mostafajoma / A-Whale-Off-the-Portfolio

Quantitative analysis techniques with Python and Pandas, for investors to determine which portfolio is performing the best across many areas: volatility, returns, risk, and Sharpe ratios.

A-Whale-Off-the-Portfolio

Background

Quantitative analysis techniques with Python and Pandas, for investors to determine which portfolio is performing the best across many areas: volatility, returns, risk, and Sharpe ratios.

I created 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 have used historical daily returns of several portfolios: some from an algorithmic portfolios, some that represent the portfolios of famous "whale" investors like Warren Buffett, and some from the big hedge and mutual funds. then I used this analysis to create a custom portfolio of stocks (TGT ,TJX, WMT) and compared its performance to that of the other portfolios, as well as the larger market (S&P 500).

---

Prerequisites

Python

Pandas

Numpy

---

Prepared the Data

1. Used Pandas to read in each of the CSV files as a DataFrame and converted the dates to a DateTimeIndex.

2. Detected and removed null values.

3. Removed dollar signs from the numeric values and converted the data types as needed.

            ||
            ||
            \/

4. Converted the S&P 500 closing prices to daily returns.

5. concatenated Whale Returns, Algorithmic Returns, and the S&P 500 Returns into a single DataFrame with columns for each portfolio's returns.

Quantitative Analysis

Analyzed the data to see if any of the portfolios outperform the stock market (i.e., the S&P 500).

Performance Analysis

1. Calculated and plotted cumulative returns. checked if any portfolio outperform the S&P 500.

Risk Analysis

1. Created a box plot for each of the returns. To determine which box has the largest spread and Which has the smallest spread.

2. Calculated the standard deviation for each portfolio. To determine which portfolios are riskier than the S&P 500.

Rolling Statistics

1. Plotted the rolling standard deviation of the firm's portfolios along with the rolling standard deviation of the S&P 500. To determine if the risk increase for each of the portfolios at the same time risk increases in the S&P.

2. Constructed a correlation table for the algorithmic, whale, and S&P 500 returns.To determine Which returns most closely mimic the S&P.

3. Picked BERKSHIRE HATHAWAY INC and plotted a rolling beta between that portfolio's returns and S&P 500 returns. To determine if the portfolio sensitive to movements in the S&P 500.

Plot Sharpe Ratios

Used the daily returns, calculated and visualized the Sharpe ratios using a bar plot.

Built with

Python