From b4ce5ae0142c0fe076397eeb791c124b01d1984b Mon Sep 17 00:00:00 2001 From: Chris Park Date: Sun, 24 May 2020 14:04:14 -0700 Subject: [PATCH 1/6] Update README.md --- README.md | 34 ++++++---------------------------- 1 file changed, 6 insertions(+), 28 deletions(-) diff --git a/README.md b/README.md index 1575cf1..2e48996 100644 --- a/README.md +++ b/README.md @@ -6,18 +6,11 @@ [![Donate PayPal](https://img.shields.io/badge/Donate%20%24-PayPal-brightgreen.svg)](https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=CLF223XAS4W72) # stocksight -Crowd-sourced stock market 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... +Stock market 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... ## About -stocksight is a crowd-sourced stock market 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. +stocksight is an open source stock market analysis 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. -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. - -## Stocksight website -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 = ""` ## Slack workspace Join the conversation, get support, etc on [stocksight Slack](https://join.slack.com/t/stocksightworkspace/shared_invite/enQtNzk1ODI0NjA3MTM4LTA3ZDA0YzllOGNiM2I5ZjAzYWM2MjNmMjI0OTRlY2ZjYTk1NmM5YmEwMmMwOTE2OTNiMGZlNzdjZmZkM2RjM2U). @@ -50,8 +43,6 @@ $ cd stocksight Stocksight Kibana dashboard stocksight kibana dashboard -Stocksight website -stocksight website dashboard ## Install - Docker @@ -98,8 +89,6 @@ https://developer.twitter.com/en/docs/basics/authentication/guides/access-tokens 5) 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. - ### Examples Run sentiment.py to create 'stocksight' index in Elasticsearch and start mining and analyzing Tweets using keywords and the stock symbol TSLA @@ -108,16 +97,10 @@ Run sentiment.py to create 'stocksight' index in Elasticsearch and start mining $ python sentiment.py -s TSLA -k 'Elon Musk',Musk,Tesla,SpaceX --debug ``` -Start 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 - -```sh -$ python sentiment.py -s TSLA -k 'Elon Musk',Musk,Tesla,SpaceX -U --debug -``` - Start 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 ```sh -$ python sentiment.py -s TSLA -k 'Elon Musk',Musk,Tesla,SpaceX -l -U --debug +$ python sentiment.py -s TSLA -k 'Elon Musk',Musk,Tesla,SpaceX -l --debug ``` Start 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) @@ -150,7 +133,7 @@ To import the saved exported visualizations/dashboard, go to Kibana, click on ma ``` usage: sentiment.py [-h] [-i INDEX] [-d] -s SYMBOL [-k KEYWORDS] [-a] [-u URL] [-f FILE] [-l] [-n] [--frequency FREQUENCY] - [--followlinks] [-U] [-w] [--noelasticsearch] + [--followlinks] [-w] [--noelasticsearch] [--overridetokensreq TOKEN [TOKEN ...]] [--overridetokensignore TOKEN [TOKEN ...]] [-v] [--debug] [-q] [-V] @@ -162,10 +145,7 @@ optional arguments: -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. + example: TSLA -k KEYWORDS, --keywords KEYWORDS Use keywords to search for in Tweets instead of feeds. Separated by comma, case insensitive, spaces are ANDs @@ -183,7 +163,6 @@ optional arguments: (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 ...] @@ -196,9 +175,8 @@ optional arguments: --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] From b39d1befd25a83128376b30e5388bf78e46e91a6 Mon Sep 17 00:00:00 2001 From: Chris Park Date: Sun, 24 May 2020 14:07:30 -0700 Subject: [PATCH 2/6] Update home.md --- docs/_pages/home.md | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/docs/_pages/home.md b/docs/_pages/home.md index fc2c277..4c7aee8 100644 --- a/docs/_pages/home.md +++ b/docs/_pages/home.md @@ -8,7 +8,7 @@ header: cta_label: " Download" cta_url: "https://github.com/shirosaidev/stocksight/releases/latest" caption: -excerpt: "Crowd-sourced stock analyzer and stock predictor using Elasticsearch, Twitter, News headlines and Python natural language processing and sentiment analysis.
Latest release v0.1-b.9

+excerpt: "Stock analyzer and stock predictor using Elasticsearch, Twitter, News headlines and Python natural language processing and sentiment analysis.
Latest release v0.1-b.9

{::nomarkdown} {:/nomarkdown}" github: - excerpt: '{::nomarkdown} @@ -19,5 +19,4 @@ intro: {% include feature_row id="intro" type="center" %} -

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.

+

stocksight is an open source stock analysis 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.

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zq_Pj<7gNW?As$CKR4(gi$Om{u^bnC^_al&_5omsvn;-~vLy{m(teaf_^CbD@0V+FT zXp!Nx9M{h`TZUQWw49?>?UK&g?b>RQ+_ZS7dH84j%X4eOmu_xKHyf Date: Sun, 24 May 2020 14:13:46 -0700 Subject: [PATCH 4/6] Update home.md --- docs/_pages/home.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/_pages/home.md b/docs/_pages/home.md index 4c7aee8..3eee16d 100644 --- a/docs/_pages/home.md +++ b/docs/_pages/home.md @@ -8,7 +8,7 @@ header: cta_label: " Download" cta_url: "https://github.com/shirosaidev/stocksight/releases/latest" caption: -excerpt: "Stock analyzer and stock predictor using Elasticsearch, Twitter, News headlines and Python natural language processing and sentiment analysis.
Latest release v0.1-b.9

+excerpt: "Stock analyzer and stock predictor using Elasticsearch, Twitter, News headlines and Python natural language processing and sentiment analysis.
Latest release v0.1-b.11

{::nomarkdown} {:/nomarkdown}" github: - excerpt: '{::nomarkdown} From 60207c4377e49241758fc6d72c346e26907627d6 Mon Sep 17 00:00:00 2001 From: Chris Park Date: Sun, 24 May 2020 15:04:08 -0700 Subject: [PATCH 5/6] Update config.py.sample --- config.py.sample | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/config.py.sample b/config.py.sample index fa19369..2e57004 100644 --- a/config.py.sample +++ b/config.py.sample @@ -2,10 +2,10 @@ elasticsearch_host = "localhost" elasticsearch_port = 9200 elasticsearch_user = "" elasticsearch_password = "" -consumer_key = "wA4nfiLzLZn8nfJUms7W44Lu9" -consumer_secret = "R3oYihq08zkWf3qLu5auiO0BcNzegs7gpW47oWgJCQuBAKyLr4" -access_token = "912155670882820096-jr9ipJ6FqyzcCc6v4c6ahPaQZmjx5JY" -access_token_secret = "3GfwcUW4rsyVvPbKnkHfRW0iDZgzQcHbO9mtvYdGF0RRI" +consumer_key = "" +consumer_secret = "" +access_token = "" +access_token_secret = "" nltk_tokens_required = ("neuralink", "solar", "tesla", "@tesla", "#tesla", "tesla", "tsla", "#tsla", "elonmusk", "elon", "musk", "spacex", "starlink") nltk_min_tokens = 1 nltk_tokens_ignored = ("win", "giveaway") @@ -17,4 +17,4 @@ twitter_feeds = ["@elonmusk", "@cnbc", "@benzinga", "@stockwits", "@muddywatersre", "@mcuban", "@AswathDamodaran", "@elerianm", "@MorganStanley", "@ianbremmer", "@GoldmanSachs", "@Wu_Tang_Finance", "@Schuldensuehner", "@NorthmanTrader", "@Frances_Coppola", "@bySamRo", - "@BuzzFeed","@nytimes"] \ No newline at end of file + "@BuzzFeed","@nytimes"] From 1edc889160571a94a9fe2da11b62f52bc4e7b579 Mon Sep 17 00:00:00 2001 From: Chris Park Date: Mon, 8 Jun 2020 14:11:28 -0700 Subject: [PATCH 6/6] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 2e48996..01fbc88 100644 --- a/README.md +++ b/README.md @@ -87,7 +87,7 @@ https://developer.twitter.com/en/docs/basics/authentication/guides/access-tokens 4) Add twitter consumer key/access token and secrets to config.py -5) 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). +5) 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 (whitelist). NLTK tokens ignored are keywords which if are found in tweet, it will not be added to Elasticsearch (blacklist). ### Examples