analicia / talks

A collection of resources from talks I will or may, present.

A collection of resources from talks I will or may, present.

Each talk will have different minimum requirements, so each folder will have virtual environment setups.

Princeton Quant Trading

The jypyter notebooks in folder princeton_quant_trading use python 3.6 and pipenv.

To setup resources:

cd princeton_quant_trading
pipenv install
pipenv shell

To run the notebook used to create the diagrams on the slides:

jupyter notebook

The jupyter notebook app will launch in your browser. Click on graphs.ipynb. Here you can view the steps used to create the graphs in the presentation. You can also go to Kernel -> Restart & Clear Output to run each cell at a time.

The sqlite3 file regular_season.sqlite contains data from the regular season 2017-2018 has the tables with the following schemas:

sqlite> .schema player_game_log
CREATE TABLE player_game_log
            (
                name TEXT,
                position TEXT,
                opponent TEXT,
                game_date TEXT,
                salary INTEGER,
                score REAL,
                minutes INTEGER,
                points INTEGER,
                made_3pt INTEGER,
                rebound INTEGER,
                assist INTEGER,
                steal INTEGER,
                block INTEGER,
                turnover INTEGER,
                double_double INTEGER,
                triple_double INTEGER
            );
sqlite> .schema players
CREATE TABLE players
            (
                name TEXT,
                team TEXT,
                birth_date TEXT,
                height INTEGER,
                weight INTEGER
            );
sqlite> .schema team_game_log
CREATE TABLE team_game_log
            (
                name TEXT,
                abbr TEXT,
                game_date TEXT,
                opponent TEXT,
                home INTEGER,
                win INTEGER,
                points INTEGER
            );