Evaluating four new investment options for inclusion in client portfolios. This program helps determine the fund with the most investment potential based on key risk-management metrics: the daily returns, standard deviations, Sharpe ratios, and betas.
This project leverages python 3.7 with the following:
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JupyterLab - JupyterLab is a web-based user interface designed for data analysis.
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pandas - Flexible and powerful data analysis / manipulation library for Python.
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numpy - The fundamental package for scientific computing with Python.
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matplotlib inline - Comprehensive library for creating static, animated, and interactive visualizations in Python.
Before running the application first install the following dependencies.
pip install jupyterlab
pip install pandas
pip install numpy
pip install matplotlibCreating daily returns DataFrame based on NAV prices of the Four Portfolios and on closing price of the S&P 500.
daily_returns = whales_df.pct_change().dropna()
daily_returns.head()
Visualizing the daily returns data of the Four Fund Portfolios and the S&P 500 using Pandas plot function.
daily_returns.plot(figsize=(10,7), title="Daily Returns")
Calculating, creating, and displaying the last five rows of cumulative returns DataFrame for the Four Fund Portfolios and the S&P 500.
cumulative_returns = (1 + daily_returns).cumprod()
cumulative_returns.tail()
Visualizing the cumulative return data of the Four Fund Portfolios and the S&P 500 using Pandas plot function.
cumulative_returns.plot(figsize=(10,5), title="Cumulative Returns of Funds and S&P 500: 2014-2020")
Visualizing the daily return data of the Four Fund Portfolios and the S&P 500 using Pandas plot function and box plot parameter.
daily_returns.plot.box(figsize=(15,20), title="Daily Returns of Funds and S&P 500")
Calculating standard deviation of the Four Fund Portfolios and the S&P 500; sorted from smallest to largest.
standard_deviation = daily_returns.std().sort_values()
standard_deviation
Calculating annualized standard deviation of the Four Fund Portfolios and the S&P 500; sorted from smallest to largest.
year_trading_days = 252
annualized_standard_deviation = standard_deviation * np.sqrt(year_trading_days)
annualized_standard_deviation.sort_values()
Visualizing the 21-day Rolling Average of the Four Fund Portfolios and the S&P 500.
daily_returns.rolling(window=21).mean().plot(figsize=(15,10), title="21-Day Rolling Average of Funds and S&P 500")
Calculating the Sharpe Ratio of the Four Fund Portfolios and the S&P 500.
sharpe_ratios = average_annual_return / annualized_standard_deviation
sharpe_ratios.sort_values()
Visualizing the Sharpe Ratios of the Four Fund Portfolios and the S&P 500 in a bar chart.
sharpe_ratios.plot.bar(figsize=(10,8),title="Sharpe Ratios")
Calculating the variance of the S&P 500 by using a 60-day rolling window and Pandas var function.
spy_rolling_60_variance = daily_returns['S&P 500'].rolling(window=60).var()
spy_rolling_60_variance.tail()
Calculating the covariance of Berkshire Hathaway Inc by using a 60-day rolling window, Pandas var function, and S&P 500.
profit_per_trade_early.plot(figsize=(10, 7), title="Profit Per Trade - Early Date", color="green")
Calculating the beta of Berkshire Hathaway Inc based on the 60-day rolling covariance; listing last five rows of info.
brk_rolling_60_beta = brk_rolling_60_covariance / spy_rolling_60_variance
brk_rolling_60_beta.tail()
Calculating the average value of the 60-day rolling beta of Berkshire Hathaway Inc.
brk_rolling_60_beta_mean = brk_rolling_60_beta.mean()
brk_rolling_60_beta_mean
Visualizing the 60-day rolling beta of Berkshire Hathaway Inc.
brk_rolling_60_beta.plot(figsize=(15,10), title="60-Day Rolling Beta: BERKSHIRE HATHAWAY INC")
To use the portfolio management application simply clone the repository and run the risk_return_analysis.py with:
python risk_return_analysis.pyBelow you will find screenshots of varios visualizations demonstrated to help drive analysis.
Brought to you by Robert Giannini. LinkedIn: https://www.linkedin.com/in/robertgianninijr/
MIT