Great! Here’s how we can structure the system design at a high level with those components in mind:
High-Level System Design
Key Components
-
Data Ingestion Layer:
- MinIO: For object storage, replacing AWS S3 during development and for backup storage.
- Airflow: For orchestrating the entire data pipeline—handling the movement and processing of data across components, including feature extraction, model training, and evaluation.
-
Data Storage and Feature Management:
- Feature Store (Feast): To store and serve features required for training and real-time inference. It can manage both historical features and real-time features, and keep track of feature lineage.
- MinIO: Will be used as a backup storage solution for saving datasets, models, and logs.
- Database: An RDBMS or NoSQL database (such as MongoDB) for holding metadata, models, and portfolio information.
-
Model Training and Experimentation:
- MLflow: To track experiments, log parameters, metrics, and models, and to serve models for inference.
- Jupyter Notebooks: For initial model development and experimentation, integrated with MLflow for logging.
-
Model Serving:
- Docker Containers: For packaging the models and services as self-contained units, ensuring reproducibility, and making deployment easier.
- FastAPI/Flask: For creating APIs for model inference, portfolio optimization, and other services like data processing.
-
Data Analytics and Visualization:
- Apache Superset: For real-time visualization and dashboarding, giving you insights into market data, portfolio performance, clustering, and risk analysis.
-
Compute and Orchestration:
- Docker: Containerizing the applications (Airflow, MLflow, APIs, etc.) and running them on local machines or cloud instances.
- Docker Swarm: For local orchestration of services when scaling out, handling load balancing and service discovery.
-
Messaging/Queue System:
- RabbitMQ or Kafka: For asynchronous communication between different services (e.g., triggering model retraining or updating portfolio weights).
-
Real-Time Data Processing:
- Airflow: Orchestrating the movement of data from external sources to MinIO and triggering feature engineering and model retraining.
- Real-time Event Processing: Use Airflow tasks to trigger actions when new market data is received.
-
API Layer:
- FastAPI/Flask: Expose endpoints for model inference, portfolio updates, and other critical functionalities.
- API Contracts: Standardized contracts defining input data (OHLCV, features, etc.), request and response formats (JSON, arrays of values, etc.).
Component Communication & Data Flow:
-
Data Ingestion:
- External market data is collected by scraping or via APIs.
- Data is stored in MinIO (object storage) or a database.
- Airflow schedules the data pipelines to ensure continuous data collection.
-
Feature Engineering & Storage:
- Airflow tasks trigger feature engineering jobs, feeding data from MinIO into the Feature Store (Feast).
- Feature Store serves both historical and real-time features.
- Processed features are stored in the Feature Store, and new features are logged in MLflow.
-
Model Training & Experimentation:
- MLflow tracks models and experiments, logging hyperparameters, metrics, and final models.
- Training jobs are executed in containers, and results are logged in MLflow.
-
Model Deployment & Serving:
- Docker containers are used to serve models through FastAPI or Flask.
- Models are queried by the portfolio management system for real-time inference.
-
Real-time Processing:
- Kafka or RabbitMQ will handle real-time data ingestion and event-driven processing.
- For example, if a new stock price is available, it will trigger model inference and portfolio updates.
-
Orchestration & Monitoring:
- Airflow manages the pipeline orchestration and monitors task success/failure.
- Apache Superset provides visualizations of portfolio performance, stock clustering, and risk analytics.
API Contracts:
-
Data Ingestion API:
- POST /api/data: Receives raw stock data (OHLCV, technical indicators, etc.).
- Request:
{ "symbol": "AAPL", "date": "2024-11-15", "open": 145.2, "high": 146.8, "low": 144.0, "close": 145.7, "volume": 10000 } - Response:
{ "status": "success", "message": "Data stored successfully" }
-
Model Inference API:
- POST /api/predict: Receives stock data for prediction.
- Request:
{ "symbol": "AAPL", "features": [0.01, -0.02, 0.03] } - Response:
{ "prediction": 0.02, "confidence": 0.85 }
-
Portfolio Update API:
- POST /api/portfolio/update: Receives the new portfolio weights.
- Request:
{ "weights": { "AAPL": 0.25, "GOOG": 0.35, "TSLA": 0.4 } } - Response:
{ "status": "success", "message": "Portfolio updated successfully" }
-
Model Management API:
- GET /api/model/{model_id}: Fetches the current model.
- Response:
{ "model_id": "abc123", "status": "deployed", "last_updated": "2024-11-15" }
High-Level Architecture Diagram:
+--------------------+
| External Market |
| Data Source |
+--------------------+
|
v
+-------------------+
| Data Ingestion |
| (Airflow) |
+-------------------+
|
v
+----------------------------+
| Feature Engineering |
| (MinIO, Feast, MLflow) |
+----------------------------+
|
v
+----------------------------+
| Model Training & Tracking |
| (MLflow, Docker) |
+----------------------------+
|
v
+--------------------+
| Model Serving/API |
| (FastAPI, Flask) |
+--------------------+
|
v
+--------------------------+
| Portfolio Management |
| (Model Inference) |
+--------------------------+
|
v
+-------------------------+
| Visualization & Dash |
| (Apache Superset) |
+-------------------------+Detailed High-Level Architecture Diagram
Here’s the detailed breakdown of the components and their interactions:
+--------------------+
| External Market |
| Data Source |
| (Crypto/Stocks) |
+--------------------+
|
v
+------------------------+
| Data Ingestion Layer |
| (Airflow, Kafka, SQS) |
+------------------------+
|
v
+----------------------------+
| Data Storage Layer |
| (MinIO, MongoDB, SQL DB) |
+----------------------------+
|
v
+---------------------------------------------+
| Feature Engineering & Management Layer |
| (Feast, Feature Store, Airflow, MLflow) |
+---------------------------------------------+
|
v
+-----------------------------------------------+
| Model Training & Experimentation Layer |
| (MLflow, Docker, Jupyter, Hyperparameter) |
+-----------------------------------------------+
|
v
+----------------------------------------------+
| Model Deployment and Serving Layer |
| (Docker, FastAPI, Flask, MLflow API) |
+----------------------------------------------+
|
v
+--------------------------------------------+
| Portfolio Management & Trading Execution |
| (Model Inference, Backtesting, Risk) |
+--------------------------------------------+
|
v
+------------------------------------------+
| Visualization & Analytics Layer |
| (Superset, Dashboards, Real-time Alerts) |
+------------------------------------------+Detailed Explanation of the Flow
-
External Market Data Source:
- Input: Market data from external sources, such as stock price feeds or cryptocurrency data APIs.
- Output: Raw market data in the form of OHLCV (Open, High, Low, Close, Volume), technical indicators, and possibly alternative data sources.
-
Data Ingestion Layer (Airflow, Kafka, SQS):
- Airflow: Orchestrates all the data pipelines. Airflow will schedule and trigger jobs for data ingestion and processing.
- Kafka/SQS: These are used for real-time message passing. Whenever new data arrives, Kafka/SQS will notify the system to trigger data processing workflows.
-
Data Storage Layer (MinIO, MongoDB, SQL DB):
- MinIO: Stores raw market data, processed data, feature engineering outputs, and model files as backup storage.
- MongoDB/SQL DB: Used for structured data storage, such as feature store metadata, model metadata, and portfolio information.
-
Feature Engineering & Management Layer (Feast, Feature Store, Airflow, MLflow):
- Feast: Manages both real-time and historical features required for model training and inference. Feast ensures feature consistency across different models and pipelines.
- Airflow: Orchestrates feature extraction jobs, triggers feature pipelines whenever new data comes in, and feeds it to Feast.
- MLflow: Logs all feature engineering steps and tracks the lineage of features.
-
Model Training & Experimentation Layer (MLflow, Docker, Jupyter, Hyperparameter):
- MLflow: Logs experiments, tracks hyperparameters, and stores model artifacts.
- Docker: Containerizes training jobs to ensure reproducibility and easy scaling. Docker allows you to run models in isolated environments.
- Jupyter: Used for interactive experimentation, model development, and tuning.
-
Model Deployment and Serving Layer (Docker, FastAPI, Flask, MLflow API):
- Docker: Once a model is trained, it is containerized using Docker, allowing for easy deployment to production.
- FastAPI/Flask: Exposes the model inference API. FastAPI (for speed) or Flask (for flexibility) serve the model in real-time for portfolio management and decision-making.
- MLflow API: If necessary, MLflow can serve the models via its API for real-time inference and also manage multiple versions of models.
-
Portfolio Management & Trading Execution Layer:
- Model Inference: Inference from the trained models is used to make trading decisions based on the portfolio’s performance, predictions, and risk analysis.
- Backtesting: Historical data is used to evaluate the model’s performance on past data.
- Risk Analysis: Ensures that the trading system adheres to risk management protocols, including diversification, position sizing, and stop-loss mechanisms.
-
Visualization & Analytics Layer (Superset, Dashboards):
- Superset: Allows you to create interactive dashboards for real-time tracking of your portfolio performance, model accuracy, and market data.
- Real-time Alerts: Alerts on important changes, like a significant drop in portfolio value or a change in market conditions.
Data Flow Example
Step-by-Step Data Flow:
-
Step 1: Data Ingestion
- Market data from APIs (stocks, crypto) is fetched and stored in MinIO as raw data (OHLCV).
- Kafka or SQS queues message the system about new data arrival.
-
Step 2: Feature Engineering
- Airflow schedules jobs to run periodically to process raw data and extract technical indicators (moving averages, RSI, MACD).
- Processed data is then ingested into Feast to serve as features for training and inference.
-
Step 3: Model Training
- MLflow tracks all experiments, logging metrics such as accuracy, precision, and recall for each model.
- Docker containers are used to train models in isolated environments to ensure reproducibility.
- After training, the model is stored and logged in MLflow.
-
Step 4: Model Deployment
- The best-performing model is deployed as a Docker container with FastAPI or Flask serving the inference API.
- The portfolio management system sends market data to the model for predictions.
-
Step 5: Portfolio Management & Trading Execution
- The model outputs buy/sell/hold signals based on its predictions (price movements, volatility, etc.).
- The trading algorithm takes those signals and places trades using an API to a broker or exchange.
-
Step 6: Real-Time Monitoring & Analytics
- Superset is used to create dashboards that track the portfolio’s performance, providing visual insights.
- Alerts notify the system in case of critical changes (e.g., portfolio drawdown or market anomalies).