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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).
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.
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.
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.
Agent skill for trading-predictor - invoke with $agent-trading-predictor
8 finance skills. Trigger: financial modeling, market data, risk analysis, quantitative finance. Design: data sources, quantitative methods, and regulatory frameworks.
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.
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.
Perform quantitative analysis of returns, correlations, risk factors, and portfolio optimization. Statistical modeling with institutional-grade rigor.
Asset allocation and portfolio optimisation via Longbridge — efficient frontier (MPT), Black-Litterman model overview, risk parity / risk budgeting, all-weather strategy, and practical allocation recommendations based on the user's Longbridge account data. Triggers: "资产配置", "组合优化", "有效前沿", "Black-Litterman", "风险预算", "风险平价", "全天候策略", "大类资产", "資產配置", "組合優化", "有效前沿", "風險預算", "風險平價", "全天候策略", "大類資產", "asset allocation", "portfolio optimization", "efficient frontier", "Black-Litterman", "risk parity", "all-weather strategy", "mean-variance optimization", "strategic allocation".
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.
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.