#First line rom Module 4 Challenge: Background
#Second line from Unit 4: Pandas email
In this assignment, I’ll use quantitative analysis techniques with Python and Pandas, and I’ll determine which portfolio is performing the best across multiple areas:volatility, returns, risk, and Sharpe ratios. In this module, I learned how to use Pandas and the JupyterLab IDE to collect, prepare, and analyze financial data.
##First line from Module 4 Challenge "Background"
##Second line From Module 4 Challenge "What You're Creating"
I have been investing in algorithmic trading strategies. Some of the investment managers love them, some hate them, but they all think their way is best. I've just learned these quantitative analysis techniques with Python and Pandas and I want to determine which portfolio is performing the best across multiple areas:volatility, returns, risk, and Sharpe ratios. I need to create a tool (an analysis notebook) that analyzes and visualizes the major metrics of the portfolios across all of these areas, and determine which portfolio outperformed the others. I will be given the historical daily returns of several portfolios: some from the firm's algorithmic portfolios, some that represent the portfolios offamous "whale" investors like Warren Buffett and some from the big hedge and mutual funds. I will then use this analysis to create a custom portfolio of stocks and compareits performance to that of the other portfolios, as well as the larger market S&P 500 Index.
#From Module 4 Challenge "Instructions"
- Prepare the Data
- Perform Quantitative Analysis
- Performance Analysis
- Risk Analysis
- Rolling Statistics
- Rolling Statistics Challenge: Exponentially Weighted Average
- Sharpe Ratios
- Create a Custom Portfolio
- Run through each line of code to view the output
