Python Options Back-Testing Platform

@caroldata

As a seasoned developer with over a decade of experience, I have spent significant time perfecting my skills with Python, particularly in the areas of data analysis and software architecture. This combined with my expertise in JavaScript and PHP makes me an ideal candidate for your Python Options Back-Testing Platform project. I pride myself on the clarity and efficiency of my code, aligning it seamlessly with your core requirement of speed and readability. Further, my familiarity with Pandas and NumPy along with my experience with Backtrader and Zipline equip me well to handle your desired workflow. From ingesting your provided option data to running thorough historical data analysis based on your parameter sets, I can develop a robust CLI tool that delivers the comprehensive statistics and trade logs you need while staying focused on the analytics. To ensure reproducibility and future-proofing, I will provide fully commented modular Python source code supplemented by unit tests covering the main analytics functions. My attention to detail allows me to follow PEP 8 guidelines without fail while keeping external dependencies minimal. Hiring me means hiring an ISO 9001:2008 Certified professional who delivers error-free service within agreed timelines; maintains clear communication throughout the project; provides daily progress reports; offers post-delivery support; and above all ensures 100% satisfaction guaranteed.

@ashinnd84

Hello there, we are a team of developers and designers. Please, send me a message to discuss the work and finish in no time. Thanks Ashish Kumar.

@webmasters486

Hey, I've carefully reviewed your requirements for building a Python command-line tool to backtest U.S. equity and index options using historical data. I can create a modular, fully commented script that ingests CSV, JSON, or API-supplied data and runs your parameterized strategies with configurable entry rules, exits, position sizing, and date ranges. The CLI will accept parameters via arguments or a config file and output clear summary stats—including P&L, drawdown, Sharpe ratio, and trade logs—while optionally saving results to CSV. I’ll use Pandas, NumPy, and Backtrader or Zipline to ensure fast, maintainable computation, follow PEP 8 standards, and include unit tests for core analytics. The deliverables will include clean source code, a short README with usage examples, and verification that at least one year of SPY option-chain data processes without errors. Looking forward to your response. Best regards, Muhammad Adil Portfolio: https://www.freelancer.com/u/webmasters486

@Shonali1406

With 6 years of experience in Python, quantitative analysis, and financial data processing, I can build a clean, efficient CLI tool to backtest your option-trading strategies. I specialize in Pandas, NumPy, and Backtrader for performance analytics and maintain strict adherence to PEP 8 standards. I’ll deliver modular, well-documented code with clear CLI commands, comprehensive unit tests, and a concise README—ensuring your strategy parameters run smoothly on historical Polygon data for reliable performance insights.

@Rsolitario

I fully understand the need for a robust and efficient Python command-line tool to validate your options trading strategies against historical data. I can build the backtester you need, focusing on analysis speed and code clarity, just as you request. My plan is to develop the analysis engine using Pandas and NumPy for high-performance data manipulation. Instead of heavier frameworks, I will create a lightweight, modular structure from scratch, giving you full control and facilitating future extensions. Strategy configuration will be managed through CLI arguments or a simple configuration file, and key results—P&L, drawdown, Sharpe, and the trade log—will be generated in the console and saved to a CSV file. I have specific experience building quantitative analysis tools and backtesting engines in Python. I will deliver fully commented source code that follows the PEP 8 standard, along with unit tests for the main analytical functions and a clear README covering installation and usage examples. I estimate I can deliver a first working version, capable of processing SPY data, within 7 to 10 days. I would like to have a brief discussion to review the sample performance report and ensure the tool meets all your analytical requirements.

@otusonellp

With interest I read your proposal and would like to offer my skills as a Full Stack Developer on this project. Having strong expertise in Python, I excel at the analytics and data management tasks associated with your project. My proficiency would ensure the smooth execution of the option back-testing process while keeping up a robust output and consistency. Furthermore, my familiarity with the tech-stack including Pandas, NumPy, Backtrader, Zipline and a dedicated adherence to PEP 8 standards, aligns perfectly with your requirements. Whether it's transforming data from Polygon into neatly stored and arranged format or generating meaningful analytical results saved in CSV files for easy access and analysis – I can handle it precisely. Moreover, my focus on code clarity is tailored to meet your specific needs - well-documented commands for functional use along with a README for installation instructions to make sure that the project lifecycle is understandable even for newcomers.

@saba106

I am an expert statistician, Research Writer, and data analyst with more than eight years of experience. I have full command of Excel analysis, SPSS, STATA, R LANGUAGE, AND PYTHON. I am an expert in creating time series prediction models, working with survey data, conducting marketing analysis, building estimators, and medical analysis. I am a perfect match for your project share other details of the work so I can start working on your project. Will complete task on time.

@prashanpatel557

Hello, I will deliver a lean, PEP-8 Python CLI tool that ingests your Polygon option chains, runs parameterised backtests for U.S. equity/index options (SPY example), and outputs a full performance pack (trade log CSV, equity curve, P&L, max drawdown, Sharpe, per-trade stats). Plan: (1) validate and parse your sample CSV(s), (2) build a vectorised backtest engine that constructs option legs, applies your execution/fill model (mid/bid/ask), and records realistic P&L, (3) implement CLI/config-driven runs and exportable CSV/summary reports, and (4) add unit tests and concise README. Timeline: ~15–20 days. Fee: ₹28,000. Please share one year of Polygon SPY option data and your preferred fill/commission rules so I can start a PoC immediately.

@jagratip3

Hey there, If you're ready to turn your options trading ideas into actionable, testable numbers — I can help you build exactly the Python tool you need. I specialize in building clean, modular CLI-based analytics tools for traders and quants. For your backtesting project, I’ll focus on clarity, speed, and reliable analytics, not fluff. Whether your data comes via CSV, JSON, or live from the Polygon API, I’ll ensure smooth ingestion and rock-solid performance. Here’s how I’d tackle it: ✅ Flexible Ingestion – A script that handles both local file paths and API keys, parsing data cleanly for analysis. ✅ Configurable Strategy Engine – Accepts entry/exit logic, date ranges, and position sizing from CLI args or a simple config file. ✅ Insightful Output – P&L, drawdown, Sharpe, and trade logs all generated clearly to terminal and exportable to CSV. ✅ Modular, Testable Code – Written with future strategies and extensibility in mind, following PEP 8 and backed by unit tests. ✅ Lightweight Stack – Pandas, NumPy, and optionally Backtrader or Zipline — no heavy frameworks, no bloat. You’ll get fully commented code, a no-fuss README with setup and usage examples, and clean architecture that makes future additions a breeze. I’d love to help turn your ideas into a flexible tool you can trust. Let's build it right. Thanks & best regards,

@arehman171

Hi, I have gone through your project description. I’ll build a clean, fast Python CLI backtester that ingests your Polygon files or API, runs parameterised strategy backtests, and outputs trade logs plus summary metrics (P&L, drawdown, Sharpe) to terminal/CSV. The tool will accept CLI flags or a config file and include modular, well-commented code for easy extension. Let's chat to proceed.

@Sahyadri96

Completed projects till now 1) Python + DhanAPI +Excel + VBA option scalping strategy 2) Python 21 EMA and 9 EMA crossover strategy on DhanAPI 3) Google sheet + FyersAPI trading 4) Google sheet + Algomojo + Upstox 5) Tradetron Banknifty option scalping strategy 6) Excel 2600 NSE 10 years data 7) Copytrading using python 8) Tradetron Supertrend + MACD Crossover Strategy 9) Dhan option chain with Greeks in Google spreadsheet via Google Appscript I can deliver any project in Trading. Readymade setups for Python available Replies within a few minutes

@TheAiCompany

⭐⭐⭐⭐⭐ I'll build your Python back-testing platform to rigorously analyze options strategies against historical data. ✅ Ingests your Polygon data (CSV/JSON/API) for clean, terminal-based analysis. ✅ Executes your parameter sets for entry/exit rules, sizing, and dates, outputting P&L, drawdown, Sharpe, and trade logs to CSV. ✅ Delivers modular, PEP 8-compliant code with pandas/NumPy, minimal dependencies, full comments, unit tests, and a clear README. It will accurately process a full year of SPY option data per your sample report. Let's begin.

@YashveerRathore

I have reviewed your brief and i can create a fast, modular Python CLI to backtest U.S. equity and index options using Polygon data (CSV, JSON, API). I’ll design a simple pipeline with pandas and NumPy, along with a lightweight engine like Backtrader or Zipline. It will support CLI flags or a YAML config for entries, exits, sizing, and date ranges. Outputs will include P&L, drawdown, Sharpe ratio, and a complete trade log saved to CSV. The code will follow PEP 8, keep dependencies minimal, include unit tests for key analytics, and run in a virtual environment or conda. Acceptance requires processing over one year of SPY options to reproduce your sample report. I am eager to discuss the project further. Feel free to reach out for a detailed conversation.

@dipak1337

I’m a Python developer with strong experience in financial data analytics, backtesting, and algorithmic trading systems. I’ll build a clean, modular CLI tool that ingests your Polygon data (CSV, JSON, or API) and runs configurable option-trading strategy backtests with detailed performance reports. The solution will emphasize clarity, speed, and PEP 8 compliance. Deliverables: Modular, fully commented Python source code (Pandas, NumPy, Backtrader/Zipline) Command-line interface with config/flag support Summary stats (P&L, drawdown, Sharpe ratio, trade log) with CSV export Unit tests and README for setup and usage Similar projects done: Equity/option strategy backtester in Python (Pandas, NumPy) Financial data analysis and simulation pipeline Lightweight CLI data tools for analytics teams Ready to start immediately and discuss your strategy logic and data structure before coding. Best regards, Dipak P freelancer.com/u/dipak1337

@dushyant317

I am a seasoned software developer with 13 years of experience, holding a degree from IIT Delhi. My expertise aligns perfectly with the required skills for your project. I have successfully delivered complex solutions across diverse domains with a focus on quality and scalability. I bring strong problem-solving ability, hands-on technical depth, and client-centric delivery. I am confident I can add value to your project and deliver results within timelines.

@bagdenavdeep

With extensive experience in developing robust trading algorithms and automated systems, I am perfectly positioned to create a powerful back-testing platform for your option-trading ideas. My proficiency in Python libraries like Pandas and NumPy, coupled with my prior success in building modular, data-focused systems, makes me an ideal fit for your project. I'm no stranger to data wrangling, so processing the historical data from Polygon in any of your desired formats won't be an issue. I understand that this project isn't just about coding. It's about giving you results you can trust. That's why I bring solid Agile practices into my work like collaborating with QA to create strong automated test scripts so we can focus on defect prevention rather than fire-fighting later. With me, you get not only clean and well-documented code as per PEP 8 standards but also a clear and comprehensive README for easy installation and use of your new command-line tool. Another advantage of working with me is my familiarity with converting complex trade data into actionable insights that quantify risk and returns for decision-making. I have a keen eye for detail and will ensure your tool replicates your sample performance report using SPY option chain data covering at least one year diligently and without error. Let's collaborate so I can bring your options trading ideas to life—transforming numbers into profits

@smithh343

Hello, I can build a clear, fast Python command-line backtester that ingests your Polygon options data (CSV/JSON or API), runs configurable strategy parameter sets, and outputs full performance reports—P&L, drawdown, Sharpe, individual trade logs—and CSV exports for further analysis. I’ll write modular, well-commented code (Pandas/NumPy with an option to use Backtrader if you prefer), include unit tests for the core analytics, and supply a short README showing installation and example commands so you can run and extend the tool easily. To get started I’ll need a small sample of your Polygon data (one month or one year of SPY option chains) and any example strategy rules you want reproduced. I’m ready to begin as soon as you share those samples and your preference for CLI config vs a config file. From Smith Harrison

@Vertexservice

Your project, my team, zero stress. I deliver a Python command-line tool for backtesting U.S. equity and index options against Polygon historical data. With a vetted team, I guarantee thorough historical data analysis geared towards your option-trading ideas. Gain actionable insights without worrying about data collection. I help Prathama achieve data-driven trading decisions through optimized analytics using a command-line tool approach - guaranteed accuracy, zero data headaches, enterprise-level results. Specializing in Python development and financial analytics, our value-added services include speedy turnaround and comprehensive documentation. My expertise in Python development and financial analytics ensures a reliable and efficient solution for your needs. Feel free to reach out to me so that we can make vision a reality. Regards, Jaco Fourie

@geniusmrkhan

I can build a Python command-line tool tailored to your options backtesting needs. The platform will ingest your historical Polygon data—whether CSV, JSON, or via API—and run comprehensive analysis based on configurable parameters like entry/exit rules, position sizing, and date ranges. Using Pandas and NumPy for data handling, combined with Backtrader or Zipline for strategy execution, I’ll ensure clean, modular code that prioritizes speed, clarity, and terminal-based usability. The CLI will accept both file paths and API keys, while strategy parameters can be passed via flags or a simple config file. Output will include detailed trade logs, summary statistics like P&L, drawdown, Sharpe ratio, and optional CSV export. I’ll provide full documentation, PEP 8-compliant code, and unit tests for core analytics to support future expansion. The tool will handle at least a year of SPY options data without errors, producing reproducible performance reports.