Design Robinhood

What is Robinhood?

Robinhood is a commission-free stock trading platform that allows users to buy and sell stocks, ETFs, and cryptocurrencies. It's similar to E*TRADE, TD Ameritrade, or Webull. The service provides real-time market data, order execution, and portfolio management.

Real-time trading with market data processing and risk management is what makes systems like Robinhood unique. By understanding Robinhood, you can tackle interview questions for similar trading platforms, since the core design challenges—trading engine, market data, order execution, and risk management—remain the same.

Functional Requirements

Core (Interview Focussed)

Order Management: Users can place buy/sell orders for stocks.
Real-time Market Data: Provide current stock prices and market data.
Portfolio Management: Track user holdings and portfolio value.
Risk Management: Prevent risky trades and ensure compliance.

Out of Scope

User authentication and accounts
Payment processing and settlement
Tax reporting and compliance
Options and derivatives trading
Mobile app specific features

Non-Functional Requirements

Core (Interview Focussed)

Low latency: Sub-second response time for order placement.
High availability: 99.9% uptime during market hours.
Consistency: Ensure order execution accuracy and consistency.
Scalability: Handle millions of concurrent users during market hours.

Out of Scope

Data retention policies
Compliance and privacy regulations

💡 Interview Tip: Focus on low latency, high availability, and consistency. Interviewers care most about trading engine, market data processing, and risk management.

Core Entities

Entity	Key Attributes	Notes
Order	order_id, user_id, symbol, order_type, quantity, price	Indexed by user_id for user orders
Stock	symbol, name, current_price, market_cap, volume	Real-time market data
Portfolio	portfolio_id, user_id, total_value, cash_balance	User portfolio information
Trade	trade_id, order_id, execution_price, quantity, timestamp	Executed trades
RiskLimit	user_id, limit_type, limit_value, current_usage	Risk management limits

💡 Interview Tip: Focus on Order, Stock, and Trade as they drive trading engine, market data, and order execution.

Core APIs

Order Management

POST /orders { symbol, order_type, quantity, price } – Place a new order
GET /orders/{order_id} – Get order details
PUT /orders/{order_id}/cancel – Cancel an order
GET /orders?status=&symbol=&limit= – List orders with filters

Market Data

GET /stocks/{symbol} – Get current stock information
GET /stocks/{symbol}/price – Get current stock price
GET /stocks?search=&limit= – Search for stocks
GET /market/status – Get market status

Portfolio

GET /portfolio – Get user portfolio
GET /portfolio/holdings – Get user holdings
GET /portfolio/performance – Get portfolio performance
GET /portfolio/transactions – Get transaction history

Risk Management

GET /risk/limits – Get user risk limits
POST /risk/check { order_details } – Check order against risk limits
GET /risk/violations – Get risk violation history

High-Level Design

System Architecture Diagram

Key Components

Trading Engine: Handle order processing and execution
Market Data Service: Process and provide real-time market data
Portfolio Service: Manage user portfolios and holdings
Risk Management Service: Enforce risk limits and compliance
Order Book: Maintain order book for each stock
Database: Persistent storage for orders, trades, and portfolios

Mapping Core Functional Requirements to Components

Functional Requirement	Responsible Components	Key Considerations
Order Management	Trading Engine, Order Book	Order validation, execution, matching
Real-time Market Data	Market Data Service, Database	Data processing, real-time updates
Portfolio Management	Portfolio Service, Database	Holdings tracking, value calculation
Risk Management	Risk Management Service, Trading Engine	Risk checks, limit enforcement

Detailed Design

Trading Engine

Purpose: Handle order processing, validation, and execution.

Key Design Decisions:

Order Validation: Validate orders for amount, timing, and risk
Order Matching: Match buy and sell orders efficiently
Execution Engine: Execute trades with market data integration
Order Book Management: Maintain real-time order book

Algorithm: Order processing

1. Receive order request
2. Validate order:
   - Check user account balance
   - Check risk limits
   - Check market hours
   - Check order format
3. If valid:
   - Add order to order book
   - Attempt to match with existing orders
   - If matched:
     - Execute trade
     - Update portfolio
     - Send confirmation
   - If not matched:
     - Keep order in book
4. If invalid:
   - Return error with reason

Market Data Service

Purpose: Process and provide real-time market data.

Key Design Decisions:

Data Sources: Aggregate data from multiple market data providers
Real-time Processing: Process market data updates in real-time
Data Validation: Validate and clean market data
Caching: Cache market data for fast access

Algorithm: Market data processing

1. Receive market data updates
2. Validate data:
   - Check for outliers
   - Verify data format
   - Check data freshness
3. Process updates:
   - Update stock prices
   - Calculate derived metrics
   - Update market indices
4. Broadcast updates:
   - Send to trading engine
   - Send to portfolio service
   - Send to clients
5. Store historical data

Risk Management Service

Purpose: Enforce risk limits and ensure compliance.

Key Design Decisions:

Risk Checks: Perform pre-trade risk checks
Limit Enforcement: Enforce position and exposure limits
Compliance Monitoring: Monitor for regulatory compliance
Risk Reporting: Generate risk reports and alerts

Algorithm: Risk management

1. Receive order for risk check
2. Check risk limits:
   - Position size limits
   - Exposure limits
   - Concentration limits
   - Leverage limits
3. Calculate risk metrics:
   - Portfolio value at risk
   - Maximum loss potential
   - Correlation with existing positions
4. If risk acceptable:
   - Allow order
   - Update risk metrics
5. If risk too high:
   - Reject order
   - Log risk violation
   - Notify user

Database Design

Orders Table

Field	Type	Description
order_id	VARCHAR(36)	Primary key
user_id	VARCHAR(36)	Order owner
symbol	VARCHAR(10)	Stock symbol
order_type	VARCHAR(20)	Order type (buy/sell)
quantity	INT	Number of shares
price	DECIMAL(10,2)	Order price
status	VARCHAR(20)	Order status
created_at	TIMESTAMP	Order timestamp

Indexes:

idx_user_id on (user_id) - User orders
idx_symbol on (symbol) - Stock orders
idx_status on (status) - Order status queries

Stocks Table

Field	Type	Description
symbol	VARCHAR(10)	Primary key
name	VARCHAR(255)	Company name
current_price	DECIMAL(10,2)	Current stock price
market_cap	BIGINT	Market capitalization
volume	BIGINT	Trading volume
last_updated	TIMESTAMP	Last price update

Indexes:

idx_last_updated on (last_updated) - Recent updates

Trades Table

Field	Type	Description
trade_id	VARCHAR(36)	Primary key
order_id	VARCHAR(36)	Associated order
execution_price	DECIMAL(10,2)	Execution price
quantity	INT	Number of shares
timestamp	TIMESTAMP	Trade timestamp

Indexes:

idx_order_id on (order_id) - Order trades
idx_timestamp on (timestamp) - Trade history

Portfolios Table

Field	Type	Description
portfolio_id	VARCHAR(36)	Primary key
user_id	VARCHAR(36)	Portfolio owner
total_value	DECIMAL(15,2)	Total portfolio value
cash_balance	DECIMAL(15,2)	Available cash
last_updated	TIMESTAMP	Last update

Indexes:

idx_user_id on (user_id) - User portfolios
idx_last_updated on (last_updated) - Recent updates

Scalability Considerations

Horizontal Scaling

Trading Engine: Scale horizontally with load balancers
Market Data Service: Use consistent hashing for data distribution
Portfolio Service: Scale portfolio calculations with distributed computing
Database: Shard orders and trades by user_id

Caching Strategy

Redis: Cache market data and portfolio values
Application Cache: Cache frequently accessed data
Database Cache: Cache order book and market data

Performance Optimization

Connection Pooling: Efficient database connections
Batch Processing: Batch market data updates for efficiency
Async Processing: Non-blocking order processing
Resource Monitoring: Monitor CPU, memory, and network usage

Monitoring and Observability

Key Metrics

Order Latency: Average order processing time
Market Data Latency: Average market data update time
Trade Execution Rate: Percentage of orders executed
System Health: CPU, memory, and disk usage

Alerting

High Latency: Alert when order processing time exceeds threshold
Market Data Delays: Alert when market data updates are delayed
Risk Violations: Alert when risk limits are exceeded
System Errors: Alert on trading engine failures

Trade-offs and Considerations

Consistency vs. Availability

Choice: Strong consistency for order execution, eventual consistency for market data
Reasoning: Order execution needs immediate accuracy, market data can tolerate slight delays

Latency vs. Throughput

Choice: Optimize for latency with real-time processing
Reasoning: Trading requires immediate order execution and market data updates

Accuracy vs. Performance

Choice: Use precise calculations for risk management
Reasoning: Risk management accuracy is critical for regulatory compliance

Common Interview Questions

Q: How would you handle market data delays?

A: Use multiple data sources, data validation, and fallback mechanisms to handle market data delays.

Q: How do you ensure order execution accuracy?

A: Use atomic operations, order validation, and real-time market data integration to ensure order execution accuracy.

Q: How would you scale this system globally?

A: Deploy regional trading servers, use geo-distributed databases, and implement data replication strategies.

Q: How do you handle risk management at scale?

A: Use real-time risk calculations, distributed risk checks, and automated risk monitoring to handle risk management at scale.

Key Takeaways

Trading Engine: Order validation, matching, and execution are essential for trading platforms
Market Data: Real-time data processing and validation are crucial for accurate trading
Risk Management: Multiple risk checks and limit enforcement ensure system safety
Scalability: Horizontal scaling and partitioning are crucial for handling large-scale trading
Monitoring: Comprehensive monitoring ensures system reliability and performance

Design Robinhood

Design Robinhood

What is Robinhood?

Functional Requirements

Core (Interview Focussed)

Out of Scope

Non-Functional Requirements

Core (Interview Focussed)

Out of Scope

Core Entities

Core APIs

Order Management

Market Data

Portfolio

Risk Management

High-Level Design

System Architecture Diagram

Key Components

Mapping Core Functional Requirements to Components

Detailed Design

Trading Engine

Market Data Service

Risk Management Service

Database Design

Orders Table

Stocks Table

Trades Table

Portfolios Table

Scalability Considerations

Horizontal Scaling

Caching Strategy

Performance Optimization

Monitoring and Observability

Key Metrics

Alerting

Trade-offs and Considerations

Consistency vs. Availability

Latency vs. Throughput

Accuracy vs. Performance

Common Interview Questions

Q: How would you handle market data delays?

Q: How do you ensure order execution accuracy?

Q: How would you scale this system globally?

Q: How do you handle risk management at scale?

Key Takeaways

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