Quantitative Research Analyst & Algorithmic Trading Platform Developer

@ZohaibRoy

⭐⭐⭐⭐⭐ Expert Research Analyst & Software Engineer for Trading Solutions ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project requirements and noticed you're looking for a research analyst and software engineer for your quantitative fund. Look no further; Zohaib is here to assist you! My team has successfully completed 50+ similar projects in research analysis and trading technology. I will conduct in-depth research and create a robust trading platform that meets your needs. ➡️ Why Me? I can easily handle your research and software engineering tasks as I have 5 years of experience in financial analysis, algorithm development, and market research. My skills include data analysis, machine learning, and Python programming. Additionally, I have a strong grip on database design and real-time data processing, ensuring a solid foundation for your trading system. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. Looking forward to discussing this with you in our chat. ➡️ Skills & Experience: ✅ Financial Analysis ✅ Research Methodology ✅ Python Programming ✅ Data Analysis ✅ Machine Learning ✅ Algorithm Development ✅ Database Design ✅ Real-time Data Processing ✅ Risk Management ✅ Trading Strategies ✅ Performance Metrics ✅ Report Generation Waiting for your response! Best Regards, Zohaib Waiting for your Response!

@ahmadayaz

I propose a comprehensive partnership to cater to your expanding needs in deep fundamental research and algorithmic trading technology. For the research analyst role, I offer expertise in quantitative analysis focusing on options and futures markets to deliver timely and insightful information. Please specify the preferred frequency and format of daily briefs, along with key risk metrics for your fund. In software engineering, I aim to design an architecture that balances complex analytics with low-latency execution using Python, pandas, TensorFlow for ML modeling, and robust database design. How should risk reporting be integrated, and are there specific regulatory compliance needs? I will provide a tailored project proposal outlining a step-by-step plan from data capture to risk reporting in the trading platform, including a case study showcasing domain experience. When do you expect the MVP of the algorithmic trading engine for initial testing? I pledge to deliver rigorously sourced research notes and a trading platform for end-to-end paper trading, P&L tracking, and position reconciliation. With a focus on quality, speed, and reliability, I am confident in exceeding your expectations. Let's collaborate for your fund's success.

@PINESCRIPTERR

Hi, The strongest approach is to connect research and engineering through one repeatable pipeline: every market thesis becomes a defined, testable strategy with cited evidence, explicit assumptions, risk limits, and measurable invalidation criteria. For research, I’d structure daily briefs around the market catalyst, affected instruments, sector fundamentals, options/futures positioning, proposed spread, expected payoff, Greeks or margin exposure, scenario analysis, and source links. Each idea would be tested without look-ahead bias and reported net of realistic fees, slippage, and liquidity constraints. For the platform, I’d use modular Python services for market-data ingestion, normalized historical storage, feature generation, walk-forward model training, event-driven backtesting, paper execution, and reconciliation. The risk layer would remain deterministic and separate from ML: position limits, exposure caps, stale-data checks, kill switches, and order-state recovery should never depend on a model’s discretion. Immutable logs and monitoring would make every signal, order, fill, P&L change, and model version traceable. The MVP should first prove one strategy end-to-end under replayed tick data and realistic latency before adding markets or live execution. Regards, Houssame

@tangramua

Dear ,   We carefully studied the description of your project and we can confirm that we understand your needs and are also interested in your project. Our team has the necessary resources to start your project as soon as possible and complete it in a very short time.   We are 25 years in this business and our technical specialists have strong experience in C Programming, Python, Statistics, Machine Learning (ML), Financial Analysis, Statistical Analysis, Data Science, NumPy, Pandas, LLM Prompt Engineering and other technologies relevant to your project.   Please, review our profile https://www.freelancer.com/u/tangramua where you can find detailed information about our company, our portfolio, and the client's recent reviews.   Please contact us via Freelancer Chat to discuss your project in details.    Best regards, Sales department Tangram Canada Inc.     

@Aaleen110

The core mission digging into papers and reports, academic studies, sell-side notes, 10-Ks, industry white papers, and pulling out insights that move markets, that is the real job here, and I will build a system to do that. I will use Python and pandas to ingest and structure this data, building APIs to pull from various sources, and also develop the ML-driven models you need to analyze it. For execution I will use a framework that integrates with exchange APIs, logging all performance in real time. You mention ML-driven models and also Python, pandas, NumPy, scikit-learn or Tensor, so I will assume you want those specific libraries or similar for the models. Where the brief leaves a fork between raw data ingestion and a more curated feed of structured spread ideas, I will build the latter. This means not just pulling reports but also creating parsers for specific financial news feeds and also options/futures contract data to derive those spread ideas directly. I am a Preferred Freelancer on Freelancer with a 5.0 rating, 100% on time and 100% on budget. What is the preferred latency for trade execution? If you hand this over today, I will have the basic Python data ingestion framework set up, capable of pulling a sample research paper and extracting key entities, and also the initial API structure for the algorithmic trading stack.

@Hubready

Hello!, This is James from Hollywood... I read your project description carefully, and I understand you’re building a fast-moving quant research and algo platform where the real value is not just code, but clean research workflows, reliable data handling, and models that support fund decisions. I bring 15+ years of experience in Python, C, statistics, ML, financial analysis, NumPy, Pandas, and production systems for trading, analytics, and AI automation. My focus is to build something practical, testable, and fast to iterate on, so your team can move from idea to signal to execution without friction. My approach would be: 1. Clarify the research goals and data sources 2. Build a clean pipeline for ingestion, cleaning, and feature generation 3. Prototype the models and backtesting logic 4. Validate with statistical checks and performance review 5. Package it into a maintainable workflow your team can extend Could you please clarify the following questions to help me better understand the project? 1. Are you mainly focused on fundamental research, trade signal generation, or a full execution workflow? 2. What data sources are already available, and do you need help building the pipeline from scratch? 3. Is the first milestone more about research tools, backtesting, or ML/LLM support? I can also share relevant work in Python trading tools, quant research dashboards, and AI-driven data pipelines I’ve built for small teams and funds.

@juanpablomacf

Funny mix of deep research and heavy engineering here — that’s basically how I’ve been working: parsing academic/market papers into actionable derivatives spreads, and building Python ML pipelines that stream live data, test models, and fire low‑latency orders with full P&L logging. I’d start by setting up a clean data ingestion layer, then a strategy sandbox where research outputs translate directly into model signals. Quick thing: do you already have preferred data vendors? Any constraints on execution venues? Should the research briefs focus on specific sectors first? Juan Pablo

@starttechno

Your project needs both disciplined market research and a practical paper-trading MVP, and I can structure the work so the two reinforce each other. I would produce daily briefs from academic papers, sell-side research, 10-Ks, white papers, and news flows, with precise citations, sector implications, options/futures spread structures, entry logic, invalidation levels, and risk metrics. Each thesis would include a back-tested performance hypothesis rather than only qualitative commentary. For the platform, I would build a modular Python stack using pandas/NumPy for research, scikit-learn or TensorFlow for model training, and FastAPI services for orchestration. Market data would flow through validated ingestion and normalization into PostgreSQL/TimescaleDB, with a strategy sandbox that prevents look-ahead bias and supports historical tick replay. A broker adapter would handle paper orders, realistic latency and slippage, fills, position/P&L reconciliation, and event-level logging. Dockerized tests would cover data quality, execution behavior, and risk limits; the design can later support lower-latency C components where needed. I can begin with the data schema, research-note template, and end-to-end paper-trade path, then add model evaluation and risk reporting. Do you already have a preferred market-data or broker API for the paper-trading MVP? Muhammad Saad

@mayr81

You’re building a dual-track operation, daily research depth plus an ML-enabled trading stack, and I’ll match that pace with rigor. Research track (daily): - Systematic paper/news ingestion across options/futures and sector fundamentals (10-K/white papers/sell-side). - Source-cited briefs with explicit hypotheses: what moves, why it moves, what to trade. - Quantified trade theses including scenario ranges, key risk factors, and a back-tested performance hypothesis (even if preliminary). - Results logged for continuous refinement (feature notes, thesis changes, realized vs expected). Trading platform MVP (end-to-end paper trading): - Architecture from data capture → feature store → strategy sandbox → execution simulation → risk & P&L logging. - Live ingest via streaming market data, normalized to a consistent schema; historical tick backtesting wired to the same interfaces. - Order execution module designed for realistic latency modeling; full reconciliation of positions and P&L. - ML workflow for model training/inference with reproducible pipelines (sklearn/TensorFlow), plus monitoring and retraining hooks. - Strategy templates so new ideas can be deployed quickly while maintaining auditability. To start, I’ll deliver an MVP that reliably runs paper trades tick-by-tick and a research brief cadence that turns sources into testable spread ideas.

@johibkhan

Hi, With my extensive experience in software application development and a specialization in efficient coding, performance optimization, scalable architectures, and building high-performance, low-latency systems, I am confident I can deliver excellent results for your fund. I have led teams working on trading systems, fintech tools, AI/ML platforms, and much more over the past 15+ years; projects that align closely with what you are seeking. Regarding the research analysis aspect of your project, I am skilled in NLP and market regime detection which will prove highly valuable when digging into academic papers and sell-side notes to identify key market-moving insights. My portfolio includes projects delivering robust backtesting engines, which will ensure that the research notes you receive from me are informed by rigorous and reliable testing. As for the software engineering part of the job, I have deep expertise in Python, TensorFlow, PyTorch, Scikit-Learn as well as designing algorithmic trading stacks from data capture to risk reporting. In addition to this technical capability, my work in blockchain/DeFi trading has given me an edge with safe coding practices and API/network/cloud security. Let's talk further about how I can leverage my skills and experience to create a purpose-built trading technology that meets your expectations

@TechWagger2026

As an experienced full-stack developer with a comprehensive skill set in various areas such as Python, Data Science, C Programming, and more, I can seamlessly carry out your project requirements as both a quantitative research analyst and developer for an algorithmic trading platform. With a track record of managing over 416 successful projects in diverse industries such as Online Delivery, Real Estate, Medical, Education - I'm confident of delivering high-quality work that helps businesses thrive even in the most competitive sectors. In terms of the software engineering requirement, I’ve extended my capabilities to machine learning-driven models as well as low-latency execution modules crucial for algorithmic trading environments. My proficiency in Python with packages like scikit-learn or TensorFlow coupled with database design skills would be invaluable when building your trading stack from scratch. Further, you can rely on me to create efficient data pipelines and real-time performance trackers to ensure smooth functioning and accurate reporting.

@ssquare489

For the trading platform, I’d design around your acceptance criteria first: historical tick replay and live feeds should pass through the same strategy and execution interfaces, so a model validated in the sandbox can move to realistic paper trading without rewriting core logic. I’d build the stack in Python with async market-data ingestion, pandas/NumPy for research workflows, scikit-learn or TensorFlow for models, and PostgreSQL/TimescaleDB for ticks, orders, positions, and performance history. The architecture would separate feed adapters, feature/model services, strategy sandbox, risk controls, execution adapters, reconciliation, and P&L reporting. My priorities would be scalability and maintainability, particularly making strategies and broker/data providers replaceable modules so new models can be tested without destabilizing execution. I’m personally involved as technical lead and would own architecture, implementation, testing, profiling, and deployment rather than handing the project to an unknown team. A closely related project is a live stock-market analysis platform we built using the GDFL WebSocket API, including real-time market-data handling and analytical dashboards. That experience is directly relevant to streaming feeds, state synchronization, and market-facing interfaces. For the MVP, I’d validate the complete path: tick replay → features/model → signal → risk checks → paper order → fills → positions/P&L reconciliation.

@rohaansheikh90

Hello, I'm Rohaan, a seasoned professional with over 5 years of expertise in Python, Data Science, NumPy, Pandas, and Machine Learning. I have a strong background in developing algorithmic trading systems and conducting in-depth research analysis. I understand your requirement for a research analyst to extract market-moving insights and an engineer to build an algorithmic trading platform. I will provide daily briefs with quantified trade theses and risk metrics, along with an MVP of the trading engine featuring data pipelines, strategy sandbox, and execution module. Let's discuss your project further. Please start a chat so we can explore how I can meet your needs effectively. Best regards, Rohaan