Automated Algorithmic Day Trading Platform

@AwaisChaudhry

Hi, I understand you need Stage 1 of an automated Python trading platform for US stocks using Charles Schwab API, hosted on a US VPS, with live market data, order execution, logs/reports, and a clean modular setup for Stage 2. I can build the API connection, market data module, trading engine, and reporting module so the bot can trade tickers during RTH using the 5-minute closed candle 100 EMA rule. I will also keep the design ready for future strategy replacement, DGX Spark/local model connection, stronger risk controls, and AI tool use like Claude. My focus will be stable execution, low latency where possible, safe error handling, clear logs, and simple documentation so you can understand each module and interface. Which Charles Schwab account/API access level do you already have, and do you have approved trading permissions for automated order execution? Do you want Stage 1 to use Schwab market data only, or should we connect a separate real-time data provider if Schwab data limits are not enough? What exact risk rules should be included in Stage 1, such as max position size, max daily loss, max trades per day, and stop trading after errors? Should the first version support paper trading/sandbox first before live trading? Best regards,

@opencollar

Hi, I see you want a trading platform that can handle different styles smoothly and connect to Charles Schwab without hiccups. I’ll build a solid base with modules for connecting to the broker, pulling live data, executing trades, and keeping records. I’ll make sure the system is flexible so you can upgrade it later with your own models and use your DGX spark for heavy lifting. Communication will be clear, quality high, which saves you time and helps you grow. After delivery, I’ll support you and help with any tweaks needed. Let’s chat about how to plan a bigger project and make this more powerful together. Regards, Nick

@n2rtechnologies

Hello, I've built algorithmic trading systems before — real ones that handle live market data, execute trades, and manage risk across multiple strategies. The stack you need (Python for ML models, C++ for speed, PHP for the backend) is exactly what we use for this type of work. We've been shipping complex financial and data-heavy platforms for over a decade. Most of our work is SaaS, custom platforms, and systems that need to process data at scale — which means we know the tricky parts: latency, data integrity, and keeping everything running 24/5 without gaps. Your budget range gives us a solid starting point. Once we dig into scope — how many strategies, what data sources, deployment requirements — I'll give you a real number. Let's hop on a quick call this week. I'll ask three questions about your architecture needs, and we can figure out if this is a fit. Message me whenever you're ready. Regards, Nurul Hasan

@diektech

Hi — Elias here from Miami. The real challenge here is not only coding the EMA strategy, but building Stage 1 with clean module boundaries so broker connectivity, market data, execution, logging, and later DGX/local model signals can be swapped or extended without rewriting the core system. I’d approach this as a Python-based VPS trading engine with separate modules for Schwab API auth/order routing, live market data ingestion, 5-minute candle/EMA calculation, strategy execution, risk controls, and detailed logging/reporting. I’d also keep the signal layer abstract so Stage 2 can replace the 100 EMA logic with local AI/model outputs from the DGX environment. I’ve worked on API-driven automation systems involving real-time data, execution logic, monitoring, modular backends, and production deployment workflows. Q1 – Do you already have Charles Schwab API access approved? Q2 – Should Stage 1 include paper trading/backtesting before live execution? Q3 – What basic risk rules should be enforced first: max position size, daily loss limit, ticker limit, or all? Happy to structure this into a safe Stage 1 architecture and milestones. Looking forward to hearing from you.

@ZohaibRoy

⭐⭐⭐⭐⭐ Build Your Automated Trading Platform for Stock Market Success ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project needs and see you're looking for an automated trading platform. Look no further; Zohaib is here to help! My team has completed over 50 similar projects for trading platforms. I will set up a robust infrastructure that ingests live market data and executes orders through your broker’s API, all while minimizing latency. ➡️ Why Me? I have 5 years of experience in building automated trading systems, specializing in Python, API integration, and real-time data processing. I have a strong grip on AI tools and local computing power, ensuring your platform is efficient and powerful. ➡️ Let's have a quick chat to discuss your project in more detail. I can show you samples of my previous work and how I can bring your vision to life. Looking forward to chatting with you! ➡️ Skills & Experience: ✅ Python Programming ✅ API Integration ✅ Real-time Data Analysis ✅ Automated Trading Systems ✅ AI Model Integration ✅ Risk Management ✅ VPS Configuration ✅ Market Data Handling ✅ Reporting and Logging ✅ Modular Architecture Design ✅ Scalping and Swing Trading Strategies ✅ User Manual Documentation Waiting for your response! Best Regards, Zohaib

@corpmember29

I WILL BUILD SCALABLE, AI-READY TRADING INFRASTRUCTURE FROM DAY ONE ENSURES SMOOTH EXPANSION INTO ADVANCED STRATEGIES LATER. With 12+ years of experience in Python, AI, algorithmic trading, APIs, and cloud infrastructure, I can develop Stage 1 of your automated trading platform with a modular architecture designed for future DGX Spark integration and custom AI models. ✅ Charles Schwab API Integration ✅ Live Market Data Ingestion ✅ Automated Trading Engine (5-Min EMA Strategy) ✅ Order Execution & Position Management ✅ Risk Controls & Trade Validation ✅ Reporting, Logging & Performance Tracking ✅ VPS Deployment (US-Based) ✅ Documentation & User Manual **Stage 1 Deliverables** • API Connection Module • Market Data Module • Trading Engine • Reporting & Logging Module The system will be built in Python using a clean modular architecture, allowing seamless replacement of trading logic and future integration with local AI/ML models running on your NVIDIA DGX Spark during Stage 2. Available to start immediately and deliver a production-ready foundation for long-term development.

@C0RETECHIES

Hi there, I've reviewed your plan for the Stage 1 trading bot. Operationally, the system will run on a US-based VPS, ingesting live market data for your specified stock list. For each stock, it will maintain a real-time 5-minute bar series. On each bar's close, the engine will calculate the 100 EMA and compare it to the price, executing buy or sell orders via the Charles Schwab API based on your crossover logic. All events will be logged for analysis, and the architecture will be designed with modular interfaces to integrate your DGX-powered models in Stage 2. Technical approach: - Backend: Python using asyncio for non-blocking I/O and pandas for time-series analysis. - Architecture: Decoupled, event-driven system: Data Ingestion, Strategy/Signal, Order Execution, and Logging services. - Broker API: Direct integration with the Charles Schwab API for market data and trade execution. - Infrastructure: Containerized via Docker, deployed to a low-latency AWS EC2 instance in a US region. Core modules: - Schwab Connector: Manages authentication, maintains connection, places/monitors orders, and fetches account status. - Data Ingestor: Subscribes to market data streams and aggregates tick data into 5-minute bars. - Trading Engine: Encapsulates the 100 EMA strategy as a pluggable component, ready for Stage 2 replacement. - Persistent Logger: Records every signal, order, fill, and system error for audit. Implementation strategy: We'll begin by establishing a stable connection to the Schwab API, then build the data ingestion logic. The EMA strategy will be implemented and tested, ideally against historical data first. After deploying to the VPS, we will conduct a paper trading phase before going live to ensure reliability and accurate execution. Regards, Rohit

@taimoorkhan311

I have carefully reviewed the project requirements and believe that I possess the expertise and experience necessary to execute this project flawlessly. With over 5 years of experience in PHP and Software Architecture, I am confident in my ability to develop an automated algorithmic trading platform tailored specifically for the stock market, capable of running various trading styles without manual intervention. If you would like to see more of my previous work or discuss further details about the project, please feel free to connect with me via chat. I am excited about the opportunity to collaborate on this project and look forward to the possibility of working together.

@Hubready

Hello! This is James from Hollywood, and I've carefully read your project description for the automated algorithmic trading platform. I have a solid understanding of your needs and a few questions to clarify: Could you please clarify the following questions to help me better understand the project? 1. What specific features do you envision for the trading algorithms? 2. Are there particular APIs or data sources you prefer for market data integration? 3. What is your timeline for project completion and testing? With over 15 years of experience in software engineering, particularly in AI and automation, I am confident in delivering a robust solution tailored to your specifications. I have built various trading algorithms for platforms like E*TRADE and Schwab, ensuring efficiency and reliability. To achieve your project's goals, I suggest a phased approach: 1. Requirements gathering and architecture design. 2. Development of core trading algorithms and integration with APIs. 3. Rigorous testing and optimization for performance. 4. Deployment and ongoing support. I’m serious about creating a platform that not only meets your expectations but exceeds them. Let’s connect and discuss your vision further!

@iosifpeterfi

Hi there, I've built automated trading systems with broker API integrations, real-time data pipelines, and modular execution engines. Your Stage 1 aligns well with my experience. My approach for your platform: 1. API Connection Module: - Charles Schwab API with OAuth authentication - Robust reconnection and rate limiting - Designed for future DGX Spark integration 2. Market Data Module: - Real-time 5-minute bar streaming during RTH - EMA calculation engine (100 EMA, extensible) - Historical backfill for indicator warm-up - Local data storage (SQLite/PostgreSQL) 3. Trading Engine: - Signal detection: price crossing 100 EMA on closed 5-min bar - Position management: entry on crossover, exit on close below - Multi-ticker support from user-defined watchlist - Order execution with confirmation tracking - Paper trading mode for safe testing 4. Reporting/Logging: - Trade log with entry/exit prices and P&L - Performance dashboard (daily/weekly stats) - System health monitoring and alerts Architecture: Modular Python codebase with clean interfaces. Each component independently replaceable for Stage 2. US-based VPS for minimal Schwab latency. Deliverables: source code, deployment scripts, user manual, and module docs. Happy to discuss architecture details.

@temoberishvili

Building an automated algorithmic trading platform sounds like a fascinating challenge. I see you're looking to develop a solution specifically for the stock market, which requires precision and strong data analysis capabilities. With around 10 years of experience in PHP, Python, and machine learning, I've tackled similar projects that demand robust API development and risk management features. Your main goal appears to be creating a reliable trading system that can operate autonomously, which is crucial for maximizing trading opportunities. Some similar things I've built: a local marketplace app with real-time data analysis, an internal tool for financial forecasting, and a regional tutoring portal that integrated automated notifications. Let’s bring your vision to life! Could you please clarify the following questions to help me better understand the project? Q1: What specific features do you envision for the risk management aspect of the platform? Q2: Are there particular APIs or data sources you plan to use for trading signals? Q3: How do you plan to handle the deployment and ongoing maintenance of the platform?

@FizzaNadeemK

Hi I can set up Stage 1 as a clean, modular trading infrastructure that connects to the Charles Schwab API, runs on a US-based VPS, and is structured specifically so you can later plug in your DGX Spark and more advanced AI-driven models without rewriting the core system. The architecture will be split into four independent modules API connection, market data ingestion, trading engine, and reporting/logging—so each part can be upgraded or replaced in Stage 2 without affecting execution stability. For the trading engine, I’ll implement your initial strategy logic (100 EMA crossover on 5-minute closed bars with RTH constraints), but keep the signal layer abstract so it can later be swapped with AI or custom model outputs. The system will include real-time data handling, order execution through Schwab’s API, and basic risk controls, with structured logging so every trade decision and API interaction is traceable and easy to debug. I’ll also design the infrastructure with future scalability in mind—so your DGX Spark can later handle heavy computation while the VPS remains focused on execution, latency, and broker communication. You’ll receive a fully working Stage 1 deployment, documentation for each module, and a clear upgrade path for Stage 2 integration. Best Regards, Fizza Nadeem K

@kishanbwts

Building a solid base platform for automated trading with your specs is doable. I’ve helped a client in US equities before set up a Python trading bot on a US VPS, connecting to a broker API for live data, order execution, and managing risk controls with low latency. For your Stage 1, I’ll create a modular system with an API connector for Charles Schwab, a market data ingestion module tuned for 5-minute bar detection, the trading engine implementing your 100 EMA entry and exit rules, plus detailed logging/reporting. I’ll make sure the architecture supports swapping the entry strategy and scaling AI computations later on your DGX spark. Do you have access credentials and API documentation from Schwab already? Also, do you prefer a specific US VPS provider for low latency, or should I recommend a couple? This setup can be ready quickly since the logic is straightforward and built on proven frameworks. I’ll deliver clear documentation for easy expansion in Stage 2. Ready to get this infrastructure running for your automated trading soon.

@yuliussmayoru

Hello! I’m comfortable designing modular Python-based market data and research infrastructure, especially where the goal is to build a strong Stage 1 foundation that can later support more advanced local analytics and model-driven workflows. Your emphasis on clean module boundaries, VPS deployment, broker connectivity, logging, and future DGX-based local computation is exactly the right way to structure a serious long-term platform. For Stage 1, I would focus on a maintainable architecture around four clear modules: broker/API connectivity, market data ingestion, a simulation or paper-execution engine, and strong reporting/logging. That gives you a working foundation for research, testing, and controlled iteration, while keeping the interfaces clean enough to replace the initial strategy logic later with your own mathematical models and local AI components. I can also document the module boundaries and deployment model carefully so Stage 2 can evolve without rework. Warm regards, Yulius Mayoru

@TechizeBuilder

Hi, I came across your project "Automated Algorithmic Day Trading Platform" and I'm confident I can help you with it. About Me: I'm a full stack developer and agency owner with over 8+ years of experience in PHP, API Development, Python. , and I understand exactly what’s needed to deliver high-quality results on time. Why Choose Me? - ✅ Expertise in required Technologies and 1 year post deployment free support - ✅ On-time delivery and excellent communication - ✅ 100% satisfaction guarantee Let’s discuss your project in more detail. I’m available to start immediately and would love to hear more about your goals. Looking forward to working with you! Best regards, Deepak

@Eliteinno

Hi there, Developing an automated trading platform involves navigating the complexities of real-time data analysis and fast execution. Without a robust infrastructure for low-latency data processing and a seamless API connection, performance can falter. With my expertise, I'll ensure a swift, scalable, and AI-integrated solution that aligns perfectly with your long-term vision and the NVIDIA DGX spark integration for local computation. Here are my questions: What specific performance metrics are you targeting for latency and execution speed? Could you confirm any particular broker API limitations or preferences you may have? Let’s discuss your project now!

@OscarMozo

Hi, I can help you You want a simple, solid first version that connects to Schwab, pulls live prices, follows a clear EMA rule on 5‑minute bars, places trades fast, logs everything, and runs on a US VPS. It should be clean, modular, and ready to plug in your own models later, including your DGX, with docs and a easy way to swap strategies. This will take a few days, I've been doing this type of work for years. I have short walkthrough videos on my Freelancer profile showing similar work. 1) Do you already have Schwab API credentials and a preferred US VPS provider? 2) For reports, do you want CSVs only, or also a small dashboard and alerts? Ideally, we have a call and go through the details together so I can make sure I understand everything correctly, address any questions, and give you a quote and timeline. Would that work? Best, Nicolas

@MIndlabsAi6

Hey there, I'm Vishal Maharaj, with 25 years of experience in PHP, C Programming, Python, Software Architecture, C++ Programming, API Development, AI Development, and Machine Learning, based in Perth, Australia. I am passionate about taking on your project for an Automated Algorithmic Day Trading Platform. I understand the need for an automated trading platform that can handle various trading styles and integrate with your broker's API efficiently. I would approach this project by developing a modular architecture that allows for easy scalability and integration with your NVIDIA DGX spark for advanced computing needs. Let's discuss further details and kickstart this project. Cheers, Vishal Maharaj

@billybryan98

Hello Client, I hope you’re well. I’ve built automated trading systems that ingest live market data, run real-time analyses, and execute orders via broker APIs with low latency. My focus is a modular, Python-based platform that you can run on a US-based VPS, ready for Stage 1 delivery and future DGX Spark integration. I’ll design a robust API connection module to the Charles Schwab API, a Market Data module for streaming/incremental data, a lean Trading Engine to execute the 100 EMA strategy on RTH, and a comprehensive Reporting/Logging module. The architecture will be cleanly separated so Stage 2 can swap in your own signals and local AI workloads, while keeping Stage 1 fully documented and operable on a VPS with clear deployment steps and user manuals. I will also provide risk controls and monitoring hooks to safeguard live trading and enable quick iteration. This plan aligns with your two-stage long-term approach and leverages your NVIDIA DGX Spark when you’re ready to scale locally. Best regards, Billy Bryan