This portfolio contains a series of data analyses, visualizations, machine learning algorithms, statistical models and data structures that I have created.
In this notebook I clean, organize, and analyze company fundamentals data in R in order to train a model that recommends a portfolio of 12 company investments that maximizes 12-month return of an overall $1,000,000 investment made at the end of quarter 4 of year 2018.
I created this project to demonstrate the flexibility of neural networks in Python by using the same neural network to identify hand written digits, identify the language of a text, and to model real number functions, such as sin(x).
This is a Java implementation of a double ended queue, which is usually referred to by its irregular acronym, Deque (pronounced "Deck"). Deques are sequence containers with dynamic sizes that can be expanded or contracted on both ends (either its front or its back).
In this notebook I examine data collected by the Department of Public Health in San Francisco, and demonstrate multiple methods for data manipulation, visualization, imputation, and representation in Python. I also investigate patterns that appear in the data, and what insights we can glean from the distributions in the dataset.
This is a collection of algorithims created in Python created in order to research, backtest, and develop trading and investment strategies. Intended for use with the open-source platform QuantConnect. I recommend interacting with the strategies using the embedded display below, otherwise, here is a link to the repository.
This strategy considers 5 ETFs (SPY – US stocks, EFA – foreign stocks, BND – bonds, VNQ – REITs, GSG – commodities). It picks the 3 ETFs with the strongest 12-month momentum, weighting them equally, and rebalances once a month. This strategy has a CAGR of 9.8%, a max drawdown of 70.5%, and a Sharpe ratio of .49.
<script src='https://www.quantconnect.com/terminal/backtest.js?sid=434dbef842cee46850dbfba07cc4534d'></script>The "All Weather" Portfolio, popularized by hedge fund manager Ray Dalio, is designed to minimize drawdowns through financial downturns. It is composed of 40% long-term bonds, 30% stocks, 15% intermediate-term bonds, 7.5% gold, and 7.5% commodities. The portfolio rebalances itself to these levels once a year. This strategy has a CAGR of 9.2%, a max drawdown of 21.7%, and a Sharpe ratio of .88.
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