How I Built A Real-time Streaming Market Data with .NET and ReactJS

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.

📋 Table of Contents

• I. Why I Built This
• II. High-Level Architecture
• III. Core Components Explained
• 1. RealMarketAPI (Entry Point)
• 2. Auth Service
• 3. RealMarketServices (The Brain)
• 4. Data Storage Strategy
• 5. Data Ingestion
• 6. RealMarketBots (Bonus Layer)
• IV. Tech Stack
• V. Biggest Challenges So Far
• VI. What’s Next
• VII. Final Words

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:

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

IV. Tech Stack

Backend: C# .NET

using FastEndpoints;
using RealTimeMarketAPI.Core.Modules.TickerModule.Handlers;
using RealTimeMarketAPI.Infrastructure.Models;

namespace RealTimeMarketAPI.Core.Modules.TickerModule.Endpoints
{
 public class GetCandlesEndpoint : Endpoint<GetCandlesRequest, ListResult<GetCandlesResponse>>
 {
 public override void Configure()
 {
 AllowAnonymous();
 Get("candle");
 Version(1);
 }

 public override async Task HandleAsync(GetCandlesRequest req, CancellationToken ct)
 {
 var result = await req.ExecuteAsync(ct);
 await Send.OkAsync(result, cancellation: ct);
 }
 }
}

Stack using: FastEndpoints, PostgreSQL, Redis

Frontend: Next.js

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

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.

You can check it out here: RealMarketAPI

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?

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