Quantitative Consultant to Refine Institutional Equites Backtest (Bloomberg)

We are a boutique asset management firm preparing to launch an institutional long-only equity fund. We have developed a 20-year historical strategy back test (on BLOOMBERG) and need an experienced consultant to review and refine our back testing architecture. The primary objective is to transition our current model from daily closing-price returns to a multi-day VWAP execution simulation that accounts for market impact, cash drag, and implementation shortfall. Responsibilities: • Execution Model Refinement: Upgrade current annual rebalance methodology to model a 3- or 5-day staggered entry/exit execution window using daily volume-weighted average prices. • Market Impact Modeling: Code dynamic slippage and transaction cost penalties. • Bloomberg Backtest Integration: Validate the 20-year backtest across 20 distinct annual holding periods using Bloomberg PORT (staggered rebalancing engine) or BQL / BQuant (Python). • Audit & Documentation: Ensure the holding period total returns, cash drag, and dividend adjustments are ready for institutional Operational Due Diligence. Skills & Experience: • Institutional Background: Prior experience as a Quant Developer, Quantitative Researcher, or Risk Analyst or consultant at an institutional asset manager or fund. • Bloomberg Expertis: Knowledge of Bloomberg PORT, BQL, BQuant (Python API), and custom index/portfolio backtesting capabilities. • Market Microstructure Expertise: Understanding of transaction cost analysis (TCA), Implementation Shortfall, VWAP execution algorithms, and market-impact modeling for small-to-mid-cap equities. • Clean Code/Math: Proficiency in Python (pandas, numpy) or R for portfolio backtesting and factor modeling. Project Details: • Scope: Short-term consultation / project-based • Budget: Hourly or Fixed-Price