HIGH FREQUENCY TRADING CPP RUST LEARNING PROJECTS

High-Frequency Trading (HFT) Development in C++ or Rust

This is an excellent domain to learn - it forces you to understand performance at the deepest levels: CPU cache lines, memory allocation, lock-free programming, network latency, and systems architecture.

Core Concept Analysis

HFT programming requires mastering these fundamental building blocks:

Concept	Why It Matters in HFT
Latency optimization	Nanoseconds determine profit/loss
Memory layout & cache	Cache misses are death; data locality is life
Lock-free data structures	Mutexes are too slow; atomics rule
Network programming	Kernel bypass, zero-copy, raw sockets
Order book mechanics	The core data structure of all trading
Protocol parsing	FIX, binary protocols, market data feeds
Deterministic execution	No GC pauses, no heap allocations in hot paths

Project 1: Limit Order Book Engine

File: HIGH_FREQUENCY_TRADING_CPP_RUST_LEARNING_PROJECTS.md
Programming Language: C++ or Rust
Coolness Level: Level 4: Hardcore Tech Flex
Business Potential: 1. The “Resume Gold”
Difficulty: Level 4: Expert
Knowledge Area: High Performance Computing / Finance
Software or Tool: Order Book Algorithms
Main Book: “Computer Systems: A Programmer’s Perspective” by Bryant & O’Hallaron

What you’ll build: A fully functional limit order book that maintains bid/ask levels, handles order matching, and reports trades - the core data structure of every exchange and trading system.

Why it teaches HFT: The order book is the fundamental data structure. You can’t understand trading systems without understanding how bids, asks, price levels, and matching work. Building one forces you to think about cache-friendly data layouts, O(1) operations, and memory management.

Core challenges you’ll face:

Price level organization: How do you store and access thousands of price levels efficiently? (teaches red-black trees vs. hash maps vs. arrays)
Order queue management: FIFO queues at each price level with O(1) operations (teaches memory pooling, intrusive lists)
Cache-friendly design: Keeping hot data together in memory (teaches CPU cache hierarchy, struct padding)
Matching algorithm: Price-time priority matching without branching (teaches branch prediction, branchless code)

Resources for key challenges:

“Limit Order Book Implementation for Low Latency Trading” - Practical walkthrough comparing data structures
aspone/OrderBook on GitHub - Reference C++ implementation achieving 300-600ns operations
“Computer Systems: A Programmer’s Perspective” by Bryant & O’Hallaron (Ch. 6) - Deep understanding of cache hierarchy

Difficulty: Intermediate Time estimate: 1-2 weeks Prerequisites: Basic C++ or Rust, understanding of data structures (maps, queues)

Real world outcome:

A CLI or simple GUI showing live order book depth (bid/ask ladder)
Ability to submit orders and see them match, with trade prints to stdout
Benchmark output showing nanosecond-level operation times
Example: “Order 12345 BUY 100@50.25 matched with Order 12344 SELL 100@50.25 → Trade executed”

Learning milestones:

Basic order book with std::map - understand the mechanics, see ~1-5μs latency
Optimized with custom allocators - drop to ~500ns, understand memory pooling
Cache-optimized with benchmarking - understand why certain layouts are faster

Project 2: Lock-Free Market Data Handler

File: lock_free_market_data_handler_hft.md
Main Programming Language: Rust
Alternative Programming Languages: C++, C, Zig
Coolness Level: Level 5: Pure Magic (Super Cool)
Business Potential: Level 1: The “Resume Gold”
Difficulty: Level 3: Advanced (The Engineer)
Knowledge Area: Lock-Free Programming, Atomics, Memory Ordering
Software or Tool: Market Data Feeds
Main Book: Rust Atomics and Locks by Mara Bos

What you’ll build: A system that receives simulated market data (or real data from a free feed), parses it, and distributes it to consumers using lock-free queues - zero mutexes, zero blocking.

Why it teaches HFT: Real HFT systems can’t afford mutex contention. This project forces you to understand atomics, memory ordering, and why lock-free is hard but necessary. You’ll see the difference between memory_order_relaxed and memory_order_seq_cst in real latency numbers.

Core challenges you’ll face:

SPSC queue implementation: Single-producer, single-consumer lock-free queue (teaches atomics, memory barriers)
Memory ordering: Understanding acquire/release semantics (teaches CPU memory models)
False sharing avoidance: Cache line padding between producer/consumer cursors (teaches hardware details)
Protocol parsing: Efficiently deserializing binary market data (teaches zero-copy parsing)

Resources for key challenges:

“Rust Atomics and Locks” by Mara Bos - The definitive Rust resource
Fear and Loathing in Lock-Free Programming - Mental models for lock-free
rigtorp/awesome-lockfree - Curated collection of lock-free resources
Lock-freedom without garbage collection - Aaron Turon on epoch-based reclamation

Difficulty: Advanced Time estimate: 2-3 weeks Prerequisites: Solid understanding of pointers/references, basic threading

Real world outcome:

Console output showing market data flowing: AAPL BID 150.25 x 500 | ASK 150.26 x 300
Latency histogram printed showing p50, p99, p999 times
Benchmark comparing lock-free vs. mutex-based versions
Ability to handle 1M+ messages/second on commodity hardware

Learning milestones:

Mutex-based queue - baseline understanding, see contention in profiler
Basic lock-free SPSC - working atomics, ~10x improvement
Cache-padded, optimized version - understand false sharing, another 2-3x gain

Project 3: Simple Matching Engine

File: simple_matching_engine_hft.md
Main Programming Language: Rust
Alternative Programming Languages: C++, C, Go
Coolness Level: Level 5: Pure Magic (Super Cool)
Business Potential: Level 4: The “Open Core” Infrastructure
Difficulty: Level 3: Advanced (The Engineer)
Knowledge Area: Order Matching, Event-Driven I/O, Networking
Software or Tool: epoll, kqueue, io_uring
Main Book: Building Low Latency Applications with C++ by Sourav Ghosh

What you’ll build: A complete matching engine that accepts orders via TCP, matches them using your order book, and publishes trades and market data - a mini-exchange.

Why it teaches HFT: This is the core of what exchanges do. Building one integrates order books, networking, protocol design, and state management. You’ll understand why exchanges measure latency in nanoseconds.

Core challenges you’ll face:

Network I/O without blocking: Using epoll/kqueue or io_uring (teaches async I/O, event loops)
Protocol design: Creating a binary order protocol (teaches serialization, wire formats)
State machine design: Order lifecycle management (teaches deterministic systems)
Fair matching: Price-time priority without favoritism (teaches algorithm correctness)

Resources for key challenges:

“Building Low Latency Applications with C++” by Sourav Ghosh - Step-by-step trading system build
GitHub: Building-Low-Latency-Applications-with-CPP - Full code repository
“The Linux Programming Interface” by Michael Kerrisk (Ch. 63) - epoll deep dive

Difficulty: Advanced Time estimate: 3-4 weeks Prerequisites: Order book project completed, basic networking knowledge

Real world outcome:

Connect with telnet or custom client and submit orders
See order acknowledgments: ACK Order#123 ACCEPTED
Watch trades execute: TRADE Order#123 x Order#456 100@50.25
Market data broadcast to all connected clients
Throughput stats: “Processed 50,000 orders/second, p99 latency: 12μs”

Learning milestones:

Single-threaded blocking version - understand the protocol and matching logic
Event-driven with epoll/kqueue - handle many connections, no blocking
Optimized with lock-free queues - separate network thread from matching thread

Project 4: Trading Strategy Backtester

File: trading_strategy_backtester_hft.md
Main Programming Language: Rust
Alternative Programming Languages: C++, Python, Julia
Coolness Level: Level 3: Genuinely Clever
Business Potential: Level 2: The “Micro-SaaS / Pro Tool”
Difficulty: Level 2: Intermediate (The Developer)
Knowledge Area: Quantitative Finance, Event-Driven Architecture, Memory-Mapped Files
Software or Tool: Backtrader, QuantConnect
Main Book: Designing Data-Intensive Applications by Martin Kleppmann

What you’ll build: A backtesting engine that replays historical market data, simulates order execution, and calculates P&L for trading strategies - the tool every quant uses daily.

Why it teaches HFT: You can’t trade without backtesting. This teaches you about market microstructure, realistic simulation (slippage, latency), and why simple strategies often fail. Plus, it’s immediately useful.

Core challenges you’ll face:

Event replay system: Processing time-ordered events correctly (teaches event-driven architecture)
Realistic simulation: Modeling fills, slippage, and latency (teaches market microstructure)
Memory-efficient data handling: Processing GBs of tick data (teaches memory-mapped files, streaming)
Performance metrics: Sharpe ratio, drawdown, P&L curves (teaches quantitative finance basics)

Resources for key challenges:

“Developing High-Frequency Trading Systems” by Donadio, Ghosh, Rossier - Covers backtesting design
Databento Blog: Rust vs C++ for trading systems - Modern perspective on language choice

Difficulty: Intermediate Time estimate: 2-3 weeks Prerequisites: Basic statistics, order book understanding

Real world outcome:

Run a strategy on historical data and see: Strategy: Mean Reversion | Sharpe: 1.8 | Max Drawdown: -12%
Generate a P&L curve (ASCII or as CSV for plotting)
Compare multiple strategies side-by-side
Output trade log showing every simulated execution

Learning milestones:

Basic replay with simple fill simulation - understand event ordering
Realistic fills with slippage modeling - understand why backtests lie
Add latency simulation - see how speed affects strategy P&L

Project 5: Custom Memory Allocator

File: custom_memory_allocator_hft.md
Main Programming Language: C
Alternative Programming Languages: Rust, C++, Zig
Coolness Level: Level 4: Hardcore Tech Flex
Business Potential: Level 1: The “Resume Gold”
Difficulty: Level 2: Intermediate (The Developer)
Knowledge Area: Memory Management, Pool Allocators, Systems Programming
Software or Tool: malloc, jemalloc
Main Book: Computer Systems: A Programmer’s Perspective by Bryant & O’Hallaron

What you’ll build: A specialized memory allocator (arena/pool allocator) that eliminates malloc from your hot path - because malloc is a latency killer.

Why it teaches HFT: HFT systems never call malloc in the critical path. This project teaches you why general-purpose allocators are slow and how to design allocation strategies for specific use cases.

Core challenges you’ll face:

Pool allocator design: Fixed-size blocks with O(1) alloc/free (teaches memory management fundamentals)
Arena allocator: Bump allocation with bulk free (teaches allocation patterns)
Thread-local pools: Avoiding false sharing in multi-threaded allocators (teaches TLS, cache lines)
Integration: Replacing std::allocator or GlobalAlloc (teaches language allocation APIs)

Resources for key challenges:

“Fluent C” by Christopher Preschern (Ch. 6: Memory Pool) - Step-by-step pool allocator
CppCon: Using C++ for Low-Latency Systems - Memory management techniques
“Rust Atomics and Locks” by Mara Bos (Ch. 8) - Thread-local storage

Difficulty: Intermediate Time estimate: 1 week Prerequisites: Understanding of pointers, basic memory concepts

Real world outcome:

Benchmark showing: malloc: 250ns avg | pool_alloc: 8ns avg
Integration into your order book showing measurable latency improvement
Memory usage stats showing zero fragmentation
Stress test allocating/freeing millions of objects without degradation

Learning milestones:

Basic fixed-size pool - understand the concept
Multiple size classes - handle varying allocation sizes
Thread-safe version - understand lock-free allocation or TLS approach

Project Comparison Table

Project	Difficulty	Time	Depth of Understanding	Fun Factor	Language Recommendation
Limit Order Book	Intermediate	1-2 weeks	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	Both work well
Lock-Free Market Data	Advanced	2-3 weeks	⭐⭐⭐⭐⭐	⭐⭐⭐	Rust (safer atomics)
Matching Engine	Advanced	3-4 weeks	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	C++ (more examples)
Backtester	Intermediate	2-3 weeks	⭐⭐⭐⭐	⭐⭐⭐⭐⭐	Either
Memory Allocator	Intermediate	1 week	⭐⭐⭐⭐	⭐⭐⭐	C++ (more control)

Recommendation

Start with: Project 1 - Limit Order Book

Here’s why:

Foundation for everything else - Every other project builds on understanding order books
Immediate results - You’ll see your code running in days, not weeks
Benchmarkable - You can measure your progress in nanoseconds
Either language works - Good starter for C++ or Rust

Language choice:

Choose Rust if: You want memory safety guarantees, you already know C++, or you’re building something that needs to be reliable without extensive testing
Choose C++ if: You want more existing HFT examples to reference, you need maximum control, or you’re targeting a job in traditional finance (most HFT shops still use C++)

Recommended learning path:

Week 1-2:   Order Book (Project 1)
Week 3-4:   Memory Allocator (Project 5) - integrate into order book
Week 5-7:   Lock-Free Queue (Project 2) - critical HFT skill
Week 8-11:  Matching Engine (Project 3) - tie it all together
Week 12-14: Backtester (Project 4) - start testing strategies

Final Overall Project: Full HFT Trading System

After completing the individual projects, combine them into a complete trading system:

What you’ll build: A full electronic trading ecosystem consisting of:

Exchange simulator (your matching engine)
Market data feed (your lock-free handler)
Trading gateway (connects to the exchange)
Trading strategy (uses your backtester-validated logic)
Risk management layer
Performance monitoring dashboard

Why it teaches everything: This integrates every concept: lock-free queues between components, order books in multiple places, custom allocators everywhere, network I/O, protocol design, and strategy execution. It’s what real HFT firms build.

Core challenges you’ll face:

System integration: Making all components work together with minimal latency
End-to-end latency measurement: Instrumenting the entire pipeline
Failure handling: What happens when components crash?
Configuration management: Tuning the system for performance
Multi-threaded orchestration: Thread pinning, affinity, and coordination

Resources for the full system:

“Building Low Latency Applications with C++” by Sourav Ghosh - The entire book builds this
“Developing High-Frequency Trading Systems” - Multi-language approach (C++/Java/Python/Rust)
Luca Sbardella: Rust for HFT - Modern Rust perspective

Difficulty: Advanced Time estimate: 2-3 months Prerequisites: All previous projects completed

Real world outcome:

Launch the full system and see it trade: [STRATEGY] Signal detected | [GATEWAY] Order sent | [EXCHANGE] Fill received | [RISK] Position updated | [P&L] +$0.02
Dashboard showing: orders/second, latency percentiles, position, P&L
Ability to run paper trading with simulated or real market data
End-to-end latency measurement: “Order decision to exchange ack: 45μs”

Learning milestones:

Components talking over TCP - basic integration
Lock-free inter-component communication - production-grade architecture
Full system with monitoring - understand operational concerns
Paper trading with real data - see your system make (simulated) money

Sources

Books:

Building Low Latency Applications with C++ - Sourav Ghosh
Developing High-Frequency Trading Systems - Donadio, Ghosh, Rossier

GitHub Repositories:

aspone/OrderBook - C++ low-latency order book
Building-Low-Latency-Applications-with-CPP - Full trading system code
awesome-lockfree - Lock-free programming resources

Articles & Blogs:

Rust vs C++ for trading systems - Databento
Rust for HFT - Luca Sbardella
Limit Order Book Implementation - Alex Abosi
Lock-freedom without garbage collection - Aaron Turon
Fear and Loathing in Lock-Free Programming - Tyler Neely