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Found 6 Skills
Expert in quantitative finance, algorithmic trading, and financial data analysis using Python (Pandas/NumPy), statistical modeling, and machine learning.
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.
Using palladium's leading trend reversal as a confirmation condition, verify whether silver's short-term price movements are supported by both industrial sentiment and risk sentiment, and mark failed trends that lack palladium participation.
Asset allocation frameworks: strategic (SAA), tactical (TAA), mean-variance optimization, Black-Litterman, risk parity, glide paths.
Scan stocks for bullish trends using technical indicators (SMA, RSI, MACD, ADX). Use when user asks to scan for bullish stocks, find trending stocks, or rank symbols by momentum.
Apply statistical methods to financial data including descriptive statistics, covariance estimation, regression, hypothesis testing, and resampling. Use when the user asks about return distributions, correlation between assets, building a covariance matrix, running a CAPM regression, testing whether alpha is significant, checking if returns are normal, or estimating confidence intervals. Also trigger when users mention 'volatility', 'how correlated are these', 'fat tails', 'skewness', 'R-squared', 'beta of a fund', 'bootstrap a Sharpe ratio', 'shrinkage estimator', 'Ledoit-Wolf', or ask why their optimizer produces unstable weights.