Python Options Algo Development

@Microlent

Hello! This should be built as a clean event-driven system, not a script bundle. Phase 1 must prioritize determinism, latency control, and separation of concerns. Proposed Architecture Core Stack • Python 3.11 • asyncio for non-blocking market feed handling • FastAPI (optional) for control/health endpoints • pandas + numpy for signal calculations • SQLite (Phase 1) with schema ready for PostgreSQL upgrade Modules Data Ingestion • Upstox WebSocket for ticks • REST for option-chain snapshots • Normalized internal data model Signal Engine • Rule-based engine • Strategy interface class for plug-and-play logic • Stateless signal evaluation per tick Risk & Execution • Paper broker layer • Dynamic SL/TP manager • Position state machine • Intraday auto square-off Logging & Storage • Structured JSON logs • Trades + signals persisted with metadata • Market snapshot tagging for replay Design Principles • Strict separation of data, signal, execution • Idempotent execution logic • Deterministic replay capability Outcome • Full unattended intraday paper cycle • Queryable trade history • Modular base ready for AI layer Happy to share trading system examples privately. Best regards, Jasmin

@GameOfWords

Python Options Algo Development I’m a full-stack software engineer with expertise in React, Node.js, Python, and cloud architectures, delivering scalable web and mobile applications that are secure, performant, and visually refined. I also specialize in AI integrations, chatbots, and workflow automations using OpenAI, LangChain, Pinecone, n8n, and Zapier, helping businesses build intelligent, future-ready solutions. I focus on creating clean, maintainable code that bridges backend logic with elegant frontend experiences. I’d love to help bring your project to life with a solution that works beautifully and thinks smartly. To review my samples and achievements, please visit:https://www.freelancer.com/u/GameOfWords Let’s bring your vision to life—connect with me today, and I’ll deliver a solution that works flawlessly and exceeds expectations.

@vasudharao9

Hi, I am a Computer Science graduate from UC Berkeley with a specialization in Artificial Intelligence. I have more than 10 years of experience working in the AI/ML space and can help you with this project. Message me to discuss this further. Thanks

@AZTURK1

Hi there, I will build a modular intraday options MVP for NIFTY / SENSEX using Python 3.x; my background in production trading pipelines and broker API integrations makes me a fit to deliver a stable Phase 1 with clean separation of ingestion, signals, risk, and logging. - Ingest live option-chains and quotes from Upstox API with asyncio-based connectors and pandas preprocessing (CSV/SQLite export) - Implement a rule-based signal engine (extensible for future AI modules) and dynamic stop-loss / take-profit logic that adapts to live ticks - Paper-trading execution simulator with position tracking, timestamped fills, and queryable SQLite/CSV store - Quality controls: staged deploy, unit tests for signal/risk modules, structured logging, and rollback/validation to ensure unattended run from open→close Skills: ✅ Python 3.x ✅ Upstox API ✅ asyncio / FastAPI / pandas ✅ Deployment: SQLite export, modular service separation, Docker-ready ✅ Risk & reliability: staged deploy, structured logging, reproducible paper-trade runs Certificates: ✅ Microsoft® Certified: MCSA | MCSE | MCT ✅ cPanel® & WHM Certified CWSA-2 I’m available to start now , what specific Upstox account/credentials and historical data access will you provide for development? Do you have Upstox API credentials and a preferred historical data range (e.g. last 6 months) for backtest and paper-trade validation? Best regards,

@KarthikTechCon

Hi, I’m Karthik, a Python engineer and system architect with 15+ years of experience building trading tools, data pipelines, and real-time analytics platforms. I can develop your modular intraday options trading system for NIFTY/SENSEX from scratch and deliver a stable Phase 1 MVP. Proposed Architecture: • Market Data Layer: Upstox API integration for option-chain and tick data ingestion using asyncio for reliability and speed. • Signal Engine: Modular rule-based strategy layer built with Python + pandas, easily extendable for future AI/ML analytics. • Risk & Execution: Dynamic stop-loss / take-profit engine with position tracking and paper-trade execution logic. • Storage & Logging: Structured logs and trade metadata stored in SQLite/PostgreSQL or CSV for analysis and debugging. • API Layer: Optional FastAPI service to monitor signals, trades, and system health. Phase 1 Outcome: ✔ Fully automated paper trading cycle (market open → close) ✔ Modular architecture separating data, signal, risk, execution, logging ✔ Queryable trade logs with timestamps and metadata ✔ Clean, maintainable code ready for AI analytics & advanced strategies I have experience working with broker APIs, financial data pipelines, and algorithmic trading frameworks. Happy to discuss strategy logic, architecture, and delivery timeline. — Karthik Python Engineer | 15+ Years Experience

@Rsolitario

Building a truly modular Phase 1 MVP for NIFTY and SENSEX intraday options requires an architecture that can handle the continuous ingestion of the Upstox API without blocking the signal engine or risk calculation. The key to this system operating unattended and scaling smoothly to future AI analytics modules lies in a strict separation of data, logic, execution, and logging from the very first block of code. To achieve this, I will structure the core in Python 3 using asyncio for efficient handling of real-time market flows. The signal engine will use pandas to process the options chain and feed a separate execution layer responsible for paper trading and dynamically adjusting stop-loss and take-profit levels. I have developed multiple algorithmic trading systems and data pipelines integrating APIs from various brokers under this same decoupled architecture. Each event, signal, and simulated fill will be logged with its timestamps and metadata in an SQLite database, ensuring reproducible code and flawless post-trade analysis. Are you available this week to briefly discuss the initial triggers for your signal engine and finalize the last technical details of the database?

@MqlcoderHub

Hi there, I can build your Python intraday options trading system MVP (Phase 1) for NIFTY / SENSEX using a clean, modular architecture. I have experience working with broker APIs, market data pipelines, and rule-based trading systems using Python. Let’s discuss your strategy rules and data preferences in detail so we can start building.

@prashanpatel557

Hello, I can build your Phase-1 modular intraday options trading system for NIFTY/SENSEX using Python with a clean architecture designed for future AI expansion. Architecture: 1. Data Ingestion Upstox API integration using WebSocket for live ticks and option-chain updates. Async data pipeline with reconnect logic and caching to prevent data loss. 2. Signal Engine Rule-based strategy module separated from data layer. Supports ATM/OTM strike selection, volatility filters, and configurable entry rules. Built so future AI modules can plug into the signal layer. 3. Paper Trading Engine Simulated order execution with position tracking, margin checks, and PnL calculation. Handles SL/TP logic with dynamic adjustment based on live data. 4. Storage & Logging Trade logs, signals, and market snapshots stored in SQLite or PostgreSQL. Structured logging for debugging and post-trade analysis. Acceptance Goals: 1. System runs full market session autonomously. 2. Signals, trades, and metadata stored for analysis. 3. Modular code ready for AI modules and analytics later. Happy to discuss architecture details and Upstox integration specifics.

@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.

@rakam26

Hello, Just read your post and it seems you are looking for a skilled Python engineer experienced in building modular intraday trading systems with broker API integrations, rule-based signal engines, adaptive risk logic, and structured logging for NIFTY/SENSEX options. With my years of extensive experience and exceptional expertise in Python 3.x, broker/data API integrations, asyncio-based real-time data pipelines, pandas-driven analytics, and clean architecture separation (data ingestion, signal engine, execution/risk, storage), I am 100% confident that I can bring your Phase 1 MVP to life in the shortest possible time with a stable, extensible foundation ready for future AI-assisted modules. Let’s connect and see how great value I can add to your business. Best Regards, Raka

@ahmedtahash

Building a modular intraday options system for NIFTY/SENSEX requires strict separation between ingestion, signal engine, risk layer, and execution tracking. I’ve worked with broker integrations including FYERS API, handling live market streams, option-chain parsing, and structured trade logging. Phase 1 will use Python (asyncio + pandas) with a clean service-based architecture: Upstox ingestion module, rule-based signal core, adaptive SL/TP engine, paper-trading executor, and structured storage (SQLite/PostgreSQL) for full auditability and post-trade analysis. Designed for unattended market-hour stability and future AI extensions. Will the signal engine rely purely on option-chain Greeks/price action, or also underlying index microstructure data? Regards, Ahmad Al-Ashery.

@yuanzhi5

Building a fresh intraday options system for NIFTY/SENSEX demands a robust backend that can process live market data and execute strategies with minimal latency. The core challenge lies in creating a reliable, event-driven architecture that can handle the volatility and speed of intraday trading without manual intervention. Based on the described scope for a fresh build, I’d suggest a budget of 1200 INR to deliver a foundational proof-of-concept. This initial phase would focus on establishing the core Python data pipeline, integrating with a key data source you specify, and demonstrating a single, back-tested strategy logic. This concrete milestone ensures we are aligned on the technical approach before scaling the system to its full potential. To move from concept to a tailored implementation plan, could you describe the primary data feed or broker API you intend to integrate for real-time ticks and order execution?

@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 I can deliver any project in Trading. Readymade setups for Python available

@Hemantt841

You’re looking to build a modular intraday options trading system for NIFTY and SENSEX, starting from scratch with a Phase 1 MVP focused on Upstox API data ingestion, rule-based signals, dynamic stop-loss/take-profit logic, and paper-trading execution. The emphasis on clean separation of data ingestion, signal logic, risk management, and logging aligns well with your goal for maintainability and future AI enhancements. With over 15 years of experience and 200+ projects completed, I specialize in Python development, API integration, and data analysis, which directly fits your need for a Python 3.x core stack using tools like FastAPI and pandas. My background includes building trading systems and data pipelines that require modular architecture and responsive performance, making me well-suited to handle your intraday options system. For Phase 1, I would architect a modular pipeline that ingests option-chain data via Upstox API asynchronously, processes signals through a rule-based engine, and manages paper trades with dynamic risk controls. All trades and signals would be logged in a lightweight SQLite database for easy querying and analysis. I estimate a 6–8 week timeline to deliver a stable, well-documented MVP within your budget range. Feel free to reach out so we can discuss your priorities and get this project moving smoothly.

@bhartimalu

Hello, I am excited about the opportunity to develop your Python intraday options system for NIFTY/SENSEX. - I have extensive experience in building trading algorithms, specifically in Python. - My plan includes requirement analysis, system design, development, testing, and deployment. - I will utilize Python, Pandas, NumPy, and relevant APIs for data handling. - I propose a timeline of 4-6 weeks with regular updates and open communication. Choose me for my proven track record and commitment to delivering robust solutions. What specific features do you envision for the system? Are there any preferred APIs you would like to use? Regards, Bharti M.

@nutankumarftp

With over 10 years of experience as a full-stack web and mobile app developer, I bring to the table unparalleled proficiency in Python and data analysis. I have successfully completed 2000+ projects with 100% client satisfaction, making me the perfect fit for your project. In addition to my robust development skills, I have prior experience working with broker APIs, data pipelines, and trading systems – giving me a strong foundation in understanding the intricacies of your project. I am highly adept at building modular, maintainable, and scalable solutions that align perfectly with your tech expectations. Architecture-wise, my approach would involve a clean separation between all the crucial components you've mentioned: data ingestion, signal logic, risk/execution, and logging/storage. This will not only ensure more efficient and easy-to-maintain code but will also set the stage for potential future enhancements like AI-assisted analytics and end-of-day reporting. I'll leverage my extensive skills in Python 3.x along with recommended libraries like FastAPI, asyncio, pandas to design a system that is capable of robust market-data ingestion from Upstox API, Rule-based signal engine , Dynamic stop-loss / take-profit logic，Paper-trading execution with position tracking and structured logging for comprehensive post-trade analysis.

@mrkk4ever

Dear Client I saw your requirements regarding to develop a auto trading bot for options trading using python. I can develop this application ASAP based your requirements. I developed many applications is similar to your requirements. Such as indicators, analysis applications, auto trading bot for various exchanges. Let share further details. Thanks and Regards Kannadasan K

@singhashishb2

Hello,I can help you build this modular intraday options trading system (NIFTY / SENSEX) from scratch and deliver a stable Phase 1 MVP. I have experience with Python-based trading logic, real-time API integrations, data pipelines, structured logging, and analytics workflows. I understand the need for low-latency ingestion, clean architecture, and reliable paper-trade execution during live market hours. Proposed Architecture (Phase 1): - Async market data + option chain ingestion via Upstox API - Modular rule-based signal engine (AI-ready for future phases) - Dynamic SL/TP with live position tracking - Paper trading execution engine (no live orders) - Structured logging + SQLite storage (queryable trade history) Stack: Python 3.x, FastAPI (control layer), asyncio, pandas, SQLite. Deliverable: End-to-end unattended paper-trade cycle with timestamped signals, fills, and metadata stored in a modular, reproducible codebase. Estimated timeline: 4–6 weeks Happy to share approach details. Ashish

@rahulb617

hi,i have already created trading platforms using fyers, binance , nifty futures and options and shoonya trading app looking at your requirements i can help you with your project dm me