Loading...
Loading...
Found 53 Skills
Master Moon Dev's Ai Agents Github with 48+ specialized agents, multi-exchange support, LLM abstraction, and autonomous trading capabilities across crypto markets
Build trading systems in the style of Renaissance Technologies, the most successful quantitative hedge fund in history. Emphasizes statistical arbitrage, signal processing, and rigorous scientific methodology. Use when developing alpha research, signal extraction, or systematic trading strategies.
Portfolio management. Display of held securities, trade records, structural analysis. Input data foundation for stress testing.
Quantitative strategy generation and optimisation framework via Longbridge — create, modify, and backtest quant strategies: parameter grid search, walk-forward validation, overfitting detection (in-sample vs. out-of-sample), strategy combination (multi-strategy correlation diversification), Sharpe / Calmar ratio optimisation. Generates Python code frameworks for local execution. Triggers: "策略优化", "策略生成", "参数优化", "网格搜索", "回测优化", "过拟合", "walk-forward", "策略回测优化", "策略組合", "策略優化", "策略生成", "參數優化", "網格搜索", "回測優化", "strategy optimization", "strategy generation", "parameter optimization", "grid search", "overfitting", "walk-forward validation", "strategy backtest", "Sharpe ratio", "Calmar ratio".
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
Compare multiple strategies or directions (long vs short vs both) on the same symbol. Generates side-by-side stats table.
Run a historical backtest using npx neural-trader with Rust/NAPI engine (8-19x faster) and walk-forward validation
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).
Use when a user asks to build, optimize, backtest, rebalance, or analyze a stock portfolio with Mean-CVaR, efficient frontiers, scenario generation, or NVIDIA cuOpt.
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.
Trade execution modelling framework (backtesting analysis only) via Longbridge — covers slippage models (linear / square-root market impact), VWAP/TWAP execution logic, market impact cost estimation (Kyle lambda), volume participation rate (POV) strategy. Helps quant traders build realistic execution assumptions in backtests. Triggers: "执行模型", "滑点模型", "VWAP执行", "TWAP执行", "市场冲击", "执行成本", "成交量参与率", "交易执行", "執行模型", "滑點模型", "VWAP執行", "TWAP執行", "市場冲擊", "執行成本", "交易執行", "execution model", "slippage model", "VWAP", "TWAP", "market impact", "execution cost", "volume participation rate", "Kyle lambda", "square root model", "POV strategy".