Profiting from Seasonal Trading Key Dates -- SPY -- BIDU

Profiting from Trading Key Dates -- SPY -- BIDU

My core goal is to provide quantifiable ideas and strategies that can be easily implemented and have high probability of success. The main problem with Seasonal trading is that it is subjective and arguably the edge is gone because everyone knows about the Seasonal impact anyway. So I have put a study together, where we can look at the impact and probability of seasonal trading and how we can mitigate risk, but also have a high probability of a reasonable profit.

The way to do this is to look at historical data of the stock. In this case we will be using Matlab® with Yahoo data. The goal is to find trading ideas where history shows that there is a good risk/reward payoff within a selected date time frame for an underlying stock.

Looking at the historical data of the SPY, I can note straight away that there is actually not much money to be made from “seasonal” date trading. This is based on the investment criteria and inputs I have used in Matlab®.

I will go through my study on the SPY, and show how my analysis was made.

SN	YD	BD	SD	LR	HR	DD	PP	BL
SPY	23.1704	09//10	22//11	3.132	21.1545	-12.326	60.8696	-18.037

Outputs:
SN = Stock Name
Years = Years of data
BD = Buy Date
SD = Sell Date
LR = Lowest Return
HR = Highest Return
DD = Draw down
PP = Percentage Profitable
BL = Biggest Loss

Looking at the SPY I have analyzed 23 years of data. The input variables were set at a max of 60 day time frame block within a year, with the lowest return set at 3% with a min 60% profitable. The output is that the only date range to buy the SPY is 09/10 with a sell date of 22/11 of every year. Over the 23 years that date range was profitable 60.86% of the time with the lowest return within that date range of 3.132%. The highest return in that time period was 21% with a drawdown of 12%. The biggest loss was 18%.

What does all this mean? Well it means that if I buy the stock on the 9/10 with a sell date of 22/11 it will be profitable 60% of the time. If the trade is profitable, the min return I can expect is 3%. But the problem lies in the biggest loss, if the trade is a losing trade; historically the biggest loss was 18%.

Looking at this output we can clearly see that even though seasonality may be an existing thing as an overall trend concept, the reality is that the only date to trade where the min return is above 3% is as noted above. Seasonality does not mean always profitable based on historical data, that is the problem, and with this data set it shows that even to get a small 3% return over 60day time frame, the SPY trader may have to endure a massive drawdown.  As an investor I would not be comfortable with the risk/reward of a potential biggest loss of 18% to have a return from 3% to 21%.

What I look for is ways to reduce the biggest loss and increase the lowest return. If we can get those figures on an even scale, it will give us opportunity to analyze the trade further.

So where to from here, well the output is that we need trade ideas. And trade returns that have a historical edge.

So let’s look a bit further and we come up with BIDU;

SN	Years	BD	SD	LR	HR	DD	PP	BL
BIDU	10.6639	05//04	24//04	5.3834	16.6997	-8.3777	90	-8.4722

Outputs:
SN = Stock Name
Years = Years of data
BD = Buy Date
SD = Sell Date
LR = Lowest Return
HR = Highest Return
DD = Draw down
PP = Percentage Profitable
BL = Biggest Loss

BIDU is an example of when we look at the buy date of 05/04 and sell date of 24/04, based on historical stock data there is a 90% chance that the return will be between 5.3% and 16%. Based on history, the biggest loss was 8%.

We have to bear in mind that BIDU only has 10 years of data to analyze, which gives the strategy only 10 data points.

Likewise with all stock codes analyzed the data set we are looking at is limited, and at the end of the day we have to work with what we have. In a perfect world it would be good to have a 1000 data set points but with this strategy that is the inherent flaw with the testing.

I would not bank your house on this data due to the short time frame, but it does give the trader another way to look at the markets and trade the key dates.

A trading example with BIDU was just recently triggered.

The stock price on the 5/4, was at 183.80. The share price as at the time of 21/4 is 192.74. Looking at this data and the max risk, we can note that the biggest loss was 8.5% during this date time frame. Based on that we can look at selling put option premium at around the 8.5% max loss level, which will increase our probability of profit as well, or alternatively buying the stock with strategy to exit at positive 5% return during that time frame. Either way buying the stock or using options the strategy was profitable this year as the stock went from 183.80 to a high of 197.5 which is greater than the lowest return of 5%.