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https://github.com/sarahm44/python-portfolio-analysis

Analysis of financial portfolios using Python, across the metrics of volatility, returns, risk and Sharpe ratios.
https://github.com/sarahm44/python-portfolio-analysis

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Analysis of financial portfolios using Python, across the metrics of volatility, returns, risk and Sharpe ratios.

Host: GitHub
URL: https://github.com/sarahm44/python-portfolio-analysis
Owner: sarahm44
Created: 2022-03-03T10:11:46.000Z (almost 3 years ago)
Default Branch: main
Last Pushed: 2022-08-05T05:57:00.000Z (over 2 years ago)
Last Synced: 2025-01-26T05:18:42.492Z (18 days ago)
Topics: financial-analysis, fintech
Language: Jupyter Notebook
Homepage:
Size: 4.81 MB
Stars: 0
Watchers: 2
Forks: 1
Open Issues: 0
Metadata Files:
- Readme: README.md

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README

        # Python Portfolio Analysis

![""](https://github.com/sarahm44/unit-4-assignment/blob/main/Images/portfolio-analysis.png)

## Contents

- [Overview](#overview)

- [Data Preparation](#data-preparation)

- [Quantitative Analysis](#quantitative-analysis)

  * [Performance Analysis](#performance-analysis)

  * [Risk Analysis](#risk-analysis)

- [Sharpe Ratios](#sharpe-ratios)

- [Custom Portfolio](#custom-portfolio)

  * [Data Preparation](#data-preparation-1)

  * [Re-run the Analysis, Comparing My Portfolio](#re-run-the-analysis-comparing-my-portfolio)

## Overview 

I was tasked with analysing a number of financial portfolios across the areas of:

* volatility

* returns

* risk

* Sharpe ratios

I created an [analysis notebook](https://github.com/sarahm44/unit-4-assignment/blob/main/whale_analysis.ipynb) that analyzes and visualizes the major metrics of the portfolios across all of these areas, and determines which portfolio outperformed the others. 

My data set included a number of different portfolios.

The three main tasks were to:

1. Read in and wrangle returns data

2. Determine success of each portfolio

3. Choose and evaluate a custom portfolio

## Data Preparation

For the data preparation I was required to do the following:

* Use Pandas to read each CSV file in as a DataFrame. 

* Detect and remove all null values. 

* Convert the S&P 500 closing prices to daily returns. 

* Join the [Whale Returns](https://github.com/sarahm44/unit-4-assignment/blob/main/whale_analysis.csv), [Algorithmic Returns](https://github.com/sarahm44/unit-4-assignment/blob/main/algo_returns.csv), and the [S&P 500 Returns](https://github.com/sarahm44/unit-4-assignment/blob/main/sp_tsx_history.csv) into a single DataFrame with columns for each portfolio's individual returns. 

See the single DataFrame below:

![""](https://github.com/sarahm44/unit-4-assignment/blob/main/Images/data_prep.png)

## Quantitative Analysis

In this section, I was required to calculate and visualize performance and risk metrics for the portfolios.

### Performance Analysis

#### Daily Returns

![""](https://github.com/sarahm44/unit-4-assignment/blob/main/Images/daily_returns.png)

#### Cumulative Returns

![""](https://github.com/sarahm44/unit-4-assignment/blob/main/Images/cumulative_returns.png)

### Risk Analysis

#### Box Plot

![""](https://github.com/sarahm44/unit-4-assignment/blob/main/Images/box_plot.png)

#### Standard Deviation

![""](https://github.com/sarahm44/unit-4-assignment/blob/main/Images/standard_deviation.png)

#### Portfolios Riskier than S&P TSX 60

![""](https://github.com/sarahm44/unit-4-assignment/blob/main/Images/riskier_than_sp.png)

#### Annualised Standard Deviation

![""](https://github.com/sarahm44/unit-4-assignment/blob/main/Images/annualised_std.png)

#### Rolling Standard Deviation with a 21-day Window

![""](https://github.com/sarahm44/unit-4-assignment/blob/main/Images/rolling_std.png)

#### Calculate Correlation

![""](https://github.com/sarahm44/unit-4-assignment/blob/main/Images/correlation.png)

#### Plot Correlation

![""](https://github.com/sarahm44/unit-4-assignment/blob/main/Images/correlation_graph.png)

#### Beta

I was required to calculate and plot Beta for a chosen portfolio (algo 1) and the S&P 60 TSX.

![""](https://github.com/sarahm44/unit-4-assignment/blob/main/Images/beta.png)

## Sharpe Ratios

### Annualised Sharpe Ratios

![""](https://github.com/sarahm44/unit-4-assignment/blob/main/Images/annualised_sharpe_ratios.png)

### Sharpe Ratios Bar Plot

![""](https://github.com/sarahm44/unit-4-assignment/blob/main/Images/sharpe_graph.png)

### Determine whether the algorithmic strategies outperform both the market (S&P TSX 60) and the whales portfolios

I found as follows: 

![""](https://github.com/sarahm44/unit-4-assignment/blob/main/Images/algo_v_market.png)

## Custom Portfolio

### Data Preparation

I created a custom portfolio of [Cardano](https://github.com/sarahm44/unit-4-assignment/blob/main/ada_data.csv), [Bitcoin](https://github.com/sarahm44/unit-4-assignment/blob/main/btc_data.csv) and [Ethereum](https://github.com/sarahm44/unit-4-assignment/blob/main/eth_data.csv). 

I used the Google Finance function to choose a portfolio, then downloaded the data needed as CSV files.

In my notebook I read in the CSVs and joined them as a single DataFrame, as below:

![""](https://github.com/sarahm44/unit-4-assignment/blob/main/Images/my_portfolio_01.png)

I calculated the weighted returns for the portfolio assuming an equal number of shares for each stock:

![""](https://github.com/sarahm44/unit-4-assignment/blob/main/Images/my_portfolio_02.png)

Then I added these to my DataFrame containing all portfolios:

![""](https://github.com/sarahm44/unit-4-assignment/blob/main/Images/my_portfolio_03.png)

### Re-run the Analysis, Comparing My Portfolio

#### Annualised Standard Deviation

![""](https://github.com/sarahm44/unit-4-assignment/blob/main/Images/my_portfolio_04.png)

#### Rolling Standard Deviation with a 21-day Window

![""](https://github.com/sarahm44/unit-4-assignment/blob/main/Images/my_portfolio_05.png)

#### Calculate Correlation

![""](https://github.com/sarahm44/unit-4-assignment/blob/main/Images/my_portfolio_06.png)

#### Plot Correlation

![""](https://github.com/sarahm44/unit-4-assignment/blob/main/Images/my_portfolio_07.png)

#### Beta

I was required to calculate and plot 60-day Rolling Beta for My Portfolio compared to the S&P 60 TSX.

![""](https://github.com/sarahm44/unit-4-assignment/blob/main/Images/my_portfolio_08.png)

#### Annualised Sharpe Ratios

![""](https://github.com/sarahm44/unit-4-assignment/blob/main/Images/my_portfolio_09.png)

#### Sharpe Ratios Bar Plot

![""](https://github.com/sarahm44/unit-4-assignment/blob/main/Images/my_portfolio_10.png)

#### My Portfolio Compared to the Market

For my portfolio, I found as follows:

![""](https://github.com/sarahm44/unit-4-assignment/blob/main/Images/my_portfolio_11.png)