A nightly AI report that summarizes my bot’s trades and risk
I 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…
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Building an integrated agent orchestrator with LangChain, LangGraph, and Temporal
Last quarter, our algorithmic execution desk migrated its multi-step portfolio rebalancing agent from a standalone LangGraph deployment to a hybrid archit…
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Backtesting LLM-generated signals without fooling yourself
We 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…
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From strategy idea to live trading: my full automation stack
Every 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…
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GitOps for a quant agent platform: ArgoCD, Kubernetes, and zero-downtime deploys
Three months ago, a naive git-push triggered a standard Kubernetes rolling update on our systematic market-making cluster. The deployment replaced our ETH…
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Building a quant research pipeline with AI agents
I 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…
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Using LLMs to generate trading signals: what actually works
The 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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