Tungsten is coming to Architect’s AX. Perpetuals, not cubes. Hedge metals supply chain risk at every stage of compute manufacturing: metals mining, chip and memory fabrication, gas generation, electricity consumption, GPU capacity procurement, and inference creation.

The first reaction to finding a high-Sharpe strategy in simulation should be to assume there’s a mistake. Markets are adversarial, data is messy, and small modeling errors compound into misleading results. Common mistakes in training systematic trading models and some solutions: Omitting fees and slippage: Nearly all equities, options, futures, and digital asset exchanges charge higher fee amounts for removing liquidity than for adding liquidity. HFT strategies that hold positions for only seconds or minutes frequently see their expected edge disappear once realistic fees are applied. Another common mistake is assuming a liquidity-taking order is fully filled at the best bid/ask instead of walking the book, consuming successive price levels up to available size. This both systematically underestimates cost and overestimates the capacity of the strategy. Accurately modeling fees and slippage requires building automated reconciliation between predicted costs and exchange-reported fees and fill prices. In-sample contamination: Statistical arbitrage strategies that fit model parameters to historical data need to avoid evaluating performance on the period used for training. The naive method of splitting data is to hold back the most recent days from the training set, which may result in training the strategy on a different market volatility and momentum regime than the recent past. Simple K-fold cross validation, a common fix from machine learning, is inappropriate for time series as this method leaks future information into the past. The most successful data splitting techniques involve purged K-fold, walk-forward optimization, or combinatorial purged cross-validation. Incompatible clocks: Backtesters that reference timestamps based on the market data capture machine’s clock overestimate achievable fill ratios. A partial solution is to refer to exchange timestamps for order book events to determine whether liquidity still exists when orders are sent. However that solution is also unreliable as exchanges have wide distributions of latencies between matching engine actions and book update dissemination. A robust heuristic is to re-run the backtest under a range of artificial order-submission delays and examine the sensitivity of both fill ratio and P&L. Survivorship bias: The choice of instrument universe itself introduces survivorship bias. For example, choosing the constituents from today’s S&P 500 index systematically selects historical winners only, as those names that underperformed have already been excluded from the basket. One solution is to record point-in-time universes to use for future historical studies, and another is to fix today’s universe and implement a walk-forward simulation until enough data has been collected.

Excellent and comprehensive discussion of the critical market opportunities in compute derivatives. Appreciate Architect’s inclusion as US exchanges approach regulatory approval and begin building robust futures markets in compute.

AI capex is projected to reach $765B in 2026, passing oil and gas for the first time. Compute is now getting its first futures contract. Here is what has to be true for it to work…

Can inference costs be commoditized? On OpenRouter there’s an 11x spread between the cheapest and most expensive provider of a single model, DeepSeek V4 Flash. Other relevant data points on open-weight inference prices, speeds, availability: • Baidu serves DeepSeek V4 Flash at $0.049 per million tokens and at 124 tokens per second. 26 of the 30 other providers are both more expensive and slower, so it’s not a tradeoff of speed vs cost. • Across the 117 models on OpenRouter with three or more providers, the median price spread between the cheapest and priciest host is 2.2x, the most extreme is 14.4x (DeepSeek v3.2). • Among all endpoints serving the above, 37% of endpoints are strictly dominated by cost, speed, and uptime. The number goes up to 52% if considering only cost and speed.

Compute Markets referenced a couple of times in today's committee meeting. Everyone agrees that these markets should exist and fast-tracked. TLDR: @FalconXGlobal @2Ragu - AI CapEx hit $670B in a single-year. These will be the largest markets ever and an opportunity for the CFTC to define compute as an asset class. AI Labs going through IPO processes are being asked (for the first time) about their compute or datacentre expenses and they want to find ways to hedge to reduce their cost of capital. @multicoin @tushar_jain requesting for an innovation exemption for compute derivatives. @blockchain doubles down on Multicoin's idea for a sandbox environment for compute. Don Wilson says compute markets gives US capital efficiency in the AI race. Duffy highlights that Kalshi's compute markets are live whilst everyone else must abide by CFTC's request for comment period.

Accurately pricing derivatives when underlyings are closed is a universal source of edge in market making, from tier-1 firm graybox ETF trading to newer operations in 24-hour prediction markets and perpetual futures. Some widely applicable pricing strategies: Related futures and currency moves. When a derivative’s underlyings are closed, market makers use other instruments as proxies that have sufficient beta to the derivative. Examples for a US ETF on Japanese stocks: the USD/JPY pair trades 24/7 through a variety of FX ECNs, interdealer platforms, and futures exchanges. Nikkei 225 index futures also trade on multiple global derivatives exchanges during extended hours. Moves in these assets during the Japanese night will on average predict the opening prints of individual stocks listed on JPX. Home market index moves. An ETF moves with a non-trivial correlation factor to other US names simply because it’s a US-listed security. The effect is easily observable during heightened volatility. It’s a common industry saying that in a market-wide selloff, all correlations go to 1. Both narrow- and broad-based indexes of US names explain some of the signals in mid-frequency alphas. US index returns comprise a small but meaningful component of derivatives’ multifactor beta models. News. A key requirement for pricing derivatives on foreign stocks is processing local news and earnings releases that relate either to the particular stocks or relevant stocks in the same sector. Quantitative trading firms use automated translation tools to process local foreign-language news and suggest idiosyncratic adjustments to traditional factor models. Recent advances in LLM-based NLP have made sentiment analysis viable for blackbox trading systems to react instantaneously to news-based signals. Microstructure. High-frequency trading firms successfully and counterintuitively price derivatives by ignoring the underlyings’ characteristics. Firms extract short-term alphas from order book characteristics, recent returns, microprices, and other microstructure features. From the perspective of a reinforcement-learning non-linear model trainer, unlabeled feature sets and time series data result in ETFs, ADRs, and common stocks being treated as mathematically equivalent. Based on the above, here is a rough model of how large market-making firms predict mid-frequency returns when traditional underlying markets are closed: ΔP = β_FUT · ΔFUT + β_FX · ΔFX + β_IND · ΔIND + [News Term] These firms also heavily prepare for circumstances under which this model fails. FX and futures have idiosyncratic moves, home-market betas can break during regime shifts, news sentiments still have positives, and microstructure signals decay fast. The market shift underway in 24/7 traditional derivative and prediction contract trading will test the model’s longevity for market making and statistical arbitrage going forward.

The top trading firms in compute markets are going to be firms that are already leasing GPU compute, training their own models, managing their own infra. It's an incredibly young market with no way to warehouse risk. Having skin is the only way to get the invaluable experience with the nuances of the market as it's developing. Firms that hesitate will be too far behind considering that this experience is the byproduct of training bespoke models.

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.

The SEC’s Regulation Crypto Assets will create needed pathways for digital securities primary issuance, but regulated secondary markets will remain impossible without the agency rescinding Rule 611, the Order Protection Rule. More on this rule and its relevance to crypto: Rule 611 under SEC Regulation NMS prevents any venue for US equities or equity options from matching trades at a worse price than a protected quote on another venue. A protected quote is a round-lot firm order at top-of-book on a lit national securities exchange. Before 611 was established, floor brokers with access to quotations on multiple exchanges could provide inferior pricing to clients. The rule accelerated the migration to fully automated execution and led to today’s competitive ecosystem of 17 lit equities exchanges, 18 lit options exchanges, and over 80 darkpool ATSs and single dealer platforms. Major market participants have grown more critical of 611 in recent years. To comply with order routing rules, exchanges and broker-dealers pay market data and connectivity fees to the growing list of NMS exchanges. Large orders that can’t be filled on one exchange get broken up slowly and suffer price impact before routing is complete. The protections are less needed in an environment where HFTs already maintain price equilibria between exchanges through arbitrage. The new proposal for SEC Regulation Crypto Assets would create a safe harbor for primary issuance of digital securities, but doesn’t address how secondary markets can develop in light of the Order Protection Rule. An ATS or on-chain venue listing a token could not comply with the rule without NMS venues listing the asset, establishing a canonical NBBO, and facilitating on/off-chain hybrid routing. The SEC proposed rescinding the Order Protection Rule in June. Doing so would provide a clearer path for digital securities trading, but introduce significant disruption and unknown impact on equity and equity options markets.

We're seeing more companies focused on building modular DCs cutting lead times from 3Y to 3m. @CrusoeAI building a Spark Factory, Helios, Bleeding edge, @runware with Sonic Inference Pods, @Radiant_Infra and several other modular AI Factories.

Another consequence of inference unit economics improving vs training: it’s profitable to run inference clouds on scrappy datacenters like stranded gas pads, old crypto mines, or telco closets. Any powered shell with decent chips can be turned into a token-refining revenue gen.

Options products unlock feasible financing for datacenter projects says @BrettHarrison "20-25% interest rate for a [datacenter] loan is completely untenable, especially if you're a startup trying to use equity raise capital" With hedging, you can guarantee to you lender a GPU-hour rate, which unlocks tons of financing to a market that is supply constrained. "Right now NVIDIA is behaving like that put option seller of last resort" Brett, CEO @Architect_Fi

PRE-ONBOARDING FOR THE AMERICAN INNOVATION EXCHANGE IS LIVE. Sign up to trade Architect’s Nvidia GPU compute futures and options. Access the first US derivatives market for compute. Competitive margin, low fees, desktop/mobile/API access, block trades supported 🇺🇸

Announcing ComputeConnect: the financial industry's first exchange-for-physical network for compute. Convert cash-settled futures into provisioned GPU capacity through standardized, exchange-cleared contracts. Built in partnership with @ComputeDesk.

Trade for size on the American Innovation Exchange. Negotiate bilateral block transactions and report as a single print, via voice or desktop or mobile. Multi-leg spread trades supported.