Stock Market Recommender

Key Libraries Employed

Webscraping

BeautifulSoup, Regex, yFinance

Technical & Fundamental Indicator Calculation

Pandas, Math, BeautifulSoup

Visualizer

Matplotlib, Backtester

Recommender

Pandas, Math

Backtester

Backtester

File Exporting and Sharing (for collaboration)

Pickle

Webscraper, Visualizer, Recommender, Backtester

All in One

Webscraper

We employ BeautifulSoup & yFinance to extract 500 ticker names, with corresponding close prices & volumes over 5+ years.
1300+ data points

Technical & Fundamental Indicator Calculation

We employ Python to create technical indicators and BeautifulSoup to webscrape company fundamental indicators.
15+ indicators

Visualizer

We employ Matplotlib to create comprehensive price & volume visualizations of each ticker, equipped with technical indicators.
200+ lines of code

Recommender & Backtester

We develop a function which recommends stock purchases for the day based on programmed trading strategies.
We employ backtester to analyze returns based on the stock recommender and various trading strategies.
10.1% Annualized Returns

Our Next Targets

– Achieving 30% annualized returns

– Increasing backtesting speed by 40%