Hire the Best Quantitative Analysts
in China

I am a PhD in Data Science with expertise in Python, machine learning, statistical modeling, and quantitative analysis. I help clients transform complex data into actionable insights through data analytics, predictive modeling, automation, and decision-support systems. My experience includes building data pipelines, developing analytical tools, conducting large-scale data analysis, and creating reproducible research workflows. Core expertise: • Python (Pandas, NumPy, SciPy, Scikit-learn) • Data Analysis & Visualization • Machine Learning & Predictive Modeling • Statistical Analysis & Optimization • Data Processing & Automation • Quantitative Research I focus on delivering reliable, well-documented, and scalable solutions that help clients solve real-world problems efficiently and accurately.

Specializing in financial modelling, quantitative analysis, trading, and operations with over 10 years of experience in securities market investments. CFA Certification. My additional areas of expertise include mandarin translation, web scrapping, automations, tutoring, and data analysis. Below is a brief summary of my technical skills: 🐍 Python: - Data Analysis and Visualization - Web Scrapping - Generative AI - Machine Learning and Deep Learning 📝 Financial Modelling and Quantitative Analysis: - Risk Management - Options Pricing - Portfolio Analysis 💹 Trading: - Algorithm Development - MT4, TradingView (Pine Script), IG - Screening and Back-testing 🪄 Automations: - Multi-Processing - Google Sheets - Scheduling - Document Manipulation - Email Automation 📈Excel and Google Sheets: - Complex Formula Calculations - Table and Graph Organization - Optimization - Statistical Modelling - VBA - App Script 🔍 SQL (MySQL, Microsoft SQL Server): - Data Querying - Functions and Keys - Relationship Management 📊 Power BI: - Data Import and Transformation - DAX Calculations - Relationship Modelling - Data Visualization

I am a seasoned high-frequency portfolio manager with extensive experience at two of the industry’s most prestigious trading firms—Jane Street and Jump Trading. My career has been defined by my leadership and innovation in the global crypto markets, where I have managed multi-billion dollar portfolios and consistently delivered industry-leading returns. At both Jane Street and Jump, I spearheaded the design, research, and deployment of a diverse suite of high-frequency trading strategies tailored to the unique microstructure and volatility of digital asset markets. My strategies spanned a broad range of alphas, including cross-exchange statistical arbitrage, market microstructure-driven market making, liquidity mining, cross-asset volatility arbitrage, and DeFi-specific yield strategies. I have successfully scaled these strategies across numerous exchanges and protocols (both centralized and decentralized), maintaining top-tier Sharpe ratios and robust risk controls throughout volatile market cycles. My technical expertise covers the entire trading stack: alpha research, feature engineering, signal generation, ultra-low latency execution, real-time risk management, and customized post-trade analytics. I am highly proficient in leveraging advanced machine learning models, alternative data, and on-chain analytics to identify and capture new sources of alpha, as well as in optimizing trading infrastructure for maximum speed and reliability. Under my leadership, my teams have consistently outperformed industry benchmarks, achieving record-setting P&L results and maintaining a leading presence in global crypto liquidity provision. My work has been recognized with numerous industry awards, including top honors in algorithmic trading competitions and accolades from the Association for Computing Machinery (ACM) for technical excellence. Beyond trading, I am deeply involved in mentoring and developing quant talent, contributing to open-source research, and shaping best practices for risk and compliance in the rapidly evolving digital asset space. My insights are regularly sought by industry peers, and I am frequently invited to present keynote talks at international conferences and forums on quantitative trading and crypto finance. With a proven track record of innovation, performance, and leadership, I remain committed to pushing the frontiers of high-frequency trading in the digital asset ecosystem.

Shiming is a top-rated software development contractor for more than a decade. Bridging clients from multiple industries to open-source technologies including machine learning, machine translation, OCR, natural language processing, and scraping. Shiming is an expert developer and team leader who has: • 15+ yrs of programming experience in Python, C, C++, Nodejs, vectorized / parallel computation. • 10+ yrs of trading and strategy research experience in financial markets. • Solid understanding of mathematics, statistics, mathematical finance, and machine learning. • Solid understanding of tree-based methods: gradient boosting machine (GBM), random forest, and the respective off-the-shelf packages: scikit-learn, lightgbm • Solid understanding of natural language processing methods and libraries • Solid understanding of cryptography, hashing, blockchain, smart contracts, and Solidity Shiming created systems for: • Sales forecast • Dynamic pricing • Targeted online advertising • Recommender • Fraud detection • PDF document content extraction and understanding • OCR of specific complex layout • Machine translation • Internal cryptocurrency demos Shiming created his own autonomous trading system, which in eight years, slowly evolved into a versatile platform for trading and strategy research. The system trades a combination of trend and momentum strategies on stocks, ETPs, and options.

Most of my work sits between quant research and messy reality. Clients usually come to me with a strategy that looks fine on paper, but breaks once you ask uncomfortable questions. What happens in a volatility spike? What happens outside the last five years of data? What happens when the data itself is wrong? That’s where I spend most of my time. On the modeling side, I use ARIMA and GARCH mainly for short-term volatility and regime awareness, not as black boxes. When the horizon gets longer or the problem becomes cross-sectional, I move to factor-based analysis instead of forcing a time-series model to do something it shouldn’t. In several projects, the real blocker wasn’t the strategy — it was the data. I’ve rebuilt missing or inconsistent equity index history so strategies could actually be tested through periods like 2008, instead of implicitly assuming a post-QE market forever. That alone has killed more “great-looking” strategies than any performance metric. A large part of my work is data analysis that doesn’t show up in glossy reports: fixing time alignment issues between prices, returns, and signals; dealing with survivorship bias that sneaks in through vendor datasets; deciding when missing data should be reconstructed, and when it should invalidate the test entirely. The goal isn’t academic cleanliness. The goal is to avoid false confidence. In Python, I usually work with pandas and statsmodels to turn half-formed ideas into backtests that can survive scrutiny, not just optimization. I’m deliberately conservative with assumptions, because most strategies fail due to data shortcuts, not math errors. I also translate this kind of research into production-grade TradingView tools, building Pine Script v5 indicators that reflect the same risk-aware logic in real-time execution environments. If you need someone to sanity-check a strategy, pressure-test it against bad history, or explain why something works only in hindsight, I can help.

Data Science Professional with a strong background in machine learning, natural language processing, and time-series analysis. I specialize in leveraging data-driven insights to solve complex real-world problems and deliver impactful solutions. Key strengths: - Expertise in data mining, data visualization, and full-stack software development Proficient in Python, SQL, and various data science libraries (PyTorch, Dask, Pandas, NumPy, scikit-learn) - Strong problem-solving skills and ability to translate complex data into actionable insights - Excellent communication and collaboration abilities, enabling effective teamwork and client relationships I thrive on challenges and am passionate about driving innovation through data-informed decision-making. With a solid foundation in data science and a proven track record of success, I am well-equipped to tackle a wide range of data-related projects. If you're looking for a dedicated and results-oriented data science professional or full-stack developer to help you harness the power of your data and drive meaningful insights, I would love to contribute my expertise to your projects. Let's collaborate and unlock the hidden value in your data together.