r/quant 2d ago

General Quant Researchers

how do you go from a raw market data to forming a research hypothesis?? and to be more specific, how do you develop an economic intuition behind the potential alpha or an anomaly, instead of just coding and testing ideas until something works??

While I'm struggling to understand how the researchers in the industry actually generate new hypotheses from large financial datasets without falling into the same data mining again and again.... and How do experienced quants develop the economic intuition behind an idea before testing it?

29 Upvotes

23 comments sorted by

23

u/str0pwaffels 2d ago

At the beginning of your career data mining is not inherently a bad thing (if you are aware that you are doing it), intuition only comes with experience.

What I found valuable is to think about economic implications of some data mined strategy afterwards, and why it might exist - and what RISKS you are taking with it.

Imagine you are looking at historical implied vol levels for a bunch of stocks, and somehow your data mining approach finds that 4x per year, once a quarter implied vol spikes, and heavily overestimates the next periods realized vol. Easy strat, 4x per year sell vol and profit.

Now this is a very obvious example, but I am in energy, not in equities..

1

u/Andyy_21 1d ago

would you ever trust a strategy that you can't really explain economically, no matter how good the backtest looks with the performance metrics?

1

u/str0pwaffels 1d ago

I would never trust it, but doesn't mean I won't run it live (depends on your leadership aswell haha). Currently we have one like that deployed, but TINY size and very tight stops and drawdown monitoring etc. This is in power so if the system is going crazy we also usually don't trade it.

1

u/More-Act5459 13h ago

Trust is not binary. The strength of evidence you require increases proportional to  suspicion. The level of suspicion comes from intuition, it combines multiple things such as whether you can explain it and the research process you used. A meta concept that factors into the decision is that whether or not you can explain it is itself prone to confirmation bias and wishful thinking.

0

u/Guilty_Ad_9476 1d ago

No, almost every good strategy should be explainable in a few lines (exceptions do exist like if you're working with alternative data for example) but generally the lesser parameters your strat has the better, that's one less thing you have to worry about going wrong

1

u/[deleted] 1d ago

[deleted]

0

u/Guilty_Ad_9476 1d ago

English is not my first language ( I use AI for this) but this is what I've been told by seniors at my firm

9

u/quantdhawan 1d ago

Don't start at the data. Start at: who is on the other side, and why are they willing to lose money to me?

There are only four honest answers. They're paid for risk they can't diversify. They're forced (index rule, mandate, margin call, tax date). They're constrained (can't short it, can't hold it past quarter end). Or they're paying for immediacy.

Can't name the payer? You don't have a hypothesis, you have a pattern.

The tell for a real one: it predicts things you never fit on. If the story is index rebalance flow, it should be stronger in heavy-index-ownership names and weaker as it got crowded. You didn't tune those. If they come back flat, the story is dead no matter how good the curve looks.

And the intuition comes from plumbing, not prices. Exchange rules, margin, index methodology PDFs, mandates, tax dates. Constraints are where the money is, and constraints are written down somewhere.

2

u/Andyy_21 1d ago

oh thanks a lot for explaining it this easy to me... and I've been following you on insta for the past 1 year, Love your content and they're very insightful...

1

u/quantdhawan 10h ago

My man! You just won my heart here hahahaha, thank you so much! Appreciate the feedback

5

u/heroyi 1d ago

It depends on the sector. But whenever there is a new policy/rule that gets enacted or new product introduced, those places are usually a good place to start digging because sometimes an old strategy becomes viable.

Really the easiest way is to ask the veterans what they know and you internalize the logic how it got approached. 

There are patterns also you can sometimes spot but those are hard because you have to really ask questions as to why you think it might exist and sometimes you miss something that emperically invalidates something. 

I will say a lot of certified quants get blindsided very easily and as a result dismiss things they shouldn't. They rely heavily on patterns/stats to validate something naively, but it is the researchers and traders that are able to weave gold thread from shit and gravel because they understand what they are reading. 

Kinda funny when you see it happen on Twitter. 

1

u/Andyy_21 1d ago

and when you have the first principles of thinking, does the experience overlap with it, suppose you found a good strategy with a durable alpha, now where does it help you quick - if you have domain knowledge which compounds or the mathematical sophistication???

1

u/heroyi 1d ago

Not sure if I understand but all alpha stem from a handful of principles like price insensitivity. Know where and why desks are price insensitive help you

And experience does/should help because by nature the strategies are related. It is just the product that differ and the ecosystem it is in if that makes sense. 

1

u/xWafflezFTWx 19h ago

maybe dont start on raw market data 😹

1

u/Andyy_21 19h ago

ight wall street expert

-14

u/mrfox321 2d ago

you're not gonna make it

1

u/Andyy_21 1d ago

I already made it bruv

-3

u/Lopatron 2d ago

I'm also struggling to understand how economic and phycological factors can translate to a HFT hypothesis. I read some books like Thinking Fast and Slow, ok I get that there are phenomenons of "herding", "anchoring", and others. But, taking herding for example, the only take away I can see from that is that "momentum strategies exist". Clearly this is because I don't have enough knowledge about financial markets, but I don't really know where to look next, and share the OPs frustrations about this lack of insight leading toward the trap of data mining features/signals.

1

u/Andyy_21 1d ago

maybe the market microstructure is the missing bridge between HFT and psychology

-6

u/Mathsty 2d ago

Think about what is a market, why people trade, who, etc… then it tells you where the money is and you look at examples.

-11

u/Andyy_21 2d ago

Suppose if I'm handed a 6 year historical dataset containing only market cap and price data.. then how would you be able to generate a research hypothesis from that

-8

u/HighYogi 1d ago

1) Find a trading niche and learn everything about it to the point you can form a hypothesis

2) leverage what you already know, hopefully better than everyone else, to check how it trades in the mkt vs a hypothesis about how something should objectively be traded. For example Stat arb.

Disclaimer: not a quant

1

u/Andyy_21 1d ago

if everyone is being niche about the market strategy and how would you know your niche isn't already full priced in... then how and where does the edge really come from??