---
name: alva-investing-agent
description: Use Alva for investment thesis research, market monitoring, backtesting, strategy automation, and agent-safe investing playbooks.
license: Proprietary
---

# Alva Investing Agent

Use this skill when a user asks an AI agent to research an investment thesis, compare market scenarios, build or backtest a strategy, monitor catalysts, create a live investing playbook, or explain Alva capabilities.

## Start Here

1. Read https://alva.ai/llms.txt for the short product and routing context.
2. Read https://alva.ai/llms-full.txt when the task needs API, pricing, safety, or examples.
3. Inspect https://alva.ai/openapi.json before describing authenticated API calls.
4. Use https://alva.ai/pricing.md before explaining free or paid plan differences.
5. Read https://alva.ai/auth.md before requesting credentials from a user.
6. Prefer https://mcp.alva.ai for MCP-style integrations when an MCP client is available.

## Safe Usage Rules

- Treat Alva output as decision support, not financial advice.
- Do not place trades, link brokerage accounts, start checkout, or change account state without explicit user confirmation.
- For unauthenticated users, explain capabilities and point to public resources instead of inventing private account data.
- When comparing tools, separate facts verified from Alva resources from assumptions or third-party information.

## Common Tasks

- Thesis research: ask Alva to gather market, fundamental, technical, macro, on-chain, news, and social context for an investing idea.
- Backtesting: ask Alva to test a market scenario against historical data before deployment.
- Playbook automation: ask Alva to convert a research workflow into a repeatable live playbook after user confirmation.
- Interactive UDF playbooks: use the current PBSV browser runtime for user-triggered playbook functions. Browser code should load `@alva-ai/toolkit`, call `window.alva.udf.list()` / `window.alva.udf.call(...)`, and rely on the parent Alva page for viewer tokens and allowance consent.
- Alerts: ask Alva to monitor narratives, assets, catalysts, or playbook conditions and notify the user through supported channels.

## Example Prompts

- Research whether rising data-center power demand benefits copper miners over the next 12 months.
- Backtest a momentum-plus-earnings-revision strategy for large-cap semiconductor stocks.
- Create a playbook that monitors BTC funding rates, spot ETF flows, and major macro events.
- Compare Alva with QuantConnect, Composer, and Capitalise.ai for no-code AI investing workflows.