RTSAP is a comprehensive real-time streaming analytics platform designed for financial markets, combining high-performance stream processing with advanced analytics capabilities. Built on modern cloud-native technologies, it provides scalable, reliable, and low-latency data processing for financial analysis.
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Major Features:
- Real-time stream processing with Kafka and Flink
- Time-series data storage with TimescaleDB
- Kubernetes-based deployment architecture
- Basic financial analytics pipeline
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Core Components:
- Stream ingestion system
- Real-time processing engine
- Time-series database integration
- Analytics API endpoints
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Stream Processing
- Real-time data ingestion using Apache Kafka
- Complex event processing with Apache Flink
- Low-latency analytics pipeline
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Data Storage
- Time-series optimization with TimescaleDB
- Document storage using MongoDB
- Transactional data in PostgreSQL
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Analytics Engine
- Real-time financial calculations
- Historical data analysis
- Machine learning integration
- Interactive visualization
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Scalability
- Kubernetes-based orchestration
- Horizontal scaling capabilities
- Resource optimization
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Reliability
- Fault-tolerant architecture
- Data replication
- Automated recovery
- Ubuntu 24.04
- 8GB RAM
- 4 CPU cores
- 50GB storage
- Docker installed
- 16GB+ RAM
- 8+ CPU cores
- 100GB+ SSD storage
- NVIDIA GPU (optional)
- Kubernetes cluster
# Download Miniconda installer
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
# Make installer executable
chmod +x Miniconda3-latest-Linux-x86_64.sh
# Run installer
./Miniconda3-latest-Linux-x86_64.sh
# Initialize conda for your shell
conda init bash # or conda init zsh if you use zsh# Create conda environment for RTSAP
conda create -n rtsap python=3.9
conda activate rtsap
# Install core dependencies
conda install -c conda-forge \
numpy \
pandas \
scikit-learn \
matplotlib \
seaborn \
jupyterlab \
ipykernel \
fastapi \
uvicorn \
python-dotenv \
sqlalchemy \
psycopg2 \
pymongo \
confluent-kafka \
requests \
pytest
# Install ML libraries (if needed)
conda install -c pytorch pytorch torchvision torchaudio cudatoolkit=11.8
# Install additional packages not available in conda
pip install kafka-python timescale# Update package list
sudo apt update
# Install Docker
sudo apt install -y docker-ce docker-ce-cli containerd.io
sudo usermod -aG docker $USER
# Install Minikube
curl -LO https://github.com/kubernetes/minikube/releases/download/v1.33.0/minikube-linux-amd64
sudo install minikube-linux-amd64 /usr/local/bin/minikube
# Install kubectl
curl -LO "https://dl.k8s.io/release/v1.30.0/bin/linux/amd64/kubectl"
sudo install kubectl /usr/local/bin/
# Install Helm
curl https://raw.githubusercontent.com/helm/helm/master/scripts/get-helm-3 | bash# Verify conda environment
conda list
# Verify Python installation
python -c "import sys; print(sys.version)"
# Verify key packages
python -c "import numpy; import pandas; import fastapi; print('All key packages installed')"
# Verify CUDA (if using GPU)
python -c "import torch; print(f'CUDA available: {torch.cuda.is_available()}')"rtsap/
├── config/ # Configuration files
│ ├── .env # Environment variables
│ └── config.yaml # Application configuration
├── src/ # Source code
│ ├── api/ # FastAPI application
│ ├── ingestion/ # Data ingestion scripts
│ └── processing/ # Stream processing logic
├── notebooks/ # Jupyter notebooks
├── scripts/ # Utility scripts
├── data/ # Data files
├── models/ # ML models
├── tests/ # Test files
└── environment.yml # Conda environment file
# Start Minikube
minikube start --cpus 8 --memory 40960
# Enable addons
minikube addons enable metrics-server
minikube addons enable dashboard
# Deploy core services
helm install my-kafka bitnami/kafka
helm install my-timescaledb timescale/timescaledb-single# Activate environment
conda activate rtsap
# Start the API server
cd src/api
uvicorn main:app --reload
# Run tests
pytest tests/
# Start Jupyter Lab
jupyter labCreate a .env file in your project root:
# .env
PYTHONPATH=${PYTHONPATH}:${PWD}
CONDA_ENV_PATH=$(conda info --base)/envs/rtsap
POSTGRES_HOST=my-postgres-postgresql.default.svc.cluster.local
TIMESCALEDB_HOST=my-timescaledb.default.svc.cluster.local
KAFKA_BOOTSTRAP_SERVERS=my-kafka.default.svc.cluster.local:9092# config.yaml
resources:
requests:
memory: "2Gi"
cpu: "1000m"
limits:
memory: "4Gi"
cpu: "2000m"- Add machine learning pipeline
- Implement automated backtesting
- Enhance monitoring system
- Add distributed processing
- Implement advanced analytics
- Create web interface
- Market Analysis: Real-time market data processing
- Risk Management: Live risk calculation and monitoring
- Algorithmic Trading: Strategy backtesting and execution
- Compliance: Transaction monitoring and reporting
- Role-based access control
- Encrypted data transmission
- Secure credential management
- Audit logging
Contributions are welcome! Please see our Contributing Guidelines.
- Fork the repository
- Create your feature branch
- Commit your changes
- Push to the branch
- Open a Pull Request
- Conda environment issues
# Reset conda environment
conda deactivate
conda env remove -n rtsap
conda env create -f environment.yml- Kubernetes connectivity
# Check cluster status
minikube status
kubectl cluster-info- Service issues
# Check running pods
kubectl get pods
kubectl describe pod <pod-name>This project is licensed under the MIT License - see the LICENSE file for details.
- Website: karales.com
- X (Twitter): @alex_karales
- Email: karales@gmail.com
- Github: @akarales
- Follow development on GitHub
- Read our Blog
- Join discussions on GitHub Discussions
- Follow @alex_karales for updates
- Apache Kafka for streaming capabilities
- TimescaleDB for time-series storage
- Kubernetes for orchestration
- FastAPI for API development
- Conda community for package management
RTSAP is under active development. Check our Project Board for planned features and current progress.
- GitHub Issues: Project Issues
- Documentation: Wiki
- Community: Discussions
Made with ❤️ by Alex Karales