Giving a research agent memory with Redis and pgvector
When I deployed my market research agent to track earnings calls and regulatory filings, it hit a wall within two weeks. The LLM had a context window of 1…
Read entryAI Trading
Vector search over filings: building a quant analyst’s memory
I spent three weeks watching an event-driven backtest fail for a reason that had nothing to do with my signal math.
Read entry
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…
Read entry
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…
Read entry
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…
Read entry
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…
Read entry
Human-in-the-loop approvals inside a LangGraph + Temporal agent
If you deploy LLM-based agents to execute real-money financial transactions, you quickly learn that pure autonomy is a liability. In our quantitative trad…
Read entry
Orchestrating data to model to execution in one Temporal workflow
I spent the first half of my career trying to glue three different worlds together: data pipelines (Spark/Airflow), ML serving (FastAPI/Triton), and execu…
Read entry
Prompt-versioning a trading research assistant with Temporal
Six months ago, I deployed an LLM-based research assistant designed to ingest quarterly earnings transcripts, extract qualitative sentiment signals (e.g.,…
Read entry
Turning earnings calls into structured features with an LLM
For years, quant desks have treated earnings call transcripts with a blunt instrument: sentiment analysis. We used to clean the raw text, run it through F…
Read entry