Python & Quantower Option Strategies -- 2

@Microlent

Hi, this looks straightforward at first, but in my experience there’s usually a key detail that can cause issues later. I’ve handled similar projects before and can outline a practical approach for you. For similar work and case studies, feel free to check my profile: https://www.freelancer.com/u/microlent Let me know if you'd like me to walk you through the plan. ~ Rajesh

@ambientinfotech

Hi sir, I can manage share task in job summery with quality features and functions. I am expert self-motivated and hardworking Python developer and we can ensure complete customer satisfaction and 100% quality work. Let’s chat Thanks

@vishal1145

Your Quantower module will fail in production if the option chain feed lags by more than 500ms during high IV events - straddles get mispriced and you'll enter at the wrong strikes. I've seen this break three live algos because devs didn't implement local caching with staleness checks. Before architecting the solution, two questions: Are you using NSE's FnO API directly or going through a broker gateway like Zerodha Kite? And for crypto, do you need cross-exchange arbitrage detection or just single-venue execution on Binance/Deribit? Here's the architectural approach: - PYTHON PACKAGE: Build a broker-agnostic adapter layer with separate modules for NSE, Binance, and Deribit - each implementing a common interface for option chains, order placement, and Greeks calculation using py_vollib. - QUANTOWER C# MODULE: Mirror the Python logic using Quantower's API with real-time Greeks updates via their OnQuote handler and parameter exposure through the Strategy Settings panel for strike selection and delta thresholds. - GREEKS ENGINE: Implement Black-Scholes with volatility surface interpolation to recalc delta/gamma every 100ms - critical for adjustment triggers when underlying moves 1% in either direction. - ADJUSTMENT LOGIC: Code rule-based hedging (rolling strikes, adding wings, converting to iron condor) with configurable thresholds - I'll expose max loss %, time decay targets, and delta neutrality bands as parameters. - BACKTESTING FRAMEWORK: Build a CSV-based replay engine that simulates order fills with realistic slippage models (0.05% for index options, 0.15% for stock options, 0.02% for crypto). I've built similar non-directional systems for two prop shops trading SPX and BTC options. The modular design you're asking for is exactly how I structure production code - swapping brokers should take 20 lines, not 200. Let's schedule a 15-minute call to walk through your adjustment rules and risk limits before I start the prototype.

@NEHABHAT92

With over 9+ years of web and mobile development experience, I'm confident that I can tackle the challenging project you've proposed. My expertise in automation and Python aligns perfectly with what you're seeking - a clean, readable codebase that efficiently handles option trading logic across multiple environments. Drawing on my extensive background, I can build for you a single, organized Python package that delves into real-time option chain data analysis, calculates crucial risk metrics with Greek-based algorithms, and effectively executes trade orders accordingly. Moreover, my understanding of modular coding will enable your platform to easily integrate new exchanges or brokers without disrupting the core strategy engine. Additionally, my familiarity with REST/WebSocket APIs ensures seamless automation and back-testing capabilities. To complement this proficiency, my team can also provide you with a detailed documentation and a video walkthrough illustrating how both the Python and Quantower setups function on demo data. Don't miss out on leveraging our well-rounded skill set to create an intelligent trading system that translates your parameters into profitable actions!

@TechizeBuilder

Hi! I reviewed "Python & Quantower Option Strategies -- 2" and I'm confident we're a great fit. We bring extensive experience building inventory management systems—stock tracking, barcodes, warehouses, purchase orders and low-stock alerts—using Java, Python. Our solutions give real-time visibility, reduce waste and streamline your supply chain end to end. Secure, scalable and easy to operate. Let's discuss your workflow, timeline and budget. Thanks!

@zyeloncomputech

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@iimtpradeepdash

Option straddle/strangle with adjustment and exchange modularity means the engine layer must route both order management and data feeds through adapters. I've built Python algo trading stacks for Indian F&O (Zerodha, Angle, Upstox) and BitMEX/Binance before, all with Greek calc, order management, and event-driven logic. I'd structure this as a standalone Python package: one source for both backtest and live, REST/WebSocket adapters as plug-ins, and config-driven entry/exit/adjustment. Trade logs to CSV or SQLite. Quantower side: mirrored strategy logic, all params exposed for instant field tweaking. Which F&O brokers and crypto exchanges need adapter code in v1, and do you already have demo credentials lined up? I can start this flow this week if the scope fits. Pradeep

@nexvative

Hi, I am an experienced Python and algorithmic trading developer with expertise in options trading systems, broker/exchange integrations, WebSocket market data, and automated execution engines. Your requirement for a production-ready non-directional options framework across both Indian F&O markets and crypto exchanges aligns closely with projects I have built involving options analytics, strategy automation, risk management, and real-time trading infrastructure. For this project, I can develop: • A modular Python trading framework supporting straddles, strangles, adjustments, partial exits, and risk management rules. • Real-time option chain ingestion through REST/WebSocket APIs. • Greeks calculation engine (Delta, Gamma, Theta, Vega, IV analytics). • Support for Indian index/F&O stocks and major crypto exchanges. • Plug-and-play broker/exchange adapters so new integrations can be added without modifying strategy logic. • Trade logging to CSV and database for analysis and auditing. • Backtesting and parameter optimization capabilities. Additionally, I can build the corresponding Quantower strategy module with configurable UI parameters for strikes, adjustment thresholds, time filters, risk controls, and position sizing while keeping the logic synchronized with the Python implementation.

@KrisuTechkul

As an experienced AI professional, my services perfectly align with your project requirements. Not only do I possess strong Python skills, but I also have an in-depth understanding of a wide range of advanced functionalities, including the ones you specifically mentioned like option chain data extraction and Greek-based risk metrics calculations. My adeptness with PEP-8 standard ensures that the code will adhere to a clean and readable style, while my focus on sensible error handling guarantees robustness for your trading logic. Moreover, the modular approach I employ in my development processes have always worked towards easy integration and adaptability, which aligns perfectly with your need of incorporating new exchanges or brokers without affecting the core strategy engine. This extends to the ability to switch on/off individual legs for testing purposes. Furthermore, my extensive experience with documentation coupled with a keen eye for details will ensure delivery of comprehensive project documents along with video walk-throughs that accurately reflect how both the versions operate on demo data. I am confident that choosing me would allow us to develop a high quality solution efficiently right from the get-go. Let's make your prototype within a week and bring your concepts to life!

@tyronet2

The part that decides whether this actually works in production is the adjustment logic, not the entry signal — a straddle/strangle is easy to open; what separates a survivable non-directional system from one that bleeds capital is how cleanly it detects when delta has drifted too far and rolls or hedges the threatened leg before the move compounds. I'd treat that as the core engine, with entry/exit as a thinner layer on top. My architecture: a strategy-engine core (Greeks calculation, adjustment-trigger logic, position state) kept completely separate from exchange/broker adapters, so India F&O and crypto exchanges plug in as interchangeable data/execution sources rather than being hardcoded into the strategy logic — this is what lets you add a new exchange later without touching the core, as you specified. Each leg gets its own on/off flag for isolated testing, which also makes debugging adjustment logic much easier since you can isolate one leg's behavior at a time. For Quantower, I'd mirror the same core engine through its custom strategy API, exposing strike selection, time filters, and adjustment thresholds as UI parameters so live tweaking doesn't require touching code.

@TechWagger2026

With over 14 years of experience, I am no stranger to the world of software development, automation, and data analysis. If you're looking for someone capable of providing end-to-end solutions for both Python modules and Quantower scripts in your option trading project, then I'd be an excellent fit. My extensive know-how spans from Python's core functionalities for data pulling, risk calculation using Greek-based metrics to ensuring clean code that's modular for seamless updates with new exchanges/brokers. In short, if you choose to work with me on this project, you'll be getting someone who can hit the ground running from Day One, a self-motivated professional committed to clean and readable coding practices while maintaining industry best practices (like PEP-8) in mind. Combining all this with my genuine passion for technology and a proven track record of completing similar projects successfully - I believe together we can create a powerful system that'll endure future growth whilst remaining agile for your tailored needs. So let's take that first substantial step and get ready to build something outstanding!

@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 10) Backtesting of Nifty options for wait and trade strategy 11) Trigger orders for Dhan Nifty options 12) Shoonya API:- Wait and trade strategy 13) Tradetron: RSI + ADX + EMA strategy 14) Python Moving avarage channel trading Algo 15) Kotak Neo: Turtle scalping strategy for options 16) Fyers Filtered option chain in Excel 17) Binance Bitcoin tradingview strategy python bot 18) Fyers Tradingview python bot 19) Dhan Python order manager I can deliver any project in Trading. Readymade setups for Python available

@iterativeTech

As a seasoned AI and cloud data engineering specialist with a strong track record in the finance industry, I bring to the table an uncanny ability to turn your desires into measurable ROI. I have been building highly-scalable and production-ready systems that align with business objectives for years, and this project aligns perfectly with my skill set. My proficiency with Python is matchless, having used the language to develop advanced analytics applications like recommendation systems, NLP models and more that allowed businesses to extract true value from their data. My automation skills are an add-on advantage, eliminating manual effort and enhancing throughput, which will come in handy when creating different strategy options and integrating new exchanges or brokers to the system without compromising the core strategy engine. Moreover, my experience in data engineering and architecture on AWS ensures that the ETL/ELT pipelines I build are not only scalable, efficient and secure but also equipped with real-time analytics as demanded by this project. To solidify my pitch, I would add that my writing style is intrinsically `PEP-8', validating my error-free approach that will ensure your `module' 파damage control becomes 바nonexistent. All in all, I believe your project requires a blend of finance knowledge, advanced technical expertise and business acumen--all of which I offer abundantly.

@MKCHAND

Hi, I am the master of trading automation solutions. See my work first then award your valuable project to me. Visit my profile and portfolio on freelancer.com. I can easily create your trading automation system. I have already developed more than 75 trading automation for options trading. You can see live demo of all also. Let’s discuss your detailed requirement in detail on chat.

@itsrehman67

Hello, I have reviewed your requirement for a dual-environment options trading system (Python + Quantower) with real-time Greeks, adjustments, and modular broker integration. This is a system-design heavy problem, and I can help you build a clean, production-grade architecture that separates strategy logic from execution layers so both backtesting and live trading remain consistent. **My approach:** • Core strategy engine in Python (modular package) • Separate adapters for data feeds (options chain, crypto exchanges, broker APIs) • Greeks + risk module as independent service layer • Execution layer handling entry/exit/adjustments with configurable rules • Trade logging system (CSV + database ready structure) • Quantower strategy mirroring same logic using exposed parameters for live tuning **Key focus areas:** • Non-directional strategies (straddle/strangle logic with adjustment rules) • Risk control + position lifecycle management • Plug-and-play exchange/broker adapters (no core changes needed) • Clean separation between signal generation and execution For Quantower, I will align the strategy with its C# framework and ensure parity with the Python engine so both systems behave identically in simulation and live environments. I prioritize clean architecture, maintainability, and reproducibility over rushed implementations. I can deliver an initial working prototype within 5–7 days for review. Best Regards, Abdul Rehman

@seethadeviv

Hello, Your project is exactly the type of complex trading automation we enjoy building. We can develop both the Python framework and Quantower strategy module with a shared, modular architecture to ensure consistency between backtesting and live execution. Our approach includes: • Real-time option chain integration for Indian F&O and major crypto exchanges • Greeks calculation and risk management engine • Automated straddle/strangle entries, partial exits, and adjustment logic • Broker/exchange adapters designed for easy future expansion • CSV/Database trade logging for analysis and reporting • Quantower strategy with configurable UI parameters for strikes, filters, timings, and adjustments • Clean, maintainable, PEP-8 compliant code with robust error handling We have experience building custom trading platforms, automation systems, API integrations, and scalable financial applications. The architecture will allow individual legs to be enabled or disabled independently, making testing and optimization straightforward. We can provide documentation, source code, and a walkthrough video demonstrating both implementations on demo data. An initial prototype can be delivered within the first week for your review and feedback. I would be glad to discuss the strategy rules, broker requirements, and deployment workflow in detail. Best Regards, Seethadevi Vaiyapuri Co-Founder | Tee3Apps

@sanddep5

With my extensive background in Full Stack Development and specialized skills in Automation, Data Analysis, Java, and Python, I am your ideal candidate for this project. Not only do I take businesses from concept to completion by building scalable and efficient applications tailored to their unique needs, but I have ample experience in option algos and exchange adapters. One of the crucial aspects of this project is the need for well-organized code that seamlessly pulls real-time data, computes risk metrics while executing precise trading orders according to your parameters. My technical expertise allows me to architect clean and readable code aligning with PEP-8 standards ensuring a modular structure. Furthermore, I have an extensive history in developing SaaS platforms which will be highly valuable while working on Quantower strategy modules with customizable inputs. Lastly, not only can you trust me to deliver an initial prototype within a week, but my work doesn’t end there – I emphasize long-term collaboration and offer robust support, ensuring your project is always up-to-date and operating at its full potential. So why wait? Let's transform your trading strategies into seamless, adaptable solutions starting today!

@Cruz1217

Upgrading from NAV to Business Central isn’t just a version bump, it’s a shift from monolithic code to modular extensions. I’d start by mapping the existing C/AL objects to AL, then refactor using events to avoid overlayering while keeping custom logic intact. I recently migrated a NAV 2015 system with 50+ custom reports and 200+ tables to Business Central SaaS. The biggest lesson was testing schema sync early, small mismatches in field types caused silent data truncation. You’ll get a clean, event-driven codebase with all reports converted to RDLC or Word layouts, ready for future updates.

@Luanvdm375

I can deliver a production-ready, non-directional options engine that runs in both plain-vanilla Python and as a Quantower custom strategy, matching your straddle/strangle and adjustment requirements across Indian F&O and major crypto pairs. My focus will be clean architecture so you can plug in new exchanges and toggle legs without touching the core logic. I’ve previously built modular option algo frameworks with real-time option chain ingestion, Greeks calculation, bracket/adjustment logic, and broker-agnostic exchange adapters, plus mirroring strategies into platform-specific scripts (including Quantower-style environments). CSV/DB logging and PEP-8, testable code are standard in my deliveries. I’d structure this as a single Python package containing the strategy engine, risk module, and adapter layer, then implement a Quantower strategy that calls the same logic, exposing parameters (strikes, time filters, thresholds) via the UI. I’ll include concise docs and a short video walkthrough using demo data, with a first prototype ready in about a week. Quick question before I refine the approach: Do you already have preferred data/execution providers for Indian derivatives and crypto, or should I propose a specific set of APIs?