Absolutely — here’s how I’d prioritize those projects in terms of maximizing hiring signal and relevance to low-latency/HFT firms, especially if you're aiming to get hired fast:

✅ Tier 1: Must-Haves (Core to 90% of Low-Latency Roles)

These scream "I can build, understand, and optimize a trading system."

1. ITCH Parser + Order Book Reconstruction

   • Parsing binary feeds, maintaining a LOB.
   • Signals deep systems knowledge + familiarity with real-world market data.

2. Signal Generation Engine

   • Demonstrates strategy design, market microstructure intuition.
   • Can be simple (e.g. price momentum or order book imbalance).

3. Execution Engine (OUCH protocol mock or real)

   • Shows end-to-end thinking: from signal to order placement.
   • If you include latency metrics here, that’s golden.

🔁 Tier 2: Strategic Add-Ons (Huge Bonus Points)

These round out your system and let you compete with seasoned candidates.

4. Backtesting & Market Data Replay Engine

   • Gives your system realism and validation.
   • Shows product sense + systematic thinking.

5. Risk Engine (Limit Checker, Position Tracker)

   • Often overlooked, but super important.
   • Shows understanding of live trading constraints.

🧪 Tier 3: Nice-to-Have, Differentiators

They’re optional but can boost you into a top candidate tier for certain roles.

6. Matching Engine (like what exchanges run)

   • Not directly needed for trader/infra roles, but very impressive.
   • Useful if you’re aiming for infra roles, or just want deep understanding.

7. Custom Network Stack / Kernel Bypass Experiment

   • Bonus for ultra-low-latency infra roles.
   • Can be a very small project, but huge flex (DPDK, io_uring, etc).

🔥 Recommended Stack Order to Build

🔚 Bottom Line:

If you build even just the first 3–4, you'll outshine 95% of candidates — especially if it’s:

• Cleanly modular
• Written in Rust (showing systems chops)
• With real ITCH samples or mock market scenarios

Want help outlining how these fit together in a repo structure or which crates to use for each layer?

more projects nice to have

🟡 4. Real-Time Event Replay Engine

Goal: Deterministically replay historical data for simulation
Why: Useful for debugging and low-latency validation

• Read timestamped events
• Send them in order via a ring buffer or queue
• Replay with real-time or simulated-time pacing

🔴 5. Strategy Executor

Goal: React to events and simulate strategy behavior
Why: Core component for any trading system

• Read LOB snapshots or ticks
• Implement simple strategy (e.g., ping/pong market maker)
• Simulate fills, update PnL

🔴 6. Risk Manager + Order Throttler

Goal: Manage exposure, rate limits, order caps
Why: Required in any production trading system

• Track outstanding orders, position, gross/net exposure
• Cancel orders on risk breach
• Throttle messages per second

🔴 7. Backtester

Goal: Offline evaluation of strategy on historical data
Why Last: Most complex, but demonstrates full system simulation

• Replay ITCH/LOB data
• Run strategy in simulation loop
• Measure PnL, latency, fill rate, queue position

🔁 Optional / Bonus Projects

Would you like me to turn this into a starter GitHub repo structure (src/bin/parser.rs, src/bin/orderbook.rs, etc.) so you can get hacking right away?

🦀 Rust Internals (System Foundations)

These are low-level topics that help you build fast, predictable, and safe systems:

⚙️ Concurrency & Synchronization

• Lock-free data structures (queues, ring buffers)
• Atomics (AtomicU64, Ordering)
• Memory fences and barriers
• Compare-and-swap (CAS) loops

🧠 Memory & Layout

• Cache lines, false sharing, alignment
• Stack vs heap, zero-cost abstractions
• Allocation strategies (e.g., bump allocators for scratch space)
• SIMD and intrinsics with std::arch

⏱️ Time & Performance

• std::time::Instant limitations and alternatives (TSC, HPET)
• rdtsc and high-res timers in userspace
• Batching vs inlining vs loop unrolling
• Avoiding syscalls in hot paths
• Profiling tools: perf, flamegraph, criterion, dhat

🔬 Runtime Behavior

• Panic-free, deterministic error handling
• Unsafe correctness (RAII with unsafe)
• Custom memory allocators
• Thread pinning & CPU affinity
• Real-time scheduling on Linux (SCHED_FIFO)

💸 Finance + HFT Domain Knowledge

This set is necessary to model the market, understand edge cases, and design realistic simulators/backtesters.

📈 Market Microstructure

• Limit Order Books (price-time priority, queue modeling)
• Market vs Limit vs Pegged orders
• Trade-throughs, slippage, and order routing

📡 Exchange Data & Protocols

• NASDAQ ITCH, OUCH, and FIX parsing
• Binary data feed decoding and event sequencing
• Latency arbitrage + stale book handling
• Exchange matching engine behaviors (matching rules, reject codes)

🧠 Strategy Design Concepts

• Market making (passive liquidity provisioning)
• Momentum, stat arb, latency-sensitive execution
• Position/risk management
• Strategy parameter search spaces

🔁 Simulation & Testing

• LOB simulators (stateful reconstruction)
• Tick-based vs event-based backtesting
• Deterministic replay of ITCH streams
• Latency-aware fill modeling (queue position simulation)

🏗️ System Components You Can Build from These

Here’s where the two areas converge — components where Rust internals + finance domain intersect:

That’s a sharp and pragmatic approach — double down on Rust internals (your edge), and cover just enough finance to make yourself hireable fast in low-latency/HFT/backend roles.

Below is a balanced roadmap with:

1. Core domain areas from Rust internals + finance/HFT
2. The Rust APIs/libraries that are most relevant for each
3. Focus areas that are likely to get you hired fast

🦀 Rust Internals (Your Strength)

1. Concurrency & Lock-Free Programming

Goal: Build ultra-low-latency data structures (queues, task schedulers, ring buffers)

🔥 In the wild: Used in matching engines, feed handlers, low-latency schedulers.

2. Memory Layout & Control

Goal: Tight control over cache-line alignment, zero-copy parsing, arena allocation

🔥 In the wild: Used in protocol parsing, feed decoding, scratchpads for fill modeling.

3. Timing & Instrumentation

Goal: Measure sub-microsecond timing, perf hotspots, and event latency

🔥 In the wild: Used to profile trading systems, latency histograms, kernel bypass path analysis.

4. CPU Pinning & Realtime Scheduling

Goal: Deploy components predictably under Linux without syscall interference

🔥 In the wild: Common for colocated low-latency services and coloc box tuning.

💸 Finance / HFT Domain

1. Market Data & Protocols

Goal: Parse binary exchange feeds and simulate order book state

🔥 In the wild: Required for all HFT feed handlers. Parsing ITCH/FIX is a top skill.

2. LOB Simulator & Matching Engine

Goal: Simulate an exchange for backtesting

🔥 In the wild: Used by every prop shop to test and train strategies internally.

3. Backtesting Framework

Goal: Replay historical ticks and simulate strategy behavior

🔥 In the wild: Used in quant research, strategy design, execution analysis.

4. Strategy & Risk Engine

Goal: Decide and throttle order flow

🔥 In the wild: Often embedded inside colocated strategy engines or execution layers.

🔨 Project Suggestion to Tie It All Together

Build a simplified, performant HFT simulation stack in Rust:

• parser/ — Parse ITCH/OUCH into events (use nom, zerocopy)
• engine/ — Matching engine with lock-free ring buffers (use crossbeam, spin)
• backtest/ — Replay tick streams and emit metrics (use rayon, csv)
• latency/ — Nanosecond timing + queue position modeling (rdtsc, time)
• strategy/ — Simple market maker or momentum strat + fill modeling

This will be your hire-me resume project — a great demo for low-latency/infra/backend roles.