EminiForecaster
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Labor Day Week and Finding Seasonal Cycles Around Stock Market Holidays
Much of the work we do is based around analyzing cycles in the market. There are various ways to do this. Fourier transforms, trigonometric regression, Hurst channels and pivot projections to name a few. Cycles in the stock market can also come under different names, such as “seasonal”, or “seasonal trading”. A seasonal is just another form of a cycle, but seasonal are date dependent functions and cycles are often date independent.
There are cycles on all different time frames. For example, if I make a composite of the market over any different unit of time, it may reveal to me various seasonal tendencies during that interval that have occurred in the past. Many of the most successful traders in the world use this kind of method.
To make a composite on an annual basis for example, we would start on Jan 1st (or the first trading day of the year) and, in the simplest scheme, sum all the various years together into one value for each calendar day of the year. For the last 20 years, it would look something like a low of the year at or around October 17th and a high of the year at or around Jun 17th. This seasonal tendency can actually be detected (plus or minus) going back as far as we have data on the stock market.
Armed with this information, one could buy October 17th and sell (or sell short) June 17th each year. Historically, this would have been very profitable.
Some years this seasonal does better than others. I have developed some amazing trading systems off this one basic principle. For many people though, trading off this cycle is just too long term. In the years you are wrong, it can hurt. Some form of risk management is required to make it more palatable. One way to do this is to trade off a weekly time frame in order to manage risk a bit better. That gives us approximately 52 segments in a year in which we manage our risk.
There are other amazing seasonals that occur in a shorter intervals that match this weekly time frame. One such cycle is holiday seasonals. We are approaching the Labor Day holiday this coming weekend. Let’s take a look.
Here is a chart showing last year’s price action (the candle stick chart) with the current price action mapped on to it going into the holiday (green line).
This weekly analysis is mapping on to the historical past with remarkable accuracy.
This type of analysis is consistent with one of the many cycle analysis methods mentioned above and is also consistent with some of the techniques we use at EminiForecaster.com to generate our accurate weekly forecasts.
Just how accurate is it to pick a weekly low with such accuracy? There are approximately 40, 10 minute bars in a day and just over 200, 10 minute bars in a week. Picking a low within 200 minutes, or 20 bars then is an accuracy of about 90%.
When the seasonal is following as it was earlier in the week, it confirms the seasonal is in effect. We can run various correlation studies to deal with this problem mathematically that feed the correlation back into the input of the computer model that successively approximates which seasonal (or cycle) we choose to trade.
To read more posts like this - EMF Blog
Much of the work we do is based around analyzing cycles in the market. There are various ways to do this. Fourier transforms, trigonometric regression, Hurst channels and pivot projections to name a few. Cycles in the stock market can also come under different names, such as “seasonal”, or “seasonal trading”. A seasonal is just another form of a cycle, but seasonal are date dependent functions and cycles are often date independent.
There are cycles on all different time frames. For example, if I make a composite of the market over any different unit of time, it may reveal to me various seasonal tendencies during that interval that have occurred in the past. Many of the most successful traders in the world use this kind of method.
To make a composite on an annual basis for example, we would start on Jan 1st (or the first trading day of the year) and, in the simplest scheme, sum all the various years together into one value for each calendar day of the year. For the last 20 years, it would look something like a low of the year at or around October 17th and a high of the year at or around Jun 17th. This seasonal tendency can actually be detected (plus or minus) going back as far as we have data on the stock market.
Armed with this information, one could buy October 17th and sell (or sell short) June 17th each year. Historically, this would have been very profitable.
Some years this seasonal does better than others. I have developed some amazing trading systems off this one basic principle. For many people though, trading off this cycle is just too long term. In the years you are wrong, it can hurt. Some form of risk management is required to make it more palatable. One way to do this is to trade off a weekly time frame in order to manage risk a bit better. That gives us approximately 52 segments in a year in which we manage our risk.
There are other amazing seasonals that occur in a shorter intervals that match this weekly time frame. One such cycle is holiday seasonals. We are approaching the Labor Day holiday this coming weekend. Let’s take a look.
Here is a chart showing last year’s price action (the candle stick chart) with the current price action mapped on to it going into the holiday (green line).
This weekly analysis is mapping on to the historical past with remarkable accuracy.
This type of analysis is consistent with one of the many cycle analysis methods mentioned above and is also consistent with some of the techniques we use at EminiForecaster.com to generate our accurate weekly forecasts.
Just how accurate is it to pick a weekly low with such accuracy? There are approximately 40, 10 minute bars in a day and just over 200, 10 minute bars in a week. Picking a low within 200 minutes, or 20 bars then is an accuracy of about 90%.
When the seasonal is following as it was earlier in the week, it confirms the seasonal is in effect. We can run various correlation studies to deal with this problem mathematically that feed the correlation back into the input of the computer model that successively approximates which seasonal (or cycle) we choose to trade.
To read more posts like this - EMF Blog
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