Complete Algo Trader Justin Jimenez

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.


---

Send in a voice message: https://podcasters.spotify.com/pod/show/completealgotrader/message
Support this podcast: https://podcasters.spotify.com/pod/show/completealgotrader/support

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!


---

Send in a voice message: https://podcasters.spotify.com/pod/show/completealgotrader/message
Support this podcast: https://podcasters.spotify.com/pod/show/completealgotrader/support

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!


---

Send in a voice message: https://podcasters.spotify.com/pod/show/completealgotrader/message
Support this podcast: https://podcasters.spotify.com/pod/show/completealgotrader/support

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!


---

Send in a voice message: https://podcasters.spotify.com/pod/show/completealgotrader/message
Support this podcast: https://podcasters.spotify.com/pod/show/completealgotrader/support

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.


---

Send in a voice message: https://podcasters.spotify.com/pod/show/completealgotrader/message
Support this podcast: https://podcasters.spotify.com/pod/show/completealgotrader/support

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.

“[Bruce] Kovner lists risk management as the key to successful trading; he always decides on an exit point before he puts on a trade. He also stresses the need for evaluating risk on a portfolio basis rather than viewing the risk of each trade independently. This is absolutely critical when one holds positions that are highly correlated, since the overall portfolio risk is likely to be much greater than the trader realizes.” 
― Jack D. Schwager, Market Wizards: Interviews With Top Traders
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!


---

Send in a voice message: https://podcasters.spotify.com/pod/show/completealgotrader/message
Support this podcast: https://podcasters.spotify.com/pod/show/completealgotrader/support