title: Calculate RSI from pandas dataframe
body: <p>I have got a df with value from forex market and I'm trying to put into the data frame the RSI, relative strength index(10), for each row in the df.</p>
  <pre><code>df.head()
  Out[3]: 
          Date      Time     Open     High      Low    Close  Volume  OpenInt
  0 2016-09-16  00:05:00  0.75183  0.75186  0.75160  0.75161       0        0
  1 2016-09-16  00:10:00  0.75156  0.75156  0.75145  0.75149       0        0
  2 2016-09-16  00:15:00  0.75156  0.75166  0.75152  0.75165       0        0
  3 2016-09-16  00:20:00  0.75164  0.75165  0.75150  0.75156       0        0
  4 2016-09-16  00:25:00  0.75156  0.75174  0.75153  0.75156       0        0
  </code></pre>
  <p>RSI is an indicator that tells you when the product is oversold or overbought; RSI = 100 - 100 / (1 + RS)  where RS is the average gain of up periods in a given time frame / the average of loss of down period in a given time frame. In my case, time frame is 10.</p>
  <pre><code>df.change = df.Open - df.Close # find out if there is a gain or a loss
  
  df.gain = df.change [df.change &gt; 0] #column of gain
  
  df.loss = df.change [df.change &lt; 0]# column of loss
  
  df.again = df.gain.rolling(center=False,window=10) #find the average gain in the last 10 periods 
  
  df.aloss = df.loss.rolling(center=False,window=10) #find the average loss in the last 10 periods
  </code></pre>
  <p>Now is where the troubles begin; I need to get the RS:</p>
  <pre><code>df.rs = df.again/df.aloss
  
  TypeErrorTraceback (most recent call last)
  &lt;ipython-input-13-2886bcd78f42&gt; in &lt;module&gt;()
  ----&gt; 1 df.rs = df.again/df.aloss
  
  TypeError: unsupported operand type(s) for /: 'Rolling' and 'Rolling'
  </code></pre>
  <p>EDIT</p>
  <pre><code>df.gain.head(6)
  Out[31]: 
  0    0.00022
  1    0.00007
  3    0.00008
  5    0.00002
  7    0.00003
  8    0.00002
  
  df.loss.head(6)
  Out[32]: 
  2    -0.00009
  6    -0.00019
  9    -0.00013
  14   -0.00002
  15   -0.00011
  20   -0.00008
  dtype: float64
  </code></pre>