--- title: "Research" description: "Sharing research can be a great way to build a reputation within the QuantConnect community, reach potential employers, and earn QuantConnect Credit." canonical: https://www.quantconnect.com/docs/v2/cloud-platform/community/research markdown: https://www.quantconnect.com/docs/v2/cloud-platform/community/research.md language: python source: https://github.com/QuantConnect/Documentation/tree/master/01%20Cloud%20Platform/14%20Community/10%20Research modified: 2026-11-12 index: https://www.quantconnect.com/llms.txt --- # Research [Documentation](https://www.quantconnect.com/docs/v2.md) > [Cloud Platform](https://www.quantconnect.com/docs/v2/cloud-platform.md) > [Community](https://www.quantconnect.com/docs/v2/cloud-platform/community.md) > Research > Code examples are shown in Python. Append `?lang=csharp` to this URL for the C# version of this page. ## Introduction The [Research](https://www.quantconnect.com/research) page contains articles from QuantConnect team members and community members that implement a particular trading strategy. Review these articles to gain a better understanding of creating full trading algorithms with LEAN and the method to build them. If you have an interesting strategy or research you want to share with the community, create a post and get featured on the Research page and distributed in the community mailing list. Sharing research can be a great way to build a reputation within the QuantConnect community, reach potential employers, and earn [QuantConnect Credit](https://www.quantconnect.com/docs/v2/cloud-platform/organizations/credit.md). ## Submit Proposals Before you investigate a trading idea, submit a proposal. The proposal should describe the type of strategy you want to investigate, the universe and asset classes it's applied to, the [datasets](https://www.quantconnect.com/datasets) it requires, results you expect to see, and any research papers you're using as a source. Once you submit a proposal, we will review it and email you on whether it's approved or rejected. We only approve research that is based on some financial concept, theory, or model. We reject research that is just a combination of technical indicators and overfit parameters. This approval process ensures you avoid spending time on research that won't end up being published. Follow these steps to submit a research proposal for the Research page: 1. Open the [Research](https://www.quantconnect.com/research) page. 2. Click **Share New Research**. 3. In the **Title** field, enter the title of your research. The title must follow standard capitalization rules. For example, "Opening Range Breakout for Stocks in Play". 4. In the **Content** field, replace the placeholder text in the **Introduction** section of the template with the introduction of your submission. During the proposal stage, the **Introduction** section should summarize your area of research. Explain what type of strategy it is, the universe and asset classes it's applied to, the [datasets](https://www.quantconnect.com/datasets) it requires, and results you expect to see. If the strategy is based on a research paper, reference the paper at the end of the introduction. 5. If you have references, list them in the **References** section. 6. Click **Publish Research**. ## Publish Content After you receive our email that your research proposal is approved, implement your strategy or research notebook and then follow these steps to add your findings to your research post: 1. Open the [Research](https://www.quantconnect.com/research) page. 2. Click on the draft of your research post. 3. On the discussion page that opens, in the top-right corner of the **Introduction** section, click the **three dots** icon and then click **Edit**. 4. Update the text of the research post. 5. Attach a backtest or notebook. 6. Click **Update**. We will review your submission. If your research follows our content guidelines and provides value to the community, we may publish it to the Research page. ## Content Guidelines To get your research onto the Research page, it must respect by the following guidelines: 1. The research is based on some financial concept, theory, or model. It’s not just a strategy of some technical indicators and overfit parameters. 2. The attached backtest or notebook is concise and contains plenty of comments. 3. If the code is Python, it follows the [PEP8 style guide](https://peps.python.org/pep-0008/). 4. The text is well-written English without grammar or spelling errors. 5. The text can't be generated by an LLM like ChatGPT. 6. In-line code snippets (not code blocks) are in bold face. 7. If there are math symbols throughout the text, it uses LateX syntax (for example, `\(x\)`). 8. Asset class names are capitalized (for example, “Equity”). 9. The content has the following "Heading 1" sections: 1. **Introduction**: This section summarizes your area of research. Explain what type of strategy it is, the universe and asset classes it’s applied to, the datasets it requires, and summarize results you found. If the strategy is based on a research paper, reference the paper at the end of the introduction. 2. **Background**: This section provides background information on foundational concepts of the strategy, utilizing LateX syntax when necessary. For example, if the strategy creates low beta portfolios, this section should define what beta is and how it’s calculated. By the end of the Background section, readers should understand what the entire strategy is, the universe it’s applied to, the factors it uses, and the portfolio construction technique. 3. **Implementation**: This section walks the reader through how to implement the strategy in LEAN. Explain each step in text and then include a short code snippet. When appropriate, link the content to relevant pages of the [QuantConnect documentation](https://www.quantconnect.com/docs/v2.md) so readers can learn more. 4. **Results**: This section describes the backtest period, notes the Sharpe ratio, and explains if the strategy underperformed or outperformed the underlying benchmark. One of the paragraphs should analyze the parameter sensitivity and feature a screenshot of [the Sharpe ratio heatmap from the optimization results page](https://www.quantconnect.com/docs/v2/cloud-platform/optimization/results.md#05-Parameter-Charts). End this section by discussing the results and some areas of further research. 5. (Optional) **References**: This section is a list of references in APA style to any source material like research papers. For more information on the APA style of reference lists, see [Basic Principles of Reference List Entries](https://apastyle.apa.org/style-grammar-guidelines/references/basic-principles) on the APA Style website. ## Examples Review the following research posts for examples that respect the content guidelines: - [Low Beta Portfolios Across Industries](https://www.quantconnect.com/research/18469/low-beta-portfolios-across-industries/p1) - [Opening Range Breakout for Stocks in Play](https://www.quantconnect.com/research/18444/opening-range-breakout-for-stocks-in-play/p1) - [Kelly Criterion Applications in Trading Systems](https://www.quantconnect.com/research/18312/kelly-criterion-applications-in-trading-systems/p1) - [Bitcoin as a Leading Indicator](https://www.quantconnect.com/research/17902/bitcoin-as-a-leading-indicator/p1) - [Automating the Wheel Strategy](https://www.quantconnect.com/research/17871/automating-the-wheel-strategy)