High-Level Architecture of an HFT System

Understand the big picture first — then we dive into code. This screen shows the main components you'll meet when building a tiny HFT service and explains the latency‑critical path you must shrink. You're a beginner in C++ & Python (and have background in Java, C, JS) — so I'll point out where those languages typically live in this stack.

ASCII diagram (simple, left-to-right data flow):

[Exchange multicast / TCP] --> [NIC / Hardware timestamp] --> [Kernel / Driver] --> [Market Data Feed Handler] | v [Strategy Engine (decision)] --> [Order Gateway] --> [Exchange] | | v v [Risk] [Logging / Telemetry]

Key components (short, practical notes):

• Market Data Feed Handler

  • Role: receive, parse, and sequence-recover exchange messages (often UDP multicast / binary protocols like ITCH).
  • Typical implementation: C++ for lowest latency (tight parsing, zero-copy), or Python for prototyping (slow path).
  • Things to watch: copying, memory allocation, and parse branching.

• Strategy Engine

  • Role: use parsed market data to decide orders. Could be simple rules (crossing SMA) or complex signals.
  • Typical flow: prototype algorithm quickly in Python (numpy, pandas), then move hot code paths to C++ (or bind with pybind11).
  • Keep decision logic in-memory and branch-minimal for microseconds.

• Order Gateway

  • Role: serialize orders and send to exchange; track acknowledgements and resend logic.
  • Typical implementation: low-level C++ for performance and strict socket handling.

• Risk and Logging

  • Risk checks should be inline and extremely fast (pre-trade); heavy risk policies are off the hot-path.
  • Logging must not block: use async/batched writers, ring buffers, or route logs off-thread.

Latency-critical path (what to optimize first):

• From the NIC timestamp to the bytes on the wire back to exchange: NIC -> Kernel -> Feed handler -> Strategy -> Order Gateway -> NIC.
• Focus on: zero/allocation-free parsing, cache-friendly data layout, avoiding syscalls in the hot path, and hardware timestamping.

Language mapping and analogies for your background:

• If you come from Java: think of C++ here as Java without the GC — you must manage memory but you get predictable pauses.
• If you come from C: same low-level control, plus modern tools (std::vector, RAII) to avoid bugs.
• If you come from JS: imagine the market feed as events on an event loop — but instead of a single-threaded loop, we design threads and lockless queues for microsecond latencies.
• Python is your rapid-prototyping notebook — don't ship it on the hot path without moving bottlenecks to C++.

Quick checklist (visual):

1[ ] NIC hardware timestamping enabled
2[ ] Feed handler: zero-copy parsing
3[ ] Strategy: branch-light, cache-friendly data
4[ ] Order gateway: async socket send, minimal syscalls
5[ ] Risk: pre-trade checks inline
6[ ] Logging: non-blocking, batched

Hands-on challenge (run the C++ program below):

• The C++ snippet simulates the component chain and prints per-stage and total microsecond latencies. It's a model — not a real network stack — but it helps you reason about which stages dominate.
• Try these experiments:
  • Change stage latencies to see which component pushes you past the critical threshold.
  • Replace the Strategy stage with a smaller value to simulate migrating Python logic to C++.
  • Edit favorite_player to your favorite athlete (or coder) — a tiny personalization tie-in to keep learning playful.

Below is an executable C++ snippet that models this pipeline. Modify the stage times and rerun to explore the latency profile.

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}

#include <iostream>

#include <chrono>

#include <thread>

#include <vector>

#include <string>

#include <iomanip>

​

using namespace std;

using Clock = chrono::high_resolution_clock;

using us = chrono::microseconds;

​

int main() {

  // Personalize this (change to your favorite player or coder):

  string favorite_player = "Kobe Bryant"; // change for fun

​

  // Each pair is: (stage name, simulated latency in microseconds)

  // These numbers are coarse simulations to help you reason about hotspots.

  vector<pair<string,int>> stages = {

    {"NIC/hardware rx (hw ts)", 30},

    {"Kernel / driver copy", 20},

    {"Feed handler parse (zero-copy)", 60},

    {"Strategy (in-memory decision)", 120},

    {"Risk check (inline)", 40},

    {"Order serialization", 30},

    {"Socket send / NIC tx", 50},

    {"Exchange ack RTT (mock)", 300}

  };

​

  us critical_threshold(500); // microseconds: quick example threshold