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Help me out in alpha discovery for the new Tearsheet Challange

Hi!

I'm starting to look for alpha in the Insider Transactions Dataset.

Could you give me any advices? In the attached notebook I get the data for these 3 metrics (unique_filers_form3_90d, unique_buyers_form4and5_90d & unique_sellers_form4and5_90d) BUT on different columns, do you know a way to have something more compact, excel-style?

Something like:

Timestamp     Equity     alpha_factor    unique_filers_form3_90d     unique_buyers_form4and5_90d      unique_sellers_form4and5_90d

2016-05-05    Equity(2 [ARNC])     0.250000                   5.0                    0.0                                    3.0  
11 responses

@Emiliano,

Add this to a cell:

import qgrid  
# Set the default max number of rows to 12  so theDataFrame we render  
# with qgrid isn't too tall.  
qgrid.set_grid_option('maxVisibleRows', 12)

# Render DataFrame with QGrid  
qgrid.show_grid(df)

# Uncomment and run this line to allow columns to overflow the cell window  
# and show a toolbar to add/remove rows and view in fullscreen  
# qgrid.show_grid(df, grid_options={'forceFitColumns': False}, show_toolbar=True)  

This and more were found at:
https://www.quantopian.com/docs/recipes/research-recipes#qgrid-example

On the content side, trying to parse these papers/indexes for some insights...perhaps make some factors based on their results.

https://sites.hks.harvard.edu/fs/rzeckhau/InsiderTrading.pdf
https://poloclub.github.io/polochau/papers/13-snam-insider.pdf
http://www.zacksdata.com/data/insider-transactions/zacks-insider-rank/

alan

thank you very much @Alan

For what concerns possible sources of ideas/alpha for the tearsheet challenge I will add this one, too:
The 7 Lessons Of Insider Transactions

Here's an updated version of my notebook:

Next thing to do:
Trying to make the final alpha factor be influenced more by stocks that have an higher number of unique filers:

If stock A has 1 total buyer and 0 total sellers during the last 90 days shouldn't have an alpha factor score as high as another stock that had 100 unique buyers and 0 or 1 or 2 .... unique sellers

In this notebook I try to address this problem:

Blockquote If stock A has 1 total buyer and 0 total sellers during the last 90 days shouldn't have an alpha factor score as high as another stock that had 100 unique buyers and 0 or 1 or 2 .... unique sellers

Testing the new alpha factor

The original one was better :(

Trying slices of 30 days

Next idea to test:

Trying to create alpha factor according to the trend in the last 90, 30 trading and 7 trading days:

Here I try to create alpha factor according to the trend in the last 90, 30 trading and 7 trading days:

Trying to use the research environment to test my alpha factors

Added winsorize() and zscore()

What do you think I miss? Any suggestions? Should I try to trade more frequently or less according to this IC decay graph?