This Jupyter notebook demonstrates data preparation, analysis, and visualizations for key risk and return metrics.
The dataset used in this analysis is:
- whale_navs.csv: Located in the "Resources" folder.
The analysis was conducted in several steps:
- Data Collection: Collecting CSV data into a jupyter notebook file.
- Analyzing: Calculating the risk of the assets in the DataFrame in comparison to the S&P 500. This analysis covers important measurements of risk and return, such as daily returns, standard deviation, Sharpe ratio, and beta. This demonstrates the risk-return characteristics of the assets and how they perform in relation to the market benchmark.
- Evaluation: An examination of each asset, utilizing rolling statistics to monitor the risk-return dynamics.
from pathlib import Path
import pandas as pd
import numpy as np
%matplotlib inline