Comprehensive Finvasia Shoonya Python Trading Bot

@NEHABHAT92

With over 9+ years of experience in web development and mobile app development, I am confident in my ability to meet the unique requirements of your Comprehensive Finvasia Shoonya Trading Bot project. My skills in Python-programming are not only broad, but also deep -- invaluable for creating your custom zero-touch login script, WebSocket stream engineering, auto-reconnection architecture and more. Additionally, my experience with QuestDB and InfluxDB Line Protocol (ILP) for data ingestion aligns perfectly with your needs for an efficient data handling process. Further, I have adept Pandas skills that will enable me to create a robust downsampling engine enabling you to convert the raw tick data into cleaner structures that will make analysis easier for you. Lastly, my proficiency in programming execution modules using API endpoints and implementing risk management limiters is essential for accurately routing market orders, handling complex multi-leg spreads, as well as protecting against excessive drawdowns. With a deep commitment to delivering high quality work and extensive experience with the range of tasks you need done, I truly believe I'm the ideal choice for your project.

@kingxanda

Hey, Thanks for your post. I'v read your description carefully. I have relevant experience I can help. some of my skills are: JavaScript, Python, Nodejs, EAs, trading etc Hope you're having a nice day my friend :)

@bhavanim08

Hello, I reviewed your Shoonya trading bot requirement carefully. This project needs more than simple API integration. It requires a stable async architecture, reliable WebSocket recovery, fast tick processing, and strict risk management for real-time options trading. I can build the complete Python-based system including: * Automated Shoonya login using pyotp * Async WebSocket streaming for Nifty & Bank Nifty ticks * Auto-reconnection with token resubscription * QuestDB ingestion using ILP without blocking the event loop * Pandas-based OHLCV downsampling engine * Order routing with partial-fill and multi-leg spread handling * Daily drawdown circuit breakers * Structured JSON/Markdown export for AI analytics I have experience building scalable real-time platforms including the CandVox AI Candidate Screening Platform and a complete analytics/dashboard system involving automation workflows, APIs, structured pipelines, and performance-focused backend development. My focus is on clean architecture, low-latency execution, reliability, and maintainable code. I can also provide modular development with regular updates, logging, and testing to ensure stable deployment. I’d be glad to discuss the execution strategy and start immediately. Best regards, Bhavani

@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

@Ritesh11gupta

I see you're looking for a zero-touch login script for the Finvasia Shoonya API. I've got solid experience with Python and API integrations. What specific features do you want included in the authentication process?

@manoval09

I need the **complete project description** to write a compelling proposal. The brief you've provided is cut off at "Automated Authentication: Develop a zero-touch login s..." and the key requirements show that the full details are missing. To write a proposal that meets Val's standards—one that mirrors the client's specific pain points and demonstrates relevant expertise with concrete technical choices—I need: 1. **Full project description** - all requirements and details 2. **Complete scope** - what the trading bot must do, specific features, integrations, authentication flow, etc. 3. **Any timeline or milestones** mentioned in the posting Please paste the **complete project description** from the Freelancer.com posting, and I'll generate a strong, specific proposal that references the actual technical requirements and demonstrates clear expertise.

@serviceaxisgroup

Hi, I can help with a zero-touch Finvasia Shoonya Python trading bot that reliably logs in, streams live Nifty 50/Bank Nifty options ticks to QuestDB, downsamples clean OHLCV, and executes spread orders with strict drawdown circuit breakers. I’ll start by wiring pyotp-based TOTP login and a resilient async WebSocket client, then build the QuestDB ILP writer with non-blocking backpressure so the event loop stays smooth. Next I’ll add reconnection + resubscribe, order state tracking for partial fills, and an EOD JSON/Markdown exporter for Claude. Which exact option symbols/strikes and OHLCV intervals do you want? Do you prefer Market, Limit, or both for multi-leg spreads—shall we use per-leg sizing rules?

@snath88

Hi, Drop me a message — I'll share a quick prototype based on what I understood. If it matches your expectations, we can move forward. Thanks!

@Br1je5h

Built Envestnet/Orion trading integration for fintech—asyncio WebSocket streams, InfluxDB pipelines, risk circuit breakers on real capital. I've architected exactly your Shoonya stack: pyotp zero-touch auth, tick-to-order latency optimization, OHLCV resampling, multi-leg order execution. Your spec is the problem I solve daily. WebSocket auto-reconnect loops, QuestDB writes under load, Pandas downsampling, risk limits—all production-grade at my BP fintech shop. MILESTONES Day 5: Core auth + WebSocket capture working, QuestDB ingestion live Day 10: Pandas resampling + order routing engine tested, risk breakers active Day 14: Full end-to-end system tested, JSON analytics export for Claude AI RISK REVERSAL If tick latency exceeds 200ms on NSE live data, I'll optimize at no charge. DIAGNOSTIC QUESTION Are you paper-trading first, or going live immediately after testing? — Brijesh, Sonraj Labs

@indreshpnk

As an AWS Certified Full-Stack Developer with a deep understanding of Python, data analysis, and risk management, I'm excited to offer my services for your Comprehensive Finvasia Shoonya Python Trading Bot project. I have extensive experience in API development, including designing for automated authentication and managing WebSocket streams effectively - both essential requirements for your trading bot. Drawing on my skills, I will create a robust architecture for your project. I have hands-on experience in developing and implementing self-healing reconnection loops and utilizing high-performance libraries like picowswith uvloop or winloop, exactly what your project needs to ensure data continuity even during intermittent connection dropouts. Moreover, my familiarity with popular data ingestion tools like QuestDB will guarantee an efficient data pipeline from the raw tick data. Combining that with my strong skills in leveraging the Pandas library to analyze and downsample data along with keen understanding of order routing and state management will provide you with value-added functionalities beyond your expectations. Finally, as an added bonus to your project, I am also proficient in machine learning and can seamlessly build the end-of-day script to extract useful OHLCV and trade logs for AI analytics purposes. I excitedly look forward to applying my years of experience and technical skills to ensure efficient trading operations while minimizing risks on your behalf.

@Ebendewater

I focus on delivering work that’s done properly, clear, polished, and aligned with exactly what you need. I’m focused on building my reputation, so I offer competitive rates while putting in extra effort to ensure high quality results, reliable communication, and work I stand behind. With a strong background in software architecture, API integration, Python, and Java, I am adept at handling the complex tasks involved in your Finvasia Shoonya Python Trading Bot project. From developing automated authentication to writing efficient data ingestion pipelines and implementing hard risk management limits, my skills and experience align perfectly with your requirements. My expertise in high-performance libraries like picows with uvloop and winloop will prove invaluable as I engineer an asynchronous WebSocket client to capture real-time tick-by-tick options data. Additionally, I can leverage Python's pandas library to build a robust downsampling engine that converts raw tick data into clean OHLCV structures.

@amit210279

Hi there, I reviewed your project — you need a Python-based Finvasia Shoonya data system that can efficiently handle market data using Pandas and build a structured, reliable solution for financial data processing, analysis, or automation. This requires strong experience in API integration, data handling, and clean financial data pipelines. My Understanding of the Project You are looking for a developer to build a comprehensive Finvasia Shoonya Python solution that can: • Connect and fetch market data via Shoonya API • Handle real-time or historical data efficiently • Structure and process data using Pandas • Possibly support strategy logic or data automation • Ensure reliability, accuracy, and clean data flow The focus is on stable API integration + accurate financial data handling, not just basic scripting. My Approach Understand exact API endpoints and data requirements Set up secure Shoonya API authentication Build modular Python structure for data fetching Use Pandas for cleaning, transformation, and storage Implement error handling and retry mechanisms Key Questions Are you using Shoonya API for real-time, historical, or both? What exact data outputs do you need (OHLC, ticks, indicators, etc.)? Should the system store data in CSV, database, or both? Next Step Please initiate a quick chat or video call on Freelancer

@gurpreets058

GSINFOTECHH OPC Pvt. Ltd. – Your Trusted Tech Partner Based in New Delhi, GSINFOTECHH OPC Pvt. Ltd. is a professional IT solutions & software development company delivering secure, scalable, and high-performance digital solutions for startups and enterprises. We help businesses convert ideas into powerful, market-ready products. Our Services •⁠ ⁠Mobile App Development (Android & iOS) •⁠ ⁠Desktop Software Development (C#, Java, .NET) •⁠ ⁠Custom Software & Web Application Development •⁠ ⁠Website Design & Development (WordPress, Joomla, Drupal) •⁠ ⁠Laravel, React JS & Node JS Development •⁠ ⁠Game Design & Development •⁠ ⁠Blockchain Solutions •⁠ ⁠AI, Automation & Custom Tools •⁠ ⁠Meta Trading Tools, Bot Scripting & Web Scraping •⁠ ⁠SEO, Digital Marketing & Branding •⁠ ⁠Video Editing & Multimedia Production Technologies We Use •⁠ ⁠React JS, Node JS, MongoDB •⁠ ⁠Python (Django) •⁠ ⁠Android Studio (Java/Kotlin), iOS (Swift) •⁠ ⁠Flutter & React Native Why Choose Us? ✔ Modern, cost-effective & scalable solutions ✔ Experienced & creative development team ✔ Transparent workflow & 100% client satisfaction ✔ Secure, optimized & future-ready technology ✔ On-time delivery & dedicated support ✔ Flexible pricing – negotiation available Let’s build something amazing together! Hire GSINFOTECHH OPC Pvt. Ltd. to take your project to the next level.

@bizvivo

Hi, I lead a team of trading software developers, data scientists and full stack engineers with 20+ years of combined experience in developing and deploying enterprise grade software solutions. We have several years of experience in automated trading bot development and can design bots based on indicators like RSI, MA, MACD, Bollinger bands, harmonic and Fibonacci. recently, we created an algorithm for trading based on advanced machine learning research. Looking forward to discussion, Best Regards, Radhika

@abdinasirhaji

As an experienced Data Analyst and Python developer, I bring to the table a diverse skill set and a keen eye for detail that will be beyond beneficial for your project. From your request for a comprehensive Finvasia Shoonya Trading bot, I am particularly skilled in several areas you've shown priority such as Zero-touch login script development utilizing pyotp's Two-Factor Authentication tokens and WebSocket Stream Engineering using libraries like picows with uvloop/ winloop. I'm also proficient at auto reconnection architecture which you need for handling server drops and would prevent data loss. My expertise extends to QuestDB data ingestion through InfluxDB Line Protocol, Pandas downsampling & resampling to manage OHLCV structure, order routing & state management along with hard risk management limits to establish circuit breakers. For the icing on the cake, I am well-versed in data serialization f In addition to these technical abilities, my experience also lies in analyzing large datasets flawlessly and presenting clear financial insights, which would add value to the final dataset your project requires. Building high-date tools is a passion of mine, and I'm excited about the opportunity to put my skills into practice with an exciting project like yours, especially because of my ability to wait calmly for a thousand characters of information; critical for ensuring asyncio compliance in Python!

@markdennis19

I can build this Shoonya-based trading data and execution system in Python with a focus on reliability, async performance, and strict risk controls. My approach would be to split it into clear modules: automated pyotp login, async WebSocket streaming for Nifty/Bank Nifty option ticks, auto-reconnect and resubscription logic, non-blocking QuestDB ILP ingestion, Pandas-based OHLCV resampling, order routing/state tracking, and daily AI-ready reporting. For performance, I would use asyncio with uvloop/winloop, an async WebSocket client, buffered tick queues, and separate ingestion workers so database writes never block the live feed. For trading safety, I would implement hard circuit breakers such as max daily drawdown, order throttling, active-position checks, and partial-fill handling before supporting multi-leg option spreads. The final system would include clean Python source code, configuration files for API keys/tokens/risk limits, setup instructions, sample QuestDB schema, test scripts, and an end-of-day JSON/Markdown export designed for Claude analysis. I have experience building Python backend automation, real-time data pipelines, AI-ready reporting workflows, and production-style systems with logging, retries, monitoring, and modular architecture.

@AdityaNarayan26

Building a high-performance automated pipeline for the Finvasia Shoonya API requires isolating the ultra-fast live tick ingestion from database writing cycles. I am a Senior AI and Full-Stack SaaS Engineer specializing in high-throughput real-time systems. I architect non-blocking async workflows in Python using uvloop and picows to capture tick-by-tick options data for Nifty 50 and Bank Nifty without dropping packets. Partnership Note: I submitted a competitive proposal to establish this core infrastructure. My goal is to deliver a production-grade algorithmic asset and earn a 5-star review. We can finalize the multi-leg execution parameters in the chat. What I will deliver: Asynchronous Ingestion Core: Zero-touch pyotp TOTP login paired with a self-healing picows WebSocket reconnection loop. Non-Blocking Telemetry Backend: Asynchronous QuestDB ingestion over InfluxDB Line Protocol (ILP) with a Pandas downsampling engine to generate clean OHLCV metrics. Execution & Risk Safeguards: Multi-leg order routing handling partial fills alongside hardware-like circuit breakers to protect your capital base. LLM Ready Logs: End-of-day serialization formatting your logs into structured Markdown for clean analysis. Timeline: 14 Days Quick question: Do your multi-leg option strategies require real-time margin utilization tracking before routing the limit orders to the Shoonya API? Looking forward to your message.

@mykolas67

Hello. The risky part in trading systems is usually not strategy logic — it’s handling websocket instability, order-state mismatches, and event-loop blocking during volatile market spikes. A bot can look fine in testing and still fail badly when reconnections or partial fills happen under load. I’d separate the system into independent async services: market data ingestion, order execution, risk engine, and QuestDB writer. The database pipeline should never sit inside the live websocket loop, otherwise delayed writes will eventually cause dropped ticks or execution lag. For Shoonya feeds, reconnection logic also needs token/state persistence so active subscriptions restore automatically after disconnects. For execution safety, I’d maintain local order-state reconciliation against broker responses instead of trusting websocket events alone. Hard circuit breakers and position validation should run independently from strategy execution. I’ve worked on API-heavy automation systems involving real-time streams, async Python services, data pipelines, and fault-tolerant backend logic. Happy to outline the async architecture, failover flow, and risk-control structure before implementation.

@ViktorUmanskyi

As an experienced Python developer with a full-stack skill set, I bring to the table a robust foundation in API development and am well-versed in utilizing Python libraries like pyotp for ultra-reliable automated authentication. My work has dealt extensively with high-performance data streams, such as the WebSocket system mentioned in your project description. Through employing libraries like picows and uvloop/winloop, I have built numerous async WebSocket clients capturing live data which aligns perfectly with your requirements. Additionally, my significant experience with databases like QuestDB and downstream processing using Pandas would be highly useful in designing and implementing the OHLCV downsampling engine you require. To make sure your trading activity is fully safeguarded, I will effectively code hard risk management limits into the algorithm that automatically halts all trading if specific thresholds are triggered. In summary, my strong familiarity with a range of technologies, proven track record of successful projects using Python, my ability to learn quickly on new fronts, meticulous approach towards delivering clean and stable code while maintaining predictable outputs perfectly places me to not only meet but exceed your expectations for this project. I'm looking forward to taking you through my portfolio and discussing any further questions you may have!