Systems Operational

Under The Hood

A deep dive into the polyglot microservices architecture powering real-time financial data processing at scale.

<1ms
Ingestion Latency
10K+
Concurrent Users
5
Languages
99.9%
Uptime SLA

System Architecture

From market data feed to your screen in microseconds

%%{init: {'theme': 'dark', 'themeVariables': { 'primaryColor': '#00F0FF', 'primaryTextColor': '#fff', 'primaryBorderColor': '#00F0FF', 'lineColor': '#D83BD2', 'secondaryColor': '#1a0d2e', 'tertiaryColor': '#0f0618', 'background': '#1a0d2e', 'mainBkg': '#1e1035', 'nodeBorder': '#00F0FF', 'clusterBkg': '#1e1035', 'clusterBorder': '#3d2a5a', 'edgeLabelBackground': '#1a0d2e'}}}%%
flowchart LR
    subgraph External["External Data Sources"]
        DB[("Databento\nMarket Feed")]
        NEWS[("News\nAPIs")]
    end

    subgraph Ingestion["Rust Ingestion Layer"]
        RUST["Rust Engine\n< 1μs latency"]
        PARSE["Protocol\nParser"]
    end

    subgraph Cache["In-Memory Cache"]
        REDIS[("Redis Cluster\nPub/Sub")]
    end

    subgraph Realtime["Elixir/Phoenix Layer"]
        PHOENIX["Phoenix\nChannels"]
        PUBSUB["PubSub\nBroadcast"]
        BEAM["BEAM VM\n10K+ conns"]
    end

    subgraph Client["Browser Runtime"]
        WS["WebSocket\nClient"]
        WASM["WebAssembly\nCompute"]
        WEBGPU["WebGPU\nRenderer"]
    end

    subgraph AI["QENEX AI"]
        ML["ML Models\nInference"]
        SIGNAL["Signal\nGeneration"]
    end

    DB --> RUST
    NEWS --> PARSE
    RUST --> REDIS
    PARSE --> REDIS
    REDIS --> PHOENIX
    PHOENIX --> PUBSUB
    PUBSUB --> BEAM
    BEAM --> WS
    WS --> WASM
    WASM --> WEBGPU
    REDIS --> ML
    ML --> SIGNAL
    SIGNAL --> PHOENIX

    style RUST fill:#D83BD2,stroke:#D83BD2,color:#fff
    style REDIS fill:#D83BD2,stroke:#D83BD2,color:#fff
    style PHOENIX fill:#44318D,stroke:#44318D,color:#fff
    style BEAM fill:#44318D,stroke:#44318D,color:#fff
    style WEBGPU fill:#00F0FF,stroke:#00F0FF,color:#000
    style ML fill:#00F0FF,stroke:#00F0FF,color:#000

The Stack

Purpose-built components working in harmony

🦀
The Core

Rust Ingestion Engine

Zero-copy parsing of market data protocols. Memory-safe by design with guaranteed thread safety. Processes millions of messages per second with sub-microsecond latency. No garbage collection pauses, ever.

<1μs
Parse Latency
0
Memory Leaks
10M+
Msgs/Second
🧪
The Nervous System

Elixir Phoenix Channels

Built on the battle-tested BEAM VM (Erlang). Native support for millions of concurrent WebSocket connections. Fault-tolerant supervision trees automatically recover from failures. Hot code upgrades without downtime.

10K+
Connections/Node
<1ms
Broadcast
99.9999%
Uptime
⚡
The Visual Cortex

WebGPU & WebAssembly

Next-generation browser APIs for GPU-accelerated computing. WebAssembly modules compiled from Rust run at near-native speed. Complex financial calculations execute client-side, reducing server load to zero.

100x
vs JavaScript
60 FPS
Render Rate
GPU
Accelerated
🧠
The Brain

QENEX AI Engine

Real-time inference on market data streams. Sentiment analysis, anomaly detection, and pattern recognition. Models trained on petabytes of historical data, deployed at the edge for minimal latency.

15+
ML Models
<10ms
Inference
PB
Training Data

Data Flow Pipeline

Watch data traverse the stack in real-time

Databento
Market Feed
Rust
< 1μs
Redis
Pub/Sub
Phoenix
< 1ms
WebGPU
60 FPS

Sample Integration

Connect to the real-time market data stream

market-stream.js 
// Connect to GPTFinancial WebSocket via Phoenix Channels
import { Socket } from "phoenix";

const socket = new Socket("wss://gptfinancial.org/socket", {
  params: { token: "your_api_key" }
});

socket.connect();

// Join the real-time market channel
const channel = socket.channel("market:realtime", {});

channel.on("tick", (payload) => {
  const { symbol, price, volume, timestamp } = payload;

  // WebGPU-accelerated chart update
  webgpuRenderer.updateCandle({
    symbol,
    price: parseFloat(price),
    volume: parseInt(volume),
    time: new Date(timestamp)
  });
});

channel.join()
  .receive("ok", () => console.log("Connected to market stream"))
  .receive("error", (err) => console.error("Connection failed", err));

Ready to Build?

Experience the power of our polyglot stack in your own applications.

Launch Platform API Documentation QENEX Corporate