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December 11, 2019

How To Win Big & Wipe-Out Losses

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Seasonal Trading
by Murray Ruggiero

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

Types of Fundamental Forces

Three types of fundamental forces cause seasonal trading patterns. The first type is based on events that have fixed or relatively fixed dates. Examples are: The pollination of corn in late June and early July, and the filing of federal tax returns on April 15.

Many seasonal forces are related to events for which the date could change – for example, the government's release of the current unemployment numbers. If these dates remain fairly constant for many years, then seasonal effects can be identified. If these dates change slightly, it may look as if the seasonal pattern has changed when, in actuality, the seasonal bias relative to the reports has not changed. For example, the Thursday before the monthly unemployment numbers is scheduled to be announced has a downward bias in the T-Bond market.

The third type of fundamental forces is based on human psychological factors. For example, in the stock market, Mondays have an upward bias because many traders exit their positions on the preceding Friday and reenter them on Monday. This Monday bias has existed at least since the 1970s, but it has been magnified since the 1987 Black Monday crash. For example, since the 1987 crash, Mondays have had an upward bias of over .60 point per trade, or about $300.0, on a futures contract on an open-to-close basis. Before the crash, the bias was about $138.00 per trade. The crash magnified the fear of hold positions over a weekend. This fear enhanced the upward bias on Mondays and changed the psychology of the market.

Calculating Seasonal Effects

Now that we understand why seasonal trading works, let's discuss different ways of calculating these measures.

The simplest method is to use price changes – different prices from open to close or from close to close. This type of seasonal analysis works very well for day-of-week and day-of-month analyses. When calculating seasonality on a yearly basis, price changes or several other methods can be used to capture the effects of these recurring fundamental forces.

One alternate method is to calculate the seasonal effect using a de-trended version of the data. The simplest way to de-trend a price series is to subtract the current price from a longer-term moving average. Another popular method for calculating seasonality is to standardize the data on a contract-by-contract or year-by-year basis – for example, by identifying the highest or lowest price on each contract or year and using it to create a scaled price.

Measuring Seasonal Forces

Let's first discuss measuring these seasonal forces based on the day of the week. Day-of-week forces can be measured in several ways. The first way is to measure the change on an open-to-close or a close-to-close basis – for example, measure the close-to-open change every Monday on the S&P 500. Another, even more powerful, variation is to compare only one day of the week throughout the month – Mondays in December, for example. As we will see later, this type of analysis can produce amazing results.

Using another form of day-of-week analysis, you would map where the high and low for a given week will occur. This information can help you pinpoint when, during a given week, you should take a position historically. On a shorter-term basis, it can tell you how strong the bear or bull market is. In bull markets, the high occurs later in the week; in bear markets, the high is earlier in the week.

The final form of day-of-week analysis is conditional day-of-week analysis. Buying or selling is done on a given day of the week, based on some condition – for example, buy on a Tuesday when Monday was a down day. This type of analysis can produce simple and profitable trading patterns.

Larry Williams, a legendary trader, developed the concept of trading day-of-month analysis. This concept is very powerful for discovering hidden biases in the markets. There are two major ways to use this type of analysis: (1) on an open-to-close or close-to-close basis, and (2) more often, by buying or selling on a given trading day of the month, and holding for N days. When a holding period is used, this type of analysis can produce tradable systems by just adding money management stops.

Let's now discuss three methods for calculating seasonality on a yearly basis. The first method originated in the work of Moore Research, which calculates seasonality on a contract-to-contract basis, using a calendar day of the year. Moore Research converts prices into a percentage of yearly range and then projects this information to calculate the seasonal.

The second method is the work of Sheldon Knight, who developed a seasonal index he calls the K Data Time Line. The calculation involves breaking down each year according to the occurrences on a given day of the week in a given month. The steps for calculating the K Data Time Line are as follows:

Identify the day-of-week number and the month for each day to be plotted – for example, the first Monday of May.

Find the 5-year price changes in the Dollar for that day, in each of the years identified.

Add the 5-year average price change for that day to the previous day’s time line value. The full-year time line value starts at zero.

Trade by selecting the tops and bottoms of the time line for your entries and exits. Buy the bottoms of the time line and sell the tops.

The final method is one that I use in my seasonal work. I call it the Ruggiero/Barna Seasonal Index. This index is part of a product we call the Universal Seasonal, a TradeStation or SuperCharts add-in that automatically calculates many different measures of seasonality if the historical data are available. This tool will work on all commodities and even on individual stocks.

The Ruggiero/Barna Seasonal Index

The Ruggiero/Barna Seasonal Index was developed by myself and Michael Barna. The calculations for this index are shown here:

Develop your seasonal and update it as you walk forward in the data.

For each trading day of the year, record the next N-day returns and what percentage of time the market moved up (positive returns) and down (negative returns).

Multiply this 5-day return by the proper percentage.

Scale the numbers calculated in step 3 between -1 and 1 over the whole trading year. This is the output value of the Ruggiero/Barna Seasonal Index.

I would like to make one point about the Ruggiero/Barna 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 seasonal index for tomorrow's trading. This allows development of a more realistic historical backtest on a seasonal trading strategy.

Besides the Ruggiero/Barna Seasonal Index, you can use the raw average returns, the percent up or down, and correlation analysis to develop trading strategies. The Ruggiero/Barna index can be calculated either by using the complete data set or by using an N-year window.

Static and Dynamic Seasonal Trading

A seasonal trade can be calculated using the complete day set, some point in the past, or a rolling window of data. This is true for day-of-week, day-of-month, and day-of-year seasonals. The question is: Which method is the best? The answer depends on the commodity being analyzed. For example, in markets with fixed fundamentals, the more data used and the longer they are used, the greater the reliability of the seasonal. If we were analyzing corn, we would want to go back, using as much data as possible On the other hand, if we were doing seasonal research on the bond market, we would not want to use any day before January 1, 1986, because, prior to 1986, the dynamics of the bond market were different.

Another important issue in calculating seasonality is basing results on in-sample trades versus walk forward testing. For example, if we say a given seasonal is 80 percent accurate over the past 15 years, based on the results of the seasonal trades over the past 15 years, that is an in-sample result. If we use one day in the past 15 years to calculate a seasonal and then only take trades in the future using a buy and sell date calculated on past data, and roll the window forward every day, this is walk forward testing. More realistic results may be possible. For example, in 1985, you might not have had a seasonal bias on a given day, but, years later, that day of the year is included in a given walk forward seasonal pattern. Suppose you calculate the seasonal walking forward using only data from 1970 to 1985. You trade in 1986 and then move the window up every year or so. In 1987, you would use data including 1986 to calculate the seasonal, and you could produce a realistic seasonal model that can be used to trade.

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How To Win Big & Wipe-Out Losses

Seasonal Trading by Murray Ruggiero

Seasonal Trading
by Murray Ruggiero