## 1. Objective
Gather and compile hourly snapshots of both supply and borrowing rates for AAVE v3 across three networks (Base, Ethereum, and Arbitrum) over the last 2 years. The goal is to provide a time-series dataset of interest rates, allowing for analysis of rate dynamics, trends, and potential opportunities or risks across different blockchains.

## 2. Data Requirements

### Essential Data Fields
- **Timestamp**: The precise hour for each data point (UTC preferred)
- **Blockchain Network**: Identify whether the data is from Base, Ethereum, or Arbitrum
- **Token**: The specific asset (e.g., USDC, DAI, ETH) that has lending and borrowing rates
- **Supply (Deposit) Rate**: The annualized rate lenders receive for depositing the token
- **Borrowing Rate**: The annualized rate borrowers pay to borrow the token

### Optional Data Fields
- **Utilization Rate**: The ratio of borrowed funds to the total supplied funds
- **Total Supply and Total Borrowed Amounts**: Context and liquidity availability indicators
- **Variable vs. Stable Rates**: Separate rates for variable and stable borrowing rates per asset

## 3. Approach to Data Collection

### Step 1: Identify Data Sources
- **Aave Subgraph API**: Official subgraph for querying historical data across networks
- **Aave Protocol Direct API**: Alternative API for rate information
- **Historical Indexing Services**: Services like Dune Analytics, Flipside Crypto, or Covalent
- **On-Chain Data Extraction**: Direct blockchain transaction logs and smart contract interactions

### Step 2: Construct and Optimize Queries
Use Aave subgraphs on each network to query data by token, network, and hourly timestamp.

### Step 3: Data Processing and Normalization
- **Data Cleaning**: Remove duplicates, handle missing data points, standardize timestamps
- **Normalization**: Ensure consistent units across all rates
- **Interpolation**: Handle missing values carefully

### Step 4: Data Storage and Access
- **Storage Format**: CSV, JSON, or SQL database
- **Versioning and Updates**: Automated scripts for dataset maintenance

## 4. Analysis Possibilities and Applications
- **Trend Analysis**: Study rate fluctuations and market demand
- **Cross-Network Comparison**: Identify rate disparities across networks
- **Risk Assessment**: Monitor rate increases and liquidity issues
- **DeFi Strategy Development**: Create optimized lending/borrowing strategies

## 5. Potential Challenges
- **Data Availability**: Incomplete historical data on newer networks
- **API/Query Limits**: Managing large dataset retrieval
- **Data Consistency**: Handling format differences across networks

## 6. Example Dataset Structure

| Timestamp (UTC) | Network | Token | Supply Rate (%) | Borrowing Rate (%) | Utilization Rate (%) |
|----------------|---------|--------|-----------------|-------------------|---------------------|
| 2022-11-01 00:00:00 | Ethereum | DAI | 1.75 | 3.25 | 65.4 |
| 2022-11-01 01:00:00 | Ethereum | DAI | 1.76 | 3.26 | 65.6 |
| 2022-11-01 00:00:00 | Arbitrum | USDC | 2.15 | 4.50 | 70.3 |
| 2022-11-01 01:00:00 | Base | ETH | 0.95 | 1.85 | 45.2 |

ai-marketplace

Hourly AAVE v3 Supply/Borrowing Rates for the last 2 years on base , Ethereum and Arbitrum

BEPRO Token

## Objective

Develop an AI-powered agent to detect potential rug pulls and scams in new crypto projects by analyzing token contracts, liquidity information, team credibility, social interactions and transaction patterns on Dexes. The solution should help users identify high-risk projects, minimizing their exposure to fraudulent activities.

## Scope of Work

### Data Collection & Preprocessing

• Gather data on project tokens, contract addresses, and liquidity metrics from blockchain explorers (e.g., Etherscan), and DeFi analytics platforms like Dexscreener or DexTools.

• Aggregate historical data on known scam tokens for model training, including red flags such as sudden liquidity withdrawals, team token holdings, and low community activity.

• Standardize and preprocess data to handle various data sources, including token creation dates, social media presence, and developer wallet activity.

###	Model Development

•	Design a classification model (e.g., **random forest**, **logistic regression**, or **neural network**) that predicts rug pull risk based on contract properties, token distribution, and liquidity changes.

•	Engineer features such as:

•	Liquidity lock duration and developer token ownership percentage.

•	Team social presence and project website quality.

•	Transaction volume trends and abnormal token transfers.

•	Tune the model for high precision in identifying high-risk tokens while reducing false positives to avoid flagging legitimate projects.

### Evaluation & Testing:

• Validate the model on a test dataset of known scam projects and legitimate tokens to assess accuracy, precision, and recall.

• Test the alert system for timely notifications, ensuring minimal latency and effective delivery.

• Conduct robustness testing to ensure the model adapts to evolving scam tactics, like stealth marketing or modified liquidity schemes.

### Deployment Readiness:

**Scope of Work**

1.	**Data Collection & Preprocessing**:

•	Gather data on project tokens, contract addresses, and liquidity metrics from blockchain explorers (e.g., Etherscan), and DeFi analytics platforms like Dexscreener or DexTools.

•	Aggregate historical data on known scam tokens for model training, including red flags such as sudden liquidity withdrawals, team token holdings, and low community activity.

•	Standardize and preprocess data to handle various data sources, including token creation dates, social media presence, and developer wallet activity.

2.	**Model Development**:

•	Design a classification model (e.g., random forest, logistic regression, or neural network) that predicts rug pull risk based on contract properties, token distribution, and liquidity changes.

•	Engineer features such as:

•	Liquidity lock duration and developer token ownership percentage.

•	Team social presence and project website quality.

•	Transaction volume trends and abnormal token transfers.

•	Tune the model for high precision in identifying high-risk tokens while reducing false positives to avoid flagging legitimate projects.

3.	**Evaluation & Testing**:

•	Validate the model on a test dataset of known scam projects and legitimate tokens to assess accuracy, precision, and recall.

•	Test the alert system for timely notifications, ensuring minimal latency and effective delivery.

•	Conduct robustness testing to ensure the model adapts to evolving scam tactics, like stealth marketing or modified liquidity schemes.

###	Deployment Readiness:

•	Package the model and a microservice to evaluation queries, ensuring it can integrate with web and mobile platforms.

•	Document setup and deployment steps, including data sourcing requirements, in a shared drive or repository for easy access.


### Requirements

•	**Experience** with fraud detection, blockchain analytics, and machine learning.

•	**Proficiency** in processing transaction data, liquidity metrics, and implementing real-time alerting.

•	**Ability** to develop a reliable, scalable solution that provides crypto users with insights on potential fraud risks.

### Deliverables

•	**Codebase**: Well-documented Python code, including data preprocessing, model architecture, and alert system scripts.

•	**Trained Model**: Exportable tenderflow or pytorch model optimized for scam detection, ready for deployment.

•	**Documentation**: Detailed README with installation instructions, data source setup, and usage guidelines.

•	**Performance Report**: Evaluation metrics, test set results, and model robustness testing summary.

## Rugpull Detection AI Agent Implementation

A production-ready solution for early detection of crypto scams and rug pulls, implementing:

### Core Features
- Real-time token analysis and risk assessment
- Multi-source data collection (Token info, Contract data, Liquidity data)
- Neural network-based classification with 92% accuracy
- REST API endpoints for easy integration

### Key Components Implemented
1. **Data Collection**
   - Token contract analysis
   - Liquidity monitoring
   - Transaction pattern detection
   - Historical data aggregation

2. **Risk Detection**
   - Stealth marketing detection
   - Liquidity scheme analysis
   - Wash trading patterns
   - Smart contract vulnerabilities

3. **Technical Implementation**
   - 18 comprehensive test cases
   - FastAPI-based REST API
   - Configurable alert system
   - Performance monitoring

### Repository Structure
The implementation follows a modular architecture:
```python
rugpull_detector/
├── src/
│   ├── data_collection/    # Data collectors
│   ├── feature_engineering/# Feature extractors
│   ├── model/             # Neural network model
│   └── api/               # REST API endpoints
├── tests/                 # Test suite
└── docs/                  # Documentation
```

### Performance Metrics
- Accuracy: 92%
- Precision: 89%
- Recall: 94%
- F1 Score: 91%
- Average response time: <2s

### Getting Started
```bash
# Clone repository
git clone https://github.com/aybanda/AI-Powered-Rugpull-Detection-Agent-with-Real-time-Analysis

# Install dependencies
pip install -r requirements.txt

# Run tests
pytest -v

# Start API
uvicorn src.api.main:app --reload
```

### API Usage
```python
# Example API request
POST /analyze
{
    "address": "0x123...",
    "chain_id": 1
}

# Response
{
    "risk_score": 0.87,
    "risk_factors": ["liquidity_risk", "contract_risk"],
    "confidence": 0.92
}
```

All code is thoroughly documented and tested. The implementation meets all requirements specified in BEPRO task #176.

AI-Powered Rugpull Detection Agent with Real-time Analysis

Create an AI-Powered Agent for Early Detection of Crypto Scams and Rug Pulls

USD Coin

## **Objective**

Develop a high-accuracy, real-time bot detection model using the PyTorch Python Framework to identify bot-like behavior in comments on web platforms. The solution should effectively differentiate between authentic human users and bots, even as bot behaviors evolve.

## **Scope of Work**

### **Data Collection & Preprocessing**:

• Use existing labeled datasets (e.g., platform comment data with bot/human labels) and/or generate synthetic data if real-world labeled data is limited.

• Clean, tokenize, and preprocess text comments. Implement NLP techniques to standardize input for the model.

### **Model Development**:

• Develop a deep learning model in PyTorch. Options could include:

• Transformer-based models (e.g., BERT, RoBERTa) fine-tuned for bot detection.

• RNN or LSTM models with attention mechanisms if appropriate.

• Logistic Regression

• Incorporate both text content and potential behavioral features (e.g., comment frequency, sentiment score, posting patterns).

###  **Training & Optimization**:

• Train the model on labeled data and optimize for high precision and recall, especially given the class imbalance often present (bots being a minority).

• Employ techniques like class weighting or oversampling to improve bot identification accuracy.

• Fine-tune hyperparameters, evaluate multiple architectures, and explore regularization methods to improve generalization.

###  **Evaluation & Testing**:

•  Validate the model on a separate test set, ideally containing real-world comments with varying bot characteristics.

•  Provide detailed metrics, including precision, recall, F1 score, and ROC-AUC.

•  Test model robustness against adversarial examples, such as bots mimicking human behavior.

##  **Deployment Readiness**:

•  Package the trained model with PyTorch and document steps for deployment in Google Drive and provide the link on the proposal

•  Include considerations for model scalability, including batch processing for real-time scenarios.

### **Requirements**

• **Experience** with NLP, deep learning models, and PyTorch.

• **Proficiency** in handling text data, model optimization, and classification in the presence of imbalanced datasets.

•  **Ability** to develop a robust, scalable solution suitable for deployment.

### **Deliverables**

• **Codebase**: Well-documented PyTorch code, including model architecture, training pipeline, and evaluation scripts.

• **Trained Model**: Exportable PyTorch model (.pth file) ready for deployment.

• **Documentation**: Detailed README with installation instructions, dataset preparation steps, and usage guidelines.

• **Performance Report**: Analysis with metrics, test set performance, and tuning details.

Develop an AI PyTorch Model to Identify Bot Activity in User Comments

## Goal

Develop an **AI-based model** to monitor and predict Ethereum gas fees in real-time, allowing users to time their transactions for cost savings. The agent should be capable of analyzing gas price fluctuations and offering insights for optimal transaction windows, helping users reduce expenses on gas-heavy networks like Ethereum.

## Scope of Work

### Data Collection & Preprocessing

• Collect historical and real-time gas price data from sourceslike Etherscan or other public blockchain analytics APIs.

• Analyze network congestion patterns and transaction types to understand factors influencing gas prices.

• Clean, aggregate, and preprocess data to identify trends and patterns for effective prediction modeling.

### Model Development

• Develop a predictive model using machine learning techniques in Python (e.g., LSTM or Prophet) to forecast short-term gas price trends over next 7 days. 

• Consider real-time adjustments based on user-specified factors, such as urgency and maximum acceptable gas fees.

Possible model architectures could include:

• Recurrent Neural Networks (RNNs), LSTMs, or GRUs for time series forecasting.

• Transformer-based models for capturing complex patterns in gas fee fluctuations.

### Evaluation & Testing

• Validate the model against historical data to measure the accuracy and reliability of gas fee predictions.

• Test the model's accuracy against historical gas fee data and assess performance with metrics such as Mean Absolute Error (MAE) and Mean Squared Error (MSE).

•  Test the API app for response speed and ensure it aligns with real-time network changes.

### Deployment Readiness

• Prepare the agent for deployment by packaging it as a microservice, integrating API calls to get predictions and trends .
 
• Document deployment steps, including data source configuration, and make it accessible in a shared drive or GitHub repository and include it on the bepro deliverable

### Requirements

• Experience with time-series forecasting, blockchain data analytics, and real-time alerting systems using RNN , Transformers,…

• Proficiency in handling transaction data and implementing machine learning models for prediction.

### Deliverables

• **Codebase**: Documented Python code, including data processing, model architecture and the pythorch trained model.

• **Trained Model**: Ready-to-deploy model (.pth file) capable of real-time predictions.

• **Documentation**: Detailed README with installation steps, data requirements, and usage guidelines

• **Performance Report**: Evaluation metrics, testing outcomes, and improvement recommendations.
 

# Deliverable
https://drive.google.com/file/d/1HPz3aChpT5kWOSdVqp9hBCVINyiA29wN

### Notes:
- I was unable to add the link above as a deliverable
- Access to the deliverable will be granted after confirmation that the requester matches the task owner
- _Extensive_ documentation exists inside `docs/` folder

---

# Ethereum Gas Fee Prediction System

An AI-powered system for monitoring and predicting Ethereum gas fees in real-time, helping users optimize transaction timing for cost savings.

## Features

- Real-time gas price data collection from Etherscan API
- LSTM, Transformer, and XGBoost prediction models
- 7-day gas price forecasting
- RESTful API for real-time predictions
- Historical data analysis and trend identification
- Performance evaluation with MAE/MSE metrics

## Installation

1. Install dependencies:
```bash
pip install -r requirements.txt
```

2. Set up environment variables:
```bash
cp .env.example .env
# Edit .env with your Etherscan API key
```

3. Run the application:
```bash
python main.py
```

## API Usage

### Start the server
```bash
uvicorn api.main:app --host 0.0.0.0 --port 8000
```

### Endpoints

- `GET /health` - Health check
- `GET /gas/current` - Current gas prices
- `GET /gas/predict` - 7-day gas price predictions
- `GET /gas/history` - Historical gas price data
- `POST /gas/optimize` - Get optimal transaction timing

## Model Usage

This project provides three machine learning models for gas price prediction:

- **XGBoost** - Best performance (R² > 0.996)
- **LSTM** - Time series patterns (R² > 0.991)
- **Transformer** - Complex patterns (R² > 0.976)

### **Quick Start**
```bash
# Start API server
uvicorn src.api.main:app --host 0.0.0.0 --port 8000 --reload

# Make predictions
curl -X POST "http://localhost:8000/gas/predict" \
  -H "Content-Type: application/json" \
  -d '{"hours_ahead": 24, "model_type": "xgboost"}'
```

## Model Training

```bash
# Train specific models
python train_model.py --model lstm --epochs 100
python train_model.py --model transformer --epochs 50
python train_model.py --model xgboost

# Train all models
python train_model.py --model all --epochs 100

# Or run directly from src/scripts/
python src/scripts/train_model.py --model lstm --epochs 100
```

## Performance Metrics

- Mean Absolute Error (MAE)
- Mean Squared Error (MSE)
- Root Mean Squared Error (RMSE)
- R-squared Score

## 📚 Documentation

Comprehensive documentation is available in the `docs/` folder:

## 🚀 Quick Start

1. **Get Etherscan API Key**: Sign up at [etherscan.io](https://etherscan.io)
2. **Configure Environment**: `echo ETHERSCAN_API_KEY=your_key > .env`
3. **Collect Data**: `python collect_data.py`
4. **Train Models**: `python train_model.py`
5. **Start API**: `python main.py`

## License

MIT License

Tasks

0

4

873