Advanced Strategy Development for QuantConnect LEAN

@marjanahmed13

Hi! My name is Marjan and I'm here to offer you my services as a skilled applicant with over a decade of experience working on Freelancer.com. l believe I am the best fit candidate for this project due to my extensive experience; I would like to have a discussion to get to know that we both are on the same page. Once the scope will be locked, I will start working on it right away.

@ykarora26

With extensive experience in quantitative trading and strategy development, I understand your need for a comprehensive research and strategy workflow using QuantConnect LEAN. Leveraging my skills in Python and market structure analysis, I can collaborate to create a robust framework for high-quality trading strategies around your proprietary data engine. To ensure a successful outcome, could you provide more details on the specific functionalities and features you envision for the research platform? Regards, Yogesh Kumar

@codeandcommand

Since you have the data ready already, I can skip straight to writing the Python strategy in LEAN, no extra setup time. I would build the alpha model, add risk controls, and run full backtests so you see real results before going live. Can start today. Initial output within 2 days. Budget and timeline shown are starting points. Once we walk through the full scope, both may shift. Want to jump on a quick call?

@nemanjam61

Hi there , I have reviewed your QuantConnect LEAN project and understand you need a full research-to-strategy framework using your custom market structure data. I can build a clean data pipeline, feature engineering layer, and automated strategy discovery with walk-forward, Monte Carlo, and robustness validation. I will ensure modular design for repeatable research and future dataset integration. Could you confirm your current data format and LEAN setup? I can start immediately.

@IsaacFullStack

Hi - given the $10-20 budget, I'll be direct: this is a multi-phase quant research framework (data pipeline, feature engineering, automated strategy discovery, walk-forward and Monte Carlo validation, ranking system) that normally runs weeks of dev time, not a couple hours. That mismatch is worth flagging before either of us wastes time. If you want to start small and prove out the workflow, I can scope Phase 1 alone: build the LEAN data import pipeline for your structural CSV exports, so future exports slot in without touching strategy code. That's a realistic $10-20 deliverable and gives you a working foundation to evaluate before committing budget to feature engineering or automated discovery. One question: is your research engine's structural data timestamped to align with LEAN's bar resolution already, or does it need resampling/alignment logic as part of the pipeline? If Phase 1 goes well, we can talk next steps for feature engineering and discovery separately. Let me know if a scoped Phase 1 works for you. Best re

@ShabbarAbbas

I will develop a complete research and strategy development workflow around your custom market structure research engine using QuantConnect LEAN, by integrating your proprietary data, creating a reusable data pipeline, and building an automated feature engineering and strategy discovery process. I have experience with QuantConnect LEAN and Python, and I've worked on similar projects, such as the development of a Telegram Trade Signal Bot, where I implemented regex logic to extract key trade data and integrated user authentication and access control. Another relevant project is the Attorney Settlement scrapping with AI, where I developed a fully automated data scraping solution using Playwright, BeautifulSoup, and LLM integration. How do you envision the workflow for capturing new market data and generating updated structural datasets with your research engine, and what are the key performance metrics you would like to use for strategy ranking and validation?

@arturg6

Hello, QuantConnect LEAN – you need a repeatable research framework that builds strategies from your proprietary market‑structure data. I'll create a custom `BaseData` loader that reads your CSV exports into LEAN's data objects in under 200 ms, so each backtest stays fast while preserving the full resolution of your bid/ask structure. I'll also add a deduplication step that resolves overlapping timestamps, preventing silent overwrites when new data replaces old files. Which prop‑firm rule—profit target or drawdown limit—should we prioritize in the first optimization cycle? Looking forward to working with you. Artur Giżycki

@trevorr24

✅ Ready to jump in and get this done the right way—clean, fast, and reliable. ✅ ----------------------- Good evening , I am a Senior Software Engineer with over 10 years of professional experience designing, developing, and delivering reliable software solutions for a wide range of business needs. I've reviewed your project deskription Advanced Strategy Development for QuantConnect LEAN I'm proficient in Predictive Analytics, Data Visualization, Data Processing, Python, Data Integration, Backtesting, Data Mining, Risk Management, Data Analysis and Statistical Analysis. Happy to start immediately and deliver something that matches the level of realism you're aiming for. Let’s connect and go over your vision.

@toqeer74

Hi, I can help you build a solid strategy development framework using QuantConnect LEAN. I will create a reusable data pipeline for your proprietary datasets, enabling efficient integration and automated feature extraction. This will lay the groundwork for systematic strategy discovery and validation. I can deliver the initial milestone in exactly 15 days. Want me to sketch a quick action plan so you can see the approach?

@artemb35

I understand that you're looking for a developer experienced in QuantConnect LEAN to create a robust quantitative research framework leveraging your proprietary market structure data. The goal is to build a repeatable workflow for discovering, testing, and deploying high-quality trading strategies, moving beyond traditional indicators. Here's how I plan to approach this project: 1. **Data Integration**: Import your datasets into QuantConnect LEAN and create a reusable data pipeline for easy future updates. 2. **Feature Engineering**: Extract meaningful features from your data, focusing on metrics like Structure Alignment, Event Density, and Compression Ratios. 3. **Automated Strategy Discovery**: Implement a systematic process to test thousands of rule and parameter combinations to discover profitable strategies. 4. **Validation**: Conduct rigorous testing, including In-Sample, Out-of-Sample, Walk-Forward, and Monte Carlo analysis to ensure strategy robustness. 5. **Strategy Ranking**: Rank strategies based on performance metrics like Net Profit, Sharpe Ratio, and Maximum Drawdown to identify reliable candidates. 6. **Final Strategy Output**: Document complete entry and exit rules, risk management guidelines, and performance reports ready for QuantConnect LEAN. I can start immediately and communicate directly to ensure we’re aligned throughout the process. Do you have a preferred timeline for the project milestones? Best, Artem

@rolandog26

⭐️Hi⭐️ I can integrate your custom market-structure dataset into a QuantConnect LEAN research pipeline and build a clean, reusable framework for feature engineering, strategy testing, and validation. My approach would focus on: • Structured data ingestion layer (your CSV → LEAN data pipeline) • Modular feature engineering from your bid/ask structural metrics • Automated strategy testing loop (parameter + rule combinations) • Built-in validation (walk-forward, OOS, Monte Carlo style checks where feasible in LEAN) • Strategy ranking system based on Sharpe, drawdown, expectancy, and stability • Clean documentation so you can rerun the full research cycle with new datasets This will be designed as a repeatable research framework, not a one-off strategy.

@DesmondTwoTwo

⚡ I WAS LOOKING FOR A PROJECT LIKE THIS. I have just handled developing a custom research platform for processing market data, similar to your Interactive Brokers Time & Sales engine. You want a reliable, repeatable quantitative research framework to discover and optimize trading strategies using proprietary data. From your brief, I'll focus on seamlessly integrating your datasets into QuantConnect LEAN and extracting meaningful features for strategy development. Let's simplify the process by creating a clear workflow, making strategy discovery and validation straightforward for you. MESSAGE ME, I ALREADY HAVE A FEW IDEAS. Regards, Stefan.

@SidUSA

Hi, A good quantitative trading system is not about using indicators. It is about having a system that can find and test trading strategies that really work. Before we start I want to ask: Do you already have your market data lined up with the price data or do we need to make sure the times match when we import it into QuantConnect LEAN? This can be a problem so I will make a system that can automatically import data create new features find strategies test them and rank them so we can use it again with other data sets without having to do too much work. I recently worked on a project called AlphaQuant. It is a platform for researching futures trading using numbers. We used Python and QuantConnect LEAN to make it. The goal was to look at market data and come up with trading strategies that use computers. My job was to build the system that brings in the data create custom features test the strategies make them better and see how well they work over time. I also had to rank the strategies based on how they work compared to the risks so we could pick the best ones to use. I worked with QuantConnect LEAN and AlphaQuant to make sure the system was good, at finding and testing trading strategies. Best Regards

@arilrivenski

How is your proprietary market structure data exposed to LEAN—CSV, Parquet, API, or a custom data provider? Which asset classes and execution environment are you targeting first (equities, futures, options, or FX) so the research pipeline can be designed around realistic constraints? This project is about building a research framework, not a single trading strategy. I'll develop a modular LEAN workflow that ingests your proprietary market structure data, automates feature generation, strategy discovery, backtesting, optimization, validation, and ranking while minimizing overfitting through robust out-of-sample testing. The architecture will keep your research engine separate from the strategy layer, making it straightforward to iterate, benchmark new ideas, and deploy only statistically robust strategies as your dataset evolves. Best regards, Aril

@CliveMSoftware

I have 8+ years building quantitative trading systems and data pipelines for algorithmic trading. I can architect your complete research framework: clean data import pipeline from your custom market structure engine, automated feature engineering that extracts structural signals, intelligent strategy discovery that tests thousands of rule combinations, and rigorous validation including walk-forward analysis and Monte Carlo testing. The deliverable will be a production-ready QuantConnect LEAN project with modular components so you can simply swap new datasets and regenerate candidate strategies. I understand your prop firm constraints and will bake them into position sizing, risk management, and exit logic from day one.

@RandyJackson12

Hi, I have strong experience in algorithmic trading and QuantConnect's LEAN engine. I can develop a robust trading strategy using your existing data and QuantConnect's backtesting framework. - Python/proven QuantConnect experience - Strategy development with proper risk management - Backtest validation with performance metrics Simple fixed price. 5 day delivery. Best, Ryan