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A full comprehensive guide on how to get started in Quantitative Finance Covers the topics: -Python & Rust -Mathematics, Statistics & Probability Theory -Optimization -Machine Learning -Numerical Methods -Derivatives and Pricing -Portfolio and Risk Management -Trading and Strategies -HFT & Market Making -Data & APIs -Projects without wasting time on a bunch of resources that would take 10 years to complete. Head over to my website to read the article for free!

Different types of distributions and where you will find them in quant finance. Normal Distribution: This is the most common one, it's often used to model returns. As everyone knows returns are far from normal but sometimes it can still make sense to model them as normal 1/n

Writing this has literally become muscle memory at this point.

The Quants has been one of my most enjoyable reads of all time. You obviously won't learn a ton since it's not some math textbook or whatever but I can still recommend it to everyone looking for some good stories.

Momentum strategies exhibit high kurtosis and negative skew. Momentum strategies also exhibit strong drawdowns. One way to mitigate this is explained in the "Momentum has its Moments" paper. It proposes that volatility usually comes in the form of negative returns and that forecasted volatility (for example via the HAR model) is a good predictor of returns.

If I had to learn math for quant finance from 0 I would learn things in the following order: - Calc I - Calc II - Linear Algebra - Calc III (Measure Theory) - Probability Theory and Statistics - (Convex) Optimization - Stochastic Calculus - Financial applications of all the above (You can also do this in parallel) From here you can honestly branch out however you want and learn what's most interesting to you but learning those will give you a great theoretical foundation.

Quant Intern Interview Question: Tell me a strategy that makes 50% anually with a sharpe > 2 and capacity > $1.000.000

The life of a quant

Time to give this bad boy a read

There are so many underrated quant resources. I've discovered those resources over 7+ years of studying quant finance. And now, they're yours to explore:

The only "indicators" / metrics you need for 99% of tasks. EMA There was once a trend of using Kalman Filters but reality is, you can spend 10x less time and just use EMAs to usually result in the exact same performance unless it's some really specific task.

Making a Python Backtest 132400% faster using vectorization! 🧵1/n

List of Project Ideas that will make you stand out 1. Triangular Arbitrage Scanner Pick one exchange like Binance and build a bot that scans the spot market for triangular arbs. You can make the project even more difficult by using shortest path algorithms for longer arb paths!

Moving Averages don't do well when data changes abruptly. We can do better by solving a 100000-dimensional Second Order Cone Program. Let's talk about Total variation denoising:

Looks what just arrived in the mail!

Quant Intern Interview Question: Tell me a strategy that makes 50% anually with a sharpe > 2 and capacity > $1.000.000

Not only does my university force me to learn R, it also forces me to learn MATLAB. 2 shitty languages in 1 semester... It is so over

Writing this has literally become muscle memory at this point.

Is there a way to quickly load a csv in C++? Gotta read hundreds of Orderbook update files and loading the data is such a big bottleneck that the code ends up being slower than python lol Pandas loads the CSVs literally 10x faster

Gonna be pulling an all-nighter reading "Advanced Futures Trading Strategies" by Robert Carver. Will report tomorrow if I lost my sanity or not.

To the PC experts out there, how stupid is this build?

Quants of Twitter, what projects would you do if you were a student trying to get into quant?

Does anyone actually read the book recommendations on here or did everyone just agree that the books are good because everyone else is saying they are good?

Newest version of the PC, any improvements? I'm NOT using it for gaming but mostly quant finance tasks so lots of data processing, algorithms etc. Need a fast cpu with lots of cores for maximum parallelization.

Quants are a little too secretive imo. Yes, many people knowing your strategy will dilute the alpha. If you work together with a hand full of researchers though then this will likely have almost no impact on the alpha while you are able to develop something much much better.

Guess I'm 20 now. Can already feel my back and knees starting to hurt.

What point is there in showing up to university if all of the material is uploaded online? The only reason I can think of is socializing but I already got a gigantic social circle outside of university. Only been to campus a few times and feeling huge FOMO.

Everything sounds cooler with Quantum in front of it. ❌Monte Carlo ☑️Quantum Monte Carlo