Crowd-sourced stock analyzer and stock predictor using Elasticsearch, Twitter, News headlines and Python natural language processing and sentiment analysis. How much do emotions on Twitter and news headlines affect a stock's price? Let's find out ...
stocksight is a crowd-sourced stock analysis open source software that uses Elasticsearch to store Twitter and news headlines data for stocks. stocksight analyzes the emotions of what the author writes and does sentiment analysis on the text to determine how the author "feels" about a stock. stocksight makes an aggregated analysis of all collected data from all sources.
Each user running stocksight has a unique fingerprint: specific stocks they are following, news sites and twitter users they follow to find information for those stocks. This creates a unique sentiment analysis for each user, based on what data sources they are getting stocksight to search. Users can have the same stocks, but their data sources could vary significantly creating different sentiment analysis for the same stock. stocksight website will allow each user to see other sentiment analysis results from other stocksight user app results and a combined aggregated view of all.
https://stocksight.diskoverspace.com Currently in BETA. Free signup. Once you are registered and login, you will be given a token which you need to add to your stocksight config file to upload your stocksight results to the website. Edit config.py and add the line
stocksight_token = "<your auth token>"
Join the conversation, get support, etc on stocksight Slack.
- Python 3. (tested with Python 3.6.5)
- elasticsearch python module
- nltk python module
- requests python module
- tweepy python module
- beautifulsoup4 python module
- textblob python module
- vaderSentiment python module
- newspaper3k python module
- Elasticsearch 5. (Elasticsearch is recommended but can be not used with --noelasticsearch)
- Kibana 5. (Kibana is recommended for searching/visualizing the Elasticsearch indices)
$ git clone https://github.com/shirosaidev/stocksight.git
$ cd stocksightStocksight Kibana dashboard
Stocksight website
Install python requirements using pip
pip install -r requirements.txt
Install python nltk data
python -c "import nltk; nltk.download('punkt'); nltk.download('stopwords')"
Create a new twitter application and generate your consumer key and access token. https://developer.twitter.com/en/docs/basics/developer-portal/guides/apps.html https://developer.twitter.com/en/docs/basics/authentication/guides/access-tokens.html
Copy config.py.sample to config.py
Set elasticsearch settings in config.py for your env
Add twitter consumer key/access token and secrets to config.py
Edit config.py and modify NLTK tokens required/ignored and twitter feeds you want to mine. NLTK tokens required are keywords which must be in tweet before adding it to Elasticsearch and uploaded to StockSight website (whitelist). NLTK tokens ignored are keywords which if are found in tweet, it will not be added to Elasticsearch and uploaded to StockSight website (blacklist).
These settings (tokens, feeds) and optional keywords for tweets, are what creates your unique fingerprint and helps with the crowd-sourcing of the stock symbol data mining and sentiment accuracy on StockSight website. The higher number of unique and valid data sources, the more accurate the sentiment analysis average should be.
Run sentiment.py to create 'stocksight' index in Elasticsearch and start mining and analyzing Tweets using keywords and the stock symbol TSLA
$ python sentiment.py -s TSLA -k 'Elon Musk',Musk,Tesla,SpaceX --debugStart mining and analyzing Tweets using keywords and upload sentiment results to stocksight website (requires website auth token) using the stock symbol and stocksight website symbol TSLA. The website symbol is the "tag name" for the sentiment data being uploaded
$ python sentiment.py -s TSLA -k 'Elon Musk',Musk,Tesla,SpaceX -U --debugStart mining and analyzing Tweets using keywords and the stock symbol TSLA and follow any url links in tweets and performing sentiment analysis on the link web page as well as the tweet
$ python sentiment.py -s TSLA -k 'Elon Musk',Musk,Tesla,SpaceX -l -U --debugStart mining and analyzing Tweets from feeds in config using cached user ids from file (if you change any of the twitter feeds in the config file, you need to delete this file and recreate it without -f)
$ python sentiment.py -s TSLA -f twitteruserids.txt --debugStart mining and analyzing News headlines and following headline links and scraping relevant text on landing page
$ python sentiment.py -s TSLA --followlinks --debugRun stockprice.py to add stock prices to 'stocksight' index in Elasticsearch
$ python stockprice.py -s TSLA --debugLoad 'stocksight' index in Kibana and import export.json file for visuals/dashboard.
usage: sentiment.py [-h] [-i INDEX] [-d] -s SYMBOL [-k KEYWORDS] [-a] [-u URL]
[-f FILE] [-l] [-n] [--frequency FREQUENCY]
[--followlinks] [-U] [-w] [--noelasticsearch]
[--overridetokensreq TOKEN [TOKEN ...]]
[--overridetokensignore TOKEN [TOKEN ...]] [-v] [--debug]
[-q] [-V]
optional arguments:
-h, --help show this help message and exit
-i INDEX, --index INDEX
Index name for Elasticsearch (default: stocksight)
-d, --delindex Delete existing Elasticsearch index first
-s SYMBOL, --symbol SYMBOL
Stock symbol you are interesed in searching for,
example: TSLA This is used as the symbol tag on
stocksight website. Could also be set to a tag name
like 'elonmusk' or 'elon' etc. Cannot contain spaces
and more than 25 characters.
-k KEYWORDS, --keywords KEYWORDS
Use keywords to search for in Tweets instead of feeds.
Separated by comma, case insensitive, spaces are ANDs
commas are ORs. Example: TSLA,'Elon
Musk',Musk,Tesla,SpaceX
-a, --addtokens Add nltk tokens required from config to keywords
-u URL, --url URL Use twitter users from any links in web page at url
-f FILE, --file FILE Use twitter user ids from file
-l, --linksentiment Follow any link url in tweets and analyze sentiment on
web page
-n, --newsheadlines Get news headlines instead of Twitter using stock
symbol from -s
--frequency FREQUENCY
How often in seconds to retrieve news headlines
(default: 120 sec)
--followlinks Follow links on news headlines and scrape relevant
text from landing page
-U, --upload Upload sentiment to stocksight website (BETA)
-w, --websentiment Get sentiment results from text processing website
--noelasticsearch Don't connect or add new docs to Elasticsearch
--overridetokensreq TOKEN [TOKEN ...]
Override nltk required tokens from config, separate
with space
--overridetokensignore TOKEN [TOKEN ...]
Override nltk ignore tokens from config, separate with
space
-v, --verbose Increase output verbosity
--debug Debug message output
-q, --quiet Run quiet with no message output
-V, --version Prints version and exits
usage: stockprice.py [-h] [-i INDEX] [-d] [-s SYMBOL] [-f FREQUENCY] [-v]
[--debug] [-q] [-V]
optional arguments:
-h, --help show this help message and exit
-i INDEX, --index INDEX
Index name for Elasticsearch (default: stocksight)
-d, --delindex Delete existing Elasticsearch index first
-s SYMBOL, --symbol SYMBOL
Stock symbol to use, example: TSLA
-f FREQUENCY, --frequency FREQUENCY
How often in seconds to retrieve stock data (default:
120 sec)
-v, --verbose Increase output verbosity
--debug Debug message output
-q, --quiet Run quiet with no message output
-V, --version Prints version and exits