Development of an Automated Token Analysis System
Development of an Automated Analysis Tool for Meme and Microcap Tokens
Hello
We are planning to develop a comprehensive system for the early detection of meme and microcap tokens. The system should analyze trading and social media data to identify potential trends, assess security aspects, and minimize fraud risks (e.g., rug pulls).
Key Requirements:
1. Data Sources & Processing
Twitter Scraper
Utilize the Twitter API to capture mentions of new tokens ($TOKEN_NAME, #TOKEN, etc.).
Analyze tweet frequency, engagement (retweets, likes), and influencer participation (>30k followers).
Perform sentiment analysis (positive/neutral/negative) to assess the overall sentiment.
DexScreener Scraper
Retrieve new token trading pairs via the DexScreener API.
Collect data on volume, number of trades, and identify sponsored listings.
Automatically filter based on trading activity (>100 trades/hour, >20 trades/5 min).
Social Media Check ([login to view URL] API)
Verify which influencers (≥30k followers) are discussing the token.
Determine engagement rates (likes, comments, follower ratio).
Aggregate a "Social Hype Score" based on mentions and reach.
Security Check ([login to view URL] API)
Automatically assess a token's security based on:
Liquidity Burn (permanent liquidity lock).
Minting & Pausability of the contract.
Security Rating (excluding tokens below 85% score).
2. Data Processing & Scoring
Store all data in a database (PostgreSQL, MongoDB, etc.).
Compute an overall score that combines trading volume, social media hype, and security.
Filter out high-risk tokens (<85% security rating or low overall score).
3. Dashboard & Notifications
Visualization: Display analyzed tokens in a dashboard (e.g., using Streamlit, Dash, or React).
Alerts: Send notifications via Telegram bot, Discord webhook, or email when relevant new tokens are identified.
4. Expansion Options (Optional)
NLP Analysis: Advanced sentiment detection (bullish/bearish trends).
Bot Detection: Filtering out fake accounts in Twitter data.
Machine Learning: Identifying suspicious pump-and-dump patterns.
Holder Analysis: Investigating whether a few wallets control the majority of a token.
Cloud Scaling: Utilizing AWS/GCP, Docker, and CI/CD for stable operation.
What We Are Looking For:
An experienced development team or a solo full-stack developer with expertise in Python, API integration, databases, and potentially frontend technologies.
Knowledge of web scraping, NLP, machine learning, or blockchain analysis is a plus.
Next Steps:
If you're interested, let's discuss an initial technical assessment and feasibility. We would appreciate a proposal with an estimation of implementation time, costs, and potential challenges.
We are looking for developers with prior experience in these areas
Core Competencies of the Developer for This Project
A suitable programmer or development team should have the following technical skills and experience:
1. Backend Development & Data Processing
Programming Languages: Python (preferred) or Node.js for API integrations and data processing.
API Development & Integration: Experience with RESTful APIs (e.g., Twitter API, DexScreener API, [login to view URL] API, [login to view URL] API).
Web Scraping: Knowledge of BeautifulSoup, Scrapy, or Selenium for HTML-based scraping (if API access is insufficient).
Databases: Experience with relational (PostgreSQL, MySQL) and NoSQL databases (MongoDB) for storing token data and analytics.
2. Data Analysis & Evaluation
Data Processing: Pandas, NumPy for efficient handling of large datasets.
Scoring Models: Developing a rating system that combines trading activity, social media hype, and security metrics.
NLP & Sentiment Analysis: Basic experience with NLP libraries (NLTK, spaCy, TextBlob) to detect positive/negative mentions in tweets.
Machine Learning (Optional): TensorFlow, scikit-learn, or PyTorch for anomaly detection (e.g., pump-and-dump patterns).
3. Frontend Development & Visualization
Dashboards & UI: Experience with Streamlit, Dash, or modern frontend frameworks (React, Vue.js) to visualize analyzed data.
Data Visualization: Matplotlib, Plotly, or D3.js for charts, tables, and graphs.
4. Security & Blockchain Knowledge
Smart Contract Analysis: Understanding Solidity and blockchain security risks (rug pulls, liquidity burn, minting functions).
Blockchain APIs: Experience with [login to view URL] or [login to view URL] for querying on-chain data.
5. Scaling & Deployment
Cloud Technologies: AWS, GCP, or Azure for hosting and data processing.
Docker & CI/CD: Experience with Docker, Kubernetes, and Continuous Integration/Deployment for scalable architecture.
Asynchronous Processing: Use of Celery, RabbitMQ, or Kafka for efficient handling of large data loads.
Additional Soft Skills & Work Approach
Structured Work Approach: Experience with agile methodologies (Scrum, Kanban).
Documentation: Clear code documentation and API overviews.
Teamwork: Ability to collaborate with other developers or analysts.
Conclusion
The ideal candidate should be a strong backend developer with skills in API integration and data analysis. Experience in data visualization and blockchain security is a significant advantage. If machine learning is used for fraud detection, ML knowledge would be beneficial but not strictly required.
Best regards,
Daniel