Design Real-Time Stock Market Dashboard

A real-time stock market dashboard is a sophisticated platform that provides live market data, interactive charts, portfolio tracking, and price alerts to millions of users simultaneously. Think Bloomberg Terminal, Robinhood, Webull, or Yahoo Finance - these systems handle billions of price updates daily while maintaining millisecond-level latency for critical trading decisions.

Designing such a platform presents unique challenges including handling high-throughput data streams, providing sub-second latency for price updates, maintaining data consistency across millions of concurrent users, and processing market data from multiple exchanges in real-time.

Step 1: Understand the Problem and Establish Design Scope

Before diving into the design, it’s crucial to define the functional and non-functional requirements. For user-facing applications like this, functional requirements are the “Users should be able to…” statements, whereas non-functional requirements define system qualities via “The system should…” statements.

Functional Requirements

Core Requirements (Priority 1-3):

  1. Users should be able to view real-time stock quotes with bid/ask spreads, volume, and market depth.
  2. Users should be able to view interactive candlestick charts with multiple timeframes (1m, 5m, 15m, 1h, 1d, 1w, 1M).
  3. Users should be able to track portfolios with real-time profit/loss calculations.
  4. Users should be able to set price alerts that trigger notifications when stocks hit target prices or percentage changes.

Below the Line (Out of Scope):

  • Users should be able to access news aggregation with sentiment analysis.
  • Users should be able to create and manage multiple watchlists with customizable columns.
  • Users should be able to access years of historical data for backtesting.
  • Users should be able to filter stocks by various criteria using market screeners.
  • Users should be able to view Level 2 market data for advanced trading.

Non-Functional Requirements

Core Requirements:

  • The system should provide ultra-low latency with less than 100ms for price updates from exchange to user.
  • The system should handle high throughput of 1M+ price updates per second during market hours.
  • The system should deliver real-time updates with sub-second WebSocket message delivery to clients.
  • The system should ensure eventual consistency for non-critical data (news, fundamentals) but strong consistency for portfolio calculations.

Below the Line (Out of Scope):

  • The system should ensure 99.99% uptime during market hours.
  • The system should gracefully degrade if external data providers fail.
  • The system should ensure zero data loss for user portfolios and alerts.
  • The system should comply with financial regulations and data privacy requirements.

Clarification Questions & Assumptions:

  • Platform: Web and mobile apps (iOS, Android) for end users.
  • Scale: 10 million daily active users with 5M peak concurrent users during market open/close.
  • Price Update Frequency: 1M updates per second across 100K securities.
  • Geographic Coverage: Global coverage with support for multiple stock exchanges.
  • Data Storage: Historical tick data requires approximately 15 PB/year.

Step 2: Propose High-Level Design and Get Buy-in

Planning the Approach

Before moving on to designing the system, it’s important to plan your strategy. For this real-time data platform, we’ll build the design sequentially, addressing each functional requirement while ensuring the architecture can handle the extreme throughput and latency requirements.

Defining the Core Entities

To satisfy our key functional requirements, we’ll need the following entities:

User: Any person who uses the platform to view market data and track investments. Includes personal information, subscription tier, notification preferences, and authentication credentials.

Quote: The real-time price information for a security. Contains the symbol, current price, bid/ask prices, volume, timestamp, and market depth information.

Candle: Aggregated price data for a specific time period. Includes the symbol, timeframe, open/high/low/close prices (OHLCV), volume, and timestamp for the period.

Portfolio: A collection of holdings owned by a user. Records the user’s positions, cost basis for each holding, current market value, and performance metrics.

Holding: An individual position within a portfolio. Contains the symbol, quantity, average cost basis, purchase date, and related transaction history.

Alert: A user-defined notification rule. Includes the symbol, condition type (above/below/percentage change), threshold value, notification channels, and current status (active/triggered/disabled).

Transaction: A record of buying or selling securities. Contains the portfolio reference, symbol, transaction type (buy/sell/dividend), quantity, price, commission, and timestamp.

API Design

Get Quote Endpoint: Used by clients to fetch the current quote for a specific symbol.

GET /quotes/:symbol -> Quote

Get Candles Endpoint: Used by clients to retrieve historical candlestick data for charting.

GET /candles/:symbol -> Candle[]
Query Params: {
  interval: "1m" | "5m" | "1h" | "1d",
  start: timestamp,
  end: timestamp
}

Get Portfolio Endpoint: Used by clients to retrieve portfolio details with real-time valuations.

GET /portfolios/:portfolioId -> Portfolio

Create Alert Endpoint: Used by users to create a new price alert.

POST /alerts -> Alert
Body: {
  symbol: string,
  condition: "ABOVE" | "BELOW" | "CHANGE_PCT",
  threshold: number,
  notificationChannels: string[]
}

Update Holding Endpoint: Used to record buy/sell transactions and update portfolio holdings.

POST /portfolios/:portfolioId/holdings -> Transaction
Body: {
  symbol: string,
  type: "BUY" | "SELL",
  quantity: number,
  price: number
}

Note: Authentication is handled via JWT tokens in headers, not in body or path params. User identification comes from session data.

High-Level Architecture

Let’s build up the system sequentially, addressing each functional requirement:

1. Users should be able to view real-time stock quotes

The core components necessary to fulfill real-time quote delivery are:

  • Client Applications: Web (React) and mobile (React Native) applications that display market data. These establish WebSocket connections for real-time updates.
  • API Gateway: Acts as the entry point for all client requests, handling authentication, rate limiting, and routing to appropriate services.
  • WebSocket Gateway: Manages persistent connections with clients for real-time data streaming. Handles subscription management where users subscribe to specific symbols.
  • Market Data Service: Provides real-time quote streaming and historical data access. Acts as the primary interface for market data.
  • Market Data Ingestion Layer: Connects to multiple market data providers (NYSE, NASDAQ, IEX Cloud, Polygon.io) and normalizes data formats into a unified schema.
  • Message Broker (Kafka): Acts as the central pipeline for all real-time data flows. Partitioned by symbol for parallel processing.
  • Redis Cache: Stores the latest quotes for instant access. Provides microsecond latency for hot data.

Real-Time Quote Flow:

  1. External exchanges send tick data to our Market Data Ingestion Layer via FIX protocol or WebSocket feeds.
  2. The ingestion layer normalizes different exchange formats into a unified schema and publishes to Kafka topics.
  3. The Market Data Service consumes from Kafka, processes the data, and updates Redis with the latest quotes.
  4. When a client subscribes to a symbol via WebSocket, the WebSocket Gateway subscribes to Redis pub/sub channels for that symbol.
  5. As new quotes arrive in Redis, they’re published via pub/sub to all WebSocket Gateway instances.
  6. Each gateway fans out the update to its connected clients who are subscribed to that symbol.
2. Users should be able to view interactive candlestick charts with multiple timeframes

We extend our design to support candlestick chart data:

  • Stream Processing Layer (Apache Flink): Performs real-time aggregation of tick data into candlesticks for various timeframes. Uses tumbling windows to create 1-minute, 5-minute, hourly, and daily candles.
  • Time-Series Database (TimescaleDB): Stores historical candlestick data with efficient compression. Optimized for time-based queries and aggregations.
  • Chart Service: Generates candlestick data and computes technical indicators on-demand. Implements a hybrid approach using Redis for recent data and TimescaleDB for historical data.

Candlestick Generation Flow:

  1. Tick data flows through Kafka into the Stream Processing Layer.
  2. Apache Flink uses tumbling windows to aggregate ticks into 1-minute candles, computing open/high/low/close/volume (OHLCV).
  3. These 1-minute candles are stored in TimescaleDB and also used to create higher timeframe candles (5m, 1h, 1d).
  4. Recent candles (last 24 hours) are also cached in Redis for instant access.
  5. When a user requests chart data, the Chart Service fetches from Redis if recent, otherwise queries TimescaleDB.
  6. Technical indicators (SMA, RSI, MACD) are computed on-the-fly on the retrieved candlestick data.
3. Users should be able to track portfolios with real-time profit/loss calculations

We introduce portfolio management components:

  • Portfolio Service: Manages user portfolios and calculates real-time performance metrics. Computes profit/loss, returns, day changes, and asset allocation.
  • PostgreSQL Database: Stores user accounts, portfolios, holdings, and transaction history. Ensures strong consistency for financial data.
  • Quote Cache: A specialized Redis cache maintained by the Portfolio Service for quick price lookups when calculating portfolio values.

Portfolio Valuation Flow:

  1. A user requests their portfolio via the client app, sending a GET request to the Portfolio Service.
  2. The service fetches all holdings from PostgreSQL, retrieving quantity and cost basis for each position.
  3. It batch-fetches current prices from Redis for all symbols in the portfolio.
  4. For each holding, it calculates: market value (quantity × current price), cost basis (quantity × average cost), unrealized P&L (market value - cost basis).
  5. It aggregates total portfolio value, total cost, total gain/loss, and return percentage.
  6. Previous close prices are retrieved to calculate day change and day change percentage.
  7. The computed portfolio metrics are returned to the client.

When users buy or sell securities, transactions are recorded in the database with proper cost basis tracking using weighted average calculations.

4. Users should be able to set price alerts that trigger notifications

We add alerting capabilities:

  • Alert Engine: Evaluates price alert rules in real-time as quotes change. Uses efficient data structures for fast threshold detection.
  • Notification Service: Dispatches notifications via multiple channels (email, push notifications, SMS) when alerts are triggered.
  • Alert Storage: PostgreSQL stores user-defined alert rules with indexes optimized for symbol and threshold lookups.

Alert Evaluation Flow:

  1. Users create alerts via the client, specifying a symbol, condition (above/below/change percentage), and threshold.
  2. Active alerts for popular symbols are loaded into Redis sorted sets for efficient range queries.
  3. As quotes arrive, the Alert Engine checks if any alerts should be triggered:
    • For “ABOVE” alerts: Find alerts with thresholds less than or equal to the current price.
    • For “BELOW” alerts: Find alerts with thresholds greater than or equal to the current price.
  4. Redis sorted sets allow O(log N) lookups using range queries by score (threshold).
  5. When an alert triggers, the Notification Service sends notifications via the user’s chosen channels (email, push, SMS).
  6. The alert status is updated to “TRIGGERED” and removed from the active evaluation set.

Alternatively, Apache Flink can evaluate alerts in the stream processing pipeline, maintaining alert state per symbol and checking conditions as quotes flow through.

Step 3: Design Deep Dive

With the core functional requirements met, it’s time to dig into the non-functional requirements via deep dives. These are the critical areas that separate good designs from great ones.

Deep Dive 1: How do we deliver 1M+ price updates per second to 5M concurrent users with sub-100ms latency?

Managing the massive throughput of market data and delivering it to millions of users in real-time is the core challenge of this system.

Problem:

During market hours, we receive approximately 1 million price updates per second across 100,000 securities. With 5 million concurrent users, if each user subscribes to just 10 symbols on average, we need to deliver 50 million messages per second. Traditional request-response patterns won’t work at this scale.

Solution: WebSocket Streaming with Redis Pub/Sub

We use a multi-layered streaming architecture:

Connection Management:

  • Each WebSocket Gateway instance maintains persistent connections with clients, typically handling 10,000-20,000 concurrent connections per instance.
  • We deploy 200-300 gateway instances to handle 5M concurrent users.
  • Sticky sessions ensure a user reconnects to the same gateway instance when possible, reducing state synchronization overhead.

Subscription Model:

  • When a client connects, it sends a list of symbols to subscribe to (from their watchlist).
  • The gateway maintains a mapping: symbol → set of connected users.
  • The gateway subscribes to Redis pub/sub channels for each unique symbol its clients are interested in.

Message Flow:

  • Market data arrives in Kafka and is processed by the Market Data Service.
  • Latest quotes are written to Redis and published to Redis pub/sub channels (one channel per symbol).
  • All WebSocket Gateway instances subscribed to that symbol’s channel receive the message.
  • Each gateway fans out the message to its locally connected clients who subscribed to that symbol.

Optimization Techniques:

Conflation: If a client can’t keep up with the update rate (network slowdown, device performance), we send only the latest price and drop intermediate updates. Humans can’t process 100 updates per second anyway.

Throttling: Even though we receive updates constantly, we limit delivery to clients at 10-20 updates per second per symbol, which is sufficient for visual display.

Delta Compression: Instead of sending full quote objects, we send only changed fields (e.g., “price: 150.25, volume: +100”).

Binary Protocol: Using Protocol Buffers instead of JSON reduces message size by approximately 50%, cutting bandwidth requirements in half.

Batching: Combine multiple symbol updates into a single WebSocket message when possible.

Deep Dive 2: How do we efficiently aggregate tick data into candlesticks for multiple timeframes in real-time?

Generating candlestick charts requires aggregating raw tick data into time windows, and we need to support multiple timeframes simultaneously.

Problem:

With 1M ticks per second, we need to compute candlesticks for multiple timeframes (1m, 5m, 15m, 1h, 1d) without overwhelming our storage or compute resources. Traditional database aggregations would be too slow.

Solution: Streaming Aggregation with Apache Flink

We use Apache Flink for real-time stream processing:

Windowing Strategy:

  • Flink consumes tick data from Kafka topics, keyed by symbol.
  • Tumbling windows of 1 minute are applied to aggregate ticks into 1-minute candles.
  • For each window, we compute: open (first price), high (max price), low (min price), close (last price), volume (sum).
  • The aggregation function maintains state within the window, updating the accumulator as each tick arrives.

Multi-Timeframe Approach:

  • Base aggregation: Raw ticks → 1-minute candles (in Flink).
  • Higher timeframes are derived from 1-minute candles: 5m candles from five 1m candles, 1h candles from sixty 1m candles, etc.
  • This cascading approach reduces computational overhead.

Storage Optimization:

  • Recent candles (last 24 hours) are stored in Redis for instant access.
  • All candles are written to TimescaleDB for historical queries.
  • TimescaleDB’s continuous aggregates automatically maintain pre-computed rollups.
  • After 30 days, 1-minute candles are compressed; 1-day candles are kept forever.

Technical Indicators:

  • Technical indicators (SMA, RSI, MACD, Bollinger Bands) are computed on-the-fly by the Chart Service.
  • When a user requests a chart, the service retrieves candlestick data and applies indicator calculations.
  • Using libraries like pandas or ta-lib, we can efficiently compute indicators over the retrieved time series.
  • Commonly requested chart configurations can be cached to reduce computation.

Deep Dive 3: How do we calculate real-time profit/loss for millions of portfolios as prices change?

With millions of users and continuously changing prices, recalculating portfolio values for every price update is computationally infeasible.

Problem:

If we have 10M users with portfolios, and we need to recalculate portfolio values on every price update (1M updates/sec), that would be billions of calculations per second, which is impossible.

Solution: Lazy Evaluation with Caching

We use a pull-based model instead of push-based:

On-Demand Calculation:

  • Portfolio values are calculated only when a user requests them (lazy evaluation).
  • When a GET request arrives for a portfolio, we fetch holdings from PostgreSQL.
  • We batch-fetch current prices from Redis for all symbols in the portfolio.
  • We compute market value, cost basis, and P&L for each position.
  • The computed metrics are returned immediately without storing intermediate results.

Caching Strategy:

  • Calculated portfolio values are cached in Redis with a short TTL (30-60 seconds).
  • Subsequent requests within the TTL window return cached values, avoiding recalculation.
  • When a user trades (buy/sell), we invalidate the cache for that portfolio.

WebSocket Updates:

  • For users actively viewing their portfolio, we can push updates via WebSocket.
  • The Portfolio Service subscribes to price updates for symbols in the user’s portfolio.
  • As prices change, it recalculates portfolio value and pushes deltas to the client.
  • This is done selectively only for active sessions, not all 10M users.

Cost Basis Tracking:

  • When users buy shares, we calculate weighted average cost basis.
  • New average cost = (existing quantity × existing average cost + new quantity × new price) / total quantity.
  • When users sell shares, we reduce quantity but maintain the same average cost.
  • Realized gains/losses are calculated at the time of sale and stored in transaction history.

Snapshotting:

  • At market close, we snapshot all portfolios for historical tracking.
  • This provides daily performance history and simplifies return calculations.

Deep Dive 4: How do we evaluate 500M+ alert rules in real-time as prices change?

With millions of users setting multiple alerts, we need an efficient mechanism to evaluate alert conditions without checking every rule for every price update.

Problem:

If we have 10M users and each sets 5 alerts on average, that’s 50M active alerts. Checking all alerts for every price update (1M/sec) would require 50 trillion comparisons per second.

Solution: Geospatial-Inspired Indexing with Redis Sorted Sets

We use an inverted index approach:

Data Structure:

  • For each symbol, we maintain two Redis sorted sets:
    • alerts:{symbol}:above - alerts waiting for price to go above threshold.
    • alerts:{symbol}:below - alerts waiting for price to go below threshold.
  • The score in the sorted set is the threshold price.
  • The member is the alert ID.

Efficient Lookup:

  • When a price update arrives for a symbol, we perform range queries:
    • For “above” alerts: ZRANGEBYSCORE alerts:AAPL:above -inf {currentPrice} finds all alerts triggered.
    • For “below” alerts: ZRANGEBYSCORE alerts:AAPL:below {currentPrice} +inf finds all alerts triggered.
  • These operations are O(log N + M) where M is the number of matching alerts.

Alert Triggering:

  • When alerts are found, we fetch full alert details from PostgreSQL.
  • We dispatch notifications via the Notification Service to email, push notification, or SMS.
  • We update the alert status to “TRIGGERED” and remove it from the sorted sets.
  • Users can configure alerts to auto-reactivate or remain triggered.

Alternative: Stateful Stream Processing

We can also use Apache Flink for alert evaluation:

  • Maintain alert state per symbol in Flink’s keyed state.
  • As quotes flow through Flink, check conditions against stored alerts.
  • When conditions are met, emit alert trigger events to a Kafka topic.
  • A consumer service picks up these events and sends notifications.

Scalability:

  • Partition alerts across multiple Flink task managers by symbol.
  • Use sharded Redis clusters to distribute the sorted sets.
  • Implement rate limiting to prevent alert spam (max 10 alerts per hour per user).

Deep Dive 5: How do we handle the 10x traffic spike when markets open at 9:30 AM ET?

Market open represents an extreme spike in activity - all stocks receive updates simultaneously, and millions of users refresh their apps at the same time.

Problem:

Normal load is around 100K updates per second. At market open, this spikes to 1M+ updates per second for the first 5-10 minutes. Additionally, user traffic spikes as everyone checks their portfolios and watchlists simultaneously.

Solution: Pre-Scaling and Backpressure Management

We implement multiple strategies:

Pre-Opening Preparation:

  • 15 minutes before market open, we auto-scale WebSocket Gateway instances from 50 to 200.
  • We increase Kafka partition counts for parallel processing capability.
  • We pre-warm caches by loading all actively-watched symbols into Redis.
  • We fetch previous close prices for all symbols to enable day change calculations.

Backpressure Handling:

  • Implement token bucket rate limiting per symbol: max 20 updates per second to clients.
  • If updates arrive faster than the rate limit, we drop intermediate updates and send only the latest.
  • Kafka consumers use backpressure to signal upstream producers to slow down if they can’t keep up.

Priority Queueing:

  • High-priority stocks (S&P 500, most-watched) get preferential processing.
  • User watchlists inform priority: symbols with more subscribers get higher priority.
  • Less liquid penny stocks can have slightly delayed updates without impacting user experience.

Graceful Degradation:

  • Under extreme load (95%+ capacity), we reduce update frequency from 20/sec to 10/sec.
  • We disable non-critical features like technical indicators and market depth.
  • We serve only the top 1,000 most-watched stocks if necessary.
  • As a last resort, we switch from streaming to polling mode (clients poll every 2-3 seconds).

Auto-Scaling Configuration:

  • Kubernetes Horizontal Pod Autoscaler monitors CPU and connection count.
  • Scale-up policy: increase by 50% every 60 seconds if load is high.
  • Scale-down policy: decrease gradually (10 pods per minute) after peak hours.
  • Minimum 50 replicas, maximum 300 replicas for WebSocket gateways.

Connection Management:

  • Implement exponential backoff for client reconnections to prevent thundering herd.
  • Use connection pooling for database connections to prevent exhaustion.
  • Implement circuit breakers to fail fast when downstream services are unavailable.

Deep Dive 6: How do we ensure data consistency across distributed components?

With data flowing through multiple stages (ingestion → Kafka → processing → storage → cache), ensuring consistency is challenging.

Problem:

Users should never see stale prices after seeing fresh ones (monotonic reads). Portfolio calculations must be accurate. Alert evaluations must not miss triggers or double-trigger.

Solution: Carefully Chosen Consistency Models

Price Data (Eventual Consistency):

  • Stock prices flow through an eventually consistent pipeline.
  • We accept that different users might see slightly different prices (within milliseconds).
  • However, for a single user session, we ensure monotonic reads by using consistent routing.
  • Each WebSocket Gateway subscribes to Redis pub/sub, ensuring it sees messages in order.

Portfolio Data (Strong Consistency):

  • PostgreSQL provides ACID guarantees for portfolio mutations.
  • When a user buys/sells, we use database transactions with SELECT FOR UPDATE to prevent race conditions.
  • Cost basis calculations are done atomically within the transaction.
  • After committing, we invalidate any cached portfolio values.

Alert Evaluation (At-Least-Once):

  • We prefer at-least-once delivery for alerts (better to send twice than not at all).
  • Distributed locks prevent the same alert from being evaluated by multiple instances simultaneously.
  • Alert triggers are idempotent - triggering the same alert multiple times is detected and deduplicated.

Kafka Offset Management:

  • Consumers commit offsets only after successfully processing messages.
  • If processing fails, the message is reprocessed (at-least-once semantics).
  • Critical operations (like alert triggers) are idempotent to handle reprocessing.

Step 4: Wrap Up

In this chapter, we proposed a system design for a real-time stock market dashboard. If there is extra time at the end of the interview, here are additional points to discuss:

Additional Features:

  • News aggregation: Use NLP and named entity recognition to extract stock symbols from news articles. Perform sentiment analysis and calculate relevance scores. Store in PostgreSQL with full-text search indexes.
  • Market screeners: Allow users to filter stocks by criteria (price, volume, market cap, P/E ratio). Pre-compute popular screens and cache results. Use Elasticsearch for complex multi-criteria searches.
  • Historical data access: Archive old tick data to S3 for cost-effective long-term storage. Use data lifecycle policies to transition from hot (Redis) to warm (TimescaleDB) to cold (S3) storage.
  • Options chain data: Extend the data model to include options contracts with strike prices, expiration dates, and Greeks (delta, gamma, theta, vega).
  • Social sentiment tracking: Stream data from Twitter, Reddit, StockTwits. Use sentiment analysis models to gauge retail investor sentiment. Aggregate mentions and sentiment scores per symbol.

Scaling Considerations:

  • Horizontal Scaling: All services are stateless and can scale horizontally. WebSocket Gateways can be added/removed dynamically based on connection count.
  • Database Sharding: Shard user data (portfolios, alerts) by userId for horizontal scaling. Shard time-series data by symbol or time range.
  • Multi-Region Deployment: Deploy to multiple geographic regions to reduce latency. Use a CDN for static assets. Route users to the nearest region.
  • Message Queue Scaling: Kafka partitions allow parallel processing. Increase partition count for high-traffic symbols. Use consumer groups to scale consumers.

Technology Stack Summary:

Real-Time Layer:

  • Apache Kafka for message streaming and data pipelines.
  • Apache Flink for stream processing and aggregations.
  • Redis for caching and pub/sub messaging.
  • WebSocket for persistent client connections.

Storage Layer:

  • TimescaleDB for time-series data (ticks, candles).
  • PostgreSQL for relational data (users, portfolios, alerts).
  • Redis for hot cache (latest quotes).
  • S3 for archival storage (historical data).

Application Layer:

  • Python/FastAPI for microservices and business logic.
  • Node.js for WebSocket Gateway (high I/O concurrency).
  • React for web frontend.
  • React Native for mobile apps.

Error Handling:

  • Data Provider Failures: Maintain multiple data provider connections (IEX, Polygon, Bloomberg). Automatically failover to backup providers. Cache recent data to continue serving while resolving issues.
  • WebSocket Disconnections: Clients implement exponential backoff retry. On reconnect, send a snapshot of all subscribed symbols to refresh state.
  • Database Failures: Use read replicas for portfolio queries. Queue portfolio updates in Kafka for replay if writes fail. Alert operations team for manual intervention.
  • Kafka Failures: Multi-replica setup (replication factor 3) ensures durability. Consumer groups rebalance automatically when instances fail.

Monitoring and Observability:

  • Key Metrics: Data freshness (exchange to user latency P50/P99), WebSocket connection count, quote throughput, alert evaluation latency, Kafka consumer lag, cache hit rate, API response times.
  • Distributed Tracing: Use tools like Jaeger or Datadog to trace requests across microservices.
  • Alerting: Set up alerts for Kafka lag > 1000 messages, WebSocket failures > 5%, database connection pool exhaustion, Redis memory > 80%.
  • Dashboards: Real-time dashboards showing system health, traffic patterns, error rates.

Security Considerations:

  • Authentication: JWT tokens for API authentication. Session management for WebSocket connections.
  • Rate Limiting: Prevent abuse by limiting API requests per user per minute.
  • Data Encryption: TLS for data in transit. Encrypt sensitive user data at rest.
  • Input Validation: Sanitize all user inputs to prevent injection attacks.
  • DDoS Protection: Use CDN and WAF to filter malicious traffic.

Cost Optimization:

  • AWS Estimated Costs: EC2/ECS for WebSocket gateways and app servers ($50K/month), RDS/TimescaleDB ($30K/month), ElastiCache Redis ($15K/month), MSK Kafka ($20K/month), S3 storage ($5K/month), Data transfer ($25K/month), Market data feeds ($100K/month). Total approximately $245K/month for 10M users.
  • Optimization Strategies: Use spot instances for non-critical workloads. Implement data compression and retention policies. Optimize Kafka partition counts. Use reserved instances for predictable baseline load.

Future Enhancements:

  • Machine Learning: Stock price predictions using LSTM or Transformer models. Anomaly detection for unusual trading patterns.
  • Algorithmic Trading: Paper trading simulator for backtesting strategies. Real-time strategy execution with risk management.
  • Advanced Analytics: Portfolio optimization suggestions. Tax loss harvesting recommendations. Dividend tracking and forecasting.
  • Multi-Asset Support: Expand beyond stocks to ETFs, options, crypto, forex, commodities.
  • Collaborative Features: Share watchlists with other users. Follow expert traders. Social trading community.

Congratulations on getting this far! Designing a real-time stock market dashboard is an incredibly complex system design challenge that combines financial domain knowledge with cutting-edge distributed systems concepts. The key is to understand the data flow from exchanges to users, choose the right technologies for each layer (streaming, processing, storage, delivery), and optimize for the extreme throughput and latency requirements that financial systems demand.

Summary

This comprehensive guide covered the design of a real-time stock market dashboard, including:

  1. Core Functionality: Real-time quotes, interactive charts, portfolio tracking, and price alerts.
  2. Key Challenges: High-throughput data ingestion, sub-100ms latency delivery, real-time aggregation, alert evaluation at scale, and handling market open spikes.
  3. Solutions: WebSocket streaming with Redis pub/sub, Apache Flink for stream processing, TimescaleDB for time-series storage, Redis sorted sets for efficient alert evaluation, and auto-scaling strategies.
  4. Scalability: Horizontal scaling across all layers, caching strategies, backpressure handling, graceful degradation, and multi-region deployment.

The design demonstrates how to build a high-performance financial data platform that handles massive throughput, provides real-time updates to millions of users, and maintains the accuracy and reliability required for trading decisions.