trader-data

Project Overview:

Modular platform for collecting, processing, and analyzing trading data using serverless AWS infrastructure, and LLMs. It demonstrates cloud-native design, scalable ML workflows, and modern DevOps practices.

🚀 Quick Start

For deployment instructions, environment setup, and configuration details, see DEPLOYMENT.md.

Repository Structure

trader-data/
├── collection/             # Data collection serverless functions
│   ├── handler.py          # Lambda handlers for data collection
│   ├── serverless.yml      # Serverless deployment configuration
│   ├── slsenvs.yml         # Environment variables
│   └── requirements.txt    # Python dependencies
├── llm/                    # LLM integration serverless functions  
│   ├── serverless.yml      # Serverless deployment configuration
│   ├── slsenvs.yml         # Environment variables
│   └── requirements.txt    # Python dependencies
├── processing/             # Data processing and ML serverless functions
│   ├── consumer/           # ECS orchestration handlers
│   ├── functions/          # Data processing and ML functions
│   ├── task/               # Containerized ML tasks
│   ├── tests/              # Unit and integration tests
│   ├── utils/              # Shared utilities
│   ├── serverless.yml      # Serverless deployment configuration
│   ├── slsenvs.yml         # Environment variables
│   └── Dockerfile          # Container image for ML tasks
├── .github/workflows/      # CI/CD GitHub Actions workflows
├── run                     # Project management and deployment script
├── DEPLOYMENT.md           # Detailed deployment guide
├── SECURITY.md             # Security policies and practices
└── README.md               # This documentation

Key Components

• Data Collection Service (collection/):

  • Serverless functions for collecting market data from various APIs
  • Automated data ingestion and storage to S3
  • Integration with external data providers

• LLM Service (llm/):

  • Integration with Large Language Models (Ollama, OpenAI, etc.)
  • Natural language processing for market analysis
  • Chat interfaces for trading insights

• Data Processing Service (processing/):

  • Candlestick pattern detection and technical analysis
  • Machine learning model training and prediction
  • ECS-based containerized processing workflows
  • PySpark integration for large-scale data processing

• Infrastructure:

  • Serverless AWS architecture with Lambda, ECS, S3, and DynamoDB
  • Infrastructure as Code with Serverless Framework
  • CI/CD pipelines with GitHub Actions

Setup

Prerequisites

• Python 3.10+
• Docker (for local development and deployment)
• AWS CLI (for deploying serverless functions)
• Serverless Framework (for Lambda deployment)

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/trader-data.git
   cd trader-data

2. Install dependencies for each service:

   # Data collection service
   cd collection/
   pip install -r requirements.txt
   pip install -r requirements-dev.txt
   npm install
   cd ..
   
   # LLM service
   cd llm/
   pip install -r requirements.txt
   pip install -r requirements-dev.txt
   npm install
   cd ..
   
   # Processing service
   cd processing/
   pip install -r requirements.txt
   pip install -r requirements-dev.txt
   npm install
   cd ..

3. Set up environment variables:

   cp .env.example .env.local
   # Edit .env.local with your AWS account details
   source .env.local

4. Configure AWS credentials:

   aws configure
   # or export AWS credentials as environment variables

Usage

Local Development

Run tests for each service:

# Test collection service
cd collection/
pytest

# Test LLM service
cd ../llm/
pytest

# Test processing service
cd ../processing/
pytest

Build and test Docker containers:

cd processing/
docker build -t TAGNAME -f task/Dockerfile .
docker run --env-file .env.local TAGNAME

Deployment

Deploy individual services:

# Using the run script (sets up dependencies automatically)
ENV=dev REGION=us-east-2 DEPLOYMENT_BUCKET=my-bucket ./run deploy-service collection
ENV=dev REGION=us-east-2 DEPLOYMENT_BUCKET=my-bucket ./run deploy-service llm
ENV=dev REGION=us-east-2 DEPLOYMENT_BUCKET=my-bucket ./run deploy-service processing

# Or deploy manually with serverless
cd collection/
sls deploy --stage dev --param="deployment-bucket=my-deployment-bucket"

Deploy all services:

# Deploy everything (see DEPLOYMENT.md for detailed instructions)
./scripts/deploy-all.sh dev

Features

📊 Data Collection & Processing

• Multi-source Data Ingestion: Collect market data from various APIs (Coinbase, etc.)
• Real-time Processing: Stream processing capabilities for live market data
• Technical Analysis: Candlestick pattern detection and technical indicators
• Data Storage: Efficient S3-based data lake with partitioned storage

🤖 Machine Learning & AI

• ML Model Training: Automated model training with PySpark on ECS
• Prediction Services: Real-time prediction APIs for trading signals
• LLM Integration: Natural language interface for market analysis
• Pattern Recognition: Advanced algorithmic pattern detection

☁️ Cloud-Native Architecture

• Serverless Functions: AWS Lambda for scalable event-driven processing
• Container Orchestration: ECS for ML training and batch processing
• Infrastructure as Code: Serverless Framework deployment
• Auto-scaling: Dynamic scaling based on demand

🔒 Security & Compliance

• Secrets Management: AWS SSM Parameter Store for sensitive data
• IAM Integration: Fine-grained access control
• Encryption: Data encryption in transit and at rest
• Audit Logging: Comprehensive logging for compliance

Key Services

Data Collection Service (collection/)

• Automated market data collection from external APIs
• Configurable data sources and collection intervals
• Error handling and retry mechanisms
• Data validation and cleansing

LLM Service (llm/)

• Integration with Ollama, OpenAI, and other LLM providers
• Natural language queries for market data
• Automated report generation
• Chat interfaces for trading insights

Processing Service (processing/)

• Candlestick pattern detection and analysis
• Machine learning model training and inference
• Large-scale data processing with PySpark
• ECS-based containerized workflows

About the Author

This project provides a comprehensive data collection and processing system for financial market analysis.
Key skills demonstrated:

• Cloud architecture (AWS Lambda, S3, ECS)
• Python (PySpark, Pandas)
• Machine Learning (feature engineering, model training)
• DevOps (CI/CD, Docker, Serverless Framework, Terraform)
• LLM integration (Ollama, Hugging Face, OpenAI API)

Future Enhancements

🔮 Planned Features

Data & Analytics

• Real-time Data Streaming: Implement Kinesis Data Streams for live market data ingestion
• Advanced Pattern Recognition: Expand candlestick pattern detection with ML-based pattern discovery
• Multi-Asset Support: Extend beyond crypto to support stocks, forex, and commodities
• Historical Data Warehouse: Implement data lake architecture with partitioned historical data

Machine Learning & AI

• Automated Model Training: Scheduled retraining pipelines with performance monitoring
• Ensemble Models: Combine multiple ML models for improved prediction accuracy
• Reinforcement Learning: RL agents for dynamic trading strategy optimization
• Sentiment Analysis: Integration with news and social media sentiment data
• Custom LLM Fine-tuning: Fine-tune models on trading-specific datasets

Infrastructure & Scalability

• Kubernetes Migration: Transition from ECS to EKS for better container orchestration
• Multi-Region Deployment: Global deployment for reduced latency and high availability
• Auto-Scaling: Dynamic scaling based on market volatility and data volume
• Cost Optimization: Implement Spot instances and reserved capacity for cost reduction

Security & Compliance

• Zero-Trust Architecture: Enhanced security with service mesh and mTLS
• Audit Logging: Comprehensive audit trails for all trading decisions
• Compliance Reporting: Automated compliance reports for regulatory requirements
• Encryption at Rest: End-to-end encryption for all sensitive data

Monitoring & Observability

• Distributed Tracing: OpenTelemetry integration for request tracing
• Custom Metrics: Business-specific metrics and alerting
• Performance Analytics: Latency and throughput optimization
• Health Checks: Comprehensive health monitoring with automatic recovery

🤝 Contributing

We welcome contributions! Areas where help is needed:

• Documentation: Improve setup guides and API documentation
• Testing: Expand test coverage and add integration tests
• Performance: Optimize data processing and ML inference
• Features: Implement any of the planned enhancements above