https://github.com/mmsaki/whale_portfolio

Determine which Whale portfolio is performing the best across multiple areas: volatility, returns, risk and Sharpe ratios.
https://github.com/mmsaki/whale_portfolio

datetime finance jupyter-notebook matplotlib numpy pandas pathlib python quantitative-finance seaborn stock-market trading-strategies

Last synced: 27 days ago
JSON representation

Determine which Whale portfolio is performing the best across multiple areas: volatility, returns, risk and Sharpe ratios.

• Host: GitHub
• URL: https://github.com/mmsaki/whale_portfolio
• Owner: mmsaki
• Created: 2022-04-02T04:49:49.000Z (about 4 years ago)
• Default Branch: main
• Last Pushed: 2022-05-18T19:35:21.000Z (about 4 years ago)
• Last Synced: 2025-07-17T13:42:54.069Z (11 months ago)
• Topics: datetime, finance, jupyter-notebook, matplotlib, numpy, pandas, pathlib, python, quantitative-finance, seaborn, stock-market, trading-strategies
• Language: Jupyter Notebook
• Homepage:
• Size: 28.7 MB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# Whale Portfolio Analysis

# Table of Contents

1. [Background](#1-background)

2. [My Analysis](#2-my-analysis)

3. [Results](#3-results)

## [1. Background](#1-background)

* Using a portfolio data to determine which portfolio is performing the best across multiple areas:

    * volatility

    * returns

    * risk 

    * Sharpe ratios

* This python code compares [my custom portfolio](/custom_portfolio/) to [the whales](/Resources/whale_returns.csv) and [two alogorithmic trading strategies](/Resources/algo_returns.csv)

* This code compares if my custom portfolio: 

    * Outperforms

    * Underperforms 

    * Or Equally perfroms 

## [2. My Analysis](#2-my-analysis)

* Analyze whale Returns of Soros, Paulson,Tiger and Berkshire 

    * Read the Whale Portfolio daily returns and clean the data 

    ![Whale Data Analysis](./images/whale_analysis.png)

* Analyze Algorithm 1 and Algorithm 2 Daily Returns

    * Read the algorithmic daily returns and clean the data

    ![Algorithm Data Analysis](./images/algorithm_analysis.png)

* S&P 500 Returns

    * Read the S&P 500 historic closing prices and create a new daily returns DataFrame from the data.

    ![S & P 500 Data Analysis](./images/sp500_analysis.png)

* Combine Returns

    ![Combined Returns](./images/combined_returns.png)

* Performance Analysis

    * Calculate and Plot the daily returns.

    * Calculate and Plot cumulative returns.

    ![Daily Returns](./images/daily_returns.png)

* Risk analysis:

    * Create a box plot for each portfolio.

    * Calculate the standard deviation for all portfolios

    * Determine which portfolios are riskier than the S&P 500

    * Calculate the Annualized Standard Deviation

    ![Risk Analysis](./images/risk_analysis.png)

* Rolling Statistics.

    * Calculate and plot the rolling standard deviation for all portfolios using a 21-day window

    * Calculate the correlation between each stock to determine which portfolios may mimick the S&P 500

    ![Rolling Stats](./images/rolling_stats.png)

    * Choose one portfolio, then calculate and plot the 60-day rolling beta between it and the S&P 500

    * Calculate and Plot Beta for a chosen portfolio and the S&P 500

        *  Try calculating the `ewm` with a 21-day half life for each portfolio, using standard deviation (std) as the metric of interest. 

        ![Beta Plot 60 day Window](./images/plot_beta.png)

* Sharpe ratio analysis

    * Calculate the Sharpe ratios and generate a bar plot

    ![Sharpe Ratios](./images/sharpe_ratios.png)

* Determine whether the algorithmic strategies outperform both the market (S&P 500) and the whales portfolios.

![Algorism Performace](./images/algorithm_performance.png)

* Create Custom Portfolio

    * Choose 3-5 custom stocks with at last 1 year's worth of historic prices and create a DataFrame of the closing prices and dates for each stock.

    ![Custom Stocks](./images/custom_stocks.png)

    * Calculate the weighted returns for the portfolio assuming an equal number of shares for each stock

        * Join your portfolio returns to the DataFrame that contains all of the portfolio returns

        ![Joined Portfolios](./images/joined_portfolios.png)

    * Calculate the Annualized Standard Deviation.

    * Calculate and plot rolling `std` with a 21-day window.

    * Calculate and plot the correlation.

    * Calculate and plot beta for your portfolio compared to the S&P 60 TSX.

        ![Joined Correlations](./images/custom_beta.png)

* Calculate the Sharpe ratios and generate a bar plot.

    * How does my portfolio do?

    ![My Portfolio Results](./images/my_results.png)

## [3. Results](#3-results)

* **File:** [Whale Analysis](./whale_analysis.ipynb)