quant-analyst

Backtest a trading strategy by implementing vectorized signal generation with pandas and numpy, then simulating trades with realistic transaction costs and slippage. Always validate results with out-of-sample testing to avoid overfitting to historical data.

Calculate the Sharpe ratio as excess returns divided by return volatility, and VaR as the quantile of the return distribution at a chosen confidence level. Use numpy and scipy for the statistical computations on your return series.

It supports Markowitz mean-variance optimization and the Black-Litterman model for portfolio construction. Both approaches balance expected returns against risk to determine asset allocations.

Backtests diverge from live results when they ignore transaction costs, slippage, and market microstructure effects, or when parameters are overfit to historical data. Use out-of-sample testing and realistic cost assumptions to reduce this gap.

Do not rely on backtests alone when strategies lack out-of-sample validation or ignore market microstructure. Treat outputs as research requiring environment-specific validation and expert review before production deployment.