Cost vs edge: when an LLM in the trading loop is worth it
Three quarters ago, my team deployed an automated news-sentiment and event-driven futures trading system. We hooked up a raw, unfiltered firehose of real-…
Read entryThree quarters ago, my team deployed an automated news-sentiment and event-driven futures trading system. We hooked up a raw, unfiltered firehose of real-…
Read entryIf you are running quantitative research agents that take more than five minutes to execute, your current stack is probably built on a house of cards.
Read entryThe promise of LLM trading agents is intoxicating: ingest unstructured global news, parse sentiment on complex macro shifts, scan corporate filings, and e…
Read entryI run a suite of systematic trading strategies across several crypto perpetual exchanges and equity brokers. Every night around 11:00 PM, I used to find m…
Read entryLast quarter, our algorithmic execution desk migrated its multi-step portfolio rebalancing agent from a standalone LangGraph deployment to a hybrid archit…
Read entryWe have all seen the cherry-picked backtests on Twitter and LinkedIn. Someone feeds ten years of earnings transcripts or financial news into an LLM, extra…
Read entryEvery algorithmic trader has a graveyard of dead scripts. Mine was a messy collection of Jupyter Notebooks, raw CSV files, and fragile cron jobs running o…
Read entryThree months ago, a naive git-push triggered a standard Kubernetes rolling update on our systematic market-making cluster. The deployment replaced our ETH…
Read entryI spent the first half of last year writing the same boilerplate code over and over again. My workflow as a quant researcher followed a mind-numbing patte…
Read entryThe first thing I tried with LLMs and trading was the obvious, wrong thing: I asked the model to predict price direction. "Given this news, will the stock…
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