Goal of this assignment is to -
- Read historical trade data from CSV files (daily NAVs or prices of various fund portfolios and S&P 500 index)
- Convert to daily returns for further analysis.
- Analyse the daily returns # 3.1 Visualize the daily returns, cumulative returns to observe patterns 3.2 Use box plots to analyse volatilty of return data 3.3 Analyse standard deviation, rolling window standard deviation to understand nature of risk 3.4 Use sharpe ratios to analyse risk-return dynamic 3.5 Use variance, coveriance and betas to diversify the portfolio (select portfolio that looks most attractive)
Risk Return Analysis program evaluates four new investment options for inclusion in the client portfolios. Program determines the fund with the most investment potential based on key risk-management metrics: the daily returns, standard deviations, Sharpe ratios, and betas
Program uses Python 3.10.6 version and Jupyter Lab
Program uses 'Pandas' library to work with dataframes and analyse timeseries data. Program mainly focuses on .loc function to narrow down timeseries and extensively uses visualization techniques to perform analysis.
Program also imports from Path and CSV libraries to read data from the file.
Program runs in jupyter. Therefore its important to install Jupyter. If you already have installed anaconda, then you already have installed Jupyter and relevant libraries.
Jupyter lab Go to -> localhost:8888/lab/tree Choose a Notebook Use run function of Notebook
Main author is : Pravin Patil. His linkedin profile is Profile
MIT License