blave-agent
Reduces friction between strategy conception and live execution for quantitative teams.
Quant infrastructure for AI agents — a free, open-source desktop workspace for macOS and Windows where your Claude Code or Codex turns a trading idea into a backtested strategy and runs it live.
- Ask Claude to backtest a trading strategy idea against historical market data quickly.
- Generate executable trading code from natural language descriptions of your strategy logic.
- Automate live trading execution once your backtested strategy passes performance requirements.
Reduces friction between strategy conception and live execution for quantitative teams. Eliminates manual infrastructure setup, allowing traders to validate ideas against historical data before deploying capital.
Quantitative trading teams and algorithmic traders using Claude to prototype, backtest, and deploy trading strategies efficiently.
https://github.com/Blave-TW/blave-agent
By Blave-TW
How to Get It
claude plugins install Blave-TW/blave-agent
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
Auto-generated from the tool's public listing — not hands-on verified. Cross-check against the source repo's README before running.
After installing, paste this into Claude:
Backtest a trading strategy idea against historical market data quickly
Trust Signals Auto-scanned
Community Pulse Quiet
No community discussions found yet. This doesn't mean the tool isn't good — it may be new or serve a niche use case.
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 110 GitHub stars; contributors unknown; last commit 0d ago; license Apache-2.0.
Things to check
- Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.
- Single maintainer. Consider the risk if this person stops maintaining the project.
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Evaluation
Scored from trust signals (evidence-eval-v1): 110 GitHub stars; contributors unknown; last commit 0d ago; license Apache-2.0.