blue line signifies if you bought the open and sold the close the same day (day session), and the orange line signifies buying the close and selling on the next day’s open (simulating overnight exposure). As expected, there are no generalities in trading truths. Some stocks exhibit that most, if not all, returns come from the overnight.

In conclusion

Not all trading truths are “truths”. There are many that still have value! The edge is not in these truths but in determining the difference between a truth and a “truth”. Most do not take the effort to investigate these trading axioms found in trading books, blog sites, chat rooms, etc. If I have one goal then it is to make traders realize the value in testing everything – it is something I wish I would have started earlier. It is a super power to quickly test these ideas and trade/act accordingly!

I think many traders shy away from digging into the data because it requires effort and they think it will be a hard and arduous journey before they find some gold. But now I think it is increasingly easier than many may think.

That’s why Build Alpha’s main goal was (and still is) to make this type of testing easier for those with no programming skills or those tired of crunching endlessly in excel. The game is changing and things like this can be easily tested now. Today, trading just comes down to those who will put in the work and who won’t: this should greatly increase your odds for success if you’re part of the former group.

--By David Bergstrom from blog Buildalpha

Humans have been trying to clock in the stock market with fancy indicators since the beginning of time. But do they work? In this article I test five different market timing indicators. They are:

• Coppock Curve
• Zweig Breadth Thrust
• Arms Index (TRIN)
• McClellan Oscillator
• No. Of New 52-Week Lows

These market indicators are all available with my historical database from Norgate Data.

The first indicator we will test is the Coppock Curve.

The Coppock Curve first appeared in a Barron’s article in 1962 and was invented by economist Edwin Coppock.

The indicator takes a weighted moving average of the rate-of-change and is designed to spot long term trend changes in market indexes.

According to Investopedia, you should buy when the Coppock Curve moves above zero and sell when the indicator moves below zero.

Developed by Marty Zweig, the Breadth Thrust indicator takes the number of advancing issues in an index divided by the total number of issues (advancing and declining). A 10-period exponential moving average is then laid on top.

According to most sources, you should buy when the indicator moves from a level of under 40% to a level above 61.5% within a 10-day period.

This is noted as a rare sentiment shift and usually comes at the beginning of a new bull market.

• Buy SPY when Zweig Breadth Thrust moves from below 40% to above 61.5% within a 10-day period.
• Sell after 20 days.

I tested this strategy between 1/1994 and 1/2017 and got an annualized return of 1.48% with a maximum drawdown of -14% and a win rate of 65% across 26 trades.

Below you can see the equity curve as taken from Amibroker:

The Arms Index was developed by Richard W. Arms in 1967 and is also referred to as the TRIN. This is another market breadth indicator. It’s calculated by dividing the advance-decline ratio by the advance-decline-volume ratio.

There are a number of interpretations on how to use TRIN and some suggest it is most useful for day traders.

According to this article, you should go long when the 10-day moving average of TRIN moves below 0.8 and sell when it moves above 1.2.

I tested buying SPY according to the above definition between 1/1994 – 1/2017 and recorded an annualized return of 3.52% with a maximum drawdown of -22% and a win rate of 65% from 20 trades.

Following is the equity curve taken from Amibroker. You can see that this is a rare signal in recent times so the settings may need to be altered:

The McClellan Oscillator is another market breadth indicator. This one is based on the smoothed difference between the number of advancing and declining securities on the NYSE or some other market index.

Ratio Adjusted Net Advances (RANA) = (Advances – Declines) / (Advances + Declines)

McClellan Oscillator = 19-day EMA of RANA –  39-day EMA of RANA

Looking online, I found it difficult to find any consistent definition on how this oscillator should be used.

According to one article, you should be bullish when the McClellan Oscillator moves above zero and bearish when it moves below zero.

I tested this on the SPY ETF and the results were poor. I also tried a number of other variations which also showed net losses. The best result came from a strategy taken from Futures Mag.

• Buy SPY when the McClellan Oscillator moves to between -70 and -100, then turns up.
• Sell SPY when the McClellan Oscillator moves to between 70 and 100, then turns down.

In other words, we are looking for the indicator to move down into an oversold range before turning back upwards.

This strategy generated an annual return of 9.61% with a maximum drawdown of –24% and a win rate of 76% over 45 trades between 1/1994 and 1/2017.

This compares to a buy and hold return between 1/1994 – 1/2017 of 9.09% with a max drawdown of -55%.

This indicator has some interesting characteristics and it is always nice to see a win rate above 70%. You can see the equity curve below:

The final stock market indicator we will look at today is the number of stocks making new 52-week lows in comparison to the index itself.

This is a classic indicator that can be used to measure strength in the prevailing trend and is also useful when looking for divergence.

You can see that in the chart below the number of stocks hitting new 52-week lows hit a peak of over 400 (out of 500) in 2008.

• Buy SPY when it closes at a new 20-day low but the number of stocks making new 52-week lows goes down.
• Sell five days later.

This strategy is based on divergence. The index has hit a new low but the stocks that make up the index have not made new 52-week lows. This suggest the downward trend is weakening and an upturn could be on the cards.

This strategy produced an annualized return of 3.74% between 1/1994 and 1/2017 with a maximum drawdown of -14% and a win rate of 62% over 100 trades.

Below you will find the equity curve produced in Amibroker:

There are countless numbers of different indicators on the market but few are able to generate profits superior to buy and hold. We have looked at five indicators here and the results are mixed.

The Coppock Curve Indicator, McClellan Oscillator, and Number of New 52-Week Lows Divergence strategy look fairly interesting and they might be useful for day traders or as market timing filters.

For example, traders could use these indicators to only take trades when certain conditions line up. These strategies might also be enhanced with extra leverage or adapted for futures.

As usual, the data proves there is no easy money available in the stock market despite what is claimed by the inventors and promoters of simple market indicators.

--by Joe Marwood from blog Jbmarwood.com

A different strategy for each day of the week? Sounds crazy, but is it?

I have written about “Turnaround Tuesday” before.

This simple strategy is actually at new highs since this was published over 10 months ago!

But what about other markets? Is it possible to just trade one market per day of the week (from open to next day’s open) on a specific day of the week and still do well? If you would have followed this simple “portfolio” below for the past 15+ years then yes... you would have. You wouldn’t even have to sit in front of the screens like the rest of us degenerates.

Simple strategy dating back to 2003. Short Oil on Monday’s open and cover on Tuesday’s open. Results based on a 1 lot.

If Monday was a down day (Monday’s close less than or equal to Monday’s open) then buy Tuesday’s open and sell on Wednesday’s open. Results based on 1 lot.

Maybe in lieu or anticipation of bad weekend news releases, buying Gold on Friday’s open and holding over the weekend to exit on Monday’s open has produced the following based on 1 lot.

Not a bad portfolio equity curve, right?! One of the hidden ‘holy grails’ in trading is combining systems together to smooth the equity growth of the account. No single super star out of any of these three strategies but combined their curve starts to look rather nice.

Sure these might not be alpha generators on their own but they may be combined with some other price patterns, filters, or targets and stops to become full strategies. They can certainly act as intermarket or multi-time frame filters for more complicated strategies.

For example, if you have a 60-minute mean reverting system that buys oil dips then maybe it should be turned OFF on Mondays! At the very least it is worth a look and this is all made very simple with Build Alpha – just point, click, test.

For example, did you know S&P500 has performed much better on even days than odd days? (April 6 is an even day).

With April  over I thought it would be a good idea to review our Market Seasonality Study. 

If you will recall, back on November 2nd of last year, we opened a seasonality trade in anticipation for a traditional strong period of the stock index markets. That is, the months of November, December, January, February, March and April. So what did the market do during those months?

Below is the image of the price chart were we entered back in November and where we re-exited at the open of May 3rd when price fell below our simple moving average.

Another winning trade.

We are now entering a traditionally weak season for the stock index market. This is a well known seasonality period. In this article I would like to take a closer look at the seasonality bias which so many investors and traders talk about. 

First, I would like to test the popular trading idea of buying the S&P in November and selling in May. I will test this on the cash market going back to 1983. The number of shares to buy will be adjusted based upon a fixed amount of risk. In this case, $5,000 is risked per trade based upon the volatility of the market. As this is a simple market study no slippage or commissions were taken into account. The EasyLanguage code looks like this:

I decided to test the strategy on the S&P cash index going back to 1960. The following assumptions were made:

* Starting account size of  $100,000.
* Dates tested are from 1960 through May 2017.
* The number of shares traded will be based on volatility estimation and risking no more than $5,000 per trade.
* Volatility is estimated with a two times 20-week ATR calculation. This is done to normalize the amount of risk per trade.
* The P&L is not accumulated to the starting equity.
* There are no deductions for commissions and slippage.
* No stops were used.

From here we can plug into the input values the buy month (November) and sell month (May). Doing this we generate the following equity graph:  

It sure looks like these months have a long bias as those are some nice results. What would the equity curve look like if we reverse the BuyMonth and SellMonth? That is, let's buy in May and sell in November. Below is the equity curve for this inverted system.

The early years of 1960 to about 1970 the equity curve went lower and lower. Then it started climbing reaching an equity peak of 1987. During this period, the strategy was producing positive results. That equity peak occurred during 1987 should be familiar. That was the year we had the massive one day market crash on October 19th known as Black Monday. That's when Dow Jones Industrial Average dropped 22% in one day!

 Since that event the behavior of market participants has been altered. This is not unlike the radical market changes which occurred after the 2000 market peek where much of the trending characteristics of the major markets were replaced by mean reversion tendencies. So far the basic seasonality study looks interesting. However, keep in mind that we do not have any stops in place. 

Nor do we have any entry filter that would prevent us from opening a trade during a bear market. If the market is falling strongly when our BuyMonth rolls around we may not wish to purchase right away.

Likewise, we have no exit filter to prevent us from exiting when the market may be on a strong rise during the month of May. It's conceivable that the market may be experiencing a strong bull run when our usual SellMonth of May rolls around.

Simple Moving Average Filter

In order to avoid buying and selling at the wrong times I'm going to introduce a 30-period simple moving average (SMA) to act as a short-term trend filter. This filter will be used to prevent us from immediately buying into a falling market or selling into a rising market. For example, if our SellMonth of May rolls around and the market happens to be rising (trading above the 30-period SMA), we do not sell just yet. We wait until unit price closes below the short-term SMA. The same idea is applied to the buying side, but reversed. We will not go long until price closed above the short-term SMA. Within EasyLanguage we can create a buy/sell confirmation flag called BuyFlag and SellFlag which will indicate when the proper go-long or sell conditions appear based upon our short-term trend filter.

if ( MinorTrendLen > 0 ) Then BuyFlag = Close > Average( Close,  MinorTrendLen )
Else BuyFlag = true;
If ( MinorTrendLen > 0 ) Then SellFlag = Close < Average( Close, MinorTrendLen )
Else SellFlag = true;

The MinorTrendLen variable is actually an input value which holds the look-back period to be used in the SMA calculation. You will notice there is an additional check to see if the look-back period is zero. This is done so we can enable or disable this SMA filter. If you enter zero for the look-back period, the code will always set our BuyFlag and SellFlag to true. This effectively disables our short-term market filter. This is a handy way to enable and disable filters from the system inputs. Below is the performance of both our Baseline system and the system with our SMA filter:

We increased the net profit, profit factor, average profit per trade, annual rate of return, and the expectancy score. The max intraday drawdown fell as well. Overall it looks like the SMA filter adds value.

MACD Filter

A standard MACD filter is a well known indicator that may help with timing. I'm going to add a MACD calculation, using the default settings, and only open a new trade when the MACD line is above zero. Likewise, I'm only going to sell when the MACD line is below zero.  Within EasyLanguage we can create a MACD filter  by creating two Boolean flags called MACDBull and MACDBear which will indicate when the proper major market trend is in our favor.

If ( MACD_Filter ) Then
Begin
MyMACD = MACD( Close, FastLength, SlowLength );
MACDAvg = XAverage( MyMACD, MACDLength );
MACDDiff = MyMACD - MACDAvg;
If ( MyMACD crosses over 0 ) Then
Begin
MACDBull = True;
MACDBear = False;
End
Else If ( MyMACD crosses under 0 ) Then
Begin
MACDBull = False;
MACDBear = True;
End;
End
Else Begin
MACDBull = True;
MACDBear = True;
End;

Below are the results with the MACD filter:

Utilizing the MACD filter and comparing it to our baseline system, we reduced every so slightly many of the performance metrics. Overall, it does not appear to be better than our SMA filter.

RSI Filter

For our final filter I will try the RSI indicator with its default loopback period of 14. Again, like the MACD calculation, I want price moving in our direction so I want the RSI calculation to be above zero when opening a position and below zero when closing a position.

If ( RSI_Filter ) Then
Begin
   RSIBull = RSI(Close, 14 ) > 50;
   RSIBear = RSI(Close, 14 ) < 50;
End
Else Begin
   RSIBull = true;
   RSIBear = true;
End;

The RSI filter performed better than the MACD filter. Comparing it to the Baseline, we see it's very similar. With the RSI filter the net profit, profit factor and RINA Index are all slightly lower.

In the end it does appear applying a SMA filter or an RSI filter can improve the baseline results. Both filters are rather simple to implement and were tested for this article with their default values. Of course, you could take this simple study much further.

Apply SMA Filter on Weak Seasonality Period?

After performing the different filter tests and choosing the simple moving average filter, I started wondering what it would look like if we applied to the weak seasonality period. Below are the before-and-after equity curves.

It appears the simple moving average filter helps a lot. While we can still see the weak seasonality period equity curve is not nearly as attractive as the strong seasonality period, it did improve to somewhat decent. 

Conclusion

It certainly appears there is a significant seasonal edge to the S&P market. The very trading rules we used above for the S&P cash market could be applied to the SPY and DIA ETF markets. I've tested those ETFs and they produce very similar results. The S&P futures market also produces similar results. It even appears to work well on some stocks.

Keep in mind this market study did not utilize any market stops. How can this study be used? With a little work an automated trading system could be built from this study. Another use would be to apply this study as a filter for trading other systems.

This seasonality filter could be applied to both automated trading systems or even discretionary trading. It may not be much help for intraday trading, but it may be worth testing. Anyway, just being aware of these major market cycles can be helpful in understanding what's going on with today's markets and where they may be going in the near future.

Hope you found this study helpful. This seasonality filter (30-period SMA) is what's used on our State of U.S. Markets webpage.

Downloads

• Seasonal Strategy (TradeStation ELD)
• Seasonality Strategy WorkSpace (TradeStation TWS)
• Seasonal Strategy (Text File)