Quant Analyst

• Developing or refining algorithmic trading strategies and need backtesting with transaction costs and slippage accounted for.
• Computing risk metrics such as VaR, Sharpe ratio, or maximum drawdown for a portfolio or individual strategy.
• Running portfolio optimization using frameworks like Markowitz mean-variance or Black-Litterman.
• Exploring statistical arbitrage or pairs trading opportunities through time series analysis and cointegration testing.

• The task is unrelated to quantitative finance, trading, or risk analysis — a general-purpose or domain-specific skill will serve better.
• You need fundamental or qualitative equity research rather than data-driven, model-based analysis.
• You require live trade execution or brokerage connectivity, which falls outside this skill's modeling and analysis scope.

Design and implement algorithmic trading strategies using vectorized operations for performance. Backtests incorporate realistic market microstructure assumptions including transaction costs and slippage, and include out-of-sample testing to guard against overfitting.

Calculate industry-standard risk metrics including Value at Risk (VaR), Sharpe ratio, and maximum drawdown. Outputs include structured risk analysis and exposure reports to support informed decision-making.

Apply Markowitz mean-variance optimization and Black-Litterman models to construct efficient portfolios. The skill emphasizes risk-adjusted returns over absolute returns throughout the optimization process.

Price options contracts and compute Greeks to support derivatives analysis and hedging strategies. Outputs are grounded in established financial models with clearly stated assumptions.

Perform statistical analysis on financial time series data using pandas, numpy, and scipy. Supports pairs trading via cointegration testing, trend identification, and return forecasting.

Build market data ingestion pipelines with a data-quality-first approach — cleaning and validating all inputs before analysis. Produces visualizations of returns, performance metrics, and parameter sensitivity analyses.

A trader wants to evaluate a pairs trading idea on two correlated equities. The skill guides cointegration testing, signal construction, and a full backtest including transaction costs, delivering performance metrics and sensitivity analysis.

An analyst needs to report on portfolio risk before a quarterly review. The skill computes VaR, Sharpe ratio, and maximum drawdown, then produces an exposure report broken down by position or asset class.

A portfolio manager wants to rebalance using a Markowitz or Black-Litterman framework. The skill runs the optimization, surfaces the efficient frontier, and highlights the risk-return tradeoff of candidate allocations.

A quant needs to price a book of options and monitor delta, gamma, and vega exposures. The skill calculates Greeks across strikes and expiries and surfaces key sensitivities to support hedging decisions.