Automated Stock Trading System

@diektech

Hi — Elias here from Miami. I see you’re looking to automate routine tasks for your trading desk. The goal here is efficiency and reliability in executing trades without manual intervention. What usually matters most in systems like this is the integration between trading algorithms and real-time data feeds. A common issue is ensuring that the system can handle varying market conditions without compromising performance. The tricky part is usually managing state and ensuring that your automation logic can adapt to unforeseen scenarios. My approach would involve designing a modular architecture that separates data ingestion from trading logic. This would enhance maintainability and allow for future expansions, such as integrating new trading strategies or data sources. I’ve built similar systems where stability and performance were critical, particularly in high-frequency trading environments. A few questions to better understand the scope: Q1 – What kind of user roles do you envision for monitoring and adjusting the trading strategies? Q2 – Are there any specific APIs you plan to integrate, and what data sources are crucial for your trading decisions? Q3 – How do you envision handling errors or anomalies in trades? Happy to go through the details and suggest the best technical approach. Looking forward to hearing from you.

@ZohaibRoy

⭐⭐⭐⭐⭐ Automate Your Stock Trading Workflow with AI & Excel Integration ❇️ Hi My Friend, I hope you are doing well. I've reviewed your project requirements and see you are looking for an automated stock trading solution. You have no need to look any further as Zohaib is here to help you! My team has already completed 50+ similar projects for automated trading workflows. I will create a system that efficiently handles market analysis, trade execution, and performance reporting, all while integrating seamlessly with Excel. ➡️ Why Me? I can easily develop your automated stock trading workflow as I have 5 years of experience in Python programming, AI integration, data analysis, and API management. My expertise includes real-time data processing, pattern recognition, and building dynamic dashboards. Additionally, I have a strong grip on Python libraries like TensorFlow and PyTorch, ensuring a robust solution for your needs. ➡️ Let's have a quick chat to discuss your project in detail. I can provide samples of my previous work and demonstrate how I can bring your vision to life. Looking forward to our chat! ➡️ Skills & Experience: ✅ Python Programming ✅ AI & Machine Learning ✅ Data Analysis ✅ API Integration ✅ Excel Automation ✅ Real-Time Data Processing ✅ Pattern Recognition ✅ Trade Execution ✅ Performance Reporting ✅ Error Handling ✅ Back-Testing ✅ Documentation Waiting for your response! Best Regards, Zohaib

@Fazeennazar

Hello, I can develop an end-to-end Python-based automated trading system combining real-time market data, AI/LLM-assisted signal generation, broker API execution, risk controls, and dynamic Excel reporting. I will design the architecture so market analysis and signal generation are separated from execution, with validation, position/risk limits, order-state tracking, retries, logging, and clear handling of broker/API failures. For the AI layer, I can work with PyTorch, TensorFlow, or appropriate LLM frameworks where they add measurable value, while keeping latency-sensitive trading logic deterministic rather than relying blindly on an LLM. I will integrate the system with your preferred brokerage API and Excel through Python/VBA or an appropriate Excel integration, providing live P&L, risk metrics, trade history, and strategy diagnostics in an automatically refreshed dashboard. I will also build a six-month historical backtest framework with reproducible results and a test environment that demonstrates the complete flow from market-data ingestion through simulated order confirmation before any live deployment. The deliverables will include the containerized/source application, Excel workbook, backtesting results, configuration files, logging, and detailed setup/maintenance documentation, with the implementation structured for future strategy extensions. Best regards

@AITSoft

Having successfully worked on numerous projects involving API Development, Data Analysis, and Software Architecture, I believe I'm the perfect fit for your Automated Stock Trading System. My proficiency in Java, Python, MySQL, and the AI libraries you've mentioned like TensorFlow and PyTorch will facilitate efficient real-time data ingestion, pattern recognition, and signal generation for effective market analysis. In terms of trade execution, my expertise in building customized business software that seamlessly integrates with diverse APIs will ensure automated order placement with tight latency control - all while prioritizing clear error handling. Additionally, my familiarity with VBA and Python Excel add-ins will aid in pushing instant P&L reports, risk metrics, and strategy diagnostics directly to dynamic Excel dashboards for easy access and daily review. Not only do I promise to deliver a robust system that runs end-to-end in a test environment (with proper back-test results), I also prioritize user-friendliness. Rest assured, my setup documentation will be concise and comprehensive so you can maintain and extend the system without the need to chase me down. To sum it up - utilizing my skills sets in automating processes, managing data, and building reliable software solutions combined with my passion for long-term client relationships - let's transform tedious tasks into prompt actions with sustainable profitability. Let's connect!

@ahmadayaz

I propose a comprehensive solution for automating your trading desk operations: 1. **Market Analysis:** Real-time data analysis using AI/ML libraries for pattern recognition with seamless Excel integration. 2. **Trade Execution:** Automated order placement via brokerage APIs with low latency and error handling. 3. **Performance Reporting:** Real-time P&L tracking and risk analysis in dynamic Excel dashboards. Technology stack: Python for AI modeling, Excel integration, and major brokerage APIs. Key Deliverables: 1. Testing-ready script/containerized app. 2. Back-testing results for 6 months. 3. Dynamic Excel workbook for performance metrics. 4. Detailed setup documentation for independent management. With a focus on AI/ML, financial technologies, and automation, I aim to deliver a successful automated trading workflow tailored to your needs. Let's discuss your requirements and timeline for a seamless transition.

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Python, and similar tools. I have worked with pytorch, and tensorflow to develop DL models, .I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@devendrathakur12

Hi, I’m a Senior Quantitative AI Engineer with 20+ years in financial modeling and Python. I have gone through your specific requirement for automated AI trading workflow. I would keep trade execution separate from the LLM layer, because model delays or malformed output should never block broker orders. I will build a Python trading worker that consumes market data and produces tested signals before sending orders through the broker API. I’ll use PyTorch for model research and pandas for backtesting, with Excel as the reporting layer rather than the execution path. And I’ll log every order decision so failed or rejected orders can be traced. Backtesting and quantitative research examples I can send. Which broker API and market data provider are you using today? Which six month historical dataset and trading rules should the first backtest cover? What Excel metrics and controls do you currently review each day? Free for a quick call this week? Or answer those three and I will map the first version. Dev Singh

@emilesd617

Hi, I am a web designer and developer with 8 years of rich experience in software development, with a background in website design, HTML, CSS, PHP, and user interface development. I am familiar with PHP, HTML, CSS, responsive web design, graphic design, UI development, and web development. For the Vectra landing page, I can create a clean product focused design based on the Windows application, using its interface, features, and visual language to make the website feel consistent with the actual product. I can structure the page with clear sections for the application overview, key features, screenshots, benefits, and strong calls to action, while keeping it responsive across desktop, tablet, and mobile. I'm an individual freelancer and can work on any time zone you want. Please contact me with the best time for you to have a quick chat. Looking forward to discussing more details. Thanks. Emile.

@denisSolution

Hello, I'm Denis, a full-stack developer who has built several automated systems combining real-time data processing with AI-driven decision making and external API integrations. What you're describing is a solid workflow: ingest market data, run pattern analysis through an LLM or ML model to generate signals, execute trades through your broker's API, and feed results back into Excel for monitoring. The key challenge isn't the individual pieces—it's keeping them synchronized, reliable, and fast enough for trading. I've worked on similar systems where latency and error handling matter deeply. My approach is straightforward: map out your broker API, data sources, and Excel integration points, then build the core pipeline. A Python service would pull market data, run your AI analysis, and execute orders with proper retry logic and monitoring. For Excel, I'd use Python add-ins or VBA to keep dashboards live without manual refresh. Signal quality is critical. Before live trading, we'd backtest thoroughly on historical data to validate your model's performance with clean data handling, proper slippage assumptions, and honest P&L reporting. One realistic concern: broker APIs have rate limits and latency spikes. We'd build in queuing, circuit breakers, and clear logging so you see exactly what happened when something doesn't execute as planned. You'll always know why a trade didn't fire. I can start right away. Let's connect and discuss the details. Thanks, Denis.

@sumitsthakur

I understand you need a fully automated stock trading workflow combining AI/LLM techniques with Excel integration. My approach would develop a Python-based system that ingests real-time market data, applies machine learning models for pattern recognition and signal generation, connects to your broker API for automated execution with proper error handling and latency controls, and delivers performance metrics to dynamic Excel dashboards. The solution will include back-testing over six months of historical data and comprehensive documentation for maintenance. This approach directly addresses your requirements for an end-to-end automated trading system with seamless Excel connectivity. Let's discuss the implementation details and timeline.

@tnt42010

Hello, I’m an experienced Python and automated-trading developer with strong experience integrating trading systems, broker APIs, real-time data, AI/LLM workflows, and Excel-based reporting. I can build the complete workflow from market data to validated signals, execution, and reporting: * Real-time market-data ingestion and AI/LLM-assisted signal generation * Python strategy/AI layer using appropriate ML/LLM libraries * Broker API integration for automated order placement and management * Low-latency execution with retries, reconciliation, logging and error handling * Real-time P&L, exposure, drawdown and risk metrics * Dynamic Excel dashboard for monitoring and daily review * Containerized/test environment with reproducible configuration * Six-month historical backtesting with realistic costs, slippage and execution assumptions * Clear documentation for deployment and future extensions I’ll keep the AI and execution layers modular so models and strategies can be replaced or improved without rewriting the trading infrastructure. Please contact me to discuss the broker, market-data source, Excel workflow and timeline.

@Hrolgar

Before pricing the live-execution piece, what broker are you targeting, Alpaca, IBKR, something else? That decides the auth model and most of the error-handling work, and it moves the number more than anything else in the brief. For a fixed budget on a build this size, I'd rather hand you a real, testable slice than a half-wired full system. Phase 1 covers the data ingestion pipeline, the signal-generation core (LLM-driven market analysis feeding rule-based entries and exits), and a full backtest against six months of history, plus a working Excel dashboard pulling P&L and risk metrics off paper-trading output. That's something you can validate against known outcomes before live money or a broker connection enters the picture. Order execution and dashboard polish become a scoped follow-on once the core is proven out. M1: data ingestion + signal generation, $250, 7 days. M2: backtest engine against 6 months of history, $250, 7 days. M3: Excel dashboard (P&L, risk) wired to paper-trading output, $250, 7 days. 750 over 21 days is my starting number off the brief as posted, the broker you pick and the LLM you run against are the two things that would move it once scope is locked. Send me the broker and data source you're leaning toward and I'll have a tighter number back tomorrow.

@bindmission

Hello, I have 10 years of experience in developing automated trading systems and AI-driven solutions. I propose building a robust system for real-time market analysis, order execution, and performance reporting. The system will leverage Python-based AI libraries and integrate seamlessly with Excel and broker APIs. I will provide comprehensive testing, back-testing, and clear documentation for easy maintenance. Let's connect to discuss the details and timeline. Regards, VishnuLal NB*

@PINESCRIPTERR

Hi, I understand you need an automated trading workflow that combines real-time market data, AI-driven analysis, broker execution and an Excel-based reporting layer for daily monitoring. I can build the system in Python with a modular architecture separating data ingestion, feature processing, signal generation, risk controls, execution and reporting. AI/ML components can use PyTorch, TensorFlow or appropriate LLM tooling where it provides measurable value, while keeping deterministic trading and risk rules separate from the model layer. For execution, I can integrate the selected broker API with order validation, position management, retries, timeouts, logging and clear handling of rejected or partially filled orders. The system can operate in a test/paper environment first so the complete flow can be validated before any live execution is enabled. For Excel, I can provide an automatically refreshed dashboard covering P&L, positions, drawdown, exposure, trade statistics and strategy diagnostics. Historical backtesting will use time-ordered data and realistic assumptions around transaction costs and execution rather than treating backtest results as a guarantee of future performance. The final handover will include the source/container, configuration, backtesting workflow, Excel dashboard and setup documentation so the system can be maintained and extended independently. Best regards, Houssame

@seefattechnologi

Hi, The important part is not simply connecting an AI model to a broker API. It is building a controlled pipeline where market data, signal generation, risk checks, order execution, and Excel reporting remain traceable and testable end to end. I can build this in Python with broker API integration, real-time market data, AI/ML signal generation, backtesting, execution safeguards, P&L/risk analytics, and an auto-refreshing Excel dashboard. I would keep the AI layer modular so the strategy can be tested and improved without changing the execution engine. I will first validate the strategy against historical data, then run the complete workflow in a paper/test environment before connecting live execution. One important question: Which broker API and market/data provider are you currently using? Please ping me to get started and provide you with great results. Thanks!!!

@ssquare489

For this trading workflow, I’d separate the AI/research layer from the execution engine. LLMs can help interpret news, summarize market context, and support signal research, but I’d keep live order placement deterministic, testable, and rule-driven so latency, risk limits, and failures remain predictable. I’d use Python with FastAPI, pandas/Polars, PyTorch or LightGBM where appropriate, WebSocket market feeds, broker APIs, PostgreSQL/TimescaleDB for history, Redis for low-latency state, and Docker for deployment. The two priorities would be integrations and maintainability, with isolated modules for data ingestion, signal generation, risk checks, execution, reconciliation, and Excel reporting. Excel can connect through xlwings/Python or a controlled workbook refresh layer, with live P&L, exposure, drawdown, win/loss, and strategy diagnostics. I’d also build a backtesting pipeline with walk-forward/time-based validation, at least six months of data, paper-trading mode, detailed logs, retries, and kill-switch controls before any live deployment. A relevant project is our live stock-market analysis website using the GDFL WebSocket API, where I worked with real-time market data, streaming updates, analysis views, and trading-focused dashboards. Estimated timeline: 5–7 weeks for data pipeline, strategy engine, broker integration, backtesting, Excel dashboards, testing, and documentation.

@HamparProd

The important part here is separating AI-assisted market analysis from deterministic trade execution and risk controls. An LLM can contribute to signals, but position sizing, order validation, P&L and execution state should remain auditable code. I would build the Python workflow around one broker API, starting in paper mode: real-time data ingestion, your defined signal logic, risk validation, automated order placement/reconciliation, six-month historical backtesting, and an Excel dashboard for positions, P&L, risk and strategy diagnostics. My background includes Python, API integration, AI/LLM systems and financial-data workflows, so I can handle the full path from model output through reliable execution and reporting. I can start immediately after award once the broker and strategy rules are confirmed.

@carlosd178

Hello, I’ve reviewed your requirements and understand you need an end-to-end trading workflow—from market data and signal generation through broker execution, position tracking and automated Excel reporting. For execution, I’d include order-state tracking, duplicate-order protection, retries/timeouts, logging and reconciliation so API/network failures cannot silently create incorrect positions. The same strategy logic would be shared between historical backtesting and paper/live execution to minimize discrepancies. Excel would receive positions, trades, P&L, drawdown, exposure and strategy diagnostics through an automated refresh layer. For the current budget, I recommend a Phase-1 MVP covering: • One market-data source • One brokerage API • AI/signal pipeline • Risk/execution engine in paper/test mode • 6+ month backtest • Auto-refreshing Excel dashboard • Containerized deployment and documentation Estimated Phase-1 delivery: 2–3 weeks. Estimated budget: $650–750. Two questions: • Which broker/API are you currently using? • Do you already have defined entry/exit/risk rules, or should strategy development be included in the scope? Best regards, Carlos

@rosscarter1011

Hey, hooking that LLM signal generation directly into Excel for a trading desk sounds like a fun workflow to set up. I usually build out the data pipeline in Python and use openpyxl or xlwings to keep those P&L sheets refreshing live without choking on the API calls. The trick is getting the broker error handling robust so bad ticks don't stall the whole loop. Are you planning to route orders through Interactive Brokers or another specific API for this?