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Found 16 Skills
Guidance for implementing high-performance portfolio optimization using Python C extensions. This skill applies when tasks require optimizing financial computations (matrix operations, covariance calculations, portfolio risk metrics) by implementing C extensions for Python. Use when performance speedup requirements exist (e.g., 1.2x or greater) and the task involves numerical computations on large datasets (thousands of assets).
Risk-return optimisation for investment portfolios via Longbridge — builds risk-adjusted return-optimal portfolios based on fund size, risk preference (conservative / balanced / aggressive), and investment horizon. Asset allocation across equities / bonds / cash / commodities / alternatives. Evaluates current portfolio efficiency versus the efficient frontier. Triggers: "风险收益优化", "组合效率", "有效前沿", "风险偏好配置", "最优组合", "风险调整收益", "大类资产配置", "投资组合优化", "風險收益優化", "組合效率", "有效前沿", "風險偏好配置", "最優組合", "risk-return optimization", "portfolio efficiency", "efficient frontier", "risk preference", "optimal portfolio", "risk-adjusted return", "asset class allocation", "portfolio optimisation", "mean variance".
Analyze dividend investment opportunities, evaluate dividend safety, growth potential and yield rate. Use this when users inquire about dividends, dividend investment or dividend yield. Supports quick screening, in-depth analysis and portfolio optimization.
Finance Guru™ Core Context Loader Auto-loads essential Finance Guru system configuration and user profile at session start. Ensures complete context availability for all financial operations.
Agent skill for trading-predictor - invoke with $agent-trading-predictor
Build trading systems in the style of D.E. Shaw, the pioneering computational finance firm. Emphasizes systematic strategies, rigorous quantitative research, and world-class technology infrastructure. Use when building research platforms, systematic trading strategies, or quantitative finance infrastructure.
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.
Construcción y optimización cuantitativa de portafolios: Markowitz (scipy.optimize + Monte Carlo), Black-Litterman (prior CAPM, views absolutas/relativas, posterior bayesiano), HRP/HERC/NCO (clustering jerárquico, risk parity, NCO con restricciones). Todo flat numpy + scipy, sin Riskfolio-Lib ni PyPortfolioOpt.
Mean-variance portfolio optimization via Conjugate Gradient — 40-60× faster than the legacy Neumann path (ADR-126 Phase 3, ADR-123 Wedge 8)
Expert personal finance coach with deep knowledge of tax optimization, investment theory (MPT, factor investing), retirement mathematics (Trinity Study, SWR research), and wealth-building strategies grounded in academic research. Activate on 'personal finance', 'investing', 'retirement planning', 'tax optimization', 'FIRE', 'SWR', '4% rule', 'portfolio optimization'. NOT for tax preparation services, specific securities recommendations, guaranteed return promises, or replacing licensed financial advisors for complex situations.
Identifies upsell and cross-sell opportunities within existing customer accounts. Analyzes product usage, feature gaps, team growth, industry benchmarks, and competitive pressure to surface revenue expansion plays scored by potential, effort, and likelihood. Generates an expansion-playbook.md with account-by-account opportunities, recommended pitch, timing, and approach.
8 finance skills. Trigger: financial modeling, market data, risk analysis, quantitative finance. Design: data sources, quantitative methods, and regulatory frameworks.