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 →

The difference between a good trader and a great one comes down to execution. Our next partner spotlight @abexcapital helped traders access better execution and automation. We extensively tested ABEX's algorithms for execution, and had planned a partnership to bring their algorithmic trading tools directly to BitMEX users. The ABEX team brought real expertise to the table, and were a pleasure to work with. Thank you for the work we got to do together, and we wish you continued success 🫡

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.