Algorithmic Trading Dev

@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

@vishal1145

Your ML models will fail in production if you're training on historical data without accounting for regime changes and market microstructure noise. I've seen firms lose $200K in a single day because their backtests didn't simulate slippage, latency, or order book dynamics. Before architecting the execution layer, I need clarity on two things: What's your target latency requirement - are we talking sub-millisecond HFT or multi-second stat arb? And what's your data infrastructure - are you streaming tick data from Bloomberg/Reuters or building custom feeds from exchange APIs? Here's the architectural approach: - PYTHON + MACHINE LEARNING: Build feature pipelines with walk-forward validation and implement ensemble models (XGBoost, LightGBM) that retrain on rolling windows to adapt to regime shifts. I'll add Shapley values for explainability so you understand why trades trigger. - C++ FOR EXECUTION: Write a low-latency order management system with FIX protocol integration and smart order routing that minimizes market impact. I've reduced execution latency from 800μs to 120μs using lock-free queues and memory-mapped files. - RUST FOR DATA PROCESSING: Build a real-time tick data parser that handles 500K messages per second without garbage collection pauses, feeding clean data into your alpha signals. - RISK MANAGEMENT: Implement pre-trade checks (position limits, drawdown triggers, correlation monitoring) and post-trade analytics with VaR calculations and stress testing against 2008/2020 scenarios. - STATISTICAL ARBITRAGE: Design cointegration-based pairs trading with Kalman filters for dynamic hedge ratios and Ornstein-Uhlenbeck mean reversion models. I've built 3 production trading systems that processed $50M+ in daily volume. One stat arb strategy I developed achieved a 2.1 Sharpe ratio over 18 months before alpha decay. Let's schedule a technical call to discuss your signal generation methodology and exchange connectivity requirements before we commit to infrastructure decisions.

@prashanpatel557

Hello, I am an algorithmic trading developer with strong experience in Python-based trading systems, quantitative research, and automated execution platforms. My work includes broker API integrations, backtesting engines, machine learning pipelines, and real-time trading dashboards. Relevant Expertise: • Python (Pandas, NumPy, TA-Lib, VectorBT, Backtrader) • Quant research and statistical analysis • Machine learning for signal generation (XGBoost, LightGBM, Optuna, SHAP) • Statistical arbitrage and pair trading frameworks • Risk management and portfolio controls • Real-time execution with broker APIs and WebSockets While Python is my primary development language, I can collaborate effectively in mixed-language environments where C++ or Rust components are used for performance-critical modules. Recent Projects: • Multi-broker algorithmic trading systems • Options and futures automation platforms • AI-driven trading signal analysis • Quantitative backtesting and portfolio analytics Availability: ₹1500 – ₹2,000/hour, available for ongoing quantitative development and research work. I’d be happy to discuss your strategy ideas, architecture, and how best to structure the development workflow.

@mahads31

My name is Mahad Sheikh, a freelance web and software developer specializing in creating tailored digital solutions that address complex business needs. With extensive experience in C++, Python, and software architecture, I'm well-equipped to meet your key requirements for this project. Drawing from my data-driven approach and strong inclination towards innovation, I have a proven track record of designing high-performance algorithmic trading systems. Having worked across various industries and international clients, my expertise extends to machine learning, statistical arbitrage and high-frequency trading. Moreover, my robust analytical skills coupled with a deep understanding of financial markets make me proficient in creating data-driven strategies for effective risk management. My commitment to quality delivery within strict deadlines positions me as an ideal long-term technology partner who will ensure your algorithmic trading system is scalable, efficient, and designed to deliver real business value. I am excited to leverage my skillset to enhance your trading performance and drive your business forward!

@tracygearth

Hello There, I work in Equity and F&O and Forex and strategies and algorithmic based training. I have used C++ and Python for back testing as well. TU

@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 17) Binance Bitcoin tradingview strategy python bot 18) Fyers Tradingview python bot 19) Dhan Python order manager I can deliver any project in Trading. Readymade setups for Python available

@souvikbasu

Building a profitable algo trading system is 20% strategy design and 80% execution reliability — I can help you nail both. I've built quantitative trading systems in Python using ML (scikit-learn, PyTorch) including backtesting engines, live execution pipelines, and risk management modules. For HFT and low-latency needs, I understand the trade-offs between Python's flexibility and C++/Rust's performance. What I bring: - Strategy development: statistical arbitrage, mean reversion, momentum models - ML integration: regime detection, feature engineering from tick/OHLCV data - Backtesting with realistic slippage/cost modeling - Risk controls: position sizing, max drawdown triggers, real-time P&L monitoring I'm available to start immediately. Can we discuss your specific markets and timeframes? Whether it's equities, crypto, or derivatives significantly shapes the architecture.

@alluankit01

Hi, I can support your algorithmic trading development work with Python, C++, and Rust, covering strategy design, backtesting, data analysis, risk controls, and performance-focused execution. I understand you need someone who can work across quantitative logic and engineering, not just write simple scripts. I can help design and test strategies around machine learning, statistical arbitrage, signal generation, portfolio/risk management, and low-latency execution where needed. My approach would be to first define the market, data source, trading frequency, risk limits, and execution environment, then build a clean research-to-production pipeline: data ingestion, feature engineering, model/strategy logic, backtesting, performance metrics, risk checks, and deployment-ready code. I’m comfortable with Python for research and ML, C++ or Rust for performance-critical components, and structured reporting so results are measurable before anything goes live. I can share relevant trading automation, backtesting, and financial data analysis experience in chat. Best regards Ankit

@indreshpnk

Hello, I understand you need a skilled quantitative developer proficient in Python, C++, Rust to build algorithmic trading systems (machine learning, statistical arbitrage, and high-frequency trading). The goal is to deliver a robust, low-latency, data-driven trading solution. Here’s what I can provide: * Design and development of high-performance trading algorithms (stat arb, ML-based strategies) * Low-latency system optimization in C++/Rust with Python for research and backtesting * Risk management modules, data pipelines, and financial data analysis I bring over 4+ years experience in Python, C++, and Rust with a strong focus on algorithmic trading systems, financial data analysis, and scalable software architecture. I have worked on backtesting engines, signal generation models, and market data processing pipelines ensuring performance, accuracy, and risk control. Just to clarify: * Which asset class are you targeting (crypto, equities, forex)? * Do you already have a data feed or need integration setup? Please come to the chat box to discuss more about your project. Best regards Indresh Kushwaha Available to discuss strategy and implementation details anytime.Thank.

@divyeshpatel373

Hi, I’m interested in this opportunity because it combines exactly the areas I work in — algorithm design, optimization systems, machine learning, and high-performance engineering. I have 7+ years of experience building complex Python-based systems for enterprise-scale platforms, including: * Optimization engines * Real-time decision systems * AI/ML pipelines * High-performance backend architectures * Predictive analytics and data-intensive I’m particularly comfortable designing systems where research, analytics, and execution pipelines need to work together efficiently and reliably. In previous projects, I’ve worked on: * Route optimization and mathematical modeling engines * Large-scale forecasting and predictive systems * Real-time orchestration platforms * ML-based automation workflows * Performance optimization for compute-heavy workloads I’d be happy to discuss your trading domain, execution environment, research workflow, and performance expectations in more detail.

@ziedb4

Hi, ​I am writing to express my interest in your quantitative developer position. With a strong background in Python and financial markets, I specialize in designing and implementing high-performance trading algorithms. ​Why I am a strong candidate for this role: ​Hands-on Experience: I have developed and successfully deployed a live trading bot on Binance, which handles real-time data integration, order execution, and risk management. ​Algorithmic Proficiency: I am well-versed in developing algorithms for Statistical Arbitrage and Machine Learning models applied to market trend prediction. ​Performance & Reliability: I understand the importance of low-latency and robust error handling in trading environments, ensuring the system remains stable under high market volatility. ​Technical Stack: I am highly proficient in Python for data analysis (Pandas, NumPy) and algorithm design, with a solid understanding of C++ for performance-critical components.

@gerard934

Over the past years, my career path has been heavily focused on honing my programming skills in C++, Python, and other relevant technologies. As a software engineer, I've had the privilege to tackle complex projects that demanded deep analytical thinking and efficient problem-solving - a trait central to algorithmic trading development. My expertise is not limited to pure coding; rather it extends into building sophisticated algorithms using machine learning techniques, crafting statistical arbitrage systems, and even high-frequency trading. In addition to my technical prowess, I possess a profound understanding of financial markets and algorithmic trading. I am well-versed with the intricacies of data analysis and risk management - essential skills for any successful quantitative developer. My proficiency in Metatrader 4/5 adds another dimension to my abilities as I've had hands-on experience with trading platforms too.

@thinknewte

Hi, I’m a quantitative developer with strong experience in Python, C++, and Rust, specializing in algorithmic trading systems and financial market analytics. I have worked on machine learning models, statistical arbitrage strategies, and low-latency trading solutions with a focus on performance, risk management, and execution efficiency. I also have 7+ years of experience working with trading algorithms and platforms including Angel One APIs, strategy automation, backtesting, and live deployment. My expertise includes market data analysis, portfolio optimization, and building scalable trading infrastructure for both intraday and HFT-style systems. Key skills: Python, C++, Rust Algorithmic & quantitative trading Statistical arbitrage & ML models Backtesting & risk management API integration (Angel One, broker APIs)

@rohitv622

Hello, I saw your requirement for a voice-controlled game. I have already built a similar project (Robo Race Voice Controller Game), so I can deliver this efficiently. Let’s discuss your requirements in detail.

@StanitaPinto

As a quant developer with a decade of experience, my skills in Python, C++, and Rust are precisely tailored to your project's requirements. I have an empirical understanding of algorithmic trading, encompassing machine learning, statistical arbitrage, and high-frequency trading, which would be great assets to bring to your team. With a track record of creating impactful solutions, I assure you that my abilities to analyze data, develop algorithms, manage risks, and solve complex problems are second to none. On the subject of previous projects, I spearheaded numerous successful initiatives similar to yours. For instance, working at Amazon sharpened my ability to execute high-performing strategies while bridging Python between different operations and executive levels. This resulted in significant cost savings (think £8m) and time reductions (a whopping 35%) through AI-enhanced ETL. I also led the development of a loan prediction model with 90% accuracy rate saving companies from potential losses. From Berlin-based analysis of Airbnb data for optimization using SQL and Python to my accomplishment in building an AI agent framework using LLMs for proficient YouTube analytics on AWS, my capacity transcends borders. I'm not just proficient in SQL, Python, dbt, Tableau; I understand their power when incorporated intelligently into complex business models like yours. Let my expertise help you get the most from your algorithmic trading endeavors!

@gurpreets058

I’m Gurpreet Singh, a professional freelance developer based in New Delhi, specializing in delivering secure, scalable, and high-performance digital solutions. I help startups and businesses turn their ideas into powerful, market-ready products. ? What I Can Do for You Mobile App Development (Android & iOS) Desktop Software Development (C#, Java, .NET) Custom Software & Web Application Development Website Design & Development (WordPress, Joomla, Drupal) Laravel, React JS & Node JS Development Game Design & Development Blockchain Solutions AI Automation & Custom Tools Meta Trading Tools, Bot Scripting & Web Scraping SEO, Digital Marketing & Branding Video Editing & Multimedia Production ⚙️ Technologies I Work With React JS, Node JS, MongoDB Python (Django) Android (Java/Kotlin), iOS (Swift) Flutter & React Native ✨ Why Work With Me? ✔ Modern, scalable & cost-effective solutions ✔ Creative and experienced development approach ✔ Transparent communication & smooth workflow ✔ Secure, optimized & future-ready technology ✔ On-time delivery with dedicated support ✔ Flexible pricing (open to discussion) ? Let’s Work Together If you’re looking for a reliable freelancer who can bring your ideas to life and deliver high-quality results — I’m here to help. Let’s build something amazing together ?

@swaroop080

ik good in creating projects in the python language i can make it in time but if you give me my expected rate

@silask88

Hi, Your models won’t survive live markets without handling regime shifts, microstructure noise, and execution friction. I design trading systems that stay robust under real conditions—not just backtests. Before buildout, I’ll align on latency targets (HFT vs stat arb) and your data stack (exchange feeds vs vendors), since this directly defines system architecture. Execution Plan: ML & Alpha Layer (Python): Feature pipelines with walk-forward validation, rolling retraining, and ensemble models (XGBoost/LightGBM). Regime detection + drift monitoring baked in. SHAP for signal transparency. Low-Latency Execution (C++): Custom OMS with FIX integration, smart order routing, and slippage-aware execution. Optimized with lock-free structures for microsecond-level performance. Real-Time Data (Rust): High-throughput tick ingestion (500K+ msgs/sec), normalization, and event-driven pipelines feeding live signals with minimal latency. Risk Engine: Pre-trade controls (limits, exposure, correlation) + real-time drawdown guards. Post-trade analytics with VaR, stress testing, and scenario replay. Strategies: Statistical arbitrage (cointegration, Kalman filters, OU processes) with adaptive hedge ratios and execution-aware signals. I’ve built systems that bridge research and production reliably. Share your target markets and infra—I’ll map this into a deployable architecture.

@abishek129

Hello, I’m interested in this opportunity. While my direct experience is stronger in low-latency systems and backend engineering than institutional trading, my background aligns closely with the technical requirements of quantitative systems. One relevant project I built was a high-performance PLC logger application in Rust for an industrial startup. The system continuously performed real-time read/write operations across multiple devices using OPC UA and Modbus protocols. I optimized the logging pipeline to reduce latency significantly and process hundreds of telemetry signals within seconds while maintaining reliability under continuous load. This experience is highly relevant to HFT-style systems because both require: • Low-latency processing • Concurrent event handling • Efficient memory and CPU usage • Real-time data pipelines • Reliable high-throughput architecture Alongside Rust, I have strong Python experience for automation, analytics, and backend systems, and I’m comfortable implementing algorithmic logic and data-processing workflows. My problem-solving background (LeetCode rating 1600+) also helps in building optimized and performance-sensitive systems. I’d be happy to discuss how my low-latency engineering experience can contribute to your quantitative development work. Best regards Abishek