portfolio-construction

Portfolio optimization under turnover and liquidity constraints adjusts allocation weights by respecting mandate limitations and liquidity thresholds. It balances target allocations against realistic trading boundaries to ensure the resulting portfolio remains implementable without exceeding risk or compliance parameters.

Pre-trade transaction cost analysis in portfolio construction estimates the market impact and direct costs of proposed trades before execution. It adjusts target allocation weights to account for these costs, ensuring that the optimized portfolio retains its expected value after realistic trading expenses are applied.

Rebalancing with new alpha signals while minimizing trading costs involves running constraint-aware optimization that respects turnover limits and mandate compliance. The process applies pre-trade impact adjustments to align the new signals with your existing positions, reducing unnecessary churn and lowering execution costs.

Yes, you can run portfolio risk attribution and factor exposure diagnostics using deterministic Python scripts. These scripts process input CSV files to evaluate risk distribution across factors and generate a JSON output file containing detailed compliance and stability metrics for the constructed portfolio.

Mandate compliance directly affects liquidity optimization by enforcing regulatory and investor-specific rules during weight allocation. The optimization process ensures that liquidity thresholds and turnover limits are met simultaneously with mandate constraints, preventing violations while maintaining efficient execution.

Theoretical portfolio optimization without transaction cost adjustments often produces untradeable allocations that ignore market impact and liquidity constraints. It can lead to excessive turnover and mandate violations, whereas constraint-aware optimization provides stable, compliant portfolios validated through pre-trade diagnostics.