Build a Custom Quantitative financial Backtesting Tool (20 Years of Historical Data)

Job Description - TWO TASKS FIRST I have developed a global stock screener in Excel that calculates the Fair Value of companies worldwide using Discounted Cash Flow (DCF) models and Financial Modeling Prep (FMP) data. I have already generated a comprehensive historical dataset spanning from 2005 to 2025. This dataset includes the calculated Fair Values, historical estimates, cash data, and various key performance indicators (KPIs) like ROIC, ROE, etc., for all covered companies. I am now looking for an experienced quantitative developer to build a reusable, flexible Backtesting Tool to analyze how an investment strategy based on these Fair Values and KPIs would have performed over the last 20 years. Key Features & Requirements Data Integration: Import and process my existing 2005–2025 historical Excel/CSV dataset. Dynamic Strategy Builder (Weighting & Filtering): The tool must allow me to input and weight different criteria to select stocks. For example: Top 10 most undervalued stocks (highest discount to Fair Value). Top 10 stocks with the highest ROIC. Top 10 stocks with the highest ROE. Combinations of the above (e.g., 50% weight on Fair Value gap, 25% on ROIC, 25% on ROE). Portfolio Simulation: Simulate portfolio performance based on these rules (including rebalancing intervals, e.g., quarterly or annually). Optimization Engine (Auto-Discovery): The tool should feature an optimization mode that automatically tests various combinations of KPI weightings and filters to identify the strategy that would have yielded the highest historical return (CAGR, Sharpe Ratio, Max Drawdown). Performance Dashboard: Clear visual output of the results (equity curves, performance metrics vs. benchmarks like S&P 500 or MSCI World). Qualifications We Are Looking For Proven experience in quantitative finance, portfolio backtesting, and data analysis. Strong proficiency in Python and financial data libraries. Understanding of corporate finance metrics (DCF, Fair Value, ROIC, ROE). Ability to write clean, reusable, and well-documented code so I can use the tool independently moving forward. SECOND I would highly appreciate your expertise in helping me refine and polish my existing dataset to make it more professional and insightful. Im not good at all with data processing and would appreciate any Input. How to Apply Please briefly describe your experience with similar quantitative backtesting projects. If possible, share a brief architectural idea of how you would approach the optimization engine for the KPI weightings.