NSE Options Trading Sandbox

@AbhyudayaCodes

Hi, Warm Greetings ! First of all, kudos for the truly different kind of innovative project idea. We are a team of expert algo trading systems developers, having vast experience in algo trading field, mainly using Python. We have built the following types of algos: > NSE/MCX/NYSE/Nasdaq Markets > Algos trading F&O, stocks, cryptos & commodities > Indicator/price movement based algos > Multi client systems > Systems with Spreads, Straddles, Iron Flys, Heikin Ashi, Renko, etc. > Algos having extremely complicated logic and signal generation procedures > Have built custom indicators and timeframes > Brokers like IBKR/Oanda/Alpaca/Tastytrade/TOS, Binance/, Zerodha/Fyers/Upstox/Finvasia/XTS, and many more ... > US Backtesters and Stock Screeners The features we provide: > Multiple Instance dashboard > Orderbook and logs for tracking > User friendly UI to manage everything > Emergency Safety buttons > Detailed statistics of the algo as per logic > Paper trading as well as Live trading > Editable input boxes for modifiable parameters The project has many different parts, and the complexity level is also high, but considering our skill set and experience, we think we will be a perfect fit for your project. We will handle the complete process from development and testing till deployment. We focus on delivering quality to our clients, keeping in mind safety, security, aesthetics, scalability. Looking forward to working with you ... Message me to discuss further

@tinuvj

Hi, I have 15+ years of experience in the software development. You can see my recent ALGO projects, reviews & demos here: ► https://www.freelancer.in/u/tinuvj ✅ TRADING SOFTWARE EXPERIENCE - Experience in Zerodha, Fyers, Angel One, Kotak, Flat Trade, IIFL, Alice Blue, ICICI Direct, Upstox, Motilal, Finvasia Shoonya APIs - Trading View Pine Script, Development & Back testing - Bridge Software to execute Trading View, Charink & Amibroker Signals - Copy Trader Software: Copy all Master account trades to unlimited client accounts - One-Click Quick Option Trading software with Auto SL, Target, TSL & Averaging - Software to monitor and auto-exit the position - Amibroker AFL Development and Backtesting - Algo Development in Node.js, Python & Pine Script ✅ RECENT-ALGOS - Tripple Super Trend Algo - Option Selling Strategies - Banknifty Price Action Algo - Tick Analysis - PriceAction Breakout - RSI-Based Option Selling - Pivot Reversal Algo - SMA & EMA Strategy - 9:20 AM Short Straddle - Heikin-Ashi Breakout - Multi Indicator Algos MACD, EMA, ADX, CCI , ST & RSI - Pivot Level Algo - Crude Options Some of my other projects. - Automated login to various Indian bank websites to fetch the account statement to perform some automated tasks. - Automated login and trade execution in various Indian stockbroker platforms - WhatsApp API & Chat Bot Using WhatsApp Web Automation Thanks & Regards Tinu Jose

@SmartCodeDev

Hey. This is a systems project more than a simple trading app and that is exactly where I’m strongest. I’ve worked on event driven platforms, market data pipelines, low latency automation and simulation environments where realistic behavior and control mattered more than basic execution. I can build a LAN first architecture that distributes market data, simulates fills, slippage, greeks and position behavior while keeping everything isolated from real capital exposure. The strategy layer can support directional inversion, automated hedging, configurable execution conditions and centralized monitoring so all 15 nodes stay synchronized. I’d also design the dashboard around operational visibility with algo controls, node status, logs, reporting and aggregate position management. I can review the PDF and map the architecture before implementation so the simulator behaves realistically rather than becoming a simple replay tool.

@vishal1145

Your 15-node LAN setup will fail the moment two algos try to place orders simultaneously without a central order router. Right now you're describing a distributed system with no conflict resolution - that's how you get duplicate fills and phantom P&L. Before I architect this, I need clarity on two things: Does your Zerodha account support their Historical API for backtesting, or are you expecting me to cache live tick data locally? Their free tier throttles at 3 requests per second, which won't support 15 nodes hammering the feed. What's your acceptable latency for order acknowledgment? If you're testing HFT strategies, a LAN-based message queue adds 5-15ms overhead. If you're running positional strategies, we can batch orders and that number doesn't matter. Here's the architectural approach: - JAVA ORDER ROUTER: Build a central matching engine that runs on one master node, accepts orders via TCP sockets from all 15 workstations, validates margin requirements, and simulates fills using NSE tick data with configurable slippage models (percentage-based or volume-weighted). - PYTHON MARKET DATA ADAPTER: Stream live NSE options chain from Zerodha Kite WebSocket, calculate Greeks using Black-Scholes with Indian volatility surfaces, and multicast updates to all nodes at 100ms intervals to keep the feed synchronized without API rate limit violations. - REVERSE MODE EXECUTION: Implement a strategy state machine that flips long/short positions by calculating delta-neutral hedge ratios and auto-generating offsetting orders - for example, if Node 3 is long 100 NIFTY 18000 CE, reverse mode shorts 100 contracts and dynamically hedges with futures based on real-time delta. - LAN-ONLY DASHBOARD: Deploy a lightweight React frontend served from the master node that shows aggregate net positions across all 15 nodes, per-strategy P&L attribution, and a kill switch that halts all algos and flattens positions in under 200ms. - WINDOWS DEPLOYMENT: Package everything as Docker containers or PowerShell scripts with dependency checks for Java 17 and Python 3.11, so your team can spin up the environment with one command and tear it down without leaving residual processes. I've built three similar prop trading simulators for desks in Mumbai and Singapore that handled 50+ concurrent strategies without order collisions. The tricky part isn't the API integration - it's ensuring your position reconciliation logic matches Zerodha's margin calculation exactly, or your paper trades will show profits that evaporate in live execution. Let's schedule a 20-minute call to walk through your PDF specs and confirm your network topology supports multicast traffic. I don't start builds where the infrastructure assumptions are unclear.

@Shrutibimpl

Hello There, As per my understanding you want a localized proprietary options sandbox for 15 workstations that simulates NSE market dynamics and paper trading with realistic slippage and Greek tracking. I have hands on experience building high frequency trading simulators and distributed LAN architectures for professional trading desks. I will deliver a high fidelity training ground where your team can sharpen their strategies without risking a single rupee of real capital. You will get a robust environment that mimics the exact pressure of the live market, including simulated latency and order fills, allowing you to stress test automated algos across your entire 15 node desk. This setup gives you the visibility to manage net positions at both the node and aggregate level, ensuring your team learns to manage risk and execution precision in a safe but realistic sandbox. I will architect the solution using a central Python server on your LAN to ingest live feeds via Kite Connect and broadcast data to your 15 nodes using ZeroMQ for low latency messaging. I will implement a custom matching engine that simulates the Zerodha API, incorporating configurable slippage and latency models to provide realistic fill prices and P&L calculations. The system will feature a centralized dashboard built with Dash or FastAPI to monitor all active algos and manage position limits across the local network without any external cloud reliance. Best regards, Bharat Joshi

@prashanpatel557

Hello, Your project aligns very closely with the kind of trading infrastructure I build. I can develop a fully local NSE Options Trading Sandbox that simulates realistic execution, Greeks, P&L, slippage, latency, and strategy behavior across your 15-node LAN environment—without exposing real capital. What I can deliver: - Real-time / near-real-time NSE options feed integration - Paper-trading execution engine with Zerodha Kite API compatibility - Configurable slippage, latency, hedging, and reverse-mode execution - Multi-node LAN architecture with centralized position/risk tracking - Dashboard for algo control, monitoring, logs, and session reports - Net position aggregation per node and desk-level exposure tracking - Fully local deployment My background includes Python-based algo trading systems, broker APIs (Upstox/Zerodha), backtesting engines, OMS workflows, and real-time execution frameworks. I also focus heavily on modular architecture, logging, and operational reliability.

@Srashtasoft

A Warm Hello! Your requirement is very clear — you want a fully isolated LAN-based NSE options trading sandbox that behaves like a real proprietary desk while remaining completely safe for testing and strategy experimentation. The combination of live/near-live NSE data simulation, paper execution routing, reverse-mode strategy handling, configurable slippage/latency, and centralized multi-node position management is absolutely achievable. I have experience building low-latency trading and automation systems with Python/Node.js, broker API integrations, real-time event handling, and distributed monitoring architectures. For your setup, I’d recommend a centralized execution engine with LAN-distributed client nodes, local WebSocket streams, paper-order matching logic, strategy orchestration, risk controls, and an internal dashboard for algo control, logs, P&L, greeks, and reporting. Zerodha Kite integration can be handled cleanly within the firewall using existing SDKs and custom wrappers. Key deliverables: LAN-only architecture for 15 Windows systems Real-time options feed & sandbox execution engine Reverse/hedge mode automation support Node-wise and aggregate position management Algo control dashboard with logs & reports Recovery, retry, and session persistence mechanisms Please share the PDF so I can properly review the workflow, execution expectations, and infrastructure details before outlining the final architecture and timeline. Best Regards, Jemin Sagar

@NEHABHAT92

As a seasoned freelancer with over 9 years in web and mobile app development, I have honed my skills in various programming languages including Python. Your ambitious project of building a NSE Options Trading Sandbox, mirroring the live market behavior but ensuring utmost safety for your intermediate skilled team aligns perfectly with my expertise and commitment. In addition to great programming skills, my portfolio also demonstrates my understanding and prior engagement with Finance industry projects. I have an indepth understandi of how trading simulation environments should function, including factors such as streaming live data, routing orders and returning fills in real-time. Furthermore, I assure you that all our implementations are intended for smooth local functioning providing reliable automated executions, thereby avoiding any dependence on unreliable cloud services. My team's proficiency in creating comprehensive documentation will ensure easy deployment process for your team along with a hassle-free usage experience like start/stop, monitoring of algos and generating intelligent session reports. With my serventions, you can acknowledge the project’s successful completion when every node is capable of executing your desired options strategies into the sandbox environment while reflecting real market outcomes

@Cmonosoft

Hi there, I carefully reviewed your requirement and understand that the goal is to build a realistic but fully sandboxed proprietary options trading environment operating entirely inside a closed 15-node LAN. Since the system is intended for strategy development without real capital exposure, the focus needs to be on realistic market simulation, execution behavior, controllable experimentation, and stable local infrastructure rather than a basic paper trading setup. I can help build a local trading simulation environment capable of consuming live or near-live NSE options data, simulating realistic order execution behavior, and supporting algorithmic trading workflows with configurable slippage, latency, fills, greeks, P&L tracking, and hedge/reverse execution logic. The system can be structured for smooth operation across all 15 Windows workstations inside the LAN with no cloud dependency, centralized local services, role-based monitoring, and a lightweight dashboard for starting/stopping strategies, reviewing logs, monitoring positions, and exporting session reports. Let's connect to discuss and start work! Thanks Saurabh

@prabhathk5

Thanks for sharing the details. I’ve reviewed your requirement and would be glad to discuss it further. I’m Prabhath, an experienced MQL4/MQL5, Pine Script, Python, and C++ developer specializing in automated trading systems and institutional-grade algorithmic solutions. I develop Expert Advisors, indicators, dashboards, data tools, and custom trading utilities for MT4/MT5, TradingView, and standalone platforms. Along with MQL5 systems, I also build fully automated trading software in Python and C++ for Indian stock markets and global exchanges (US, EU, and others). These solutions can be tailored for stocks, indices, futures, forex, and crypto based on project needs. As an active trader, I work with ICT, SMT, market structure, liquidity models, order blocks, FVGs, VWAP, and volume-based logic, ensuring each strategy follows the client’s trading methodology. My expertise includes institutional-grade EA and indicator development, ICT/SMT-based trading systems, Pine Script automation, Python and C++ systems for Indian and global markets, backtesting, paper trading and live trade integration, strategy optimization, and low-latency execution. I also fix, optimize, and enhance existing trading systems to make them stable and production-ready. Where permitted, I can share demos or walkthroughs of previously completed projects while respecting client confidentiality. Thank you for your time and consideration.

@KrisuTechkul

Hi, we are a team of 20+ AI/ML Engineers based in Delhi - have completed 300+ projects with 100% client satisfaction & long term association. I As an experienced AI professional, I possess a unique skill set that perfectly aligns with your project requirements. My solid foundation and significant experience in Python, coupled with a deep understanding of options trading and using APIs such as Zerodha’s Kite API, make me an ideal choice for developing your NSE Options Trading Sandbox. Having identified gaps in the market, I've designed bespoke AI tools to address complex business challenges and ensure measurable results. My proficiency in machine learning, deep learning, and neural networks stands unrivaled-it would significantly contribute to your goal of creating a dummy trading environment that reflects live NSE options data accurately whilst safeguarding your capital. Furthermore, my talent extends beyond design; I diligently integrate cutting-edge technologies into operations without encumbering the process with inappropriate cloud dependencies. I assure you a seamless operation across your 15 Windows workstations through a local switch, as desired.

@alluankit01

✨ I can build this NSE options sandbox as a local LAN based paper trading environment where your team can test strategies safely without touching real capital. I would design it with a central local server for market data, strategy execution, order simulation, fills, greeks, P&L, logs, and net position tracking, with all 15 Windows nodes connecting over the LAN. For broker connectivity, I can work with Zerodha Kite API or an equivalent provider, while keeping the execution layer in paper mode with configurable latency, slippage, forward mode, reverse mode, and hedge behavior. The dashboard would allow your team to start and stop algos, monitor node wise and total positions, review order history, download session reports, and test strategy behavior in a controlled way. I can also provide deployment scripts and clear setup documentation so the system runs behind your firewall with no cloud dependency. Please share the PDF spec, expected data source, and preferred broker API access, and I can map the exact architecture and delivery timeline around your desk workflow. ✨ Best regards Ankit

@Visharaa2024

I am interested in developing your LAN-based NSE options trading sandbox platform designed for realistic strategy testing without live capital exposure. I can build a high-performance paper trading environment that streams live or near-live NSE options data, integrates with Zerodha Kite API or equivalent broker APIs, and accurately simulates real market behavior including fills, greeks, slippage, latency, margin impact, and real-time P&L calculations. The platform will support automated algorithm execution in both forward and reverse modes, allowing strategies to dynamically flip direction, hedge positions, and stress-test under configurable market conditions. I will ensure stable deployment across all 15 Windows workstations within your local network, with zero cloud dependency and fully local communication. The system will also include centralized net position tracking, equivalent exposure management, execution monitoring, detailed order logs, downloadable reports, and an intuitive dashboard for starting, stopping, and supervising algos in real time. With strong expertise in Python, Node.js, broker API integrations, low-latency systems, and long-running trading infrastructure, I can deliver a reliable, scalable, and realistic simulation environment tailored specifically for your proprietary options desk requirements.

@anilptk

Hello, I am anilptk, a seasoned developer with 10 years of experience in Java, Python, and other related technologies that perfectly align with your NSE Options Trading Sandbox project requirements. I understand your need for a dummy trading environment that replicates live NSE options data, handles order routing to Zerodha in "paper" mode, and accurately reflects fills, greeks, and P&L as if trading real capital. My expertise allows me to design and implement a solution that enables automated algorithm executions, stress tests strategies, and provides a seamless operation across 15 Windows workstations connected via LAN with no cloud dependencies. I will ensure clear deployment instructions, scripts, and a user-friendly dashboard for easy algo management and reporting. Please start the chat so we can discuss your project further. Best regards,

@techmadigital

I already built algo trading desks, broker integrations, paper trading engines, MT5 systems, PineConnector automation, and realtime risk management infrastructure. I reviewed the PDF and understood the complete architecture including: • Ghost broker execution layer • Multi node LAN setup • Reverse/copy trading engine • RMS & strike fence logic • Net position management • Auto square off • Zerodha/Kite execution flow • Multi leg options execution • Realtime MTM & reconciliation engine I can build this with fully local LAN based deployment without cloud dependency. Also experienced with NSE options, Nifty/BankNifty/Sensex workflows, low latency websocket systems, and high frequency order routing. Let’s discuss the execution architecture and deployment flow.

@rawheyx

I understand you need a realistic LAN-based options trading sandbox that can simulate NSE market behaviour across multiple local workstations while supporting automated strategy execution, configurable slippage/latency modelling and centralized monitoring without relying on cloud infrastructure. This aligns closely with my experience building backend systems, automation workflows and real-time API-driven platforms. I can help structure the trading engine, local execution architecture and broker API integration layer so strategies can execute safely in paper mode while maintaining realistic fills, greeks and P&L simulation. I also understand the importance of stable LAN communication, centralized order management and reproducible deployment for multi-node environments. The final deliverable will include the sandbox execution system, local deployment setup, monitoring dashboard, logging/reporting workflows and configurable strategy control mechanisms prepared for continuous internal testing.

@shivamhardia

Hi, NSE options simulation requires handling real-time Greeks calculations, tick-level market data processing, and low-latency order routing across your 15-node setup. I built a similar trading system for a South India client that processed 70-90 orders for all their selected customers and stocks. Your paper trading environment needs WebSocket streams from NSE data feeds, local order matching engine with configurable slippage models, and position aggregation across all nodes. I'll architect this with Node.js backend handling market data distribution, React dashboards for each workstation, and MongoDB for order logs and session storage. The system will simulate realistic fill behavior using historical NSE volatility patterns and current bid-ask spreads. Each algo can run forward/reverse modes with your specified latency parameters, and the master dashboard will show net positions, risk metrics, and P&L in real-time. Everything runs locally with no external dependencies once deployed. What's your current data feed setup for NSE options, and do you need the system to handle specific option strategies like straddles or iron condors out of the box?

@mohitsps

As an accomplished Technical Lead with over 10 years of experience, I possess the necessary skills and knowledge to build your advanced sandbox-style proprietary options desk. Throughout my career, I have led the development and scaling of high-performance web platforms and AI-driven systems. With my strong command over Python, I have built scalable APIs, microservices, and distributed systems - the very kind required to mirror live NSE-options market behaviors while ensuring safety for experimentation. Last but definitely not least, my reputation for ensuring reliable, secure and compliant systems has led to scaled platforms with millions of users and billions operations. Just like your project demands, I will not comprise on providing you clean instructions or scripts that enable easy deployment and a simple dashboard for comprehensive monitoring. Bearing in mind all these advantageous factors together with my commitment to client satisfaction, I am confident - awarded this project - exceeding your expectations is guaranteed.

@sjohn117

Hi, I am Sean, a Full-Stack Developer with over 10 years of experience, specializing in building secure, scalable financial applications. I understand that you need a proprietary options trading sandbox that mirrors live NSE-options market behavior for strategy development without the risk of real capital. I have successfully developed similar environments that stream financial data and execute trades through broker APIs in a safe dummy setting. My experience with APIs and financial software development enables me to deliver a solution that includes live order routing, accurate P&L reporting, and automated algorithm execution. I will implement a robust system that operates seamlessly across your 15 Windows workstations, ensuring no cloud dependencies while providing detailed logs and an intuitive dashboard for your team. Additionally, I'm capable of reviewing the attached PDF to align my strategy with your requirements accurately. The first working milestone can be delivered within one week, allowing your team to start testing strategies confidently. What specific features are you looking to prioritize for the initial version of the trading sandbox? Best regards, Sean