saosebastiao

> On average, each millennial parent receives $11,011 per year in combined financial support and unpaid labor

That's the average?!?! What's the 90th percentile like, cause I've been sitting on a big fat zero since before I was a legal adult. And I know I'm not alone. These numbers are blowing my mind right now.

I have a really tough time comprehending why it is such a difficult problem to solve. The FCC could have solved it by now, independently of congressional legislation, considering the fact that they regulate the issuance of phone numbers. Those overseas companies still have to get their US phone numbers from an FCC regulated body.

What am I missing?

Ocaml has 100% less bikeshedding and no propensity for unending puritanical holy wars. It's a language for getting things done, as opposed to thinking about finding a way to do something that satisfies an obsessive compulsive desire to eliminate 100% of sides effects for no practical reason.

Do you have any recommendations for wireguard providers? I'd love to try it out, but I don't want to host my own server.

I just about dumped PIA for the same reason. Downloaded their newest update, and it is soooo much better.

One based indexing is used in tons of domains within mathematics.

Fortran chose 1-based indexing for a very obvious reason...it was the best translation from the mathematics literature that they were trying to implement. Because matrix notation uses 1-based indexing! MatLab, a language designed specifically as a high level language for matrix mathematics, chose it for the same reason. R, a language for statistics, chose 1-based indexing because it is a statistical language, and counting is one of the most fundamental operations in statistics, and 1-based indexing is the form used for counting.

Mathematicians obviously have no problem switching back and forth between 0-based and 1-based indexing for different domains, so it boggles my mind that computer scientists have turned it into such a huge holy war, and even more mind-boggling that 0-based zealots claim to have mathematics on their side.

The most prominent programming languages designed specifically for mathematical purposes (Mathematica, MatLab, Julia, R, Fortran), all have 1-based indexing. That should be a sign to you that 1-based indexing has a legitimate mathematical rationale. Djikstra wasn't speaking on behalf of the mathematics community. The idea that if you have deep faith in mathematical beauty that you'll come to the same conclusion is absurd.

Why? You've already come to the conclusion that anybody that disagrees with you is an idiot, and you think your argument is fully reasoned and covers all bases. Why should I believe that you would change your mind given new information?

It sounds like you haven't considered any of the multiple reasons or situations where 1-based indexing works out better.

> Having worked for over a decade at Google, I sometimes wonder if the founders still use the products. Larry Page used to always harp on latency (rightly), and now Google products are slower than ever.

Is this for real? I've never felt like google's products have prioritized latency. Android latency has been atrocious from the very beginning. And gmail and google docs are easily some of the slowest webapps out there.

If your work defines who you are, then sure. I don't trade as a purpose for my life...I trade to make money, full stop.

The finance industry loves deep neural networks because it provides them a way to extract money from all of the naive tech engineers who took a deep learning class on Coursera and thought they could apply neural networks to trading. The general consensus is that deep learning for general trading purposes is complete bullshit because it unavoidably overfits.

There are some quality uses for NN, but price or volatility prediction is not one of them.

It doesnt refute your theory, it's just that your theory applies very specifically to HFT. Alpha generation in HFT is incredibly simple, and HFT analysis typically relies on very basic math: linear regression and recursive filters. HFT relies on execution far more than modeling to generate returns.

But HFT is inherently low capacity. You can't put a billion dollars on an order book and expect to make the same returns as you would with a thousand. That's the reason HFT firms are almost always proprietary trading firms...they don't need or want more capital.

A hedge fund, like Rentech, is typically on much higher timeframes because their size necessitates higher capacity strategies. This could mean holding periods of minutes for the million dollar funds to days for the billion dollar funds. As you get to higher and higher timeframes, your mathematics are going to need to be dramatically more sophisticated in order to beat the market. The math you are looking at in that quora link is about pairs trading which is where pairs of instruments mean revert over time. I would expect Rentech to be doing this type of trading over long timeframes and holding a trade for days to weeks.

Hey, awesome that you're posting here. I actually just asked a question as a post on the algotrading subreddit, but I'm hoping you can maybe help here. I think this would be broadly helpful, so I'm asking here instead of emailing.

Is there any authoritative literature on the execution aspect of algotrading? I'm talking specifically about the things that are hard, if not possible, to analyze via backtesting. As I've moved to shorter and shorter timeframes, I feel like there's a huge execution gap that I'm suffering from. I've got good strategies that work but can definitely be improved with better execution. For example:

    How to optimize limit pricing to get better fill rates or better entries or exits

    Market microstructure analysis and modeling. How to analyze book depth and order flow to predict short term (shorter than 5-10s) behavior

    Applications for and optimizations of various order types like IOC, MIT, LIT, FOK, Iceberg, Basket, etc.

    How to handle the million ways that algotrading can do unexpected things: market gaps, trading halts, network outages or latency spikes, news events and volume explosions, etc.

    Metastrategies: creating strategies that "trade" strategies to turn them off or swap them out based on market conditions.

    Statistical process control, or other ways to detect when an algorithm is no longer working as expected.

    Use of metainformation to dynamically inform strategies, such as implied volatility or any of the option greeks.

Do you know of any resources to point me to?