Another even simpler way of finding turning points is to use "fractals" (a highly misleading name). I have used these in the past to find support/resistance levels in FX market and they work quite well.

http://www.investopedia.com/articles/trading/06/fractals.asp

Thanks, Mikko. Much easier and more reliable.

Any suggestions on how you go about finding support/resistance? I was thinking of looking at the histogram of the turning points and extracting levels that have lots of points. Then, invalidating the support/resistance points if they've already been broken.

Hey guys, this is very cool but the Notebook doesn't work, I am getting this error

--------------  
IndexErrorTraceback (most recent call last)  
<ipython-input-36-a8992727d9f0> in <module>()  
----> 1 bottoms, tops = plotTurningPoints(asset, 1)

<ipython-input-35-a12f49a2439b> in plotTurningPoints(price_series, n)  
     14     ax.plot(price_series)  
     15     ticks = ax.get_xticks()  
---> 16     ax.set_xticklabels([price_series.index[i].date() for i in ticks[:-1]]) # Label x-axis with dates  
     17     ax.set_title('Share Price with Turning Points')  
     18     ax.plot([b[0] for b in bottoms], price_series[[b[0] for b in bottoms]], 'gD')

/usr/local/lib/python2.7/dist-packages/pandas/tseries/base.pyc in __getitem__(self, key)
    190         getitem = self._data.__getitem__  
    191         if lib.isscalar(key):  
--> 192             val = getitem(key)  
    193             return self._box_func(val)  
    194         else:

IndexError: index 735842 is out of bounds for axis 0 with size 160  

Found this - haven't tested it tho. It's from2015, might need reformatting:
https://www.quantopian.com/posts/how-to-code-resistance-areas

@ Randy Walker I love your code, I'm thinking of filtering it by areas of major and minor support and resistance. The way I would go about this is to compare the standard deviation of the absolute distance between pivots. What do you think about this? I feel like the standard deviation of the distance is the way to go but I could be wrong.

Here is the process I'm thinking about:
MODIFICATION PROCESS:
1. Incorporate pivot area method that returns a list of raw pivots
in order along with a classification of a top or bottom.

1. Assign a distance variable for each pivot area by measuring the
   distance to the last pivot and the next pivot if any. Avg out?
   return a list with the corresponding data.

2. Using the python Statistics library compile the data and assign
   a standard deviation for each pivot. Sample size is the desired
   time period.

3. Separate pivot list into tops and bottoms by using given methods.

4. For tops and bottoms lists designate major or minor based on size
   of standard deviation.

5. Plot out using horizontal lines. Done!

Hey guys, I have the same error. Anyone who solve this?

IndexErrorTraceback (most recent call last)
in ()
----> 1 bottoms, tops = plotTurningPoints(asset, 1)

in plotTurningPoints(price_series, n)
14 ax.plot(price_series)
15 ticks = ax.get_xticks()
---> 16 ax.set_xticklabels([price_series.index[i].date() for i in ticks[:-1]]) # Label x-axis with dates
17 ax.set_title('Share Price with Turning Points')
18 ax.plot([b[0] for b in bottoms], price_series[[b[0] for b in bottoms]], 'gD')

/usr/local/lib/python2.7/dist-packages/pandas/tseries/base.pyc in getitem(self, key) 190 getitem = self.data.getitem_
191 if lib.isscalar(key):
--> 192 val = getitem(key)
193 return self._box_func(val)
194 else:

IndexError: index 735842 is out of bounds for axis 0 with size 160