Podcasts about Hft

Martins from Gravity Team joins Sam to discuss the evolution of market making in Web3. Starting as a prop trading desk in 2017, Gravity now trades 1% of all spot crypto volume, provides liquidity to token projects, and invests in Web3 infrastructure. Martins explains what founders should look for in a market maker, why stablecoins and payments are the next wave, and how Gravity is building a global trading platform with offices from Latvia to Singapore.Key Timestamps[00:00:00] Introduction: Sam introduces Martins from Gravity Team and outlines the episode's topics. [00:01:00] Origin Story: Martins shares how he discovered Bitcoin in 2017 and launched a trading desk. [00:02:00] Early Arbitrage: Realizing inefficiencies in the Thai crypto market sparked Gravity's beginning. [00:04:00] What is Gravity: Martins explains Gravity's transition from prop trading to market making and investments. [00:05:30] Differentiator: Gravity's strength lies in bridging Web2 ↔ Web3 through tech-driven liquidity services. [00:06:30] Market Making 101: Why projects need market makers and what they should look for. [00:09:00] One-Stop Shop: Gravity offers OTC, treasury, and even investment—beyond just spreads. [00:11:00] Regulation Shift: Why 2025 will mark the rise of stablecoins and Web3 payment rails. [00:14:00] Prop Trading Arm: Gravity still runs HFT strategies, not just market making for clients. [00:16:00] Investment Focus: Gravity's criteria for backing projects like Cookie3 and Usual Labs. [00:18:00] Strategic Synergies: Gravity looks to invest in other trading teams and infrastructure. [00:19:30] Key Trends: The stablecoin surge and institutional interest in payments. [00:20:30] Infrastructure Gap: Web3 needs a “Stripe for stablecoins” to reach mass adoption. [00:21:00] Biggest Lesson: Martins would've scaled faster and taken more risk early on. [00:23:00] Managing Risk: Why mission-aligned strategies beat short-term gains. [00:25:00] Final Ask: Gravity is hiring globally, investing in trading talent, and open to partnerships.Connecthttps://gravityteam.cohttps://www.linkedin.com/company/gravity-team-ltd/https://www.linkedin.com/in/martins-benkitis/DisclaimerNothing mentioned in this podcast is investment advice and please do your own research. Finally, it would mean a lot if you can leave a review of this podcast on Apple Podcasts or Spotify and share this podcast with a friend.Be a guest on the podcast or contact us - https://www.web3pod.xyz/

Jordi Alexander is the founder and CEO of Selini Capital. Selini Capital specializes in market making, high-frequency trading (HFT), and early-stage venture investing. He also co-hosts the wonderful crypto-focused podcast Steady Lads.In this interview with Bitcoin.com News' David Sencil, Jordi talks about his firm's activities in crypto, the impact of meme coins and the need for regulation, the potential of AI in crypto, the dynamic nature of the crypto market, and more.

Championship winning crew chief Jonathan Toney joins Davey Segal (6:42) for an insightful and feel-good conversation that spans over two decades with one single team. Toney explains how working with Sheldon Creed in the NASCAR Xfinity Series really isn't too different so far than it was working with Cole Custer, the rash of runner-up finishes Creed has scored, team chemistry at Haas Factory Team in this new iteration of the organization and more. Toney also dives into all the change the team has seen over his time, from Haas CNC Racing to Stewart-Haas Racing and now HFT. He explains why he's stayed loyal to the company despite numerous offers to go elsewhere over the years, why the Xfinity Series is the right place for him to be as a crew chief right now from a work/life balance perspective, the conversations leading up to 2023 before he finally got his chance to be a crew chief for the first time and how rewarding it was to win a championship with Custer in his first year at the helm. Toney also reminisces on the 2011 title run with Tony Stewart, what it was like to be a part of that team, some memorable tales from Homestead-Miami Speedway on championship weekend and more, including the vibe at the team when Stewart came in at first and his Hickory, North Carolina roots. Davey also recaps the tripleheader weekend of action at Martinsville Speedway and addresses the debacle of an Xfinity race that saw Sammy Smith penalized, previews Darlington Raceway and Papa Segal pays homage to Raymond Parks in this week's wayback segment.

Grip Strip Podcast Episode 260 discusses several recent racing events. In NASCAR's Tripleheader at Las Vegas, Josh Berry secured his first Cup Series win for the Wood Bros, marking their 101st victory. Different pit strategies affected the Hendrick team's performance, while pit road issues impacted drivers like Briscoe and Busch. Heading to Homestead, Justin Allgaier won the XFinity race, with notable teams like JRM, Gibbs, RCR, and HFT distancing themselves from others. Corey Heim achieved his first Truck Series victory, with Majeski and Ankrum winning stages and Corey Day earning his first pole. The F1 Australian Grand Prix saw Norris win amid rain. Key points included issues for Eclair and Lewis, poor rookie performances, and some rare mistakes from veterans. Other topics covered include GSP Roundup, IMSA 12 Hours of Sebring, FIA F2 & F3, Supercars, MotoGP in Argentina, NHRA Arizona Nationals, and previews for upcoming events in F1 and IndyCar.

One last Gold sponsor slot is available for the AI Engineer Summit in NYC. Our last round of invites is going out soon - apply here - If you are building AI agents or AI eng teams, this will be the single highest-signal conference of the year for you!While the world melts down over DeepSeek, few are talking about the OTHER notable group of former hedge fund traders who pivoted into AI and built a remarkably profitable consumer AI business with a tiny team with incredibly cracked engineering team — Chai Research. In short order they have:* Started a Chat AI company well before Noam Shazeer started Character AI, and outlasted his departure.* Crossed 1m DAU in 2.5 years - William updates us on the pod that they've hit 1.4m DAU now, another +40% from a few months ago. Revenue crossed >$22m. * Launched the Chaiverse model crowdsourcing platform - taking 3-4 week A/B testing cycles down to 3-4 hours, and deploying >100 models a week.While they're not paying million dollar salaries, you can tell they're doing pretty well for an 11 person startup:The Chai Recipe: Building infra for rapid evalsRemember how the central thesis of LMarena (formerly LMsys) is that the only comprehensive way to evaluate LLMs is to let users try them out and pick winners?At the core of Chai is a mobile app that looks like Character AI, but is actually the largest LLM A/B testing arena in the world, specialized on retaining chat users for Chai's usecases (therapy, assistant, roleplay, etc). It's basically what LMArena would be if taken very, very seriously at one company (with $1m in prizes to boot):Chai publishes occasional research on how they think about this, including talks at their Palo Alto office:William expands upon this in today's podcast (34 mins in):Fundamentally, the way I would describe it is when you're building anything in life, you need to be able to evaluate it. And through evaluation, you can iterate, we can look at benchmarks, and we can say the issues with benchmarks and why they may not generalize as well as one would hope in the challenges of working with them. But something that works incredibly well is getting feedback from humans. And so we built this thing where anyone can submit a model to our developer backend, and it gets put in front of 5000 users, and the users can rate it. And we can then have a really accurate ranking of like which model, or users finding more engaging or more entertaining. And it gets, you know, it's at this point now, where every day we're able to, I mean, we evaluate between 20 and 50 models, LLMs, every single day, right. So even though we've got only got a team of, say, five AI researchers, they're able to iterate a huge quantity of LLMs, right. So our team ships, let's just say minimum 100 LLMs a week is what we're able to iterate through. Now, before that moment in time, we might iterate through three a week, we might, you know, there was a time when even doing like five a month was a challenge, right? By being able to change the feedback loops to the point where it's not, let's launch these three models, let's do an A-B test, let's assign, let's do different cohorts, let's wait 30 days to see what the day 30 retention is, which is the kind of the, if you're doing an app, that's like A-B testing 101 would be, do a 30-day retention test, assign different treatments to different cohorts and come back in 30 days. So that's insanely slow. That's just, it's too slow. And so we were able to get that 30-day feedback loop all the way down to something like three hours.In Crowdsourcing the leap to Ten Trillion-Parameter AGI, William describes Chai's routing as a recommender system, which makes a lot more sense to us than previous pitches for model routing startups:William is notably counter-consensus in a lot of his AI product principles:* No streaming: Chats appear all at once to allow rejection sampling* No voice: Chai actually beat Character AI to introducing voice - but removed it after finding that it was far from a killer feature.* Blending: “Something that we love to do at Chai is blending, which is, you know, it's the simplest way to think about it is you're going to end up, and you're going to pretty quickly see you've got one model that's really smart, one model that's really funny. How do you get the user an experience that is both smart and funny? Well, just 50% of the requests, you can serve them the smart model, 50% of the requests, you serve them the funny model.” (that's it!)But chief above all is the recommender system.We also referenced Exa CEO Will Bryk's concept of SuperKnowlege:Full Video versionOn YouTube. please like and subscribe!Timestamps* 00:00:04 Introductions and background of William Beauchamp* 00:01:19 Origin story of Chai AI* 00:04:40 Transition from finance to AI* 00:11:36 Initial product development and idea maze for Chai* 00:16:29 User psychology and engagement with AI companions* 00:20:00 Origin of the Chai name* 00:22:01 Comparison with Character AI and funding challenges* 00:25:59 Chai's growth and user numbers* 00:34:53 Key inflection points in Chai's growth* 00:42:10 Multi-modality in AI companions and focus on user-generated content* 00:46:49 Chaiverse developer platform and model evaluation* 00:51:58 Views on AGI and the nature of AI intelligence* 00:57:14 Evaluation methods and human feedback in AI development* 01:02:01 Content creation and user experience in Chai* 01:04:49 Chai Grant program and company culture* 01:07:20 Inference optimization and compute costs* 01:09:37 Rejection sampling and reward models in AI generation* 01:11:48 Closing thoughts and recruitmentTranscriptAlessio [00:00:04]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO at Decibel, and today we're in the Chai AI office with my usual co-host, Swyx.swyx [00:00:14]: Hey, thanks for having us. It's rare that we get to get out of the office, so thanks for inviting us to your home. We're in the office of Chai with William Beauchamp. Yeah, that's right. You're founder of Chai AI, but previously, I think you're concurrently also running your fund?William [00:00:29]: Yep, so I was simultaneously running an algorithmic trading company, but I fortunately was able to kind of exit from that, I think just in Q3 last year. Yeah, congrats. Yeah, thanks.swyx [00:00:43]: So Chai has always been on my radar because, well, first of all, you do a lot of advertising, I guess, in the Bay Area, so it's working. Yep. And second of all, the reason I reached out to a mutual friend, Joyce, was because I'm just generally interested in the... ...consumer AI space, chat platforms in general. I think there's a lot of inference insights that we can get from that, as well as human psychology insights, kind of a weird blend of the two. And we also share a bit of a history as former finance people crossing over. I guess we can just kind of start it off with the origin story of Chai.William [00:01:19]: Why decide working on a consumer AI platform rather than B2B SaaS? So just quickly touching on the background in finance. Sure. Originally, I'm from... I'm from the UK, born in London. And I was fortunate enough to go study economics at Cambridge. And I graduated in 2012. And at that time, everyone in the UK and everyone on my course, HFT, quant trading was really the big thing. It was like the big wave that was happening. So there was a lot of opportunity in that space. And throughout college, I'd sort of played poker. So I'd, you know, I dabbled as a professional poker player. And I was able to accumulate this sort of, you know, say $100,000 through playing poker. And at the time, as my friends would go work at companies like ChangeStreet or Citadel, I kind of did the maths. And I just thought, well, maybe if I traded my own capital, I'd probably come out ahead. I'd make more money than just going to work at ChangeStreet.swyx [00:02:20]: With 100k base as capital?William [00:02:22]: Yes, yes. That's not a lot. Well, it depends what strategies you're doing. And, you know, there is an advantage. There's an advantage to being small, right? Because there are, if you have a 10... Strategies that don't work in size. Exactly, exactly. So if you have a fund of $10 million, if you find a little anomaly in the market that you might be able to make 100k a year from, that's a 1% return on your 10 million fund. If your fund is 100k, that's 100% return, right? So being small, in some sense, was an advantage. So started off, and the, taught myself Python, and machine learning was like the big thing as well. Machine learning had really, it was the first, you know, big time machine learning was being used for image recognition, neural networks come out, you get dropout. And, you know, so this, this was the big thing that's going on at the time. So I probably spent my first three years out of Cambridge, just building neural networks, building random forests to try and predict asset prices, right, and then trade that using my own money. And that went well. And, you know, if you if you start something, and it goes well, you You try and hire more people. And the first people that came to mind was the talented people I went to college with. And so I hired some friends. And that went well and hired some more. And eventually, I kind of ran out of friends to hire. And so that was when I formed the company. And from that point on, we had our ups and we had our downs. And that was a whole long story and journey in itself. But after doing that for about eight or nine years, on my 30th birthday, which was four years ago now, I kind of took a step back to just evaluate my life, right? This is what one does when one turns 30. You know, I just heard it. I hear you. And, you know, I looked at my 20s and I loved it. It was a really special time. I was really lucky and fortunate to have worked with this amazing team, been successful, had a lot of hard times. And through the hard times, learned wisdom and then a lot of success and, you know, was able to enjoy it. And so the company was making about five million pounds a year. And it was just me and a team of, say, 15, like, Oxford and Cambridge educated mathematicians and physicists. It was like the real dream that you'd have if you wanted to start a quant trading firm. It was like...swyx [00:04:40]: Your own, all your own money?William [00:04:41]: Yeah, exactly. It was all the team's own money. We had no customers complaining to us about issues. There's no investors, you know, saying, you know, they don't like the risk that we're taking. We could. We could really run the thing exactly as we wanted it. It's like Susquehanna or like Rintec. Yeah, exactly. Yeah. And they're the companies that we would kind of look towards as we were building that thing out. But on my 30th birthday, I look and I say, OK, great. This thing is making as much money as kind of anyone would really need. And I thought, well, what's going to happen if we keep going in this direction? And it was clear that we would never have a kind of a big, big impact on the world. We can enrich ourselves. We can make really good money. Everyone on the team would be paid very, very well. Presumably, I can make enough money to buy a yacht or something. But this stuff wasn't that important to me. And so I felt a sort of obligation that if you have this much talent and if you have a talented team, especially as a founder, you want to be putting all that talent towards a good use. I looked at the time of like getting into crypto and I had a really strong view on crypto, which was that as far as a gambling device. This is like the most fun form of gambling invented in like ever super fun, I thought as a way to evade monetary regulations and banking restrictions. I think it's also absolutely amazing. So it has two like killer use cases, not so much banking the unbanked, but everything else, but everything else to do with like the blockchain and, and you know, web, was it web 3.0 or web, you know, that I, that didn't, it didn't really make much sense. And so instead of going into crypto, which I thought, even if I was successful, I'd end up in a lot of trouble. I thought maybe it'd be better to build something that governments wouldn't have a problem with. I knew that LLMs were like a thing. I think opening. I had said they hadn't released GPT-3 yet, but they'd said GPT-3 is so powerful. We can't release it to the world or something. Was it GPT-2? And then I started interacting with, I think Google had open source, some language models. They weren't necessarily LLMs, but they, but they were. But yeah, exactly. So I was able to play around with, but nowadays so many people have interacted with the chat GPT, they get it, but it's like the first time you, you can just talk to a computer and it talks back. It's kind of a special moment and you know, everyone who's done that goes like, wow, this is how it should be. Right. It should be like, rather than having to type on Google and search, you should just be able to ask Google a question. When I saw that I read the literature, I kind of came across the scaling laws and I think even four years ago. All the pieces of the puzzle were there, right? Google had done this amazing research and published, you know, a lot of it. Open AI was still open. And so they'd published a lot of their research. And so you really could be fully informed on, on the state of AI and where it was going. And so at that point I was confident enough, it was worth a shot. I think LLMs are going to be the next big thing. And so that's the thing I want to be building in, in that space. And I thought what's the most impactful product I can possibly build. And I thought it should be a platform. So I myself love platforms. I think they're fantastic because they open up an ecosystem where anyone can contribute to it. Right. So if you think of a platform like a YouTube, instead of it being like a Hollywood situation where you have to, if you want to make a TV show, you have to convince Disney to give you the money to produce it instead, anyone in the world can post any content they want to YouTube. And if people want to view it, the algorithm is going to promote it. Nowadays. You can look at creators like Mr. Beast or Joe Rogan. They would have never have had that opportunity unless it was for this platform. Other ones like Twitter's a great one, right? But I would consider Wikipedia to be a platform where instead of the Britannica encyclopedia, which is this, it's like a monolithic, you get all the, the researchers together, you get all the data together and you combine it in this, in this one monolithic source. Instead. You have this distributed thing. You can say anyone can host their content on Wikipedia. Anyone can contribute to it. And anyone can maybe their contribution is they delete stuff. When I was hearing like the kind of the Sam Altman and kind of the, the Muskian perspective of AI, it was a very kind of monolithic thing. It was all about AI is basically a single thing, which is intelligence. Yeah. Yeah. The more intelligent, the more compute, the more intelligent, and the more and better AI researchers, the more intelligent, right? They would speak about it as a kind of erased, like who can get the most data, the most compute and the most researchers. And that would end up with the most intelligent AI. But I didn't believe in any of that. I thought that's like the total, like I thought that perspective is the perspective of someone who's never actually done machine learning. Because with machine learning, first of all, you see that the performance of the models follows an S curve. So it's not like it just goes off to infinity, right? And the, the S curve, it kind of plateaus around human level performance. And you can look at all the, all the machine learning that was going on in the 2010s, everything kind of plateaued around the human level performance. And we can think about the self-driving car promises, you know, how Elon Musk kept saying the self-driving car is going to happen next year, it's going to happen next, next year. Or you can look at the image recognition, the speech recognition. You can look at. All of these things, there was almost nothing that went superhuman, except for something like AlphaGo. And we can speak about why AlphaGo was able to go like super superhuman. So I thought the most likely thing was going to be this, I thought it's not going to be a monolithic thing. That's like an encyclopedia Britannica. I thought it must be a distributed thing. And I actually liked to look at the world of finance for what I think a mature machine learning ecosystem would look like. So, yeah. So finance is a machine learning ecosystem because all of these quant trading firms are running machine learning algorithms, but they're running it on a centralized platform like a marketplace. And it's not the case that there's one giant quant trading company of all the data and all the quant researchers and all the algorithms and compute, but instead they all specialize. So one will specialize on high frequency training. Another will specialize on mid frequency. Another one will specialize on equity. Another one will specialize. And I thought that's the way the world works. That's how it is. And so there must exist a platform where a small team can produce an AI for a unique purpose. And they can iterate and build the best thing for that, right? And so that was the vision for Chai. So we wanted to build a platform for LLMs.Alessio [00:11:36]: That's kind of the maybe inside versus contrarian view that led you to start the company. Yeah. And then what was maybe the initial idea maze? Because if somebody told you that was the Hugging Face founding story, people might believe it. It's kind of like a similar ethos behind it. How did you land on the product feature today? And maybe what were some of the ideas that you discarded that initially you thought about?William [00:11:58]: So the first thing we built, it was fundamentally an API. So nowadays people would describe it as like agents, right? But anyone could write a Python script. They could submit it to an API. They could send it to the Chai backend and we would then host this code and execute it. So that's like the developer side of the platform. On their Python script, the interface was essentially text in and text out. An example would be the very first bot that I created. I think it was a Reddit news bot. And so it would first, it would pull the popular news. Then it would prompt whatever, like I just use some external API for like Burr or GPT-2 or whatever. Like it was a very, very small thing. And then the user could talk to it. So you could say to the bot, hi bot, what's the news today? And it would say, this is the top stories. And you could chat with it. Now four years later, that's like perplexity or something. That's like the, right? But back then the models were first of all, like really, really dumb. You know, they had an IQ of like a four year old. And users, there really wasn't any demand or any PMF for interacting with the news. So then I was like, okay. Um. So let's make another one. And I made a bot, which was like, you could talk to it about a recipe. So you could say, I'm making eggs. Like I've got eggs in my fridge. What should I cook? And it'll say, you should make an omelet. Right. There was no PMF for that. No one used it. And so I just kept creating bots. And so every single night after work, I'd be like, okay, I like, we have AI, we have this platform. I can create any text in textile sort of agent and put it on the platform. And so we just create stuff night after night. And then all the coders I knew, I would say, yeah, this is what we're going to do. And then I would say to them, look, there's this platform. You can create any like chat AI. You should put it on. And you know, everyone's like, well, chatbots are super lame. We want absolutely nothing to do with your chatbot app. No one who knew Python wanted to build on it. I'm like trying to build all these bots and no consumers want to talk to any of them. And then my sister who at the time was like just finishing college or something, I said to her, I was like, if you want to learn Python, you should just submit a bot for my platform. And she, she built a therapy for me. And I was like, okay, cool. I'm going to build a therapist bot. And then the next day I checked the performance of the app and I'm like, oh my God, we've got 20 active users. And they spent, they spent like an average of 20 minutes on the app. I was like, oh my God, what, what bot were they speaking to for an average of 20 minutes? And I looked and it was the therapist bot. And I went, oh, this is where the PMF is. There was no demand for, for recipe help. There was no demand for news. There was no demand for dad jokes or pub quiz or fun facts or what they wanted was they wanted the therapist bot. the time I kind of reflected on that and I thought, well, if I want to consume news, the most fun thing, most fun way to consume news is like Twitter. It's not like the value of there being a back and forth, wasn't that high. Right. And I thought if I need help with a recipe, I actually just go like the New York times has a good recipe section, right? It's not actually that hard. And so I just thought the thing that AI is 10 X better at is a sort of a conversation right. That's not intrinsically informative, but it's more about an opportunity. You can say whatever you want. You're not going to get judged. If it's 3am, you don't have to wait for your friend to text back. It's like, it's immediate. They're going to reply immediately. You can say whatever you want. It's judgment-free and it's much more like a playground. It's much more like a fun experience. And you could see that if the AI gave a person a compliment, they would love it. It's much easier to get the AI to give you a compliment than a human. From that day on, I said, okay, I get it. Humans want to speak to like humans or human like entities and they want to have fun. And that was when I started to look less at platforms like Google. And I started to look more at platforms like Instagram. And I was trying to think about why do people use Instagram? And I could see that I think Chai was, was filling the same desire or the same drive. If you go on Instagram, typically you want to look at the faces of other humans, or you want to hear about other people's lives. So if it's like the rock is making himself pancakes on a cheese plate. You kind of feel a little bit like you're the rock's friend, or you're like having pancakes with him or something, right? But if you do it too much, you feel like you're sad and like a lonely person, but with AI, you can talk to it and tell it stories and tell you stories, and you can play with it for as long as you want. And you don't feel like you're like a sad, lonely person. You feel like you actually have a friend.Alessio [00:16:29]: And what, why is that? Do you have any insight on that from using it?William [00:16:33]: I think it's just the human psychology. I think it's just the idea that, with old school social media. You're just consuming passively, right? So you'll just swipe. If I'm watching TikTok, just like swipe and swipe and swipe. And even though I'm getting the dopamine of like watching an engaging video, there's this other thing that's building my head, which is like, I'm feeling lazier and lazier and lazier. And after a certain period of time, I'm like, man, I just wasted 40 minutes. I achieved nothing. But with AI, because you're interacting, you feel like you're, it's not like work, but you feel like you're participating and contributing to the thing. You don't feel like you're just. Consuming. So you don't have a sense of remorse basically. And you know, I think on the whole people, the way people talk about, try and interact with the AI, they speak about it in an incredibly positive sense. Like we get people who say they have eating disorders saying that the AI helps them with their eating disorders. People who say they're depressed, it helps them through like the rough patches. So I think there's something intrinsically healthy about interacting that TikTok and Instagram and YouTube doesn't quite tick. From that point on, it was about building more and more kind of like human centric AI for people to interact with. And I was like, okay, let's make a Kanye West bot, right? And then no one wanted to talk to the Kanye West bot. And I was like, ah, who's like a cool persona for teenagers to want to interact with. And I was like, I was trying to find the influencers and stuff like that, but no one cared. Like they didn't want to interact with the, yeah. And instead it was really just the special moment was when we said the realization that developers and software engineers aren't interested in building this sort of AI, but the consumers are right. And rather than me trying to guess every day, like what's the right bot to submit to the platform, why don't we just create the tools for the users to build it themselves? And so nowadays this is like the most obvious thing in the world, but when Chai first did it, it was not an obvious thing at all. Right. Right. So we took the API for let's just say it was, I think it was GPTJ, which was this 6 billion parameter open source transformer style LLM. We took GPTJ. We let users create the prompt. We let users select the image and we let users choose the name. And then that was the bot. And through that, they could shape the experience, right? So if they said this bot's going to be really mean, and it's going to be called like bully in the playground, right? That was like a whole category that I never would have guessed. Right. People love to fight. They love to have a disagreement, right? And then they would create, there'd be all these romantic archetypes that I didn't know existed. And so as the users could create the content that they wanted, that was when Chai was able to, to get this huge variety of content and rather than appealing to, you know, 1% of the population that I'd figured out what they wanted, you could appeal to a much, much broader thing. And so from that moment on, it was very, very crystal clear. It's like Chai, just as Instagram is this social media platform that lets people create images and upload images, videos and upload that, Chai was really about how can we let the users create this experience in AI and then share it and interact and search. So it's really, you know, I say it's like a platform for social AI.Alessio [00:20:00]: Where did the Chai name come from? Because you started the same path. I was like, is it character AI shortened? You started at the same time, so I was curious. The UK origin was like the second, the Chai.William [00:20:15]: We started way before character AI. And there's an interesting story that Chai's numbers were very, very strong, right? So I think in even 20, I think late 2022, was it late 2022 or maybe early 2023? Chai was like the number one AI app in the app store. So we would have something like 100,000 daily active users. And then one day we kind of saw there was this website. And we were like, oh, this website looks just like Chai. And it was the character AI website. And I think that nowadays it's, I think it's much more common knowledge that when they left Google with the funding, I think they knew what was the most trending, the number one app. And I think they sort of built that. Oh, you found the people.swyx [00:21:03]: You found the PMF for them.William [00:21:04]: We found the PMF for them. Exactly. Yeah. So I worked a year very, very hard. And then they, and then that was when I learned a lesson, which is that if you're VC backed and if, you know, so Chai, we'd kind of ran, we'd got to this point, I was the only person who'd invested. I'd invested maybe 2 million pounds in the business. And you know, from that, we were able to build this thing, get to say a hundred thousand daily active users. And then when character AI came along, the first version, we sort of laughed. We were like, oh man, this thing sucks. Like they don't know what they're building. They're building the wrong thing anyway, but then I saw, oh, they've raised a hundred million dollars. Oh, they've raised another hundred million dollars. And then our users started saying, oh guys, your AI sucks. Cause we were serving a 6 billion parameter model, right? How big was the model that character AI could afford to serve, right? So we would be spending, let's say we would spend a dollar per per user, right? Over the, the, you know, the entire lifetime.swyx [00:22:01]: A dollar per session, per chat, per month? No, no, no, no.William [00:22:04]: Let's say we'd get over the course of the year, we'd have a million users and we'd spend a million dollars on the AI throughout the year. Right. Like aggregated. Exactly. Exactly. Right. They could spend a hundred times that. So people would say, why is your AI much dumber than character AIs? And then I was like, oh, okay, I get it. This is like the Silicon Valley style, um, hyper scale business. And so, yeah, we moved to Silicon Valley and, uh, got some funding and iterated and built the flywheels. And, um, yeah, I, I'm very proud that we were able to compete with that. Right. So, and I think the reason we were able to do it was just customer obsession. And it's similar, I guess, to how deep seek have been able to produce such a compelling model when compared to someone like an open AI, right? So deep seek, you know, their latest, um, V2, yeah, they claim to have spent 5 million training it.swyx [00:22:57]: It may be a bit more, but, um, like, why are you making it? Why are you making such a big deal out of this? Yeah. There's an agenda there. Yeah. You brought up deep seek. So we have to ask you had a call with them.William [00:23:07]: We did. We did. We did. Um, let me think what to say about that. I think for one, they have an amazing story, right? So their background is again in finance.swyx [00:23:16]: They're the Chinese version of you. Exactly.William [00:23:18]: Well, there's a lot of similarities. Yes. Yes. I have a great affinity for companies which are like, um, founder led, customer obsessed and just try and build something great. And I think what deep seek have achieved. There's quite special is they've got this amazing inference engine. They've been able to reduce the size of the KV cash significantly. And then by being able to do that, they're able to significantly reduce their inference costs. And I think with kind of with AI, people get really focused on like the kind of the foundation model or like the model itself. And they sort of don't pay much attention to the inference. To give you an example with Chai, let's say a typical user session is 90 minutes, which is like, you know, is very, very long for comparison. Let's say the average session length on TikTok is 70 minutes. So people are spending a lot of time. And in that time they're able to send say 150 messages. That's a lot of completions, right? It's quite different from an open AI scenario where people might come in, they'll have a particular question in mind. And they'll ask like one question. And a few follow up questions, right? So because they're consuming, say 30 times as many requests for a chat, or a conversational experience, you've got to figure out how to how to get the right balance between the cost of that and the quality. And so, you know, I think with AI, it's always been the case that if you want a better experience, you can throw compute at the problem, right? So if you want a better model, you can just make it bigger. If you want it to remember better, give it a longer context. And now, what open AI is doing to great fanfare is with projection sampling, you can generate many candidates, right? And then with some sort of reward model or some sort of scoring system, you can serve the most promising of these many candidates. And so that's kind of scaling up on the inference time compute side of things. And so for us, it doesn't make sense to think of AI is just the absolute performance. So. But what we're seeing, it's like the MML you score or the, you know, any of these benchmarks that people like to look at, if you just get that score, it doesn't really tell tell you anything. Because it's really like progress is made by improving the performance per dollar. And so I think that's an area where deep seek have been able to form very, very well, surprisingly so. And so I'm very interested in what Lama four is going to look like. And if they're able to sort of match what deep seek have been able to achieve with this performance per dollar gain.Alessio [00:25:59]: Before we go into the inference, some of the deeper stuff, can you give people an overview of like some of the numbers? So I think last I checked, you have like 1.4 million daily active now. It's like over 22 million of revenue. So it's quite a business.William [00:26:12]: Yeah, I think we grew by a factor of, you know, users grew by a factor of three last year. Revenue over doubled. You know, it's very exciting. We're competing with some really big, really well funded companies. Character AI got this, I think it was almost a $3 billion valuation. And they have 5 million DAU is a number that I last heard. Torquay, which is a Chinese built app owned by a company called Minimax. They're incredibly well funded. And these companies didn't grow by a factor of three last year. Right. And so when you've got this company and this team that's able to keep building something that gets users excited, and they want to tell their friend about it, and then they want to come and they want to stick on the platform. I think that's very special. And so last year was a great year for the team. And yeah, I think the numbers reflect the hard work that we put in. And then fundamentally, the quality of the app, the quality of the content, the quality of the content, the quality of the content, the quality of the content, the quality of the content. AI is the quality of the experience that you have. You actually published your DAU growth chart, which is unusual. And I see some inflections. Like, it's not just a straight line. There's some things that actually inflect. Yes. What were the big ones? Cool. That's a great, great, great question. Let me think of a good answer. I'm basically looking to annotate this chart, which doesn't have annotations on it. Cool. The first thing I would say is this is, I think the most important thing to know about success is that success is born out of failures. Right? Through failures that we learn. You know, if you think something's a good idea, and you do and it works, great, but you didn't actually learn anything, because everything went exactly as you imagined. But if you have an idea, you think it's going to be good, you try it, and it fails. There's a gap between the reality and expectation. And that's an opportunity to learn. The flat periods, that's us learning. And then the up periods is that's us reaping the rewards of that. So I think the big, of the growth shot of just 2024, I think the first thing that really kind of put a dent in our growth was our backend. So we just reached this scale. So we'd, from day one, we'd built on top of Google's GCP, which is Google's cloud platform. And they were fantastic. We used them when we had one daily active user, and they worked pretty good all the way up till we had about 500,000. It was never the cheapest, but from an engineering perspective, man, that thing scaled insanely good. Like, not Vertex? Not Vertex. Like GKE, that kind of stuff? We use Firebase. So we use Firebase. I'm pretty sure we're the biggest user ever on Firebase. That's expensive. Yeah, we had calls with engineers, and they're like, we wouldn't recommend using this product beyond this point, and you're 3x over that. So we pushed Google to their absolute limits. You know, it was fantastic for us, because we could focus on the AI. We could focus on just adding as much value as possible. But then what happened was, after 500,000, just the thing, the way we were using it, and it would just, it wouldn't scale any further. And so we had a really, really painful, at least three-month period, as we kind of migrated between different services, figuring out, like, what requests do we want to keep on Firebase, and what ones do we want to move on to something else? And then, you know, making mistakes. And learning things the hard way. And then after about three months, we got that right. So that, we would then be able to scale to the 1.5 million DAE without any further issues from the GCP. But what happens is, if you have an outage, new users who go on your app experience a dysfunctional app, and then they're going to exit. And so your next day, the key metrics that the app stores track are going to be something like retention rates. And so your next day, the key metrics that the app stores track are going to be something like retention rates. Money spent, and the star, like, the rating that they give you. In the app store. In the app store, yeah. Tyranny. So if you're ranked top 50 in entertainment, you're going to acquire a certain rate of users organically. If you go in and have a bad experience, it's going to tank where you're positioned in the algorithm. And then it can take a long time to kind of earn your way back up, at least if you wanted to do it organically. If you throw money at it, you can jump to the top. And I could talk about that. But broadly speaking, if we look at 2024, the first kink in the graph was outages due to hitting 500k DAU. The backend didn't want to scale past that. So then we just had to do the engineering and build through it. Okay, so we built through that, and then we get a little bit of growth. And so, okay, that's feeling a little bit good. I think the next thing, I think it's, I'm not going to lie, I have a feeling that when Character AI got... I was thinking. I think so. I think... So the Character AI team fundamentally got acquired by Google. And I don't know what they changed in their business. I don't know if they dialed down that ad spend. Products don't change, right? Products just what it is. I don't think so. Yeah, I think the product is what it is. It's like maintenance mode. Yes. I think the issue that people, you know, some people may think this is an obvious fact, but running a business can be very competitive, right? Because other businesses can see what you're doing, and they can imitate you. And then there's this... There's this question of, if you've got one company that's spending $100,000 a day on advertising, and you've got another company that's spending zero, if you consider market share, and if you're considering new users which are entering the market, the guy that's spending $100,000 a day is going to be getting 90% of those new users. And so I have a suspicion that when the founders of Character AI left, they dialed down their spending on user acquisition. And I think that kind of gave oxygen to like the other apps. And so Chai was able to then start growing again in a really healthy fashion. I think that's kind of like the second thing. I think a third thing is we've really built a great data flywheel. Like the AI team sort of perfected their flywheel, I would say, in end of Q2. And I could speak about that at length. But fundamentally, the way I would describe it is when you're building anything in life, you need to be able to evaluate it. And through evaluation, you can iterate, we can look at benchmarks, and we can say the issues with benchmarks and why they may not generalize as well as one would hope in the challenges of working with them. But something that works incredibly well is getting feedback from humans. And so we built this thing where anyone can submit a model to our developer backend, and it gets put in front of 5000 users, and the users can rate it. And we can then have a really accurate ranking of like which model, or users finding more engaging or more entertaining. And it gets, you know, it's at this point now, where every day we're able to, I mean, we evaluate between 20 and 50 models, LLMs, every single day, right. So even though we've got only got a team of, say, five AI researchers, they're able to iterate a huge quantity of LLMs, right. So our team ships, let's just say minimum 100 LLMs a week is what we're able to iterate through. Now, before that moment in time, we might iterate through three a week, we might, you know, there was a time when even doing like five a month was a challenge, right? By being able to change the feedback loops to the point where it's not, let's launch these three models, let's do an A-B test, let's assign, let's do different cohorts, let's wait 30 days to see what the day 30 retention is, which is the kind of the, if you're doing an app, that's like A-B testing 101 would be, do a 30-day retention test, assign different treatments to different cohorts and come back in 30 days. So that's insanely slow. That's just, it's too slow. And so we were able to get that 30-day feedback loop all the way down to something like three hours. And when we did that, we could really, really, really perfect techniques like DPO, fine tuning, prompt engineering, blending, rejection sampling, training a reward model, right, really successfully, like boom, boom, boom, boom, boom. And so I think in Q3 and Q4, we got, the amount of AI improvements we got was like astounding. It was getting to the point, I thought like how much more, how much more edge is there to be had here? But the team just could keep going and going and going. That was like number three for the inflection point.swyx [00:34:53]: There's a fourth?William [00:34:54]: The important thing about the third one is if you go on our Reddit or you talk to users of AI, there's like a clear date. It's like somewhere in October or something. The users, they flipped. Before October, the users... The users would say character AI is better than you, for the most part. Then from October onwards, they would say, wow, you guys are better than character AI. And that was like a really clear positive signal that we'd sort of done it. And I think people, you can't cheat consumers. You can't trick them. You can't b******t them. They know, right? If you're going to spend 90 minutes on a platform, and with apps, there's the barriers to switching is pretty low. Like you can try character AI, you can't cheat consumers. You can't cheat them. You can't cheat them. You can't cheat AI for a day. If you get bored, you can try Chai. If you get bored of Chai, you can go back to character. So the users, the loyalty is not strong, right? What keeps them on the app is the experience. If you deliver a better experience, they're going to stay and they can tell. So that was the fourth one was we were fortunate enough to get this hire. He was hired one really talented engineer. And then they said, oh, at my last company, we had a head of growth. He was really, really good. And he was the head of growth for ByteDance for two years. Would you like to speak to him? And I was like, yes. Yes, I think I would. And so I spoke to him. And he just blew me away with what he knew about user acquisition. You know, it was like a 3D chessswyx [00:36:21]: sort of thing. You know, as much as, as I know about AI. Like ByteDance as in TikTok US. Yes.William [00:36:26]: Not ByteDance as other stuff. Yep. He was interviewing us as we were interviewing him. Right. And so pick up options. Yeah, exactly. And so he was kind of looking at our metrics. And he was like, I saw him get really excited when he said, guys, you've got a million daily active users and you've done no advertising. I said, correct. And he was like, that's unheard of. He's like, I've never heard of anyone doing that. And then he started looking at our metrics. And he was like, if you've got all of this organically, if you start spending money, this is going to be very exciting. I was like, let's give it a go. So then he came in, we've just started ramping up the user acquisition. So that looks like spending, you know, let's say we're spending, we started spending $20,000 a day, it looked very promising than 20,000. Right now we're spending $40,000 a day on user acquisition. That's still only half of what like character AI or talkie may be spending. But from that, it's sort of, we were growing at a rate of maybe say, 2x a year. And that got us growing at a rate of 3x a year. So I'm growing, I'm evolving more and more to like a Silicon Valley style hyper growth, like, you know, you build something decent, and then you canswyx [00:37:33]: slap on a huge... You did the important thing, you did the product first.William [00:37:36]: Of course, but then you can slap on like, like the rocket or the jet engine or something, which is just this cash in, you pour in as much cash, you buy a lot of ads, and your growth is faster.swyx [00:37:48]: Not to, you know, I'm just kind of curious what's working right now versus what surprisinglyWilliam [00:37:52]: doesn't work. Oh, there's a long, long list of surprising stuff that doesn't work. Yeah. The surprising thing, like the most surprising thing, what doesn't work is almost everything doesn't work. That's what's surprising. And I'll give you an example. So like a year and a half ago, I was working at a company, we were super excited by audio. I was like, audio is going to be the next killer feature, we have to get in the app. And I want to be the first. So everything Chai does, I want us to be the first. We may not be the company that's strongest at execution, but we can always be theswyx [00:38:22]: most innovative. Interesting. Right? So we can... You're pretty strong at execution.William [00:38:26]: We're much stronger, we're much stronger. A lot of the reason we're here is because we were first. If we launched today, it'd be so hard to get the traction. Because it's like to get the flywheel, to get the users, to build a product people are excited about. If you're first, people are naturally excited about it. But if you're fifth or 10th, man, you've got to beswyx [00:38:46]: insanely good at execution. So you were first with voice? We were first. We were first. I only knowWilliam [00:38:51]: when character launched voice. They launched it, I think they launched it at least nine months after us. Okay. Okay. But the team worked so hard for it. At the time we did it, latency is a huge problem. Cost is a huge problem. Getting the right quality of the voice is a huge problem. Right? Then there's this user interface and getting the right user experience. Because you don't just want it to start blurting out. Right? You want to kind of activate it. But then you don't have to keep pressing a button every single time. There's a lot that goes into getting a really smooth audio experience. So we went ahead, we invested the three months, we built it all. And then when we did the A-B test, there was like, no change in any of the numbers. And I was like, this can't be right, there must be a bug. And we spent like a week just checking everything, checking again, checking again. And it was like, the users just did not care. And it was something like only 10 or 15% of users even click the button to like, they wanted to engage the audio. And they would only use it for 10 or 15% of the time. So if you do the math, if it's just like something that one in seven people use it for one seventh of their time. You've changed like 2% of the experience. So even if that that 2% of the time is like insanely good, it doesn't translate much when you look at the retention, when you look at the engagement, and when you look at the monetization rates. So audio did not have a big impact. I'm pretty big on audio. But yeah, I like it too. But it's, you know, so a lot of the stuff which I do, I'm a big, you can have a theory. And you resist. Yeah. Exactly, exactly. So I think if you want to make audio work, it has to be a unique, compelling, exciting experience that they can't have anywhere else.swyx [00:40:37]: It could be your models, which just weren't good enough.William [00:40:39]: No, no, no, they were great. Oh, yeah, they were very good. it was like, it was kind of like just the, you know, if you listen to like an audible or Kindle, or something like, you just hear this voice. And it's like, you don't go like, wow, this is this is special, right? It's like a convenience thing. But the idea is that if you can, if Chai is the only platform, like, let's say you have a Mr. Beast, and YouTube is the only platform you can use to make audio work, then you can watch a Mr. Beast video. And it's the most engaging, fun video that you want to watch, you'll go to a YouTube. And so it's like for audio, you can't just put the audio on there. And people go, oh, yeah, it's like 2% better. Or like, 5% of users think it's 20% better, right? It has to be something that the majority of people, for the majority of the experience, go like, wow, this is a big deal. That's the features you need to be shipping. If it's not going to appeal to the majority of people, for the majority of the experience, and it's not a big deal, it's not going to move you. Cool. So you killed it. I don't see it anymore. Yep. So I love this. The longer, it's kind of cheesy, I guess, but the longer I've been working at Chai, and I think the team agrees with this, all the platitudes, at least I thought they were platitudes, that you would get from like the Steve Jobs, which is like, build something insanely great, right? Or be maniacally focused, or, you know, the most important thing is saying no to, not to work on. All of these sort of lessons, they just are like painfully true. They're painfully true. So now I'm just like, everything I say, I'm either quoting Steve Jobs or Zuckerberg. I'm like, guys, move fast and break free.swyx [00:42:10]: You've jumped the Apollo to cool it now.William [00:42:12]: Yeah, it's just so, everything they said is so, so true. The turtle neck. Yeah, yeah, yeah. Everything is so true.swyx [00:42:18]: This last question on my side, and I want to pass this to Alessio, is on just, just multi-modality in general. This actually comes from Justine Moore from A16Z, who's a friend of ours. And a lot of people are trying to do voice image video for AI companions. Yes. You just said voice didn't work. Yep. What would make you revisit?William [00:42:36]: So Steve Jobs, he was very, listen, he was very, very clear on this. There's a habit of engineers who, once they've got some cool technology, they want to find a way to package up the cool technology and sell it to consumers, right? That does not work. So you're free to try and build a startup where you've got your cool tech and you want to find someone to sell it to. That's not what we do at Chai. At Chai, we start with the consumer. What does the consumer want? What is their problem? And how do we solve it? So right now, the number one problems for the users, it's not the audio. That's not the number one problem. It's not the image generation either. That's not their problem either. The number one problem for users in AI is this. All the AI is being generated by middle-aged men in Silicon Valley, right? That's all the content. You're interacting with this AI. You're speaking to it for 90 minutes on average. It's being trained by middle-aged men. The guys out there, they're out there. They're talking to you. They're talking to you. They're like, oh, what should the AI say in this situation, right? What's funny, right? What's cool? What's boring? What's entertaining? That's not the way it should be. The way it should be is that the users should be creating the AI, right? And so the way I speak about it is this. Chai, we have this AI engine in which sits atop a thin layer of UGC. So the thin layer of UGC is absolutely essential, right? It's just prompts. But it's just prompts. It's just an image. It's just a name. It's like we've done 1% of what we could do. So we need to keep thickening up that layer of UGC. It must be the case that the users can train the AI. And if reinforcement learning is powerful and important, they have to be able to do that. And so it's got to be the case that there exists, you know, I say to the team, just as Mr. Beast is able to spend 100 million a year or whatever it is on his production company, and he's got a team building the content, the Mr. Beast company is able to spend 100 million a year on his production company. And he's got a team building the content, which then he shares on the YouTube platform. Until there's a team that's earning 100 million a year or spending 100 million on the content that they're producing for the Chai platform, we're not finished, right? So that's the problem. That's what we're excited to build. And getting too caught up in the tech, I think is a fool's errand. It does not work.Alessio [00:44:52]: As an aside, I saw the Beast Games thing on Amazon Prime. It's not doing well. And I'mswyx [00:44:56]: curious. It's kind of like, I mean, the audience reading is high. The run-to-meet-all sucks, but the audience reading is high.Alessio [00:45:02]: But it's not like in the top 10. I saw it dropped off of like the... Oh, okay. Yeah, that one I don't know. I'm curious, like, you know, it's kind of like similar content, but different platform. And then going back to like, some of what you were saying is like, you know, people come to ChaiWilliam [00:45:13]: expecting some type of content. Yeah, I think it's something that's interesting to discuss is like, is moats. And what is the moat? And so, you know, if you look at a platform like YouTube, the moat, I think is in first is really is in the ecosystem. And the ecosystem, is comprised of you have the content creators, you have the users, the consumers, and then you have the algorithms. And so this, this creates a sort of a flywheel where the algorithms are able to be trained on the users, and the users data, the recommend systems can then feed information to the content creators. So Mr. Beast, he knows which thumbnail does the best. He knows the first 10 seconds of the video has to be this particular way. And so his content is super optimized for the YouTube platform. So that's why it doesn't do well on Amazon. If he wants to do well on Amazon, how many videos has he created on the YouTube platform? By thousands, 10s of 1000s, I guess, he needs to get those iterations in on the Amazon. So at Chai, I think it's all about how can we get the most compelling, rich user generated content, stick that on top of the AI engine, the recommender systems, in such that we get this beautiful data flywheel, more users, better recommendations, more creative, more content, more users.Alessio [00:46:34]: You mentioned the algorithm, you have this idea of the Chaiverse on Chai, and you have your own kind of like LMSYS-like ELO system. Yeah, what are things that your models optimize for, like your users optimize for, and maybe talk about how you build it, how people submit models?William [00:46:49]: So Chaiverse is what I would describe as a developer platform. More often when we're speaking about Chai, we're thinking about the Chai app. And the Chai app is really this product for consumers. And so consumers can come on the Chai app, they can come on the Chai app, they can come on the Chai app, they can interact with our AI, and they can interact with other UGC. And it's really just these kind of bots. And it's a thin layer of UGC. Okay. Our mission is not to just have a very thin layer of UGC. Our mission is to have as much UGC as possible. So we must have, I don't want people at Chai training the AI. I want people, not middle aged men, building AI. I want everyone building the AI, as many people building the AI as possible. Okay, so what we built was we built Chaiverse. And Chaiverse is kind of, it's kind of like a prototype, is the way to think about it. And it started with this, this observation that, well, how many models get submitted into Hugging Face a day? It's hundreds, it's hundreds, right? So there's hundreds of LLMs submitted each day. Now consider that, what does it take to build an LLM? It takes a lot of work, actually. It's like someone devoted several hours of compute, several hours of their time, prepared a data set, launched it, ran it, evaluated it, submitted it, right? So there's a lot of, there's a lot of, there's a lot of work that's going into that. So what we did was we said, well, why can't we host their models for them and serve them to users? And then what would that look like? The first issue is, well, how do you know if a model is good or not? Like, we don't want to serve users the crappy models, right? So what we would do is we would, I love the LMSYS style. I think it's really cool. It's really simple. It's a very intuitive thing, which is you simply present the users with two completions. You can say, look, this is from model one. This is from model two. This is from model three. This is from model A. This is from model B, which is better. And so if someone submits a model to Chaiverse, what we do is we spin up a GPU. We download the model. We're going to now host that model on this GPU. And we're going to start routing traffic to it. And we're going to send, we think it takes about 5,000 completions to get an accurate signal. That's roughly what LMSYS does. And from that, we're able to get an accurate ranking. And we're able to get an accurate ranking. And we're able to get an accurate ranking of which models are people finding entertaining and which models are not entertaining. If you look at the bottom 80%, they'll suck. You can just disregard them. They totally suck. Then when you get the top 20%, you know you've got a decent model, but you can break it down into more nuance. There might be one that's really descriptive. There might be one that's got a lot of personality to it. There might be one that's really illogical. Then the question is, well, what do you do with these top models? From that, you can do more sophisticated things. You can try and do like a routing thing where you say for a given user request, we're going to try and predict which of these end models that users enjoy the most. That turns out to be pretty expensive and not a huge source of like edge or improvement. Something that we love to do at Chai is blending, which is, you know, it's the simplest way to think about it is you're going to end up, and you're going to pretty quickly see you've got one model that's really smart, one model that's really funny. How do you get the user an experience that is both smart and funny? Well, just 50% of the requests, you can serve them the smart model, 50% of the requests, you serve them the funny model. Just a random 50%? Just a random, yeah. And then... That's blending? That's blending. You can do more sophisticated things on top of that, as in all things in life, but the 80-20 solution, if you just do that, you get a pretty powerful effect out of the gate. Random number generator. I think it's like the robustness of randomness. Random is a very powerful optimization technique, and it's a very robust thing. So you can explore a lot of the space very efficiently. There's one thing that's really, really important to share, and this is the most exciting thing for me, is after you do the ranking, you get an ELO score, and you can track a user's first join date, the first date they submit a model to Chaiverse, they almost always get a terrible ELO, right? So let's say the first submission they get an ELO of 1,100 or 1,000 or something, and you can see that they iterate and they iterate and iterate, and it will be like, no improvement, no improvement, no improvement, and then boom. Do you give them any data, or do you have to come up with this themselves? We do, we do, we do, we do. We try and strike a balance between giving them data that's very useful, you've got to be compliant with GDPR, which is like, you have to work very hard to preserve the privacy of users of your app. So we try to give them as much signal as possible, to be helpful. The minimum is we're just going to give you a score, right? That's the minimum. But that alone is people can optimize a score pretty well, because they're able to come up with theories, submit it, does it work? No. A new theory, does it work? No. And then boom, as soon as they figure something out, they keep it, and then they iterate, and then boom,Alessio [00:51:46]: they figure something out, and they keep it. Last year, you had this post on your blog, cross-sourcing the lead to the 10 trillion parameter, AGI, and you call it a mixture of experts, recommenders. Yep. Any insights?William [00:51:58]: Updated thoughts, 12 months later? I think the odds, the timeline for AGI has certainly been pushed out, right? Now, this is in, I'm a controversial person, I don't know, like, I just think... You don't believe in scaling laws, you think AGI is further away. I think it's an S-curve. I think everything's an S-curve. And I think that the models have proven to just be far worse at reasoning than people sort of thought. And I think whenever I hear people talk about LLMs as reasoning engines, I sort of cringe a bit. I don't think that's what they are. I think of them more as like a simulator. I think of them as like a, right? So they get trained to predict the next most likely token. It's like a physics simulation engine. So you get these like games where you can like construct a bridge, and you drop a car down, and then it predicts what should happen. And that's really what LLMs are doing. It's not so much that they're reasoning, it's more that they're just doing the most likely thing. So fundamentally, the ability for people to add in intelligence, I think is very limited. What most people would consider intelligence, I think the AI is not a crowdsourcing problem, right? Now with Wikipedia, Wikipedia crowdsources knowledge. It doesn't crowdsource intelligence. So it's a subtle distinction. AI is fantastic at knowledge. I think it's weak at intelligence. And a lot, it's easy to conflate the two because if you ask it a question and it gives you, you know, if you said, who was the seventh president of the United States, and it gives you the correct answer, I'd say, well, I don't know the answer to that. And you can conflate that with intelligence. But really, that's a question of knowledge. And knowledge is really this thing about saying, how can I store all of this information? And then how can I retrieve something that's relevant? Okay, they're fantastic at that. They're fantastic at storing knowledge and retrieving the relevant knowledge. They're superior to humans in that regard. And so I think we need to come up for a new word. How does one describe AI should contain more knowledge than any individual human? It should be more accessible than any individual human. That's a very powerful thing. That's superswyx [00:54:07]: powerful. But what words do we use to describe that? We had a previous guest on Exa AI that does search. And he tried to coin super knowledge as the opposite of super intelligence.William [00:54:20]: Exactly. I think super knowledge is a more accurate word for it.swyx [00:54:24]: You can store more things than any human can.William [00:54:26]: And you can retrieve it better than any human can as well. And I think it's those two things combined that's special. I think that thing will exist. That thing can be built. And I think you can start with something that's entertaining and fun. And I think, I often think it's like, look, it's going to be a 20 year journey. And we're in like, year four, or it's like the web. And this is like 1998 or something. You know, you've got a long, long way to go before the Amazon.coms are like these huge, multi trillion dollar businesses that every single person uses every day. And so AI today is very simplistic. And it's fundamentally the way we're using it, the flywheels, and this ability for how can everyone contribute to it to really magnify the value that it brings. Right now, like, I think it's a bit sad. It's like, right now you have big labs, I'm going to pick on open AI. And they kind of go to like these human labelers. And they say, we're going to pay you to just label this like subset of questions that we want to get a really high quality data set, then we're going to get like our own computers that are really powerful. And that's kind of like the thing. For me, it's so much like Encyclopedia Britannica. It's like insane. All the people that were interested in blockchain, it's like, well, this is this is what needs to be decentralized, you need to decentralize that thing. Because if you distribute it, people can generate way more data in a distributed fashion, way more, right? You need the incentive. Yeah, of course. Yeah. But I mean, the, the, that's kind of the exciting thing about Wikipedia was it's this understanding, like the incentives, you don't need money to incentivize people. You don't need dog coins. No. Sometimes, sometimes people get the satisfaction fro

Happy Sunday and welcome back to The Gwart Show. Today we are joined by Jordi Alexander, founder of Selini Capital, discusses HyperLiquid's success, the future of AI in crypto, market making strategies, and shares insights on Ethereum's potential changes and Solana's surprising performance. Follow our guests on Twitter:@gametheorizing 00:00 Start 00:56 Jordi intro 01:23 Selini Capital 03:11 Prop shop vs VC money 04:35 What is HFT? 05:38 Which DeFi protocols for Selini Capital? 06:35 Volume of Hyperliquid 07:59 Marketmaking CeFi vs DeFi? 09:02 Why no Wall Steert marker makers? 10:19 How good is Hyperliquid? 13:30 Will Hyperliquid survive? 16:18 Small validator set 18:59 Hype token 21:30 ETH vibes these days? 23:06 Does the Ethereum Foundation even matter? 25:00 Does SOL flip ETH? 27:17 AI agents 30:08 Agents vs Bots 33:08 AI tokens 34:46 AI for endless promotion 36:43 Pigeon dox 40:31 DeSci

Налоговые ловушки Х5, инвестиции не для всех и реальная инфляция. В этом выпуске вас ждет батл в студии «Финама». Спорим по поводу синтетических инструментов и сокращения уборщиц в Газпроме, обсуждаем нюансы налогообложения, жалобы инвесторов, цены в вашем магазине у дома и алгофонд в юанях. Кто топит за простого инвестора, а кто способен приумножить и без того большие капиталы, а главное − какую позицию занимаете вы?======ПОЛЕЗНЫЕ ССЫЛКИ====== Инвестиции для всех

Extrait : « … un paradoxe vivant, car, rends-toi compte, durant toute sa carrière, il a été banni des radios, rarement invité à la télé, jamais tu ne l'as entendu en musique de fond chez Zara ou dans un Courte-paille, seule la presse régionale lui a parfois fait les yeux doux, et pourtant, pratiquement chacun de ses 18 albums a été disque d'or ou de platine, il en a vendu des millions d'exemplaires, un paradoxe te dis-je. Et ça, il le doit à ses milliers de fans de la première heure, ils lui vouent amour et admiration éternelle, ils remplissent tous ses concerts à ras bord, quelle que soit la taille de la salle. Du reste, plus que des fans, ce sont des cerbères, des gardiens du temple qui, si toutefois tu avoues t'en tamponner le coquillard de HFT, sont tout disposés à t'arracher les yeux et les testicules, et à tout remettre à l'envers … »Pour commenter les épisodes, tu peux le faire sur ton appli de podcasts habituelle, c'est toujours bon pour l'audience. Mais également sur le site web dédié, il y a une section Le Bar, ouverte 24/24, pour causer du podcast ou de musique en général, je t'y attends avec impatience. Enfin, si tu souhaites me soumettre une chanson, c'est aussi sur le site web que ça se passe. Pour soutenir Good Morning Music et Gros Naze :1. Abonne-toi2. Laisse-moi un avis et 5 étoiles sur Apple Podcasts, ou Spotify et Podcast Addict3. Partage ton épisode préféré à 3 personnes autour de toi. Ou 3.000 si tu connais plein de monde.Good Morning Music Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

Talus network is enabling AI smart agents to come onchain and coordinate with each other to automate much of the tedious workflows that you may not want to bother with. It's a race to build the best onchain AI trader—and it's winner take all. We're at the precipice of onchain AI today just like the advent of HFT bots swarming tradfi in the 2000s.#blockchaintech #technews #web3news #interchainfm #cryptocurrency #cryptopodcasts #ai #aiagents #depinprojects

Biggest challenges with crypto market making? Who's winning the crypto HFT race? Why the UAE is the new global crypto hub? Alchemy Family Founder Anthony Agoshkov joins to discuss some of the latest trends in quant trading, market making, and the evolving role of AI in crypto, as well as some of the unique networking opportunities provided by Alchemy Family and their philosophy behind creating meaningful connections in the space. A must-listen episode if you're interested in the evolution of crypto algo trading and the future of HFT in crypto.   

Over the past five years, Elliot Caswell has let the BBC follow him as he searched for his first job after leaving college, but so far he's faced nothing but barriers.That frustration has coincided with the publication of a House of Lords report into how the transition from education to work could be improved for young disabled people. Stephen Veevers, CEO of HFT, an organisation which helps disabled people prepare for employment, gives his thoughts on the report and offers some success stories too.Plus, when Norwegian gamer Mats Steen died aged 25 his parents feared their son had lived a lonely life as a result of Duchenne muscular dystrophy. But, when they posted a final update on his blog they were inundated with messages.Mats, it turned out, had lived a full and vibrant life online. Emma speaks with his parents – Trude and Robert – and two of his online friends - Xenia and her autistic son Mikkel - who learned to connect with each other with Mats's help. Now a Netflix movie is set to be released on 25 October, about Mat's extraordinary, hidden life.Presented by Emma Tracey Produced by Daniel Gordon and Emma Tracey Edited by Beth Rose

Broc Peden and Addison Harbour co-host this surgical episode of HFT to kick off the 2024 season. This college football season is sure to be action packed as the sport moves to a 12 team playoff. The team break down the season and give their expert week one college football picks.

Benedict Brady from Ergonia and Doug Colkitt from Ambient Finance join The Gwart Show, covering various aspects of decentralized finance (DeFi), high frequency trading (HFT), and automated market makers (AMMs).  Follow our guests on Twitter: @0xdoug @bqbrady Follow Gwart and the podcast: @gwartygwart @blockspacepod Timestamps: 00:00:30 Guest Introductions 00:02:13 HFT, AMMs and CLOBs 00:9:10 Improving the Ethereum L1 for apps  00:18:10 zkEVMs, validator and rollups  00:22:021 Trading on AMMs 00:24:55 Why AMMs on Solana? 00:35:03 MEV  00:40:00 Is ~any~ MEV illegal? 00:47:16 Everything will run on AMMs?  00:52:55 Block builder centralization 01:00:40 What if tradfi built blocks? 01:02:08 Solana orderflow is a goldmine 01:05:30 Stakeweight QS 01:10:35 Memecoins

Join Bryan from quantlabs.net.com as he delves into the intriguing world of a Quant Researcher at Citadel Securities in Miami. Discover the daily routine of Will Softhold, a PhD physicist from the University of Cambridge, who now thrives in the high-frequency trading (HFT) environment.   Join our new community at https://www.quantlabsnet.com/registration From early morning swims to late-night coding sessions, this episode provides a detailed look at the challenges and rewards of working in one of the most prestigious financial firms. Learn about the importance of supervisory roles, the dynamics of team collaboration, and the critical role of systematic trading algorithms in the FX market. A Day in the Life of a Quant Researcher at Citadel Securities: Decoding the Algorithmic Magic (quantlabsnet.com) Whether you're an aspiring quant or just curious about the inner workings of finance, this episode offers valuable insights and practical advice. Tune in to understand what it takes to succeed in the fast-paced world of quantitative research.

In this episode, Brian from quantlabs.net delves into a fascinating article from eFinancialCareers.com about Better Hand Financial Technologies (BHFT), a high-frequency trading firm based in Dubai. BHFT has been making waves with its remote work model and strategic hires from top competitors.   New website at Home | Quantlabs (quantlabsnet.com) Key hires include Ilya Malinovsky, former head of HFT at Tower Research Capital and Credit Suisse, and Ruslan Reskipov, previously head of derivatives and algo trading at Renaissance Capital. The firm has also attracted talent like Rook Teeter, former managing director at KCG Holding and Tower Research. BHFT stands out by using Rust instead of the traditional C++ for its trading systems, attracting a diverse team that includes chess champions, martial arts winners, and world-class math and science talents. Despite the remote work setup, the company boasts a friendly, multicultural team with a modern tech stack. The episode also covers BHFT's current job listings, including roles for senior quant traders and researchers, with a notable focus on the Chinese and Indian trading markets. Brian wraps up by sharing updates about his new website, quantlabsnet.com, and invites listeners to join the new community group.

Good day, good day, everybody. Brian here from wantlabs.net. Today is May 28th. I'm going to have some big news coming down the pipe soon. So keep your eyes and ears and all that peeled out for it. Anyways, I came across another interesting article. I do like this BetterSystemTrader.com podcast. It's pretty good. They did a posting called 10 Insights from the Man Who Solved the Market. This is referring back to the book Jim Simons Medallion Fund. It's the book put out by Gregory Zuckerman a few years ago. So this guy, Andrew Swan Scott, read it and thought he'd provide his insights. So let's go through these.   Get your free trading tech books here books2 - QUANTLABS.NET The first one is originality matters. Ignore conventional wisdom about the markets. Innovate and explore unique trading strategies and ideas. Look at the market differently from the herd. I totally agree with that because when you look at the markets, there's usually a star performer out there if you go out and dig for that among all the different data sources. And it could be a long-term trending strategy that you could use based on that. A good one in the past was USD Japanese Yen. Another one that was lesser known was USD Turkish Lira or Euro Turkish Lira. They did really good over the years, but now central bankers have stepped in and taken away those opportunities. Now, I don't know about the yen. That could continue, but that could lead to something that could be the next big catalyst to take us all down. But at the end of the day, looking for those sort of things, finding them, that's what gives you what they call trading edge for sure. Number two, collaborative success. Partner with talented individuals. Foster a collaborative environment to enhance problem-solving and innovations. Again, I cannot stress this. We're hoping to have an interview with Ernie Chan and other people that have kind of trailblazed the whole quant trading space, specifically for those that are coming from the retail trading space. Because as we know, to do true HFT, high-frequency trading, and true low-level quant research and that, people can kind of do it, but you have to have a fairly big account to take advantage of it to really do the pay-to-play thing directly right on the exchanges. As a retail trader going through a retail trading broker, that's pretty hard to do. So you have to find people that are kind of in the same area as you in terms of your account size, maybe your technical chops, as well as your mathematical experience. For myself, I guess I can share this now is that I will be moving my site over to a better technology, to Wix. I'm hoping to build out a better group community through that. That's part of the WIC's features, I guess. I did have an amazing group a few years ago. They're still around. I just want to bring more people together so that they can engage with each other behind the scenes. All at a paywall, but membership privileges have its costs. So that's where you get the collaborative success. So that's what I'm hoping to bring to the table. Okay, number three, embrace scientific rigor. Apply a rigorous scientific approach in model Model testing and validation ensuring robustness and statistical significance. This isn't really HFT, but to make life easier for a retail trader, I find using tools, very popular, very in-depth tools like TradingView helps you here. You can see instantly without going through any of the wonky backtesting packages out there, frameworks. works. Out of the box, you're ready to go. When you're working with an open source trading strategy, if you build your own, buy one, lease one, whatever on TradingView, you get the ability to see it. What's its profit potential? What is its profit factor? Which is another way of saying, if I'm going to put a dollar in, how much can I expect to get return from that via the profit factor? These are right there out of the box. So when you have these sort of instantly viewable to you from a high level, it makes your life a lot easier and a lot less stressful. A lot of people want to build and roll their own solutions. I'm not against that. But when you get up to my age, you will start to see how valuable time will be. That's all I could tell you. Efficient capital allocation. Okay. Develop systems to optimize capital allocation across various strategies to maximize returns and manage risk. One thing I can mention here, a lot of the boutique hedge funds, boutique AGFT shops, a lot of them will trade in the space of options. I don't think you can operate under a really successful trading strategy with a $1,000, $5,000 account. You need to have something fairly significant to play those kind of, I don't want to say gains, but kind of strategy capabilities. You need probably $20,000, $25,000 because you have to add all your premium and all that fun stuff. And that's why one of the reasons why ETFs are popular because they're not really risky. You can dream like a stock and there's no margin requirement any of that so these sort of things matter but if you do do the options training you can do very well if you get something that actually actually works okay let's talk number six leverage with caution use leverage strategically to amplify your returns now most people i talk to who keep their account in check from early blowing up. One of the things they do is they use no more than six times. I've seen in crypto years ago, finance would have 100 times and so on. And that's high risk when things are not going your way, especially when the assets are in a consolidation phase or downward spiraling or falling knife environment. You don't want to use leverage there. If it's a long-term trend and it's doing well, then yeah, add your leverage. Some people may go up to 12, let's say, but no more will not go more than six. Data quality is key. Prioritize the acquisition and cleaning all extensive data sets for accurate model development and testing. Again, this is where I like TradingView. you. You can get all kinds of data sources. You'll get, let's say if you're using free data sources like Yahoo Finance, expect to get the gaps. The gaps are going to be deadweight to you and they're going to be hard to work with. So you got to use good quality data. Obviously, there's lots of sources and most of them, I think all of them are going to be paid. And if you're not willing to do that, you're going to, I don't know, if you're just playing around and experimenting, fine. But if you ever want to get serious, you got to pay for the data. And as I say what you get what you pay for is what you get so if you're going to get free stuff I expect to have a low quality. Experiences and results that's all I can tell you they have the ability to enable you to have. Enable you to have decent success there. But obviously, you got to find your proper strategy. Short-term focus, number eight, concentrate on short-term trading opportunities where predictive power is stronger and more actionable. I know one successful trader, he most likely will hear this, he's trading on one minute. Now, he's probably successful there based upon his experience, based upon his history, and he's probably blown up a lot of accounts. So the short focus can help, but this is, again, for a guy who's built and defined high-frequency trading. And obviously, sub-second, sub-minute matters if you're successful. If you're coming from the free trade world, you want to work with basically. Basically long-term, daily, four-hour. When I was writing for Seeking Alpha, they wouldn't accept articles that timeframes less than four hours. So that's just to give you a scenario depending upon who you are and where you're coming from in terms of knowledge in the world of trading. Execution matters. Invest in technology and processes for efficient trade execution to minimize market impact and slippage the one i've come up with between trading view and the auto trading that's how trading view defines it with something like traders post is exceptional i could be sleeping at night and it trades and it's 100 fully synced that's all i can tell you there unless you try it you're not going to know except luck's rule there is a lot of luck i'm not going to deny that to you and yeah so we'll leave it at that you know basically it's like betting in a casino where if you're betting. If you're betting in Vegas, well, that means you may be riding on luck. You may have to do 10 little trades, take 10 little losses, but maybe that 11th trade may be the big one that you're seeking. But how often does that happen? No one knows. If you have a strategy that may be able to predict that, fine. And then you can work off of that for luck. So basically what this guy was saying, the author of this article here, Peter, this Andrew guy said, what Simmons, Simons and his team achieved is remarkable. We'll probably never see anything like this in our lifetime. Who knows? Computers may come up and do stuff and they may be doing it already through the AI, but we just don't know about it because these are not publicly known. They're not going to go on the internet and say, hey, look at me. I bought a Ferrari because I made an amazing AI trading solution. And if they are, they're just probably BSing. And if they're not showing they're creating journal to achieve that, well, there's a problem there. We may never know the details, but there are enough hints to guide all traders. Very true. Yeah. And then there's interview posted here with the guy, Gregory Zuckerman. Also, I think I can say about this is that Simons was very reluctant to do the interview and to do the book. And apparently, there are some parts in the book that Simons didn't want to get revealed. But this Zuckerman still went ahead and published it. And I don't think Simons was too happy about it. I wanted to leave that as well. And I'll talk to you soon. Have a good day. Remember, get on our training books, quantlabs.net slash books. That may change soon. So do it while you can. Over and out. Thank you.

Join Brian from quantlabs.net as he delves into the complexities of high-frequency trading (HFT) and provides valuable insights for software engineers aiming to transition into this competitive field. This episode covers the essential technical and trading concepts you need to grasp, from order books to matching engines, and explores the specialized knowledge required to excel in HFT firms. Get our free trading tech books books2 - QUANTLABS.NET Breaking into High-Frequency Trading: Essential Career Insights - QUANTLABS.NET Brian discusses the journey of transitioning into HFT, misconceptions about the field, and the key areas of expertise needed. Topics include order book dynamics, pricing engines, option pricing, and the intricacies of protocols like FIX. Additionally, he shares practical tips for impressing in interviews and thriving in the fast-paced environment of HFT shops. Don't miss this comprehensive guide to navigating the world of high-frequency trading and setting yourself up for success in one of the most lucrative sectors in finance.

G'day everybody! In this episode, we delve into the nitty-gritty and complex world of high-frequency trading (HFT). Through a detailed analysis of a recent technical post on quant.stackexchange.com, we explore the critical role of market-makers in HFT, their requirements, strategies, and response times in dynamic market conditions. Get on our Email Newsletter while getting some free tech trading books books2 - QUANTLABS.NET   Market makers operate swiftly, adjusting their quotes to reflect real-time market dynamics. The time taken for sending quotes and receiving a hit in HFT could be around 1.5 milliseconds, which exemplifies the speed with which these players operate. The actual figures might vary, depending on the sophistication and speed of the underlying infrastructure. Talk about this on our Discord When a quote is hit, the response time isn't instant because of the computational processes required. Market makers must analyse the complexity of their algorithms, prevailing market conditions, and improve their technical framework for better efficiency and reduced latency. Regulatory and operational considerations also play a part in their decision making.   High-Frequency Trading: Unveiling the Market Maker's Dance - QUANTLABS.NET   Needing no cooling-off period nor limitations, the speed at which market makers replace their quotes depends on the computational capability of their system. They rapidly adjust their quotes in high-frequency trading. If multiple quotes are hit simultaneously, they reassess their strategies, potentially widening their spreads or dynamically adjusting their pricing to manage their risk exposure. Don't be fooled into thinking it's all about speed, though. High-frequency trading also necessitates technologically advanced data processing capabilities, refined algorithms, sophisticated risk management models, and constant vigilance of regulatory compliance. Market makers must be continually looking for strategic enhancements in each of these areas to maintain a competative edge. All things said, this is a fascinating peek into the complex world of HFT and market making. If these concepts intrigue you, perhaps it's worth delving deeper to get a better understanding of what's happening behind the scenes in the world of finance.

In this enlightening episode, join our host, Brian from quantlabs.net as he delves deep into the exciting world of latency and high frequency trading, with a primary focus on C++. This episode promises to pull you deep into the nuts and bolts of performance optimization, providing a comprehensive exploration of latency-sensitive applications and how they impact the Memory Subsystem. It's a world of code, geekiness, and high-level trading intelligence that promises to inform and captivate any tech-minded enthusiast.   Get out FREE trading tech books here    Follow along as Brian navigates a revealing article found on the CPP subreddit, labeled 'Latency Sensitive Applications and the Memory Subsystem: Keeping the Data in the Cache - Johnny's Software Lab'. From exploring the advantages of hardware-based HFT systems and the importance of effective cache management techniques - the conversation spans critical areas of software and hardware performance optimization in trading environments. Not just an overview, Brian also takes a deep dive into coding techniques designed to minimize latency and maximize performance. You'll gain insight into coding examples, discussions around the use of hardware and software cache warming, the impact of different caching approaches on performance, and why a customized, well-articulated architecture is a must-have in achieving low latency. Get on our Discord to talk about this  Beyond just coding, Brian also looks at the broader landscape, touching on the importance and role of various hardware in reducing latency and even extends the conversation to the cloud space, touching on the challenges and opportunities in leveraging Cloud vNICs. Demystifying Latency: A C++ Deep Dive into High-Frequency Trading - QUANTLABS.NET Whether you're interested in quant, trading, or improving your understanding of C++ techniques for optimizing latency, this podcast promises to leave you with a wealth of knowledge and insights that will help you navigate the world of high-frequency trading. Come along for this engaging journey through the intersection of streamlining software performance and trading technology.

This podcast by Brian from QuantLabs.net dives into the world of High-Frequency Trading (HFT) and the role of KDB+ in this domain. KDB+: Developed by KX, KDB+ is a high-performance software used for data handling in HFT. It excels at working with large time-series datasets and is known for its: Efficiency Uncomplicated code structure Python integration Cloud interoperability KDB+ and Ticker Plant: Ticker Plant can be used to feed data into KDB+, making it a popular combination for HFT applications. Cost: A major barrier to entry for KDB+ is its high cost, estimated to be around $100,000 per year. This limits its use primarily to the well-funded fintech industry. Future Potential: Despite the cost, KDB+ remains a dominant player due to its performance and features. Brian discusses the potential of KDB+ to evolve even further with advancements in technology and AI. Call to Action: Brian invites listeners to join his Discord community to discuss KDB+ and related topics. Exploring the Power of KDB+ for High-Frequency Trading and Data Analytics - QUANTLABS.NET Welcome everyone, Brian from QuantLabs.net is here with another intriguing episode. In this episode, Brian dives deep into the world of High-Frequency Trading and advanced data analytics, emphasizing the role of KBD+ in it. Touching upon a previous episode of the podcast on the same topic, he delves into the intricacies of software like the Ticker Plant. Brian explains that KDB+, produced by KX, is a high-standard enterprise-level software known for its efficient data handling capabilities. As he deconstructs the workings of this software, he highlights how Ticker Plant could write all incoming records to a log file, pushing all data to the RDP. This software, although widely unknown, is an industry standard. The focus then shifts to the price aspect of KDB+, and the barriers it poses for widespread market adoption. Discussing a comment on Hacker News, Brian brings to light the exorbitant cost of KDB+, estimated at around a hundred thousand dollars per year. As per the comment, software is extremely lucrative and can only be afforded by the fintech industry. Despite the cost, KDB+ comes highly praised. A comment Brian brings up highlights KDB+ as an elegant solution for running analytics on large data sets, especially those with time series. Known for its performance, uncomplicated code structure, Python integration, and cloud interoperability, KDB+ has been a dominant player in electronic trading analytics on Wall Street for over 20 years. In conclusion, Brian discusses the potential of KDB+, which opens avenues for potential business opportunities. He emphasizes how innovations in technology and AI could lead to exploring beyond the limitations imposed by network cards. Following his exploration of KDB+ and its potential, he invites listeners to join his Discord community and actively engage in stimulating discussions.   Join our Discord for quant trading and programming news https://discord.gg/k29hRUXdk2 Get our free trading tech books here books2 – QUANTLABS.NET Know what I trade on my Substack Quantlabs Substack | Substack

Welcome to another episode of the Quantlabs podcast. In this episode, Brian delves into the exciting and ever-evolving world of Quant development as it applies to high-frequency trading (HFT). High-frequency trading is a complex and nuanced topic, but Brian brings it down to earth with practical insights and thoughtful commentary. He sheds light on the importance of retail trading bots, the innovative use of Discord and Telegram as trading platforms, and the rise of unique Cryptocurrency trading techniques.   Exploring High-Frequency Trading and Data Processing with DPDK - QUANTLABS.NET One of the highlights of the episode is when Brian introduces us to the concept of Data Plane Development Kit (DPDK)—an intriguing open-source project that take centre stage in discussions around rapid packet processing. This segment not only clarifies what DPDK is and why it matters for high-frequency trading, but also explores the potential of using this library for high-speed data processing in large scale applications. A deep dive into the nuts and bolts of the open-source project, its licensing, users, functionality, and supported hardware follows. The episode ends with an invitation for listeners to engage further on these topics via Discord or delving deeper with materials available on Substack. For those interested, Brian also generously offers access to free tech books he's written, available at quantlabs.net. This podcast provides a fountain of knowledge for anyone interested in the intersection of Quant development and high-frequency trading, regardless of their level of expertise.   Join our Discord for quant trading and programming news https://discord.gg/k29hRUXdk2 Get our free trading tech books here books2 – QUANTLABS.NET Know what I trade on my Substack Quantlabs Substack | Substack

Welcome to the latest episode of our podcast where host Brian from quantlabs.net speaks on the fascinating world of High Frequency Trading (HFT). A shout rests on his lips for his audience as he appreciates their unwavering support that's taken his podcast to new heights on both YouTube and various podcast networks. Brian retreats into the two areas his listeners crave - High Frequency Trading and Trading Systems. Dive into the insights shared by Naveen Kumar Suppala, a Software Development Leader and Principal Global Quant, Research and Dev professional based out of India. Suppala brings to the table a series of articles filled with the golden nuggets of wisdom on key subjects such as the memory layout, HFT C++ core techniques, low latency programming, market order vs limit order backtesting, kernel bypassing in HFT, and more. A goldmine of valuable information awaits as we traverse through these various areas. The journey doesn't stop here. Brace yourselves as we navigate through a Github repository filled with the intricate concepts of the Operating System (OS). Find out how some HFT shops customize the Linux kernel and build systems on top of these tailored operating systems. The repository covers diverse areas such as hardware basics, the layers involved in an OS, task scheduling, multitasking issues, synchronization objects, and much more aimed at enhancing your understanding of the OS nuances. Join our Discord for quant trading and programming news https://discord.gg/k29hRUXdk2 Get our free trading tech books here books2 – QUANTLABS.NET Know what I trade on my Substack Quantlabs Substack | Substack A call to action from Brian invites you to engage in insightful discussions over on his Discord channel, receive updates about his trading on Substack, and explore the trading books via quantlabs.net. Dive into the episode to amplify your knowledge on High Frequency Trading and Trading Systems!   A Deep Dive into Techniques, Tools, and Systems - QUANTLABS.NET linkedin.com/in/naveensuppala/recent-activity/articles/   github.com/braboj/tutorial-os?tab=readme-ov-file#operating-systems

In this podcast episode from QuantLabs, Brian delves into the complex world of high-frequency trading (HFT), with a particular focus on trends noticed in major companies such as Jane Street and IMC. One company had the highest paying internship. As HFT continues to evolve, it's crucial to stay updated with the latest developments and understand what these industry leaders are looking for.   Get some free trading Tech PDF books  books2 - QUANTLABS.NET Among the topics discussed, HFT competitions feature most prominently, particularly IMC's Prosperity Trading Competition. With an attractive cash prize, the contest attracts over 10,000 applicants and has seen participants go on to land high-paying internships and jobs in leading trading firms. Brian points out the impressive figures earned by successful competitors and shares insights into what these companies look for in prospective employees. Join our Discord for quant trading and programming news https://discord.gg/k29hRUXdk2 The episode also explores the roles and requirements of HFT companies in-depth. For instance, IMC, a high-focus trading and market-making firm based in Amsterdam, is discussed comprehensively - their ongoing projects, hiring process, as well as what it takes to secure a job there. Listeners seeking career advancement will benefit from Brian's detailed job description analyses and the insider tips offered. Don't forget to subscribe to my Substack  for more trading tips and strategies! Let's keep learning and growing together. https://quantlabs.substack.com/ Moreover, Brian also shares some pointers on learning the requisite tech skills, such as working with FPGA filters via MATLAB. The podcast culminates in an insightful discussion on the predictive models leveraged for informed trading improvements, unveiling some of the strategies employed by the industry's best. Overall, the episode serves as an invaluable guide for those interested in HFT and its relevant careers, offering an informative and enriching deep dive into this complex, fast-paced industry.   https://www.efinancialcareers.com/news/imc-trading-competition-internships https://www.levels.fyi/internships/ https://prosperity.imc.com/

Welcome to a fascinating discussion with Brian from Quantlabs.net. Recorded on March 3rd, 2024, Brian provides an insightful overview of his current focus on high-frequency trading (HFT) and quantitative trading strategies. The conversation also covers the interaction of these strategies with TradingView, an accessible, low-cost platform offering access to future market-level data.   Get your free trading tech book pdf books2 – QUANTLABS.NET   Join our Discord for quant trading and programming news https://discord.gg/k29hRUXdk2 One of the key issues highlighted by Brian is TradingView's use of PineScript, a scripting language that can pose a barrier to people unfamiliar with coding. However, he points out that accessing and learning from the over 100,000 scripts available via this platform can be an invaluable resource for those willing to delve into it. He discloses that these scripts may be reverse-engineered and applied to a live trading system using higher-level languages like C++, Java or Python, feeding into his broader mission of providing an automated trading solution not currently offered by TradingView. Brian then delves into the world of automated trading, elaborating on why TradingView does not directly support this method of trading due to challenges associated with regional regulations. By contrast, he offers a solution that uses TradingView data alongside a Python listener to generate signals, allowing for automated trading via brokers or crypto exchanges not supported by TradingView. My TradingView account is Trader bryandowningqln — Trading Ideas & Charts — TradingView Don't forget to subscribe to my Substack  for more trading tips and strategies! Let's keep learning and growing together. https://quantlabs.substack.com/   However, Brian is keen to highlight that this system does not fit the mold of true HFT, which would necessitate access to an exchange and hardware and software infrastructure that can cost thousands of dollars and only becomes economically viable for serious traders with sizeable trading accounts due to the high margin requirements. Subsequently, he indicates that the focus of his YouTube channel, QuantLabs, will center on topics such as trading strategies, programming techniques, and integration with Python, TradingView, C++ or Java. Brian also outlines his current campaign to diversify his reach across various platforms, such as Substack and his new Python Django-based website, driven by his focus on achieving maximum returns through social media platforms and generating more email opt-ins. Lastly, he addresses potential concerns associated with YouTube and their censorship rules by signaling his intention to explore alternative platforms while continuing to use YouTube for specific aspects of his outreach. You can join Brian's journey in automated trading and HFT by subscribing to his YouTube channel at QuantLabs, opting into his email list at Quantlabs.net/books or exploring his TradingView profile, Brian Downing QLN.

Welcome to this episode with Brian from QuantLabs.net. It was an emotional start as we paid tribute to Brian's late dog, Open Source Cody. The highlight of this episode is the exploration of legacy coding projects, notably C++ and Python files, as well as PDF training resources related to High-Frequency Trading (HFT) and quantitative analysis, dating back to 1998. Brian expressed the possibilities of consolidating this vast data trove and leveraging it to build a private Language Library Model (LLM). Get your free trading tech book pdf books2 – QUANTLABS.NET   Join our Discord for quant trading and programming news https://discord.gg/k29hRUXdk2   The episode further delved into the rose-tinted future of programming. Reminiscent of a prediction from NVIDIA's CEO founder, Brian questioned the longevity of programming, envisioning a future proliferated by AI. Despite this, he acknowledged that there would always be a need for human intervention, especially with simulation in a trading environment. Amid the uncertainties, Brian emphasized the importance of preserving coding projects, strategy ideas, and research papers. With increasing advancements in AI and LLM, these resources could be valuable if used strategically. For instance, they could offer an edge to private teams working on similar projects. However, releasing them to the public would be inappropriate, according to Brian. Consistent with the diversification of his resources, Brian announced exciting developments at QuantLabs. He shared that he would be implementing a trading idea from a follower based in Puerto Rico. After building his new website, Brian expressed the intent to focus more on C++ and Python for research and potentially leverage AI. Don't forget to subscribe to my Substack  for more trading tips and strategies! Let's keep learning and growing together. https://quantlabs.substack.com/ For ease of trade execution, Brian revealed his platform of choice as TradingView. He detailed how he had found a workaround, using TradingView's PineScript strategy to issue signals to their external environment, thereby allowing trades to be executed from it. While acknowledging some issues with this setup, Brian expressed hope that moving it to the cloud could resolve them. The episode concluded with discussions on the growth of Brian's Discord and his speculation on the distortive impact of the US's massive debt on global markets. Despite the challenges of an unpredictable trading environment, individuals and HFT shops must strategize accordingly, he advised.

I have stumbled upon some old high-frequency trading (HFT) source code and research papers that have been hidden away for years. This treasure trove of information could provide valuable insights and knowledge that could benefit our trading strategies.   Get your free trading tech book pdf books2 – QUANTLABS.NET   Join our Discord for quant trading and programming news https://discord.gg/k29hRUXdk2   I believe that by revisiting and studying these old materials, we could potentially uncover new techniques and approaches that could give us a competitive edge in the market. Imagine the possibilities of combining cutting-edge technology with tried and tested methods from the past.   I am reaching out to you because I believe that together, we can leverage this opportunity to enhance our trading capabilities and stay ahead of the curve. I invite you to join me in this journey of rediscovery and exploration, as we delve into the world of HFT source code and research papers.   If you are interested in turning this into a collaborative effort, I encourage you to consider pursuing this further by enrolling in a Master of Laws (LLM) program specializing in financial technology and algorithmic trading. This could provide us with the necessary tools and knowledge to fully harness the potential of this rediscovered treasure.   Let's seize this opportunity and take our trading to new heights. Are you in?   Looking forward to hearing your thoughts and potentially embarking on this exciting adventure together.   P.S. Don't forget to subscribe to my Substack and YouTube channel for more trading tips and strategies! Let's keep learning and growing together.   https://quantlabs.substack.com/  

Since the 1930's research using human fetal tissue has been used in numerous scientific and medical advances that have saved millions of lives, including the development of vaccines and treatments for diseases. Despite its substantial contribution to medicine and science, significant public debate and misinformation persists surrounding the ethical use of human fetal tissue in biomedical research.  The ISSCR, led by its Public Policy Committee, have been tireless champions and advocates for sound science policy across the globe. This includes advocacy for fetal tissue research and working to inform policymakers and the public on the vast medical applications and advances that have, and will, come from the use of HFT in biomedical research. Towards that end, the ISSCR and the Lawrence Goldstein Policy Fellows have authored a recent paper in Stem Cell Reports entitled, Human Fetal Tissue is Critical for Biomedical Research. HostMartin Pera, Editor-in-Chief, Stem Cell Reports and The Jackson Laboratory@martinperaJAX GuestsLawrence (Larry) Goldstein, PhD, is a Distinguished Professor Emeritus, Department of Cellular and Molecular Medicine and Director Emeritus of Stem Cell Program at the University of California, San Diego. He is the namesake for the ISSCR's Lawrence Goldstein Science Policy Fellowship which is training the next generation of scientists to impact public policy.  Tyler Lamb, JD, is the ISSCR's Director of Policy and leads the Society's global policy efforts.  Tamra Lysaght, PhD, University of Sydney, Australia, is an Associate Professor in Health Ethics.Justin Brumbaugh, PhD, University of Colorado Boulder, USA, is an Assistant Professor in Molecular Cellular & Developmental Biology. Supporting Materials Drs. Brumbaugh, Lysaght, and Goldstein, along with Brian Aguado, are authors of the recently published paper, Human Fetal Tissue is Critical for Biomedical Research.  About Stem Cell ReportsStem Cell Reports is the Open Access journal of the International Society for Stem Cell Research (ISSCR) for communicating basic discoveries in stem cell research, in addition to translational and clinical studies. Stem Cell Reports focuses on original research with conceptual or practical advances that are of broad interest to stem cell biologists and clinicians.Twitter: @StemCellReportsAbout ISSCRWith more than 4,800 members from 75+ countries, the International Society for Stem Cell Research (@ISSCR) is the preeminent global, cross-disciplinary, science-based organization dedicated to stem cell research and its translation to the clinic. The ISSCR mission is to promote excellence in stem cell science and applications to human health.ISSCR StaffKeith Alm, Chief Executive OfficerYvonne Fisher, Managing Editor, Stem Cell ReportsKym Kilbourne, Director of Media and Strategic CommunicationsJack Mosher, Scientific AdvisorVoice WorkBen Snitkoff

We chat with Liquidity Goblin about all things crypto. He shares his insights into market making, HFT, and other strategies he deploys in his firm. --- Send in a voice message: https://podcasters.spotify.com/pod/show/riskbiscuits/message

Come along for the strange world of HFT.    All of our wonderful links are here on the linktree: https://linktr.ee/allts   A nice description brought to you from AI:   High-frequency trading (HFT) is a type of algorithmic trading characterized by the use of powerful computers and sophisticated algorithms to execute a large number of orders at extremely high speeds. The goal of high-frequency trading is to take advantage of small price fluctuations in financial markets, often holding positions for a very short duration, ranging from milliseconds to microseconds. Key features of high-frequency trading include: Speed: HFT relies on the speed of execution. These systems can analyze market conditions and execute trades in fractions of a second, allowing them to react to market changes faster than human traders. Algorithmic Trading: HFT algorithms use complex mathematical models and statistical arbitrage strategies to identify and exploit short-term trading opportunities. These algorithms can analyze vast amounts of market data in real-time to make rapid trading decisions. Volume: HFT systems often execute a large number of orders in a short period, aiming to profit from the cumulative impact of these trades on stock prices. Low Latency: To achieve high speeds, HFT systems invest heavily in reducing latency, the time it takes for a trading system to receive market data, process it, and send out orders. This involves using high-performance hardware and co-locating servers in proximity to stock exchange data centers. Market Making: Some HFT firms act as market makers, continuously quoting buy and sell prices for financial instruments. They profit from the bid-ask spread and aim to capture small price movements. While high-frequency trading has been praised for improving market liquidity and efficiency, it has also been a subject of controversy. Critics argue that HFT can contribute to market instability, create unfair advantages for well-funded firms, and potentially lead to market manipulation. Regulatory bodies in various countries have implemented measures to address some of these concerns and ensure a fair and transparent market environment.

We're on Patreon now! Find us at https://www.patreon.com/AudioUnleashed Buy-now links for products mentioned herein (As Amazon Associates, we may earn a small cut from qualifying purchases): Master Handbook of Acoustics, Sixth Edition 6th Edition by F. Alton Everest: https://amzn.to/4aixQbO Sound Reproduction: The Acoustics and Psychoacoustics of Loudspeakers and Rooms by Floyd Toole: https://amzn.to/3ZlTLtE Loudspeaker Design Cookbook 8th Edition: Volume 1 by Vance Dickason: https://amzn.to/42pdwk0 This week, Brent and Dennis talk about one of the best values in hi-fi, dig into the physics of something that doesn't seem likely to work, and debate whether Spotify is ripping off small artists. Further Reading:  “WiiM AMP review: flying high on value for money” by John Darko: https://darko.audio/2023/11/wiim-amp-review-flying-high-on-value-for-money/ Synergistic Research HFT: https://www.synergisticresearch.com/acoustics/passive/hft/ “HFT & EFQ” by Steve Marsh: https://6moons.com/audioreviews2/synergistic/1.html “Spotify made £56m profit, but has decided not to pay smaller artists like me” by Damon Krukowski: https://amp.theguardian.com/commentisfree/2023/nov/30/spotify-smaller-artists-wrapped-indie-musicians “Headphone Handbook” on Twenty Thousand Hertz: https://www.20k.org/episodes/headphonehandbook SVS Audiophile Happy Hour with Audio Unleashed Podcast Hosts - Episode #71: https://www.youtube.com/watch?v=Kpd8PYJXSP0&t=3132s&ab_channel=SVS  

Anant Jatia, the founder and CEO of Greenland Investment Management, joins Moritz Seibert for a commodities-focused conversation. In this episode, Anant explains how Greenland developed from currency market making to what today is predominantly an HFT-driven commodities arbitrage fund. Anant has a detailed understanding of the workings of physical commodity markets and focuses Greenland's trades on location and substation arbitrage trades. Moritz and Anant discuss several exemplary trades in detail, including sizing and portfolio implementation, and focus on the tail exposures which could materialize while the trade is active. This is a detailed and somewhat technical episode and a must-listen for anyone interested in commodities arbitrage trading.-----EXCEPTIONAL RESOURCE: Find Out How to Build a Safer & Better Performing Portfolio using this FREE NEW Portfolio Builder Tool-----Follow Niels on Twitter, LinkedIn, YouTube or via the TTU website.IT's TRUE ? – most CIO's read 50+ books each year – get your FREE copy of the Ultimate Guide to the Best Investment Books ever written here.And you can get a free copy of my latest book “The Many Flavors of Trend Following” here.Learn more about the Trend Barometer here.Send your questions to info@toptradersunplugged.comAnd please share this episode with a like-minded friend and leave an honest Rating & Review on iTunes or Spotify so more people can discover the podcast.Follow Moritz on Twitter.Follow Anant on LinkedInEpisode TimeStamps: 02:09 - Introduction to Anant Jatia06:39 - Anant's experience from working at AQR08:46 - How did Anant's firm evolve over time?10:44 - How difficult is High-Frequency Trading?14:33 - What is their current trading strategy?17:07 - An example of one of their trades21:17 - Staying away from the physical side23:33 - Tracking the market and executing trades27:18 - Managing the cost of trades29:58 - How did they get through the Covid19 period?34:37 - How many markets are...

The biggest lie in quant finance is that you can do everything required at the highest level of a model. This includes data engineering, model development (quant research), implementation (quant dev), and be the end user (often a trader or operations team). This full stack quant does exist however they are never the best at everything and they often work in a very specific niche. Examples of a niche could be, auto loans, equity HFT, commodities, securitization, or etc. The systems, languages, math, stats, financial theory, and business uses are all different.I would encourage people to focus on one main area (data engineer, quant, quant dev, or business user) and dig as deep as possible. There are a lot of topics inside of each of these and it is easier to jump around business types than to try and cover too many areas with non-overlapping skills. Support the show

Keone Hon is the founder and CEO of Monad Labs — the development team behind a new Layer-1 blockchain that aims to bring pipelined execution to the Ethereum Virtual Machine. In this episode, Keone shares how his background at various HFT firms led him to identify problems with existing blockchain infrastructure and ultimately inspired him to rebuild the EVM entirely to optimize it for high-performance trading activity. Monad has raised $19 million from investors including Dragonfly Capital, Naval Ravikant, Cobie, Hasu and others. OUTLINE: 03:33 - Monad Overview 05:17 - Another Layer-1? 06:26 - ‘Pipelining' 08:32 - Web3 Social 11:38 - Bridges to Monad 12:11 - High Performance DeFi 13:34 - DeFi Summer 2.0? 16:01 - Monad's Funding 16:49 - Onchain HFT 19:05 - The Future of Trading 22:55 - Onchain Sports Betting 23:19 - Surviving Crypto Winter 28:30 - Closing Thoughts

In the fast-paced and ever-evolving world of trading, success is not solely determined by knowledge and experience, but also by understanding the human element. Today, we embark on a captivating journey into the life of Archimed La Luce (Creed), a remarkable individual who has made a significant impact in the trading industry. From humble beginnings to becoming a Principal Investment Officer at Quasar Markets, Creed's story offers valuable insights into the way of investing, reading the markets, importance of mentorship, continuous learning, and the psychology of traders.Creed's remarkable journey in the trading industry highlights the importance of the human element in achieving success. From his humble beginnings to co-founding Quasar Markets, Creed's story exemplifies the power of mentorship, continuous learning, and understanding the psychology of traders. By recognizing that trading is not solely about numbers and charts, but also about the emotions and actions of individuals, Creed has made a significant impact in the trading community. Through his endeavors, he strives to empower traders with the necessary tools and insights to thrive in the fast-paced world of trading. About Archimed (Creed) La LuceMeet Archimed La Luce, affectionately known as "Creedmoor" in the financial markets. With over 10 years of experience in the industry, Archimed is a seasoned professional who has made his mark as the Principal Investment Officer at Quasar Markets Inc.Beyond his role at Quasar, Archimed is also the visionary Founder of Nomadic Trading and Consulting, as well as The Copper Umbrella Fund. Through these ventures, he has created a legacy of success, fostering fruitful partnerships and encouraging like-minded experiences for both friends and clients alike.Archimed's trading expertise spans across all asset classes, and what sets him apart is his deep understanding of human reactionary logic. He approaches trading from a psychological standpoint, recognizing the impact of human emotions on market movements.What makes Archimed stand out even further is his open-mindedness and willingness to collaborate with new individuals. He is always excited to work with fresh perspectives and forge new connections, embracing the dynamic nature of the financial world.Archimed La Luce "Creedmoor" is an emblem of wisdom and innovation in the financial markets, and his journey continues to inspire and influence countless professionals in the industry. Contact Agnieszka Wood | Ahead Coach: Website: aheadcoach.comTwitter: @Ahead_CoachYouTube: @aheadcoachFacebook: Agnieszka WoodInstagram: ahead.coachLinkedIn: Agnieszka WoodContact Archimed La Luce:Mobile number: XXX-944-9666Quasar: www.quasarmarkets.comConsulting/Hedge fund: https://copperumbrella.com/—Transcript[00:00:04.170] - AgnieszkaI am Agnieszka Wood. Welcome to the Confidence in Trading podcast. Let me introduce my special guest on today's show, Creed La Luce. Hi Creed. Welcome to my podcast and thank you so much for making the time for this conversation. I know how busy you are.[00:00:19.940] - CreedI want to say, Agnieszka, thank you for having me on the podcast. I look forward to the questions and let's see how this conversation goes. I always love conversations that provoke thought overall.[00:00:31.630] - AgnieszkaTotally. And that's exactly what this podcast is about and for. So Creed, you have a long list of achievements on your resume, especially considering relatively short time you are walking on this earth, which is really impressive, this whole list. Except for being a trader and trading coach, you do a lot of different things. I see many names on your resume: American Charging Solutions, the Copper Umbrella Fund, Nomadic Trading and Acquisitions. And I'm dying to hear more about all the things you do. I know that your current main role is principal investment officer at Quasar Markets. That's very exciting and I definitely want to hear more about that, especially how recent that initiative is. But the main reason why I'm so excited to have you here is because of one of your recent interviews. I listened to you talked about how to trade and how to perceive the stock market. And you mentioned that you are not trading the price action, but you trade traders psychology. And I thought it was such a fresh perspective and I'm so curious to hear more about it. Welcome to episode number 13, the Human Element in Trading. So, Creed, could you tell us a little bit more about yourself and all the things you're keeping yourself busy with?[00:02:03.660] - CreedI'll keep it brief for everyone on here. The thing about it is I came from humble backgrounds on a blue collar aspect of things and got involved with the market after pursuing some other endeavors. And what really led me to this is starting to understand where does the money come from and how does the money work. At the end of the day, we do not get a job because we really like it. You get a job to pay the bills overall. You get a hobby to enjoy things. Now, don't get me wrong, if you enjoy your job, that is another aspect. And like with trading, I eat, sleep and breathe trading. So I do enjoy my job overall, but at the end of the day, we have to pay the bills. And the ideology from it was, well, where is the money? The biggest wealth generator in the world is where the markets, global markets overall. So that's something I started to dive into, asking individuals and gathering something very important that I hope people from listening to this will go and search out, be it for yourself or something else, is mentorship overall. That is something I sought out very early on and starting to be, for lack of a better term, the dumbest.[00:03:15.560] - CreedGuy in the room that felt he had to ask 10,000 different questions, but knowing when to shut up and listen and making sure to take notes so I could go back and study the words, the phrases, everything that I heard. Because ladies and gentlemen, we've got to be frank. If someone has been in the markets for 15, 2025 years, they are going to say and do things that you have never been exposed to. It is your responsibility as a good student of the markets and of your mentor to go and do your own due diligence, your own research. Everything you learn is exactly like a trade. You need to do your due diligence on it. After several years of going through the markets, profitable trader. I was with a couple of different communities and I was an educator with the Bullish Bears for five, six years, something of that nature on there. And what I found very important and I would say to traders going through.[00:04:17.150] - CreedFor this is that you should eventually become a mentor because it keeps you honest, it keeps you accountable to what you're doing. Because as a good human, as a good person, how can you honestly tell somebody to keep a stop loss when you yourself are not even keeping a stop loss? So that is something to keep in mind. After my time as an educator on there, right before the global pandemic that pursued, I had several individuals that asked me, hey, as we're learning to work within the markets, we have capital that we would love for you to work with. I tossed the idea around a little bit, but really wasn't sure if I wanted to manage money for people overall well enough. People said, hey, we'll do this, that and the other. And that's what actually led to the Copper Umbrella now TrailBack the nomadic trading and consulting that was part of the education sphere. Whereas the Copper Umbrella fund was a friends and family fund that was set up exactly for that friends and family. Throughout my career as an educator said, look, I either do not have time or I see that my journey is going to take time. Will you please work this capital for me[00:05:28.890] - AgnieszkaJust to understand, how old were you when that happened?[00:05:35.850] - Creed20, 26, 27, something of that nature. So I guess we'll go ahead and let the cat out of the bag. As of right now, ladies and gentlemen, I am 29 years old and you'll hear everything else and I get it. Before you turn off the podcast, what the heck does this young buck know? Keep in mind that if you spend a decade plus doing a singular thing, trust me, you will learn the ins and out if you are a good student of that thing and desire continued progression. So that is something to keep in mind because I do know people that have been in a single job for ten years and be quite frank, really haven't done much. So that's something to keep in mind is that while age is a good determining factor, it should not be your finite factor on things.[00:06:29.150] - AgnieszkaIt's not just the time. Letting the time pass, but actually using the time to do to put effort into the right thing, right?[00:06:38.050] - CreedAgreed. Agreed. And we all have that time and I think that's something very important to keynote on is we all have 24 hours in a day.[00:06:48.080] - CreedHow we choose to spend those 24 hours both for refreshment labor and education is up to us. So that is something to keep in mind. But anyhow, the fund itself came together, managed that money, still managing that fund overall and then was brought on by another company for an education aspect of things. As they had saw my path they wanted me to help build the community on it. Did that for about a year or so. And now I'm on to two things that were started this year on that the American Charging Solutions which is an EV company which once again most of my endeavors actually come from my friends and clients on there and even sometimes my mentors or mentees. And that charging stations, those charging solutions. People ask me what do you think of EV? What do you think about this? Can I take my money that I've made here and can we move it over here? So once again another business idea is born.[00:07:46.750] - CreedBut now my big endeavor that is going on which I firmly believe that I am not one to gloat, which I know is technically gloating in its own with that comment but it is going to be the thing that I finally said look, I'm ready to go to the next level. And that was backed up by I didn't just come to that decision on my own. I asked many people that have 20, 30, 35 years in the market at Goldman, JPM, people that matter within the business know just somebody that's here's an idea and that is now that I am the founding investment officer for Quasar Markets. What even is that?[00:08:32.130] - AgnieszkaThat's what I wanted to ask.[00:08:34.160] - CreedQuasar Markets was the brainchild of myself and my two other partners on this and we've brought on other partners but it was our brainchild together on this. And what it is is it is the Amazon of finance. What do I mean by that? The Amazon of finance on this is that you as a trader right now, even myself before I had the fund, I had three, four, five screens with where is the flow, where's the order flow, where is my charting, where is my news, et cetera, et cetera, et cetera. And the thing about it is that we have now created something that quite literally does not just bring everything, it brings the true premium of everything into this platform so that the hit of a toggle you are able to add and remove it to your platform. But something that I find is very key. If you connect with one of our educators on there and you go, I really like the way that this individual trades, I don't know, monkey bars, order flow, whatever, will actually have a profile and you're able to select all of the stuff that they have so you know that you're not missing anything.[00:09:45.180] - CreedAnd that's a big thing. Sometimes teachers and I'm guilty of it too. What we'll do is we'll talk in a presumption that somebody knows this well, what we're trying to get out of this is there are no presumptions. We are completely agnostic to everything. All the information is there.[00:10:01.240] - CreedEven more so though, what we're providing with this is that as you as a trader create this as a business overall, that business for you is going to allow you to facilitate transactions in the world. If you want to buy a house, if you want to buy gold, if you want to pay for pizza down at the local diner, we have the capability to do that. But even more so, and where I come into things, not just on the educational side that I'm managing, but the funded trader program and the fund management program. Don't get me wrong, if you manage to make 30, 40, 50% in a month or even in a quarter, lock that capital in and make sure you keep your taxes in play with things, but then put that money into something longer overall. And that's where the fund management services come in is it allows people to have that ability to think about your future. We have one of the highest failure rates in any industry. In fact, when I went and looked it up earlier, it's over 92% of people that do short term trading. Short term trading being six months and under on a position has a failure rate of over 92%.[00:11:13.030] - CreedWe are trying to flip that around with our requirements and you guys can keep an ear out for when we're doing more, but that gives you the high level view. But I will be managing the educational side and the fund investment side that is not just US based, we're global. We already have the handshakes in there. And in fact, I'm working with a wonderful firm out of Estonia there and portion in Germany that they have a beautiful seasonality profile for people who are one of those individuals that just says, you know what, I can't read market psychology or I do not want to learn to read market psychology. I want something that still gives me good gains, but is pretty robust. So we got a lot of fun stuff in the works.[00:11:57.540] - AgnieszkaWow, that sounds amazing. And also really very complex. Just looking at that from the perspective. Okay, so I'm a retail trader, right, that is struggling with consistency. At what time frame am I looking at that I can get on that platform and what can I expect? Is this going to help me to trade better? What is this going to do for me?[00:12:20.290] - CreedSo as an individual, and we really.Have to break this down in the psychology. No one will ever be able to trade better without self input. Quite frankly. I could take an individual, drop them in the middle of Harvard, and if they did not take the time, even though they have all of the opportunity, if they did not take the time to best utilize that opportunity, they will never grow. That's just how that works. Right now for this platform, we will have everything launched, barring complications. We all know how Murphy likes to get with things. We're looking at about 90 days to have this up and running, and we will actually start the fund management services November, but bigger notoriety on it Q one of next year. Now, the best part about this, and I'll say this again, is the mentorship aspect of it. We have partnered with Prosper Trading Jerremy Newsom over at Real Life. We've partnered with ABAC Mundeum Capital, so many other people that it's not just about being able to get money to the trader, but you knowing how to go through the steps and processes of what is the best way to do this. I do not care if you're building a house, a birdhouse, or working at the local grocery store.[00:13:45.900] - CreedThere is a standard operating procedure within the markets and risk management. And that's what we're aiming to do. If you happen to like Scott or one of the guys over there at Prosper Trading, awesome. You can go through that educational sphere on there and work with them. And then you say, okay, I only have $50,000 of my money and I really do not want to risk my full $50,000.[00:14:11.180] - CreedCool. You're able to come to any of our funded trader programs that you feel comfortable with, be it Jerremy's, be it Scott's over at Prospers, be it mine here at the firm and go, okay, I want to have capital put to this. So you're able to spend $200, $500, a $1000, prove that you learned something through this education, and the only risk capital that you actually have up there is what you paid for the initial test, for lack of a better term.[00:14:45.680] - CreedNow, what I advise people to do.[00:14:48.910] - CreedIn my opinion, is that it is a good idea to set aside what we would consider a one quarter tuition from a local college. What I mean by that here in the US. And in many other countries, you have technical schools and a lot of times those are somewhere between $3,000 to $5,000 for a quarter of information and technology on there. With that being the case, let's break that number down. Let's say you're going to do $5,000.[00:15:17.220] - CreedFor easy on that. Just good easy number, I would say. Take $2500 to $3,000 of that and spend that money on information and education.[00:15:28.890] - CreedThe other $2,000, there you go.[00:15:31.950] - CreedThat's your initial cost for testing or your cost for the books. Oh, but nobody has to pay for testing. Well, yes, you do. Whenever you're getting state licenses or anything like that, you actually do have to pay for testing. So that is a part of everyday life, whether or not people like it.[00:15:47.490] - CreedBut this day and age, it used to be who was closer to the exchange plus information. Now it is your inherent ability to.[00:16:03.340] - CreedBe able to receive, perceive and act upon the information being presented to you. That is why we've designed the platform. How it is designed is so that the only thing that would delay your information is, quite frankly, your Internet speed overall, because we have put in the partnerships in that. Hypothetically, if I don't know, there was a new strain of pandemic coming through the UK. And that news was to hit the global wires, boom, boom.[00:16:32.690] - CreedIt is to your system how quick you process it and whether or not you're being responsible. My mechanics out there, you know, you're supposed to be wearing your safety glasses. Well, same thing. If you do not have your newsfeed up there, guess what? You're not going to see the news.[00:16:48.480] - AgnieszkaRight[00:16:48.840] - CreedIt's just not going to happen. So make sure that you do the due diligence and the prep for it and you'll be able to see that with the educators. There's live trading and all of that.[00:16:57.560] - CreedIt's one of those things that we are trying to put legitimately trying to put as much material in front of the trader to go, okay, not analysis paralysis. You're actually going to have analysis. Action is what I'm looking for on this.[00:17:13.760] - CreedAnd there's a learning curve. I won't lie to you guys and gals on this one. If you were to sign on right now, in my opinion, to start to feel confident in what you're doing, on average, I think it would take someone around four to seven months to understand the platform, the trade, digest the information, and also get time behind the keyboard. That's the thing.[00:17:46.560] - CreedSo many people just go, I read a thousand books, let me do it. I'll steal one from Dan Pena on this one. It was I would rather have someone that has had a hundred deals than read a hundred books.[00:17:58.560] - CreedDon't get me wrong, you need knowledge. You need books, you need mentors, but you have to actually get into the dust to understand what's going on.[00:18:08.060] - AgnieszkaAbsolutely. Knowing is not the same as knowledge, right. And you do need the action and the experience. That sounds all absolutely so exciting and I cannot wait until that's going to go live so we can all see and experience it. So let's talk about now about you as a trader, because all the things you're doing is very entrepreneurial. Are you more entrepreneur or do you see yourself more as a trader or both? What's the proportion?[00:18:45.870] - CreedSo that's the funny part overall. Many people that I know that are within the markets, they had a few good wins, but they made their money off of taking those wins and investing them in other things. Startups, they didn't really make it purely from the market on that. Myself, ever since 2000? No, 13. Yeah, 2013, 2014, something of that nature, because it took me about a year to start to be profitable on that. So, as you guys see, I'm on the other side of the four to seven month statistic. It took me about a year. And when I say profitable, I mean paying my bills. I don't mean making $10 a year. I'm talking about you are able to withdraw money from your account and actually pay a bill. And actually, I remember my first bill.[00:19:44.810] - CreedIt was a light bill when I had just finished university, and that was the big component of it. So myself, I draw my money from the markets. I have other investments, I have other things, but my money comes from the markets because I am purely a trader. And in fact, even right now, not my client accounts, but my personal account on this is probably the least amount of assets I've had in it because I have been distracted with trying to get this platform or not trying actually getting every day I have meetings about every 30 minutes or so getting this platform going. Overall, right now, I think I have.[00:20:27.450] - CreedMaybe actually, let me look real quick.[00:20:30.270] - CreedI'll tell you, I have nine. I have nine stocks, keep in mind. I have well over an eight figure account. That's the personal account.[00:20:44.700] - CreedAnd wow, that's sad. I really shouldn't have looked at that.[00:20:49.630] - CreedBut that's the thing. You have to know when to stay out.[00:20:52.080] - AgnieszkaThat's for swing trading, right? Or is that long term investing, that's swing?[00:20:56.450] - CreedOkay, so that breaks up another part of it. If I was to look at my. So it is, in my opinion, as a trader, when you're working with this, you have to break down your idea, your trader psychology when it comes into this. And I've always said that you need to have about three to four accounts. You need to have an actively managed account, which is typically your day trading, and any trades that are under three to six months on that, for me, it's anything under three months on there. Now, if you have something that's a longer term idea on things, that's your long term account, that you'll typically only work with that account because you should have automated orders in it. Four to six times a year is the only time you should really touch that account. And you go through and it takes a while to get everything together. The other account that, in my opinion, people should look into is a dividend account. For that, the profits that you make from the day trade account, the short term account, go into either your dividend account or they go into your six month and longer horizon account on there. And I firmly believe everybody should have a 10,15, 20 year account that okay, at the end of each quarter, I take 5%, 10% of net profits and put that into the retirement account overall for that.[00:22:17.320] - CreedAnd there are several different Roths you can put up. And if you want to see a fun one, go look up what's called a backdoor Roth on this, and it allows you to do more than your standard Roth, and you can go down that rabbit hole. But that's the thing, is separate your accounts to meet your goals. Many traders and individuals have a difficulty.[00:22:40.110] - CreedWith realizing that, okay, where do I want to be in three, five, or ten years?And in my opinion, do not make it finite, make it in the aspect of, I would like to be at this region, this area, and for me to accomplish that, I am going to set and do these things. Because you may hit a hurdle global pandemic, you may take a big hit in an account. You may have, unfortunately, a spousal loss or a friend loss or whatever things happen.[00:23:14.830] - CreedSo you need to have goals, but you need to know what is a finite goal and what is an amendable goal overall. Because before COVID before even me starting the other hedge fund, I retired. I straight up retired. I said, you know what? I've made my money. I'm good. I can pay myself $50,000 to $100,000 a year, whatever I choose, and just go relax for a while.[00:23:40.330] - CreedWell, I did that for about four six months, and then everyone started asking me about the hedge fund, and I realized look[00:23:45.140] - AgnieszkaThe shortest retirement ever.[00:23:47.850] - CreedYeah, retirement is not all it's cracked up to be, trust me. But I'm a little bit jaded in that as well is because while, yes, I came up from a very blue collar to give you ladies and gents, my graduating class was 22 people.[00:24:02.480] - CreedSo I came up from a very small area to where people do not get to travel as much as I do. But thanks to the generosity of clients and to the fact that I was able to set money to the side, to be able to do is actually even this year and most of last year, I am typically either in a different country or a different region of the US at least one week out of the month, minimum.[00:24:28.630] - AgnieszkaThat's like a dream of many people, right?[00:24:32.150] - CreedWell, dreams are like support and resistance levels.[00:24:39.530] - CreedThe intensity that people will hold them is completely different, and it actually comes down to the individual. I mean, even algorithms have a threshold within them. If you want to see a fun one, go back and look up the Thor algorithm for HFT trading.[00:24:58.950] - CreedIt was, oh God, mid 90s on there.[00:25:03.110] - CreedI may be a little bit off on that date. And it was for UBS. And that's the thing, is those thresholds are different from everyone, so my dreams are different than others. I always joke that the next time I will retire is when I have 500 million in free cash flow. I will give away 450,000,000 of it, take another 50 million, buy an island and then just go away you know, because I like nice things. I have a paddock, I actually just ordered in a McLaren on there because I'm a car guy. For those unaware, one of my university degrees is for automotive restoration, but I'm not going out and doing that all of the time. I believe in goals. I mean, even this watch, this was actually one of my first goals. This is a chorus vertex. It's like 700, $800, but for someone, that is quite literally two weeks pay for some people in the world. So for me at that point in time, this was a big goal overall for me to get.[00:26:08.620] - CreedI didn't come from any of that. But now I actually just placed an order for another watch because I have to go to the UN in September. So your goals and dreams should and will change as you advance within your trading career and within any career, trading is just a tool to give you access to other things, be it the money or the knowledge of the markets. You may not be profitable in what you're doing with your trading because you just can't hold a stop loss or whatever, but you're still building your ability for analytics. Guess what, there's 10,000 plus other jobs out there for business analytics, market analytics, et cetera. So you're not wasting time. You may not have made money and you paid tuition to the market, but you learned a valuable skill on analytics.[00:26:58.300] - AgnieszkaI love that point of view. I'm so curious about your opinion on that. Looking at the very high rate of failure, do you think it's even possible for a retail trader to live just off trading? And I mean, like for majority of people, right? And to become really rich, because paying bills, that's one thing, but I mean really built capital from just day trading, and I'm talking day trading, not the way that you're saying putting aside and investing and just day trading.[00:27:33.520] - CreedIt's a little bit of a loaded question because first we have to define what is rich. Rich to me is different to rich to other. Rich to me when I started, rich to me now is completely different. I mean, I have numbers in my phone that are heads of state, president, stuff like that, which me just starting out as a trader, come on.[00:27:54.950] - CreedRich to me was, oh man, I'm making $5,000 a month, this is awesome. So it's a matter of perspective yeah.[00:28:02.350] - AgnieszkaThat you can afford, I don't know, going on vacations a few times a year, having a nice car, like financial independence. That's what I mean rich.[00:28:11.540] - CreedWould it be fair for us to say what it would take to purchase freedom? Would that be a fair assessment?[00:28:19.090] - AgnieszkaThat's an interesting question.Yeah.[00:28:20.980] - CreedSo if an individual said that the cost of freedom for them to do what they want, give to what they want, et cetera, is, I don't know $250,000 a year, pretty round number overall. And in fact, that would put you well within the top 3% of income earners of the US. That is not that difficult to do, especially if you keep parameters in play.[00:28:48.390] - CreedCreed what do you mean that's not that difficult to do? Well, keep it in perspective. If you have a million dollar account Creed how do I get a million dollar account? Funded trader program. You spend your $3,000, you prove you know what you're doing, blah, blah, blah. You take a million dollar account and you, at no point in time, have more than 1% allocated per trade.[00:29:13.100] - CreedNow, that's different from risk, that's full allocation, right? So that means that you're going to have $10,000 allocated to it. You take $10,000 and just use the spy for an example. Right now, the spy statistically, using the ATR average true range or average trading range on a 14 period time frame, lets you know that we on average are making about a 1% move, i.e. $4.[00:29:38.410] - CreedSo you know you're going to make about $4 of movement in a day at maximum. Now, you know your max allocation. You know your highest probability amount of movement. If I was to buy in at the money, sell a vertical, whatever. Well, right now when we look at I'm actually looking at an spy, and granted, it is Friday, so don't take these numbers to heart. And at the money is going for about 80 cent. Okay?[00:30:08.710] - CreedSo $0.80 on that. Let's just use a dollar for an easy number on there. Ten contracts will cost you  a $1000. A 100 contracts cost you ten grand on ten grand for every $0.01 movement in the cost of that option is $1,000 profit to you.[00:30:32.110] - CreedNow, if we think about this and you think there are roughly 255 trading days in there, let's say your strategy is only 75% effective if you scalp three pennies on an options contract on that account using only the spy, not accounting for anything else, no wheel trades, nothing like that. You've just made about, what, $300,000 a year after taxes.[00:30:57.230] - AgnieszkaWow.[00:30:57.880] - CreedSo it is 100% feasible to purchase freedom, but you will get in your own way.[00:31:06.600] - CreedHeck, I'll be quite frank, even with the accomplishments that everyone sees on that, and it's one of those things. I still get in my own way from time to time. And I'm fine with admitting that because my circle of friends around me, if I'm being too humble or too cocky they'll say, hey, step it up or bring it down totally overall. And that's something important.[00:31:32.990] - CreedTrading is lonely. The psychology of trading is only because what was it, about three months ago? Two, three months ago, like that? I was doing some zero DTES, and Janet Yellen came out and said something market trashed. Market just sank. In the course of, like, five minutes, we dropped about $5.[00:31:52.080] - CreedBut guess what? I had a max loss on those zero DTES because by the time my limit order got well, it wasn't a full max loss, but it was a pretty heavy lot. I was doing something like 2000 contracts, so it would have been a total order of 4000 contracts.[00:32:05.050] - CreedI lost like 70 or $80,000 in five minutes. Now, be honest, there are not a lot of people that you can go to and go, honey, I lost $70,000 today.[00:32:18.940] - CreedI made it up the next two days on it. But could ask yourself sincerely, could you sit down with your friend or your coworker right now and go, man, I just lost 70 grand?[00:32:28.680] - CreedNo, that's more than a lot of people's three year salary.[00:32:32.010] - AgnieszkaFor those unaware, I had a very similar situation. It was not because the market dropped. It was at the time when I was struggling still, like being silly and not keeping my rules, not putting my stops and hoping for the price to go back that one day. It was pretty intense. I went out after the market closed to a store, and I was coming back and I was walking with my husband and I was completely quiet. And he didn't know it yet, but I knew I want to tell him what just happened. And it happened that there was a Porsche parked, and I looked at the car and I said to him, I could buy this car with the money I just lost. Cash. It was so confronting to me because suddenly there was the money, the value that it's standing right here. And I would have never thought that this could have happened to me, that I would allow that. And that was a very pivotal point to confront myself with that it's like, okay, that's it. That cannot happen anymore. This was absolutely outrageous. So it's a big thing to actually take a lesson from it.[00:33:47.930] - AgnieszkaAnd I know a lot of people who have lost a lot of money in the market that way.[00:33:51.790] - CreedIs it outlandish to say that it's almost better for someone to take a relatively account big loss early on, so you feel that sting. And actually this is something I do not believe I've shared on any other podcast until now, but about, I'd say about eight months into my journey, I actually became so numb to the wins and losses that I didn't even care.[00:34:31.830] - CreedWhether I won or lost on that or let me rephrase that, made a profit. Because won and loss really, it ties a word that doesn't need to be within your trading. But I did not care about the trade. I did not care about that. It was almost like going through the motions of making coffee. And at that point in time, I was still on not the size I put on now, but I was putting on 15, 20,30 contracts at a time. And I think the account was probably somewhere around something around 100, 150, something like that. So you start running those numbers, you go, wow, okay, you lose $5,000 on that account, you've just lost 0.7% of the account or whatever on there.[00:35:12.100] - AgnieszkaRight.[00:35:12.640] - CreedSo I actually had an instance to where I had to check myself and go, whoa, you just lost the equivalent of being able to take a trip to Jamaica on you, just for lack of better term, you just don't give a darn.[00:35:29.970] - CreedOverall, that's a problem. And that's something I have not heard.[00:35:35.560] - CreedA lot of other people or in fact anyone of the best minority talk about, is that some traders and some people, our brains get to the point of loss is loss.[00:35:47.150] - CreedThere's a difference between an educated loss and you're being sloppy and slacking kind of loss, because you may have your stop loss in, you may go through that, but I would say you need to have about 5% to 7% emotion on a loss. And what I mean by that is that that emotion says something didn't happen. I feel a little something on this. I need to go back and look. And for any of my students, for any of my clients, my friends, et cetera, I've always advised in keeping an emotions journal. And I know for the guys out there, don't get me wrong, I've done a whole bunch of other stuff on that you got to keep. Mine own a ranch in Montana. There's not exactly a pansy aspect, but your emotions as a trader on there matter, especially when you're putting on real capital. I mean, I hate to say for those out there, yes, get started with $5,000, $10,000, that's great. But at the end of the market, you got to keep in mind that the markets are over $3 trillion. You could stack just the money from the forex market back and forth to the moon. I think it's something like 25 times or something like that. And that's just one market. That's not everything else. So you need to keep those emotions in check. And that's what I do when I'm trading the psychology of the market.[00:37:15.470] - AgnieszkaI'm so happy we get to this because I can talk with you forever. I don't know how much patient people have to listen, but this is really so interesting. So, yes, tell me about the trading psychology, trading people and not the price action. What do you mean with that?[00:37:29.410] - CreedYes, that's a term that a friend of mine coined a long time ago for me, actually, back when I was an educator on there. And he goes, So you're really not trading flow. You're trading emotions. You're trading people.[00:37:45.660] - CreedI went, yeah, I guess you're right. I guess I'm trading people.[00:37:48.980] - CreedAnd let's conceptualize this for a moment. What is the market? The market is nothing but an auction system, a tool that allows ideas to be facilitated in a solid object, such as money overall. Now, let's try and bring that in.[00:38:10.270] - CreedWhat do I mean by ideas? When a company is going up or down, and be it a commodities contract, a currency, a company, whatever, when it is moving, it is moving off of what? New information. And the people who are looking at that market reacting to that information creed. What about Algos? Guess what? Algos are programmed by people that put in parameters. Even the medallion fund. All those Those algorithms are adjusted with new information that is piped in overall. Some of the smartest minds are on that. But you know what they do? They're making adjustments, human adjustments.[00:38:49.350] - CreedEven AI run programs. What is it doing? It is compulating information that humans did. When the Nordstrom Pipeline blew up. Guess what? That was a human action that caused this thing that the AIS, the Algos as well saw.[00:39:11.480] - CreedSo at the end of the day you cannot remove humans from the market. They may not be the one clicking the button to make the trade, but humans are the ones doing and making.[00:39:25.640] - CreedThe actions that affect the underlying. So now we know every aspect of the market is affected by humans, even. If it's the weather. For those that say that, guess what? Weather goes bad. Humans are affected. They can't drive stuff. Humans. So when we get to the chart, now.[00:39:47.630] - CreedThe common term on this, and you can go to the CMT and look this up as well, charted market technicians, not country music, television. You will start to learn about something called supply and demand zones. Now, you'll see this plastered all over 10,000 different videos, et cetera. And I buck the idea of supply and demand zones for essentially one reason. They try to separate what is going on. But keep in mind, for a market to do anything, there has to be a buy and a sell within this.[00:40:34.080] - CreedOtherwise, we're still in discovery phase of what's going on. When we reach these nodes, these pockets of supply or pocket of demand, they're the same thing. In fact, if you go and look at any of my videos on this or any of my material, I have written stuff out there. You can go find my PDFs and all that. I call them a business zone. Why? Because at this area, a demand came in for supply at this other area.[00:41:07.640] - CreedSo as it was coming in and buyers step in, guess what? Supply came to demand. That's the only way that that transaction works, right? So it is a business zone, not a solely a supply zone, solely a demand zone. So now that we have that out of the way, we can start saying. To ourselves, okay, we now know humans affect the market.[00:41:31.120] - CreedWe now know that these pockets are not separate. They are the same thing overall. The only thing is, is it above current price or below current price support resistance. That remains true.[00:41:45.250] - CreedWe know that certain people for a fact prefer to only trade technology, biopharmaceutical, industrials. So you have a certain ideology in that market. Typically if someone grew up in rural. Idaho, what are they probably going to trade?[00:42:06.080] - CreedThey're probably going to trade something around the agriculture section or they're going to trade something around technology. For those unaware, Idaho has a lot of technology in it. It's just kind of out there, a lot of servers.[00:42:15.460] - CreedBut I digress, if you know that. That particular participant is in that background, that type of a previous education, that type of an ideology, then you're able to disseminate that.[00:42:31.320] - CreedOkay, these people have typically this type. Of a risk tolerance overall. So if we know that the people trading Caterpillar tend to be much more skittish from very volatile things, what's going to happen?[00:42:51.580] - CreedYou're going to have a low ATR average true range. It is going to take more participation to break a resistance and it is going to be a better buying opportunity at support. So in typical as price descends down from, let's say the first of the month and gets to that lower business zone, what's going to happen? Oh my God, it's at a discount because I'm buying this for a long term move. Caterpillar is typically used for a dividend. Play on thing long term stop. So you now understand that participant, let's break it down again.[00:43:29.240] - CreedYou know the humans that are doing it, you know how they're doing it, you know the way that they act and move within the market. So you as a trader staying agnostic to everything that's going on can go.[00:43:44.470] - CreedOkay, I know that these people in this market act in this way.[00:43:50.650] - CreedIf on Apple I need 100,000 trades to break a resistance, okay, that's something where there's more volatility. But in Caterpillar I may need 200, 300, 400,000 and you're actually able to go back and see what the typical volume break is, find the average of what that is. There you go.[00:44:13.780] - CreedNow you have a number you can work with and say, okay, if I see more than 100,000 transactions at this area, so if it starts building up, 60, 70, 80, oh man, we're getting to that area, I should prepare to look for a break again. I'm a confirmation trader, not a presumption trader. I've presumed enough things in my life that have usually end up getting me burnt. I'll sacrifice the extra 10-15 cents.[00:44:41.460] - CreedDid they break it? That's just my thing. And once you see that volume building up and it confirms a break out of that resistance or support is holding. You're going to say, okay, the mentality of the participants has now stated that. At this business zone there is enough.[00:45:03.440] - CreedParticipation and demand that they are willing to pay above this standard business price. Right in here. Example of that is if anyone has ever went for a limited edition, I'm going to be funny Beanie Baby furby, whatever, and they cost you $20. But all of a sudden you can no longer find your sky blue furby. Guess what? Instead of it only being $20, it's going to be 25, 27, 30.[00:45:32.970] - CreedSame with this. There is a finite amount of people willing to participate at any given time. That is a finite number. More people can come in, but there are only so many people in the market at that given time. So it's not like you can really. Work with a lot of that.[00:45:53.670] - CreedBut you can see that. This number of people coming in has stated we are willing to pay more. What I like to look for is a standard breakout retest and it is why I utilize a specific candle called a Heiken Ashi candle on there HA candles for some other platforms. The HA candle is formulated through a sense of averages overall, as I discussed earlier on this. We have an abundance of individuals willing to pay more above this standard area. What I look for in the candles when I'm working for this is 60% or greater of the body of that candle, regardless of time frame. This works across any time frame, which surprised me, to be frank, when I started running the studies on it.[00:46:45.240] - Creed60% or greater of the participants have stated, yes, our average price will be above this standard. If true, what does that tell you? A majority of participants are willing to pay more so they break out.[00:47:03.910] - CreedWe tend to see a little bit of a retest to the downside as people start to say, are you sure you want to pay more? Are you positive? Yes, darn it, I want more. Give me as much as I can have on this. The average continues to push on there. As the average pushes, you start to get that FOMO into this for all the people that were down here that said we are going to do the break, then you have the test. I like to look for a candle break or I'm sorry, a break of the high of the candle that broke the business zone.[00:47:40.170] - AgnieszkaRight.[00:47:40.580] - CreedIf that average is above that line. There, guess what, not all but a majority of the participants that stated, yes, we believe that there is more value to be had up here, so we are willing to take the risk of a purchase. Now in the idea that we can. Sell for more here, that's not me saying it. That's not me being looking at the market and form fitting data that is the True Blue transactions have stated, yes, we're willing to do this. The transactions that move within the market are direct representation of the humans, the idea, the psychology of the market. So I could care less if Apple comes out with the Vision Pro 37.[00:48:28.610] - CreedI could really care less because I'm able to see does the money of the market care?[00:48:36.360] - CreedI don't care if President XYZ Setter did this.[00:48:40.650] - CreedDoes the money in the market care?[00:48:43.660] - CreedBecause I trade a good account. I'm not trading a $7 trillion account, okay. I'm not trading the economy of Botswana. So you do not have the weight to really change that idea. Now, if you're running with Penny stocks and I actually did manage to do this with zero DTES on Spy one time because I fat fingered an order and I had something like I think it was like twelve or 13,000 contracts per side on there. You guys and Gals can go run the exposure on that one. I didn't mean to do it and I flipped it out really quick. It was a bad trade. I made money, but it was a bad trade overall, I digress.[00:49:26.930] - CreedYou are not going to move the ideas of the market.[00:49:29.880] - CreedAnd if you can move the ideas of the market, you're not going to be on it.[00:49:34.410] - CreedLike, for myself, I cap myself at 25 million on an account. That's what I cap myself on on there. I've noticed that as I trade and do other things, I working with everything do not feel comfortable trading anything over that at any single point in time.[00:49:50.160] - CreedOverall, I know guys that trade larger accounts so that's the thing.[00:49:54.910] - AgnieszkaLet me ask you about that. How did you build that immunity? Because a lot of traders don't have large accounts like that, right? And the process of sizing up requires building an immunity to your risk tolerance, right? How did you started build your risk tolerance? For example, let's say at the beginning, maybe you were risking, I don't know, $100 per trade, then maybe $1,000. And do you still remember how that process went or were you just like, I'm not thinking about that and doesn't do anything to me.[00:50:34.380] - CreedSo this is the formula I use to build my account and what I've taught actually no. Now it's probably thousands of people, now that I really think about it, to build theirs and it's a metric that seems kind of odd because the words I use and you may have to slow down replay this part, but just bear with me. So let's say you're starting with a $10,000 account on there.[00:51:03.810] - CreedRealistically to see any type of progress that is appreciationable. Let's say you've already built a little bit. You've got your indicators, you got that, but you still got a 10K account.[00:51:13.480] - CreedOkay?[00:51:13.750] - CreedWe're talking risk management here, right?[00:51:15.980] - CreedYou would never allocate anything more than 10% of the account value to any singular trade so we would have $1,000. Creed why would you only allocate 10% when you have 10,000 to work with? Because anything can happen, especially in this market. So if that position you put on for whatever news comes out, you become unaccountable to what you're doing. Whatever, you take a total loss you only lost one 10th of the account.[00:51:48.260] - CreedAnd in this economy, $1,000 is $1,000. Don't get me wrong, I still argue over a $4 cup of coffee. But the thing about it is you can come back from $1,000 loss. It may take you a couple of weeks, but you can come back from $1,000 loss. Now, amongst that allocation, you're able to figure out your trading metrics, okay, I have $1,000 I can work with. With that $1,000, I'm expecting a $4 move in the market. My options cost is $2. i.e.I can purchase five contracts overall. Within that five contracts, I am willing to risk down to this level or above or take profit at this level.[00:52:37.190] - CreedAnd that's why for books and for lack of a better term, BS Media, the idea of, oh, we had a one to three or a one to six, I have almost never I had.[00:52:50.810] - CreedOne trade on ReWalk that was actually a pure one to three. It's always like one to 2.7, one to 2.9, one to all this and trying to form fit a one to two, a one to three, anything like. That just doesn't work because we are in a market of finite numbers. 2.5437 is 2.5437 all day long, regardless of what it is. If you said, hey, I'm going to put a limit order into the market and get in at 250, your order fills at 249.98.[00:53:27.000] - CreedGuess what? You are wrong.[00:53:28.800] - CreedYou did not get in at 250. It's not by much. Don't get me wrong. Yes, it is a nuance. But when we're looking at those numbers of I have a strict one to three, it doesn't work because that puts a finite in it.[00:53:41.500] - CreedWhat I do to change that up and to help build that account up is I go, okay, I have my $4 range to work with on here. My next resistance level, my confirmed resistance level is one dollars up. Oh, wait a second.[00:53:58.080] - CreedMy next support level is a $1.50 down. Something's not right on this.[00:54:06.880] - CreedWhat I'm going to do is I'm going to wait for my 60% or better print above that resistance level because what happens now, okay, my second resistance can be dollar up, $2 up. But if I'm at a neutral area here now I set my stop at open. I have that one dollars move up on here. I've only got one dollars risk, true risk right in here, whereas I have another dollar and a half $2 move up, whatever that actual resistance is. And inverse, if you're shorting. So that's how you can do that.[00:54:44.320] - CreedNow, as you have those gains in your account, you go from 10,000 to12,000, you remove one percentage point. What I mean by that 10,000 – 10% is 1000. 9% of 12,000 is roughly 1000, 14,000 – 8%.[00:55:08.340] - CreedContinuing, continuing, continue until you're at PDT on there to, at which point, in my opinion, allocating anything more than 5% of account for a new trader and even an intermediate trader, there's no point to allocate anything more than five. Because once you get to PDT now you can start doing a bunch of day trades and you really need to hold that risk metric in there so you don't end up below PDT.[00:55:35.550] - CreedAfter 25,000, I stopped and stop whenever I'm teaching people on that, removing any percentage on it, because 5% of 50,000 is different from 5% of 25,000. So you're still growing how much you're allocating and working with, but the emotions attached to that are still the same.[00:56:01.330] - CreedAnd also I almost never look at my account.[00:56:05.540] - CreedIn fact, pretty much the only time I ever see any account values on things are when I see my taxes at the end of the year, something like that. And that's the thing to keep in mind is even then I really don't look at it unless I get a margin call for XYZ reason on things. Like, I had a pretty big position on soybean a while back in one of my accounts, and soybean tanked.[00:56:27.310] - CreedIt didn't do what it was supposed to do on there. And I ended up getting a small call on that because it was with a starter account. And I went, that's different I didn't even know the account was near anything like that.[00:56:38.510] - CreedSo what happened? All right, send the money over, close the trade out, send the money over. Get over it, things happen.[00:56:42.960] - CreedAnd when you're using that type of a risk metric, it just really helps you accelerate things. Something that I would say to keep in mind though, is that to give you the best chance, once again, remove anything that has to deal with a notional value, convert it to percentages points, ticks, anything like that. And then even when you're journaling and keeping track of things, remove any aspect of money. Because as I was joking earlier, yes, I'll still complain over a $4 cup of coffee, but when you see that dollar sign, that pound sign, et cetera, it talks to our lizard brain and says, hey, you've made or lost this amount. And more people statistically have a problem with loss, and you generate more energy in your emotions from loss than you actually do in gains. And I think that's something really unique. So if you can remove as much as you can as a notional everyday reminder from your platform, your journaling, all you're doing is saying, okay, I'm just adding points to what I'm doing on this. I started the day with 5000 points, okay? The gamification of what I'm doing on this, and don't get me wrong. Trading is not a game. But think of it in that manner. At the end of the day, with everything I did, I'm now up to 5200 points. Or you know what, I got hit with a Whammy and now I'm down to 48 35.[00:58:19.890] - CreedAnd you say it in points, as we say with Affirmations and everything else.[00:58:24.520] - CreedAs you say, so it shall be. Well, if you continue to think in that manner, yes, eventually you have to look at a number on things as far as a cash value, but while you're trading journaling all that, convert it into the points, percentages.[00:58:39.870] - CreedBecause why add another emotional headache to this already very difficult thing that we're doing? I mean, as we said before, we got over a 90% failure rate on things, so why add that hassle? But that's the long way of saying how I did it and how I developed it and how I continue to do it. Because there's a very big difference in moving and working 10,000, 50,000, even $100,000 to when you get to the seven, eight, nine figure game on things.[00:59:18.980] - CreedOr let me rephrase that job not game job. There is because you start to reach an aspect to where you physically cannot allocate any more money to a trade. Otherwise you become the market. If you only have 100,000 people with ten shares apiece on there, guess what? You got a million shares.[00:59:44.550] - AgnieszkaRight.[00:59:44.880] - CreedCool.[00:59:45.260] - CreedIf you decide to put in an order for 25% of that float overall, you're the big shark. Who are you selling to? Who are you going to be able to sell to? And you sure as heck are not going to be able to sell that whole thing at one point in time. So this is where you start to balance out and you start to go.[01:00:07.090] - CreedOkay, well, I've got a seven figure account. I only like to trade ten specific stocks. Each one of those things allows me.To only allocate, I don't know, 10,000 shares and 50 covered options on there.[01:00:20.570] - CreedOkay, you now know your basket. The good part about that though is that you can now average and get an expectation of if I know I have a 70% success ratio and this is my typical size overall, you can actually extrapolate out to give yourself the best bet of okay, I think I will make a million dollars this year.[01:00:40.690] - CreedAt 70% success rate.[01:00:42.640] - CreedMan, for me to purchase Freedom, I need 2 million. I need 3 million. So what do you got to do? You got to go find another market. Well, because you have now created the strategy and honed in on your analytics. A person is a person. A trade is a trade. If you are trading a REIT or if you're going to purchase a house.[01:01:08.710] - CreedGuess what you got to do? Where's the resistance? I.e. what's the lowest point the buyer is willing to go on that house?[01:01:14.660] - AgnieszkaRight[01:01:15.080] - CreedWhere's the support base minimum.[01:01:17.340] - CreedSo you take your skills that you learned over here, apply it to another market and start creating your other streams of income to go with it. The only thing I would advise against and this is just my own personal opinion on there, is if you are going to be a mentor and you can show and back and do everything you're going to do, charge more than $99 an hour. Because I really messed that up and that was my cost for those unaware.[01:01:43.580] - CreedWhen I went because I wanted to give everything that I could overall. But there was a cost. I only at one point in time charged $99 for my information and an hour of my time. I'm at a point now that I quite frankly cannot do.[01:02:05.930] - CreedThat the value of the information and the amount of time or value that people are willing to put into it is directly correlated to how much that you cost. And I'm not about trying to make the money. I'm trying to make fact of, okay, you did such a good cost input on this that you're actually going to find value and succeed at what you're doing. And that's what I want more. I want more success stories from my students and I hate to say it.[01:02:40.640] - CreedBut you do have to have a barrier of entry. Someone that is not willing to commit to what's happening. Don't get me wrong, you can trade the market as a hobby, as a game, binary options, stuff like that.[01:02:53.820] - CreedBut if you're serious about wanting to purchase freedom then you have to make a commitment and you have to find good mentors. That is a non negotiable on there.[01:03:08.160] - CreedYou have to stay committed and the time frame is different. The fastest I've ever seen anybody go from zero to hero was about two and a half, three months. And this is just someone that was a freak of nature and went, I'm trading one thing, only one thing and I'm always going to be looking at it when I get off of work. Okay, well guess what? Their 10,000 hours went to one thing very quickly because they weren't bouncing around.[01:03:29.890] - CreedThey said it was a MACD. Yeah, they used a MACD and Heiken Ashi's and they only focused I think it was Apple, it was either Apple or amazon and that was the only thing. So think about it, that individual they're not hopping to different symbols, they're not changing indicators, they're not going through and looking at guru X, Y and Z. So you get two or three months on there. You've got let's say on the average 2000 hours on one stop, right? And one set of yeah, you're going.[01:04:00.860] - CreedTo have some success. I'd be surprised if you did that's right overall on there. That's the long gambit about it and that's how I look at things when I'm trading people and understanding the participant of the market and it's difficult. On average I will trade about three to five equities at one point in time. I will put ES and SPX Nasdaq and QQQ whenever I'm trading those futures. I can also then have a trade initiated on the underlying because like we said before, you may only be able to put so much money into one specific thing. Well, if you know the Es, the SPX and the Spy move with each other, congratulations. They all run in correlation. So now you're able to devote three X capital. So you're not becoming the market, but you're still taking advantage of the same move. You can go and see this through many different things. Don't get in the bonds to begin with. I'll just be straight with that. In my firm opinion, you need to understand a regular market and quite frankly understand the options market itself and how to trade the options market before you get into bonds.[01:05:12.090] - CreedBecause, and I know I'm going to get crucified for this one, but bonds are a much more sophisticated option is what they are. When you really break down the idea of them on a 10,000 foot overview, they really are just a much more sophisticated option. So avoid bonds to begin with. Once you understand terminology, you got some time in, you're showing a little bit of profit on stuff, then hop over the bonds because that opens up a whole nother thing that we can talk about later on on stuff.[01:05:41.760] - AgnieszkaSo the question on that not on the bonds, but options or stocks for someone who is still trying to build consistency.[01:05:48.990] - CreedHow big is the account?[01:05:52.840] - AgnieszkaOn average let's say above the 25 so that you can actually day trade for day trader. Well, because that's the reason a lot of traders would go for options because they don't have such a big account, right? But imagine you do have it and you have the choice.[01:06:10.550] - CreedStandard leverage for a lot of accounts is two to three X. So you have 25,000 in cash. They're going to allow you 50 to 75,000 in purchase of stock.[01:06:20.710] - CreedSo with that being the case, in my opinion and that's firmly what it is, if it is under $15, just do the stock because most of the time the options have no flow. Yeah, you get a little bit on NEO and sometimes you get stuff on Ford, et cetera.[01:06:40.460] - CreedBut as a totality of stuff, if it's under $10-$15, just stick to doing the stock.[01:06:47.670] - CreedIf it's over that and you start to see the flow of the options to actually have some good volume and that is dependent on each individual overall. And keep in mind, open interest is not volume. That's a big thing with options. Open interest just means the order is out there. Doesn't mean anybody's actually transacting with stuff.[01:07:08.760] - CreedSo if it's over $15, there is good order flow within the volume, then yeah, go to options. Why not? As long as you're not seeing something like, okay, for the totality of the day, there were 400 options. So tomorrow I'm going to buy and at the money, and I'm going to buy 100 of them. No, I'm not going to become a quarter of the market overall. I might buy ten and look for a move on there five to ten. So now you have the issue of okay, well, I can only allocate five to ten on this. So you bring that up, you have it over in the window.[01:07:42.010] - CreedAutomate your orders. It's the biggest thing. Automate your orders and then you can go, okay, well, I've devoted my capital to here. Let me go over this stock over here. So work in an area of prioritization. You can leverage your money better with options both ways, profit and loss. Keep that in mind. With stock, you're not at as much exposure as you are with the option and you can hold on to that stock. Whereas with the option, it has an expiration overall.[01:08:17.430] - CreedSo I'm not saying to be a bag holder, but when you buy stock, you're also buying a little bit more time to maybe you missed an analysis or maybe there is a report coming out keep that in mind.[01:08:30.260] - CreedSo if you wanted a nice hybrid covered calls, you get stock and you can work options.[01:08:37.140] - CreedThat's actually a friend of mine, Jeremy Veerland. He puts one through there and he does a modified will strategy. And I've ran that strategy and eventually end up at net zero on your shares if you do everything right and takes a while, don't get me wrong, it takes like two, three months to end up hitting that but think about that. You purchased your shares and you saw that, let's say it's Ford. You purchased your shares, 100 shares here, and you have that option strategy, and you're just selling calls and puts both sides as you make that profit on there because Ford tends to stay in this range. Cool.[01:09:07.170] - CreedYou're making $50, $100 per $100 on here, and it cost you $1,000.[01:09:12.720] - CreedBy the time you have ten trades done, you're at a net zero on what this cost you. And you still have this to rent out for those covered calls. But you also own the stock, and it will pay you a dividend right later on. So now you made one transaction to buy the shares, but you're leveraging those shares three different ways. Covered call, cash, secured put, and then a dividend payment if it is a dividend stock. So there's a whole bunch of different ways to do that. So I guess it depends.[01:09:46.630] - AgnieszkaCreed, we can go on and on. But knowing how busy you are, how much time I have taken already of yours. I am so grateful that you really were able to allocate this time for me and for my listeners because you have just given so much wisdom and so much knowledge and so many great not just tips, but really the way to how to see the market, how to set yourself up for success. This was wonderful. I want to ask you,

Join us as we talk to Ravi Kumar, the Cofounder of Upstox about their story. Ravi and his younger brother Raghu were born in Madhya Pradesh, India, then moved to France before settling in Canada for 8 years. Ravi completed his high schooling there and developed a strong interest in startups. He later moved to California to pursue a Bachelor of Science in Information and Computer Science at UC Irvine, graduating in 2004. In September 2006, the brothers relocated to Chicago and co-founded RK Trading Partnership, a successful HFT firm that earned them over a million dollars in just two years. However, the 2008 crash forced them to return to India. In 2009, Ravi co-founded Upstox.

In Episode 6, Saadia introduces a news-worthy update about the multi-jurisdictional saga regarding the recognition and enforcement of a USD 15 billion arbitration award issued between the heirs of the Sultan of Sulu and Malaysia [TIME 06:16]. Then, for HFT, Brian recounts the trials and tribulations of file sharing and how institutions are adapting their technology to facilitate case management [TIME 25:15].

Jason Buck is back in this follow-up, Part II episode rehashing all the goings on at Global EQD '23, and he's continuing the conversation on equity market hedging, not to mention interest rate, currency, and more hedging – plus volatility markets and investment strategies with host Jeff Malec.  Jason and Jeff discuss what was discussed, including the influence of Fed policy, abnormally low volatility in equities, cross-asset volatility, and the relationship between skew and put options. Tune in to discover the risks associated with high-frequency trading (HFT) firms in the zero-day-to-expiration market and gain insights into balance and risk management.  Explore supply and demand dynamics, black swan events, and the perspective of market makers. Gain valuable knowledge on the interplay between institutional and retail traders, the importance of diversification in building portfolios, and the comparison between systematic and discretionary approaches to investing — SEND IT! Chapters: 00:00-01:31=Intro 01:32-10:07= Leaders in Vol – Monetizing Vol, Skew vs puts & commodity Vol can't be suppressed 10:08-26:00= The volume of ODTE options & when retail flow becomes toxic 26:01-32:55= Complexity, probability & path dependency: Building a resilient portfolio 32:56-48:43= Systematic vs discretionary, the importance of dispersion 48:44-58:54= Fixed income factors and a panel too short In case you missed it: Check out Part I of this EQD Breakdown here! Follow along on Twitter with Jason ⁠@jasoncbuck ⁠and ⁠@MutinyFunds⁠ for all updates and also check out the website ⁠mutinyfund.com⁠ for more information. Don't forget to subscribe to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Derivative⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, follow us on Twitter at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@rcmAlts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and our host Jeff at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@AttainCap2⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, or ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ , and ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Facebook⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, and ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠sign-up for our blog digest⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Disclaimer: This podcast is provided for informational purposes only and should not be relied upon as legal, business, or tax advice. All opinions expressed by podcast participants are solely their own opinions and do not necessarily reflect the opinions of RCM Alternatives, their affiliates, or companies featured. Due to industry regulations, participants on this podcast are instructed not to make specific trade recommendations, nor reference past or potential profits. And listeners are reminded that managed futures, commodity trading, and other alternative investments are complex and carry a risk of substantial losses. As such, they are not suitable for all investors. For more information, visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.rcmalternatives.com/disclaimer

Keone Hon is the CEO and Co-founder of Monad Labs, the team supporting the high-performance Monad blockchain. He is a software developer and blockchain researcher.  Before founding Monad, he spent eight years at Jump Trading, leading an HFT team.  In 2021, Keone joined Jump's crypto division and led a team of engineers focused on blockchain research and dApp development.In this conversation, we discuss:- JUMP Trading- High frequency trading- The relationship between HFT and crypto trading- Security on the Monad blockchain- L1 pros and cons vs. the pros and cons of L2s- The narrative always changes- Roll-ups- Scaling blockchain tech at the L1 level- Creating better blockchain-based mobile appsMonad LabsWebsite: www.monad.xyzTwitter: @monad_xyzDiscord: discord.gg/monadKeone HonTwitter: @keoneHDLinkedIn: Keone Hon   ---------------------------------------------------------------------------------  This episode is brought to you by PrimeXBT.  PrimeXBT offers a robust trading system for both beginners and professional traders that demand highly reliable market data and performance. Traders of all experience levels can easily design and customize layouts and widgets to best fit their trading style. PrimeXBT is always offering innovative products and professional trading conditions to all customers.  PrimeXBT is running an exclusive promotion for listeners of the podcast. After making your first deposit, 50% of that first deposit will be credited to your account as a bonus that can be used as additional collateral to open positions.  Code: CRYPTONEWS50  This promotion is available for a month after activation. Click the link below:  PrimeXBT x CRYPTONEWS50 

FatManHashflow on Twitter: "Hashflow - a DeFi exchange - has rugged early supporters of their tokens through implementation of a DAO proposal authored by their CTO. At the time of writing, an estimated 7-8 figures USD worth of locked $HFT tokens belonging to users have been voided to the DAO treasury. - https://twitter.com/FatManHashflow/status/1667954514157248513 Mark Cuban on Twitter: "This is an SEC WEB PAGE about the howey test and tokens that often conflicts with what @SEC_Enforcement has said publicly. - https://twitter.com/mcuban/status/1667914770651987970?ref_src=twsrc%5Etfw ross on Ooki DAO: "The CFTC just won a court case against a DAO and "as members" some founders are now on the hook for $643,542, and the DAOs websites and ops have been ordered to shut down. - https://twitter.com/z0r0zzz/status/1667272302294360064?s=20 Using free crypto airdrops and NFTs for promotions will be banned in the U.K. - https://twitter.com/coindesk/status/1667122397441318913?s=52&t=K41FvHwlVL5YAFz7rppRhg Binance CEO CZ responds as data points to billions in exchange outflows - https://cointelegraph.com/news/binance-ceo-changpeng-zhao-address-billions-exchange-outflows Democrats' ‘war on crypto' will lose its key voters: Winklevoss twins - https://cointelegraph.com/news/democrats-crypto-war-alienate-youth-voters-winklevoss a16z opening London crypto office citing ‘predictable' environment - https://cointelegraph.com/news/a16z-open-london-office-international-expansion Mission: DeFi - EP 101 - DeFi to grow #AI & open source applications - Max Howell(@mxcl) of @teaxyz explains the powerful model - https://www.missiondefi.com/mission-defi-ep-101-defi-to-grow-ai-open-source-applications-max-howellmxcl-of-teaxyz-explains-the-powerful-model/ Former SEC Chairman Jay Clayton on Enforcement Actions: Crypto Should Be Treated With Nuance – Regulation Bitcoin News - https://news.bitcoin.com/former-sec-chairman-jay-clayton-on-enforcement-actions-crypto-should-be-treated-with-nuance/ If Microstrategy Chose ETH Over BTC, the Firm Would Be up 54% and Ahead by More Than $2B, Data Reveals – Bitcoin News - https://news.bitcoin.com/microstrategy-chose-eth-over-btc-the-firm-be-up-ahead-by-more-than-2b/ Gensyn - https://www.gensyn.ai/ Blockchain-Based, AI Compute Protocol Gensyn Closes $43M Series A Funding Round Led by a16z Crypto - https://www.coindesk.com/business/2023/06/11/blockchain-based-ai-compute-protocol-gensyn-closes-43m-series-a-funding-round-led-by-a16z/?utm_medium=referral&utm_source=rss&utm_campaign=headlines Did Gary Genslers SEC Break the Law by Suing Coinbase? - https://www.coindesk.com/consensus-magazine/2023/06/09/the-new-crypto-bill-gary-gensler-doesnt-want-you-to-know-about/ BinanceUS to suspend dollar deposits, Vitalik pledges $100m to Covid relief – https://www.dlnews.com/articles/markets/binanceus-to-halt-usd-deposits-as-vitalik-donates-to-india/?utm_source=telegram&utm_medium=organic_social&utm_campaign= Joe Cawley and Brad Nickel cover the DeFi news of the day, new opportunities in the space including liquidity pools, yield farming, staking, and much more. This is not financial advice. Nothing said on the show should be considered financial advice. This is just the opinions of Brad Nickel, Joe Cawley, and our guests. None of us are financial advisors. Trading, participating, yield farming, liquidity pools, and all of DeFi and crypto is high risk and dangerous. If you decide to participate, do your own research. Never count on the research of others. We don't know what we are talking about and you can lose all your money. Never invest more than you can afford to lose, because you probably will lose it all. --- Support this podcast: https://podcasters.spotify.com/pod/show/missiondefi/support

Michael Feng is the Co-Founder @ Hummingbot (https://hummingbot.org/). Backed by Initialized Capital, Slow Ventures, & more, Hummingbot is open source software that helps you build high-frequency crypto trading bots that specialize in market making and arbitrage strategies. They average $100M+ 24H volume, have 1M+ all-time downloads, and support 13+ HFT strategies. In this episode we dive deep on recently decentralizing their operations to HBOT token holders, why they restructured as a not-for-profit organization, the key differences between TradFi and crypto markets for traders, & much more.Recorded Tuesday May 30th, 2023.

Crypto Hot Seat: Brandon Mulvihill, Crossover Markets On this episode we discuss: What is Crossover Markets, the crypto venue for HFT, exchanges vs brokers and more Bitcoin and Ether volatility, skew, major options positioning And much more…

This is Eric Golden and my guest today is Jordi Alexander. Jordi is a poker player turned quantitative trader. While Jordi was trained in high-frequency trading, he has a deep passion for macro and taking fundamental positions that computer models may disagree with. We take full advantage of Jordi's breadth of knowledge in this wide-ranging conversation. We first dive into the mysterious world of high-frequency trading and Jordi's experience at GETCO, Tower, and then leaving to build his own firm, Selini Capital. We discuss Crypto's product market fit, how Jordi assesses the value of Bitcoin and Ethereum, the Balaji bet, and more. Please enjoy my conversation with Jordi Alexander. For the full show notes, transcript, and links to the best content to learn more, check out the episode page here. ----- This episode is brought to you by OKX. You may have seen OKX on McLaren's Formula 1 race car or Manchester City's football kit. But what is OKX? OKX has over 730 spot trading pairs, 280 derivatives markets, and 1000 options markets. It processes 400,000 requests per second with 99.95% uptime. That's why over 20 million traders and institutions choose OKX when they want to trade. Visit okx.com to learn more. ----- Web3 Breakdowns is a property of Colossus, LLC. For more episodes of Web3 Breakdowns, visit joincolossus.com/episodes. Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here. Follow us on Twitter: @Web3Breakdowns | @ericgoldenx | @patrick_oshag  Show Notes (00:00:32) - (First question) - Exploring how high-frequency traders (HFTs) have generated profits in recent years (00:02:50) - The utilization of technology to forecast the movement of asset classes (00:04:24) - Why he focuses on prediction over speed in trading strategies (00:05:36) - Methods for evaluating the effectiveness of a trading strategy (00:06:42) - The aggressive tactics that HFT firms employ to protect their advantages (00:09:27) - How COVID-19 changed the landscape of news trading (00:11:13) - The importance of talent and finding niches in HFT (00:12:37) - His decision to pursue an entrepreneurial path and establish Selini Capital (00:13:47) - How culture and incentives in the HFT industry impacted the genesis of Selini (00:20:17) - Finding an edge as a smaller team against giants with more resources (00:21:30) - Why he and his team started to transition to crypto (00:24:41) - His perception of other trading firms as Selini moved into the crypto space (00:27:17) - A breakdown of how the team at Selini Capital is structured   (00:29:40) - Why Selini avoids taking outside capital from LPs  (00:30:29) - How Selini operates in both the crypto and fixed-income asset classes   (00:31:11) - Why his non-technical background positions him to approach the market differently (00:36:09) - Using the dog and the fox parable to describe his view of crypto in the long term    (00:38:06) - The roles human psychology and survival strategies play in the crypto landscape (00:40:25) - Drawing comparisons between financial markets and gambling (00:43:11) - Advice on what exposure newcomers should have in the crypto space (00:45:07) - His thoughts on Balaji's outlook on Bitcoin (00:47:43) - Creating new money in a way that is globally accepted and fair for everyone (00:50:04) - His thoughts on crypto regulation and international adoption abroad (00:54:32) - What he's most excited to build over the next six months and six years 

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: LLMs and computation complexity, published by Jonathan Marcus on April 28, 2023 on LessWrong. Epistemic status: Speculative. I've built many large AI systems in my previous HFT career but have never worked with generative AIs. I am leveling up in LLMs by working things out from base principles and observations. All feedback is very welcome. Tl;dr: An LLM cannot solve computationally hard problems. Its ability to write code is probably its skill of greatest potential. I think this reduces p(near term doom). An LLM takes the same amount of computation for each generated token, regardless of how hard it is to predict. This limits the complexity of any problem an LLM is trying to solve. Consider two statements: "The richest country in North America is the United States of ______" "The SHA1 of 'abc123', iterated 500 times, is _______" An LLM's goal is to predict the best token to fill in the blank given its training and the previous context. Completing statement 1 requires knowledge about the world but is computationally trivial. Statement 2 requires a lot of computation. Regardless, the LLM performs the same amount of work for either statement. It cannot correctly solve computationally hard statements like #2. Period. If it could, that would imply that all problems can be solved in constant time, which is provably (and obviously) false. Why does this matter? It puts some bounds on what an LLM can do. Zvi writes: Eliezer Yudkowsky does not see any of this as remotely plausible. He points out that in order to predict all the next word in all the text on the internet and all similar text, you need to be able to model the processes that are generating that text. And that predicting what you would say is actually a good bit harder than it is to be a being that says things - predicting that someone else would say is tricker and requires more understanding and intelligence than the someone else required to say it, the problem is more constrained. And then he points out that the internet contains text whose prediction outright requires superhuman capabilities, like figuring out hashes, or predicting the results of scientific experiments, or generating the result of many iterations of refinement. A perfect predictor of the internet would be a superintelligence, it won't ‘max out' anywhere near human. I interpret this the opposite way. Being a perfect predictor of the internet would indeed require a superintelligence, but it cannot be done by an LLM. How does an LLM compute? What kinds of problems fall into category 2 (i.e., clearly unanswerable by an LLM)? Let's dig in to how an LLM computes. For each token, it reviews all the tokens in its context window "at least once", call it O(1) time. To produce n tokens, it does O(n^2) work. Without being too precise about the details, this roughly means it can't solve problems that are more complex than O(n^2). Consider some examples (all tested with GPT-4): Addition, O(1) It's not always accurate, but it's usually able to do addition correctly. Sorting, O(n log n) I asked it to sort 100 random integers that I'd generated, and it got it right. My guess is that it doesn't have the internal machinery to do a quick sort, and was probably doing something more like O(n^2), but either way that's within its powers to get right, and it got it. Matrix multiplication, O(n^3) I generated a 3x3 matrix called A and told it to compute AA. This was interesting, let's look at what it did: Pretty cool! It executed the naive matrix multiplication algorithm by using O(n^3) tokens to do it step-by-step. If I ask it to do it without showing its work, it hallucinates an incorrect answer: The result was the right shape, and the elements had approximately the right number of digits. Slight problem: the elements are all incorrect. Whoops. This makes sense though....

I flew out to Chicago to interview Brett Harrison, who is the former President of FTX US President and founder of Architect.In his first longform interview since the fall of FTX, he speak in great detail about his entire tenure there and about SBF's dysfunctional leadership. He talks about how the inner circle of Gary Wang, Nishad Singh, and SBF mismanaged the company, controlled the codebase, got distracted by media, and even threatened him for his letter of resignation.In what was my favorite part of the interview, we also discuss his insights about the financial system from his decades of experience in the world's largest HFT firms.And we talk about Brett's new startup, Architect, as well as the general state of crypto post-FTX.After talking with Brett for 3 hours, I found him to be extremely intelligent, thoughtful, and ethical.Watch on YouTube. Listen on Apple Podcasts, Spotify, or any other podcast platform. Read the full transcript here. Follow me on Twitter for updates on future episodes.Similar episodesSide note: Paying the billsTo help pay the bills for my podcast, I've turned on paid subscriptions on Substack.No major content will be paywalled - please don't donate if you have to think twice before buying a cup of coffee.But if you have the means & have enjoyed my podcast, I would appreciate your support

“New Mexico Elk and Navigating the Draw Process with Jordan Christensen from The Draw” Jordan Christensen from The Draw stops by for an EXTRA long episode. Normally I'd break this apart into 2 or 3 parts to keep the under 30-minute algorithm happy, but the information imparted was so intertwined there was no way I could break it apart. So go work out for an hour and a half, head up to ice camp and listen along because this information packed episode is epic. Jordan shares his rise in the outdoor industry, and his journey from outfitter to taxidermist to hunting consultant for Cabelas. We talk about permit draws and lotteries and how he helps other hunters navigate the process via The Draw. One of the states and species we highlighted in depth was Elk hunting in New Mexico, he explains the difference between a $750 DIY OTC draw tag vs the $10,000 available landowner tags. And which one is right for you. He talks about what Guided Pools even are and how it works. Then we chat about how We are the Draw service works and the costs you incur by using them. (which is surprisingly low.) We talk about drawing tags, cost of, odds and probably a ton of informational tidbits I'm forgetting about several states and species. And in the end I ask him, “I have no idea what I want to hunt or where, what do you recommend?” And his answer surprised me. The state and specie(s) suggested were extremely doable. Share this on social media with the answer to that question, tag the HuntFishTravel Podcast in it for a chance to win a free HFT tee shirt on April 15th! Links: The Draw Website The Draw Facebook The Draw Instagram

He's a guitarist, a composer, a producer, an audio engineer and a teacher. Gaurav Chintamani joins Amit Varma in episode 316 of The Seen and the Unseen to share his reflections on music and life. (FOR FULL LINKED SHOW NOTES, GO TO SEENUNSEEN.IN.) Also check out: 1. Gaurav Chintamani at Instagram, Twitter, LinkedIn, SACAC and his own website. 2. Advaita on YouTube Music, YouTube, Spotify, Instagram and Twitter. 3. Raman Negi on YouTube Music, YouTube, Spotify, Instagram and Twitter. 4. The Dirt Machine on YouTube Music, YouTube and Spotify. 5. Grounded in Space -- Advaita. 6. The Silent Sea -- Advaita. 7. Shaksiyat -- Raman Negi. 8.  Mehroom -- Raman Negi. 9. Lullaby for the Anxious Bones -- Raman Negi. 10. It's About Time -- The Dirt Machine. 11. Kleptocrat -- The Dirt Machine + Amartya Ghosh (The song that started with dripping water). 12. Carol of the Bells -- Ishaan Chintamani. 13. Gaurav Chintamani on the School of Bass Podcast. 14. The Life and Times of Shanta Gokhale — Episode 311 of The Seen and the Unseen. 15. Hard work vs. Long work -- Seth Godin. 16. Warren Mendonsa Plays the Universal Pentatonic — Episode 273 of The Seen and the Unseen. 17. The Beatles on YouTube Music, Spotify and Wikipedia. 18. Bob Dylan on YouTube Music, Spotify and Wikipedia. 19. Choo Lo -- The Local Train. 20. Episodes of The Seen and the Unseen on the creator ecosystem with Roshan Abbas, Varun Duggirala, Neelesh Misra, Snehal Pradhan, Chuck Gopal, Nishant Jain, Deepak Shenoy and Abhijit Bhaduri. 21. Four Thousand Weeks -- Oliver Burkeman. 22. Redemption Song -- Bob Marley. 23. The Beach (book) -- Alex Garland. 24. The Beach (film) -- Danny Boyle. 25. John Cage on YouTube Music, Spotify and Wikipedia. 26. A Scientist in the Kitchen — Episode 204 of The Seen and the Unseen (w Krish Ashok). 27. Over the Hills and Far Away -- Led Zeppelin. 28. Amit Varma's favourite lofi playlists on YouTube and Spotify. 29. Music for Airports -- Brian Eno. 30. The Formula Behind Every Perfect Pop Song — Seeker. 31. The Double ‘Thank-You' Moment — John Stossel. 32. Deezer -- The French streaming service that Gaurav mentions. This is how they pay their artists. 33. Entry and Exit in Agriculture -- Episode 1 of The Seen and the Unseen (w Pavan Srinath and Karthik Shashidhar). 34. Altitude -- Advaita on The Dewarists. 35. A Hard Day's Night -- The Beatles. 36. Thriller -- Michael Jackson. 37. Guns N' Roses, Pantera and The Doors. 38. The Sky is Crying -- Stevie Ray Vaughan. 39. Jai Arjun Singh Lost It at the Movies — Episode 230 of The Seen and the Unseen. 40. Lou Majaw on YouTube Music and Spotify. 41. Acquired Senses (a demo version) -- HFT. 42. Natasha Badhwar Lives the Examined Life — Episode 301 of The Seen and the Unseen. 43. Suyash Rai Embraces India's Complexity -- Episode 307 of The Seen and the Unseen. 44. Jeff Beck on YouTube Music, Spotify and Wikipedia. 45. Jimi Hendrix on YouTube Music, Spotify and Wikipedia. 46. Imposter Syndrome. 47. Aakar Patel on Twitter. 48. A Meditation on Form — Amit Varma. 49. Wanting — Luke Burgis. 50. René Girard on Amazon and Wikipedia. 51. Lifespan: Why We Age – and Why We Don't Have To — David Sinclair. 52. Waking Up - A New Operating System for Your Mind -- Sam Harris. 53. The Adda at the End of the Universe — Episode 309 of The Seen and the Unseen (w Vikram Sathaye and Roshan Abbas). 54. Dirty Mind -- Jeff Beck. 55. The Haas Effect. 56. The Advaita jam in the Kolkata hotel room. 57. Here, There and Everywhere -- Geoff Emerick. 58. Paul McCartney listens to John Lennon's Beautiful Boy. 59. Watching the Wheels -- John Lennon. 60. Chris Cornell's covers of Watching the Wheels, Redemption Song, Long As I Can See The Light, Nothing Compares 2 U, I Will Always Love You and Thunder Road. 61. Penny Lane -- The Beatles. 62. Strawberry Fields Forever -- The Beatles. 63. The Bends -- Radiohead. 64. The White Album -- The Beatles. 65. Sticky Fingers, Exile on Main Street and Goat's Head Soup -- The Rolling Stones. 66. Time out of Mind -- Bob Dylan. 67. Amitava Kumar Finds the Breath of Life — Episode 265 of The Seen and the Unseen. 68. A Day in the Life -- The Beatles. 69. Stevie Wonder on YouTube Music, Spotify and Wikipedia. 70. Friends, Crime, & The Cosmos -- Abhishek Upmanyu. 71. This Be The Verse — Philip Larkin. 72. Somebody That I Used To Know -- Mike Dawes. 73. Happy -- Pharrell Williams. 74. Blow by Blow -- Jeff Beck. 75. Cause We've Ended as Lovers -- Jeff Beck. 76. Miles Davis on YouTube Music, Spotify and Wikipedia. 77. Bitches Brew -- MIles Davis. 78. Pat Metheny on YouTube Music, Spotify and Wikipedia. 79. John Scofield on YouTube Music, Spotify and Wikipedia. 80. I Can See Your House from Here -- Pat Metheny and John Scofield. 81. SD Burman on YouTube Music, Spotify and Wikipedia. 82. John Williams on YouTube Music, Spotify and Wikipedia. 83. The Study of Orchestration -- Samuel Adler. 84. Maurice Ravel on YouTube Music, Spotify and Wikipedia. 85. Claude Debussy on YouTube Music, Spotify and Wikipedia. 86. Johann Sebastian Bach on YouTube Music, Spotify and Wikipedia. 87. Cello Suite No. 1 in G Major -- Johann Sebastian Bach, played by Yo Yo Ma. 88. Thomas Dybdahl on YouTube Music, Spotify and Wikipedia. 89. The Old Man and the Sea -- Ernest Hemingway. 90. The Great Gatsby -- F Scott Fitzgerald. 91. Crime and Punishment — Fyodor Dostoevsky. 92. Atomic Habits — James Clear. 93. Stanley Kubrick on IMDb and Wikipedia. 94. Martin Scorsese on IMDb and Wikipedia. 95. Goodfellas -- Martin Scorsese. 96. Raging Bull -- Martin Scorsese. 97. Bernard Herrmann. 98. Psycho -- Alfred Hitchcock. 99. The Sopranos, Breaking Bad and Better Call Saul. 100. Seven Samurai -- Akira Kurosawa.. 101. The Girl From Kashmir — Episode 295 of The Seen and the Unseen (w Farah Bashir). 102. Dance Dance For the Halva Waala — Episode 294 of The Seen and the Unseen (w Jai Arjun Singh and Subrat Mohanty). 103. Akira Kurosawa and Yasujirō Ozu. 104. Mulholland Drive and Twin Peaks -- David Lynch. 105. Taxi Driver, New York Stories, Casino, Kundun and Silence -- Martin Scorsese. 106. A Whiter Shade of Pale -- Procul Harum. Check out Amit's online course, The Art of Clear Writing. And subscribe to The India Uncut Newsletter. It's free! Episode art: ‘Pieces of Me' by Simahina.

Yesha Yadav is Associate Dean and Professor of Law at Vanderbilt University Law School. She is one of the world's leading experts on financial and securities regulation. Before Vanderbilt, Yesha worked as legal counsel with the World Bank and before that she practiced regulatory and derivatives law at Clifford Chance. This week's podcast covers why the US Treasury market is fundamentally broken, the rise of HFT and algo trading, the diverges uses of Treasuries, and much more.

 In this episode, we are joined by David Hochman, the VP of analytics at BuyAlerts.com, a platform that provides its members with swing trade alerts for options and stocks. He is part of a trading team of professional analysts with a proven track record overseeing and discussing every trade to ensure the highest probability of success. Before joining BuyAlerts, he worked at ForeFront Capital, then at Credit Suisse, where he worked alongside the CrossFinder dark pool HFT team. David has always defined his edge based on the numbers. Tune in to learn more!

Christina Qi is the CEO of Databento and she is Co-Chair of the Board of Invest in Girls, bringing financial literacy education to underserved populations across the US. She formerly founded Domeyard LP, a hedge fund focused on high-frequency trading (HFT) that traded up to $7.1 billion USD per day. Listen to this podcast and know about her amazing investing journey on how she started Domeyard from her dorm room with $1000 in savings, about 9 years ago, after failing to earn a job offer after a Wall Street internship. Please Enjoy!          Would you please consider being 1% and leaving a short review on Apple Podcasts/ iTunes if you enjoy the podcast? It takes less than 30 seconds, and it makes a world of difference in reaching new interesting guests! To sign up for Kevin's Podcast email Newsletter and to view the show notes & past guests please visit-https://officialkevindavid.com/podcast Follow Kevin: https://mmini.me/@FollowKD