Let me share 5 of the resources I’ll be using to switch careers to Data Science in the FinTech industry.

I left my job as a controls engineer in July.

On the surface, it was a great job. I was able to travel all over the country, I worked with a team of awesome guys, and I was earning good money for a twenty-five-year-old. The position even came with a “fun budget” perk that enabled me to do things like fly a helicopter over Baltimore, kayak on the crystal blue waters of Tulum, and hunt wild boars in the rolling hills of Santa Rosa.

But like many Americans part of the ongoing Great Resignation, I found myself reflecting on the path I was taking in my career. Even though I was extremely privileged to hold such a high-paying and rewarding position, I knew that I wouldn’t have been happy with my life if I continued down that path. I wasn’t truly passionate about the work I was doing. So I left.

During my last months at that job, I worked on several personal coding projects in my spare time. I was reading finance textbooks, studying online courses, and developing my own dashboards and web apps “for fun” (my girlfriend calls this being a workaholic, but I like being productive in my spare time).

By the time I left, I knew that I wanted to pursue a career that involved programming and finance, but I wasn’t sure whether to focus on software development or data science. I ultimately decided to pursue data science because of my penchant for data-driven decisions, problem-solving, and storytelling.

I want to share my transitional journey with you by sharing five resources that I will be leveraging to make a smooth career change into FinTech as a data scientist.

Learn how to identify what your tail risk is so that you’re prepared for the next market crash.

I can’t tell you when the next market crash is coming. I have a couple of guesses, but I don’t think predicting the timing is the most important part to consider anyway. Earthquakes and other natural disasters are similarly unpredictable, but it’s not about prediction — it’s about preparation.

When the next disaster strikes, you need to have a plan for how you are going to handle it. And the first step to having a plan for a crisis is to know exactly what is at risk.

So today I’m going to explain a few financial terms to you so that you have a thorough understanding of what’s at stake in your own portfolio. I’m also going to show you how you can implement these concepts with python so that you can actually apply what you’ve learned. I’m going to go over drawdowns, historical value at risk, expected shortfall, parametric value at risk, and we’ll look at some random walk and Monte Carlo simulations. Let’s get started!

Let me share 5 valuable sources that I referenced while learning about factor investing.

I’m finding that the deeper I explore quantitative finance, the more difficult it becomes. This past week I was working through a subsection of the DataCamp course “Introduction to Portfolio Risk Management in Python” about factor investing.

The practices were simple enough, but the course suffers from a persistent lack of context throughout the material. After completing the exercises, I found myself wondering “What does this model even tell me, and why do I care?”

That led me down the rabbit hole. I ended up researching over forty different sources in my attempt to establish a thorough understanding of factor investing. I distilled this information into my latest story, “Diversify Your Risk Exposure With Factor Investing”.

It’s a long story, but I wanted the detail of my explanations to match the difficulty I perceived in approaching the subject as an uninformed novice. If you don’t feel like taking the time to read all of my research, let me cut some time out by sharing five of the key sources I referenced while learning factor investing.

Click here to continue reading my latest story on Data Driven Investor!

Increase your chances of catching a rising trend with this new portfolio management technique!

You may not feel this way, but for many investors and traders, the stock market is a game, and we all want to move up on the leaderboard. That’s really why you’re here right? You want to beat the market, just like every other investor. I do too; that’s why I research portfolio management theory, and it’s why I write these stories.

In my last story, I explored how to increase your portfolio returns using different portfolio management strategies. If you read that story, you would have learned how you can optimize your portfolio’s weight distributions to match your desired balance between risk and return while simultaneously multiplying your gains.

In this story, I want to revisit one of the earlier steps of that process that I skimmed over — how do you actually determine which stocks will give you the best return?

To answer this question, I’m going to share a method with you called factor investing. I found this topic to be relatively easy to implement but also incredibly arduous to fully comprehend.

I’m going to break down the concepts as best as I can. By the end of this story I want you to walk away with an understanding of the relationship between risk and return; what factor investing is; how to implement a multi-factor model; and how to evaluate different models.

Click here to continue reading my story on Data Driven Investor!

I spent a lot of time researching portfolio management for my last article, so allow me to share five of the best sources I found.

Let me tell you, learning quantitative finance is not easy.

I’ve been taking a Feynmanian approach to learning data science, algorithmic trading, and quant finance for the past couple of months. Although it’s highly effective and I absolutely recommend it, it has rigorously pushed me to learn everything I can about these subjects. That rigor requires a great deal of patience while scouring for research and detailed explanations of the subject matter.

Usually, I would like to crank out two stories in addition to this weekly round-up each week, but I spent so much time on my last story about portfolio optimization methods that I didn’t have enough time for another story.

I’d like to think that spending more time on one thing just means I’ll progress faster towards becoming an expert on the subject. So, in keeping with that story’s topic, let me share with you five of the resources that I found most valuable while researching portfolio optimization methods.

Let’s look at four portfolio optimization methods to find the right fit for your investing style.

One of my all-time favorite books about investing and trading is Jack Schwager's “Market Wizards”. If you haven’t read it yet (there’s a free pdf on google and an audio version on YouTube), it’s a series of interviews with some of the world’s best traders and investors from the 80’s. The interviews provide exceptional insight into the methods and attitudes of these skilled market participants, and the subjects of discussion cover a wide breadth of topics across different markets and trading styles.

Even though each interviewee was unique, there were a few key principles that were mentioned by many of them. One of those principles is risk management.

As an individual investor and trader, I too believe it is critical to maintain risk in individual positions and across the entire portfolio. However, with myriad ways to manage risk at both of these levels, it can be difficult to discern what is the best approach for your unique style and risk tolerance requirements.

So, today I want to examine four portfolio optimization methods to show how they work and to evaluate why you might choose one strategy over another.

Click this link to continue reading my story on Data Driven Investor!

Learn the fundamentals of easily creating your own trading algorithm with the free tools available on QuantConnect.

Anyone who has attempted to build their own trading algorithm knows just how complex and difficult that undertaking can be. It requires a deep understanding of programming, an affinity for troubleshooting, knowledge of financial markets, and access to tremendous amounts of expensive data. It is no small feat to develop one’s own automated trading platform and then develop a profitable, competitive algorithm to execute it. However, QuantConnect offers 6 competitive and affordable resources that make it easy for you to start making money as an algorithmic trader.

Click this link to continue reading my story on Data Driven Investor!

Learn how to begin building trading strategies to be back-tested in the QuantConnect Lab.

Although QuantConnect is an excellent platform that simplifies the process of researching, testing, and deploying trading algorithms, it comes with a steep learning curve. I found that their boot camp tutorials and documentation do not thoroughly explain all of the available functions and properties. Additionally, there does not seem to be an abundance of third-party tutorials on YouTube either, which has made it quite difficult to learn QuantConnect’s full functionality.

Therefore, I will be exploring QuantConnect’s features through this series in an effort to clarify what can be done on the platform. I will provide helpful examples of algorithms, functions, and properties so that you can learn how to automate your own strategies. With that said, let’s get into some beginner steps for developing algorithms on QuantConnect.

Click this link to continue reading my story on Data Driven Investor!

In the second part of this series, I will explain how to cut losses with trailing stop orders through order events on QuantConnect.

There’s a trading adage espoused by many professional traders and investors that every new trader should learn to practice: “cut your losers and ride your winners.” 

By having a disciplined approach to risk management, you can minimize the amount of money lost and lock in the gains made by getting out before the market goes against you.

In this story, I will walk through one of QuantConnect’s tutorials to explain how to cut losing positions and lock in profits with a trailing stop order. I will also attempt to supplement the tutorial with references to official documentation and 3rd party references.

This story covers trailing stop orders along with lesson 2 of their “Boot Camp 101”. If you are just beginning to use QuantConnect, check out my story that covers lesson 1 of the boot camp. Otherwise, let’s dive in.

Click this link to continue reading my story on Data Driven Investor!

Let me share five impressive resources that taught me something valuable about quantitative finance.

As someone who loves learning about quantitative finance, programming, and math, I’ve found it difficult to discover in-depth technical explanations of quantitative methods on the web.

However, I’ve learned that if you’re persistent enough, you can discover a wealth of high-quality information that is often available for free or at a low cost. Sometimes it takes a while to find this information since you need to search in the right places, use the right keywords, and be able to sort the wheat from the chaff.

Today I want to try to save you some time and frustration by aggregating a list of five resources that I found value in this week. Whether you’re a seasoned professional that wants to stay informed, or you’re a hobbyist seeking to learn, check out these five resources for quantitative finance.