Quantitative Options Developer (Python): Forensic Backtest of Premium Selling Strategies (2018-2025)

Quantitative Options Developer (Python): Forensic Backtest of Premium Selling Strategies (2018-2025) Project Overview: We are a private Family Office seeking an experienced Quantitative Developer to build and execute a highly specific options backtest. The project involves comparing the historical performance of two distinct premium-selling architectures: a Defined-Risk Spread portfolio versus an Undefined-Risk portfolio (The Wheel strategy). We have a meticulously documented Master Blueprint detailing the exact entry, management, profit-taking, and crash-mechanic rules. Your objective is to translate this blueprint into a robust Python script, run the simulations across 8 years of historical data, and provide the exact performance metrics requested. Data Requirements: Testing Window: January 1, 2018 – December 31, 2025. Underlying Assets: SPY, RUT, GOOG, AMZN, GDX. Data Type: End-of-Day (EOD) Options Chains with Bid/Ask and Greeks (specifically Delta). CRITICAL NOTE REGARDING DATA PROCUREMENT: You must provide the data to run this backtest. If you do not already possess commercial access to this historical data (e.g., Databento, CBOE, ORATS), you must acquire it yourself. The total cost of acquiring this data MUST be included in your final, all-inclusive project quote. Project Milestones & Key Deliverables: Milestone 1: Technical Specification (Method Statement) & Architecture Approval. Prior to writing any code, you will submit a brief 1-to-2 page Technical Specification detailing exactly how you will execute the Master Blueprint. This must outline your data ingestion method, how you will calculate the cash-drag/Fed Funds rate, the specific logic loop for the Wheel strategy's "Trap Mechanic", and your slippage assumptions. Coding and backtesting will only commence upon approval of this document by our CIO. Milestone 2: The Python Source Code & Execution Report. Clean, annotated, and auditable Python code. An Excel/CSV report detailing the following metrics for 5 specific scenario combinations: Compound Annual Growth Rate (CAGR), Maximum Drawdown (isolating the March 2020 crash), Capital Efficiency, and Time Underwater (exact days capital was trapped generating $0 yield). A brief consultation/walkthrough of the output to verify custom mechanics fired correctly. Required Qualifications: High-level proficiency in Python (Pandas, NumPy). Provable experience in quantitative finance, specifically options backtesting. Deep understanding of options mechanics, margin utilization, and early assignment logic on American-style equities. Screening Questions (Please answer when applying): Do you currently have access to the required historical EOD options data for the tickers and dates requested? If not, what provider will you use, and can you confirm that cost is fully baked into your bid? How do you typically handle the cash-drag/interest accrual math when simulating stock assignments in a portfolio? What is your estimated timeline and all-inclusive fixed-rate bid (including data costs) for delivering both the Technical Specification and the finalized reports? (Please review the attached Master Blueprint containing the exact structural rules for the simulation before bidding). Apply tot his job

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