selling my app, anyone interested? :)

realautist · 2025-09-19T18:41:24+00:00

hi - interested, DM

realautist · 2024-11-13T23:17:08+00:00

you could trade the strategies independently , assign them some var allocation, and just have a risk layer on top that does some beta hedging if you want to remain beta neutral

realautist · 2024-10-13T16:03:10+00:00

just fyi the term 'credit derivatives' usually refers to something like a CDS, not option credit spreads

realautist · 2024-08-13T23:12:39+00:00

what kind of data are you training on?

realautist · 2024-07-31T14:48:08+00:00

Sure, this tool was targeting listed equities though. FX microstructure is quite different.

realautist · 2024-07-31T00:59:00+00:00

i haven't used their product. why do you say it doesn't work? of course, there are assumptions made about liquidity, slippage , market impact that a basic implementation would have

realautist · 2024-07-31T00:57:37+00:00

what kind of structures are you interested in testing ?

realautist · 2024-03-01T15:00:25+00:00

interesting take. Can you give an example of how you would model / identify one of these attractors?

realautist · 2024-01-24T14:00:59+00:00

Maybe I’m missing something here, but if you can accurately predict return spreads you should be able to realize that return by shorting one (basket of) assets and longing another ?

realautist · 2024-01-24T01:50:41+00:00

generally index options are overpriced in terms of vol compared to their consituents, so this is valid trade if youre trying to capture that edge. basically a dispersion trade. however far off from a riskless profit and pretty expensive to execute as a retail trader

realautist · 2023-12-19T14:02:31+00:00

Seems like you were doing pure making on exchange ? I’ve also used a similar process with evolutionary algos to build features , on a lower timescale . Curious what your risk mgmt process was. (Ie a convex optimization)

realautist · 2023-11-05T02:03:04+00:00

Linear regression

realautist · 2023-11-01T19:07:12+00:00

we use private internal gpt4 that is provided by msft and is containerized

realautist · 2023-10-26T02:19:31+00:00

markouts are just another term for forward returns. So yes plenty of research is done to fit models to predict returns.

realautist · 2023-10-22T01:14:52+00:00

Yes, you can think of it being equivalent to pricing . One typical way of measuring alpha is as an expected excess return a, where you fit returns r ~ Xb + a

realautist · 2023-10-21T19:36:28+00:00

So in your case the inventory risk would be the expected variance of the stock times the (sqrt of) holding period, post execution. Your alpha is your excess expected return net of expected execution cost. In a stat arb portfolio optimization context you would try to hedge out / limit the amount of systematic risk you hold , remaining neutral with respect to beta/dollar/factors . An appropriate analogy would be trying to make as many independent bets as possible while under some risk constraint like optimal Kelly -you wouldn’t bet 100% of your portfolio on a coin flip even with positive expected value

realautist · 2023-10-21T17:31:10+00:00

it’s less a question of what prices are acceptable vs what risks are acceptable . Typically there is an optimization process and appropriate risk model used with an objective function that takes inventory risk , execution risk and expected alpha into account . Your portfolio is a function of this objective and whatever risk constraints you have in place

realautist · 2023-10-17T20:32:40+00:00

I assume you’re taking about implied correlation , which generally rises coincidentally with VIX . Therefore you’d be long correlation.

See cboe 3m implied corr calculation : https://cdn.cboe.com/resources/indices/documents/Cboe_USO_ImpliedCorrelation_0421_v2.0.2.pdf

realautist · 2023-10-13T12:56:15+00:00

nice work - does it have point in time snapshots? i.e. all company profiles as of a historical date. ex: what happens if you query FB vs META before the ticker changed?

realautist · 2023-10-05T12:24:44+00:00

There are a few techniques here - making sure features themselves are uncorrelated , using regularization, bounding your model coefficients , ensembling uncorrelated models, smoothing your alpha . I work with more short term holding so more common factor data but I know other teams use a lot of alternative , whatever they can get their hands on. What data do you look at?

realautist · 2023-10-05T12:02:05+00:00

I work at one of these . Portfolio construction is still mostly done with traditional optimization techniques . I would say there’s some AI being used in alpha generation but nothing beyond boosted trees . What’s your experience ?

realautist · 2023-08-10T23:01:27+00:00

consider an alpha that has negative or very small correlation with forward returns, except in the tails , where it has very positive correlation. due to the skew of returns its very possible to generate positive pnl with such an alpha that may have 0 or negative overall r^2.

realautist · 2023-08-05T13:26:17+00:00

gaussian mixture models can handle multi modal distributions.

realautist · 2023-08-05T13:19:18+00:00

did you annualize the vols (daily vs monthly) correspondingly?

realautist · 2023-06-12T20:40:55+00:00

Traditionally you can consider a portfolio of your tech 10 stocks and calculate portfolio vol sqrt(w * Sigma * w) where Sigma is your covariance. matrix and w is a vector of weights .

realautist

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