Risk-Return-Analysis

This Jupyter notebook demonstrates data preparation, analysis, and visualizations for key risk and return metrics.

Dataset

The dataset used in this analysis is:

• whale_navs.csv: Located in the "Resources" folder.

Analysis Steps

The analysis was conducted in several steps:

1. Data Collection: Collecting CSV data into a jupyter notebook file.
2. 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.
3. Evaluation: An examination of each asset, utilizing rolling statistics to monitor the risk-return dynamics.

Libraries and Dependencies

from pathlib import Path

import pandas as pd

import numpy as np

%matplotlib inline

Sources

Bootcamp Spot

Google