Progress Update 8

    So this week, I got a significant amount of work done. I completed the "Analyze Financial Data with Python Skill Path" on Codecademy earlier today. Adding the certificate for the skill to my LinkedIn feels like I am collecting these skills like trading cards. 

    Completing this skill course was easier and a lot faster since some of the lessons were overlapping with the basic Python 3 course that I took before. So they were auto-completed before I even started the course. 

    The one thing about this skill course that I was looking forward to was learning how to pull and use data from financial APIs. But once I actually got to that section of the course, I started to question what exactly I expected of it before-hand. I guess I was slightly disappointed because I saw the keyword: financial APIs and immediately assumed that that was the "one-size-fits-all" skill I needed to create my trading algorithm. But it only was creating charts from lists of data that is being spoon-fed to me. Yes, the course is called "Analyze.." not "Implement Strategies..." so I don't know why I expected it to be the 1 and only skill I need to create the best trading algorithm in the world. But I guess I now know how to plot a stylish trading equity chart with all sorts of colors and fancy formatting for my trading algorithm.

    One thing that was interesting about this skill course was the "Portfolio Optimization" lesson. It tried to teach me how to grab a list of stocks/financial assets and plop them into a "portfolio" by price-weighing them by the stock's expected returns. Then I was supposed to optimize the portfolio by "brute-forcing" the portfolio through linear regression functions to find the optimal position size of every asset in the portfolio.

    That process for creating a really good stock portfolio sounds highly complicated and technical. Let me tell you 2 things as to why this doesn't seem like the best approach in the world:

1. The way of calculating expected returns is entirely reliant on past performance.
2. This way of investing avoids volatility like a disease.

    The reason why creating portfolios based on assets' past performance to determine how much to allocate to that asset is not good is because that doesn't take a look at current price action to effectively determine when to buy, how much to buy, and where to place a stop on specific assets to follow the trends. If I wanted to "stock-pick" my way to a good portfolio based on general stocks that don't have a lot of volatility, I would be better off buying into a basic index fund or ETF that already exists.

    The reason as to why avoiding volatility like a disease is a bad thing is because if you follow the trends, volatility will be your bread and butter to making good money.

    I realize that picking apart this way of investing that is taught in essentially an introductory course of both Python programming and Finance is unfair. This is a basic skill for quantitative analysts and the more aware you are of other's trading and investing strategies, the better off you will be. I just wanted to bring the problems associated with it to light.

    Now that I have completed these courses and understand Python, now is the time to start the process of converting my TradingView strategy into something I can actually put my money behind. My initial thought when starting this journey was that I have to integrate my Python code with discount brokerage's API and turn it "on". But in the short term, I have found a better way to implement my strategy: through QuantConnect.com.

    QuantConnect is a platform where I would be able to code my strategy in Python directly into the site. They offer security protection for my code so nobody like TradingView will be able to steal my code and undercut me with my own creation. QuantConnect has immense back-testing features and forward-testing features so I would be able to put my own money behind my algorithm. If everything looks good after immense back-testing and forward-testing, I would also be able to lease out my algorithm to financial institutions to use for themselves. That would make a secondary income stream to increase my earnings.

    The reasons why I am choosing QuantConnect to move forward with this project is because all of these features are incredible to have and I also get a year of their premium tools for free if I send them a copy of my student ID.

    I'll start work on QuantConnect soon and will update the blog accordingly. I'm super excited to finally start doing the fun stuff!

-Jamie