About
Ever get the feeling that you are are just not doing what you are supposed to be doing? I did, and I could not shake it. I woke up every day working in technology but thinking about investing and trading. So I leaped…
I sold my business and dove into researching anything and everything about the stock market. It turns out that my technology background ended up being incredibly useful for analyzing what works in the market – data science is powerful stuff!
Now I’m doing what I love. I’m investing, trading, and researching full-time Monday through Thursdays, and on Fridays, I’m working on analyzingalpha.com, attempting to be the resource I wish I had when first learning how to make money in the markets.
My only regret is that I didn’t leap earlier…
#chaseyourdreams
Find Out More or Connect?
You can learn more about my professional career at:
If you’re interested in chatting, please reach out to me on:
Recognition and Awards
I’ve added a few degrees and certifications over the years that may be of interest.
Recognition / Award / Location | Description/Url |
---|---|
Columbia Business School | Value Investing Executive Education |
New York University | Advanced Valuation |
New York University | Applied Corporate Finance |
University of San Francisco | Deep Learning I |
Pittsburgh University | Executive Fellowship |
Thiel College | BSc in Computer Science |
Blog Articles I’ve Written
I write regularly on investing, trading and data science.
- Statistical Arbitrage: Defined & Strategies
- CANSLIM: What Is It & Does It Work?
- Dow Theory: History, Principles & Strategy
- Support and Resistance: Fully Explained
- Relative Strength Index (RSI): An Ultimate Guide
- The 18 Best Investing & Trading Movies
- Trend Following: A Definitive Guide
- Financing and Operating Leases in Valuation
- The 20 Best Trading Quotes
- Turtle Trading: History, Strategy & Complete Rules
- The 21 Best Stock Market Investing Quotes
- Timing the Markets with ETF Fund Flows
- Algorithmic Chart Pattern Detection
- Sampling: Simplified & Methods Summarized
- Keltner Channels: Explained & Coded
- How to Create a Financial Statement Database
- Quantamental: What It Is & Why It Works
- Risk-Reward Ratio: Defined & Determined
- Stop Loss: Explained & The Best Strategy
- Sector Momentum: Explained & Backtested
- Stock Sectors: What to Know & How to Invest
- Cryptocurrency Price History Visualization
- Stocks, Bonds and Gold Returns from 1922+
- Algorithmic Trading: Is It Worth It?
- Look-Ahead Bias: What It Is & How to Avoid
- Time Series Analysis with Python Made Easy
- The Top 22 Python Trading Tools for 2021
- Data Manipulation with Python using Pandas
- Backtrader: Getting Started Backtesting
- Importing Stock Data Using Python
- Types of Trading: Trading Styles Explained
- How to Create an Equities Database
- Survivorship Bias Risk
- The S&P 500 Historical Components & Changes
- Python for Finance: An Easy Introduction
- The Cost of Capital
- How to Create a Zipline Equity Bundle
- The Cost of Debt
- How to Install Zipline on Ubuntu Linux
- The Cost of Equity
- Beta & Relative Risk
- Equity Risk Premium
- The Risk-Free Rate
- A Simple Trading Strategy in Zipline
- Python Data Visualization for Finance
- How to Value a Company: A Complete Guide
- How to Read Financial Statements
- Micron: Consolidation and Cash Flows
- Floor & Decor: Decorative Disruption
- Zendesk: Here Comes The Sun
- How to Start Investing: A Quick Intro
- Twilio: Flexing its Muscles
Guest Posts & Publications
- A Quant Quickstart with Intrinio & Backtrader
- Quant Quickstart II: Working with Multiple Securities
- Quant Quickstart III: Mean Reversion Strategy
- Quant Quickstart IV: Adding Fundamental Data
- Quant Quickstart V: A Value Strategy
- Alpaca & Backtrader: Tools of the Trade (Part 1)
- Alpaca & Backtrader: Tools of the Trade (Part 2)
- How to Use Unemployment Figures to Build a Trading Algorithim (Part 1)
- How to Use Unemplolyment Figures to Build a Regime Filter (Part 2)
- Statistically Significant Mean Reversion Strategies (Part 1)