Transcription
A $200,000 quant education for free. Now, if you [snorts] want to become a quant, systematic, or algorithmic trader, there are four lecture series from the best universities in the world that can give you the same foundation that students pay over $200,000 for.
Now, quant trading is all about using math, statistics, [music] and code to find edge in the market. Whether you're building algorithms like myself, backtesting strategies, or running a systematic approach, this education ties it all together. You don't actually need to attend MIT, but you do need to learn the right concepts in the right order. This is actually the foundation that firms like Jane Street are built on top of. Jane Street did $40 billion in revenue in 2025, roughly the year before, all by extracting edges from the market. And the math, the statistics, and the code, that's the foundation you're going to learn from this series.
Now, before we dive in, let me be real with you. These lectures won't turn you into a quant overnight. I've shared this list on Twitter a while back, and it ended up being seen by over a million people, so I thought it was worth doing a full video road map, and I'll link all the playlists in the description below.
Now, for myself, I'm mostly self-taught, but I've worked alongside teams from Chase, [music] Seven Points Capital, SMB, Waterwheel, and many family offices. And the same pattern shows up everywhere. Foundations first, specialization second, and then real learning happens during building.
So, let's get to this road map. First, we're going to take a stop at MIT, then we're going to go over to Yale, then Oxford, and then finally end it off at Columbia. And we're doing all of this for free.
Now, the first one is MIT. They primarily focused on financial mathematics. It's 24 videos, 20 hours long, and it's on the MIT Open Courseware YouTube channel. Primarily, what it's about is the pure math applied to finance. A couple of the key topics include linear algebra for portfolio optimization, factor models, things like that. Then you have probability theory, so this is the foundation primarily of all edges. Then you've got time series analysis, very useful for mean reversion, momentum, those sort of things. And then you've also got volatility modeling that can be incredibly useful for position sizing, risk, and options.
Now, there is going to be a catch. It's quite dense. They are real MIT lectures, and they are professors writing equations on the whiteboard. You will often need to pause, rewind, take notes, just like you would do back in university. But the depth is the point of this series. This is best for anyone that's serious about quant trading or algorithmic trading in general, and this is going to be the bedrock, primarily the maths. As you can see, the first couple of lectures are kind of an introduction side of things. So, introduction to financial terms and concepts, then linear algebra, then probability theory, over stochastics, regression analysis, and etc. etc. Each one of these videos are around an hour long or maybe an hour plus, so definitely have a notepad or whatever you like to take notes in, and you can also even pause the transcript into AI to kind of help you during it. So, you can ask it quick questions of what does it mean by this, or I maybe confused by this concept.
Now, going over to step two, this is when we visit Yale. This is primarily where they go over financial theory. It's 26 videos, and it's about 26 hours long. Primarily, what this is is this is a full semester reviewing the key statistical concepts in finance. A couple of the key topics include portfolio diversification, which is primarily the only free lunch in finance. You've got the efficient frontier, which is around optimal risk versus return mix. You've then got arbitrage pricing theory, so how factor models work price pricing assets. And you've also got a yield curve arbitrage, primarily which is a an intro to relative value trading.
Now, as with the last one, there's going to be a catch. And for this one, personally in my opinion, some of these references do feel a bit outdated, but the core concepts such as portfolio theory, the basics of arbitrage, risk management, are all timeless, and they're still going to be worth learning from. Now, this is primarily going to be best for anyone that has already gone through the MIT one and then wants to pair it with it, or maybe if you're watching them at the same time, but that's going to be a pretty intense education. They do cover some of the same topics, so potentially you can watch one on MIT and then watch it from Yale, see if which one explains it better, or if you learn something else from rewatching it. And in a similar fashion, they kind of start it off slow by going over, you know, why finance, then computing equilibrium, then you've got efficiency, assets and time, present value, and a lot of the basics, and then kind of building up from there.
Now, an important reminder for this. A lot of people, and especially the million people on Twitter that saw this, a lot of them bookmarked it and never really watched it. And how do I know? Well, I did take a look at how many views these series had before I published that and then after about a week after that tweet or so, and it went up by a couple thousand, but it wasn't by a million or anything like that. And even for the people that did watch it, let's say they went through the 40 hours of content, and they felt really educated, right? They had consumed a lot of knowledge. The key part in algorithmic trading and really trading in general, you have to apply it. You have to open Python, you have to write some code, you have to break it, fix it, run a backtest, see it underperform live, figure out why. And this is where the actual learnings happen.
Now, I'm mostly self-taught. I've been coding since I was around 12. I originally started coding because of making money online through Minecraft servers, and then I've been trading for the past like almost 7 years now, and obviously a lot of coding has gone into my own algorithmic process. And over that time, I've created successful business, which kind of helps either family offices, successful traders, or sometimes funds as well on the quant side of things, where we're either doing development, consultation, or just really taking someone's someone's idea and turning it into an algorithmic trading operation, which can execute trades based on that logic. And from speaking with tons and tons of engineers and also interviewing a lot of engineers, the people that get good at quant trading or algorithmic trading are the ones who are building things and not just consuming content. Don't get me wrong, consuming content is, I would still say, around 30 to 40% of the learning. You have to start from somewhere. But once you consume a bit of content, try and apply it, try and test it out. And nowadays with AI, you really have no excuse. AI can kind of write most of the code for you anyway if you're a bit lazy, and it's a great way to just start tinkering with things.
And this leads me onto the next one, which is a bit more hands-on. And this is a quantitative trading strategy playlist, and it's from a lecturer that was lecturing at Oxford, and he does 34 videos in total. Most of them are more hands-on, and they're primarily around Python implementation of trading strategies. And he does also go over real code, real data, and real execution. A couple of the key topics is financial data plus Python, so what most quants use most of the time. It depends on what they're working on. Sometimes they'll need way faster languages, but if for the very basics, you can use Python a lot of the time. You've also then got limit order book mechanics, so understanding kind of how exchanges work, how you submit orders, how you don't submit orders, things like that. You've got things like trailing stops as well for execution methods that really do matter when combining with a strategy. And then he also goes over some of the risk return and annualization, so primarily the math behind Sharpe ratios, Sortino ratio, and a couple of different performance metrics you're going to use in backtesting.
Now, I will say, watching this one without really having any foundation in either a bit of coding or probability theory, those sort of things, it's going to be a lot more difficult for you to actually apply these things. So, I would recommend getting a foundation on that first, and then you can consume this content and go for it. Now, don't expect this content to teach you a profitable strategy. It's teaching you how to approach it in code, and kind of giving you a real-life example of him doing it as well. As I mentioned, some of these parts do feel a bit outdated, but to be honest, for free, I would say they're still an amazing series you can go through and learn a lot from.
And then for the final stop, we've got price momentum at Columbia. This is just two lectures, it's about, I think, 2 hours long, give or take, or maybe three. Actually, let me just correct this. I think it's about 1 hour long for the two videos. I know one of the main videos is around 24 minutes long, so this one is very easy to go through, and primarily it just specializes on a deep dive on momentum, one of the most robust return drivers across really all markets. It goes over why momentum works, and some of the kind of two main differences of momentum, and two of the main flavors of momentum, which is cross-section and time series as well. And uh primarily in the lecture, he's referencing the Value and Momentum Everywhere research paper, which is one of the most famous research papers and has some amazing people on it like Cliff Asness, and is overall one of the kind of like time pieces for momentum. If you want to start trading, you can kind of read through that paper, get the very basics from it. Now, this is going to be best for anyone building or considering a momentum strategy. And to be honest, even if you're not, momentum is one of the main strategies in really any portfolio, so it's really worth understanding. Now, it is a bit of an outdated version of momentum. It's still going to technically work, but there are many things and improvements you can add on top. But for understanding the basics, I think it's a great, great lecture.
Now, there's an incredible amount of value and hours to kind of go through for all these particular series. So, what is the right approach? Well, the mistake most people are going to make is they're going to bookmark this, or they're going to save this video, and then they're going to say, "I'll watch it later," and they never do. First thing to do is just pick one series, start with MIT or Yale, and just block two to three hours per week, and treat it like a class. Take some notes, pause, rewatch. Don't jump between series, in my opinion, but you can obviously watch these in, you know, 1.5 speed or 2x speed if you want to do that. And then four, build as you go. Code, backtest, break things, and fix them. You will learn so much from trying to do some of the things that are mentioned in the lecture, or just trying to even visualize them. The goal here isn't to get through 40 hours of content. The goal is to actually learn and apply the material. And to be honest, two to three hours per week compounds like crazy over 6 months to a year.
So, you don't need $200,000 to start quant trading or even algorithmic trading. You just need the time and the right resources. These lectures are the same ones taught at MIT, Yale, and Oxford, and Columbia. It's the same content that their students pay six figures to access. All free, all online, and all linked in the description. And if you want some extra help going from theory to actually building algos with my help, that's exactly what we focus on inside the Crypto Momentum Group, helping you build your first live algo in crypto. Feel free to click the link in the description if you want to check it out. I hope this was valuable, and I'll see you in the next video.