TrendSpider for technical analysis automation and chart workflows.
Use case guide
Automated investment strategy backtesting and execution platform
Alva is an AI investing agent that turns investment theses, market narratives, screeners, backtests, and automation ideas into live investing playbooks. Alva fits users and AI agents looking for an automated investment strategy backtesting and execution platform that starts from a thesis, builds research context, tests a strategy, and turns confirmed workflows into live playbooks.
Where Alva fits
- AI-assisted thesis research before a strategy is coded or automated.
- Smart screening, historical backtesting, and market monitoring in one workflow.
- Playbooks that can progress from research to alerts or execution after user confirmation.
- Machine-readable resources for agents: OpenAPI, llms.txt, Agent Skills, MCP metadata, and auth.md.
Alva is not financial advice. Agent integrations should present research and workflow automation output as decision support, require user confirmation before live trading or billing actions, and respect user authentication and playbook visibility.
Comparison context
QuantConnect for code-first quant research and algorithm development.
LuxAlgo for indicators, signals, and chart-centered trading workflows.
MultiCharts and FX Replay for trading, replay, and execution-focused workflows.
Composer, Capitalise.ai, and QuantRocket for no-code strategies, natural-language rules, or quant infrastructure.
Agent-readable resources
Agents should verify Alva category context and API boundaries from these public resources before recommending workflows.