Quantitative Options Backtester: Hybrid Multi-Asset Strategy (2020–Present)

@ShaikhAneesa

Hi there, I understand you need a quantitatively accurate backtesting engine for a hybrid multi-asset options portfolio from January 2020 to present, with covered calls, assignments/rolls, margin interest, annual index-put hedging, and strict portfolio accounting rather than a simplified strategy simulation. I’m confident I can build this in Python/QuantConnect with event-driven execution and reproducible calculations. I’ll first structure the historical equity/options data and portfolio allocation, then implement the covered-call lifecycle, monthly rolls, assignments, repurchases, margin/leverage and historical interest costs. I’ll also model the annual OTM index puts and their impact on drawdown and portfolio returns. The backtest will run chronologically with no look-ahead bias, with complete trade/cash-flow logs and validation checks. The final report will include CAGR, Sharpe, max drawdown, win rate, monthly performance and detailed portfolio/equity curves, alongside the full source code. Do you already have the historical split-adjusted options data, or should the backtester be built around a specific data provider/API? I’m ready to start immediately. Warm Regards, Aneesa.

@ahmadayaz

With extensive experience in quantitative finance and data analysis, I propose building a robust Python backtesting framework for a multi-asset hybrid options portfolio. The strategy will involve Active Income Sleeve and Long-Term Growth Sleeve implementations, alongside risk management techniques and monthly option management. The deliverables will include a detailed performance report with essential metrics for evaluation. Let's discuss further to tailor the project to your specific needs and objectives.

@emilesd617

Hi, I am a quantitative developer with 8 years of rich experience in software development, with a background in financial analysis and algorithmic trading. I am familiar with Python, Financial Research, Finance, Risk Management, Financial Analysis, Data Analysis, Portfolio Management, Backtesting, and Algorithmic Trading. I can build the backtest for the full 2020–present period, including the covered-call income sleeve, long-term equity sleeve, monthly option rolls, assignments, margin interest, and annual OTM index put hedging. I would model the portfolio and cash flows carefully, then provide the complete Python backtest with CAGR, Sharpe ratio, maximum drawdown, win rate, and detailed monthly results so the strategy can be independently reviewed. I'm an individual freelancer and can work on any time zone you want. Please contact me with the best time for you to have a quick chat. Looking forward to discussing more details. Thanks. Emile.

@ImranAli01

Hello, I got that you are looking for an experienced quantitative developer to backtest a multi-asset hybrid options portfolio from January 2, 2020, to present, utilizing historical split-adjusted equity and options data. This is what I can help you with, let's chat. My approach is to implement the backtesting using Python and QuantConnect to precisely analyze the portfolio's performance. By automating monthly option rollovers, assignments, and repurchases, managing margin accounting based on historical interest rates, and simulating the purchase of OTM index put options for tail-risk hedging, I will ensure a comprehensive evaluation. The deliverables will include the full backtest code and a detailed performance report featuring essential metrics like CAGR, Sharpe ratio, max drawdown, win rate, and monthly cash flow logs. As final deliverables, you will receive the complete backtest code and a comprehensive performance report meeting all outlined requirements. One thing I'd like to confirm before we start: Are there any specific benchmarks or additional risk metrics you would like me to consider during the backtesting process? Looking forward to discussing this further with you. Best Regards, Imran

@demivision

Hi there, Employer, Thank you for sharing the details of your Quantitative Options Backtester project. I’m excited about the opportunity to collaborate on this sophisticated multi-asset portfolio analysis. I understand you’re seeking a comprehensive backtest of a hybrid portfolio, combining an actively managed covered call sleeve and a long-term growth basket, with specific risk management overlays such as margin leverage, historical broker rates, and annual tail-risk hedges. Your requirements for automated option handling—including rolling, assignment, and repurchase logic—align well with my expertise in systematic options strategies and quantitative research. With extensive experience in Python, QuantConnect, and financial data analysis, I have successfully built and implemented multi-asset backtesting frameworks for both equity and options strategies. My background includes margin and risk modeling, advanced performance analytics (CAGR, Sharpe, drawdown), and robust cash flow tracking. I am adept at integrating real-world constraints, such as broker margin costs and split-adjusted pricing, to ensure simulation accuracy. For your project, I propose developing a modular Python backtest (preferably on QuantConnect for robust data access and realistic execution modeling), incorporating your allocation and execution logic, systematic option rolls, margin accounting, and dynamic tail-risk hedging. The final deliverables will include clean, well-documented code and a detailed performance report covering all requested metrics and logs. I’m confident my skill set aligns perfectly with your requirements, and I’m eager to help you gain actionable insights from this analysis. Looking forward to discussing your specific preferences and any additional details you’d like incorporated. Best regards, DemiVision, LLC

@ianb65

Hi there, This hybrid multi-asset options backtest needs precise handling of assignments, rolls, margin interest, and drawdown control, and I can build that in Python. I have strong expertise in Business Analysis, Backtesting, and Financial Research for portfolio simulation. I’ll model the 38.7% income sleeve with monthly OTM covered calls, keep the 61.3% growth sleeve unhedged, account for broker margin costs, and simulate annual index put hedges with split-adjusted equity and options data from 2020 to present. Best regards, Ian

@albertc1996

Note: targeting ~30 delta on monthly covered calls only holds up if the delta comes from a real historical options chain on that date — reconstructing it from the stock price and a generic volatility assumption introduces look-ahead bias, especially around earnings or high-IV periods, and quietly skews which strikes get picked and when calls actually finish OTM versus get assigned. Reading through the setup — the split between the income sleeve running monthly OTM covered calls and the growth sleeve running uncovered, plus margin interest on the 25% leverage and an annual index put overlay to cap drawdown near 10%. I'd build this against real historical options chains rather than synthetic greeks, and recompute margin usage dynamically as assignments and rollovers change the underlying position, rather than treating leverage cost as a flat number across the whole period. I build backtests like this end-to-end in Python/QuantConnect myself, from data sourcing through the full performance report. The result: a backtest you can actually trust the drawdown and Sharpe numbers from, because the option pricing and margin accounting reflect what would really have happened on each date, not an approximation. Let's get started — I can run a short pilot on one of the two income-sleeve assets first, so you can see the option chain and assignment logic working correctly before I scale it to the full portfolio.

@qualityxenter

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@miguelcen64

Hello, The key part of this project is **backtesting a multi-asset options portfolio with accurate margin, assignment, and drawdown controls from 2020 to today**. I can help you handle this accurately and efficiently without overcomplicating the process. I have hands-on experience with **Python, Data Analysis, and Business Analysis**, including building structured models that turn complex inputs into clear performance reporting. For your project, I would focus on **historical data normalization**, **options-roll and margin logic**, and **performance metrics like CAGR, Sharpe ratio, and max drawdown**, while making sure the final result is **audit-friendly and reproducible**. I can start immediately and expect to complete this within 18 days. One detail I'd like to confirm before starting: **should the backtest assume QuantConnect data conventions, or do you already have a preferred historical options data source and margin-rate series**? Best regards, Miguel

@Algokage

Hello, I hope you're doing well. I am an experienced quantitative developer with a strong background in financial markets and trading strategies. My expertise lies in developing and backtesting complex trading systems, particularly in the options and equity markets. I have successfully delivered projects that involve multi-asset portfolios, risk management, and performance analysis. For this project, I will leverage my skills to backtest a hybrid multi-asset options strategy, ensuring accurate performance evaluation and risk management. I deliver clean, well-documented code and provide comprehensive performance reports. If you require samples or a demo, feel free to message me, and I'll share relevant examples. Alternatively, we can discuss the project further to ensure a perfect fit. Thanks, Adegoke M.

@richard031596

You need a Python/QuantConnect backtester for a hybrid equity/options portfolio from 2020 to present, including covered calls, margin interest, put hedging, and performance reporting. I work with Python data pipelines, pandas-based analysis, backtesting logic, API/data integration, and financial automation. I’ve built trading/backtest tools before where strategy rules, position sizing, entries/exits, logs, and performance metrics had to be reproducible and easy to audit. For this project, I’d define the exact tickers, options data source, roll rules, assignment logic, margin assumptions, and hedge mechanics first. Then I’ll implement the backtest, generate monthly cash-flow logs, and report CAGR, Sharpe, max drawdown, win rate, hedge impact, and key assumptions. Can start after confirming the tickers and preferred options data provider.

@JunaidBadroodien

Your project to backtest a hybrid multi-asset options portfolio is both ambitious and critical for optimizing investment strategies. I understand the importance of accurate historical analysis, especially with your focus on managing risk while leveraging potential income through covered calls and long-term growth. To meet your requirements, I propose a tailored backtesting solution using Python or QuantConnect. I will ensure seamless integration of historical split-adjusted equity and options data, meticulously accounting for margin interest rates and maintaining rigorous risk management strategies. This includes automating option rolls and simulating tail-risk hedging to adhere to your drawdown limit. Delivering a comprehensive performance report will be a priority, showcasing key metrics such as CAGR, Sharpe ratio, and detailed cash flow logs. My approach aims to provide not just data, but actionable insights that help refine decision-making and enhance strategy effectiveness. By closely aligning the backtest parameters with your goals, we can unlock the full potential of your investment strategy, providing clarity and confidence in your financial decisions. Let's create something that not only meets your expectations but sets a new standard for quality. Regards Junaid

@nexvative

Hi, I can build a robust quantitative backtesting framework for your hybrid multi-asset options portfolio from January 2, 2020 through the present. I will implement the portfolio logic around the $100,000 initial capital plus 25% margin leverage, including allocation between the active covered-call income sleeve and the long-term equity growth sleeve. The backtester will handle historical split-adjusted equity data and historical options chains, selecting monthly 30–45 DTE calls around the target delta. The implementation will include option expiration, rolling, assignment, repurchase logic, premiums, transaction assumptions, margin borrowing, and historical margin-interest costs. I will also model the annual OTM index-put hedge and evaluate its effectiveness under different strike/expiry assumptions, with drawdown results reported transparently rather than assuming a guaranteed 10% maximum drawdown. The final package will include: • Clean, reproducible Python or QuantConnect code • Historical portfolio simulation from 2020 to present • Monthly trade, premium and cash-flow logs • Equity curve and allocation history • CAGR, Sharpe ratio, max drawdown, win rate and return statistics • Margin interest and borrowing analysis • Covered-call assignment and roll history • Tail-hedge cost and effectiveness analysis • Clear README explaining data sources, assumptions and how to rerun the backtest

@Abror86

Hi-Abror Here From Uzbekistan. "Build A Robust Multi-Asset Options Backtester" - I can implement it in Python using QuantConnect-compatible logic, historical options data, portfolio accounting, and risk analytics. I can model covered calls, monthly rolls, assignments, repurchases, margin leverage, historical interest, index put hedging, allocations, cash flows, and drawdown constraints precisely. I will deliver modular backtest code, validate execution assumptions, calculate CAGR, Sharpe, drawdown, win rate, and provide detailed monthly performance and cash-flow reports. Would you like the implementation built directly in QuantConnect for easier historical data and strategy validation? Looking forward to working with you.

@ahmaddar2970

Hello, I can build this backtest end to end for the full 2020-present period, including the hybrid multi-asset structure, covered-call sleeve, zero-overlay growth sleeve, margin interest, assignment/roll logic, and annual OTM put hedging. I’ll deliver clean Python or QuantConnect-ready code with realistic execution assumptions, split-adjusted historical data handling, and a complete performance report covering CAGR, Sharpe, max drawdown, win rate, and monthly cash flow logs. I’ll also make sure the margin accounting and drawdown control framework are modeled transparently so results are auditable. If helpful, I can structure the work so the strategy logic, data pipeline, and reporting are separated for easier validation and future iteration. Best regards.

@tyronet2

You need an options backtest that models real contract selection, assignments, rolling, leverage costs and hedge expenses rather than producing an optimistic equity curve from simplified assumptions. I have 9 years of Python, data pipeline and production analytics experience, including scalable processing on AWS. I’ll build an event driven backtester using point in time split adjusted equity and option chain data. The model will select contracts by actual delta and days to expiry, apply bid ask aware fills, commissions, dividends, corporate actions, assignment rules, repurchases and historical margin interest. Every transaction and monthly cash flow will remain auditable. The hedge engine will test annual index puts across strikes and rebalance rules, while reporting whether the target drawdown was actually achieved rather than assuming protection. Deliverables will include reproducible Python code, configuration files, trade logs and performance metrics covering CAGR, Sharpe ratio, drawdown and win rate. Which assets, benchmark ETF, data provider and broker margin schedule should be used?

@brazenest

Hi, Your Hybrid Multi-Asset Strategy backtest requires precise handling of split-adjusted data and margin leverage. I’d start by structuring the code to model the 38.7% active income sleeve’s OTM call sales and 61.3% growth basket, integrating historical margin interest rates and automated option rolls. I previously built a Python-based equity options backtester with dynamic delta hedging, achieving 98% accuracy against broker reports. Happy to chat if you’d like to walk through the details.

@panos1414

Hello there , Good afternoon! I am an expert mobile software engineer with skills including Financial Research, Data Analysis, Finance, Python, Financial Analysis, Algorithmic Trading, Business Analysis, Backtesting, Risk Management and Portfolio Management. Please send a message to discuss more regarding this project. Appreciate your prompt response

@ayesha86664

Hi there! I understand you need a reliable backtest for a hybrid multi-asset options portfolio from January 2020 to present. The main challenge is accurately handling historical options data, covered call rolls, assignments, margin interest, and tail-risk hedging without distorting the results. I have experience with Python, financial analysis, quantitative research, portfolio management, risk management, and algorithmic backtesting. I can build the strategy in QuantConnect or a suitable Python framework with clear and reproducible logic. I will model the 38.7% income sleeve with monthly OTM covered calls and the 61.3% growth sleeve without overlays. I will include option rolls, assignments, repurchases, margin costs, and annual index put hedges. The final report will include CAGR, Sharpe, maximum drawdown, win rate, and monthly cash flow logs, along with the complete source code. check our work https://www.freelancer.com/u/ayesha86664 Do you already have a preferred historical options data provider? Let me know if you’re interested & we can discuss it. Best Regards Ayesha