Python Stock Trading Automation

@sdqtrades

Given the need for a well-documented, Linux-compatible Python script for stock trading automation, I believe I am the professional you are looking for. Being an expert in Automation and Software Architecture, I have hands-on experience in various trading scripts/bots including Python Trading Algo Development. I'm well-versed with Python 3 code, running headless on a Linux VPS and also ensure clear separation between data ingestion, signal generation, order execution, and logging—all essential aspects of your project. With a strong command over web scraping and smart data extraction, I will ensure the script pulls real-time NYSE prices effectively to calculate moving-average-based trend signals to open or close positions as per your requirements. Furthermore, logging each step without crashing or hanging is a safeguard you can count on me to achieve. While maintaining complete confidentiality on all projects, NDA is not new to me. Being fluent in multiple languages allows me to understand and implement your ideas seamlessly. My promise of fast yet quality delivery combined with my ownership of the source lines emphasizes my diligence towards customer satisfaction. Let's discuss the details soon and get started on making your project a success!

@muneeb6767

I have experience building Python trading automation systems with broker API integrations, real-time market data, strategy execution, risk management, and detailed trade logging, and can deliver a modular, VPS-ready trend-following bot with clear documentation and configurable settings.

@Jennycreation

hello sir , i already work on many Cryptocurrency trading system . i can show you demo also and can do as per your requirement. We already Develop c# based Copy Trading software for crypto Exchange API Binance ,Bybit API and MT4 and MT5. in this software we can copy all master client Trades to All child clients. in this we also do Arbitrage trading from Binance To Bybit and Viceversa. software will use the official Binance And Bybit API. i can show demo for you. let me know your response. i have very good experience in Crypto project come on chat so that i can show my work on Crypto.. i can start work from right now i already built trading plateform where i use Zerodha/Alice blue/Angel/Profitmart api for trading order, buy/sell, square off possition /margin etc feature. we also did automated trading using amibroker csv file. let me know i can show you demo.

@loxtic

Hello, how are you doing? I have considerable experience delivering Python-based trading tools, with projects that handle data ingestion, signal generation, order execution, and logging in a clean, modular way. I can build a headless Linux VPS bot that uses a chosen broker API for NYSE access, includes a simple JSON/YAML config, robust safeguards, and a CSV/SQLite trade log. I’ll keep the code well-commented, add a brief README, and provide a quick-start guide plus a demo of a live or sandbox trade. Let me know further if interested.

@ukokab

As a seasoned automation specialist with over a decade's experience, I have continually been praised by clients for significantly reducing their workflow timeline while maintaining extreme precision. My extensive knowledge of Python, paired with my mastery of streamlining business processes, makes me the perfect fit for your project. For the past 10 years, I've successfully developed tools and systems to leverage APIs and automate trading systems in the financial markets. This has given me an in-depth understanding of various broker API's like (Alpaca, Interactive Brokers, TD Ameritrade) which will be instrumental in delivering your needs effectively.-An important aspect that sets me apart is my unmatchable attention to detail and robust code management—including determining indicators and risk mitigating parameters— till I deliver effective products. Moreover, I have strong experience in working on headless Linux VPS environments like what you require for this task. I am efficient at creating organize modular codebases thereby ensuring that different stages and processes (data ingestion, signal generation, order execution, and logging) are distinct and properly documented in order to facilitate easy backtracking if need be.

@Hrolgar

A headless trading bot like this needs the risk layer to be boring and explicit, especially if the same code can point at paper and live trading later. I'd keep this conservative: Alpaca paper trading first, moving-average signals on NYSE tickers, and hard limits around position size, max daily loss, order type, and live-mode enablement. I'd structure it as separate modules for broker access, market data, strategy, risk checks, order execution, config, and logging. Trades and decisions would go into SQLite, with CSV export if you want it, so you can audit why a trade was or wasn't placed. Config would stay outside the code, with paper/live mode clearly controlled and live trading disabled unless you deliberately turn it on. For 30000 INR in 4 days, I'd deliver the source code, config template, README, quick-start guide, SQLite/CSV logging, and a reproducible demo showing a sandbox order on an NYSE ticker. This is an indicative estimate from the brief; I'll give you a firm quote once the scope is locked. Quick check before I start: are you already set on Alpaca, or should I keep the broker wrapper generic enough to swap another REST broker in later?

@ziedb4

Hi, I build exactly this—headless Python trading bots with clean architecture and real broker integration. My plan for your script: Alpaca API (paper + live) — best fit for NYSE access with a clean REST interface and easy sandbox demos. Modular design — separate layers for data ingestion, signal generation, order execution, and logging, so each part is testable and easy to extend. Trend logic — MA-crossover signal: open on trigger, close on reversal or stop-hit. Configurable MA periods. Config-driven — JSON/YAML for symbols, position sizing, risk params, and API keys. No code edits to retune. Safeguards — max daily loss, max position size, and a manual kill switch. SQLite trade log — every fill and step recorded for later review, with graceful error handling (no crashes/hangs). Deliverables: full commented source + README, a recorded sandbox demo executing a live trend trade, and a quick-start guide for adding symbols/indicators. I've delivered several Python trading and backtesting tools (PyQt desktop apps, MT5 frameworks), so robust, non-crashing execution loops are familiar territory

@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

@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!

@mussaibb7

I'd love to take on this project to create a Python Stock Trading Automation script. A clean, well-documented script with a robust feature strategy can significantly improve trading outcomes. I'd emphasize feature engineering, validation strategy, model tuning, reproducibility, metrics, and experiment discipline to ensure a high-quality build. My primary strength lies in delivering closely related work through multi-client ML inference APIs. I successfully deployed a multi-client ML inference API with sub-200ms latency and production-grade cloud delivery, showcasing my ability to manage complex systems. To tackle this project, I'll follow a structured approach: first, I'll work with you to clarify deployment targets, API boundaries, and whether we're building from scratch or enhancing an existing service. Next, I'll design a reproducible notebook or scripts to define the training and validation flow, followed by model deployment and metrics tracking. Throughout the process, I'll maintain open communication to ensure we're targeting the biggest lift first. Can you share the current baseline and validation setup so I can target the biggest lift first?

@jagratip3

Dear Hiring Manager, I can build you a clean, production-ready Python trading bot that executes a fully automated trend-following strategy for NYSE stocks using a reliable broker API such as **Alpaca (recommended for fast paper/live testing)** or Interactive Brokers if needed. The system will be written in **pure Python 3**, structured into clear modules for data ingestion, signal generation (moving-average trend detection), order execution, risk management, and logging. It will run headless on a Linux VPS and be designed for stability, transparency, and easy maintenance. Key features I will implement: • Real-time NYSE price streaming via broker API • Moving-average based trend detection for entry/exit signals • Automated market/limit order execution • Risk controls: max daily loss, position sizing limits, and manual kill switch • Full trade logging using CSV or SQLite • Config file (YAML/JSON) for symbols, strategy settings, and API keys Deliverables: • Fully commented Python source code + README • Paper-trading demo using broker sandbox (proving at least one live trade) • Quick-start guide for adding symbols and adjusting strategy parameters I am ready to start immediately and deliver a robust, extensible trading system. Best regards, JP

@jackj012

Hi, I can build this as a clean Python 3 trend-following bot with a modular structure: market data, signal generation, execution, and logging kept separate. I’ll make it Linux VPS friendly, use a simple JSON/YAML config, and include safeguards such as daily loss limits, max position sizing, and a manual kill switch. For the broker connection, I can wire it to Alpaca paper trading or an Interactive Brokers sandbox, whichever you prefer, and demonstrate a live sandbox trade on a NYSE symbol. Deliverables will include the full source code with inline comments, a concise README, and CSV/SQLite trade logs. I’ll also make sure the bot can enter on moving-average signals and exit on reversal or stop-loss triggers. If you’d like, I can start with a moving-average crossover MVP and then harden it with your preferred risk rules.

@tarasd6

Hello, I have experience building Python-based trading bots with broker API integrations, real-time data processing, and risk-managed execution systems. I understand you need a headless Python 3 trading script for NYSE that implements a trend-following strategy with moving averages, automated order execution, position management, and full trade logging. I can build this with a clean modular structure (data ingestion, signal generation, execution, and risk control), using a broker like Alpaca or Interactive Brokers for easy paper trading and REST API access. The system will include configurable parameters via JSON/YAML, safety controls like max daily loss and position limits, and a kill switch for manual shutdown. You will also get a README, well-commented code, and a simple setup guide so it can run easily on a Linux VPS. Before starting, I’d like to confirm your preferred broker and risk parameters so I can align the implementation precisely with your requirements. Looking forward to working with you.

@subhankarsinha

I am a strong fit for this project because I combine solid Python development experience with data engineering and automation expertise. I can deliver a clean, modular trading bot with clear separation of market data ingestion, signal generation, order execution, risk controls, and logging. My approach emphasizes reliability, maintainability, and thorough documentation so the system can be easily extended in the future. I will implement a moving-average trend-following strategy with configurable parameters, broker API integration (Alpaca or Interactive Brokers), real-time position monitoring, trade logging (CSV/SQLite), and safety mechanisms including max daily loss limits, position sizing controls, and a manual kill switch. The solution will run headlessly on a Linux VPS and include a concise README, setup guide, and live/paper-trading demonstration. I focus on delivering production-ready code rather than just a proof of concept.

@Ashton4x

Hello, Python Stock Trading Automation sounds simple on the surface — the real challenge is implementing a robust trend-following strategy while ensuring seamless NYSE market access and real-time data processing. That's exactly the kind of work I enjoy. Here's what I'll deliver: 1. Clean Python 3 script on a Linux VPS with clear code separation. 2. Configuration file for easy adjustments. 3. Implemented safeguards and detailed trade log functionality. 4. Comprehensive documentation for easy setup and future adjustments. Timeline: Turnaround: 14 days for full delivery. One question before I start: How do you prefer risk parameters to be configured to ensure optimal trade management? That'll help me tailor the solution to your specific requirements. Ready to get started whenever you are.

@piercep981

Proposed fixed price: 12,461 INR (~$132 USD). Re: Python Stock Trading Automation. Your Python Stock Trading Automation project is a strong fit for my background, and I'd like to make it straightforward for you. I'm a senior Python and C# developer with 25+ years of experience building systems that run headlessly, handle real-time data, and stay crash-resistant under load. The architecture you've described — clean separation between data ingestion, signal generation, order execution, and logging — is exactly how I approach systems work, and it maps directly to patterns I've applied across enterprise and backend projects for clients including Boeing, Google, and Verizon. A few specifics that line up with your requirements: **Python, headless Linux, and structured config:** I write idiomatic Python 3 with a functional programming style, which naturally produces the kind of clean, testable module boundaries you're asking for. JSON/YAML-driven config with hot-adjustable parameters is standard practice for me — your symbols, sizing rules, and API keys stay out of the code. **Safeguards and kill switches:** I've built safety-gate systems professionally — Verizon's internal security infrastructure, access-control and audit-trail work — so the max-daily-loss cap, position-size fence, and manual kill switch aren't afterthoughts. They'll be first-class, fail-closed components wired in before the order execution layer ever runs. **Trade logging:** SQLite is my default for lightweight, portable, reviewable logs. Every fill, signal trigger, and rejection will be recorded with enough context to reconstruct what happened and why. For the broker, I'd lean toward Alpaca's paper trading sandbox — clean REST interface, solid Python SDK, NYSE access, and easy to demo in a recorded session without touching real capital. I can deliver clean, commented source, a working sandbox demo, and a quick-start guide that makes it easy for you to add symbols or swap indicators without digging into the core logic. Happy to discuss indicator choices or risk parameters before I start — let me know what works. Philip Pierce

@Gurpreetsing343

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.

@gaurinavale22

Hello, I am an AI Engineer and Python Developer with experience in building data-driven applications, API integrations, and automation systems. For your stock trading automation project, I would design the system using a modular architecture: • Data Layer: Integration with brokerage APIs (Alpaca, Interactive Brokers, etc.) using REST and WebSocket connections for live market data. • Strategy Engine: Plug-and-play strategy modules so new trading strategies can be added easily. • Execution Layer: Automated order placement with position sizing, risk management, logging, and error handling. • Backtesting Module: Historical testing using Pandas, NumPy, and Backtrader. Preferred Tech Stack: * Python 3.x * Pandas & NumPy for data processing * Backtrader for backtesting * Asyncio/WebSockets for real-time streaming * SQLite/PostgreSQL for logging and trade history * Docker for deployment Proposed Milestones: 1. API integration and live data ingestion. 2. Strategy engine and sample strategy implementation. 3. Order execution and risk management. 4. Paper trading and validation. 5. Documentation and live deployment support. Deliverables: ✔ Fully functional Python code. ✔ Configuration files for API keys and parameters. ✔ README for setup and deployment. ✔ Demonstration of successful paper trades. ✔ Well-documented, extensible codebase. Estimated timeline: 10-14 days. I would be happy to discuss your preferred broker and specific trading strategies in more detail.

@KartikMishra0711

I will prepare this and deliver this to you in 7 days, and will be ready to update any feature required. Please give me any complain you will have on the email provided in the README. Please note that I am a 16 years old and this is one of my first paid projects.