This project is a Python-based financial data analysis tool that integrates stock market data, inflation metrics, and statistical modeling to provide insights into asset returns adjusted for inflation.
Comprehensively covers the Python programming environment and features. Topics include fundamental programming concepts such as variables, data types, assignments, arrays, conditionals, loops, functions, and I/O operations using Python.
A machine learning (ML) algorithm to annotate diverse protein complexes (biological particles with well-defined structures) in 3D cellular images, accelerating discoveries in biomedical science and advancing disease treatment.
Screener is a Python-based tool designed for analyzing the S&P 500 stocks. It calculates various financial metrics such as expected returns, standard deviation, variance, and covariance, and identifies optimal portfolio weights for minimum variance portfolios (MVP).
This project contains code for The Johns Hopkins University Applied Physics Laboratory 2023 BYTES Challenge, examining the effect of U.S. states' COVID-19 policy enactment dates on overall death totals.
Define candidates for optimal portfolio using comparison between expected returns and the risk from each stock in the SP500. Then calculate and save the Find the Minimum Variance Portfolio (MVP) optimal weights to CSV files.
This application uses the Flask framework to interact with the Alpaca API for trading and real-time data updates via WebSocket. It provides endpoints to start a WebSocket connection, handle trades, and respond to webhooks from TradingView.
In this lab, you will be creating new classes that are derived from a class called BankAccount. A checking account is a bank account and a savings account is a bank account as well.