garlasco
6 posts
msg #89877
- Ignore garlasco |
3/18/2010 10:18:00 PM
Many stocks have the tendency to better perform in a specific period of the year: for example, February is usually a good month for CF, MOS and TEVA and very bad for C; March is great for X and GE, April is good for AAPL, BHI and ADSK, while July is tipically the month of AMGN and IBM. In order to find potential candidates, I use a filter that allows me to identify those stocks that have been constantly strong during a specific period of the year (it can be a specific month or week) in the last 4 years (unfortunately, I cannot access more than 1000 days of data). Of course, I don't rely only on seasonality: once I have identify this starting list, I usually drop those stocks that have (or had in the past) earnings reports in that specific period, since I don't want my results to be biased by strong reactions to earning news. Then, I focus my attention only on those stocks that have nice bullish charts (basing patterns, established uptrends, etc.). Of course this methodology doesn't work 100% of the times, but since I'm following it, I've got very good results: for example that's how I've chosen to buy CF and TEVA on February 1st or GE and X on March 1st.
This is the filter I'm using for finding an initial list of April buy candidates, as of March 18th (the only problem is that I need to change every day the "days ago" numbers: maybe there is a better way write it?). I look for SP500 stocks that have increased more than 5% from March 31st to April 30th of the years 2006, 2007, 2008 and 2009 (sorry, don't now how to make it clickable):
sp500
show stocks where close 222 days ago is more than 5 percent above close 243 days ago
and close 474 days ago is more than 5 percent above close 496 days ago
and close 727 days ago is more than 5 percent above close 747 days ago
and close 978 days ago is more than 5 percent above close 997 days ago
set{ind_change, close 222 days ago- close 243 days ago}
set{ind_ratio, ind_change / close 243 days ago}
set{apr09, ind_ratio * 100.0}
and add column apr09
set{ind_change1, close 474 days ago- close 496 days ago}
set{ind_ratio1, ind_change1 / close 496 days ago}
set{apr08, ind_ratio1 * 100.0}
and add column apr08
set{ind_change2, close 727 days ago-close 747 days ago}
set{ind_ratio2, ind_change2 / close 747 days ago}
set{apr07, ind_ratio2 * 100.0}
and add column apr07
set{ind_change3, close 978 days ago-close 997 days ago}
set{ind_ratio3, ind_change3 / close 997 days ago}
set{apr06, ind_ratio3 * 100.0}
and add column apr06
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