Python Ultra-Low-Latency Trading Engine - Alpha

@ZohaibRoy

⭐⭐⭐⭐⭐ Build a High-Performance Trading Engine with Low Latency ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project requirements and see you're looking for a developer to create a high-performance trading engine. You don't need to search further; Zohaib is here to help you! My team has completed 50+ similar projects for trading systems. I will utilize advanced techniques in Python and Linux to minimize latency and ensure efficient execution. ➡️ Why Me? I can easily build your trading engine as I have 5 years of experience in Python development, specializing in low-latency systems, multi-core architecture, and real-time dashboards. My expertise includes shared memory communication and performance optimization. I have a strong grip on Linux system behavior and concurrency architecture. ➡️ Let's have a quick chat to discuss your project in detail. ➡️ Skills & Experience: ✅ Python 3 (Concurrency & Multiprocessing) ✅ Low-Latency System Design ✅ Linux System Programming ✅ Multithreading & Multiprocessing ✅ Memory Management & Shared Memory IPC ✅ Financial Market Microstructure Knowledge ✅ WebSocket & Real-Time Streaming ✅ React.js (Performance-Optimized UI) ✅ Performance Profiling & Benchmarking ✅ Cloud Deployment (Google Cloud/Docker) ✅ Real-Time Data Rendering ✅ Order Execution Logic Waiting for your response! Best Regards, Zohaib

@irsolutions

I have over a decade of experience in web and mobile development, with a strong focus on high-performance and low-latency solutions. I understand the need for a Python Ultra-Low-Latency Trading Engine - Alpha and the challenges that come with building such a system in a high-tax environment. My expertise in Python, multithreading, and system-level engineering makes me the ideal candidate for your project. One strategic insight for approaching your requirements is to leverage shared memory communication and CPU core pinning for optimal performance. I have successfully implemented similar strategies in past projects, including complex eCommerce platforms and high-traffic applications, demonstrating my ability to handle the complexity of your task. I encourage you to reach out to discuss the roadmap for your project further. I am confident that my skills and experience are aligned with your technical needs, and I am excited about the opportunity to work on this cutting-edge project.

@Milvetti

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

@iosifpeterfi

I'm Iosif Peterfi, 15+ years delivering resilient systems with a practical, no-nonsense focus. This is my speciality: designing high-performance, regulated latency platforms that run on multi-core hardware, with precise CPU pinning, shared memory IPC, and real-time observability to keep latency predictable and compliant. You're building a high-performance trading engine for a regulated, high-tax environment. The goal is to minimize latency from signal to order, enforce Order-to-Trade Ratio controls, and support 200 ms cancel logic, all while staying compliant. You want Python 3.13t, multi-core pinning, lock-free IPC, end-to-end latency measurement, a real-time dashboard, and separate processes for the engine, monitoring, and the frontend, deployed on Linux with Docker and Google Cloud. I'll deliver a focused, PM-friendly plan that reduces risk and accelerates value. Deliverables include a pinned-core engine skeleton with modules for risk (Sentinel), regime detection (Sonar), signal calculation (Oracle), and execution, an IPC layer with shared memory and lock-free buffers, a real-time monitoring service, and a React cockpit that renders RTT, CPU load, WOBI heatmaps, and OTR status. You'll get a global kill switch, deployment runbooks, and security hardening to protect regulatory posture. Milestones target environment setup, IPC integration, latency instrumentation, and production readiness within two to four weeks.

@salmanullahbaig

Your project caught my attention right away. Building an "Ultra-Low-Latency Trading Engine" using Python, Let me know if you'd like to jump on a quick call to go over the details.

@KarthikTechCon

Bid Proposal (Freelancer.com Format) Hello, I’m Karthik with 15+ yrs experience in high-performance systems & low-latency architectures. Your requirement fits well with my expertise in concurrency, Linux tuning, and real-time pipelines. I’ll build the engine using Python 3.13t (free-threaded) with: • CPU core pinning (sched_setaffinity) for all modules • Lock-free IPC via shared_memory / mmap ring buffers • Sub-ms latency optimization + perf_counter_ns tracking • OTR-compliant execution (limit orders, cancel logic) Frontend: React + WebSocket dashboard (Canvas/WebGL) fully decoupled via monitoring service to avoid impact on engine. Deployment: Dockerized, Linux (Ubuntu), GCP-ready. Answers: ✔ Shared memory/mmap – Yes ✔ CPU pinning – Yes ✔ Latency – perf_counter_ns + histograms ✔ Isolation – separate processes + async streaming Can deliver a robust, production-ready prototype. Warm Regards, Karthik B Resonite Tech

@Zawiya3

With over a decade of experience in Python development, my expertise goes beyond basic retail trading bots. My understanding of low-level performance optimization, concurrency architecture, and Linux systems is second to none. This, combined with my proficiency in using the Python 3.13t build to avoid GIL contention delays, aligns well with your project's demand for multi-threading and multiprocessing architectures that eliminate unpredictable latency spikes. Notably, as an accomplished data scientist, I can implement weighted order book imbalance (WOBI) heatmaps, manage liquidity imbalance efficiently while maintaining order-to-trade ratio (OTR), and deploy controlled order flooding logic that complies with regulatory standards on behalf of your project. Furthermore, leveraging my capabilities in cloud architecture and designing Google Cloud compatible solutions will ensure a seamless deployment process within the Linux-based environment you prefer.

@sk10327sssk

Hi, This is exactly the kind of system I build — not retail bots, but low-latency trading infrastructure and execution engines. I’m a Quant Developer & Senior Tech Solution Architect with 7+ years of experience designing high-performance trading systems across global markets including National Stock Exchange of India (NSE), Bombay Stock Exchange (BSE), Singapore Exchange (SGX), Dubai Financial Market (DFM), and US/Canada markets. I’ve built multiple execution engines, market data pipelines, and microstructure-based strategies, including systems handling real-time order book imbalance, latency-sensitive execution, and broker-compliant order flow. Your requirements — shared memory IPC, CPU pinning, free-threaded Python, latency instrumentation, and execution control loops — align strongly with my current work. I’ve also developed strategies using Pine Script and Amibroker for signal generation and backtesting layers. I understand this is not a basic bot and requires strict control over latency, concurrency, and system behavior — exactly where I operate. You can review more at: easyalgo_in or my profiles Let’s connect — I can design this system cleanly, production-grade, and aligned with your latency constraints.

@rbtech1984

Hello There!!! ★★★★ ( Build ultra low latency multi-core trading engine with real-time observability ) ★★★★ I’ve carefully read your requirement, this is a high-performance trading engine using Python 3.13t with core pinning, shared memory IPC, and sub-ms latency monitoring, plus a React dashboard for real-time insights. ⚜ Python 3.13t concurrent engine design ⚜ CPU affinity & multi-core pinning ⚜ Shared memory / mmap IPC setup ⚜ Lock-free ring buffer comms ⚜ Latency tracking (ns precision) ⚜ WebSocket real-time dashboard ⚜ Docker + Linux deployment I’ve worked on low latency systems, multiprocessing arch and realtime pipelines. Familiar with Linux tuning and perf profiling, enjoy solving deep system challanges. Yes, used shared memory & mmap. Yes, CPU pinning via sched_setaffinity. Latency via perf_counter_ns + histograms. Dashboard isolated via microservice + WS. Let’s discuss deeper, this is exciting project. Warm Regards, Farhin B.

@Cmonosoft

Hello there, We are ready to sign your NDA if require. We are a Registered company and a bunch of full stack developer with over 10 years of experience. We have experience building high-performance Python systems using multiprocessing, shared memory, and Linux-level optimization to reduce scheduling overhead and latency spikes. For the trading engine, We will implement a multi-process architecture using Python 3.13 free-threaded build and isolate critical modules (Sentinel, Sonar, Oracle, Execution) on dedicated CPU cores using Linux CPU affinity. Communication between modules will use multiprocessing.shared_memory and memory-mapped buffers with a lock-free ring buffer design to ensure sub-millisecond message passing. My Answers - 1. Yes, We have implemented shared memory communication using Python shared_memory and mmap for low-latency data pipelines. 2. Yes, We have used Linux CPU affinity (sched_setaffinity) to pin workloads to specific cores for deterministic performance. 3. Internal latency is measured using nanosecond timers (perf_counter_ns) with module-level tracing and histogram logging. 4. The dashboard will communicate only with a monitoring service via WebSockets so UI activity never impacts the trading engine. Let's initiate a call to discuss in detail. Thanks Saurabh

@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.

@rohan3188

A high-frequency trading engine built for speed. Not a retail bot. Real infrastructure. Python 3.13 free-threaded. No GIL. True parallelism across multiple cores. Each module pinned to its own CPU core. Sentinel on core 2. Sonar on core 3. Oracle on core 4. Execution on core 5. No OS migration. No context switching. Predictable latency. Communication through shared memory and lock-free ring buffers. No Python queues. No locking overhead. Sub-millisecond signal to execution. Latency measured in nanoseconds with perf_counter_ns. Histograms stream live to your dashboard. The React dashboard monitors everything. Real-time RTT graphs. CPU usage per core. Market regime. WOBI heatmap. NEV. Fill rates. OTR status. One global kill switch. Click it. Engine stops. No broker API calls from the dashboard. Safe. Deployed on Linux. Dockerized. Google Cloud ready. I've done this before. Shared memory. CPU pinning. Microsecond profiling. Financial microstructure. Let's build something that actually performs.

@danielboggs

Hi, I’m Daniel. I read your project carefully and understand that this is not a retail bot. You need a low-latency trading engine for microstructure-based execution in a high STT environment, built on Python 3.13 free-threaded, with pinned CPU cores, shared memory IPC, sub-millisecond internal communication, strict OTR control, passive order logic, and a React monitoring cockpit that stays fully isolated from broker execution. I can build this with a process-separated Linux architecture where Sentinel, Sonar, Oracle, and Execution run on dedicated cores using sched_setaffinity, while shared memory and lock-free ring buffers handle internal data flow with minimal overhead. I have worked with shared memory and mmap patterns, and I would measure latency with perf_counter_ns at every module boundary, track RTT and histograms, and stream only monitoring data to the dashboard through a separate service so UI activity never affects engine performance. The dashboard will focus on fast Canvas-based observability, live WebSocket metrics, and a true kill switch through shared memory signaling, not direct broker access. I can communicate in real time in your time zone and provide a simple demo or core engine module within 12 hours of starting. Which broker or execution API will this engine connect to in the first release? Do you already have the signal model and market data feed defined, or should I design that layer too? Best regards, Daniel

@aadips

Building a sub-microsecond order execution engine in a high-STT environment where Order-to-Trade Ratio compliance is non-negotiable requires multi-core CPU pinning, shared memory IPC, and kernel-bypass networking rather than a standard Python async framework. Our approach: architect a C-extension Python core using ctypes or Cython for the hot path, pin threads to isolated CPU cores using taskset and cpuset cgroups on Linux, use POSIX shared memory or mmap for zero-copy signal propagation between the signal generator and executor, and implement the broker API layer (Zerodha Kite, Dhan, or Upstox) with non-blocking I/O. The observability dashboard would sit separately, reading from the shared memory ring buffer without touching the latency-critical path. OTR compliance would be enforced via pre-dispatch rule checks before any order hits the exchange. We have built production-grade quantitative systems in Python with Linux-level performance optimization. Webneco carries a 4.9 rating from 118 clients and 97% on-budget delivery. Which broker API and exchange connectivity are you targeting initially: NSE co-location, standard internet, or both?

@SkillCrafts

I’ve specialized in optimizing Python for high-frequency environments where every microsecond determines slippage and execution quality. Having developed custom execution layers for HFT setups, I understand the balance between Python’s development speed and the raw performance required for competitive order routing. My focus is on eliminating bottlenecks in the "hot path" to ensure your engine maintains sub-millisecond consistency even during peak market volatility. To achieve ultra-low latency in Python, I will implement a multi-process architecture using shared memory to bypass the Global Interpreter Lock and minimize IPC overhead. We will utilize Cython for performance-critical components—like order book reconstruction—while leveraging `uvloop` for asynchronous I/O to handle market data feeds with minimal jitter. I’ll also apply Numba for JIT compilation of math models and optimize the networking stack using `socket` level tweaks like TCP_NODELAY and SO_BUSY_POLL to minimize kernel transitions. This ensures that while the core logic remains maintainable, the execution speed competes with native C++ implementations for your alpha release. Are we targeting a specific FIX/FAST protocol or a binary WebSocket feed, and what is your current target tick-to-trade latency for this alpha? I’m also curious if you plan on deploying in a co-located environment to better tailor the networking stack and interrupt handling. I am available for a brief sync to discuss the technical roadmap or review your current architecture—ready to help you hit those benchmarks.

@jaroslavcaprata

Hi, there. I will build your ultra-low-latency engine using Python 3.13t with shared memory IPC, lock-free buffers, and CPU core pinning to achieve sub-millisecond communication and execution latency under 1ms. I will design multi-process architecture with isolated modules, optimized scheduling, and precise latency tracking using perf_counter_ns for real-time observability. I have delivered 5+ high-performance systems with measurable latency optimization. I will implement memory-efficient pipelines, avoiding GIL bottlenecks and reducing processing overhead by 30% while ensuring stable multi-core utilization. I will build a React dashboard with WebSocket streaming, ensuring real-time updates under 100ms without impacting engine performance. Strict separation will guarantee zero interference between UI and execution. I will deploy a Dockerized, Linux-based system with monitoring services, secure controls, and kill switch logic for instant shutdown. I will ensure compliance, stability, and full documentation for production readiness. If this sounds good, connect in chat and we can start. Thanks, Jaroslav Caprata

@sonika1419

**DO NOT PAY ME UNTIL I COMPLETE! :)** Hello my valuable client :) My profile is new over here but I have 7 years of experience in this field. I have completely understood about your project. Also I will provide you free maintenance on your project for 1 year after project completion. I can definitely complete this in your timeframe. Give me one chance to prove myself. Hit the chat button to get started. If you will not like my work then you dont need to pay me any money so dont worry and have faith in me :) I am eagerly waiting for your message.

@Hemantt841

Achieving ultra-low-latency execution in a microstructure-sensitive, high-STT trading environment demands meticulous control over system-level behavior and concurrency architecture. The challenge lies in eliminating unpredictable latency spikes by leveraging multi-core CPU pinning, lock-free shared memory communication, and precise latency measurement—all while ensuring compliance with stringent Order-to-Trade Ratio constraints. This project’s emphasis on a Python 3.13 free-threaded build to bypass GIL limitations highlights the need for deep expertise in modern Python concurrency and Linux system internals. Delivering this trading engine involves architecting isolated modules pinned to dedicated CPU cores, implementing lock-free ring buffers via multiprocessing.shared_memory, and capturing nanosecond-level latency metrics with time.perf_counter_ns(). The real-time React dashboard will be designed for high-performance rendering using Canvas/WebGL, streaming live operational telemetry over WebSocket without imposing overhead on the trading engine. The system’s modular design ensures emergency kill-switch responsiveness and regulatory adherence, while Dockerized deployment on Ubuntu within Google Cloud guarantees scalable, production-grade reliability. Commitment to precision and performance will guide every stage—from rigorous latency profiling and kernel-level tuning to seamless multi-process orchestration. Clear communication and iterative validation will ensure the solution meets your exacting standards, delivering a robust, observability-rich trading platform that transforms market signals into compliant, lightning-fast order execution. Let’s connect to discuss your priorities and align on a roadmap to realize this high-stakes vision.

@Hrolgar

To answer your screening questions upfront: 1. Yes — I've used multiprocessing.shared_memory and mmap for IPC in production systems. Lock-free ring buffers over shared memory for sub-ms comms between processes. 2. Yes — os.sched_setaffinity for core pinning on Linux. Combined with isolcpus kernel param to keep the OS scheduler off the trading cores entirely. 3. time.perf_counter_ns() at every module boundary, streamed into a latency histogram. I also profile with perf and py-spy to catch hidden stalls. 4. Dashboard connects to a monitoring microservice over WebSocket — never touches the engine or broker directly. Shared memory flag for the kill switch, one-directional write. I've built multi-process Python systems with this exact architecture — pinned cores, shared memory IPC, real-time monitoring dashboards. I'm also comfortable with Python 3.13t's free-threaded mode. For the React dashboard I'd go Vite + Canvas-based rendering for the heatmaps and RTT graphs. WebGL if the data density demands it. No heavy charting libs — just raw performance. The engine modules (Sentinel, Sonar, Oracle, Execution) each get their own pinned core with ring buffer channels between them. Docker Compose for deployment, separate containers for engine, monitor, and frontend. Happy to walk through the architecture in detail — when works for a call?