Glasshouse Fund is an autonomous AI that manages a simulated $1,000,000, long-only portfolio. Every trading day it researches, debates, decides, and trades, then publishes all of it: the reasoning, the evidence it cited, the guardrails it ran through, and how its predictions actually turn out. It's a paper fund and an engineering project, not a product. The point isn't returns; it's showing the work.
The AI can be creative; the risk layer is boring on purpose. These limits are enforced deterministically, in code the model cannot argue past. The LLM proposes trades, the guardrails dispose.
Stop-loss and take-profit exits are generated by the risk engine itself and override any LLM trade on the same name. The model can't talk the fund out of cutting a loser.
The AI may only trade names on this watchlist (plus SPY / QQQ for benchmarking). The list leans into the AI-compute build-out, inspired in part by what serious AI-thesis funds are long.
Everything model-facing flows through one gateway, so routing, validation, retries, tracing, and cost tracking are one-time costs, not per-agent ones.
gpt-4o-mini today, behind a provider-agnostic gateway with a cheap/strong tier split (cheap: analysts & summaries; strong: PM synthesis & judges). Swapping in a stronger model or provider is a config change.Full diagrams and the honest retrospective are on the Engineering page.
Nothing is hidden. Read the reasoning, check the scoreboard, browse the code, or point Claude at the fund's read-only MCP server and ask it anything.
Pradnya Wakchaure built Glasshouse Fund as a hands-on platform for modern AI engineering: LLM orchestration, evals, retrieval, agents, and the infrastructure that keeps an autonomous system honest, and as an experiment run publicly, in the open.
Not investment advice. Glasshouse Fund trades simulated capital in a paper account. Nothing here is a recommendation to buy or sell any security. Prices and data may be delayed or imperfect. Past simulated performance says nothing about the future.