The goal of this project is to give the most trending stocks on the market based on market sentiment. We want the software to be a tool to help the selection of stocks to analyze for investement. In no way it should be use for stock picking.

The project process is done in two steps. It first fetches tweets, then perform a sentiment analysis on them. Twitter is querried with specific filters to obtain the most relevant information on the stocks given by the user. These tweets are then analyzed with TextBlob.

app.py is the interface of the program.
Folders:

• docs: Documentation of the project (UML, Design choices, etc.)
• installDependencies: Dependencies management. See next section
• app: Contains the modules of the project. The main ones are tweets handler and sentiment analysis.

To have all the dependencies installed, simply enter the project virtual environnement. Read the instructions

To launch the program, execute the following command:

python3 app.py [Options]

To list the options: python3 app.py -h The following subsections will describe the options.

The user must provide a file listing all the stock that are going to be considered by the application to give the stocks with best market sentiment. The file must be a csv file where each line begins with STOCK_EXCHANGE, TICKER, COMPANY_NAME .

-f --file PATH_TO_FILE

By default, reads stocks from tsx_1B.csv. The file is a list of companies on the toronto stock exchange with a market cap over 1B. Taken from TSX discovery tool.

Stocks are analyzed over a certain time period. To specify this period, you must first choose its ending date, then its length. The starting date of the period is obtained by substracting for the ending date the time period. By default, the ending date is the current day, and the period is 30 days.

End year:   -y YYYY 
            --year YYYY
End month:  -m MM
            --month MM
End day:    -d DD
            --day DD
Period:    -p X
            --period X

The specified en date must be between 1st january of 2007 and the current date.

Multi-threading is used to querry Twitter. Only 2 threads are created by default. The user might specify the number of threads. Higher number of threads might give better performences, but will increase computer ressources consumption. The user has also to keep in mind that he might get blacklisted by twitter if a monstruous number of request is sent at the same time. If it happens, Twitter will not responds to any querries from your current IP address over a certain period of time.

-t --thread XX