Python Ultra-Low-Latency Trading Engine

@ZohaibRoy

⭐⭐⭐⭐⭐ Build a High-Performance Trading Engine for Low-Latency Execution ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project details and see you're looking for a developer to create a high-performance trading engine. Look no further; Zohaib is here to help you! My team has successfully completed over 50 projects in low-latency systems. I will use advanced techniques to reduce latency and ensure compliance while delivering within your budget. ➡️ Why Me? I have 5 years of experience in developing low-latency systems using Python, specializing in concurrency, shared memory, and performance optimization. My expertise includes Linux system programming and real-time data streaming. I also have a strong grip on technologies like React for creating responsive dashboards. ➡️ Let's have a quick chat to discuss your project in detail and I can show you samples of my previous work. Looking forward to discussing this with you! ➡️ Skills & Experience: ✅ Python 3 (Advanced Concurrency) ✅ Low-Latency System Design ✅ Linux System Programming ✅ Multithreading & Multiprocessing ✅ Memory Management & IPC ✅ Financial Market Microstructure ✅ WebSocket & Real-Time Streaming ✅ React.js (Performance-Optimized UI) ✅ Performance Profiling & Benchmarking ✅ Cloud Deployment (Docker, Google Cloud) ✅ Real-Time Observability Dashboards ✅ Order Execution Logic Management Waiting for your response! Best Regards, Zohaib

@irsolutions

I possess advanced expertise in Python development, specifically in building ultra-low-latency trading engines like the one you require. I understand the need for high-performance execution strategies in a challenging tax environment, where minimizing latency is critical for regulatory compliance. In my extensive experience in fintech projects, I have successfully developed trading solutions that prioritize speed and system optimization. My knowledge of Python 3, CPU core pinning on Linux, and shared memory IPC align perfectly with your technical requirements. I have also implemented performance-optimized React dashboards for real-time monitoring and control, ensuring a seamless user experience. I am confident that my skills in low-latency system design, Linux system programming, multithreading, and financial market microstructure will enable me to deliver a robust trading engine that meets your expectations. If you are looking for a developer who can bring your vision to life while ensuring top-notch performance and reliability, I am here to help. Feel free to reach out to discuss your project further.

@OutsourceMan

⭐⭐⭐⭐⭐ Valuable Client, Your architecture demands true system-level engineering. CnELIndia, led by Raman Ladhani, can deliver a production-grade engine using Python 3.13t (free-threaded) with a multi-process, multi-core pinned architecture on Linux. Implementation Plan: • Dedicated processes: Sentinel, Sonar, Oracle, Execution Engine • CPU core pinning via os.sched_setaffinity with isolated cores and disabled scheduler migration • Lock-free ring buffer over multiprocessing.shared_memory (mmap-backed) for sub-ms IPC • Zero-allocation message structs (preallocated memory pools) • Latency tracking via time.perf_counter_ns() with histogram aggregation streamed over WebSocket to React dashboard • Dockerized Ubuntu deployment (separate engine, monitor service, React frontend) on Google Cloud Dashboard isolation: Monitoring microservice reads shared memory and publishes via WebSocket—no direct broker linkage. Direct Answers: • Yes, extensive shared_memory and mmap IPC experience • Yes, implemented Linux CPU core pinning & scheduler tuning • Latency measured per-module + end-to-end using perf_counter_ns and ring-buffer timestamps • Dashboard isolated via separate process + shared memory bridge to avoid engine interference We specialize in low-latency, concurrency-heavy financial systems and microstructure-aware execution engines.

@bindmission

Hello, I have 10 years of experience in developing high-performance trading systems and optimizing low-latency applications. I propose to build your Python Ultra-Low-Latency Trading Engine using Python 3.13 Free-Threaded build, ensuring minimal delay and optimal resource management. My expertise in multi-core CPU architecture and shared memory communication will ensure that the engine meets the stringent requirements. I am skilled in designing real-time observability dashboards using React and WebSocket, providing real-time monitoring and control. I am committed to delivering a system that is both high-performing and compliant with financial regulations. Regards, VishnuLal NB

@Milvetti

Hi, we can help you with your low-latency Python 3.13t trading engine & React dashboard. We offer lifetime bug fix guarantee. As Milvetti, we help traders automate their strategies. Price is an estimate and may vary by scope.

@quan9824

With over 8 years of experience, I not only possess the requisite advanced Python skills, but also an in-depth knowledge of concurrency, multiprocessing, and advanced system-level engineering for low-latency systems. I am well-versed in code optimization and understand the significance of minimizing latency to ensure effective signal generation and order execution. My proficiency in using shared memory communication through multiprocessing.shared_memory and lock-free ring buffer architecture makes me a prime candidate for this project's need for sub-millisecond internal communication. My expertise extends beyond Python. I have a solid understanding of Linux system programming and have actively engaged with tasks relating to CPU affinity, process scheduling, and system performance tuning. This project's demand for linux system behaviour will be met resolutely by my current skillset. Lastly, as emphasized in your project description; adherence to tight deadlines while ensuring quality work is paramount in my work ethic. My strong problem-solving skills and knack for debugging will guarantee any obstacles that are encountered during the project are swiftly resolved without compromising project timeline. To reiterate what my clients before you have appreciated about me: 100% job completion rate, 100% on-time delivery, and best-reviewed projects are just some of my merits that make me best suited for your unique needs.

@engmalaa

With over a decade of experience in software and automation, my team at Avalon AI is uniquely positioned to tackle the complex task of building your Python Ultra-Low-Latency Trading Engine. We are well-versed in the intricacies of Python 3, especially advanced concurrency and multiprocessing techniques. In fact, we have an in-depth understanding of the Global Interpreter Lock (GIL) and can provide solutions using free-threaded builds and shared memory architectures that eliminate any potential for GIL contentions. I bring a comprehensive background in low-latency system design and Linux system programming to minimize latency between signal generation and order execution, ensuring compliance with Order-to-Trade Ratio constraints. My knowledge of CPU affinity, process scheduling, performance tuning, and memory management will play a crucial role in designing multi-core optimized applications for your trading engine. Further differentiating us from the pack is our proficiency in multithreading & multiprocessing architecture – a skill explicitly called out in your requirements. Additionally, I have hands-on experience with deploying sophisticated applications in a Linux-based environment using Docker containers- ideal for your desired setup. Ultimately, choosing Avalon AI means you're getting an experienced and dedicated team who prioritize clean, scalable solutions- hallmarks that will undoubtedly be reflected in your powerful, reliable trading engine.

@GameOfWords

Python Ultra-Low-Latency Trading Engine 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.

@salesapple

This is a systems engineering problem, not a trading script. If latency discipline is not designed at architecture level, no amount of micro-optimizing Python code will fix it. I understand that you are targeting deterministic behavior under Linux with strict control over scheduling, memory and inter-process communication. I have worked with Python multiprocessing, shared memory and performance-sensitive backend systems on Linux. I understand the difference between GIL-bound builds and free-threaded execution, and why CPU affinity and cache locality matter in sub-millisecond systems. The architecture should isolate signal computation, risk control and execution into pinned processes communicating through shared memory or memory-mapped ring buffers, not standard queues. Answers to your filters: Yes, I have worked with shared memory and mmap for inter-process data exchange. Yes, I have used CPU affinity and understand Linux scheduler behavior. Internal latency should be measured using perf_counter_ns at each boundary and logged into histogram buckets, not just averages. The dashboard must be isolated through a monitoring microservice reading shared memory, never touching the execution loop or broker API directly. One question, are you targeting a specific exchange or broker API that imposes hard OTR thresholds we must model in the Sentinel layer? Regards Verma

@Shrutibimpl

Hello, I have a few quires regarding the high-performance trading engine. 1) Which specific broker API will the execution engine connect to? 2) Do you have a preferred Linux distribution or kernel version for the deployment? 3) What is the expected peak messages-per-second rate for the market data feed? I will develop the core trading system using the free-threaded Python build to achieve true multi-core parallelism and eliminate execution bottlenecks. I will implement CPU core pinning and shared memory buffers to ensure sub-millisecond communication between the risk, signal, and order modules. The execution logic will prioritize passive orders while strictly monitoring regulatory constraints like the order-to-trade ratio. I will also build the real-time React dashboard with high-performance rendering to provide a live cockpit for monitoring system latency and market entropy. I have developed similar project in the past. Thanks, Bharat

@Azursol

Hello,! I’m excited about the opportunity to help with your project. Based on your requirements, I believe my expertise in Python aligns perfectly with your needs. How I Will Build It: I will approach your project with a structured, goal-oriented method. Using my experience in Python, Linux, Algorithm, Software Architecture, Metatrader, Trading, Financial Software Development, API Development, HFT, Bot Development, I’ll deliver a solution that not only meets your expectations but is also scalable, efficient, and cleanly coded. I ensure seamless integration, full responsiveness, and a strong focus on performance and user experience. Why Choose Me: - 10 years of experience delivering high-quality web and software projects - Deep understanding of Python and related technologies - Strong communication and collaboration skills - A proven track record — check out my freelancer portfolio. - I’m available for a call to discuss your project in more detail - Committed to delivering results on time, every time Availability: I can start immediately and complete this task within the expected timeframe. Looking forward to working with you! Best regards, Ali Zahid India

@zu02

Hello, I specialize in Python 3.13t concurrency and microstructure-based trading systems. My recent work (AEGISVAULT OMEGA-ONE) already implements free-threaded architecture, lock-free IPC, OTR-safe execution, and predictive risk gating. This aligns directly with your requirements for a low-latency trading engine. Key strengths: • Python 3.13t free-threaded, process-first design • CPU core pinning and Linux system-level optimization • Shared memory IPC (multiprocessing.shared_memory, mmap, lock-free buffers) • Market microstructure features (WOBI, liquidity imbalance, passive execution) • OTR compliance with Execution Budget Governor logic • Real-time observability dashboards with WebSocket streaming Proposed milestones: Day 1–2: Engine skeleton + CPU affinity setup ($300) Day 3–4: Shared memory IPC + latency measurement ($350) Day 5: Execution logic + OTR compliance ($250) Day 6–7: React dashboard + monitoring microservice ($225) Total: $1,125 USD Timeline: 7 days All work secured via Freelancer milestones. Best regards, Zurab Esartia

@vinod00k

Hi, This is clearly a serious low-latency system, not a retail bot — and that’s exactly the kind of engineering I work on. I’ve built multi-process Python systems using shared memory (mmap / multiprocessing.shared_memory), lock-free patterns, and Linux CPU affinity (os.sched_setaffinity) to reduce jitter and core migration. I understand why Python 3.13 free-threaded is required and how to structure modules across pinned cores. Quick answers: Shared memory / mmap? Yes — implemented ring-buffer style IPC for high-speed process communication. CPU core pinning? Yes — isolated processes per core to avoid context switching and cache invalidation. Latency measurement? time.perf_counter_ns() at module boundaries + histogram logging. Dashboard isolation? Separate monitoring service reading shared memory; WebSocket streaming only — no direct engine interaction. Comfortable with Dockerized Linux deployment and a performance-optimized React (Canvas-based) dashboard. If you're targeting sub-ms deterministic behavior, I’d be glad to discuss further.

@AlexDeveloper911

Hello, We would like to grab this opportunity and will work till you get 100% satisfied with our work. We are an expert team which have many years of experience on Python, Linux, Algorithm, Software Architecture, Metatrader, Trading, Financial Software Development, API Development, HFT, Bot Development Please come over chat and discuss your requirement in a detailed way. Thank You

@prashanpatel557

Hello, This is a true systems-engineering problem, not a retail bot, and I can architect it correctly under the constraints you’ve outlined. Backend Architecture (Python 3.13t Free-Threaded) Process Model • Separate processes: • Sentinel (Risk / OTR) • Sonar (Regime / entropy) • Oracle (Signal engine) • Execution Engine • Dedicated core pinning via os.sched_setaffinity • No OS migration (isolcpus optional at kernel level) Latency Measurement • time.perf_counter_ns() at: • Signal generation • Execution dispatch • Broker ACK • Histogram + percentile logging (p50/p95/p99) • Shared memory telemetry stream React Monitoring Cockpit • WebSocket telemetry stream • Canvas/WebGL rendering (no heavy SVG) • Independent monitoring microservice • Kill-switch writes atomic flag to shared memory • Engine polls shared memory — zero broker coupling Answers to Screening Questions Shared memory experience? Yes — lock-free buffers using shared_memory + struct packing. CPU pinning? Yes — sched_setaffinity, NUMA awareness, cache locality considerations. Latency measurement? Nanosecond timestamps + delta propagation across modules. Dashboard isolation? Separate process + shared memory read-only telemetry; no engine RPC calls. This will be engineered as a deterministic, production-grade low-latency system. Ready to discuss expected latency and exchange connectivity layer.

@Algokage

Hello, I can deliver a high-performance, ultra-low-latency trading engine tailored to your microstructure-based execution strategies in a high-tax environment. I’ll leverage Python 3.13t, multi-core CPU pinning, shared memory communication, and real-time observability to minimize latency while ensuring regulatory compliance. My approach includes optimizing concurrency, memory management, and Linux system behavior, with a React dashboard for real-time monitoring. With 5+ years of experience in low-latency systems, financial market microstructure, and cloud deployment, I’ll ensure a robust, production-ready solution. Message me for samples or to discuss further. Thanks, Adegoke. M

@Cmonosoft

Hi there, I propose to build a high performance trading engine engineered specifically for microstructure based execution in high STT environments. This will not be a retail automation script but a system level architecture focused on deterministic latency, concurrency isolation, and regulatory discipline. First check my skills - Python 3 advanced concurrency and shared memory Low latency system design Linux system programming and CPU affinity Multiprocessing architecture Memory mapped IPC Market microstructure WebSocket streaming React performance UI Latency profiling Cloud deployment with Docker The engine will run on Python 3.13 free threaded build to eliminate Global Interpreter Lock contention and enable true parallel execution. Each core module including Sentinel, Sonar, Oracle, and Execution Engine will be pinned to dedicated CPU cores using Linux affinity controls to prevent migration and reduce cache invalidation. Let's schedule a call to discuss in detail. Thanks Saurabh

@SmartCodeDev

This is a serious system — and that’s exactly the kind of work I like. I’ve worked with shared memory, mmap-style IPC, multiprocessing, and Linux-level performance tuning before. Using Python 3.13 free-threaded makes sense here, and I’m comfortable with CPU core pinning via os.sched_setaffinity to avoid migration and cache misses. I agree that standard queues and GIL-bound builds aren’t acceptable in a low-latency setup like this. For IPC, I’d use shared memory with a lock-free ring buffer design, and measure latency using time.perf_counter_ns() at each module boundary with histogram logging. The dashboard would run completely isolated through a monitoring service so it never impacts execution timing. I understand order book microstructure, passive execution logic, OTR constraints, and controlled cancel strategies. Dockerized Linux deployment on GCP is fine. If you’re building this properly — not as a retail bot — I’m very interested.

@rahulb058

Hi, This is not a “bot project.” This is a systems engineering project disguised as trading software — and that distinction matters. You’re building a latency-sensitive, multi-process, core-pinned execution engine operating under OTR constraints in a high-STT environment. That requires OS-level discipline, deterministic scheduling, and strict separation between trading logic and observability layers. I’ll address your technical filters directly.