HPA in practice: why CPU-based autoscaling kept scaling at the wrong times
Three months ago, our crypto market data ingestion pipeline hit a wall. During high-volatility events, execution latency spiked from 15ms to over 800ms. O…
Read entryAlgorithmic Trading × AI Engineering
Engineering deep dives, from backtest to live trade.
Algorithmic trading × AI engineering — field notes from production.
pgvector vs Qdrant for a 10M-chunk index: how I chose
Six months ago, our hybrid search setup was burning money and dropping queries. We were serving RAG-augmented financial reports over a dataset of 10 milli…
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Using function-calling agents to run my research workflow
Six months ago, my quantitative research workflow was a chaotic sprawl of fragmented scripts, half-baked Jupyter notebooks, and manual file shuffling. Eve…
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Deploying an LLM agent service on Kubernetes: probes, resources, and rollouts
When deploying a standard stateless microservice on Kubernetes, the playbook is simple: write a fast health check endpoint, set modest CPU requests, and u…
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How I automated my trading bot reports with Claude API
I run a portfolio of algorithmic market-making and momentum strategies across several perpetual swap markets. While the bots execute autonomously, the hum…
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An LLM news-sentiment pipeline feeding a systematic trading book
Building an automated trading system that acts on news sentiment has been a classic quantitative finance goal for over a decade. Historically, we relied o…
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GitHub Actions self-hosted runners on Kubernetes: cost, caching, and cleanup
At my algorithmic trading firm, our execution algorithms and alpha research pipelines depend on rapid feedback loops. If a quantitative researcher has to…
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Measuring whether an LLM signal adds alpha or just noise
Six months ago, I designed what I thought was an exceptional trading signal. I set up an asynchronous pipeline to scrape transcripts of earnings calls, fe…
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Running PostgreSQL on Kubernetes with the CloudNativePG operator
I run an algorithmic trading system that processes high-frequency tick-level data and serves real-time feature vectors to ML models. Historically, our dat…
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GitHub Actions to ArgoCD: a GitOps pipeline for an AI microservice
Deploying a standard web API is straightforward. Deploying an AI microservice that loads a 1.2GB sentence-transformer model into memory, requires CUDA dri…
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