##AUTHOR: Juan Diego Tascón ##EMAIL: juantascon@horlux.org ##CREATED: 2014-05-05

The charts are very simple, please read the code comments for more details.

One thing is missing though, the example images on the description had a weird X axis, it is more useful[1][2] if the x axis represents time, in this implementation it represents the number of past days.

• [1] stockcharts.com
• [2] biz.yahoo.com

I came up with 4 simple independet invesment strategies:

• uponce: invest if on the last 20 days close price passes at least once over upper band

• dowonce: invest if on the last 20 days close price passes at least once under lower band

• moreup: invest if on the last 20 days the number of times close price passes over the uppper band is greater than the number of times close price passes under the lower band

• moredown: invest if on the last 20 days the number of times close price passes under the lower band is greater than the number of times close price passes over the uppper band

I wasn't sure which strategy would be best so I went further and developed a helper script (analyzer.py) that analyzes each strategy efficiency by comparing an hipotetical investment made DELTA days ago with the selling price TODAY (not really today but the last registered closing price). This approach will allow me to come up with better strategies and test their performance instantly against other candidates.

I took a sample of 21 stocks, all from the technology sector and I used DELTA=20 to compare the price 20 days before TODAY with the price of the same stock TODAY.

The results seems to indicate that "moredown" and "downonce" are the best candidates. My own personal conclusion is that in the short term the more times a stock lower its price below the lower bollinger band the more likely is that the price increases on the short term as well. It is "weird" that a price goes below the lower bollinger band, when it happends the price might tend to normalize itself by going back up. Becareful though because these was only tested on tech stocks, it might differ on other stocks.

In the end to be honest I wouldn't use any of these for real investments, the stock market is too caotic, with way too many variables, this makes it hard to define completely safe invesment strategies, there will always be risk involved.

• bolly.py:

  • generates (outputs to $PWD/$SYMBOL.png) bollinger bands, ex: bolly.py plot AMZN FB
  • prints wether or not you should invest given a strategy, ex: bolly.py suggest AMZN FB -s moredown

• analizer.py:

  • analyzes which strategies are better

• main.go:
  • generates (outputs to $PWD/$SYMBOL.png) bollinger bands, ex: go run main.go -p AMZN FB
  • prints wether or not you should invest given a strategy, ex: go run main.go -s moredown AMZN FB
  • each symbol is processed using goroutines so that blocking syscalls won't stop other symbol's processing