System requirements

Functional:

• User Registration and Authentication:
• Implement strong password hashing algorithms (e.g., bcrypt) for secure password storage.
• Integrate with social login providers (e.g., Google, Facebook) for a convenient user experience (optional).
• View Market Data:
• Allow users to filter and customize market data views (e.g., watchlists, specific exchanges).
• Integrate with charting libraries for advanced technical analysis tools.
• Display news feeds and economic data relevant to specific stocks or sectors.
• Place Orders:
• Allow users to specify order quantities and durations (e.g., Day Orders, Good Til Cancelled)
• Implement advanced order types like trailing stop-loss and iceberg orders (for sophisticated traders).
• Portfolio Management:
• Integrate with market data feeds to provide real-time portfolio valuation.
• Allow users to download transaction history and performance data in various formats (e.g., CSV, PDF).
• Offer basic portfolio rebalancing tools and investment strategy suggestions (optional).
• Real-Time Notifications:
• Allow users to customize notification preferences (e.g., email, SMS, in-app alerts) for different events.
• Integrate with communication platforms like web sockets for real-time push notifications with minimal latency.

Non-Functional:

• Performance:
• Define latency targets for order processing and market data updates (e.g., milliseconds for order execution).
• Implement performance monitoring tools and dashboards for continuous system optimization.
• Scalability:
• Design the system using message queues and asynchronous processing to handle high volumes of concurrent users.
• Utilize cloud-based infrastructure with auto-scaling capabilities to accommodate spikes in trading activity.
• Reliability:
• Implement redundant data storage across geographically distributed servers to ensure high availability.
• Design the system with disaster recovery plans in place to minimize downtime in case of outages.
• Security:
• Enforce secure communication protocols (e.g., HTTPS) for all data transmission.
• Implement multi-factor authentication (MFA) as a mandatory security layer for all user accounts.
• Regularly conduct security audits and penetration testing to identify and address vulnerabilities.
• Compliance:
• Integrate with KYC/AML verification services to comply with anti-money laundering regulations.
• Maintain detailed audit logs of all user activities and transactions for regulatory reporting purposes.
• Design the platform to be adaptable to future regulatory changes.

By elaborating on these requirements, we provide a clearer picture of the functionalities and performance expectations for the trading platform. This detailed breakdown helps guide the design and development process to ensure a user-friendly, secure, and reliable trading experience.

API design

The real-time stock trading platform will require a well-defined set of APIs to facilitate communication between different components and potentially external applications. Here's a breakdown of some key expected APIs:

1. User Management APIs:

• Register User: Allows users to create new accounts with username, password, and other relevant information.
• Login User: Validates user credentials and provides authentication tokens for accessing other APIs.
• Get User Profile: Retrieves user information like account details, preferences, and settings.
• Update User Profile: Enables users to update their profile information.

2. Market Data APIs:

• Get Real-time Quotes: Provides real-time market data for specific stocks or indices, including price, volume, and change.
• Get Historical Data: Retrieves historical price data for a specified period.
• Get Order Book: Retrieves the current order book for a specific stock, showing buy and sell orders at different price levels.
• Subscribe to Market Data: Allows users to subscribe to real-time updates for specific stocks or market data feeds.

3. Order Management APIs:

• Place Order: Submits an order to buy or sell a security, specifying order type (market, limit, stop-loss, etc.), quantity, and price (if applicable).
• Cancel Order: Allows users to cancel an existing order before it's filled.
• Get Order Status: Retrieves the current status of an order (pending, filled, cancelled).
• Get Order History: Provides a list of past orders placed by the user.

4. Portfolio Management APIs:

• Get Portfolio Positions: Retrieves user's current holdings and their corresponding quantities and market values.
• Get Portfolio Performance: Provides performance metrics like total return, realized and unrealized P/L.
• Download Transaction History: Allows users to download a history of their transactions in a specified format.

5. Notification APIs:

• Subscribe to Notifications: Enables users to subscribe to real-time notifications for order updates, price alerts, and market events.
• Send Notification: (Internal API) Used by other components to trigger notifications based on specific events (e.g., order execution, price threshold reached).

Security Considerations:

• All APIs should be authenticated using secure tokens obtained through user login.
• Implement rate limiting to prevent abuse and ensure system stability under high load.
• Validate and sanitize user input to prevent security vulnerabilities like injection attacks.

Additional APIs:

• Depending on platform features, additional APIs might be needed for functionalities like:
• Managing watchlists
• Accessing news feeds and economic data
• Integrating with external charting tools (optional)

By defining a well-structured set of APIs, we ensure clear communication between different components and enable potential integration with external applications. Remember, this is a high-level overview, and specific API details like request parameters and response formats will be defined during the development process.

Database Selection for Stock Trading Platform

The CAP theorem states that in a distributed system, you can only have at most two of the following properties:

• Consistency: Every read reflects the latest write operation.
• Availability: Every request receives a (non-error) response, even if it's outdated data.
• Partition Tolerance: The system continues to operate even when network partitions occur.

Here's a breakdown of suitable databases for different types of data in the stock trading platform, considering the CAP theorem:

1. User Data (User_Management_Service):

• Database Type: SQL Database (e.g., PostgreSQL, MySQL)
• Reasoning: Structured data like user profiles, accounts, and settings require strong consistency and relational queries for efficient management.
• CAP Theorem: Balanced - User data is relatively static and requires strong consistency for security and reliability. Most SQL databases offer ACID transactions for data integrity.

2. Market Data (Market_Data_Service):

• Database Type: Time-series Database (e.g., InfluxDB, TimescaleDB) or NoSQL Database (e.g., Cassandra)
• Reasoning: Time-series data (stock prices, order book changes) requires fast writes and retrieval based on timestamps.
• CAP Theorem: Availability Focused - Real-time market data updates are crucial. Short-term inconsistencies can be tolerated for faster updates. Time-series databases are optimized for high write throughput and efficient time-based queries. Cassandra offers eventual consistency, ensuring data converges over time.

3. Order Data (Order_Management_System & Trade_Execution_Engine):

• Database Type: High-performance Key-Value Store (e.g., Redis) or In-Memory Database (e.g., Apache Ignite)
• Reasoning: Order data requires fast read/write access for order placement, execution, and management.
• CAP Theorem: Availability Focused - Order processing needs high responsiveness. Short-term inconsistencies can be resolved later through reconciliation processes. High-performance key-value stores offer fast access and scalability for order data.

4. User Portfolio & Transaction History (Portfolio_Management_Service):

• Database Type: SQL Database (e.g., PostgreSQL) or Document Database (e.g., MongoDB)
• Reasoning: Portfolio data requires consistency and efficient retrieval of user holdings and historical transactions.
• CAP Theorem: Balanced - Portfolio data is critical for users and requires consistency with a reasonable level of availability. SQL databases with strong consistency guarantees or document databases with eventual consistency models can be considered based on specific needs.

5. Audit Logs & Regulatory Data (Compliance_Service):

• Database Type: SQL Database (e.g., PostgreSQL)
• Reasoning: Audit logs and regulatory data require high durability and accurate record keeping.
• CAP Theorem: Consistency Focused - Regulatory compliance demands a strong focus on data integrity and retrievability. SQL databases with ACID transactions ensure data consistency for auditing purposes.

Additional Considerations:

• Hybrid Approach: You may consider a hybrid approach where different database types are used for different functionalities based on the CAP theorem trade-offs.
• Data Replication: Data replication across geographically distributed servers can improve availability and fault tolerance.

Data Partitioning Strategies for Stock Trading Platform

Partitioning Strategy:

The best partitioning strategy for this platform depends on the type of data and access patterns. Here are two key approaches:

1. Horizontal Partitioning by User ID:

• Reason: This approach partitions data across different databases or shards based on the user ID. It promotes efficient access to user-specific data (portfolio, orders) and scales well with increasing user base.

1. Vertical Partitioning by Data Type:

• Reason: This approach separates data based on its type (user data, market data, order data) into different databases or shards. It simplifies database schema design for specific functionalities and potentially improves performance for certain queries.

Partitioning Algorithm:

A common partitioning algorithm for this scenario is Hash Partitioning.

• Reason: Hash Partitioning uses a hash function on the user ID (or other chosen key) to distribute data across partitions/shards. This ensures even distribution and avoids hotspots on specific databases, especially for horizontal partitioning.

Sharding Strategy:

The best sharding strategy aligns with the chosen partitioning approach:

• For Horizontal Partitioning: Use Range Sharding.
• Reason: Range Sharding assigns a specific range of user IDs to each shard. This simplifies routing queries to the appropriate shard based on the user ID and improves scalability for horizontal partitioning.
• For Vertical Partitioning: Use Database Sharding.
• Reason: Database Sharding involves separate databases for each data type. This simplifies data management for specific functionalities but requires careful coordination for queries that span multiple data types.

High-level design

here's a high-level design for the real-time stock trading platform, identifying the key components needed for a complete end-to-end solution:

User Interface (UI):

• Provides a user-friendly interface for users to interact with the platform.
• Allows users to search for stocks, view market data, place orders, manage portfolios, and receive notifications.
• Ensures a responsive and secure user experience.

User Management Service:

• Handles user registration, login, and authentication.
• Stores user information securely.
• Integrates with KYC/AML verification services for compliance.

Market Data Service:

• Retrieves real-time market data from exchanges and financial data providers.
• Provides users with stock quotes, order book depth, historical data, and news feeds.
• Utilizes message queues and caching mechanisms for efficient data delivery.

Order Management System (OMS):

• Processes user orders for buying and selling stocks.
• Integrates with the Risk Management component to evaluate risk before order execution.
• Routes orders to the appropriate exchange or execution venue.
• Manages order lifecycles (placement, modification, cancellation, and execution).

Trade Execution Engine:

• Executes orders in the market by interacting with exchanges or other execution venues.
• Ensures best execution practices to get users the best possible price.
• Handles order rejections and partial fills.

Risk Management Service:

• Evaluates potential risks associated with user orders based on pre-defined risk limits and user profiles.
• May consider factors like portfolio exposure, margin usage, and order size.
• Can reject orders that exceed risk thresholds or suggest alternative strategies.

Portfolio Management Service:

• Tracks user holdings and calculates portfolio performance.
• Provides users with real-time updates on their portfolio value and P/L.
• Offers functionalities like transaction history download and basic portfolio rebalancing tools (optional).

Notification Service:

• Sends real-time notifications to users about order executions, price alerts, and important market events.
• Utilizes push notifications and email to ensure timely delivery.
• Allows users to customize notification preferences.

Compliance Service:

• Ensures adherence to all relevant financial regulations (KYC, AML, etc.).
• Manages audit logs for user activity and transactions for reporting purposes.
• May integrate with external compliance services for regulatory checks.

Data Storage:

• Stores user data, market data, order history, and other platform information securely.
• Utilizes a scalable and reliable database solution to handle high data volumes.
• May implement data redundancy across geographically distributed servers for high availability.

Administration Console:

• Provides an interface for administrators to monitor system health, manage user accounts, and configure platform settings.
• Allows for risk parameter adjustments and user management.

Communication Interfaces:

• APIs for communication between different components within the platform.
• May include APIs for external integrations with charting tools or news feeds (optional).

Security Measures:

• Secure communication protocols (HTTPS) for all data transmission.
• Encryption of sensitive user data at rest and in transit.
• Regular security audits and penetration testing to identify and address vulnerabilities.
• User access controls and multi-factor authentication (MFA) for added security.

This high-level design provides a comprehensive overview of the key components needed for a real-time stock trading platform. Each component can be further elaborated on during the detailed design phase, defining specific functionalities, data structures, and communication protocols. By having a well-defined architecture, you can ensure a robust, secure, and scalable platform that meets the demands of modern stock trading.

Request flows

Here is the sequence diagram for Search Stock and Get Current Price

Here is the sequence diagram for Placing an Order

Here is the sequence diagram for Getting Order Confirmation

Detailed component design

Order Management System (OMS) in Detail

The Order Management System (OMS) plays a critical role in the stock trading platform by handling user orders, ensuring efficient execution, and maintaining order lifecycles. Here's a deeper dive into its functionalities:

Core Responsibilities:

• Order Placement: Receives user orders specifying stock symbol, quantity, price (for limit orders), and order type (market, limit, stop-loss, etc.).
• Order Validation: Validates orders against user account information, available funds (for buying), and other pre-defined limits.
• Risk Management Integration: Interacts with the Risk Management component to evaluate potential risks associated with the order before execution.
• Order Routing: Based on order type and market conditions, routes orders to the appropriate execution venue (exchange, Electronic Communication Network (ECN), etc.).
• Order Execution: Sends orders to the chosen execution venue for matching with available buy/sell orders.
• Order Status Tracking: Keeps track of the order status (pending, filled, partially filled, cancelled).
• Order Management: Allows users to modify or cancel open orders before execution.
• Order History: Provides users with a record of past orders, including execution details and timestamps.

Edge Case: Handling Simultaneous Orders

Ensuring fair execution priority for simultaneous orders from multiple users is crucial for a robust OMS. Here are two common approaches:

1. Timestamp-Based Order Queuing: Orders are placed in a queue based on the exact time of receipt (microsecond precision). Orders are executed in the order they appear in the queue, ensuring fairness.
2. Price-Time Priority: Orders with better prices (limit orders closer to the National Best Bid and Offer) are prioritized within the timestamp queue. This approach rewards aggressive orders while maintaining a fair execution sequence for orders placed at the same time and price.

The chosen approach depends on the platform's focus (fairness vs. price priority) and regulatory requirements.

Gap: Error Handling and Transaction Integrity

Detailed error handling mechanisms are essential for the OMS to maintain transaction integrity:

• Order Validation Errors: Clearly communicate errors during order placement (e.g., insufficient funds, invalid order type).
• Network Disruptions/Server Downtime: Implement order persistence mechanisms (e.g., message queuing) to ensure orders are not lost and are submitted for execution when connectivity is restored.
• Partial Fills: Handle scenarios where only part of the order quantity can be filled. Update order status and notify users with options to cancel the remaining unfilled portion.
• Order Rejection/Cancellation: Clearly communicate rejection reasons (risk violation, exchange error) and provide options for users to modify or cancel orders.
• Transaction Logging: Maintain a detailed audit log of all order activities (placement, modification, execution, cancellation) with timestamps for accurate record-keeping and troubleshooting.

By implementing robust error handling and recovery mechanisms, the OMS can ensure a reliable and efficient order management experience for users.

Additional Considerations:

• Order Book Management: The OMS may maintain an internal order book to aggregate user orders and improve execution efficiency.
• Order Routing Strategies: Advanced OMS can employ sophisticated routing algorithms based on factors like market depth, execution venue fees, and potential for price improvement.
• Order Filling Strategies: The OMS can support various order filling strategies (all-or-nothing, fill-or-kill, immediate-or-cancel) to cater to different user preferences.

A well-designed OMS is a critical component of any stock trading platform, ensuring smooth order handling, efficient execution, and a seamless user experience.

Trade Execution Engine Design and Technology

The Trade Execution Engine (TEE) sits at the heart of order execution within the stock trading platform. Here's a breakdown of its design considerations, technology choices, and scaling strategies:

Design Principles:

• Low Latency: Every millisecond counts in order execution. The TEE needs to be optimized for low latency communication with exchanges and other execution venues.
• High Availability: The TEE should be highly available to ensure uninterrupted order processing and execution.
• Scalability: The system needs to handle high volumes of orders efficiently and scale to accommodate increasing trading activity.
• Resilience: The TEE should be able to handle network disruptions, exchange outages, and partial order fills gracefully.

Technology Stack:

• Messaging Queue: A message queue like Kafka or RabbitMQ can be used for decoupled order routing and asynchronous communication between the OMS and TEE.
• FIX Protocol (Financial Information eXchange): FIX is a widely adopted messaging standard for communication with exchanges and ECNs. The TEE can leverage FIX libraries to send and receive order messages.
• Lightweight Messaging Frameworks: Technologies like ZeroMQ or WebSockets can be used for faster, real-time communication with execution venues if latency is critical.
• Order Routing Logic: The TEE may implement routing algorithms to choose the optimal execution venue based on factors like price, liquidity, and execution fees.
• Order Management Libraries: Libraries can be used to manage order lifecycles, including status updates, partial fills, and cancellations.
• Persistence Layer: A database or message queue can be used to persist order data in case of failures or for historical record-keeping.

Scaling Strategies:

• Horizontal Scaling: The TEE can be deployed across multiple servers to distribute processing load and increase capacity. Load balancers can be used to distribute incoming orders efficiently.
• Microservices Architecture: Breaking down the TEE into smaller, independent microservices (e.g., order routing, exchange connection) can improve scalability and maintainability.
• Caching: Caching frequently accessed data from exchanges (e.g., market depth) can reduce latency and improve execution speed.
• Circuit Breaker Pattern: Implementing a circuit breaker pattern can automatically suspend connections to temporarily unavailable exchanges, preventing cascading failures.