Hi,nice tools. I found alphalens code on github and there is an example folder in it which shows two examples. There's also .jpg pictures showing the result , I'm wondering how to save the results as a .jpg file? Expecially the dataframe

You can right click on any image while the notebook is loaded in your environment. You should see an option to "Save Image As". It will download as an image file to your computer.

Greetings from Sweden! First post!

I am relatively new to python and quantopian, so rookie-warning question here ;)

I am trying to customize a notebook template for factor analysis and I've been trying to take out pieces from the Alphalens IC tear sheet and Returns tear sheet to only plot the things I desire and by that save time by leaving out the things I do not need in these. However, I have not been successful in extracting the bars plot.

This line here:
al.plotting.plot_quantile_returns_bar(mean_return_by_q.apply(al.utils.rate_of_return, axis=0));

First, without messing with the cloned notebook and just running it, I get "TypeError: ('rate_of_return() takes exactly 2 arguments (1 given)', u'occurred at index 1D')" .

Since this post is 3 years old, I can imagine things could have changed code-wise.

I've checked out https://quantopian.github.io/alphalens/alphalens.html#alphalens.utils.rate_of_return. Where the rate_of_return function takes on two arguments,

"Parameters:
period_ret: pd.DataFrame
DataFrame containing returns values with column headings representing the return period.
base_period: string
The base period length used in the conversion It must follow pandas.Timedelta constructor format (e.g. ‘1 days’, ‘1D’, ‘30m’, ‘3h’, ‘1D1h’, etc)"

so I passed in:
al.plotting.plot_quantile_returns_bar(mean_return_by_q.apply(al.utils.rate_of_return(mean_return_by_q_daily, base_period='1D'), axis = 0)

Doing that, I get "AttributeError: 'DataFrame' object has no attribute 'name'"

So, does anyone know how one would go about extracting the barschart as it is calculated here in this notebook, with the growth rates for each time period, rather than just the means?

Any help is appreciated! :)

Hi,
Try to change to

al.plotting.plot_quantile_returns_bar(mean_return_by_q.apply(al.utils.rate_of_return, axis=0, args=('1D',) ))  

Hi,

my first post and already a question on how to implement code - sorry for this.

I read Trading Evolved and the book made me curious to jump into quantopian and formulate trading strategies. From the lecture I see the point in using alphalens and want to test on the following method on how to calculate momentum:

from scipy import stats

def momentum_score(ts):
x = range(self.window_length)
log_ts = np.log(ts)
slope, intercept, r_value, p_value, std_err = stats.linregress(x,log_ts)
annualized_slope = (np.power(np.exp(slope),252)-1)*100
score = annualized_slope *(r_value **2)
return score

Is there anyone who could show me how to implement this in the 'Class MyFactor(CustomFactor)'-structure?
I've tried different variations, but keep getting the ValueError: all the input array dimensions except for the concatenation axis must match exactly.

Thanks so much for your help!