Custom Python Options Trading Algo -- 2

@onkarkulkarni8

Hello, I checked your "Custom Python Options Trading Algo -- 2" project and it looks like understanding the existing workflow will be important before making any changes. I've worked on similar PHP projects involving php, java, python, software architecture, numpy, trading, api integration, backtesting, pandas and prefer delivering work in small milestones so everything stays easy to review and adjust if needed. If you can share a few more details about the current setup and expected outcome, I'll suggest the best approach and provide an accurate timeline. ⭐ 5.0/5 from a recent client: "Project was delivered before Time with Best professional Knowledge One could ever held. Thanks for the support" Final timeline and cost will be confirmed in chat after a complete understanding and documentation of the project expectations in detail.

@vishal1145

Your backtesting module will fail if it doesn't account for options Greeks changing intraday — most retail algos assume static IV and get crushed when volatility spikes. This gap between historical replay and live execution is where 80% of strategies break. Quick questions - are you planning to handle multi-leg spreads (iron condors, butterflies) or single-leg calls/puts? And what's your target latency tolerance between signal generation and order execution? Here is the architectural approach: - PYTHON TRADING STACK: Build event-driven engine using pandas for tick data processing, NumPy for Greeks calculation, and Zipline/Backtrader for historical replay with slippage modeling. - API INTEGRATION: Implement WebSocket listeners for real-time options chain updates via Interactive Brokers TWS API with automatic reconnection logic and order state reconciliation. - RISK CONTROLS: Code pre-trade validation layer that checks position limits, Greeks exposure (delta, gamma, theta), and circuit breakers before every order hits the broker API. I've built 4 production algo-trading systems for prop shops that process 500+ options contracts daily without missing fills. Let's schedule a 20-minute technical call to review your exact strategy logic before I architect the event loop.

@KavyaTechNepal

Namaste, I can build a fast, reliable Python trading system tailored to your options strategy with clean, modular architecture. I have experience integrating broker APIs, implementing real-time execution, risk management, backtesting, and automated trading workflows. I can develop your strategy with Kite Connect, Interactive Brokers, or any REST/WebSocket API, including configurable stop-loss, take-profit, position sizing, daily loss limits, historical backtesting, detailed reports, and complete documentation with a handover session. Once you share your trading rules, I'll optimize the execution for speed and reliability. Best regards, Amit G. Kavya Tech Nepal Freelancer Profiles: [https://www.freelancer.com/u/iamamitgupta1994](https://www.freelancer.com/u/iamamitgupta1994) [https://www.freelancer.com/u/KavyaTechNepal](https://www.freelancer.com/u/KavyaTechNepal)

@kishanbwts

I’ve built live-trading scripts before that read real-time market data and execute option trades via broker APIs like Interactive Brokers. For this project, I would focus on translating your entry/exit rules into clean, fast Python code that handles orders and risk controls exactly as you specify. A key step is ensuring robust and low-latency connectivity to your chosen broker—do you prefer WebSocket or REST for order routing? Also, will your rules require tick-by-tick data or is 1-minute granularity enough for decisions? These details help shape efficient data handling and order flow. Back-testing will use historical option prices and simulate your strategy with configurable stop-loss, take-profit, and daily loss caps. I’ll make sure performance outputs are clear so you can assess and tune before going live. I’m ready to start coding this project and set up a hand-off call for your smooth transition to managing and extending the system yourself. Just share your specific entry/exit logic when you’re ready.

@ashinnd84

Hello there, we are a team of senior AI ML Full Stack Web and Mobile App Developers. Please, send me a message to discuss the work and finish in no time. Thanks Ashish Kumar.

@sufyanaslamch

Hello Sir, I have carefully read your requirements and can develop a structured Python-based options trading system that connects to live market data, evaluates your rules in real time, and places orders through your selected broker API. The project will include: • Options entry and exit strategy implementation • Kite Connect, Interactive Brokers, or REST/WebSocket integration • Live market data handling • Stop-loss, take-profit, position sizing, and daily loss limits • Order validation and duplicate-order protection • Backtesting with detailed trade and performance reports • Logging, alerts, and error recovery • Paper-trading mode before live deployment • Clear documentation and handover support I will build the system using Python, pandas, NumPy, and a suitable backtesting framework. The strategy, broker connection, risk controls, and reporting will remain modular so you can update them later without rewriting the full project. I have experience with Python automation, APIs, WebSockets, trading workflows, and backtesting. I can share relevant examples privately. My hourly rate is ₹1,000. A working first version can usually be completed within 2–4 weeks, depending on the strategy rules and broker API. I am waiting for your kind response. Thanks

@larsenm2020

As an experienced trader turned software developer, I am uniquely positioned to bring your vision of a fully automated Python algorithm to life. My previous projects, which include coding option strategies and developing low-latency trading applications in Python, showcase my prowess in delivering solutions tailored specifically for the finance industry. What sets me apart is my emphasis on speed, reliability, and clean integration with popular Python trading stacks like pandas and NumPy. Additionally, when working with APIs such as Kite Connect or Interactive Brokers, I prioritize robustness to ensure smooth connectivity to brokers. My PHP and Python skills are proficient enough to handle all your requirements, from building a well-structured project implementing your specific option rules to designing risk controls like stop-losses, take-profits, position sizing, and daily loss caps. Moreover, let's not forget about the all-important back-testing module - an integral part of any trading algorithm development process. I can create a detailed historical data replay functionality that would give you a comprehensive understanding of the performance metrics before going live. With thorough documentation and a comprehensive hand-off call from me, you'll be self-sufficient in running, tweaking, and extending the code independently after project completion. Choose me for excellence that extends beyond just writing code;

@prashanpatel557

Hello, Your project is an excellent match for my expertise in Python algorithmic trading, options trading systems, broker API integration, and automated execution. I’ve developed live trading applications with Upstox, Zerodha Kite, Interactive Brokers, WebSocket market data, backtesting engines, risk management, and VPS deployment for options trading. I can build a modular, production-ready Python trading system that implements your options buying/selling strategy, processes live market data in real time, executes orders through your preferred broker API, and includes configurable stop-loss, take-profit, position sizing, trailing stop, and daily loss limits. The solution will also include a reusable backtesting module, detailed logging, performance reports, clean documentation, and a structured project that’s easy to extend with future strategies. Before we begin, I’d like to confirm one detail: Which broker API do you plan to use for live trading (Kite Connect, Interactive Brokers, or another), and does your strategy generate signals on candle close or require tick-by-tick execution? I’m ready to start immediately and can deliver a clean, scalable, and well-tested trading application with deployment support and a complete handover session.

@FreeLancers360

Hi there, The moment I read "Custom Python Options Trading Algo -- 2", I knew it was a strong match for exactly what we do best. I don’t just want to tick the boxes here; my aim is to hand you something that genuinely goes beyond what you pictured. From your brief I can see this involves php — all areas we handle in-house. We specialise in PHP, Java, Python, Software Architecture, which lines up directly with what you need. How we'd approach it: - Map out the scope, pages and data models up front - Build the frontend and backend with clean, scalable code - Test across devices and browsers, then deploy - Hand over with documentation and post-launch support Happy to work hourly with transparent time tracking and regular check-ins. Expect smooth progress updates throughout, full respect for your specifications, and zero surprises along the way. I can start right away and keep you updated at every step — let’s make this a great one. Best regards, FreeLancers360 Let’s connect in chat and get started — message me anytime and I’ll reply right away!

@tyronet2

You need a Python-based options trading algorithm that can process live market data, execute broker API orders, and enforce strategy rules with backtesting and risk controls. I can help build the trading engine structure, API integration, and data workflow needed for this automation. I have strong Python backend experience building real-time systems and API-driven applications. At Marin Software (US), I developed Python services with AWS infrastructure, event-driven workflows, and data processing pipelines. I also worked with REST APIs, WebSocket-style integrations, and performance-focused backend systems. For this project, I can implement the strategy engine, broker connectivity (REST/WebSocket APIs), pandas/NumPy-based data handling, backtesting modules, and configurable risk controls like stop loss, position sizing, and daily loss limits. I focus on clean architecture so the strategy logic can be adjusted without rewriting the whole system.

@jaroslavcaprata

I can help turn your options trading rules into a structured Python automation system focused on reliable execution, broker connectivity, and strategy validation. The main challenge is not only implementing entry and exit logic, but building a stable workflow around live data handling, order execution, risk controls, and testing before real deployment. I would structure the project as a modular Python application with separate components for market data feeds, strategy logic, broker API integration, risk management, and reporting. The system can support REST/WebSocket broker connections, process live option data, execute orders based on your rules, and include configurable controls such as stop-loss, take-profit, position sizing, and daily loss limits. Before going live, I would implement a backtesting layer using historical data to validate the strategy behavior and generate performance metrics. The final codebase would include clear documentation so you can adjust strategy parameters and maintain the system independently. One question: which broker API and market data source do you currently plan to use, and do you already have historical options data available for backtesting?

@nexvative

Hi, I can build your options trading automation as a clean, production-ready Python system with a modular strategy engine and robust broker API integration. I’ll implement your exact entry/exit rules, live market data via REST/WebSocket, and configurable risk controls including stop-loss, take-profit, position sizing, and daily loss limits. I’ll also provide a historical backtesting module with performance metrics so the strategy can be validated before live deployment, along with logging, error handling, configuration management, and safe paper/live trading modes. Tech stack can include Python, Pandas, NumPy, TA-Lib/backtrader, and Kite Connect or Interactive Brokers depending on your preference. The final code will be well documented and easy to extend. I have experience building Python-based algorithmic trading, market-data, and automation systems. Share your strategy rules and preferred broker, and I can propose the architecture and development milestones.

@hugog17

Hi there ! Two things decide whether an options algo survives live trading, and both usually get skipped. Restart safety: if the script dies mid session with open positions, it must pull real positions back from the broker on restart, never trust what was in memory. I build that in from day one. The daily token: Kite Connect access tokens expire every morning. Handle that badly and the algo just sits there doing nothing at open. Rest of it: - WebSocket ticks, orders over REST, inside the rate limits - Limit orders with a buffer, market orders on options slip badly - Stop loss, target, position sizing, and a daily loss cap that flattens everything and stops trading - Backtest module with proper metrics Honest note: option backtests need historical chain data by strike and expiry. Which broker are you on, and what data do you have? Timeline : 12 days Budget : ₹1,100/hr Thanks!

@PrateekP3003

40 hours/week, available for work You can track project progress via the tracker. Hi! I am a Python engineer specializing in algorithmic trading and financial automation, available full-time, with hands-on experience building low-latency trading systems, broker API integrations, backtesting frameworks, and automated risk management. I focus on delivering reliable, production-ready trading software that can execute strategies accurately in live markets. I can assist you with: 1. Trading System Architecture Designing modular, event-driven trading engines Broker integration (Kite Connect, Interactive Brokers, REST/WebSocket APIs) Low-latency order execution and live market data processing Robust logging, monitoring, and recovery mechanisms 2. Strategy Development Python implementation of options buying/selling strategies Backtesting using Backtrader, pandas, NumPy, and TA-Lib Configurable stop-loss, take-profit, position sizing, and daily loss limits Performance analytics and strategy optimization 3. Deployment & Support Clean, well-documented Python codebase Configuration-driven strategy parameters for easy customization Environment setup, testing, and deployment guidance Knowledge transfer and post-delivery support Let's discuss your strategy rules, preferred broker API, and execution requirements so I can build a robust, scalable trading system tailored to your workflow. Best regards, Prateek

@thiagofoz87

Your project requires more than a simple trading bot — it needs a stable event-driven execution engine capable of handling live market streams, order routing, and strict risk controls without introducing latency or inconsistent state during volatile market conditions. I can build the system as a modular Python application with separate layers for market data ingestion, strategy execution, broker integration, risk management, and backtesting. This structure makes it easier to evolve strategies without rewriting execution logic. For broker connectivity, I can integrate REST + WebSocket flows with platforms such as Kite Connect or Interactive Brokers, including reconnect handling, order state synchronization, and execution logging. The strategy engine can evaluate entry/exit rules in real time using pandas/NumPy/TA-Lib while keeping the execution path lightweight. The project would also include: - configurable stop-loss, take-profit, position sizing, and daily drawdown limits - historical replay and backtesting with detailed trade metrics - structured logging and error handling for live execution safety - configuration-driven strategy parameters for easier tuning - documentation and a hand-off session covering deployment and extension points I can also help define a reliable architecture for paper trading before enabling live execution, which is critical for options automation. If you already have partial strategy rules or preferred broker APIs, I can adapt the implementation around those constraints.

@alluankit01

Hi, I can develop your Python-based options trading algorithm with live market data processing, real-time entry/exit rule execution, broker API integration, backtesting, and configurable risk controls. The best solution is to first review your exact options strategy logic, preferred broker API, instruments, order types, timeframe, capital rules, and risk limits. I’ll then build a clean Python project that connects to live data, evaluates your rules efficiently, places orders through the broker API, and records every action for review. I’m comfortable with Python, pandas, NumPy, TA-Lib, backtrader-style backtesting, Kite Connect, Interactive Brokers, REST/WebSocket APIs, options strategy automation, order routing, stop-loss, take-profit, position sizing, daily loss limits, logging, and error handling. Deliverables will include: * Structured Python trading project * Options entry/exit rule implementation * Live market data integration * Broker API order execution * Stop-loss and target controls * Position sizing logic * Daily loss cap * Backtesting module * Performance metrics * Logs and error handling * Setup documentation * Handover call I’ll focus on reliable execution, clean architecture, and practical risk management so you can test safely before going live. No profit is guaranteed, but the system will be built for disciplined strategy execution. Best regards Ankit

@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

@trb2004

❤️Hi there❤️ Your project to automate your options trading strategy using a Python algorithm aligns perfectly with my expertise. I have hands-on experience in coding option strategies and low-latency trading apps in Python. I can deliver a well-structured Python project with option-specific rules, smooth broker connectivity, risk controls, back-testing capabilities, and clear documentation within the specified timeframe with 100% accuracy. Please take a look at my profile for more details. This project involves developing a Python trading script to automate your options strategy, including real-time market data analysis, order routing, risk controls, and back-testing capabilities. I am confident in my ability to deliver this project efficiently and effectively. I look forward to the opportunity to collaborate with you on this exciting project. Please feel free to reach out so we can discuss your requirements further. Warm regards, Thaveesha

@tradeamplify1

Hi, Your project aligns well with our expertise in fintech and algorithmic trading software development. We have a team of experienced programmers with strong expertise in Python, broker API integration, and automated trading systems. With effective communication and a structured development approach, we are confident in delivering a reliable, scalable, and production-ready solution that meets your trading requirements, including live execution, risk management, and backtesting. We have developed several fintech products and trading automation solutions. Once we connect, I'll be happy to walk you through our relevant projects and portfolio so you can see the quality of our work. We look forward to discussing your strategy and delivering the solution you're looking for. Best Regards, Durgesh Pandey