Python Ultra-Low-Latency Trading Engine - Alpha - 2

@Milvetti

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

@ashinnd84

Hello there, we are a team of Full Stack Developers and interested in your project. Please, send me the project complete details to start the work. Thanks Ashish Kumar.

@Web1devloper

As an experienced Python developer with a strong background in low-latency systems and Linux system programming, I am well-equipped to tackle the unique challenges of your ultra-low-latency trading engine project. Over my eight-year career in data analytics and science, I've developed a deep understanding of CPU architecture control, shared memory communication, and multi-core CPU optimization - all key skills for building an efficient trading engine. Crucially, I have a proficiency in Python 3 that includes an advanced understanding of concurrency and multiprocessing. This makes me fluent in designing multi-core optimized applications and employing low-level performance optimization techniques for minimizing latency. Furthermore, my knowledge of GIL contention and experience with free-threaded builds such as the required Python 3.13t makes me the ideal candidate to mitigate potential delay issues. Apart from my technical expertise, I am well-versed in creating effective data visualizations and dashboards using tools like Power BI and Tableau. This aligns perfectly with your requirement for a real-time monitoring and control cockpit built on React - a stack I am proficient in utilizing, including WebSocket streaming and lightweight charting using Canvas or WebGL-based technologies.

@technologiesit

As an experienced Full Stack Developer with more than a decade in the industry, I understand that your project requires a unique set of skills, specifically tailored towards high-performance, low-latency trading engines. My broad skill set has allowed me to gain valuable insights into advanced system-level engineering, Linux system programming, and memory management and sharing - all of which are pivotal for your project's success. At the frontend level, I'm familiar with the React stack and have worked with WebSocket streaming and lightweight charting (Canvas/WebGL) providing benchmark performance. My thorough understanding of the importance of high-performance rendering using Canvas rather than SVG dovetails seamlessly with your needs in terms of the Oracle Panel specifically. To top things off, I have hands-on experience working in Linux-based environments including Ubuntu and am comfortable with Dockerized setups. I strive to ensure efficient and secure solutions that can be scaled easily. Moreover, being compatible with Google Cloud will allow us to ensure optimal hosting for enhanced performance.

@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.

@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 I can deliver any project in Trading. Readymade setups for Python available

@dishirajput7

Building a high-performance, ultra-low-latency trading engine like the one you described is my wheelhouse. As a skilled Python developer with a deep understanding of concurrency and multiprocessing, I'm well-versed with techniques to circumvent the Global Interpreter Lock (GIL) and ensure true parallelism. This allows for the sub-millisecond internal communication your project requires, eliminating unpredictable latency spikes. Furthermore, I have solid experience in low-latency system design, having successfully reduced microsecond-level bottlenecks in previous projects. I am also proficient in Linux system programming and comfortable with core level activities like CPU affinity, process scheduling, and performance tuning. With my expertise in memory management and shared memory, I am well-prepared to build communication channels using multiprocessing.shared_memory and lock-free ring buffer architecture as per your requirements. My knowledge of time.perf_counter_ns() will be instrumental in providing accurate latency measurements throughout your system for determining round-trip times (RTT), processing times, and creating detailed latency histograms.

@ChadMatthee

I bring the precise expertise needed for this project. Your need for a clean, high-performance, low-latency trading engine with multi-core CPU architecture control and true parallelism via Python 3.13 Free-Threaded build aligns perfectly with my background. I specialize in advanced Python concurrency, Linux system programming including CPU core pinning, and shared memory IPC using multiprocessing.shared_memory and lock-free ring buffers. My experience extends to building real-time observability dashboards with React and WebSocket integration, designed for minimal UI impact on backend processes. I’m well-versed in performance profiling to meet stringent regulatory requirements like Order-to-Trade Ratio constraints. I would like to discuss your project in further detail. Best Regards, Chad Matthee

@himanshus642

Hi — this isn’t a typical trading bot; it’s a low-latency execution system where architecture decisions directly impact performance. I’ve built high-performance backend systems with concurrency, shared memory, and real-time processing, and I’m comfortable working at this level of optimization. How I’d approach your engine: * Python 3.13t (free-threaded): true parallel execution without GIL bottlenecks * Multi-core design: dedicated processes pinned to CPU cores to avoid context switching * IPC: shared memory + lock-free ring buffers for sub-ms communication (no queues) * Latency tracking: nanosecond precision using perf_counter_ns with histogram logging * Execution logic: passive orders, controlled cancel cycles (~200ms), OTR-aware flow Dashboard: React + WebSocket streaming with Canvas-based rendering for real-time monitoring (no performance-heavy UI). Isolation: Trading engine, monitoring service, and dashboard fully decoupled so UI never impacts execution. Answers: * Shared memory/mmap: Yes, for low-latency IPC systems * CPU pinning: Yes, using Linux affinity controls * Latency measurement: nanosecond timers + module-level tracking * Dashboard isolation: separate service + WebSocket bridge Timeline: staged build (engine → IPC → execution → dashboard) If you want, I can map the exact process architecture and memory layout before starting.

@littlejohnsantos

A Quantitative Crypto Trading System is designed to bring consistency and intelligence to trading decisions. It automatically identifies the current market condition, such as trending, ranging, or high-risk environments. Based on this classification, it adapts strategies and parameters in real time. This eliminates the need for fixed strategies that fail under changing market conditions. The system reduces emotional decision-making and human error. It operates using data-driven logic, indicators, and market behavior analysis. Traders benefit from improved timing, risk control, and execution. It can run continuously, monitoring opportunities without manual intervention. The system is scalable and can be delivered as a SaaS solution. Overall, it transforms uncertain trading into a structured and adaptive process.

@daremat

⭐️⭐️⭐️⭐️⭐️Hello! This sounds like an exciting and highly specialized project. I have extensive experience in building high-performance, low-latency systems, including multi-core architecture and real-time applications. In a similar project, I optimized a trading system where latency was reduced by over 30%, using Python's free-threaded build and memory-mapped buffers for inter-process communication. For your trading engine, I will ensure that the order-to-trade ratio is managed efficiently, latency is minimized, and your system adheres to regulatory requirements. Would you like me to start with a prototype latency measurement or focus first on setting up the core engine?

@markdennis19

Have you worked with Python shared memory or mmap before? - Yes, I have extensive experience using Python's multiprocessing.shared_memory and mmap for inter-process communication in low-latency systems. I have used these techniques to implement high-performance memory management and reduce overhead in multi-core applications. Have you implemented CPU core pinning on Linux? - Yes, I have implemented CPU core pinning using os.sched_setaffinity on Linux systems to ensure that critical application components are assigned to specific CPU cores, reducing context switching and improving overall performance in multi-core environments. How would you measure internal engine latency? - I would use time.perf_counter_ns() to measure latency at the millisecond and microsecond levels across various parts of the trading engine. This will allow precise tracking of end-to-end order execution time, module processing delays, and round-trip times, ensuring that each component is operating efficiently. - How would you prevent the dashboard from affecting trading engine performance? To prevent the dashboard from affecting the trading engine, I would ensure the frontend is asynchronous and runs in a separate process. Using technologies like WebSockets for real-time streaming ensures that the UI doesn't block or slow down the backend. Additionally, I would optimize data fetching and rendering for high performance, minimizing the impact on the trading engine’s operations.

@mohitmalviya1999

Novarsistech Pvt. Ltd., Indore Hello, We reviewed your requirement for a Python ultra-low-latency trading engine, and this aligns well with our expertise at Novarsistech Pvt. Ltd., Indore. Our team has strong experience in: Low-latency system design & performance optimization Multi-threading, concurrency & shared memory architecture High-performance Python (NumPy, asyncio, C-extensions) Linux system-level tuning & real-time execution systems Building scalable trading and analytics platforms We understand this is not a basic bot and requires microsecond-level optimization, robust architecture, and compliance handling, which we are fully capable of delivering. ? We can ensure: Optimized execution pipeline (signal → order) High-performance, scalable architecture Clean, maintainable, production-ready code Real-time monitoring/dashboard integration Let’s connect to discuss your architecture and execution goals in detail. Best Regards, Novarsistech Pvt. Ltd. Indore ? +91 9111720303

@arshkumar213

I Can build a low-latency trading engine using Python 3.13 (free-threaded) with shared memory IPC, CPU core pinning, and precise latency tracking. Includes React dashboard via WebSocket, kill switch, and Docker deployment. Focus on performance, stability, and clean scalable architecture. Ready to start.