Hire the Best Quantitative Finance Freelancers
in Pakistan

Greetings! I am a PhD candidate in Statistical Machine Learning (ML), and a certified Machine Learning Scientist and Data scientist (Upwork & IBM) with both Master’s and Bachelor’s degrees in Statistics. My strong academic foundation equips me advanced data science and ML workflows. I specialize in developing predictive models for financial markets—particularly in crypto and stock price forecasting—using time series analysis, deep learning, and ensemble methods. My work also involves designing and backtesting algorithmic trading strategies, optimizing model performance under live market conditions, and building interactive dashboards for real-time data visualization and decision support. 🌟 Top Rated on Upwork – Consistent record of client satisfaction and delivery excellence. 🔍 Why Hire Me? ✅ Advanced ML Expertise I leverage cutting-edge techniques to deliver highly accurate and innovative predictive solutions. ✅ Comprehensive Tool Proficiency Programming: Python, R Dashboards & Visualization: Tableau, Power BI, Streamlit Statistical Software: SPSS, STATA, E-Views, Minitab Database & Cloud: SQL, Google Cloud (for scalable, production-ready ML deployments) ✅ Quantitative Research Strength Deep experience in algorithm design and time series forecasting enables trend prediction with exceptional precision. ✅ End-to-End Execution From data ingestion to predictive modeling and visualization, I manage the full pipeline with performance and clarity in mind. ✅ Clear Communication I break down complex ML models and insights into intuitive, actionable formats for stakeholders and non-technical audiences. 💼 What I Deliver: Predictive Modeling Time Series Forecasting & Analysis Interactive Dashboards in Tableau, Power BI, and Streamlit Risk Assessment & Optimization Models Feature Engineering & Model Tuning Visual Analytics & Business Intelligence

I'm a Senior Quant trader and researcher designing institutional-grade, execution-ready trading systems for hedge funds, proprietary trading desks, and advanced fintech teams across both Forex and crypto markets. I specialize in turning robust alpha hypotheses into live, low-latency trading engines with proper risk, monitoring, and fail-safes built in from day one. My core edge sits at the intersection of market microstructure, statistical arbitrage, execution algorithms, and production software engineering. From high-frequency execution to liquidity-providing market-making, I build systems that are latency-aware, resilient under stress, and designed for real capital, not just attractive backtests. What I build High-performance execution and HFT-style engines: microstructure-aware execution logic, latency optimization, queue positioning, and adaptive execution tactics across multiple venues. Statistical arbitrage and market-making strategies: spread modeling, co-integration, pairs and baskets, inventory and quote-risk controls, and continuous PnL/risk monitoring. Trading aggregators and smart order routing: multi-exchange connectivity, routing logic, internal crossing, venue selection, slippage control, and real-time risk/hard-limits. ML- and LLM-augmented research pipelines: feature engineering for non-stationary series, signal stability testing, reinforcement-learning style policy evaluation, and sentiment/NLP inputs where they add real edge. Crypto and DeFi execution: CEX/DEX arbitrage, AMM routing, perpetual swaps, and integrations across on-chain protocols and off-chain venues. Platforms and stack Trading platforms (TradFi): MetaTrader 4/5 (MQL4/MQL5), NinjaTrader, Sierra Chart, TradingView (Pine Script), QuantConnect, Interactive Brokers, ThinkOrSwim. Crypto venues: Binance, Bybit, BitMEX, OKX, Deribit, dYdX, and major DEXs such as Uniswap, Sushiswap, and GMX. Low-latency & backend: C++, C#, Rust, Python, with practical experience building production trading and automation tools and server-side components. Infrastructure: Kafka, Redis, TimescaleDB/PostgreSQL, Liquibase, Docker, Kubernetes, and event-driven data pipelines suitable for live trading and research. AI & ML: TensorFlow, PyTorch, Scikit-learn, Pandas, and LLM APIs to augment alpha research, monitoring, and execution decision layers. How I approach ML in trading Financial markets are non-stationary, regime-driven, and adversarial, which means most naive ML pipelines decay quickly or overfit to noise. My process is hypothesis-first: alpha and market intuition come before models, with ML used to refine, stress-test, and scale strong ideas rather than search blindly for patterns. I rely on robust validation frameworks: walk-forward analysis, stability and sensitivity testing, Monte Carlo and resampling approaches, and realistic execution modeling including slippage and microstructure effects. The end goal is always the same: execution-ready systems that can be deployed, monitored, and iterated in production, not just impressive backtest charts. Partnership and engagement I work best with hedge funds, prop desks, and fintech teams that value deep market understanding, clear risk frameworks, and engineering discipline as much as raw performance. Beyond delivering code, I help teams refine their alpha pipeline, improve execution quality, and design architectures that can evolve as markets change. If you are planning to build or upgrade an institutional-grade trading stack whether it is a stat-arb engine, options or derivatives infrastructure, multi-venue execution layer, or ML-driven research platform share a brief description of your current setup, venues, and constraints, and we can map out a concrete plan

Hi. I have Master Degree in Economics & Finance. I am professional Data Analyst and Programmer with Certified MS Office Expert Certificate. I provide different statistical/financial econometrics/Quantitative Finance and trade services in Eviews, VBA, Python and R Studio Programing, incorporated with MQL4/5 & C# for Metatrade modules. I am here to provide you Financial Econometrics and computer programing with best solution for your projects. I have experience of many years in Economics and Financial Analysis in business sector, providing SUPPLY DEMAND Dynamic Analysis, Money Market and Financnial Market Technical & Trend Analysis, CAPM, Black Schole, Heston Option Trading, etc. My customer is my first priority and I provide my best efforts to them and assure 100% results. My Expertise are: ✅ Statistical Data Analysis ✔️ Qualitative & Descriptive Data ✔️ Data Validations ✔️ Cross Sectional Data ✔️Time series Data ❕ Stationary Series ❕ Non stationary Series ✔️ Panel Data Data ❂ Financial Economics & Econometrics ☞ Quantitative Finance & Financial Models ❕ CAPM, Sharp Ration, Odd Ratio, Simulation, Markovits, Blackschole Model, Binomial Option ☞ Option trading ❕ European & American Option: Strike price & Volatility ☞ AR, ARIMA, ARCH, GARCH, VAR, ARDL ☞ Stionarity, Heteroskedasticity, Multicollinarity, Autocorrealtion test ☞ STOCK, COMMODITY, FOREX, BOND Market Fundamental and Technical Analysis ✅ Computer Programming ♻️ MS Excel Macro/VBA/Dash Board, R-Studio, Python, VBA, SQL, HTML ♻️ All types of Modules & Chart, logical conditional algorithm ♻️ All types Uer Forms, Graphic User Interface (GUI), Non-Linear Programing ✅ Fintech Trading & Metatrader Trading Bot ☞MQL5/4 Metatrade with Python, Rstudio, VBA Integration ☞ AI, Machine/Deep Learning ☞BACKTEST & SWING TEST Strategies ❕ Arithmetic & Exponential Moving Average, RSI, MACD, Momentum, Chaikan Money Flow, Could strategy, etc.

I am an MBA (Finance) with in-depth knowledge of quantitative analysis, financial data analysis using R, Stata, E Views, and IBM SPSS using multiple econometric techniques. I am an expert in all types of financial and non-financial data entry and data mining techniques. I have excellent command over MS Excel and MS Word. I am almost an expert in Web Research either to copy data from the web or to find Facebook, Twitter, etc. I have experience with the collection of product info from an e-commerce website to excel/Google sheets. I can perfectly convert pdf into word and excel and further refinement of converted data. I have 4-year experience in financial research which includes data collection from the world's best sources and Bloomberg. I provide the following services:- Quantitative Analysis using R. Data Collection from Web, Bloomberg, IFS, and World Bank. Data Mining and presentation in a perfect way. Data Entry into online websites and any type of output as per the requirements of the client. APA Style citations and Proofreading. Additionally, I make sure the quality of the work by: Double-checking the work. Providing the best pay off the money of the client. Bring the satisfaction more than the expectation of the client.

Hello and welcome! I’m Shahban Ali, an Economist and Data Analyst, professional with extensive academic, research, and teaching experience. I hold a PhD in Data Analysis for Finance and Economics from HSE University, Russia, a leading institution in economics and quantitative research. My career blends a deep understanding of economics with modern data science techniques, delivering research that bridges theory and real-world insights. I’ve authored over 15 research papers in areas like behavioral finance, cryptocurrency markets, and economic forecasting. Notable Research: The Role of Herding Behavior in Cryptocurrency Crashes Impact of Crude Oil Prices on Exchange Rates Advancing Digital Marketing through Machine Learning AI and Big Data–Driven Privacy in Smart Grids What I Can Help You With? Economic data collection, cleaning, and modeling Econometric analysis and hypothesis testing Policy evaluation and forecasting Financial data analysis and visualization Academic writing and publication support Personalized Economics tutoring Subjects I Teach & Research: Economics: Microeconomics, Macroeconomics, Econometrics, Public Economics, International Trade, Behavioral & Development Economics, Financial Economics, Managerial Economics, Labor Economics, Game Theory, Environmental & Health Economics Finance: Corporate Finance, Investment Analysis, Financial Markets, Risk Management, Derivatives, Fixed Income, Portfolio Management, Behavioral Finance My teaching approach simplifies complex ideas with practical examples, ensuring clarity and understanding. Technical & Analytical Skills: Statistical Tools: Stata, EViews, SPSS, R Programming: Python, SQL Visualization: Power BI, Tableau, Excel Machine Learning: Forecasting, Sentiment Analysis Why Work With Me: 3+ years of research and education experience Published in international journals Strong analytical and communication skills Reliable and committed to quality work If you seek a professional who combines economic intuition with data-driven analysis, or a tutor to deepen your understanding of economics and finance, let’s collaborate to make your project stand out. I’d love to help you succeed.

I'm a Top Rated quantitative developer on Upwork, currently completing my MS in Data Science at Harvard, with production experience building and shipping algorithmic trading systems across futures, options, and equities. My work spans the full pipeline: from signal research and rigorous backtesting through execution architecture and live broker deployment. Over the years I've collaborated with professional traders, systematic fund managers, and independent CTAs on strategy research, execution system builds, and quantitative infrastructure — working with live accounts and real capital, not toy portfolios. - Research Philosophy Most algorithmic trading research fails before it reaches live markets. The reasons are almost always the same: data snooping bias, fragile parameterization, fill simulation that doesn't reflect actual execution, and strategy logic never stress-tested against regime change. My research process is designed to make it hard to fool yourself. That means strict chronological train/test splits with no parameter bleedthrough, walk-forward optimization over anchored and rolling windows, block bootstrap for inference on autocorrelated returns, and parameter sweep frameworks built to expose fragility rather than maximize backtest metrics. A strategy that only works on one parameterization isn't a strategy, it's a coincidence. I care about robustness across parameter space, regime stability, and understanding exactly why a signal works before scaling it. I work extensively in QuantConnect's LEAN engine for research-grade backtesting and live account deployment, though deployment targets vary by strategy and client stack. IB TWS via ibapi, Tradovate, and NinjaTrader are all in regular rotation depending on execution requirements. I build custom analytics layers on top of backtest output: Sharpe decomposition, drawdown attribution, execution cost modeling, and signal decay analysis, because standard tearsheet metrics rarely tell the full story. - Strategy Coverage My research spans futures (NQ, ES), SPX and index options, ETFs, and crypto. Strategy types I've built and validated in production include intraday momentum and trend-following systems, mean reversion and stat-arb frameworks, breakout and scalp architectures with dynamic sizing tiers, options strategies including iron condors, defined-risk vertical spreads, 45-DTE and 0DTE SPX structures with delta-based hedge logic, and GEX (gamma exposure) driven directional frameworks using open-interest-weighted dealer positioning data. It's an area where the signal literature is still thin and where getting the methodology right matters more than most people acknowledge. - Execution Engineering The research-to-live gap is where most systems break. I build execution infrastructure that closes that gap: Interactive Brokers TWS via ibapi with state machine architectures, order ladder geometry, true breakeven calculations with commission and slippage factored in, and hedge math for multi-leg positions. On the NQ/ES side I've built and audited scalp bot execution logic on Tradovate, integrated Databento for professional tick data pipelines with proper validation against reference data, and developed Pine Script strategies and indicators on TradingView for signal prototyping before committing to full implementation. I'm also familiar with NinjaTrader's execution environment and ATM framework. Code I deliver is production-standard: structured logging, explicit edge-case handling, clean separation of signal and execution layers, and documented at a level where the system is maintainable after handoff. - What I Work On: - Strategy Research & Backtesting: QuantConnect/LEAN, Backtrader, Zipline, VectorBT, QuantLib, Python - Execution Systems: IB TWS/ibapi, Tradovate API, NinjaTrader, REST/WebSocket broker integrations, FIX Protocol - Retail Platform Development: MQL4/MQL5 (MetaTrader 4/5), Pine Script (TradingView), NinjaScript (NinjaTrader 8), EasyLanguage (TradeStation), ThinkScript (Thinkorswim), cAlgo/C# (cTrader), AFL (AmiBroker) - Broker APIs: Interactive Brokers, Alpaca, TD Ameritrade, Tradovate, Oanda, Databento - Crypto Platforms & APIs: Binance, Bybit, OKX, Kraken, Coinbase Advanced Trade, Bitfinex, Deribit (options/derivatives), KuCoin, Gate io, Freqtrade, Hummingbot, 3Commas, Pionex, Gunbot, ccxt (unified exchange library) - Statistical Testing: Walk-forward testing, parameter sweep analysis, Monte Carlo simulation, bootstrap inference - Full-Stack Delivery: Data ingestion → signal research → backtesting → live deployment