Execution Cost & Strategy Optimization for U.S. Equity Trading

Customer

Challenge

Reducing Transaction Costs in High-Turnover Strategies

Diagnose execution efficiency, quantify transaction costs, and identify structural improvements to reduce drag while preserving alpha generation.

• Attribution and traceability: Connect model signals to actual fills and reconcile discrepancies
• Cost structure: High turnover magnified commissions and slippage, often dominating realized returns
• Order design: Existing order-types behaved effectively like market orders, creating unnecessary friction
• Portfolio churn: Daily position resets amplified costs; alternative rebalancing frequencies were unexplored
• Scalability: Required reproducible analytics with transparent cost attribution

Solution

Comprehensive TCA and Execution Framework

Marsbridge assembled a quantitative execution-research team—Quant Lead, Execution Analyst, Data Engineer, Research Associate—to transform raw trade data into an analytics-ready pipeline, producing both diagnostics and tested redesigns.

Data linkage & TCA analytics

Linked order, fill, and prediction data to create per-trade performance panels. Quantified execution costs (fees, slippage, market impact) under multiple routing and fee-tier assumptions. Built visual and tabular dashboards for cost attribution.

Execution redesign

Modeled routing and order-type alternatives to reduce cost and improve fill consistency. Designed guarded limit rules to avoid excessive price chasing. Created parametric timing controls for order submissions.

Portfolio structure optimization

Evaluated rebalancing frequency scenarios to minimize churn while maintaining signal exposure. Designed thresholding filters to skip marginal trades. Developed blended frameworks combining multiple holding periods.

Intraday trade-management logic

Implemented an intraday data engine for tracking realized performance vs. thresholds and tested profit-take/exit parameters through simulations.

Technologies & tools

Process

Kickoff & scoping—establish benchmarks and metrics. Data integration—clean and merge order and fill data. Baseline analysis—quantify current costs and inefficiencies. Redesign phase—simulate routing, timing, and holding-period alternatives. Backtesting—evaluate performance under new structures. Handover—deliver documented code, templates, and reports.

1. Kickoff & scoping
2. Data integration
3. Baseline analysis
4. Redesign phase
5. Backtesting
6. Handover

Results

• Cost Transparency: Unified TCA linking signals to fills
• Reduced Friction: Optimized routing and timing controls
• Lower Churn: Alternative rebalancing frequencies

Provided unified, auditable TCA process linking model predictions, fills, and execution results. Identified and validated methods to reduce total trading costs through routing and timing improvements. Proposed portfolio structures that reduced churn while maintaining model exposure.