System requirements

Functional:

User Account Management:

• Traders should be able to create, update, and manage their accounts.
• Each user will have access to a portfolio, order history, and account balances.

Order Routing:

• Traders should be able to place buy and sell orders for various financial instruments (stocks, options, etc.).
• The system should support different order types (e.g., market orders, limit orders, stop-loss orders).
• Orders must be routed to the appropriate exchanges or liquidity providers based on market conditions.

Market Data Processing:

• The system should stream live market data (e.g., stock prices, bid/ask prices, volume).
• Provide real-time updates to traders regarding price changes and market events.
• Support historical data for analysis and back-testing.

Trade Execution:

• Ensure fast execution of orders with low-latency in response to market conditions.
• Confirm orders and trade completion, showing detailed trade execution reports.

Risk Management:

• Implement real-time risk management tools, including margin checks, exposure limits, and stop-loss mechanisms.
• Provide alerts for potential risks and trading limits being breached.

Compliance and Reporting:

• Implement real-time compliance checks (e.g., regulatory reporting, anti-money laundering).
• Provide trade reports for tax filings, audit trails, and compliance monitoring.

Notifications and Alerts:

• Send notifications for order status (filled, partially filled, cancelled), price alerts, and risk alerts.
• Support both real-time alerts and periodic notifications (e.g., portfolio value changes).

Market Order Matching:

• Match orders based on price-time priority, ensuring fair and efficient order matching.

Non-Functional:

Performance:

• The system should support real-time order execution with sub-second latency for buy/sell orders.
• It must process large volumes of market data and orders per second without delays.

Scalability:

• The system should scale horizontally to handle increased market activity and more traders as the user base grows.
• The architecture should support rapid scaling of order routing, market data processing, and trade execution.

Reliability:

• The system must be highly available with minimal downtime, ensuring continuous market access.
• Implement failover mechanisms and redundant systems for mission-critical components like order execution and risk management.

Security:

• Ensure secure user authentication (e.g., two-factor authentication) and encrypted communications (SSL/TLS).
• Provide mechanisms to prevent unauthorized trading and financial fraud.

Data Consistency:

• Data must be consistent across multiple systems (orders, trades, account balances) in real-time.
• Use eventual consistency where appropriate for systems like market data streaming.

Regulatory Compliance:

• The system must comply with global financial regulations (e.g., SEC, MiFID II).
• Provide logging and audit trails for all transactions and order activities.

Availability and Fault Tolerance:

• Ensure 99.99% uptime with automatic failover for key components.
• Implement load balancing and system replication to mitigate risk of failure.

Monitoring and Analytics:

• Provide real-time monitoring of trading performance, order statuses, and market data.
• Collect detailed analytics on order execution times, risk breaches, and user activities.

User Experience:

• Provide an intuitive, responsive, and fast trading interface.
• Support both web and mobile platforms with seamless updates for real-time market conditions.

Capacity estimation

1. Active Users

• Assumptions:
  • 10 million active traders using the system.
  • Each trader places an average of 10 orders per day.
• Daily Active Users (DAU): 10 million users
• Peak Concurrent Users: 10% of DAU (1 million concurrent users) during market open times.

2. Orders and Trade Volume

• Assumptions:
  • Each user places 10 orders per day on average.
  • Some users place larger volumes of trades, especially institutional traders.
  • Trade executions are typically high-frequency and processed in real-time.
• Total Orders per Day: 10 million users * 10 orders = 100 million orders per day.
• Peak Orders per Hour: Assume 10% of orders happen during peak hours, i.e., 10 million orders per hour.
• Order Execution Latency: Sub-second, ideally 500ms per order to ensure quick execution during peak market conditions.

3. Market Data Processing

• Assumptions:
  • The system needs to process and stream real-time market data for various assets (stocks, options, futures).
  • Each asset has real-time updates at varying frequencies (e.g., stock prices updated every second).
  • 1000 active assets (stocks, bonds, options) with multiple data points being updated every second.
• Market Data Streams:
  • 1000 assets with 1 update per second = 1,000 updates per second for each asset.
  • Total market data updates per second = 1000 assets * 1 update per second = 1,000 updates per second.
• Peak Load: Consider peak trading hours with rapid price fluctuations, which might spike market data processing to 10,000 updates per second.

4. Database and Storage Requirements

• Assumptions:
  • Each order requires metadata (e.g., order ID, user ID, timestamp, financial instrument details) stored in a relational database like PostgreSQL.
  • Trade execution details and portfolio balances are also stored in databases.
  • User profiles, order history, and real-time positions need to be updated frequently.
• Order Database:
  • Assuming each order record requires about 500 bytes of storage.
  • 100 million orders per day * 500 bytes per order = 50 GB of storage per day for orders alone.
  • Storage per Year: 50 GB/day * 365 days = 18.25 TB per year.
• Market Data:
  • Each market data update might be 200 bytes (including asset, timestamp, bid/ask price, volume).
  • 1,000 updates per second * 200 bytes per update = 200 KB per second.
  • Daily Market Data Storage: 200 KB/sec * 60 sec * 60 min * 24 hours = 17.28 GB per day for market data storage.

5. Network Bandwidth

• Assumptions:
  • Market data updates are streamed to users with very low latency. The system must support low-latency trading data feeds.
  • Each update is about 200 bytes, and each user actively consumes multiple market updates per second.
• Peak Data Transfer per Second:
  • 1 million concurrent users * 10 updates per second per user = 10 million updates per second.
  • Network throughput = 10 million updates per second * 200 bytes per update = 2 GB per second for market data delivery to users.

6. Risk Management and Analytics

• Assumptions:
  • The Analytics Service tracks key trading metrics, such as executed trades, risk exposure, margin usage, etc.
  • This service collects data on user activities, orders, and market trends, which requires significant storage and computational power for real-time processing.
• Real-time Risk Checks:
  • Each order might trigger 10 risk checks (e.g., margin requirements, exposure limits).
  • The system processes 100 million orders per day, meaning 1 billion risk checks per day.
  • Data processing latency for risk management should be sub-second.
• Analytics:
  • Real-time metrics like total orders executed, asset volatility, and exposure must be tracked in DynamoDB or Amazon Redshift for fast retrieval and analysis.
  • High-frequency analytics will be stored in DynamoDB, aggregating daily for in-depth analysis.

7. Infrastructure and Compute

• Assumptions:
  • The system will utilize microservices for scalability, with services like order routing, market data processing, trade execution, and risk management deployed in separate containers (e.g., AWS ECS or Kubernetes).
  • Auto-scaling policies will be implemented to handle traffic surges, especially during market open times or volatile market conditions.
• Compute Needs:
  • Each microservice will scale based on the number of active users, processing orders, market data updates, and risk checks.
  • Peak Compute Load: During peak hours, the system needs to process up to 10 million orders per hour, requiring substantial computational resources for order matching, risk management, and real-time market data processing.

API design

1. User Authentication APIs

• POST /login
  • Description: Authenticates a user by verifying credentials (email, password) and returns a JWT token for subsequent requests.
• POST /register
  • Description: Registers a new user by submitting required data (email, password, username) and creates a new account.
• POST /logout
  • Description: Invalidates the user's session and JWT token to log them out of the system.
• POST /password-reset
  • Description: Initiates a password reset process, sending a reset link to the user’s registered email.

2. User Profile APIs

• GET /user/profile
  • Description: Fetches the authenticated user's profile details, including account balance, trade history, and portfolio.
• PUT /user/profile
  • Description: Updates the user's profile information, such as username, email, or password.
• GET /user/portfolio
  • Description: Retrieves the user's current portfolio, including stocks, options, and their current values.

3. Order Management APIs

• POST /orders
  • Description: Places a new buy or sell order for a specific financial instrument (e.g., stock, option). Can support different order types (market, limit, stop-loss).
• GET /orders/{order_id}
  • Description: Fetches the details of a specific order by its ID, including status (pending, filled, canceled).
• PUT /orders/{order_id}
  • Description: Modifies an existing order (e.g., price change for a limit order) before it is filled.
• DELETE /orders/{order_id}
  • Description: Cancels an open order.

4. Market Data APIs

• GET /marketdata
  • Description: Provides real-time market data for a list of financial instruments, including current prices, bid/ask spreads, and volume.
• GET /marketdata/{symbol}
  • Description: Retrieves real-time data for a specific financial instrument (e.g., stock price, options data).
• GET /marketdata/history
  • Description: Fetches historical market data (e.g., closing prices, volume) for a specific instrument over a defined time range.

5. Trade Execution APIs

• POST /trade/execute
  • Description: Executes the placed order (buy/sell) on the appropriate exchange or liquidity provider. Returns trade execution status and details.
• GET /trade/{trade_id}
  • Description: Fetches details of a completed trade, including execution price, quantity, and the exchange on which it was executed.

6. Risk Management APIs

• GET /risk/portfolio
  • Description: Retrieves risk metrics for the user’s portfolio, such as margin usage, exposure, and liquidity risk.
• GET /risk/check
  • Description: Checks the current order or portfolio for risk violations, such as margin breaches or stop-loss triggers.
• POST /risk/alert
  • Description: Triggers an alert if certain risk thresholds (e.g., margin limits, exposure limits) are breached.

7. Compliance and Reporting APIs

• GET /compliance/report
  • Description: Generates compliance reports, including trade history, market manipulation checks, and anti-money laundering reviews.
• GET /compliance/audit-trail
  • Description: Fetches a detailed audit trail of all user activities, orders, and trades for compliance purposes.
• GET /tax/report
  • Description: Generates tax reports detailing the user’s taxable events (e.g., capital gains, dividends) for the given financial year.

8. Notifications APIs

• GET /notifications
  • Description: Fetches the list of notifications for the authenticated user, such as order status changes, trade fills, and risk alerts.
• POST /notifications/mark-read
  • Description: Marks one or more notifications as read, clearing them from the user’s notification list.

9. Analytics and Market Insights APIs

• GET /analytics/user-performance
  • Description: Retrieves analytics about the user’s trading performance, including profit/loss, trading volume, and win rate.
• GET /analytics/market-trends
  • Description: Provides real-time and historical market trends, including volatility, volume trends, and major price movements.
• GET /analytics/sector-performance
  • Description: Retrieves analytics on sector-wide performance, such as top-performing stocks or industries over a specific time frame.

10. Order Matching and Execution APIs

• POST /order-matching
  • Description: Matches an incoming order (buy or sell) with existing orders in the order book based on price-time priority.
• GET /order-matching/{order_id}
  • Description: Provides information about the status of order matching (e.g., matched, partially filled, or pending).

Database design

1. User Database

• Purpose: To store user information such as login credentials, profile data, and account balance.
• Database Details:
  • Table Name: users
  • Columns:
    • user_id (Primary Key, INT)
    • email (VARCHAR, unique)
    • password_hash (VARCHAR)
    • first_name (VARCHAR)
    • last_name (VARCHAR)
    • profile_picture_url (VARCHAR, nullable)
    • created_at (TIMESTAMP)
    • updated_at (TIMESTAMP)
• Tech Used: PostgreSQL (Relational Database)
• Reason to Choose:
  • PostgreSQL is chosen because it offers strong ACID compliance, allowing for secure storage of user data and reliable transaction handling. It also supports complex queries, which are essential for managing user data and ensuring integrity across the system.

2. Orders Database

• Purpose: To store all order-related data, including order metadata (e.g., buy/sell, status) and associated order items (e.g., stock, option).
• Database Details:
  • Table Name: orders
  • Columns:
    • order_id (Primary Key, INT)
    • user_id (Foreign Key, INT, references users)
    • order_type (VARCHAR, e.g., market, limit)
    • status (VARCHAR, e.g., pending, filled, canceled)
    • price (DECIMAL)
    • quantity (INT)
    • created_at (TIMESTAMP)
    • updated_at (TIMESTAMP)
  • Table Name: order_items
  • Columns:
    • order_item_id (Primary Key, INT)
    • order_id (Foreign Key, INT, references orders)
    • symbol (VARCHAR, e.g., AAPL for Apple stock)
    • instrument_type (VARCHAR, e.g., stock, option)
    • price (DECIMAL)
    • quantity (INT)
• Tech Used: PostgreSQL
• Reason to Choose:
  • PostgreSQL is ideal for handling relational data with complex relationships, ensuring the integrity of orders and their associated items. Its support for transactions ensures that orders are processed atomically, especially during high-frequency trading scenarios.

3. Market Data Database

• Purpose: To store real-time and historical market data such as stock prices, bid/ask prices, and volumes.
• Database Details:
  • Table Name: market_data
  • Columns:
    • data_id (Primary Key, INT)
    • symbol (VARCHAR, e.g., AAPL for Apple)
    • price (DECIMAL)
    • bid_price (DECIMAL)
    • ask_price (DECIMAL)
    • volume (INT)
    • timestamp (TIMESTAMP)
• Tech Used: Elasticsearch (NoSQL, Search Engine)
• Reason to Choose:
  • Elasticsearch is used because it is optimized for fast, scalable search queries and real-time data ingestion. It can handle high-frequency updates and provide low-latency access to market data, which is essential for real-time stock trading systems.

4. Trade Execution Database

• Purpose: To store the details of executed trades, including the execution price, trade volume, and other transaction-specific data.
• Database Details:
  • Table Name: trades
  • Columns:
    • trade_id (Primary Key, INT)
    • order_id (Foreign Key, INT, references orders)
    • execution_price (DECIMAL)
    • execution_quantity (INT)
    • timestamp (TIMESTAMP)
• Tech Used: PostgreSQL
• Reason to Choose:
  • PostgreSQL provides strong support for transactional integrity, ensuring that trade executions are accurately stored and tied to the corresponding orders. This is crucial for maintaining the accuracy and consistency of the trade history.

5. Risk Management Database

• Purpose: To store data related to user risk, such as margin levels, exposure limits, and risk alerts.
• Database Details:
  • Table Name: risk_alerts
  • Columns:
    • alert_id (Primary Key, INT)
    • user_id (Foreign Key, INT, references users)
    • alert_type (VARCHAR, e.g., margin breach, exposure breach)
    • alert_message (TEXT)
    • status (VARCHAR, e.g., active, resolved)
    • created_at (TIMESTAMP)
    • resolved_at (TIMESTAMP)
  • Table Name: risk_checks
  • Columns:
    • check_id (Primary Key, INT)
    • user_id (Foreign Key, INT, references users)
    • check_type (VARCHAR, e.g., margin, exposure)
    • check_value (DECIMAL)
    • status (VARCHAR, e.g., passed, failed)
    • created_at (TIMESTAMP)
• Tech Used: PostgreSQL
• Reason to Choose:
  • PostgreSQL is chosen because it offers strong transactional capabilities and supports complex queries necessary for managing risk conditions in real-time. Its ACID compliance ensures the consistency of risk data, especially when running checks across users’ portfolios.

6. Notification Database

• Purpose: To store notifications related to orders, trades, and alerts.
• Database Details:
  • Table Name: notifications
  • Columns:
    • notification_id (Primary Key, INT)
    • user_id (Foreign Key, INT, references users)
    • notification_type (VARCHAR, e.g., order status, trade confirmation)
    • message (TEXT)
    • status (VARCHAR, e.g., read, unread)
    • created_at (TIMESTAMP)
• Tech Used: Redis (In-memory Data Store)
• Reason to Choose:
  • Redis is chosen for storing notifications because it allows for fast in-memory access to recent notifications, providing low-latency delivery to users. Redis is perfect for use cases where data needs to be quickly retrieved and updated frequently.

7. Compliance Database

• Purpose: To store compliance data such as audit trails, reports, and regulatory checks for each order and trade.
• Database Details:
  • Table Name: compliance_reports
  • Columns:
    • report_id (Primary Key, INT)
    • user_id (Foreign Key, INT, references users)
    • report_type (VARCHAR, e.g., AML check, order validation)
    • status (VARCHAR, e.g., approved, pending)
    • generated_at (TIMESTAMP)
  • Table Name: audit_trail
  • Columns:
    • audit_id (Primary Key, INT)
    • user_id (Foreign Key, INT, references users)
    • action (VARCHAR, e.g., order placed, trade executed)
    • timestamp (TIMESTAMP)
• Tech Used: PostgreSQL
• Reason to Choose:
  • PostgreSQL is selected for compliance and audit logging because of its ACID properties and ability to handle complex transactions while maintaining integrity for sensitive data.

8. Tax Reporting Database

• Purpose: To store data related to taxes, including capital gains and dividend transactions for users.
• Database Details:
  • Table Name: tax_reports
  • Columns:
    • tax_report_id (Primary Key, INT)
    • user_id (Foreign Key, INT, references users)
    • taxable_event (VARCHAR, e.g., buy, sell, dividend)
    • amount (DECIMAL)
    • created_at (TIMESTAMP)
• Tech Used: PostgreSQL
• Reason to Choose:
  • PostgreSQL provides the necessary features for handling detailed transaction history and complex calculations that are required for tax reporting, offering the flexibility needed for various tax jurisdictions and rules.

High-level design

1. Client (Web/Mobile)

• Functionality: The Client is the user interface that allows traders to interact with the stock trading system. It provides features such as placing orders, viewing portfolio and market data, receiving notifications, and managing their profile.
• Responsibilities:
  • Submit orders (buy/sell) and interact with market data.
  • Display real-time updates, including order status, trade execution, and market changes.
  • Display notifications and alerts (e.g., trade confirmation, risk alerts).
• Data Flow: Sends API requests to the API Gateway, receives responses containing market data, order details, and notifications.

2. Load Balancer

• Functionality: The Load Balancer distributes incoming requests from clients across multiple backend servers to ensure availability, reliability, and efficient resource utilization.
• Responsibilities:
  • Ensure high availability and fault tolerance by distributing traffic evenly across multiple instances of backend services.
  • Handle sudden traffic spikes and ensure no single server gets overloaded.
• Data Flow: Receives client requests and forwards them to the API Gateway, optimizing traffic management.

3. API Gateway

• Functionality: The API Gateway serves as the entry point for all client requests. It acts as a reverse proxy, routing requests to the appropriate backend services based on the request type.
• Responsibilities:
  • Handle authentication (verify JWT tokens) and forward user requests to relevant services.
  • Aggregate data from various services (e.g., user orders, trade executions) and send back unified responses.
  • Enforce security policies, such as rate-limiting and access control.
• Data Flow: Forwards requests from the Client to backend services like Authentication Service, Order Service, Market Data Service, etc.

4. Authentication Service

• Functionality: The Authentication Service is responsible for verifying users' identities and managing their session using JWT tokens.
• Responsibilities:
  • Authenticate users based on their login credentials.
  • Generate and verify JWT tokens for session management.
  • Handle user registration and password recovery.
• Data Flow: Receives login requests from the API Gateway, validates user credentials with PostgreSQL data, and returns a JWT token to the Client.

5. Order Service

• Functionality: The Order Service handles user orders, including creation, modification, and cancellation.
• Responsibilities:
  • Accept buy/sell orders from users and store them in the PostgreSQL Orders Database.
  • Validate orders and ensure they meet requirements (e.g., order type, price).
  • Update order status based on execution or cancellation.
• Data Flow: Receives order requests from the API Gateway, updates the PostgreSQL Orders Database, and notifies users about order status changes.

6. Trade Service

• Functionality: The Trade Service is responsible for executing trades, including order matching, confirmation, and trade execution.
• Responsibilities:
  • Match buy and sell orders based on price-time priority and execute trades.
  • Confirm trade execution and store trade details in the PostgreSQL Trades Database.
  • Communicate with Market Data Service to check for the best available prices.
• Data Flow: Receives orders from the Order Service, executes trades, stores trade details in the database, and sends execution confirmation to the Client.

7. Market Data Service

• Functionality: The Market Data Service provides real-time market data (e.g., stock prices, bid/ask spreads) to the platform and users.
• Responsibilities:
  • Fetch and process live market data streams from financial markets or data providers.
  • Provide real-time updates to traders about price changes, volumes, and other relevant data.
  • Store historical market data in Elasticsearch for querying.
• Data Flow: Receives market data from external sources, processes it, and makes it available to Order Service and API Gateway for display to users.

8. Risk Management Service

• Functionality: The Risk Management Service monitors and enforces risk-related policies, including margin checks, exposure limits, and stop-loss triggers.
• Responsibilities:
  • Monitor user positions to ensure they stay within acceptable risk limits.
  • Trigger Risk Alerts when margin levels are exceeded or exposure thresholds are breached.
  • Provide real-time risk management tools to users and send alerts via the Notification Service.
• Data Flow: Accesses user portfolio and order data from PostgreSQL and triggers alerts if risk thresholds are breached. It interacts with the Notification Service to inform users.

9. Compliance Service

• Functionality: The Compliance Service ensures that all trades and orders adhere to financial regulations, including anti-money laundering (AML) checks and trade reporting.
• Responsibilities:
  • Verify trades for regulatory compliance, such as KYC (Know Your Customer) and AML checks.
  • Generate compliance reports, audit trails, and tax reports.
  • Ensure that all orders and trades meet industry standards for financial regulations.
• Data Flow: Retrieves transaction data from the Trade Service, performs checks, stores compliance records in PostgreSQL, and generates reports.

10. Notification Service

• Functionality: The Notification Service sends real-time notifications to users about their orders, trades, risk alerts, and market updates.
• Responsibilities:
  • Generate and send notifications (e.g., order status, trade execution, alerts).
  • Store notifications temporarily in Redis for fast access.
  • Ensure notifications are sent immediately when certain events (like trade execution) occur.
• Data Flow: Receives notification requests from services like Trade Service, Order Service, and Risk Management, stores notifications in Redis, and sends them to users via email, push, or in-app notifications.

11. Databases

• PostgreSQL: Stores structured data such as user profiles, orders, trades, risk data, and compliance records. Provides ACID compliance for transactional integrity.
• Elasticsearch: Stores and indexes market data for fast search and retrieval, particularly useful for real-time price updates and historical market analysis.
• Redis: Used to cache notifications and user session data for quick access, improving performance and reducing load on the main databases.

Request flows

User Login Flow:

• The client sends login credentials to the API Gateway.
• The API Gateway forwards the request to the Authentication Service.
• The Authentication Service validates credentials using the PostgreSQL User Data database and returns a JWT token for the session.
• The API Gateway sends the JWT token back to the client.

Order Placement Flow:

• The Client places an order through the API Gateway.
• The Order Service stores the order details in the PostgreSQL Orders Database.
• The Market Data Service fetches relevant market data (e.g., price, bid/ask) from Elasticsearch.
• The order is forwarded to the Trade Service for execution.
• Once executed, the Notification Service sends the order confirmation to the user.

Trade Execution Flow:

• The Client sends a request to execute the trade.
• The Trade Service checks the order status, finalizes the execution, and updates the PostgreSQL Trades Database.
• A notification is sent via the Notification Service to confirm the trade execution.

Risk Management Flow:

• The Risk Management Service performs checks (e.g., margin, exposure) for each order.
• It queries the PostgreSQL Orders and User Portfolio to ensure the order meets risk requirements.
• The result is returned to the Client, indicating whether the trade passes or fails based on risk criteria.

Compliance Flow:

• The Compliance Service ensures that all orders comply with regulatory standards.
• The Compliance Service checks the PostgreSQL Orders Database and ensures that the order adheres to compliance rules (e.g., anti-money laundering).
• Notifications about compliance status are sent to the Client.

Market Data Flow:

• The Client requests real-time market data (e.g., stock price) from the API Gateway.
• The Market Data Service queries Elasticsearch for the latest market data.
• The Market Data Service returns the data to the Client.

Notification Flow:

• The Client requests notifications from the API Gateway.
• The Notification Service queries Redis for cached notifications and returns them to the client for display.

Detailed component design

1. Authentication Service

1. Component Overview:

The Authentication Service is responsible for managing user authentication and session management. It ensures users can securely log in, maintain their session with JWT tokens, and manage their credentials.

2. Key Functions:

• Login: Validates user credentials and generates a JWT token.
• Registration: Allows users to create an account with valid credentials.
• Password Reset: Handles password recovery for users who forgot their credentials.
• Session Management: Validates and refreshes active sessions using JWT tokens for security and scalability.

3. Scalability Considerations:

• Horizontal Scaling: The Authentication Service can be horizontally scaled behind a load balancer to handle large volumes of login requests during peak hours.
• Session Caching: Utilize Redis to cache active sessions, reducing the load on the database for token validation.

4. Relevant Algorithms and Data Structures:

• JWT Token Generation: For secure and stateless session management.
• Hashing Algorithms: Use bcrypt or Argon2 for securely storing passwords.

5. Challenges:

• Token Expiry: Ensure expired tokens are handled, and users are forced to re-authenticate.
• Brute Force Attacks: Protect user accounts from brute force login attempts by implementing rate limiting and account lockouts.

2. Order Service

1. Component Overview:

The Order Service is responsible for handling the entire lifecycle of orders, including creation, modification, cancellation, and querying the status of orders.

2. Key Functions:

• Create Order: Validates order details (e.g., quantity, price) and stores them in the database.
• Modify Order: Allows users to modify open orders before they are executed.
• Cancel Order: Processes requests to cancel unfilled or partially filled orders.
• Query Order Status: Allows users to check the current status of their orders (e.g., filled, pending).

3. Scalability Considerations:

• Horizontal Scaling: Multiple instances of the Order Service can be deployed behind a load balancer to handle increased traffic.
• Database Sharding: PostgreSQL can be sharded by user_id or order_date to optimize query performance.

4. Relevant Algorithms and Data Structures:

• Priority Queue: To ensure orders are processed in a price-time priority order.
• ACID Transactions: PostgreSQL ensures that order processing maintains integrity using ACID properties.

5. Challenges:

• Concurrency Handling: Prevent race conditions by using optimistic concurrency control.
• Partial Fills: Handle partially filled orders by updating status and allowing users to modify the remaining portions.

3. Trade Service

1. Component Overview:

The Trade Service executes trades by matching buy and sell orders, ensuring the trade is processed according to market rules.

2. Key Functions:

• Match Orders: Matches buy and sell orders based on price-time priority.
• Execute Trades: Confirms the execution of a trade and updates the trade status.
• Update Status: Updates the order and trade status (e.g., filled, partially filled, canceled).

3. Scalability Considerations:

• Horizontal Scaling: The Trade Service can be scaled horizontally to handle high-frequency trade requests, especially during market hours.
• Asynchronous Execution: Trades are processed asynchronously to ensure the system does not become bottlenecked.

4. Relevant Algorithms and Data Structures:

• Order Matching Algorithm: Implements price-time priority to match buy and sell orders.
• Message Queues: Use Kafka or RabbitMQ for asynchronous trade processing.

5. Challenges:

• Partial Execution: Ensure that partially executed orders remain in the system until fully processed or canceled.
• Latency: Handle high-frequency trades with low-latency execution using asynchronous processing.

4. Market Data Service

1. Component Overview:

The Market Data Service is responsible for collecting and providing real-time market data, such as stock prices, volumes, bid/ask spreads, and other market information.

2. Key Functions:

• Stream Market Data: Collect and process live market data streams from data providers or exchanges.
• Real-Time Updates: Provide real-time updates to users and other services.
• Historical Data: Fetch and provide historical market data for analysis.

3. Scalability Considerations:

• Horizontal Scaling: Use multiple instances of the Market Data Service to handle large volumes of incoming data.
• Elasticsearch: Store market data in Elasticsearch for efficient querying and fast retrieval.

4. Relevant Algorithms and Data Structures:

• Inverted Index: Used by Elasticsearch for fast data retrieval based on market symbol or other criteria.
• WebSockets: For low-latency data streaming to users.

5. Challenges:

• Data Feed Failures: Handle failures from data providers by implementing backup feeds or fallback mechanisms.
• High Traffic: Manage high-frequency data streams during volatile market conditions by employing sharding and caching.

5. Risk Management Service

1. Component Overview:

The Risk Management Service ensures that the system does not take on excessive risk by monitoring margin levels, exposure, and other risk factors.

2. Key Functions:

• Margin Checks: Ensure users have enough funds to place orders or hold positions.
• Exposure Management: Monitor portfolio exposure and trigger alerts if the user’s risk profile exceeds predefined limits.
• Real-Time Alerts: Notify users when their positions approach risk limits or margin calls.

3. Scalability Considerations:

• Real-Time Processing: Use Redis for fast in-memory processing of user positions and risk calculations.
• Horizontal Scaling: The Risk Management Service can scale horizontally to accommodate increased traffic, especially during periods of high volatility.

4. Relevant Algorithms and Data Structures:

• Risk Algorithms: Use algorithms to calculate value-at-risk (VaR), margin requirements, and exposure limits.
• In-Memory Caching: Use Redis for fast risk assessment and alerts.

5. Challenges:

• High Volume of Transactions: Handle large numbers of real-time risk checks with low latency.
• Stop-Loss Orders: Ensure that stop-loss orders are processed immediately to protect users from significant losses during volatile market conditions.

6. Compliance Service

1. Component Overview:

The Compliance Service ensures that all trades and financial activities comply with global regulations, such as KYC, AML, and trade reporting.

2. Key Functions:

• AML Checks: Perform Anti-Money Laundering checks on trades and user activity.
• KYC Verification: Ensure users are verified according to regulatory standards.
• Audit and Reporting: Generate reports for internal and external compliance audits.

3. Scalability Considerations:

• Batch Processing: Use batch processing to handle regulatory reporting outside of peak trading hours.
• Database Sharding: Partition compliance data by user_id or trade_id for better query performance.

4. Relevant Algorithms and Data Structures:

• AML Detection Algorithms: Use heuristics and machine learning models to detect suspicious trading activity.
• Audit Trails: Ensure all transactions are logged in an immutable, append-only structure for compliance tracking.

5. Challenges:

• Real-Time Compliance: Ensure that trades are processed without delay while still adhering to compliance rules.
• Data Privacy: Ensure that sensitive user data (e.g., KYC documents) is securely stored and complies with privacy regulations.

7. Notification Service

1. Component Overview:

The Notification Service sends real-time notifications to users for events like trade confirmations, order status updates, margin calls, and other alerts.

2. Key Functions:

• Send Notifications: Push notifications to users about their orders and trades.
• Manage Notification Preferences: Allow users to configure their notification preferences (e.g., via email, push notifications, or SMS).
• Event-Triggered Notifications: Notify users when certain events occur, such as price changes or risk breaches.

3. Scalability Considerations:

• Redis for Caching: Cache frequently accessed notification data in Redis for fast retrieval and delivery.
• Horizontal Scaling: Implement auto-scaling of the Notification Service to handle high volumes of notifications during peak trading hours.

4. Relevant Algorithms and Data Structures:

• Event-Driven Architecture: Use an event-driven architecture to trigger notifications based on user actions and system events.
• Queueing Mechanisms: Use message queues (e.g., Kafka) to manage high volumes of notifications.

5. Challenges:

• Notification Overload: Prevent spamming users with excessive notifications by implementing rate-limiting and batching techniques.
• Delivery Failures: Implement retries for failed notifications and use fallback mechanisms, such as email or SMS.

Trade offs/Tech choices

PostgreSQL vs NoSQL (Elasticsearch): Chose PostgreSQL for structured transactional data (orders, trades, users) for strong ACID compliance. Used Elasticsearch for fast search and retrieval of market data due to its high scalability and ability to handle real-time queries efficiently.

Horizontal Scaling vs Vertical Scaling: Opted for horizontal scaling of services like Order Service and Trade Service to handle high traffic during market hours, allowing dynamic scaling based on load rather than relying on vertical scaling, which has limited scalability.

Synchronous vs Asynchronous Processing: Used asynchronous processing (e.g., Kafka queues) for order execution and trade matching to avoid blocking during peak times, but some critical operations (like order creation) are processed synchronously to ensure data consistency.

PostgreSQL vs Redis for Caching: Used Redis to cache notifications and session data to reduce database load and improve latency. However, PostgreSQL is used for transactional data, as it guarantees data integrity.

Failure scenarios/bottlenecks

Database Overload: High order volume can overwhelm PostgreSQL leading to slow queries. Mitigation: Use read replicas and sharding to distribute the load.

Network Latency: Market data and trades might experience delays. Mitigation: Use WebSockets and CDN for fast, real-time data delivery.

Rate Limiting: During peak trading hours, too many requests can cause system slowdowns. Mitigation: Implement rate limiting and backpressure mechanisms.

Concurrency Issues: Multiple simultaneous orders may result in race conditions. Mitigation: Use optimistic concurrency control and locking mechanisms to avoid conflicts.

Cache Invalidation: Stale data in Redis could lead to inaccurate notifications. Mitigation: Implement cache expiration and TTL to ensure up-to-date data.

API Gateway Bottlenecks: Heavy traffic might overwhelm the API Gateway. Mitigation: Use auto-scaling and load balancing to handle high traffic volumes.

Trade Matching Failures: Failed or partial trade executions can leave orders in an inconsistent state. Mitigation: Ensure transactional integrity and use atomic operations for matching.

Real-Time Data Feed Failure: Loss of market data can disrupt trading. Mitigation: Use backup data providers and retries to ensure data availability.

Future improvements

Improvement: Implement machine learning models for predictive analytics and smarter trade recommendations.

• Mitigation: Use edge computing to process data locally and minimize latency.

Improvement: Introduce multi-cloud architecture for better availability and disaster recovery.

• Mitigation: Replicate data across multiple regions to ensure failover during network or server failures.

Improvement: Enhance real-time data pipelines for faster processing and fewer bottlenecks.

• Mitigation: Implement Kafka or AWS Kinesis for high-throughput streaming data handling.

Improvement: Integrate blockchain for immutable audit trails in trading and compliance.

• Mitigation: Ensure multi-layer security with encryption and continuous monitoring to avoid data tampering.

Improvement: Use AI-powered monitoring tools to detect and alert on anomalies in real-time.

• Mitigation: Implement auto-healing mechanisms to resolve issues automatically before they impact performance.