# User story: How a composer approaches quantitative research Canonical: https://brew.new/templates/alpaca/user-story-how-a-composer-approaches-quantitative-research Brand: alpaca.markets Category: general ![Preview of User story: How a composer approaches quantitative research](https://cdn.brew.new/email-preview-96b01aea7366fef2-tracking_r57rkkb1mdsj1xa3x45k194fq98gd7d5-1792167720629.png) ## Email content Alpaca Markets Hi there, What does composing music have to do with building a trading system? For Alpaca community member Joshua Kyan Aalampour, both involve choosing structures, working within constraints, and revising the work as evidence arrives. In a new Trader to Trader article, he explains how that approach is shaping ORIA (Orthogonal Risk Integrated Alpha), his quantitative research system designed to combine multiple market models while accounting for overlapping risk exposure. ORIA combines three research approaches: directional signals, shocks across related assets, and sharp ETF moves that may continue or reverse. Its risk-management framework is designed to identify and limit shared market, sector, and style exposures, adjust allocation based on changing conditions, and apply predefined risk limits. “The workflow took me longer to get right than the mathematics.” After observing differences between historical backtesting and subsequent paper-trading results, Joshua refined his evaluation process around a workflow that: ✅ Uses Alpaca Market Data for historical research inputs ✅ Tests ideas across rolling, unseen periods ✅ Separates overlapping samples to reduce data leakage ✅ Uses closing data to simulate execution at the next open in Alpaca paper trading The article goes deeper into ORIA, the mathematics behind it, and the evaluation and execution choices that come before live deployment. Read the full article Building a systematic research workflow with Alpaca? Reply and tell us what you’re exploring. Curious to learn more about agentic trading with Alpaca? Check out some additional resources below: Alpaca’s Skills Library for AI Agents Building AI Trading Applications with Alpaca Alpaca’s MCP Server Alpaca’s Command-Line Interface Blogs on agentic trading The Alpaca Team X / Twitter LinkedIn Github Insights generated by our MCP server, CLI and connected AI agents are for educational/ informational purposes only and should not be taken as investment advice. Alpaca does not recommend any specific securities or investment strategies. Past performance from models does not guarantee future results. Please conduct your own due diligence before making any decisions. All firms mentioned operate independently and are not liable for one another. 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