> For the complete documentation index, see [llms.txt](https://docs.marketcompass.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.marketcompass.ai/tech-solutions/architecture.md).

# Architecture

At Market Compass, we leverage cutting-edge technology solutions to gather, analyze, and visualize data from various sources, providing our users with actionable insights for informed investment decisions. Here's an overview of our technology stack and the solutions we employ:

<figure><img src="/files/aAxSAqmLCM3x5azN3UC9" alt=""><figcaption></figcaption></figure>

1. [**Data Gathering**](/tech-solutions/data-collection.md)
   * Utilizing mining activity on [Subnet 17](/subnet/subnet-17.md) on the Commune AI chain to gather data.
   * Querying the X (Twitter) API to collect real-time data on cryptocurrency-related topics.
   * Scraping data from 4chan, newsletters, Discord, and Telegram groups to capture diverse perspectives.
   * Storing collected data in a robust big data database for efficient retrieval and analysis.
2. [**Data Analysis**](/tech-solutions/data-analysis.md)
   * Analyzing data in different contexts, including project identification, narrative analysis, sentiment analysis, and distinguishing between fundamental data and marketing content.
   * Leveraging state-of-the-art AI models such as ChatGPT, Grok, Bard, Gemini, and others to extract valuable insights from the gathered data.
   * Assessing social sentiment through analysis of comments, views, and other engagement metrics.
3. [**Data Visualization**](/tech-solutions/data-visualization.md)
   * Presenting analyzed data in an easy-to-use platform interface for users to access and interpret.
   * Generating sentiment charts, narrative mindshare charts, and project mindshare charts to visualize trends and patterns.
   * Correlating price data with sentiment and mindshare to provide users with comprehensive insights into market dynamics.
   * Offering price prediction models based on sentiment analysis to aid users in making informed trading decisions.
4. **Trading Bot**
   * Integrating trading bots as a nice-to-have feature to automate trading based on sentiment analysis and market insights.
   * Empowering users to execute trades efficiently and capitalize on opportunities identified through our platform.

### **Technology Stack**

* Programming Languages: Python, Node.js
* Cloud Infrastructure: Amazon AWS (Amazon Web Services)
* AI Models: ChatGPT, Grok, Bard, Gemini, and others
* Data Sources: X (Twitter) API, 4chan, newsletters, Discord, Telegram groups
* Database: Robust big data database for efficient data storage and retrieval


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