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MarketOwl - Trend Analysis Tool for Portfolio Managers

Tool for Portfolio Managers to track market, industry sentiment and credibility trends for S&P 500 companies

How to run?

Command to get sentiment and credbility analysis for transcripts without parallelization

python marketowl.py -s textblob -c flesch -data transcript

Command to generate credibility analysis by using

  1. Gunning Fog python marketowl.py -s n -c gunning-fog -summary n -qa n -keywords n -data transcript -file Transcript_Extract_10_companies.csv

For 755 rows of transcripts, it took 8968.298764944077 seconds

  1. Flesch Reading Ease python marketowl.py -s n -c flesch -topic n -data transcript -file Transcript_Extract_10_companies.csv For 755 rows of transcripts, it took 11754.16016292572 seconds

  2. Smog Index python marketowl.py -s n -c smog -topic n -data transcript -file Transcript_Extract_10_companies.csv For 755 rows of transcripts, it took 8666.06145977974 seconds

  3. Dale Challe python marketowl.py -s n -c dale-chall -topic n -data transcript -file Transcript_Extract_10_companies.csv For 755 rows of transcripts, it took 16478.965087890625 seconds.

You need to specify analysis to perform, the below will throw error python marketowl.py -data transcript -file Transcript_Extract_10_companies.csv

Sentiment analysis specified python marketowl.py -data transcript -file Transcript_Extract_10_companies.csv --sentiment 'vader_sent'

Credibility & summary specified python marketowl.py -data transcript -file Transcript_Extract_10_companies.csv -c gunning-fog -summary y

Keywords on sectional python marketowl.py -data transcript -file Transcript_Extract_Sections.csv -keywords simple -sectional y

Transformers Pipeline

Note: It is not recommended to run the code on all transcripts since it takes forever to run! However, you can still do question answering on all transcripts with MarketOwl.py. Currently I set default question to "What drives revenue growth?" If you want to change the default question for MarketOwl.py, there is an option at line 87 of MarketOwl.py; just switch the string inside QuestionAnswering().

  • To see how to run transformer models (transformer-based summary and question answering), please check the Jupyter Notebook main_tony.ipynb. For more elaborated documentation please check https://huggingface.co/transformers/model_summary.html.
  • Currently I set the running device to CPU. If you want to change it to GPU, line 165 of transpipeline.py has the option to change it (change -1 to 0). However, please make sure that you have enough VRAM since it crashes on my laptop, which only has 4GB of VRAM.
  • I think it is better to build a more interative interface to present the results of question answering. Our current code can produce the results but how they are presented can be improved in the future.