# MARKET SENTIMENT
This menu aims to extrapolate market's sentiment regarding a pre-loaded ticker, and the usage of the following commands along with an example will be exploited below.
[REDDIT](#REDDIT)
* [wsb](#wsb)
- show what WSB gang is up to in subreddit wallstreetbets
* [watchlist](#watchlist)
- show other users watchlist
* [popular](#popular)
- show popular tickers
* [spac_c](#spac_c)
- show other users spacs announcements from subreddit SPACs community
* [spac](#spac)
- show other users spacs announcements from other subs
[STOCKTWITS](#STOCKTWITS)
* [bullbear](#bullbear)
- estimate quick sentiment from last 30 messages on board
* [messages](#messages)
- output up to the 30 last messages on the board
* [trending](#trending)
- trending stocks
* [stalker](#stalker)
- stalk stocktwits user's last messages
[TWITTER](#TWITTER)
* [infer](#infer)
- infer about stock's sentiment from latest tweets
* [sentiment](#sentiment)
- in-depth sentiment prediction from tweets over time
[GOOGLE](#GOOGLE)
* [mentions](#mentions)
- interest over time based on stock's mentions
* [regions](#regions)
- regions that show highest interest in stock
* [queries](#queries)
- top related queries with this stock
* [rise](#rise)
- top rising related queries with stock
# REDDIT
## wsb
```
usage: wsb [-l N_LIMIT] [-n]
```
Print what WSB gang are up to in subreddit wallstreetbets. [Source: Reddit]
* -l : limit of posts to print. Default 10.
* -n : new flag, if true the posts retrieved are based on being more recent rather than their score. Default False.
## watchlist
```
usage: watchlist [-l N_LIMIT]
```
Print other users watchlist. [Source: Reddit]
* -l : limit of posts to print. Default 5.
## popular
```
usage: popular [-l N_LIMIT] [-s S_SUBREDDIT] [-d N_DAYS]
```
Print latest popular tickers. [Source: Reddit]
* -l : limit of posts retrieved per sub reddit. Default 50.
* -s : subreddits to look for tickers, e.g. pennystocks,stocks. Default: pennystocks, RobinHoodPennyStocks, Daytrading, StockMarket, stocks, investing, wallstreetbets.
* -d : look for the tickers from those n past days. Default 1.
## spac_c
```
usage: spac_c [-l N_LIMIT] [-p]
```
Print other users SPACs announcement under subreddit 'SPACs'. [Source: Reddit]
* -l : limit of posts with SPACs retrieved. Default 10.
* -p : popular flag, if true the posts retrieved are based on score rather than time. Default False.
## spac
```
usage: spac [-h] [-l N_LIMIT] [-d N_DAYS]
```
Print other users SPACs announcement under subreddit 'SPACs'. [Source: Reddit]
* -l : limit of posts with SPACs retrieved. Default 5.
* -d : look for the tickers from those n past days.. Default 5.
# STOCKTWITS
## bullbear
```
usage: bullbear [-t S_TICKER]
```
Print bullbear sentiment based on last 30 messages on the board. Also prints the watchlist_count. [Source: Stocktwits]
* -t : ticker to gather sentiment from.
## messages
```
usage: messages [-t S_TICKER] [-l N_LIM]
```
Print up to 30 of the last messages on the board. [Source: Stocktwits]
* -t : get board messages from this ticker. Default pre-loaded.
* -l : limit messages shown. Default 30.
## trending
```
usage: trending
```
Stocks trending. [Source: Stocktwits]
## stalker
```
usage: stalker [-u S_USER] [-l N_LIM]
```
Print up to the last 30 messages of a user. [Source: Stocktwits]
* -u : username. Default newsfilter.
* -l : limit messages shown. Default 30.
# TWITTER
## infer
```
usage: infer [-n N_NUM]
```
Print quick sentiment inference from last tweets that contain the ticker. This model splits the text into character-level tokens and uses the DistilBERT model to make predictions. DistilBERT is a distilled version of the powerful BERT transformer model. Not only time period of these, but also frequency. Inspired by https://towardsdatascience.com/sentiment-analysis-for-stock-price-prediction-in-python-bed40c65d178. [Source: Twitter]
* -n : num of latest tweets to infer from. Default 100.
## sentiment
```
usage: sentiment [-n N_NUM] [-d N_DAYS_PAST]
```
Plot in-depth sentiment extracted from tweets from last days that contain pre-defined ticker. This model splits the text into character-level tokens and uses the DistilBERT model to make predictions. DistilBERT is a distilled version of the powerful BERT transformer model. Note that a big num of tweets extracted per hour in conjunction with a high number of days in the past, will make the algorithm take a long period of time to estimate sentiment. Inspired by https://towardsdatascience.com/sentiment-analysis-for-stock-price-prediction-in-python-bed40c65d178. [Source: Twitter]
* -n : num of tweets to extract per hour. Default 100.
* -d : num of days in the past to extract tweets. Default 7.
Additional examples:
# GOOGLE
## mentions
```
usage: mentions [-s S_START]
```
Plot weekly bars of stock's interest over time. other users watchlist. [Source: Google]
* -s : starting date (format YYYY-MM-DD) from when we are interested in stock's mentions. Default: the one provided in main menu.
## regions
```
usage: regions [-n N_NUM]
```
Plot bars of regions based on stock's interest. [Source: Google]
* -n : number of regions to plot that show highest interest. Default 10.
## queries
```
usage: queries [-n N_NUM]
```
Print top related queries with this stock's query. [Source: Google]
* -n : number of top related queries to print. Default 10.
## rise
```
usage: rise [-n N_NUM]
```
Print top rising related queries with this stock's query. [Source: Google]
* -n : number of top rising related queries to print. Default 10.
