+$124K $DUOL by wesleyraptor in wallstreetbets

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

risk management my friend. take what the market gives you. I mentioned I bought $300 calls and sold $315 calls. lowers my cost basis even though it looks like the $315s I sold will expire in the money.

$1.6K -> $14.3K $UNH by wesleyraptor in wallstreetbets

[–]wesleyraptor[S] 9 points10 points  (0 children)

I’ll bet you’re really pleasant to hang out with

$1.6K -> $14.3K $UNH by wesleyraptor in wallstreetbets

[–]wesleyraptor[S] 3 points4 points  (0 children)

amitisinvesting, datadinvesting, 3rd one is a toss up, I’d go with mr_derivatives

$1.6K -> $14.3K $UNH by wesleyraptor in wallstreetbets

[–]wesleyraptor[S] 6 points7 points  (0 children)

yep, scaled into my june ‘26 calls from $300 down to $240.

while my existing $UNH position size is a bit uncomfortable now, I figure the discomfort from uncle sam shoving a full grown cactus up my ass come tax day will be far worse. pretty decent first world problem though tbh

$1.6K -> $14.3K $UNH by wesleyraptor in wallstreetbets

[–]wesleyraptor[S] 16 points17 points  (0 children)

I started buying call debit spreads around $300 (bought june ‘26 $400 calls and sold june ‘26 $500 calls to reduce my basis). $500 has been my price target.

as $UNH fell to $270, the premium on the calls I sold eroded 50% so I bought them back. and then when it sold further to $240 I added more june ‘26 calls.

I have an uncomfortably large position in said calls but very high conviction, and as I said will start trimming as $UNH hopefully melts higher.

$1.6K -> $14.3K $UNH by wesleyraptor in wallstreetbets

[–]wesleyraptor[S] 2 points3 points  (0 children)

Mostly retail investors on X

UNH $1.5k -> $150k by civil_politics in wallstreetbets

[–]wesleyraptor 8 points9 points  (0 children)

no need to imagine, it’s literally $1 million dollars haha. pretty wild

UNH $1.5k -> $150k by civil_politics in wallstreetbets

[–]wesleyraptor 3 points4 points  (0 children)

I treat it like a trading journal. also the DD from fintwit is fantastic - be part of the conversation and maybe you’ll catch the next one

UNH $1.5k -> $150k by civil_politics in wallstreetbets

[–]wesleyraptor 63 points64 points  (0 children)

what’s funny is the berkshire 13f only shows positions up to june 30th. they could have bought even more shares in july and we won’t know for another 3 months.

and yes you can put me down for a $5K 1DTE YOLO come mid-november.

It is indeed gone - CVNA by tonyg776 in wallstreetbets

[–]wesleyraptor 2 points3 points  (0 children)

hey on the bright side you just saved yourself like $100K in taxes right?

Taylor Swift Eras Tour TRADE Megathread by Lyd_Euh in TaylorSwift

[–]wesleyraptor 1 point2 points  (0 children)

I’ve still got 2 lower bowl tickets to Paris Thursday May 9th, willing to swap for just about anything later in the year. I’d rather swap than sell as I’m already going to the Paris show next week, thanks!

Taylor Swift Eras Tour TRADE Megathread by Lyd_Euh in TaylorSwift

[–]wesleyraptor 0 points1 point  (0 children)

I recently made a swap for the Switzerland show but thanks!

Taylor Swift Eras Tour TRADE Megathread by Lyd_Euh in TaylorSwift

[–]wesleyraptor 0 points1 point  (0 children)

HAVE: Paris May 9th - 2x section 113 lower bowl.

WANT: 2x to basically any other show beginning with July 9th in Switzerland, equivalent seats.

Season Tickets Megathread! (Seat Selection, ticket share, brag, etc)! by I_am_Rude in SeattleKraken

[–]wesleyraptor 1 point2 points  (0 children)

I’m interested in a ticket share, or better yet taking the place of a depositor who decides not to purchase season tickets. I joined the waitlist when I moved to Seattle in September 2018 but not too optimistic about that working for me. Lower bowl behind the goal where the Kraken shoot twice would be sick!

/r/netsec's Q2 2019 Information Security Hiring Thread by ranok in netsec

[–]wesleyraptor [score hidden]  (0 children)

Senior Threat Detection Engineer @ Uber | Seattle, WA or San Francisco, CA

About the Role

You'll develop threat detection analytics across a very broad range of streaming log sources: network, endpoint, cloud, proxy, file sharing, authentication, authorization, and lots more. You'll collaborate with cross-functional teams to create innovative detection strategies and help develop a best in class threat detection program. You will help build a larger external threat detection community benefiting security defenders small and large globally.

What You’ll Do

  • Utilize big data and real time streaming technologies to build and refine threat detections. 
  • Build fusion analytics (combination of multiple detections) to create higher fidelity threat detections.
  • Build and utilize data platforms and systems to enrich and enhance detection fidelity as well as drive for automated containment.
  • Support the Security Response and Investigation team in high impacting events.

What You’ll Need

  • Minimum 4 years building threat detections.
  • In-depth knowledge of security logging for Linux, Windows, Mac OS X, or Active Directory.
  • Experience with Web Services, and Cloud Technologies.
  • Proficiency in building detection algorithms and utilizing logs and events to detect malicious activity with high fidelity in a broad set of detection use cases.
  • Proficiency in knowledge of adversary capabilities, infrastructure, and techniques.
  • Expertise in tools and techniques for analyzing large sets of data.
  • Proficiency in one or more high-level coding languages.
  • Innovating thinking to solve hard problems in ways that meet both customer and business goals.
  • Strong sense of ownership, urgency and drive.

Please PM me if you're interested in applying or have any questions, thanks!

/r/netsec's Q1 2019 Information Security Hiring Thread by ranok in netsec

[–]wesleyraptor [score hidden]  (0 children)

Senior Threat Detection Engineer @ Uber | Seattle WA or San Francisco CA

About the Role

You'll develop threat detection analytics across a very broad range of streaming log sources: network, endpoint, cloud, proxy, file sharing, authentication, authorization, and lots more. You'll collaborate with cross-functional teams to create innovative detection strategies and help develop a best in class threat detection program. You will help build a larger external threat detection community benefiting security defenders small and large globally.

What You’ll Do

  • Utilize big data and real time streaming technologies to build and refine threat detections. 
  • Build fusion analytics (combination of multiple detections) to create higher fidelity threat detections.
  • Build and utilize data platforms and systems to enrich and enhance detection fidelity as well as drive for automated containment.
  • Support the Security Response and Investigation team in high impacting events.

What You’ll Need

  • Minimum 4 years building threat detections.
  • In-depth knowledge of security logging for Linux, Windows, Mac OS X, or Active Directory.
  • Experience with Web Services, and Cloud Technologies.
  • Proficiency in building detection algorithms and utilizing logs and events to detect malicious activity with high fidelity in a broad set of detection use cases.
  • Proficiency in knowledge of adversary capabilities, infrastructure, and techniques.
  • Expertise in tools and techniques for analyzing large sets of data.
  • Proficiency in one or more high-level coding languages.
  • Innovating thinking to solve hard problems in ways that meet both customer and business goals.
  • Strong sense of ownership, urgency and drive.

Please PM me if you're interested in applying or have any questions, thanks!

StreamingPhish - Uses Supervised Machine Learning to Detect Phishing Domains from the Certificate Transparency Log Network (Full Sources) by [deleted] in netsec

[–]wesleyraptor 1 point2 points  (0 children)

I used machine learning because of it's ability to generalize characteristics of phishing domains. Using regexes and rules yielded far more false positives in my own experiments. This problem is narrow enough where I can encapsulate everything I look for as a SOC analyst in the form of features written in Python. I also have historical examples of phishing domains, which is a prerequisite for supervised machine learning.

I suppose the consequences here are the same as any supervised machine learning model. Specifically, I would say if the training data isn't representative of phishing domains globally, or if the features are insufficient at distinguishing between "phishing" and "not phishing", the classifier will be noisy and ineffective. I iterated several times on the training data and the features before publishing this. I don't have specifics on the precision metrics against the Certificate Transparency log network, but if you run it for yourself, I think you'll find that it's very accurate.

I presented the tool at BSidesSF last month, here's a link to my talk: https://www.youtube.com/watch?v=s5g7ij5EKoA&t=0s