Quantitative Trading Research Platform

@toriquldev123

Hello There! I’m Md Toriqul Islam, and I’m excited to partner with you. I can dive into your project immediately. I have rich experience in Python, QuantConnect LEAN, Interactive Brokers API, async programming, event-driven systems, PostgreSQL, and quantitative research. I understand most of your quantitative research platform is already built, and you need an experienced developer to integrate, optimize, and automate the remaining modules. I can work on the OpenClaw controller, IBKR data pipeline, feature engineering, LEAN integration, validation workflow, and modular architecture while keeping the system lightweight, scalable, and independently testable. I am skilled in Python, QuantConnect LEAN, ib_insync, PostgreSQL, REST/WebSocket APIs, and Algorithmic Trading. I’m ready to start immediately and would be happy to discuss this project. Looking forward to hearing from you. Best regards, Md Toriqul Islam

@Ahmedyar547005

Hey , I just went through your job description and noticed you need someone skilled in Database Management, Statistical Analysis, Python, PostgreSQL, Risk Management, Data Analysis, Artificial Intelligence, API Development, Data Processing and Statistical Modeling. That’s right up my alley. You can check my profile — I’ve handled several projects using these exact tools and technologies. Before we proceed, I’d like to clarify a few things: Are these all the project requirements or is there more to it? Do you already have any work done, or will this start from scratch? What’s your preferred deadline for completion? Why Work With Me? Over 260 successful projects completed. Long-term track record of happy clients and repeat work. I prioritize quality, deadlines, and clear communication. Availability: 9am – 9pm Eastern Time (Full-time freelancer) I can share recent examples of similar projects in chat. Let’s connect and discuss your vision in detail. Kind Regards, Zain

@goranm16

Hello, I read your specification carefully and this maps closely to work I already do. I build event-driven Python systems around ib_insync, capturing Time & Sales incrementally and persisting structural events into TimescaleDB for replay. Splitting Bid and Ask into two independent Non-Print engines that track voids, skipped prices, persistence, velocity and acceleration is exactly the kind of market structure modeling I enjoy. Generating the 100 Line Break resolution engines as independent stateful modules, then computing features like compression, resolution agreement and liquidity imbalance for clean CSV export, fits naturally ahead of your existing LEAN Bridge. I can wire OpenClaw as the controller that launches each stage, verifies completion, ranks strategies, enforces prop firm limits and retrains on degradation. Async, low RAM and CPU throughout, with every module independently testable. I work Central European time and can start right away. Best regards, Goran.

@TanvirProttoy

Hi there, I've carefully reviewed your quantitative research platform requirements, and the most likely technical risk is state desynchronization between IBKR market data ingestion, structural engines, feature generation, and LEAN strategy validation, causing inconsistent research outputs or invalid backtest results. The first step would be auditing the existing framework, LEAN Bridge, database structure, and data flow to identify incomplete modules and define the integration points before extending the system. From there I would complete the event-driven Python architecture with incremental processing, replayable storage, modular research pipelines, validation automation, and monitoring components, using experience with quantitative systems, APIs, asynchronous processing, and trading infrastructure where reliability depends on deterministic data handling. I look forward to discussing your current implementation so we can complete the remaining modules and build a robust research pipeline around your existing framework.

@durgesh8527

As a seasoned full-stack developer with extensive knowledge in Python, I am confident in my ability to successfully complete and enhance your Quantitative Trading Research Platform. My proficiency in quantitative research and analysis, coupled with my deep understanding of meta-trading platforms like QuantConnect LEAN, will enable me to seamlessly integrate the remaining components of your project. My skills in database management, specifically PostgreSQL and TimescaleDB, are crucial in ensuring the efficient storage and retrieval of historical IBKR Time & Sales data. Additionally, I have a rich background in Liquidity Analysis and Non-Print Market Structure data handling which resonates well with your project's objectives. The goal of my 12-year career has consistently been to enable seamless automation through clean code, mitigate risks through diligent validation strategies and provide valuable insights for informed decision making. By leveraging my experience across various domains like FinTech, AI & Automation, I can engineer an innovative research pipeline that produces only the statistically validated trading strategies for deployment. Choose me for a competent yet cost-effective completion of your Quantitative Trading Research Platform!

@nicolasalexandro

I understand you need a quantitative trading research platform focused on autonomous, non-print Q, with most of the work already completed. The budget of $10-$30 suggests a specific, targeted enhancement or troubleshooting phase. I have experience developing and refining algorithmic trading tools and quantitative research platforms, ensuring accuracy and autonomy in non-print systems. My background includes working with similar constraints and delivering efficient solutions. I will assess the current build, identify necessary improvements, and implement optimizations within budget. Happy to review your current setup and get this back to a stable state.

@codeandcommand

Since most of the platform is already built, the key is knowing what is actually left. With Python and PostgreSQL I can get into an existing codebase fast, whether that is plugging gaps in the data pipeline, finishing the risk layer, or connecting the statistical models to live data feeds. The bid reflects what is in the description. Once I see the repo and what remains, both the timeline and final number will get clearer. Can start today. Want to send over the code so I can take a quick look?

@IbrahimAltaf

Hi, I’ve reviewed the scope. Since most of the framework is already built, I can help complete and integrate the remaining Python modules rather than rebuilding everything from scratch. My approach would be to first audit the existing OpenClaw/LEAN bridge, then connect the missing pieces: IBKR Time & Sales ingestion, non-print event storage, Line Break structure engines, feature exports, validation workflow, live strategy monitoring, and prop-firm risk controls. I have experience with Python automation, event-driven systems, PostgreSQL/TimescaleDB, API workflows, data pipelines, and strategy validation logic, so I can work within your existing architecture and keep the system modular, testable, and resource-light for local Windows execution. Before starting, I’d want to review the attached Master Director spec and current repo structure to identify the exact remaining tasks and avoid expanding beyond your fixed scope. Ready to help complete the missing components and make the pipeline run end-to-end.

@arilrivenski

Hello, I hope you're doing well. I have hands-on experience completing Python research pipelines that integrate Interactive Brokers data and QuantConnect LEAN, having delivered several similar builds. For a recent project I finished the pipeline that ingested IBKR Time & Sales via ib_insync, stored events in TimescaleDB, and produced replayable CSVs for strategy research; I handled incremental processing and tuned a Line Break style engine for structure detection. I'd usually start by profiling the existing codebase, adding an async event loop around the Non-Print Data Engine, and wiring incremental writes to TimescaleDB while keeping memory low. Open to a quick message exchange to confirm the IBKR export format, the replay DB schema, and which LEAN bridge hooks you currently use. Thank you, Aril

@snath88

"Hi, drop me a message — I'll share a quick Quant research platform prototype based on what I understood. If it matches your expectations, we can move forward. Thanks!"

@arturg6

Hello, "IBKR Time & Sales" data ingestion is the core bottleneck you described – the current pipeline needs a reliable, low‑overhead way to pull and store every tick. I’ll build an async ib_insync wrapper that streams the raw IBKR feed into a TimescaleDB schema, using a lightweight queue so RAM stays under 200 MB even on full‑day histories. Each module (Bid/Ask engines, Line‑Break generators, feature calculators) will be isolated as a Python package with clear REST/WebSocket hooks, letting you swap or upgrade parts without breaking the rest of the research flow. One edge case I often see is duplicate timestamps when the feed bursts – I’ll add a deduplication layer that tags events with a monotonic sequence ID, ensuring the replayable database remains consistent for back‑testing. Do you already have a preferred schema for the structural events, or should I design one from scratch? Looking forward to working with you. Artur Giżycki

@artemb35

I understand you're seeking an experienced Python/QuantConnect LEAN developer to finalize and enhance your modular quantitative research platform that transforms IBKR Time & Sales data into validated trading strategies. I can help streamline the remaining components and ensure the system operates efficiently with low RAM and CPU usage. Here's my build plan: 1. **Data Acquisition**: Implement a script to download historical IBKR Time & Sales data using the ib_insync library. 2. **Non-Print Data Engine**: Develop the Bid and Ask Non-Print engines to detect and store critical events in PostgreSQL or TimescaleDB. 3. **Market Structure Engine**: Automatically generate 100 independent Multi-Resolution Line Break engines to assess statistical edges. 4. **Feature Engineering**: Calculate and document essential features like Structural Velocity and Liquidity Imbalance, ensuring export functionality. 5. **QuantConnect LEAN Integration**: Leverage your existing LEAN Bridge for feature consumption, strategy generation, and validation. 6. **Validation & Risk Management**: Implement automated validation checks and a live strategy manager to monitor performance and enforce prop firm rules. I can start immediately and communicate directly to ensure a smooth workflow. Could you clarify if you have any specific performance benchmarks in mind for the system? Looking forward to collaborating on this project. Artem

@antonk52

Hello, As an experienced backend and systems engineer, specialized in API development and Python, I am confident that I can deliver outstanding results for your Quantitative Trading Research Platform. My track record in designing clean, scalable architectures combined with my deep knowledge in cloud infrastructure and high-performance server-side logic make me the ideal candidate to complete, integrate, enhance, and automate the remaining components of your project. With a focus on long-term maintainability rather than quick fixes, I ensure that all my systems are reliable and optimized for performance. Given your project's specific requirements around low RAM and CPU usage on a Windows environment for proprietary Non-Print market structure research data, these skills will be crucial. My experience with AI implementations will also be helpful for developing the OpenClaw AI system to manage the entire workflow. Moreover, given my proficiency in PostgreSQL, I can adeptly handle the storage of historical data as well as document and export feature calculations as desired. Additionally, my prior work with RESTful API design combined with my C++ development proficiency will come into play while integrating your existing QuantConnect LEAN Bridge seamlessly. By hiring me, you can be confident that I'll not only complete the project but also ensure it's stable and secure over time. Let’s build something reliable together! Thanks!

@ayesha86664

Hi there! I understand you need an experienced Python developer to complete and improve an existing quantitative research platform. The main challenge is integrating multiple research modules into a reliable, low resource, automated pipeline. I have experience with Python development, data processing, API integration, databases, statistical analysis, and event driven systems. I have worked on automation workflows, data pipelines, backend architectures, and research focused applications with scalable designs. I will carefully review your existing framework and complete the remaining modules without changing the core structure. I will improve integration between IBKR data processing, feature engineering, database storage, and QuantConnect LEAN workflows. I will focus on modular code, efficient processing, proper logging, testing, and easy future improvements. I can help organize each component so it remains independent, maintainable, and ready for further research expansion. My approach will prioritize stability, performance, and accurate data handling. check our work [https://www.freelancer.com/u/ayesha86664](https://www.freelancer.com/u/ayesha86664) Could you share the current project status and which modules are already completed versus pending? Let me know if you’re interested & we can discuss it. Best Regards Ayesha

@SEOGlobalLLC

Hi, I understand most of your Python/QuantConnect LEAN framework is already built and you need the remaining components completed, integrated, enhanced, and automated for a Windows-based Non-Print quantitative research pipeline. My team has strong experience with Python, async/event-driven systems, IBKR-style data workflows, PostgreSQL/TimescaleDB, REST/WebSocket APIs, structured logging, quantitative validation, and modular research architecture. I will review the existing code and project overview, connect the missing workflow pieces, keep RAM/CPU usage low, preserve testable modules, and focus on safe integration rather than unnecessary rewrites. Budget: $30 fixed, as requested. Approach: Complete the assigned missing components, validate module handoffs, test CSV/database/LEAN exports, and document all changes. Could you confirm which modules are already working and which remaining component should be completed first? Best regards, Aaron SEO Global Team

@nutankumarftp

As a seasoned full-stack developer with over a decade of experience, I have deep proficiency in Python and PostgreSQL – two critical skills needed for this project. I’ve designed and developed numerous web and mobile applications from scratch, ensuring their reliability and scalability throughout the process. Moreover, I’m experienced in API development which will come in handy for integrating Interactive Brokers’ data into your platform. My technical expertise further aligns with the project goals, as I’ve hands-on skills in handling large datasets, designing efficient databases, and optimising system performance. Additionally, my exposure to modern technologies like REST and GraphQL gives me a solid foundation for building a modular research platform, according to your requirements. In conclusion, my extensive experience with quantitative data analysis, automated system design, and my holistic approach to ensuring clean, scalable code make me an ideal fit for building your quantitative trading research platform. I'm eager to take on the challenge and deliver effective results within your specified budget. Let's collaborate today and take your trading strategies to new heights!

@saadhey

Greetings, I see you're looking to finalize a modular quantitative research platform that turns Interactive Brokers' Time & Sales data into validated trading strategies. It’s essential to create a repeatable pipeline that not only generates but also continuously improves these strategies efficiently. With my background in Python development and experience working with QuantConnect LEAN, I can help enhance and automate the remaining components of your project. I understand the importance of low resource usage, so I would design the system to run smoothly on Windows while ensuring each module remains independently testable and replaceable. I have worked on similar projects where I implemented event-driven processing and database management, which aligns with your requirements for capturing and processing non-print data effectively. I’m excited to contribute to this innovative research platform and help you achieve your goals.

@enricos0

Hi there, With my expertise in Python, event-driven systems, and databases, I am confident in delivering a clean, professional, and seamless quantitative trading research platform for you. Your detailed project description aligns perfectly with my skills. Specifically, I will focus on building the Non-Print Data Engine to capture essential market data and create the Market Structure Engine for generating independent Line Break engines. My experience in feature engineering and system automation will ensure a user-friendly and efficient workflow. I am the right person for this project because of my commitment to speedy communication, fast turnaround, and dedication to exceeding client expectations. I aim for seamless integration and automated processes to achieve optimal results. I am available for a quick chat! Regards, Adam F

@andrewkor27

Hi, I have checked attached txt file carefully. I have ever used QuantConnect and Interactive Brokers API when I build trading bot. Your project is exactly the kind of modular research framework I enjoy working on. Since much of the foundation is already in place, I can focus on completing the remaining components, optimizing performance for Windows, integrating them seamlessly with your existing LEAN Bridge, and ensuring the entire pipeline runs efficiently with minimal CPU and RAM usage. My goal is to deliver a scalable, maintainable research platform that continuously generates, validates, deploys, and improves statistically robust trading strategies. A couple of questions: Which modules are already completed, and which components require the most development or refactoring? What database is currently being used for storing replayable market structure events (SQLite, PostgreSQL, or another solution)? I would implement a fully modular, event-driven architecture where each stage (data ingestion, non-print structure engines, feature engineering, LEAN integration, strategy validation, deployment, and retraining) operates as an independent service with standardized interfaces. This approach makes the system easier to maintain, optimize, and extend while ensuring reliable automation, efficient resource usage, and seamless future enhancements. I am starting as a new freelancer here, but I have 9+ years experiences at companies. Thanks