Learn To STOP Curve Fitting!
Download this free guide on how to stop curve fitting. Following these four simple steps can improve your trading dramatically!
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.
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 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:
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.
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
Murray Ruggiero is the chief systems designer, and market analyst at TTM. He is one of the world’s foremost experts on the use of intermarket and trend analysis in locating and confirming developing price moves in the markets. Murray is often referred to in the industry as the Einstein of Wall Street.
He is a sought-after speaker at IEEE engineering conventions and symposiums on artificial intelligence. IEEE, the Institute of Electrical and Electronics Engineers, is the largest professional association in the world advancing innovation and technological excellence for the benefit of humanity. Due to his work on mechanical trading systems, Murray has also has been featured on John Murphy’s CNBC show Tech Talk, proving John’s chart-based trading theories by applying backtested mechanical strategies. (Murphy is known as the father of inter-market analysis.)
After earning his degree in astrophysics, Murray pioneered work on neural net and artificial intelligence (AI) systems for applications in the investment arena. He was subsequently awarded a patent for the process of embedding a neural network into a spreadsheet.
Murray’s first book, Cybernetic Trading, revealed details of his market analysis and systems testing to a degree seldom seen in the investment world. Reviewers were universal in their praise of the book, and it became a best seller among systems traders, analysts and money managers. He has also co-written the book Traders Secrets, interviewing relatively unknown but successful traders and analyzing their trading methodologies. Murray has been a contributing editor to Futures magazine since 1994, and has written over 160 articles.
As chief systems designer, Murray digs into the depths of niche and sub-markets, developing very specialized programs to take advantage of opportunities that often escape the public eye, and even experienced high level money managers.
Please log in again. The login page will open in a new window. After logging in you can close it and return to this page.