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Found 67 Skills
Quantitative trading expertise for DeFi and crypto derivatives. Use when building trading strategies, signals, risk management. Triggers on signal, backtest, alpha, sharpe, volatility, correlation, position size, risk.
Build and test Polymarket prediction market trading strategies for YES/NO token trading. Provides 6 tools: get_all_prediction_events (browse markets, $0.001), get_prediction_market_data (analyze price history, $0.001), create_prediction_market_strategy (generate code, $1-$4.50), run_prediction_market_backtest (test performance, $0.001). Trade on real-world events (politics, economics, sports, crypto). Currently simulation only (live deployment coming soon).
TianQin SDK (tqsdk) - Python量化交易框架，用于期货/期权/股票交易策略开发、回测与实盘交易
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 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.
Build financial models, backtest trading strategies, and analyze market data. Implements risk metrics, portfolio optimization, and statistical arbitrage. Use PROACTIVELY for quantitative finance, trading algorithms, or risk analysis.
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.
Use to perform market backtests with PlausibleAI Backtester, including symbol discovery, strategy validation, strategy mining, and batch execution.
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".
This skill should be used when the user asks to forecast aggregate sentiment and opinion dynamics over time—sentiment indices from text streams; temporal rollups; leading/lagging KPI links; time-series and sequence models (ARIMA, Prophet, state-space, ML); nowcasting; spikes, bots, and bias; walk-forward backtests; intervals and scenarios; volume/velocity/topic features; BI or brand dashboards. Triggers: sentiment forecasting, forecast sentiment, sentiment index, opinion trend forecast, social sentiment time series, brand sentiment trajectory, nowcast sentiment, sentiment leading indicator, aggregate polarity forecast, sentiment backtest, walk-forward sentiment, sentiment spike prediction. Not for per-text labeling (sentiment-analysis-engineer), demand forecasting without sentiment (predictive-logistics-developer, data-scientist), trade advice (methodology only), marketing copy (content-creator), macro without text sentiment (financial-analyst partial).
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.