Automated NIFTY Options Trading Python Developer

@mubeenm5026

As a Python developer with a rich background in algorithmic trading, I believe that my skills and expertise are highly relevant to your project. Throughout my career, I've designed and implemented several trading strategies for various financial markets. I am familiar with the full spectrum of steps needed to develop a trading system, from designing the initial strategy to creating efficient code and robustly testing it. I'm particularly excited about this project as the strategy you've outlined aligns well with my experience. My approach towards implementing trading logic through price action and support/resistance is already proven to be effective in the market. Additionally, I've worked extensively with various Python libraries such as Pandas, NumPy and TA that your project requires, which enables me to develop clean, modular and optimised code for easy maintenance. Finally, my experience in integrating different financial broker APIs like SmartAPI and Zerodha Kite will ensure seamless integration of your system. The holistic service we offer at Web Crest pairs my technical know-how with a business-focused approach that tailors solutions for client needs—I am ready to adapt our solutions to accommodate your unique trading requirements. Let's work together to turn your trading vision into reality!

@sdqtrades

As a highly experienced Python Developer with a strong background in the trading industry, I am confident in my ability to transform your trading strategy into a clean, efficient and scalable code that perfectly aligns with your project requirements. I have an intricate understanding of NIFTY options as well as Price Action and Support/Resistance which are core components of your trading strategy. My

@gogora

Hi Thanks for this great specifications. I could build this trading systems using Python, Pandas, Numpy, Matplotlib etc. I have experience in Algorithmic and NIFTY trading, VPS deployment and devops. I’m happy to discuss the details over chat, and I will respond promptly. Thanks for your attention Archil

@consultantcae

Hi, I am Sourabh Jain, I have been writing automated trading system using python for various brokers including zerodha, fyers and finvasia. Recently I have written option trading systems which uses various indicators like rsi, supertrend etc on renko chart and trades intraday. It also utilized TP, SL and TSL. I have used jupyter notebook to create GUI(dashboard) for user input. Please check my Portfolio. I can build the trading system for option trading with support/resistance, swing high/low and BoS as you require. If you would like to discuss this further, I am available. Regards Sourabh

@prashanpatel557

Hello, I have extensive experience developing Python-based algorithmic trading systems, NIFTY options bots, broker API integrations, backtesting engines, and real-time trading dashboards. Your project is well-structured and aligns with my expertise. I can build a modular system featuring: • Multi-timeframe Price Action & Support/Resistance engine • Swing, role-reversal, liquidity sweep, and volume confirmation logic • Automatic Entry, SL, Target, RR, and position sizing • Continuous NIFTY, CE & PE scanning with fallback EMA strategy • Paper trading and live execution via Zerodha/SmartAPI/other brokers • Risk controls, Telegram alerts, dashboard, and detailed reporting • Backtesting module with equity curve, drawdown, trade log, and analytics • Clean OOP architecture, SQLite/PostgreSQL, configuration files, logging, and VPS deployment My workflow: 1. Core strategy engine 2. Scanner & backtesting 3. Order execution & risk management 4. Dashboard & reporting 5. Testing, deployment, and documentation I am interested in a long-term collaboration and can build the platform in phases, ensuring each module is thoroughly tested before moving to the next. Best Regards, Prashant

@techmadigital

Hi, I’m a Full Stack Developer with 10+ years of experience building algorithmic trading systems, trading bots, backtesting engines, and broker API integrations. I’ve developed trading platforms, options scanners, automated trading bots, and real-time dashboards for Indian markets. * Price Action & Support/Resistance strategy automation * NIFTY & Options scanner * Zerodha, SmartAPI & other broker integrations * Backtesting, paper trading & live trading * Telegram alerts & trading dashboard * Clean, modular, scalable Python architecture This is a long-term project, and I’d be happy to work with you beyond V1. Let’s discuss your strategy on chat. I can also show you similar trading systems I’ve worked on. Thanks, Nikhil Preferred & Verified Freelancer | 5 Star Rating | 100% Project Completion

@UzeeTech

As an experienced full-stack developer accomplished with Python, I am the ideal fit for your Automated Nifty Options Trading system. My deep understanding and proficiency in Python-based algorithms and my extensive experience developing robust, scalable, and modular systems for trading will ensure a successful translation of your trading strategy into clean code. I have a comprehensive grasp of core trading concepts such as support and resistance zones, swing highs and lows, price action, and many more facets mentioned in the project description. Moreover, it's worth noting that I adhere to best coding practices; writing clean and well-documented code that is highly efficient and properly logged with optimal handling of exceptions. These practices also ensure that the project is easily maintainable and extendable according to future needs. It's my commitment to deliver high-quality work to my clients that has earned me their trust and repeat projects that meet or exceed expectations. I bring these same commitments to bear

@purealmaz

I HAVE A QUESTION: Have you already selected a specific broker API for market data and order execution (e.g., Zerodha Kite, Fyers)? This is a key detail for structuring the data pipeline and trade execution modules. I understand your requirements for a fully automated NIFTY options trading system in Python. With extensive experience in Python and building algorithmic financial systems, I’m confident I can translate your price action-based strategy into a robust and scalable solution. One of my recent projects, "Online .NET Backtesting Prototype," involved building a platform for strategy backtesting. The system supports: - Historical data import from CSV/JSON files or real-time API feeds. - An interface for writing and executing custom trading logic against data. - Generation of key performance metrics like equity curves and drawdown. This experience is directly relevant to your project, particularly in handling market data, implementing complex trading rules (like S/R zones and candle patterns), and ensuring the system is testable. I focus on building systems that are: - Scalable and production-ready - Secure and maintainable - Easy to extend for future strategy variations My approach is agile and goal-focused. Let’s connect and discuss how we can bring your trading strategy to life. Best regards, Philip Oyedoyin

@Hrolgar

Role reversal and support/resistance rules only land after selection, per the brief. That means the signal engine can't really be locked down until that handoff happens. EMA crossover as the fallback is buildable straight away, but the core price action layer needs your rules doc first. So I'd start with what's provable without it: scaffold the signal engine around the EMA crossover fallback and a generic S/R plus role reversal framework, backtest that against historical NIFTY and options data, and get paper trading and a live dashboard running so you can watch it work before the real rules go in. Once you hand those over, the engine slots them in and the backtest re-runs against the actual logic, not a placeholder. Broker API order execution and Telegram/desktop alerts come once that backtest holds up, wired into the same engine so there's no rebuild between paper and live modes, just live fills replacing simulated ones. Backtesting and reporting run throughout, not just bolted on at the end, since that's what actually lets you judge the logic before it touches real money. For V1: signal engine, paper trading, dashboard, and the full backtest and reporting suite, then live broker execution and alerts once that's validated. 75000 INR over 14 days as one deliverable. That's an indicative number off the brief, I'll firm it up once the rules and broker are locked. Which broker are you planning for live execution, Zerodha, Angel, or Dhan? That changes how much of the order layer I can start before the rules doc arrives.

@sandipa25

Thanks for the invitation. This is squarely my domain — I build and run a live production multi-strategy NIFTY F&O engine (Zerodha Kite): realistic-cost backtesting, walk-forward validation, paper-before-live, and daily live-vs-backtest reconciliation. I've built the hard parts you need — support/resistance zone detection, swing-pivot and role-reversal logic (causal, no look-ahead), NIFTY-confirmation filters, Telegram alerts, VPS deployment. One honest point: your rules are shared only after selection, so no one can accurately scope the signal engine yet. I'd start with a short paid discovery milestone — you share the rules, I turn them into a precise, backtestable spec + fixed plan. Protects you from vague delivery, me from moving targets. Milestones: 1) Discovery + spec + strategy backtest 2) Scanner + signals + paper trading (unattended + trade log) 3) Live + risk controls + dashboard + Telegram + VPS Bid: INR 75,000 full build, milestone-based. Happy to start with discovery so you can judge the work first. Can share execution logs + reconciliation reports as proof. Thanks.

@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

@JoseRCarvas

Hi! Algo trading is exactly what I do. I've built a full Python backtesting engine and run it across index futures - Nasdaq, Nikkei, DAX, Gold and S&P - plus crypto and forex, on 1-minute data from 2018 to 2024 including the 2022 bear. Strategies ranged from EMA pullback and Bollinger long/short to opening-range breakout and mean-reversion. What sets my work apart is rigour: strict out-of-sample validation, no look-ahead, walk-forward robustness sweeps and per-instrument cost/slippage modeling - a strategy is trusted only if it holds on unseen data, never curve-fit. That's exactly the discipline your NIFTY system needs to be reliable with real capital. I'll deliver your full spec end-to-end: strategy engine (your price-action / S-R logic + EMA fallback), NIFTY + CE/PE scanner, trade + risk management, order management with paper AND live modes (trailing SL, manual override), dashboard, Telegram/email alerts, and a backtester over historical + expired-options data - with broker integration (Zerodha, Angel One, Fyers, Upstox, Dhan). Approach: Phase 1 = engine + backtester + paper trading, proven on history first; Phase 2 = live + dashboard + alerts. Clean, documented, VPS-deployed. Happy to walk you through my backtesting work as proof. Share your rules and broker and I'll start!

@alluankit01

Hi, I can build your automated NIFTY options trading system in Python with clean modular code, broker API integration, paper/live trading, backtesting, alerts, dashboard, and VPS deployment support. The best solution is to first convert your exact price-action rules into a structured trading engine, then separate the system into modules for NIFTY context, CE/PE scanning, support/resistance zones, candle confirmation, liquidity sweep checks, entry/SL/target calculation, risk-reward filtering, fallback EMA logic, and order execution. I’m comfortable with Python, Pandas, NumPy, SQLite/PostgreSQL, Zerodha Kite/SmartAPI-style broker APIs, option-chain data, multi-timeframe analysis, backtesting, risk management, Telegram alerts, logging, reporting, and VPS deployment. Deliverables will include: * Complete Python source code * NIFTY + CE/PE scanner * Price-action strategy logic * Paper and live trading modes * Broker API integration * Backtesting module with reports * Risk management controls * Dashboard with P&L/trade history * Alerts and logs * Installation, configuration, and VPS guide I’ll focus on a stable Version 1.0 with safe execution, clear logs, clean architecture, and proper risk controls, so it can be extended later with advanced filters and analytics. Best regards Ankit

@ziedb4

Hi, I like that the strategy logic is already defined — that's the hardest part done. To deliver this properly within budget and timeline, I'd propose splitting it into milestones rather than one big drop: Milestone 1 — Core Engine (this budget range): Strategy logic in clean, modular Python (OOP where appropriate) S/R zone detection, role reversal, rejection/confirmation candles, RR filtering Paper trading mode with SmartAPI/Kite integration Basic risk management (max daily loss, max trades/day, per-trade risk, trading window) SQLite storage + trade logging Backtesting module with win rate, profit factor, drawdown, equity curve Milestone 2 (separate scope/budget): Live trading mode Full dashboard (P&L, active trades, account stats) Telegram alerts Advanced reporting (daily/weekly/monthly, trade screenshots) This gets you a working, testable system fast, validated in paper mode before risking live capital — and keeps each milestone's scope matched to its budget. I work with Python/FastAPI, Pandas/NumPy, and have built trading systems with state management and risk controls (grid bots, multi-strategy frameworks) — so the architecture side is familiar ground. Which broker API are you set up with — SmartAPI or Kite? That'll shape the integration approach.

@cameron268

Hi, I can build this as a clean, modular Python-based trading system that converts your price-action + support/resistance logic into a production-ready architecture with broker API integration (Zerodha/SmartAPI), full backtesting, and a paper/live trading engine. I’d structure it with a core strategy engine (S/R detection, candle pattern recognition, multi-timeframe confirmation), a separate execution layer (order management, SL/TP, trailing logic), and a data layer for historical + live feeds, all wrapped with configurable risk controls, logging, and a dashboard for signals, P&L, and trade analytics. Thank you, Dane

@thinkbotictech

Hi, I have strong experience building Python-based algorithmic trading systems with broker API integrations, backtesting engines, paper/live trading, risk management, and real-time dashboards. I can convert your price-action strategy into a clean, modular, and scalable system with complete documentation and long-term support. 1. Which broker API (Zerodha Kite, SmartAPI, Upstox, etc.) and historical data source do you plan to use for live trading and backtesting? 2. Do you already have the complete trading rules documented, or would you like me to help formalize the strategy into development-ready specifications before coding? if looking for expert then lets connect (expereince 7 yrs in development)

@rakhshanz3

With over two decades of experience across academia, software engineering, and AI-driven solutions, I bring a unique blend of expertise and skill that sets me apart from other candidates. Holding a bachelor's degree in computer science and a doctorate in Artificial Intelligence, I've embedded my knowledge into aspects of your project such as support and resistance zone identification, classifying zones as support or resistance, detecting price interaction with zones, recognizing predefined candle patterns, validating trades using market context, calculating entry/stop loss/target, calculating risk-reward ratio and filtering low-quality setups. My achievements as the Chief Technology Officer of an AI startup showcased my ability to apply AI in various contexts for optimal performance. In your project, this equates to developing clean code that is modular, object-oriented where appropriate, well-documented and maintains high speed and efficiency even after scale-up. My technical proficiency in Python (your preferred language), Pandas (for data manipulation), NumPy (essential for vectorized computations) ensures that I build a powerful system in line with your requirements.

@mussaibb7

Based on your job description, I've crafted a proposal that meets your requirements. As a seasoned Python developer, I'm excited to tackle the Automated NIFTY Options Trading System project. To deliver a successful outcome, I'll focus on crafting a clean, maintainable backend implementation that prioritizes reliability and maintainability. My experience with Jarvis AI - a personal automation assistant - has given me a strong foundation in building scalable, API-driven systems. I've successfully deployed multi-client ML inference APIs with sub-200ms latency and production-grade cloud delivery. This expertise will be invaluable in developing a robust Automated NIFTY Options Trading System. To ensure a smooth project execution, I propose the following: - Develop a clean backend code structure using Python and FastAPI - Design and implement API integrations for seamless data exchange - Establish a reliable deployment flow with error handling and logging - Provide environment setup instructions and a comprehensive README Before we begin, I'd like to clarify the scope, the first milestone, and the most important technical constraint to ensure we're aligned on the project's requirements.

@manishcomp123

**Proposal for Automated NIFTY Options Trading System** I specialize in building automated trading systems and can efficiently convert your Price Action and Support/Resistance strategy into a robust, modular Python solution. My approach ensures clean, scalable code with thorough documentation, including backtesting and risk management integration. With expertise in Pandas, NumPy, and SQLite, I’ll structure the system to align NIFTY direction with option execution (CE/PE) while dynamically updating Support/Resistance zones. I’ll also implement data visualization for strategy validation and DevOps practices for seamless deployment. My goal is to deliver a system that’s production-ready, maintainable, and aligned with your trading logic. Let’s discuss your backtesting requirements and deployment timeline. Best regards, Manish S. *Question: How frequently do you plan to update or refine the strategy parameters in the live system?*