https://github.com/wardbradt/Sentimental-Stock-Prediction

Analyzes the sentiment of SEC quarterly earnings reports to predict future stock prices
https://github.com/wardbradt/Sentimental-Stock-Prediction

data-visualization sentiment-analysis stock-market stocks

Last synced: about 2 months ago
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Analyzes the sentiment of SEC quarterly earnings reports to predict future stock prices

• Host: GitHub
• URL: https://github.com/wardbradt/Sentimental-Stock-Prediction
• Owner: wardbradt
• License: gpl-3.0
• Archived: true
• Created: 2017-11-11T18:23:47.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2018-02-18T17:33:47.000Z (over 6 years ago)
• Last Synced: 2024-07-30T02:22:53.925Z (about 2 months ago)
• Topics: data-visualization, sentiment-analysis, stock-market, stocks
• Language: Python
• Homepage: https://wardbradt.github.io/Sentimental-Stock-Prediction/
• Size: 399 KB
• Stars: 25
• Watchers: 1
• Forks: 5
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Sentimental Stock Prediction

## Example Usage

Firstly, you must set `quandl.ApiConfig.api_key` at the top of sentimental.py to your quandl API key. 

You can get a key [here](https://www.quandl.com/tools/api).

```

from sentimental import generate_earnings_day_reports

# can be any stock ticker

ticker = "NVDA"

generate_earnings_day_reports(ticker)

```

Now when you view index.html, you will be able to view the visualization for your ticker.

## To Do:

- Find a better way to find the discussion/ overview section of each report than the current implementation in `find_overview_str`

- Fix price axis scaling