Quantitative Relation Puns

I got interested in both algo trading and Forex about the same time. I figured that if I was going to trade in the Forex market or any market there after, I was going to use algorithms to do the trading for me. I wanted to minimize the "human factor" from the trading equation. With the research I have done so far, it seems that human psychology and its volatile nature can skew ones ability to make efficient and logical trades consistently. I wanted to free myself from that burden and focus on other areas, specifically in creating a system that would allow me to generate algorithms that are profitable more often then not.

Consistently generating strategies that are more profitable then not is no easy task. There are a lot of questions one must first answer (to a satisfactory degree) before venturing forward in to the unknown abyss, lest you waste lots of time and money mucking about in the wrong direction. These following questions are what I have been trying to answer because I believe the answers to them are vital in pointing me in the right direction when it comes to generating profitable strategies.

Can quantitative analysis of the Forex market give an edge to a retail trader?

Can a retail trader utilize said edge to make consistent profits, within the market?

Are these profits enough to make a full time living on?

But before we answer these questions, there are even more fundamental questions that need to be answered.

To what degree if any is back-testing useful in generating successful algo strategies?

Are the various validation testing procedures such as monte carlo validation, multi market analysis, OOS testing, etc... useful when trying to validate a strategy and its ability to survive and thrive in future unseen markets?

What are the various parameters that are most successful? Example... 10% OOS, 20% OOS, 50%......?

What indicators if any are most successful in helping generate profitable strategies?

What data horizons are best suited to generate most successful strategies?

What acceptance criteria correlate with future performance of a strategy? Win/loss ratios, max draw-down, max consecutive losses, R2, Sharpe.....?

What constitutes a successful strategy? Low decay period? High stability? Shows success immediately once live? What is its half life? At what point do you cut it loose and say the strategy is dead? Etc....

And many many more fundamental questions....

As you can see answering these questions will be no easy or fast task

Hello r/IRstudies!

I am currently studying for a Master in International Relations and I would like to know and learn more about Quantitative methodologies in IR but my program has more of a qualitative focus.

So what Quantitative methodologies, in your opinion should any student/amateur of IR know ? (And could you provide books and sources to learn them if possible ?)

Thanks! I'm looking forward to hearing your opinions.

• M.

For : MS in Quantitative Finance/ Financial Engineering/Computational Finance

1. GRE : 325 (169Q 156V)
2. CGPA - 7.1 (IIT Kharagpur, India)
3. work experience : 5 years of data science (Product based)
4. 4 research paper in data science ( First Author : NAACL, Asonam; Second author - AAAI, ECAI)
5. 1 year as a Startup Co-founder (40k users on playstore, social media app)
6. 1 year self learning experience of trading & options (Algorithmic coding)
7. FRM - Level 1 Qualified.

Please suggest Ambitious, Moderate and Safe universities for my profile. I'm an Indian applicant

Thank You

This may be a weird question but I’ve been told by so many people that there are numerous non-math heavy jobs in high finance (which aren’t related to quantitative finance) but I have no clue what they are or how to search for them.

So what exactly are some careers/jobs in high finance that pay decent? I only know of Investment Banking and thats about it.

Hey everyone,

So I’m a qualitative researcher and currently trying to defamiliarise my self with quant. I have been learning about probability sampling and one thing I noticed is that the sample size require for small populations is very high. For instance, using a sample size calculator, I have calculated a sample size of 278 needed for a population size of 1000 based on 95% confidence and a 5% margin of error. However, the sample size for a population let’s say 10x bigger is only 370 based on the same conditions.

Hi all, I am looking for a data set in the finance realm, preferably related to investments with both quantitative and qualitative characteristics. Does anyone happen to know of any reliable sources? Or a repository I could check out that has data sets with both qualitative and quantitative variables?

Currently majoring in Computer science and am looking at masters programs in my state that lead me towards careers in tech or trading. One university in my state, Stony Brook, has two masters programs Statistics and Quantitative Finance, both in the Applied Mathematics and Statistics School. They seem to take a lot of pride in their Quant finance program which they make clear is very popular/competitive and of their ability to place many of their QF students in internships at hedge funds and major investment companies. Few other QF programs offer internships. And they even talk about how Stony Brook is headed by Robert Frey, a Stony Brook Graduate who was a "Key creative mind" at renaissance technologies (he had an early retirement from there). Some of their core classes are Portfolio Theory, Financial Derivatives and Stochastic Calculus, Computational Finance, Quantitative Risk Management and quite a few more.

Now this program sounds great, but I feel like a statistics program is arguably better in general. Its more flexible in the case that I want to work in tech or even the careers I mentioned in the title, quant and algo-trading. And its also probably less competitive. I dont know, what do you guys think. The careers Im interested in tech are data mining, AI, and recommender systems which happen to fall under data science and machine learning, already popular fields, I know

I track the following, in no particular order :

1. (nightly) resting heart rate (RHR)
2. (nightly) heart rate variability (HRV)
3. (nightly) respiratory rate (RR)
4. (workout) heart rate (HR)
5. food of course (macros/Na/K/Mg/fat types/water)
6. blood glucose
7. urine acetone strips
8. breath acetone meter

1-3 is tracked through an Oura Ring, 4 with a Garmin FR, 5 through Cronometer and 8 through some generic meter from Amazon.

On my todo list :

1. blood ketones (it's in the mail !!!!!!!! can't wait)
2. electrolyte blood meter (horiba or maybe this)
3. home-made EKG (something like this maybe)

Some quick observations :

• when potassium-deficient RHR goes up because of hypotension and RR increases
• RR decreases early and drastically on keto
• RHR decreases & HRV increases on keto but takes a couple weeks
• HR also decreases on keto / when not electrolyte-deficient
• blood glucose is a lot more stable (should I even mention this) and is inversely correlated with acetone (as expected) - provided you avoid coconut/MCT oil because else you get artificially high acetone readings
• urine and breath acetone agree quite well in general, and you can measure breath as much as you want (plus it reacts more quickly/is less dependent on hydration)
• I mostly want an ECG because electrocardiogram variations are correlated with potassium deficiency (my genetic condition makes me chronically potassium deficient), but also because it's kinda cool lol

Like, what could I start studying for now to prepare me for the application, and the Summer 2020 internship?

CTC, LLC. Chicago Trading Company - Hedge Fund

I was looking at this company a few weeks ago before the 6/30/2021 13F filings were posted on the SEC website. Originally, I thought I wasn't reading it correctly or someone had made a minor mistake. Nonetheless, I still found it kind of weird because the difference between the filing format of the 12/31/2020 13F and the 3/31/2021 13F was noticeably different.

December's 13 F filing was very neat, clean, and gave you all the information without needing to dig for it. The font was all the same color right at the top they gave you the compliance officers name and displayed the portfolio value (probably proudly, like fuq yuh check dis big dick energy out, we return more than your dad does when he said he is going to the gas station for cigarettes). Below the portfolio value total, you have a Holdings Table that shows all of their positions, and again it was really easy to look at and read and interpret. They were holding such stocks as Apple, Amazon, SPY - you know your typical blue-chip powerhouse stocks. But there were also some stocks that you would probably consider to be “Reddit stocks” such as NIO, Palantir, and Tesla. A+ folio in my book, Cathie Wood vibe almost. Whatya know, they were holding the ARK ETF TR :).

Recap:

Reporting Period: 12/31/2020

Portfolio Value: $3,114,929,000

Filing Format: Normal, not weird

So moving on to the March 13 F filing - Immediately upon opening it, I thought I was looking at a different form type than the one I was previously looking at a few minutes ago. So I went back to the December form and double-checked.

December: SEC FORM 13F-HR

March: SEC FORM 13F-HR

Ok, same form. Why does March look like someone made this with a computer running a crayon-based operating system, printed it, faxed it over to themselves, scanned it as a PDF and finally uploaded it to the SEC website (compared to December)? Maybe it is not the same company?

December: Form 13F File Number: 028-13225

March: Form 13F File Number: 028-13225

No, it is the same one… well what the frick then. Now there is a blue font, the formatting is all over the place and I don’t see where their Holding Table is. Oh wait, I found it. A tiny little button marked as “form13fInfo_20210401.html” hyperlinks you to a separate window.

https://preview.redd.it/654wcf5mj5681.png?width=344&format=png&auto=webp&s=13007c6a162887c617aca3a6f4b679668f0f1aa1

Again, blue font and is not formatt

I got interested in both algo trading and Forex about the same time. I figured that if I was going to trade in the Forex market or any market there after, I was going to use algorithms to do the trading for me. I wanted to minimize the "human factor" from the trading equation. With the research I have done so far, it seems that human psychology and its volatile nature can skew ones ability to make efficient and logical trades consistently. I wanted to free myself from that burden and focus on other areas, specifically in creating a system that would allow me to generate algorithms that are profitable more often then not.

Consistently generating strategies that are more profitable then not is no easy task. There are a lot of questions one must first answer (to a satisfactory degree) before venturing forward in to the unknown abyss, lest you waste lots of time and money mucking about in the wrong direction. These following questions are what I have been trying to answer because I believe the answers to them are vital in pointing me in the right direction when it comes to generating profitable strategies.

Can quantitative analysis of the Forex market give an edge to a retail trader?

Can a retail trader utilize said edge to make consistent profits, within the market?

Are these profits enough to make a full time living on?

But before we answer these questions, there are even more fundamental questions that need to be answered.

To what degree if any is back-testing useful in generating successful algo strategies?

Are the various validation testing procedures such as monte carlo validation, multi market analysis, OOS testing, etc... useful when trying to validate a strategy and its ability to survive and thrive in future unseen markets?

What are the various parameters that are most successful? Example... 10% OOS, 20% OOS, 50%......?

What indicators if any are most successful in helping generate profitable strategies?

What data horizons are best suited to generate most successful strategies?

What acceptance criteria correlate with future performance of a strategy? Win/loss ratios, max draw-down, max consecutive losses, R2, Sharpe.....?

What constitutes a successful strategy? Low decay period? High stability? Shows success immediately once live? What is its half life? At what point do you cut it loose and say the strategy is dead? Etc....

And many many more fundamental questions....

As you can see answering these questions will be no easy or fast task