Added filtering module, also support loading from a single-column CSV of tickers #40
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Change time output to minutes instead of seconds Add ability to print all results together an the end Output anomalous tickers to a results.txt file
opens anomalous tickers in Robinhood in a new browser window
Creates graphs for all tickers in results.txt Allows option for single ticker or tickers of your choice
Example results.txt file
change line numbers for controlling the script
Add/update descriptions grapher.py and openRobinhood.py
Fix formatting error
Provides option to save graphs to the figures directory or to print them to screen
Create figures dir if it doesn't exist
remove previous addition of printing anomalous results together at the end
Parallelization
Revert "Parallelization"
Add various functionality
Revert "Add various functionality "
This removes the unused finding function, and replaces it with the normal algorithm. Then, all the indexing through the Pandas Dataframes are refactored in preference for the faster Pandas calls.
Pandas branch
Remove additional /r in README
flask_freezer to generate the static page flask_server to host it yourself (dynamic stuff coming later) 6 month window
template.html for generating static page with website_generator dynamic.html for serving yourself with flask_server refactored globals to class variables in market_scanner print all results at end when running market_scanner line 34 fixes the off-by-1 bug (if it exists, will test it on Monday).
try to pull secret key from env var added default parameters, now these are set when you instantiate the object made exportList a class variable
example of filtering, grabbing tickers up to the 95th percentile of trading volume. Brings us down to ~5000 tickers
Closed
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the loading can also easily be modified to make a list of tickers to ignore, a la #24 |
elizabethshirley
approved these changes
Aug 2, 2020
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working on merging |
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stocklist_filter.pydoes the filtering & outputs the tickers in a single-column CSVright now it just grabs tickers up to the 95th percentile of daily volume. Should probably filter on market cap (or weight volume by mcap)
main_func()has a new parameter,doFilter(False by default), that loadslist_of_tickersfromfiltered_tickers.csvwhen set to True