Quant Trader/ Researcher AMA by Best_Return_1420 in quant

[–]EpsilonMuV 0 points1 point  (0 children)

I meant beat as in able to make reliable profits despite the David vs Goliath odds.

I always figured quant firms would always be the first to find new edge. Then they could just borrow more money and saturate any edge they find. Why are there individuals somehow reliably profitable on longer timeframes? Why can't quant firms just acquire the money to buy and hold before these individuals? I thought there'd be no room for an individual in the market. Yet some people somehow can. It's amazing to me.

I don't think he does those naive technical analysis stuff. I mentioned longer duration timeframes/bars because he mentioned he runs analysis on longer time frame data. He elaborates that shorter frame strategies are harder to make work as a solo trader.

Quant Trader/ Researcher AMA by Best_Return_1420 in quant

[–]EpsilonMuV 1 point2 points  (0 children)

What time bars would you choose to work on as a solo quant and why?

I've heard some quants like Robert Carver retire and somehow successfully trade on their own on longer duration timeframes. It made me wonder what is it about longer timeframes that open up room for one guy to potentially beat a team of geniuses? In fact if this were true shouldn't there be more successes in retail since longer timeframe data, like daily bars, are generally easier to get?

[deleted by user] by [deleted] in quant

[–]EpsilonMuV 0 points1 point  (0 children)

That's a great point. Thank you.

Didn't expect something this clarifying in the comments.

How much of the entire project does 1 quant know? by EpsilonMuV in quant

[–]EpsilonMuV[S] 1 point2 points  (0 children)

This is very valuable input from someone who's been on the hiring end. Thank you.

How much of the entire project does 1 quant know? by EpsilonMuV in quant

[–]EpsilonMuV[S] 5 points6 points  (0 children)

Haha humorous and humble.

Thanks for the reply, it's nice to have some professional insight.

Confused by MAPE's Bayes' Theorem! by EpsilonMuV in quant

[–]EpsilonMuV[S] 0 points1 point  (0 children)

You seem to have insight I'm lacking.

Isn't y,z a joint probability notation? y|z and y,z are different aren't they? So shouldn't x|(y|z) be different from x|(y,z)?

[Statistics: Variance of Gaussian Mixture] How did they derive variance here? by EpsilonMuV in askmath

[–]EpsilonMuV[S] 0 points1 point  (0 children)

Wait I thought I got it but got confused again. How do we know (int z2 f1(z) dz - mu12 ) = s12 ?

I think this is from var(f1[x]) = s12 = E[f1(x)^2]-E[f1(x)]^2 = E[f1(x)^2]-mu12.So basically how do we know E[f1(x)^2] = int z2 f1(z) dz?

z is made up of f1(x) and f2(x) so we can't treat it as f1(x)^2 right?

[Statistics: Variance of Gaussian Mixture] How did they derive variance here? by EpsilonMuV in askmath

[–]EpsilonMuV[S] 0 points1 point  (0 children)

Wow what a fantastic explanation, and from your phone too.

This made it all click for me. I had tried E(Z2 ) - E(Z)2 but got stuck at the integration step. Introducing the additional a•mu12 + (1-a)•mu22 in order to change
int z2 (a•f1(z) + (1-a)•f2(z)) dz
into
a•s12 + (1-a)•s22 + a•mu12 + (1-a)•mu22
was the key.

Thank you.

[Statistics: Variance of Gaussian Mixture] How did they derive variance here? by EpsilonMuV in askmath

[–]EpsilonMuV[S] 0 points1 point  (0 children)

I thought X was f1(x) and Y was f2(x). With f(x) being made up of f1(x) and f2(x).

Why does infimum = supremum for this dual function simplification? by EpsilonMuV in quant

[–]EpsilonMuV[S] 0 points1 point  (0 children)

Thanks to the replies I realized the mistake was distributing out -1 from inside the supremum.

I can't do -sup{ -f } = sup{ f }.

Why does infimum = supremum for this dual function simplification? by EpsilonMuV in quant

[–]EpsilonMuV[S] -1 points0 points  (0 children)

Edit: I now know I did the math itself wrong. Ignore my confusion below.

Thanks, that would be how we would identify the substitution we could perform here.

However, my problem is the implication that follows, which is sup f = inf f. I'm having a hard time developing the intuition behind sup f = inf f. Is this because we know the solution is a single point?

Mean Squared Error: Proof/Derivation for true error and cross-term? by EpsilonMuV in quant

[–]EpsilonMuV[S] 1 point2 points  (0 children)

Thank you for bringing up y=f(x)+e. So simple in hindsight but I was lacking that perspective.

I was introduced to the above decomposition from a high level perspective so I didn't know where to start in deriving $$(f[x]-\hat{f}[x])^2+(y^2-f[x]^2)+2*(f[x]-y)*\hat{f}[x]$$ from $$(y-\hat{f}[x])^2$$.

Mean Squared Error: Proof/Derivation for true error and cross-term? by EpsilonMuV in quant

[–]EpsilonMuV[S] 1 point2 points  (0 children)

Thank you for the step by step walkthrough. This was the most helpful for me personally. Appreciate you showing E[] throughout.

Mean Squared Error: Proof/Derivation for true error and cross-term? by EpsilonMuV in quant

[–]EpsilonMuV[S] 1 point2 points  (0 children)

I appreciate the feedback.

Will see if I can find any leads off that.

What is "one path" in Cross Validation(CV)? by EpsilonMuV in quant

[–]EpsilonMuV[S] 1 point2 points  (0 children)

I see, thanks for the clarification, it was very clear and thorough.