SGP-0003 is a middle finger to every microstructure-sensitive application on Solana. It signals to every developer that the cost model for the chain may be upended at any time, even if no applications support it. How can you put your business logic on-chain in this world?

Introducing #Rodin# Gen-2.5🚀 🔥World’s 1st 10 MILLION polygon #3D# GenAI — down to skin microstructures. 1️⃣1M-poly in 4s 2️⃣Adaptive thinking effort - just like LLMs 3️⃣3D-native textures, no blind spot 4️⃣Batch up to 10 results 💥Manual BANG to Parts & more... 🚨$1 for first mo!

i am never paying for a trading course again. instead of paying $3000 i am using expert tutor bots that i have created (for free) to learn market structure to teach me where the levels are, and microstructure to teach me whether a break is real or not. have them for free 👇 > it finds your level first > it quizzes you > it never gets bored of "but what about this chart?" structure shows you the levels. microstructure explains why price reacts at them. Market Microstructure tutor: Market Structure Tutor: tag @bot and you can download them!

Not like we haven’t seen before. More $USELESS FUD, right in the middle of a bull market. What have I said repeatedly? FUD during a bullish macro trend ultimately becomes fuel for higher prices on the HTFs. And once again, the fear is building while the macro structure remains firmly bullish. Oh well. Some people never learn. I will let them be. Time will prove who is right. GOD WILLING

Expect more English in my posts going forward. The logic is simple: Crypto is a global playing field. The core infra updates, raw API specs, and serious discussions on market microstructure—the primary sources are almost always in English. Code and markets speak the same language.

Architect is hiring for three lateral roles: options trading quantitative researcher, compute capital market sales, and structured derivatives specialist. Details on what we’re building and the new team members we’re looking for Quantitative Researcher: We’re building: automated high-frequency options trading strategies on legacy and new(er) exchanges What you’ll work on: volatility modeling, ML for parameter optimization, order placement simulation, new venue exploration Stack: Rust, Python, AWS, ClickHouse What we’re looking for: field experience in derivatives trading and research, strong system design instincts, multiple approaches to options pricing theory, bias towards shipping Nice to have: familiarity with the latest ML toolkits and exchange microstructure Location: your preference Compute Capital Markets Sales: We’re building: the first US-regulated exchange purpose-built for compute futures, options, swaps, forwards, and capacity, plus AI supply chain commodities What you’ll work on: building relationships and facilitating trades with neoclouds, lenders, insurance providers, inference companies, datacenters, AI labs, interdealer brokers, retail brokers, hedge funds, and asset managers What we’re looking for: network of established and startup AI companies, fluency with compute capacity pricing/structures, translation skills between finance and AI, aversion to slop, commitment to competitive markets for compute Nice to have: experience in compute procurement at a hyperscaler, neocloud, or lab Location: SF Structured Derivatives Specialist: We’re building: specialized contracts for trading and hedging compute, compute derivatives, and a range of physical commodities supporting the US datacenter build-out What you’ll work on: modeling and structuring OTC products with Architect’s traders/technologists and AI counterparties for compute, memory, inference, and energy trading What we’re looking for: experience trading OTC derivatives contracts at banks or trading firms, sell-side institutional network, enthusiasm for working at startups Nice to have: relevant FINRA/NFA certifications or passion for test-taking Location: your preference

AI has broadly become synonymous with LLMs, but most AI/ML models used in quantitative trading are decades old. Boosted trees and PCA still account for more alpha than anything built on transformers. More detail on several ML techniques in quant trading: Gradient boosted trees and random forests: A standard strategy in short-horizon trading is to start with an intuitive set of microstructure features and train a non-linear model on them. Random forests provide a baseline ensemble that’s hard to overfit, while gradient boosted models fit fainter structures at the risk of overfitting. Features can include microprices, characteristics of the present order book, recent returns of related instruments, momentum signals, and orderflow directionality. Pre-transformer natural language processing: Throughout the 2010s, NLP PhDs were a sought-after resource for quantitative hedge funds. A variety of techniques such as Loughran-McDonald finance-specific sentiment dictionaries, TF-IDF and bag-of-words classifiers, and LDA topic modeling were used to extract sentiment from news, earnings reports, SEC filings, and FOMC minutes. Transformers are more generalized tools for language processing but some of the older methods in the toolkit are still used for their simplicity, predictability, and cost-effectiveness. Dimensionality reduction: A statistical arbitrage researcher may have thousands of correlated instruments and only a few years of clean historical data, so the covariance matrix has more parameters than observations to estimate them from. Principal component analysis is the standard remedy. A handful of leading components explain most of the variation in a universe of equities, and hedge funds will frequently neutralize the leading factors and trade the residuals. Autoencoders generalize the same idea to nonlinear structure, but PCA persists because its output is stable, interpretable, and cheap to recompute. Computer vision: From around 2010 to 2017, hedge funds gained a large informational edge by building automated pipelines to extract alternative data from satellite imagery. The models were unremarkable by research standards, mainly derivatives of convolutional neural networks. Some of the most famous applications include counting parked cars in retail lots, gauging construction progress at industrial sites, estimating crop yields from near-infrared reflectance rather than visible light, and reading crude inventory off the shadow cast by a floating tank roof. The trade worked best during a time when imagery was scarce and the engineering was novel. Today image-based alternative data is sold by third-party vendors for relatively inexpensive consumption. The large memory footprint, slow inference, and black-box nature of LLMs make them a poor fit for alpha generation at trading firms. Hand-tuned models based on intuitional feature selection are still the norm. For niche industries like quantitative finance, there is ample room for competition with generalized frontier models.

First Principles Ep. 5 with Paul Milgrom One of the most important auction designs in modern history was sold to the FCC on a 3.5-inch disk. Nobel Prize winner Paul Milgrom on auctions, price discovery, and how mechanism design became real-world infrastructure — from spectrum auctions to DeFi. Hosted by @Tim_Roughgarden with @skominers. 00:00 Intro: economics assumptions that are “just wrong” 02:19 Scott Kominers and Tim Roughgarden on the genius of Paul Milgrom 05:35 An intro to the  Glosten-Milgrom model: The paper that created entire fields of economics 07:48 The auction theory breakthroughs of the 1980s 17:15 Why market microstructure matters for DeFi 24:17 When math teaches economics something new 32:38 How theory became spectrum auction design 36:22 The floppy disk that helped convince the FCC 41:05 What changed when auctions moved online 45:28 The auction that reorganized television 1:07:30 What economics and computer science can learn from each other 1:13:22 Futures markets for compute 1:15:12 Paul Milgrom’s advice for builders

As someone who loves trading technicals I think learning about markets via technicals (like I did) is one of the worst ways to start It’s a rigid framework where grown men argue with each other about the exact Japanese name for a specific candlestick or a box they’ve drawn on an arbitrary time frame It doesn’t teach you the foundations - why markets move, different types of participants, microstructure, order types and their impact, perps vs spot, and all that stuff - market ‘plumbing’ as a category One of the biggest issues with being hyperfocused on technicals is that they don’t teach you principles and market effects Most technical setups can be decomposed into broad buckets which are well-established (trend, mean reversion, momentum, order flow / price impact, vol clustering etc.) A lot of technical analysis is an often unknowing attempt to map those broad market effects into a recognisable pattern But even a technical-first view is better served by understanding the underlying market effect first and then decomposing it, as opposed to focusing on the specific pattern without ever looking at what’s happening under the hood “This type of triangle tends to go up” is a lot less useful than “this type of flow tends to resolve higher over N time frame”, even if you use the same triangle to identify it Another example: if you’re drawing a support level and buying it, you’re assuming some version of buyers being more aggressive than sellers in that area over a given time frame and predicting a higher price as a result - but what does that mean? Shorts closing / taking profit, allowing for mean reversion? Aggressive sellers being absorbed by passive buyers? Some price insensitive buyer predictably stepping in at a value area? Sellers getting margin called and forcibly trading at bad prices/causing a dislocation? Clustering of orders creating some sort of imbalance? And so on. There’s definitely a risk of overthinking this stuff, and you can make money from charts alone But if you haven’t thought about the underlying market effects and ‘plumbing’ for your setups you’ll likely be stuck in rigid pattern matching that doesn’t generalise and isn’t subject to deeper investigation and more nuanced application Even if your main lens remains TA-focused, there is no harm in understanding the stuff you’re trading on a product level (eg perp contract specs, OI, funding, mark/last/index etc) and on a foundational level (why and how markets move) Especially now that you can jam this stuff into an LLM and keep saying “dumb it down” until you get it, no excuse not to do your homework This is something I really wish I did much earlier in my trading life, so hopefully it resonates with a fellow trader stuck in TA psychosis spending his mum’s credit card on a fourth Udemy candlestick course Anyway GM