if you're looking for some weekend reading to get into algorithmic trading, start with these three. Systematic Trading, Robert Carver ex-AHL, ex-Barclays. the best starting point of the three. Building Winning Algorithmic Trading Systems, Kevin Davey the practitioner's book. monte carlo, walk-forward, etc Testing and Tuning Market Trading Systems, Timothy Masters the hardest of the three and the most useful once you're past the basics.

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.

The $150 premium options sellers are getting completely incinerated. Wall Street algorithmic trading spent all week pricing in a disaster. The conference call hasn't even started yet and shorts are already crying in liquidity pools.😆#snow#

Coming soon: $TREAD TREAD (@treadfoundation) is the token used within an algorithmic trading terminal and agentic trading ecosystem for digital asset markets. Trading starts Sep 16 Get ready →

Navier-Stokes and other frontier math discoveries have shown that multi-agent ensembles are required to escape the typical creativity constraints of LLMs. Is this also applicable to algorithmic trading research, which similarly lacks direct answers in LLM training data? The general method for producing a proof of an unsolved math problem has been to set up large teams of agents to - create multiple formations of the problem - selectively research or exclude known results - search for counterexamples or use counterexample attempts to tighten problem bounds - randomly apply unrelated areas of mathematics - use formal verification methods to check other agents’ work - project manage and referee across all existing agents Single-agent systems tend to plateau to a mean knowledge level. This covers the majority of tasks at an efficient cost per token, as the answers to most queries lie explicitly in the latent space of LLMs. Clear examples where single agents fall short are complex system design and numerical analysis, both of which describe algorithmic trading research. The ensemble method has potential to discover new alpha in algorithmic trading, but at the cost of substantial compute and tokens. Frontier model companies won the competition for quant/engineering talent against large trading firms a few years ago, but are not yet deploying them in this direction.

In 2015 I formed a small group of engineers at Jane Street to rebuild the firm’s core trading system from the ground up, and we ended up cutting latency by two orders of magnitude. Some of the techniques we used, relevant for algorithmic trading systems and exchanges today: Zero allocation: Whenever a program allocates memory for an object on the heap, the runtime pays a steep penalty in latency. The simplest solution is to avoid memory allocation entirely. Jane Street famously uses OCaml, a strongly typed programming language that by default produces garbage collected by a dynamic collector. Most other firms use languages with manual memory management, but it was a strict part of Jane Street’s tech culture that all risk-sensitive code had to be written in OCaml. It took a collaborative effort across multiple groups within Jane Street’s technology org to create zero-allocation core libraries, combining the type safety of a functional programming language with the memory profile of a language like C. We built the new main trading loop in this hybrid OCaml/C-style, producing zero new allocations in the critical path from tick to trade. In modern languages like Rust, it is substantially easier to achieve precise memory management while still benefiting from type safety and compile-time guarantees. Kernel bypass: A primary goal of a low-latency trading system or exchange is to pull a network packet containing market data or order flow through the network card’s interface and into the program’s memory space as fast as possible. The standard Linux OS kernel uses slow abstractions to support a wide variety of network drivers, at the expense of the entire system’s end-to-end latency. When we started with an empty program that contained no business logic and only forwarded packets through when received, the end-to-end latency was already too slow. To fix this issue, we employed a standard practice in the HFT industry in which we bypassed the OS’s kernel stack entirely by leveraging our network card vendors’ proprietary APIs to DMA packets straight from the NIC into memory. This technique brought our empty-packet-forwarding baseline into the latency regime we needed in order to build out the rest of the trading, risk, and protocol code. Local IPC: Kernel bypass is necessary when reading routed packets off a network from a third party such as another exchange or client connection. When communicating between internal instead of external processes, the fastest transports avoid network stacks entirely. Processes within the same box can transfer messages using shared memory or Unix domain sockets. This allowed us to continue with our familiar process boundaries for separable components without sacrificing significant performance. We had to write custom logic to emulate many of the features of network- and transport-layer protocols, with the result of creating a reusable, zero-overhead IPC mechanism. Working on this problem was one of the most intellectually rewarding experiences of my early career. The above latency optimization techniques are fairly commonplace in the HFT trade but hard to learn outside the industry setting. Half of our team at Architect comes from Jane Street and other trading firms, and we value using our domain knowledge to build exchanges for the public rather than trading software that never leaves an HFT’s walls.

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A new estimate claims AI systems now initiate 25% of crypto trades. But “AI” is doing a lot of work in that sentence. Did you know much of what gets called an AI trading agent is just a bot executing pre-programmed triggers or API calls. It's the same algorithmic trading crypto has used for years. We shouldn't confuse automated execution with autonomous decision-making. Real agents can interpret the market, choose what risk to take, and adapt their strategy themselves.