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 entryAlgorithmic Trading × AI Engineering
Algorithmic trading × AI engineering — field notes from production.
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 entryIt was 10:14 PM on a Tuesday when the pager went off. Our primary execution router, which processes high-throughput routing signals for our algorithmic tr…
Read entryDeploying a 70-billion parameter model like Meta-Llama-3-70B on a local workstation is the holy grail for trading desks and quantitative shops looking to…
Read entryOur production algorithmic trading infrastructure processes thousands of order executions and millions of real-time market data ticks daily. For years, ou…
Read entryAt 03:14 UTC, our automated trading backend started dropping connections from three major institutional liquidity providers. In our execution logs, the er…
Read entryIf you read academic literature on market microstructure, Order Book Imbalance (OBI) is presented as an easy source of alpha. The classic formula:
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 entryWe run high-frequency, event-driven sentiment analysis pipelines that parse corporate earnings transcripts, SEC filings, and real-time news feeds. Our pip…
Read entryIt was 2:00 AM on a Tuesday when I realized my backtester was lying to me. I was reviewing the performance of a daily-horizon mean-reversion strategy on t…
Read entryIn quantitative trading, tracking execution costs down to the tenth of a basis point is a prerequisite for survival. When I shifted from high-frequency ex…
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