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code performance review

So over 50% of my Pipeline generation time is spent generating prior returns in a custom factor, via the code below. I'm populating and using my own prices DataFrame by design. I did some crude timing tests and the vast majority (by ~ 2 orders of magnitude) of time is spent selecting the relevant asset prices via prices[[symbols(sid) for sid in assets]]. Can the performance of this code be improved?

def compute(self, today, assets, out, close):  
    todays_price_index = prices.index.get_loc(today)  
    prices2 = prices[[symbols(sid) for sid in assets]].as_matrix()  
    out[:] = (prices2[todays_price_index] - prices2[(todays_price_index - self.window_length)]) / \  
              prices2[(todays_price_index - self.window_length)]  
3 responses

Dictionarys are faster than dataframes. Using a dictionary may make that part of your code faster, but it may be better to use the slower dataframe depending on what else you need to do in your code: https://stackoverflow.com/questions/22084338/pandas-dataframe-performance

Why not just use the pre-built Returns() factor Quantopian provides?

A few questions...

  1. What does the 'prices' dataframe look like? Maybe include the code which creates it.
  2. I assume 'prices' is defined at a scope making it available to the compute function. Is that true?

Also, a few comments. This pipeline will only work in the notebook environment (which you probably already knew). Just wanted to verify. Pipelines are run 'asynchronously' in an algo and cannot effectively access data outside of the custom factor class scope. If the reason you are using 'prices' in this manner is to get access to 'forward adjusted' prices then be very careful. Lookahead bias abounds. However, if all you are calculating is returns, then these will be the same regardless of how prices are adjusted. Might as well not go through all this effort and simply use the built in returns method that @Jamie Veitch mentioned.

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