Inside My Real-Time Market Data API: Architecture, Challenges, Reality

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Last month I shared how I turned a side project into a SaaS. Today, I’m opening the hood and showing you the actual architecture, tech decisions, and lessons learned.

I. Why I Built This

I got tired of:

• Paying $200–$800/month for APIs that go down during high volatility
• Inconsistent latency (especially WebSocket)
• Terrible historical data quality
• Vendors suddenly changing pricing or throttling indie developers

So I decided to build my own — focused on reliability, transparency, and developer experience.

II. High-Level Architecture

Here’s the current system:

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III. Core Components Explained

1. RealMarketAPI (Entry Point)

• Simple REST + WebSocket gateway
• Handles authentication (JWT + API keys)
• Rate limiting & usage tracking

2. Auth Service

• Validates tokens
• Checks subscription tier
• Rejects invalid requests early

3. RealMarketServices (The Brain)

This is where the magic happens. It’s split into three main services:

• Ticker Service — Ultra-low latency price updates
• Stream Service — WebSocket broadcaster (using Redis Pub/Sub)
• Historical Service — Candle & tick data delivery

4. Data Storage Strategy

• Redis → Hot short-term ticker data + Pub/Sub
• PostgreSQL + TimescaleDB → Long-term candles and relational data
• Candles DB → Dedicated time-series optimized storage

5. Data Ingestion

• Multiple workers fetch from various Brokers/Exchanges
• Retry logic + circuit breakers + fallback sources
• Data is normalized into a unified format

6. RealMarketBots (Bonus Layer)

One of my favorite parts:

• Scrapes news & trends using RSS + SerpAPI
• Generates content summaries with Gemini
• Sends Telegram alerts
• Auto-publishes to Facebook and X

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IV. Tech Stack

Backend: C# .NET

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Frontend: Next.js

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• Real-time: Redis + custom WebSocket server
• Database: PostgreSQL
• Observability: Datadog

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V. Biggest Challenges So Far

• WebSocket Scale — Maintaining thousands of concurrent connections with low latency is hard.
• Data Consistency — Different exchanges have different timestamp formats and precision.
• Cost Control — Exchange API fees + bandwidth can explode quickly.
• Testing — You can’t easily replay real market conditions.

VI. What’s Next

• TradingView widget
• Official SDKs (Python, Node.js, Go)
• More symbols & exchanges
• Dedicated enterprise nodes

VII. Final Words

Building a market data API taught me one important lesson: Reliability beats features.

Most users don’t need 10,000 symbols. They need 3 symbols that actually work at 3 AM when Bitcoin crashes.

That’s what I’m optimizing for.

Let me know in the comments:

• What market data pain point do you have?
• Would you use this for algo trading, dashboards, or AI agents?

I read every comment.