Backtest quantitative strategies across global equities, crypto, and futures markets.
npx skills add https://github.com/0xZKnw/vibe-trading-tap --skill vibe-trading-0xzknw
Please help me install this Agent Skill. Skill: vibe-trading Source: https://github.com/0xZKnw/vibe-trading-tap/tree/main/agent Command: npx skills add https://github.com/0xZKnw/vibe-trading-tap --skill vibe-trading-0xzknw
This Skill solves the complexity of quantitative finance research by providing a unified, AI-driven interface for backtesting, factor analysis, and multi-agent strategy development without requiring manual coding for every step.
Use the vibe-trading skill to backtest a MACD strategy on AAPL for the year 2024.
High-intent search queries and answers about installing and using this skill.
You can backtest trading strategies across global equities, crypto, and futures markets without manual coding by deploying this AI-driven quantitative research environment to orchestrate multi-agent workflows and execute one-line benchmarking.
Factor analysis evaluates specific quantitative signals to predict asset returns. The Alpha Zoo feature provides 452 pre-built quantitative alphas, allowing you to benchmark and integrate these established factors directly into your backtesting workflow.
Yes, you can feed a broker CSV export into the Shadow Account feature to diagnose your trading behavior, extract implicit trading rules, and backtest those rules against real market data to optimize your strategy.
Yes, the toolkit supports multi-agent swarm orchestration, allowing you to deploy specialized agent teams for complex quantitative tasks including risk management, earnings research, and global macro strategy development.
The unified agentic framework supports diverse market data sources including equities, crypto, and futures across global and A-share markets, integrating analytical tools for options pricing and technical pattern recognition.
The environment supports both A-share and global markets for quantitative backtesting and factor analysis, though users should ensure their specific market data sources align with the unified agentic framework for accurate multi-agent orchestration.
hxhyyyBacktest trading strategies and analyze quantitative factors across global markets.
mahmoud20138Automate risk management, portfolio allocation, and performance tracking for trading portfolios.
ruvnetOrchestrate distributed agent swarms via MCP tools and CLI commands.
day18708433173-cryptoRun quantitative backtests and factor analysis across 18 global market-data sources.
