Brett Harrison

Brett Harrison@BrettHarrison · 15 days agoSector impact

@Kevihaiceth I've experienced them making fewer mistakes than humans trying to copy out byte-layouts or JSON records by hand. Half the issues that arise are because the specs themselves are out of date, so no matter what there needs to be a testing harness

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AI: Theme-oriented investment view, reinforcing conviction in infrastructure and new narratives.

Brett Harrison@BrettHarrison · 15 days agoOpinion output

@davidzmorris Sure, DM

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AI: Continues the public long-term stance, emphasizing execution efficiency and industry direction.

Brett Harrison@BrettHarrison · 15 days agoMarket impact

While LLMs are generally deficient at creating production-grade algorithmic trading systems, they are adept at a number of discrete tasks and components that go into building automated trading strategies or larger distributed systems in finance. Here are the top few: Order entry protocols: All trading operations that connect directly to exchanges involve a time-consuming, developer-intensive task of adapting each exchange’s proprietary order protocol to their algorithmic trading system. Most venues provide detailed specification documents for FIX, binary, and JSON-based messaging that can be read by LLMs, saving tens of hours of protocol normalization work. If the exchange provides a sandbox environment, an LLM can test its own adaptation in a REPL-style loop. Market data: Similar to order entry, LLMs are great at normalizing L1, L2, and L3 feeds for consumption by strategies and modeling processes. LLMs are also able to build the rote connectivity and authentication stack required to retrieve the data streams. LLMs lack the level of taste required to develop the right abstraction for feed normalization that preserves venue-specific details while also enabling developers to write venue-agnostic strategies. However, once such abstractions are established, there is little need to manually adapt market data protocols. Research database management: It’s common practice to record every event that passes through a trading system in databases: book updates, orders, cancels, trades, fair value updates, error messages, etc. General-purpose time-series databases are the common solution, and form the foundation of research, monitoring, and debugging stacks. Assuming that the trading system uses a well-known database technology such as Postgres or ClickHouse, LLMs can handle schema generation, table compaction, version migrations, backups, and automated reports. User interfaces: Whether an algorithmic trading system is black-box (minimal human input during the order lifecycle) or gray-box (automated order placement with manual human intervention on parametrization), all traders leverage some form of user interface for trade, risk, and P&L monitoring. Trading engineers tend not to be the most skilled GUI developers, but internal interfaces do not need to be pretty to be highly functional. Out of all areas of software development, frontier coding models have reached expert level in building terminal- and web-based applications. In building our derivatives exchanges, we’ve designed our APIs and external-facing components with both institutional traders and agentic developers in mind. This should be the financial industry norm. Most of our institutional customers have made use of our Claude skill for protocol integration, and we expect other brokerages and exchanges to support these emerging methods of trading system development in addition to agentic trading.

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AI: Focused on platform operations and ecosystem expansion, highlighting long-term gateway positioning.

Brett Harrison@BrettHarrison · 16 days agoSector impact

@heiwushiJ The maintenance thresholds are preset, but when to wait for funds vs unwind is a judgment call

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AI: Theme-oriented investment view, reinforcing conviction in infrastructure and new narratives.

Brett Harrison@BrettHarrison · 16 days agoMarket impact

We have the same risk management procedures as traditional futures exchanges+clearing firms. We set leverage multiples based on asset volatility, which lets us provide margin call warnings and top-up grace periods. If we need to liquidate (we haven't yet) we unwind manually to prevent cascades. And we contribute our own funds to a default waterfall to absorb margin shortfall instead of using ADL.

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AI: Focused on platform operations and ecosystem expansion, highlighting long-term gateway positioning.

Brett Harrison@BrettHarrison · 16 days agoSector impact

New equities perps on Architect’s AX as we begin onboarding individual traders. We built AX for institutions. Our new customers will benefit from the robust liquidity and billions in volume they’re trading, and from their non-negotiable requirement: no ADL or autoliquidation… In under six months of operation, AX is now just behind the top 5 RWA perps DEXs, executing $350M/week, and we’re scaling aggressively. AX’s unprecedented growth is enabled by architectural stability and a rejection of DEX risk management: market-selling failing positions at times of scarce liquidity (auto-liquidation) while tearing up winning positions to cover shortfalls (ADL). This prevents almost every financial institution from trading on their venues and harms their customers, especially individuals. We’re growing AX into the largest global traditional asset derivatives exchange for institutions and individuals, and we’re building it to last.

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AI: Theme-oriented investment view, reinforcing conviction in infrastructure and new narratives.

Brett Harrison@BrettHarrison · 17 days agoOpinion output

@HangukQuant Yes exactly, back to optimizing in a bare-metal environment with deterministic latency

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AI: Continues the public long-term stance, emphasizing execution efficiency and industry direction.

Brett Harrison@BrettHarrison · 17 days agoPolicy impact

Optimizing Exchange Latency At HFTs I built low-latency trading systems for traditional and cloud-native exchanges including Nasdaq, NYSE Arca, CME, ICE, SGX, JPX, Coinbase/GDAX, and others. A summary/discussion of the current split in low-latency network infrastructure Colocation: Traditional exchanges are hosted in physical datacenters. Colocation involves renting rack space with cross-connects to the exchange matching engine. While it is expensive and time-consuming to set up, physical colocation guarantees an equalized path to the exchange. By contrast, it is fast and inexpensive to set up cloud servers but difficult to guarantee the lowest latency. To be colocated with an exchange running in AWS, firms first need to understand not only what region the exchange is deployed in (e.g. us-east-1) but also the availability zone (e.g. use1-az4). Even within a single availability zone, there is variance in how close a randomly placed EC2 instance lands to the matching engine. Non-determinism: Networking setups for traditional exchanges offer deterministic access to matching engines by running the same cable lengths to all client boxes. Public clouds have a significant degree of non-determinism due to routing within a cloud region. Two different EC2 instances in us-east-1/use1-az4 could have different latencies to the same matching engine in the same zone. Firms mitigate this issue by spinning up multiple instances and choosing the one with lowest ping times, as well as using cluster placement groups and AWS technology such as ENA Express. Unicast instead of multicast: Traditional exchanges disseminate market data over UDP multicast, which puts a single copy of the book on the wire and delivers it to every participant simultaneously. Public clouds don't offer multicast in any form usable for market data: VPCs have no native support, and Transit Gateway multicast is built for enterprise applications rather than microsecond-sensitive feeds. Cloud-native exchanges therefore publish over TCP unicast, which involves a separate copy for every subscriber and a fan-out order that no longer treats them equally. HFTs recover what they can by bypassing the kernel for their networking stack. They use a poll-mode driver such as DPDK over ENA that lets the NIC DMA frames directly into userspace ring buffers, with a userspace TCP stack handling the protocol above it. Inter-region latency: Expensive microwave, millimeter-wave, and subsea fiber links carry the lowest-latency paths between physical datacenters, and none of them terminate inside a cloud region. To move data between cloud regions and traditional venues, firms combine the long-haul routes of established market data vendors with specialized cloud on- and off-ramps, paying a penalty at each transition. Since price discovery still originates largely from traditionally colocated exchanges, trading on public cloud venues adds to a firm's market data infrastructure footprint rather than replacing it. Partnerships between CME and Google and between Nasdaq and Amazon aim to deliver infrastructure that combines the flexibility and global reach of public clouds with the deterministic latency of purpose-built exchange datacenters. If that convergence arrives, most of the practices above stop paying. The advantage in trading on cloud-native exchanges may shift back toward the firms with decades of colocated experience rather than those who have built expertise in cloud-latency engineering.

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AI: Policy-driven commentary with emphasis on compliance rollout and clearer rules.

Brett Harrison@BrettHarrison · 18 days agoOpinion output

@yeabu369 In this case it's buying futures instead of writing options but the payoff diagram is similar.

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AI: Continues the public long-term stance, emphasizing execution efficiency and industry direction.

Brett Harrison@BrettHarrison · 18 days agoOpinion output

RT @vikramcantsingh: https://t.co/V74Q8VEryj

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AI: Continues the public long-term stance, emphasizing execution efficiency and industry direction.