Forex Statistical Arbitrage Algorithm Development

@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 Matlab, Python, and similar tools. 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

@Shrutibimpl

Hello, 1) Which specific forex broker API are you planning to use? 2) Do you have a preference for the programming language, such as Python or a compiled language like C++? 3) What is the maximum number of simultaneous currency pairs you want to monitor? I will build a stat-arb engine using Python and NumPy/Pandas for fast calculations. I will design a pipeline to pull live data for EUR/USD, GBP/USD, and other major pairs plus a way to get old data for testing. I will code the math to find mean-reversion and cointegration trades by checking for stable relationships between pairs and entering when they drift apart. The project includes a tester to show the Sharpe, drawdown, and win rate. I will also build in safety rules for trade size and stops to pause if things go south. I will give you a script to keep the bot running 24/5 on a VPS with alerts and logs. Thanks, Bharat

@mayankinnovative

Hi, As per my understanding: You want a fully automated stat-arb engine trading major FX pairs (EUR/USD, GBP/USD, etc.), exploiting short-term mean reversion and cointegration. It must cover tick/1s data ingestion, institutional-grade backtesting with optimisation, embedded risk controls, and low-latency broker execution—ensuring live results track backtests within tight drift tolerances. Implementation approach: I will architect a modular Python core (NumPy/Pandas + vectorised signal layer) with optional C++ execution bridge if latency profiling justifies it. Data pipeline will handle historical pulls and real-time streaming with time-sync validation. Strategy module will include cointegration testing (Johansen/ADF), z-score spreads, and rolling recalibration. Backtester will model slippage, transaction costs, trade distribution, Sharpe, max DD, and parameter sweeps. Risk engine will enforce dynamic sizing, volatility-adjusted stops, and circuit breakers. Deployment will run 24/5 on VPS with structured logging, rotation, and alerting. Forward-test reconciliation module will compare live vs expected P&L drift. A few quick questions: Which broker/API (FIX, REST, MT5)? Target timeframe (seconds/minutes)? VPS location preference? Capital allocation per pair?

@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.

@Web1devloper

As an experienced software developer with an extensive skill set in low-latency languages such as C++, NumPy and Pandas for algorithmic trading, I’m confident in my ability to design and implement a profitable forex statistical-arbitrage engine tailored to your needs. My technical capabilities are complemented by a nuanced understanding of the forex market, specifically major pairs like EUR/USD and GBP/USD. For over 8 years, I have adeptly created dynamic solutions that handle real-time data ingestion as well as furnish reliable back-testing frameworks reporting Sharpe ratios, drawdown metrics, win rates and trade distributions. My approach is not just delivering on the surface measures, but ensuring risk controls are also effectively baked into the code for position sizing, dynamic stop-loss management and circuit breakers to safeguard against excessive drawdowns. These parameters will ensure consistent profit potential while protecting the integrity of your accounts.

@kaustubhbawa123

Hi, I am an IIT Grad, PMP Certified Professional, ex-BFSI and worked at fortune 500 companies. I will make it a reality for you. As a Forex Statistical Arbitrage Algorithm, I will develop a mixed approach strategy using Python for high-level logic and NumPy/Pandas for data manipulation, while utilizing C++ for low-latency connections to your broker's API due to its performance capabilities. Kindly click on the chat button so we can discuss and get started. Will share you my prior projects done and my resume too. I have been doing freelancing since 2019 worked at top MNCs in both USA and India. Lets connect

@Hemantt841

I understand you require a fully automated statistical-arbitrage engine for major forex pairs that handles live tick data, back-testing with detailed metrics, and robust risk controls. Your focus on short-term mean-reversion and cointegration, along with deployment on a VPS with alerts, highlights the need for precision and reliability in both strategy logic and execution. With over 15 years of experience and 200+ projects completed, I specialize in Python development with strong expertise in NumPy, Pandas, and API integration, ensuring efficient data handling and low-latency execution. My background includes building financial algorithms that incorporate back-testing frameworks and risk management, tailored for forex and other markets. I will develop the strategy logic in Python, leveraging NumPy/Pandas for data processing and integrating directly with your broker’s API for real-time execution. The solution will include a comprehensive back-testing module with slippage modeling, parameter optimization, and risk controls embedded at the code level. Deployment scripts will automate running the bot on your VPS with log rotation and alerting. A clean, well-documented Git repository will be delivered within an agreed timeframe after initial testing phases. Let’s connect to discuss your goals and how I can help bring this forex stat-arb engine to life efficiently.

@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.

@marcioazambujo

Hi, there. I’m very interested in building your statistical-arbitrage engine for the major forex pairs, fully automated from signal generation to execution. I can implement the strategy in Python (or C++ for ultra-low-latency needs), using NumPy/Pandas for efficient calculations and integrating directly with your broker’s API. The system will handle live tick or one-second data ingestion and maintain a historical price pipeline for robust back-testing and parameter optimisation. The back-testing framework will report Sharpe ratio, max drawdown, win rate, trade distribution, and allow stress-testing with a realistic slippage model. Risk controls including position sizing, dynamic stop-losses, and circuit breakers will be built in, pausing the strategy automatically if drawdowns exceed limits. Deployment scripts will enable 24/5 operation on a VPS, with automatic log rotation and email/SMS alerts for key events. Deliverables will include fully-commented source code in a Git repository, a concise README, and verification that live forward-testing replicates at least 95 % of back-tested logic with minimal P&L drift. I hope to hear from you. Thank you.

@QuickMentor

Forex Statistical Arbitrage Algorithm Development I'm excited after reviewing your project details! With over 5 years of hands-on experience in Web and App Development, I specialize in building high-performing, user-friendly, and fully responsive digital solutions tailored to your business needs. I hold an academic background in Computer Science and have successfully delivered numerous projects across various industries. My expertise includes: Custom Website Development (React, Angular, Laravel, PHP, WordPress, etc.) Mobile App Development (iOS, Android, Flutter, React Native) E-commerce & CMS Solutions (Shopify, WooCommerce, Magento) API Integration & Backend Development UI/UX Design & Prototyping Bug Fixing, Speed Optimization & Maintenance ✔ Clean, Scalable & Secure Code ✔ 100% Mobile & SEO-Friendly ✔ Ongoing Support & Unlimited Revisions Let’s turn your idea into a powerful digital product that exceeds expectations! Check my profile: https://www.freelancer.com/u/QuickMentor Looking forward to working with you!

@dohng

Hi there, hope you’re doing well, I can build a fully automated FX statistical-arbitrage engine covering major pairs, from signal generation through execution. I’ve worked with mean-reversion and cointegration models, live tick/1-second data pipelines, and broker API integrations, primarily in Python (NumPy/Pandas) with the option to optimize latency-critical paths if needed. I’ll deliver a clean, modular system with historical + live data handling, a robust back-testing and optimisation framework (Sharpe, drawdown, win rate, slippage modelling), and embedded risk controls including position sizing, dynamic stops, and drawdown circuit breakers. The deployment will be VPS-ready with logging, alerts, and a clear README, and I’m comfortable validating forward-test behaviour against back-test logic on a demo account. Happy to discuss broker API, data granularity, and modelling assumptions, and share relevant stat-arb experience during the next step.

@Robertx2002

Your focus on low-latency stat-arb for major forex pairs aligns with my extensive experience in building automated trading engines. I propose a modular Python core using NumPy/Pandas for rapid development, augmented with C++ extensions if needed for latency-critical components. Real-time tick ingestion and historical data pipelines will integrate seamlessly, backed by a robust back-testing suite reporting Sharpe, drawdowns, and trade statistics with parameter optimization. Embedded risk controls and deployment automation will ensure reliable 24/5 operation with alerting. How critical is ultra-low latency compared to development speed for your initial MVP? Let’s discuss how to deliver a scalable, maintainable stat-arb engine that meets your live validation goals.

@lokdeshwark

As a dedicated, multidisciplinary professional, I am well-equipped to fulfill your needs for the Forex Statistical Arbitrage Algorithm Development project. My proficiency in Data Analysis and Python, alongside my background in digital marketing and software development, makes me the ideal candidate. Having developed various successful quant models in previous projects, I keenly understand the precise level of meticulousness necessitated by stat-arb algorithms. I leverage these skills in my use of Python with NumPy/Pandas for analytical purposes but remain open to other languages and approaches such as C++ where latency becomes critical. My data handling capabilities are robust and systematically driven which guarantee real-time tracking of trends for high-liquidity trading pairs you want to exploit. My skill set extends beyond mere statistical arbitrary to maintaining live tick data ingestion, building historical price pulls for thorough backtesting and developing risk controls ensuring your investments stay safe whilst generating profit. All these comes with a comprehensive back-testing framework reporting metrics aligning with your needs - Sharpe ratio,max drawdown, win rate et al. For your project's success, I recognize the importance of proper documentation with clear and concise code accompanied by a thorough README file.

@khrystynad4

Hello, Your statistical-arbitrage engine requirement aligns well with my experience in algorithmic trading systems and low-latency execution design. For your project, I would structure the system in modular layers: 1. Data Layer – Live tick / 1-sec ingestion via broker API – Historical data pipeline with storage (Parquet/SQL) – Data validation + outlier filtering 2. Strategy Engine – Pair selection + cointegration validation – Spread construction and dynamic z-score triggers – Volatility-adjusted position sizing 3. Risk Controls – Dynamic stop-loss & exposure caps – Max drawdown circuit breaker – Trade throttling during high-impact volatility 4. Backtesting Framework – Event-driven simulator – Slippage & transaction-cost modeling – Metrics: Sharpe, Sortino, max DD, trade distribution – Parameter walk-forward optimization 5. Deployment – VPS-ready execution script – Logging + rotation – Email/SMS alerts – Forward-test reconciliation (live vs expected PnL drift <0.3%) I can implement in Python (NumPy/Pandas + async I/O) or hybrid Python/C++ if latency profiling justifies it. If helpful, I can first outline the statistical validation approach for your major-pair universe before coding begins. Looking forward to discussing your broker API and infrastructure constraints. Best regards

@victorc309

Hi — this is squarely in my wheelhouse. I've built end-to-end stat-arb systems (cointegration + mean-reversion) on major FX pairs using Python/NumPy/Pandas with C++ hot-path modules where latency matters. What I'll deliver: Signal engine — Engle-Granger & Johansen cointegration tests, Kalman-filter hedge ratios, z-score entry/exit logic across EUR/USD, GBP/USD, USD/JPY, AUD/USD, USD/CHF and crosses. Data pipeline — tick/1s ingestion via broker WebSocket (OANDA/IBKR), historical pull for backtest seeding, stored in Parquet/TimescaleDB. Backtest framework — event-driven engine with realistic slippage/spread model, reporting Sharpe, max DD, win rate, trade distribution. Grid + Bayesian param optimisation (Optuna). Risk layer — Kelly-based position sizing, ATR-adaptive stops, portfolio-level circuit breaker (equity curve DD trigger), max exposure caps per pair and aggregate. Deployment — systemd service, logrotate config, Telegram/email alerting, health-check cron, one-command VPS setup script. All code Git-delivered, well-documented, with README and Docker option. I'll support the 2-day demo forward-test and tune until live/backtest P&L drift < 0.3%. Happy to share a redacted backtest tearsheet from a prior FX pairs-trading system. Let's talk specifics.

@neetus507

We are pleased to submit this proposal for the development of your software solution. Our team specializes in designing, developing, and deploying scalable, secure, and user-friendly applications tailored to business needs. We understand that your goal is to build a reliable and efficient system that improves operations, enhances user experience, and supports future growth. Our approach ensures high-quality development, timely delivery, and ongoing support. 2. Project Understanding Based on the provided requirements, the project involves: Designing and developing a custom software application Creating a responsive and intuitive user interface Backend development and database integration API integrations (if required) Testing, deployment, and post-launch support We will follow an agile development methodology to ensure flexibility and continuous improvement throughout the project lifecycle.

@Rathodak004

I believe I am the best candidate for this project because I have a strong technical foundation in mechanical engineering along with hands-on experience in electronics and programming-based experiments. I am comfortable working with both hardware and problem-solving tasks. I am adaptable, quick to learn new tools, and committed to delivering quality results within deadlines. Additionally, I work well in team environments and take responsibility for my tasks.

@CodeFixerPro

Hello, I have strong experience with Python-based data analysis and quantitative model development. I can build a statistical arbitrage framework including data ingestion, backtesting with performance metrics (Sharpe, drawdown, trade distribution), and risk controls. The system will be structured for live deployment on a VPS with proper logging and alerting. Due to the scope and complexity, I estimate around 14 days for a robust implementation. I’d be happy to discuss technical details before starting.