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Found 67 Skills
Use when developing or documenting trading strategies - guides edge hypothesis formation, validates statistical significance, documents strategy rules systematically (entry, exit, risk management). Activates when user says "research this strategy", "document my approach", "test this idea", mentions "trading strategy", "edge", or uses /trading:research command.
NautilusTrader algorithmic trading platform reference. NautilusTrader 量化交易框架参考。 Use this skill when: - Working with NautilusTrader API (使用 NautilusTrader API) - Implementing trading strategies (实现交易策略) - Running backtests (运行回测) - Configuring data feeds and adapters (配置数据源和适配器) - Debugging NautilusTrader code (调试 NautilusTrader 代码) - Understanding trading concepts like positions, orders, and fills (理解持仓、订单、成交等概念) Keywords: NautilusTrader, strategy, backtest, trading, adapter, Binance, quantitative, 量化, 策略, 回测
Backtest crypto trading strategies from natural language ideas. Use when: user describes trading ideas, wants to validate strategies, mentions "backtest", "trading strategy", "buy low sell high", "RSI", "MACD", "oversold", "overbought", "crypto strategy", "validate strategy", "backtest", "DCA", or similar.
Portfolio management. Display of held securities, trade records, structural analysis. Input data foundation for stress testing.
Guides quantitative research for markets and finance—research question framing, data sourcing and quality checks, descriptive and inferential statistics, time series and panel methods (high level), factor and signal research, backtest design and pitfalls (lookahead, survivorship), risk metrics (volatility, drawdown, Sharpe limitations), regime and stress analysis, and reproducible notebooks or reports with explicit limitations and uncertainty communication. Use when the user mentions "quantitative research", "quant researcher", "factor research", "signal backtest", "time series analysis", "panel regression", "alpha research", "Sharpe ratio analysis", "survivorship bias", "lookahead bias", "econometric analysis", or "risk factor model". Not for production ML pipelines (data-scientist, ml-research-engineer), equity narrative reports (equity-research skills), SOX accounting (financial-statements), legal investment advice, or trading execution systems (senior-software-engineer).
Build and deploy agentic finance applications on the Alva platform. Access 250+ financial data sources (crypto, equities, macro, on-chain, social), run cloud-side analytics, backtest trading strategies, and release interactive playbooks -- all from your AI agents.
Crypto 单因子量化研究服务 Skill。当用户说"写一个因子"、"研究因子"、"量化打工"、 "提交因子"、"因子回测"时加载此 Skill。 Agent 负责编写因子插件代码并通过 HTTP 接口与服务器交互， 服务器负责所有数据处理和计算，Agent 本地无需任何市场数据。
Run a historical backtest using npx neural-trader with Rust/NAPI engine (8-19x faster) and walk-forward validation
Construct a business cycle model using leading and coincident indicators, and interpret two business cycle phases: Expansion (Risk-On) and Contraction (Risk-Off), and generate "Iceberg" and "Sinking" event signals based on the theory.
Run a heavy neural-trader job (long walk-forward, big Monte-Carlo, parameter sweep, model training) on the Anthropic Managed Agent cloud runtime instead of locally
Measure the valuation range (overvalued/undervalued) of the mining stock sector relative to the metal itself using the ratio of Silver Mining Stock Price to Silver Price, and derive 'bottom/top' signals and scenario projections through historical percentiles and analogous intervals.
Comprehensive trading skills system with multi-broker support, strategy execution, and autonomous trading capabilities