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Found 5 Skills
Comprehensive guide for FinLab quantitative trading package for Taiwan stock market (台股). Use when working with trading strategies, backtesting, Taiwan stock data, FinLabDataFrame, factor analysis, stock selection, or when the user mentions FinLab, trading, 回測, 策略, 台股, quant trading, or stock market analysis. Includes data access, strategy development, backtesting workflows, and best practices.
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).
TQX (tqx.trade) HK/US stock quant: factor analysis, strategy backtests, and agent-driven trading (paper or live) via the TQX CLIs. Use when the user wants to run factor IC/IR analysis, backtest a Python trading strategy on Hong Kong or US stocks, or set up agent-automated trading on a paper or live account (e.g. "backtest a moving-average strategy on AAPL", "analyze a momentum factor on HK stocks", "let the agent trade my TQX account", "join the TQX trading competition").
JoinQuant Official Strategy Development Guide, covering backtesting, paper trading, data APIs, trading functions, factors and technical indicators. Use this when users write JoinQuant strategies, perform backtesting, use paper trading, query JoinQuant APIs, work with get_price/order/run_daily, Alpha factors, technical indicators, or mention joinquant, JoinQuant, jqdata strategies. For local data retrieval, use the jqdatasdk skill.
Quantitative strategy frameworks: pairs trading/cointegration, volatility regime strategies, seasonality/calendar effects, multi-factor models (IC/IR), factor research and screening, correlation analysis, statistical methods (ADF/GARCH), strategy optimization, execution modeling, hedging, and ML-based prediction (sklearn). Also provides CLI access to run indicator scripts against K-line data. Triggers: "量化", "因子", "配对交易", "协整", "波动率策略", "季节性", "多因子", "IC", "机器学习", "对冲", "量化策略", "協整", "波動率策略", "季節性", "多因子", "對沖", "quant", "pairs trading", "cointegration", "volatility strategy", "seasonality", "multi-factor", "factor model", "IC IR", "machine learning", "hedging", "walk-forward", "配對交易", "機器學習", "因子選股"