Portfolio Algorithmic Trading

objective

Execute portfolio algorithmic trading work with reproducible research, explicit controls, and deployable outputs.

workflow

1. define objective function, constraints, and benchmark selection.
2. construct allocations with explicit cost and capacity assumptions.
3. attribute active return into factor, selection, and implementation terms.
4. stress portfolio under macro, liquidity, and concentration shocks.
5. rebalance only when expected benefit exceeds turnover and impact costs.

required diagnostics

• active-risk attribution by factor, sector, and region.
• tracking-error drift and benchmark mismatch diagnostics.
• turnover concentration and implementation-cost drag.
• scenario outcomes for correlated drawdown events.
• active-risk attribution by factor and sector
• turnover concentration and capacity drag