The purpose of this project was to assume the role of a quantitative analyst for a FinTech investing platform. The task at hand is to offer clients a one-stop online investment solution for their retirement portfolios that’s both inexpensive and high quality.
The goal is to systematically evaluate four new investment options (funds) for possible inclusion in the client portfolios. In so doing, determine the fund with the most investment potential based on key risk-management metrics: the daily returns, standard deviations, Sharpe ratios, and betas.
Beyond the scope of the assignment, the author sought to conduct additional analysis of the data obtained and demonstrate further visualization with combined data plots, overlay plots, and heatmap visualization. Supplemental and/or extra analysis beyond the scope of the project is noted as 'supplemental' were approrpiate.
The code script analysis performed:
Individually set path to CSV file called 'whale_navs.csv' from the Resources folder using the Path module.
Use read_csv to read in csv file, generate dataframe setting the DatetimeIndex with 'date' as as index.
Set the parse_dates and infer_datetime_format parameters
Individually prepare the dateframe by -
converting the dataframe of NAVs and prices to daily returns and
cleaning missing and / or erroneous data using dropna() missing values.
Review the first or last five rows of the DataFrame using head() or tail() functions.
Quantitative analysis has several components: performance, volatility, risk, risk-return profile, and portfolio diversification.
Daily returns were obtained with the Pandas pct_change() function together with dropna() to create the daily returns DataFrame.
Dataframe was then visualized with plot() function. Cumulative returns are generated with the Pandas cumprod() function.
`cumulative_returns = (1 + daily_returns).cumprod() - 1`
Cumulative returns are then visualized with .plot() function. Volatility is visualized using daily return data to create box plots to visualize the volatility of the 4 funds and the S&P 500. The risk profile of each fund was analyzed using standard deviation and the beta.
$\mu = \frac{\sum{x_{i}}}{n}$
The average value for a given list or Series
${S}^2 = \frac{\sum{ (x_{i} - \mu })^{2}}{ n - 1}$
The squared average change around the mean
The square root of the variance
The Pandas std() function is used to calculate the standard deviation for each of the four portfolios and for the S&P 500
This was further analyzed via plot visualization of annualized and rolling 21-day values.
`daily_returns_std = daily_returns.std()`
`annualized_std = daily_returns_std * np.sqrt(trade_days_year)`
`daily_returns.rolling(window=21).std().plot()`
SUPPLEMENTAL ANALYSIS was then introduced by the author.
The corr function was used to calculate correlations for each fund pair.
The heatmap function from the Seaborn library was used to create a heatmap of correlation values visualization.
Additional information about the heatmap method from seaborn on the documentation page.
Using the mean() function, daily return DataFrame was used to calculate the annualized average return data for the four fund portfolios and for the S&P 500.
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$\mu = \frac{\sum{x_{i}}}{n}$ `annual_average_returns = (daily_returns.mean()*trade_days_year)`
Using the numpy np.sqrt() function, 252 trading day, and presumed risk-free return rate of zero, risk-return profile was then examined through calculating
the Sharpe ratios for the four fund portfolios and for the S&P 500.
`sharpe_ratios = annual_average_returns / (daily_returns.std() * np.sqrt(trade_days_year))`
The Sharpe ratios for the four funds and for the S&P 500 were then visualized in a bar chart
The ratio between the average annual return and the annualized standard deviation (risk-free rate =0).
SUPPLEMENTAL ANALYSIS was then introduced by the author. The author then introduced a 3% (0.03) risk-free return rate. Sharpe ratios under these conditions were plotted, facilitating comparitive analysis by visualization of the new/adjusted bar chart vs the previous bar chart
The ratio between the annual average return minus risk free rate of return and the annualized standard deviation; includes a significant risk free return rate 3%
At this point, two funds were selected for continuation of analysis as greatest potential candidates.
The Pandas var() function was used to calculate the variance of the S&P 500 by using a 60-day rolling window
for the two selected funds, covariance was calculated using the 60-day rolling window, the daily return data, and the S&P 500 returns.
${S}^2 = \frac{\sum{ (x_{i} - \mu })^{2}}{ n - 1}$
Review the last five rows of the covariance of the funds was performed with tail() function. The beta of each fund was calculated by division of
the covariance of each fund by the variance of the S&P 500. The Pandas mean() function was used to calculate the average value of the 60-day rolling
beta of the funds.
At this point the primary required analysis was complete. The author then conduct some additional supplemental calculations for visualization.
Examples are demonstrated below.
This project leverages Jupyter Lab v3.4.4 and python v3.7 with the following packages:
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Path - From 'pathlib', Object-oriented filesystem paths, used to identify a file
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pandas - Software library written for the Python programming language for data manipulation and analysis.
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read_csv - From 'pandas', read a comma-separated values (csv) file into DataFrame
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concat - From 'pandas', concatenate pandas objects along a particular axis, allows optional set logic along the other axes.
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numpy - Software library, NumPy is the fundamental package for scientific computing in Python, provides vast functionality.
For additional and / or supplemental processing and visulaization this project also makes use of the following packages:
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matplotlib.pyplot - Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python; matplotlib.pyplot is a collection of functions that make matplotlib work like MATLAB.
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seaborn - Software library for making statistical graphics in Python. It builds on top of matplotlib and integrates closely with pandas data structures.
MacBook Pro (16-inch, 2021)
Chip Appple M1 Max
macOS Monterey version 12.5.1
Homebrew 3.5.10
Homebrew/homebrew-core (git revision 0b6b6d9004e; last commit 2022-08-30)
Homebrew/homebrew-cask (git revision 63ae652861; last commit 2022-08-30)
anaconda Command line client 1.10.0
conda 4.13.0
Python 3.7.13
pip 22.1.2 from /opt/anaconda3/envs/dev/lib/python3.7/site-packages/pip (python 3.7)
git version 2.37.2
In the terminal, navigate to directory where you want to install this application from the repository and enter the following command
git clone git@github.com:Billie-LS/Portfolio-key-risk-management-metrics-analysis.gitFrom terminal, the installed application is run through jupyter lab web-based interactive development environment (IDE) interface by typing at prompt:
> jupyter labVersion control can be reviewed at:
https://github.com/Billie-LS/Portfolio-key-risk-management-metrics-analysisLoki 'billie' Skylizard
Vinicio De Sola @GitHub
Corey Recai
Santiago Pedemonte @GitHub
MIT License
Copyright (c) [2022] [Loki 'billie' Skylizard]
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.