> Trading System Scribblings. A Long Term Position Trade

INTRODUCTION

The familiar seasonality of buying the S&P in Autumn (The Fall) and selling in the spring, sometimes called "Sell in May and Go Away". We all have heard about this one, but does it hold up to reality?

THE STRATEGY


First, I would like to test the popular trading idea of buying the S&P in October and selling in May. I will test this on the cash market going back to 1983. A fixed number of shares (100) was used for all trades. Slippage or commissions were not taken into account. The EasyLanguage code looks like this:

Code:
Inputs:
iShares( 100 ),
BuyMonth(10),
SellMonth(5);

If ( Month( Date ) = BuyMonth ) And ( MP = 0 ) Then
Buy(“Buy Month”) iShares contracts next bar at market;

If ( Month( Date ) = SellMonth ) Then
Sell(“Sell Month”) iShares contracts next bar at market;
From here we can plug into the input values the buy month (October) and sell month (May). Doing this we generate the following equity graph.





It sure looks like these months have a long bias. Those are some nice results, but are these the best months? We can use tradestation’s optimization feature on the BuyMonth and SellMonth input values to test all possible combinations. The number of possible combinations are 144. I performed the optimization and was not surprised to find the best combination of BuyMonth and SellMonth based on net proft turned out to be our original inputs.

The worst combination turns out to be the exact opposite setup. Buy in May and sell in October. While this article focuses on the long side of trading only, it’s interesting to note that this period may be a time to foucus on short strategies. Below is the equity graph of the worst performing months.



From 1983 to 1987 this period was producing positive results. That equity peak is 1987 and that year should be familiar. That was the year we had the massive one day market crash on October 19th known as Black Monday. The 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 chacteristics of the major markets was replaced by mean reversion tendencies.

So far the basic seasonality study looks interesting. However, keep in mind 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 puchase 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.

SHORT-TERM TREND FILTER

In order to avoid buying and selling at the wrong times I’m going to introduce a 20-period simple moving average (SMA) to act as a short term trend filter. This filter will be used to prevent us from immediently 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 20-period SMA), we do not sell just yet. We wait 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.

Code:
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 short-term 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 equity graph with our short term filter applied.





We smoothed out the equity curve a bit by reducing those drawdowns that took place during 2000 and 2008. We can see this in the slightly improved performance results. While we made slightly less in net profit, we increased our profit factor a lot and reduced our maximum intraday drawdown.

LONG-TERM TREND FILTER

What would happen if we also combined our short-term filter with a long-term filter? Using a 200-period SMA as a long-term filter is common practice for me. A long-term filter is used to divide the market into two major camps: bullish and bearish. It’s designed to indicate the major market trend and the dominate trading psychology so I can have my automated system adapt accordingly. If we are in a long-term bear market, why should we even attempt to purchase? During this times we may be much better off sitting on the sidelines. This seems to make sense and often adding a 200-period SMA to your trading system can make a big difference. In summary, our long-term filter is designed to keep us out of bear markets and the short-term filter is to help us better time our exact entry and exit locations.

Within EasyLanguage we can create a long-term market filter by creating a boolean flag called MajorTrend which will indicate when the proper major market trend is in our favor.

Code:
If ( MajorTendLen > 0 ) Then MajorTrend = Close > ( Average( Close, MajorTendLen ) )
Else MajorTrend = true;
Before we make a purchase we verify our long-term and short-term flags.

Code:
If ( LongMode And MP = 0 And BuyFlag And MajorTrend ) Then
   Buy("Season Buy") ishares contracts next bar at market
Else If ( ShortMode And SellFlag ) Then
<<<<<<< HEAD
   Sell("Season Sell") next bar at market;




Utilizing the long-term filter we reduced the number of trades, smoothed out the equity curve, reduced drawdowns and increased the average net profit per trade. Overall, a significant improvement.

COMBINING OUR TWO FILTERS

The next step is to combine both the long-term and short-term filters. Below is the summary showing all our different systems. The combined long-term and short-terms filters perform better than our baseline system and the short-term filter. However, when compared to the long-term filter only, it’s not as good. It seems simply applying a regime filter to our seasonal strategy produces the best results.



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 market. I’ve tested those ETFs and they produce very similar results. The S&P futures market also produces similar results. 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. For example, based upon our seasonality study using the long-term filter only we come up with this general filter:

Take Long Trades Only When
  • The closing price is above the 200-day moving average
  • The current month is either October,November,December ,January,February,March,A pril,May

This general filter could be applied to both automated trading systems or even discresionary trading. It may not be much help for intraday trading, but it may. Further testing would be needed. 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

By J Swanson

======= Sell("Season Sell") next bar at market;




Utilizing the long-term filter we reduced the number of trades, smoothed out the equity curve, reduced drawdowns and increased the average net profit per trade. Overall, a significant improvement.

COMBINING OUR TWO FILTERS

The next step is to combine both the long-term and short-term filters. Below is the summary showing all our different systems. The combined long-term and short-terms filters perform better than our baseline system and the short-term filter. However, when compared to the long-term filter only, it’s not as good. It seems simply applying a regime filter to our seasonal strategy produces the best results.



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 market. I’ve tested those ETFs and they produce very similar results. The S&P futures market also produces similar results. 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. For example, based upon our seasonality study using the long-term filter only we come up with this general filter:

Take Long Trades Only When
  • The closing price is above the 200-day moving average
  • The current month is either October,November,December ,January,February,March,A pril,May

This general filter could be applied to both automated trading systems or even discresionary trading. It may not be much help for intraday trading, but it may. Further testing would be needed. 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

By J Swanson