GPU-Accelerated Backtesting for HFT with WGSL and Rust

High-frequency trading (HFT) backtesting requires processing enormous amounts of market data with minimal latency. GPU acceleration using WGSL (WebGPU Shading Language) and Rust provides a powerful solution for this computationally intensive task.

Why GPU Acceleration for HFT Backtesting?

  1. Massive parallelism - GPUs can process thousands of trades/orders simultaneously
  2. Low latency - GPU compute shaders execute strategies with microsecond precision
  3. Throughput - Process years of tick data in minutes/hours instead of days

Architecture Overview

graph TD
    A[Market Data] --> B[Rust Preprocessing]
    B --> C[GPU Buffer]
    C --> D[WGSL Compute Shader]
    D --> E[Strategy Execution]
    E --> F[Results Buffer]
    F --> G[Rust Postprocessing]
    G --> H[Performance Metrics]

Implementation with WGSL and Rust

1. Market Data Preparation (Rust)

#![allow(unused)]
fn main() {
use wgpu;
use bytemuck::{Pod, Zeroable};

#[repr(C)]
#[derive(Debug, Copy, Clone, Pod, Zeroable)]
struct MarketTick {
    timestamp: u64,    // nanoseconds since epoch
    price: f32,       // normalized price
    volume: f32,      // normalized volume
    bid: f32,
    ask: f32,
    // ... other market data fields
}

fn prepare_gpu_data(device: &wgpu::Device, ticks: &[MarketTick]) -> wgpu::Buffer {
    let buffer = device.create_buffer(&wgpu::BufferDescriptor {
        label: Some("Market Data Buffer"),
        size: (std::mem::size_of::<MarketTick>() * ticks.len()) as u64,
        usage: wgpu::BufferUsages::STORAGE | wgpu::BufferUsages::COPY_DST,
        mapped_at_creation: false,
    });
    
    queue.write_buffer(&buffer, 0, bytemuck::cast_slice(ticks));
    buffer
}
}

2. WGSL Compute Shader for Backtesting

// market_tick.wgsl
struct MarketTick {
    timestamp: u64,
    price: f32,
    volume: f32,
    bid: f32,
    ask: f32,
};

struct StrategyParams {
    lookback_window: u32,
    threshold: f32,
    // ... other strategy parameters
};

struct TradeEvent {
    timestamp: u64,
    price: f32,
    size: f32,
    direction: i32, // 1 for buy, -1 for sell
};

@group(0) @binding(0) var<storage, read> market_data: array<MarketTick>;
@group(0) @binding(1) var<storage, read> strategy_params: StrategyParams;
@group(0) @binding(2) var<storage, read_write> trade_events: array<TradeEvent>;

@compute @workgroup_size(256)
fn main(
    @builtin(global_invocation_id) global_id: vec3<u32>,
    @builtin(local_invocation_id) local_id: vec3<u32>
) {
    let idx = global_id.x;
    
    // Skip if we're out of bounds
    if (idx >= arrayLength(&market_data)) {
        return;
    }
    
    // Simple mean reversion strategy example
    if (idx > strategy_params.lookback_window) {
        var sum: f32 = 0.0;
        for (var i: u32 = 0; i < strategy_params.lookback_window; i = i + 1) {
            sum = sum + market_data[idx - i].price;
        }
        let moving_avg = sum / f32(strategy_params.lookback_window);
        let current_price = market_data[idx].price;
        
        // Generate buy/sell signals
        if (current_price < moving_avg - strategy_params.threshold) {
            trade_events[idx] = TradeEvent(
                market_data[idx].timestamp,
                market_data[idx].price,
                1.0, // size
                1    // buy
            );
        } else if (current_price > moving_avg + strategy_params.threshold) {
            trade_events[idx] = TradeEvent(
                market_data[idx].timestamp,
                market_data[idx].price,
                1.0, // size
                -1   // sell
            );
        }
    }
}

3. Rust Backtesting Pipeline

#![allow(unused)]
fn main() {
async fn run_backtest(
    device: &wgpu::Device,
    queue: &wgpu::Queue,
    market_data: &[MarketTick],
    strategy_params: StrategyParams,
) -> Vec<TradeEvent> {
    // Create buffers
    let market_buffer = prepare_gpu_data(device, queue, market_data);
    let params_buffer = create_params_buffer(device, queue, &strategy_params);
    let trade_buffer = create_output_buffer(device, market_data.len());
    
    // Load WGSL shader
    let shader = device.create_shader_module(wgpu::ShaderModuleDescriptor {
        label: Some("Backtest Shader"),
        source: wgpu::ShaderSource::Wgsl(include_str!("market_tick.wgsl").into()),
    });
    
    // Create compute pipeline
    let pipeline = device.create_compute_pipeline(&wgpu::ComputePipelineDescriptor {
        label: Some("Backtest Pipeline"),
        layout: None,
        module: &shader,
        entry_point: "main",
    });
    
    // Create bind group
    let bind_group = device.create_bind_group(&wgpu::BindGroupDescriptor {
        label: Some("Backtest Bind Group"),
        layout: &pipeline.get_bind_group_layout(0),
        entries: &[
            wgpu::BindGroupEntry {
                binding: 0,
                resource: market_buffer.as_entire_binding(),
            },
            wgpu::BindGroupEntry {
                binding: 1,
                resource: params_buffer.as_entire_binding(),
            },
            wgpu::BindGroupEntry {
                binding: 2,
                resource: trade_buffer.as_entire_binding(),
            },
        ],
    });
    
    // Dispatch compute shader
    let mut encoder = device.create_command_encoder(&wgpu::CommandEncoderDescriptor {
        label: Some("Backtest Encoder"),
    });
    
    {
        let mut cpass = encoder.begin_compute_pass(&wgpu::ComputePassDescriptor {
            label: Some("Backtest Compute Pass"),
        });
        cpass.set_pipeline(&pipeline);
        cpass.set_bind_group(0, &bind_group, &[]);
        cpass.dispatch_workgroups(
            (market_data.len() as u32 + 255) / 256, // ceil(num_ticks / 256)
            1, 
            1
        );
    }
    
    queue.submit(std::iter::once(encoder.finish()));
    
    // Read back results
    let trade_events = read_trade_events(device, queue, &trade_buffer, market_data.len()).await;
    trade_events
}
}

Performance Considerations

  1. Memory Layout Optimization

    • Structure market data for GPU coalesced memory access
    • Use SoA (Structure of Arrays) instead of AoS for better parallelism

  2. Asynchronous Processing

    • Overlap data transfers with computation using multiple command buffers
    • Pipeline multiple backtest runs

  3. Reduction Patterns

    • Use parallel reduction for aggregating PnL, statistics
    • Implement tree-reduction in WGSL for performance metrics

  4. Batch Processing

    • Process data in chunks that fit GPU memory
    • Stream data from storage as needed

Advanced Techniques

  1. Multi-GPU Support

    • Distribute different time periods or instruments across GPUs
    • Use WGSL's workgroup sharing for cross-GPU communication

  2. Realistic Market Simulation

    • Implement order book reconstruction in GPU memory
    • Simulate latency and network effects

  3. Genetic Optimization

    • Run parameter optimization directly on GPU
    • Evaluate thousands of parameter sets in parallel

This approach leverages Rust's safety and performance with WGSL's GPU compute capabilities to create a high-throughput backtesting system suitable for HFT strategies. The combination allows processing years of tick data in minutes while maintaining the flexibility to implement complex trading strategies.

--

Your proposed approach of using wgpu/WGSL for research/backtesting while leveraging Vulkan/GLSL (with hand-written SPIR-V for critical parts) in production is a thoughtful separation that plays to the strengths of each technology stack. Here's my analysis:

Strengths of This Approach

  1. Research/Backtesting with wgpu:

    • Cross-platform simplicity: WGSL's WebGPU foundation means your research code runs everywhere (including browsers for quick visualization).
    • Rust integration: wgpu is native to Rust, avoiding FFI overhead and benefiting from cargo's ecosystem.
    • Rapid iteration: WGSL is easier to modify than SPIR-V, and the toolchain is simpler than Vulkan's.
    • Good enough performance: For backtesting, wgpu's overhead is negligible compared to the gains from GPU parallelism.

  2. Production with Vulkan/GLSL → SPIR-V:

    • Maximum performance: Hand-optimized SPIR-V (via spirv-tools or manual tweaking) can squeeze out every cycle for latency-sensitive HFT.
    • Low-level control: Vulkan's explicit API avoids driver overheads (e.g., descriptor set management).
    • Vendor-specific optimizations: You can target NVIDIA/AMD intrinsics (VK_KHR_shader_float_controls) or use SPV_KHR_* extensions.
    • Determinism: Critical for production trading—Vulkan offers more predictable execution than WebGPU's abstraction layer.

  3. Shared Knowledge Transfer:

    • Both WGSL and GLSL are ALGOL-style shading languages, so algorithmic logic can often be ported with minimal changes.
    • SPIR-V is the common intermediate representation (WGSL → SPIR-V → Target ISA), so optimizations learned in one domain can apply to the other.

Potential Challenges

  1. Divergent Code Paths:

    • WGSL and GLSL have subtle differences (e.g., WGSL lacks switch, uses array<T, N> instead of T[N]). You might need shader transpilation (like naga) to share logic.

  2. Performance Discrepancies:

    • A kernel that runs well in wgpu might behave differently in Vulkan due to driver optimizations or memory model differences (e.g., WGSL's stricter atomic guarantees).

  3. Tooling Fragmentation:

    • Debugging WGSL requires different tools (wgpu-rs tracers) vs. Vulkan (RenderDoc, NSight).
    • SPIR-V hand-tuning requires deep knowledge of the spec (e.g., OpCapability/OpExecutionMode).

Recommendations

  1. Unify the Shader Pipeline:

    • Consider writing core algorithms in a subset of GLSL that compiles to both WGSL (via naga) and SPIR-V.
    • Example: Use #define macros to abstract differences: 
      #ifdef WGSL
#define ARRAY(T, N) array<T, N>
#else
#define ARRAY(T, N) T[N]
#endif

  2. Profile Rigorously:

    • Validate that wgpu backtesting results match Vulkan executions (especially for edge cases like NaN handling or barrier semantics).

  3. SPIR-V Hand-Writing Strategy:

    • Focus on hot loops (e.g., order book matching) where hand-rolled SPIR-V can outperform compiler output.
    • Use spirv-opt for baseline optimizations before manual tweaking.

  4. Consider Rust GPU (rust-gpu):

    • If you're already in Rust, rust-gpu lets you write SPIR-V kernels in Rust syntax, potentially bridging the gap between research/production.

When to Hand-Write SPIR-V

Reserve this for:

  • Atomic operation patterns not expressible in GLSL (e.g., fine-grained memory semaphores).
  • Vendor-specific intrinsics (e.g., NVIDIA's shfl_sync).
  • Kernels where the compiler generates suboptimal code (e.g., unnecessary register spills).

Final Verdict

Your approach is sound, especially for HFT where the production environment demands every microsecond. The key is to:

  1. Keep research (wgpu) and production (Vulkan) codebases aligned via shared algorithmic primitives.
  2. Use SPIR-V hand-tuning surgically—only where it materially impacts latency/throughput.
  3. Invest in tooling to compare results across both stacks (e.g., validate backtest trades match production behavior).

This hybrid strategy gives you the best of both worlds: developer velocity in research and deterministic performance in production.

Brutally Honest Answer: GPU-Accelerated Backtesting in HFT – What It’s Really About

Short Answer:
Yes, but not in the way most people think. GPU backtesting is only valuable if:

  1. You’re solving an HFT-specific bottleneck (not just speeding up pandas).
  2. Your implementation mirrors real trading infrastructure (event-driven, not vectorized).
  3. You can prove it impacts PnL (faster backtesting → better strategies → more money).

What GPU Backtesting Should Do in HFT

✅ 1. Ultra-Fast Limit Order Book (LOB) Simulation

  • Problem: Reconstructing LOBs from tick data is O(n²) per event (slow on CPU).
  • GPU Solution: Parallelize order matching (price-time priority) across cores.
  • Why HFT Cares:
    • Realistic fills require nanosecond-level event processing (GPUs can do 1000x faster).
    • Example: 
      #![allow(unused)]
fn main() {
// WGSL kernel for LOB reconstruction  
@compute @workgroup_size(64)  
fn update_lob(@builtin(global_invocation_id) id: vec3<u32>) {  
    let event = events[id.x];  
    if (event.is_cancel) {  
        lob.cancel_order(event.order_id); // Parallel cancellation  
    } else {  
        lob.add_order(event); // Parallel insertion  
    }  
}  
}

✅ 2. High-Frequency Strategy Optimization

  • Problem: Testing 10,000 parameter combos on CPU takes hours.
  • GPU Solution: Run massively parallel Monte Carlo sims (e.g., market-making spreads).
  • Why HFT Cares:
    • Faster iteration → find edge before competitors.
    • Example: 
      # CUDA-accelerated market-making backtest  
def kernel(strategies):  
    tid = cuda.threadIdx.x  
    pnl = 0.0  
    for tick in data:  
        pnl += strategies[tid].update(tick) # 10k strategies in parallel  
    results[tid] = pnl

✅ 3. Microstructure Modeling (Toxicity, Adverse Selection)

  • Problem: Calculating VPIN, queue position decay is CPU-intensive.
  • GPU Solution: Run real-time toxicity filters across all ticks.
  • Why HFT Cares:
    • Avoid toxic flow → 18% better fill rates (your claim).
    • Example: 
      #![allow(unused)]
fn main() {
// GPU-accelerated VPIN calculation  
@compute fn vpin_analysis(tick: Tick) -> f32 {  
    let imbalance = (tick.bid_volume - tick.ask_volume).abs();  
    atomic_add(&global_vpin, imbalance); // Parallel reduction  
}  
}

What GPU Backtesting Should NOT Be

❌ 1. Speeding Up Vectorized Pandas Code

  • Why Useless:
    • HFT strategies are event-driven, not vectorized.
    • Real trading has latency, partial fills, cancellations—GPUs can’t help if your model ignores these.

❌ 2. "Look How Fast My Moving Average Is!"

  • Why Useless:
    • No HFT firm cares about technical indicators (they’re noise at nanosecond scales).
    • GPUs excel at parallel stateful logic (e.g., order books), not trivial math.

❌ 3. Python + CUDA "Backtests"

  • Why Useless:
    • Python’s GIL and overhead kill latency (HFT firms use C++/Rust).
    • Example of what not to do: 
      # Useless GPU backtest (HFT firms will laugh)  
import numpy as np  
from numba import cuda  

@cuda.jit  
def moving_average(prices, window):  
    # 🤦 HFT doesn’t care about this

When GPU Backtesting Actually Helps Get Hired

Project TypeHFT Hiring ValueWhy?
GPU-accelerated LOB simulator✅ EliteMirrors real exchange matching
VPIN toxicity detection✅ StrongShows microstructure knowledge
Parallel Monte Carlo optimizer✅ GoodProves you can find edge faster
Python pandas on GPU❌ GarbageNo relevance to HFT

How to Make It Hire-Worthy

  1. Use Real Data
    • Test on NASDAQ ITCH/OUCH or Crypto L3 feeds (not Yahoo Finance).
  2. Model Real Trading Effects
    • Include latency, fees, partial fills, cancellations.
  3. Benchmark Against CPU
    • Show orders-of-magnitude speedup (e.g., "10ms vs. 10sec").
  4. Tie to PnL
    • Example:
      • "GPU-accelerated VPIN detection → 18% fewer toxic fills → +$500k/year."

Final Verdict

  • If your GPU backtester:
    • Models real HFT challenges (order books, microstructure) → Top-tier project.
    • Just speeds up retail strategies → Waste of time.
  • Ask yourself:
    • "Would Citadel’s quant team use this?"
    • If yes, highlight it on your resume. If no, pivot to FPGA or formal methods.

Want a GPU project HFT firms would love? Build:

  • A WGSL/Rust LOB simulator that reconstructs order books from ITCH data.
  • A CUDA-accelerated market-maker with real fill logic.
  • A GPU-powered VPIN engine that flags toxic flow in real-time.

Still unsure? Drop your code—I’ll tell you if it’s HFT-worthy.

Yes, But Here’s the Brutal Truth About GPU Backtesting in HFT

Short Answer:
Yes, GPU-accelerated backtesting uses GPUs to test trading strategies on historical data (like NASDAQ ITCH/OUCH). But 99% of GPU backtesting projects are useless for HFT hiring.

Why?

1. Most GPU Backtesting is Just "Fast Python" (Useless for HFT)

  • Retail GPU backtesting = Speeding up pandas/NumPy on cleaned CSV data.
  • Real HFT GPU backtesting = Event-driven, tick-by-tick processing of raw binary market data with:
    • Order book reconstruction
    • Fill simulation (partial fills, queue position, cancellations)
    • Microstructure effects (latency arbitrage, adverse selection)

2. HFT Firms Don’t Care About "Backtesting Speed" Alone

  • They care about:
    • Accuracy (does it match real exchange behavior?)
    • Latency (can it run in production?)
    • PnL Impact (does it find real edge?)
  • Example:
    • ❌ "My GPU backtester runs 1000x faster than Backtrader!" → Who cares?
    • ✅ "My GPU LOB simulator matches CME’s fill logic with 99.9% accuracy" → Hire this person.

What Actually Matters in GPU Backtesting for HFT

✅ 1. Event-Driven Processing (Not Vectorized)

  • Bad: 
    # Useless GPU vectorized backtest (HFT ignores this)  
sma = np.mean(prices[-50:])  # 🤡
  • Good: 
    #![allow(unused)]
fn main() {
// WGSL kernel for event-driven order processing  
@compute fn handle_order(order: Order) {  
    if order.price >= best_bid {  
        let fill = match_order(order); // Real fill logic  
        atomic_add(&pnl, fill.qty * fill.price);  
    }  
}  
}

✅ 2. Raw Market Data Parsing (ITCH/OUCH, PITCH)

  • Bad: Testing on CSV mid-price data.
  • Good: Processing binary ITCH feeds with:
    • FAST protocol decoding (GPU-parallelized)
    • Order book reconstruction (realistic depth updates)

✅ 3. Microstructure-Aware Fill Simulation

  • Bad: Assuming "instant fills at mid-price."
  • Good: Modeling:
    • Queue position decay
    • Cancel-to-trade ratios
    • Toxic flow detection (VPIN, Hawkes processes)

GPU Backtesting vs. HFT Realities

AspectRetail GPU BacktestingHFT-Grade GPU Backtesting
Data InputCleaned CSVRaw ITCH/OUCH binary feeds
Processing ModelVectorized (batched)Event-driven (per-tick)
Fill LogicMid-price assumptionsRealistic queue position, adverse selection
LatencyMillisecondsNanoseconds (FPGA beats GPU)
Use in HFTNever usedOnly for specific tasks (e.g., Monte Carlo)

When GPU Backtesting Is Worth It for HFT Hiring

  1. You’re Accelerating a Real HFT Bottleneck

    • Example:
      • Monte Carlo market-making sims (testing 100k parameter sets in parallel).
      • High-frequency alpha research (brute-forcing pattern detection).

  2. You’re Using GPUs Like an HFT Firm Does

    • Not: "I ran a moving average on GPU."
    • But: "I GPU-accelerated LOB reconstruction to backtest latency-sensitive arbitrage."

  3. You Can Prove It Impacts PnL

    • ❌ "My backtester is fast."
    • ✅ "GPU-optimized fill logic improved strategy Sharpe from 1.5 → 2.3."

What Should You Build Instead? (Higher ROI for HFT)

If you want to actually get hired, focus on:

  1. FPGA Market Data Parsing (ITCH → order book in 80ns).
  2. Formal Verification of Trading Logic (TLA+ for matching engines).
  3. Rust-Based Event-Driven Backtester (no GPU, but HFT-ready).

Final Verdict

  • If your GPU backtester:
    • Processes raw ITCH data, models real fills, and beats CPU by 1000x → Elite project.
    • Just speeds up vectorized retail strategies → Waste of time.
  • Ask yourself:
    • "Would Jump Trading use this, or is it just a tech demo?"

Want a GPU project that will impress HFT firms? Build:

  • A WGSL-based limit order book simulator that reconstructs NASDAQ ITCH data tick-by-tick.
  • A CUDA-accelerated VPIN toxicity detector for real-time adverse selection avoidance.

Still unsure? Drop your code—I’ll tell you if it’s HFT-worthy. 🚀

WGSL vs GLSL

That’s a really sharp observation — GLSL being better for real-time, ns-level latency makes total sense, especially if you're pushing toward native pipelines or working with tight control over the GPU stack (OpenGL/Vulkan directly or via CUDA interop). It's lower-level, battle-tested, and compiler paths are better understood and tuned for perf.

🧪 Using WGSL for Backtesting?

Yes — you can use WGSL for backtesting, and in fact, it can be a really clean and efficient approach if used properly:

✅ Why it makes sense:

  1. Parallelizable Simulation:

    • If you’re simulating a massive number of trades, order books, or strategies over historical data, WGSL via WebGPU can process large batches in parallel on the GPU.
    • Think: millions of ticks processed in parallel = very fast offline analysis.

  2. Deterministic Execution:

    • WGSL (unlike some older shader langs) was designed with determinism and safety in mind, which is perfect for reproducible backtests.

  3. Cross-Platform:

    • Because WebGPU is the target, WGSL lets you write GPU-accelerated backtesters that run in browsers, Node.js, or natively via WGPU.

  4. Good Enough Latency for Offline:

    • For backtesting, you don’t care about sub-ns per-tick latency — you care about total throughput and simulation correctness.
    • WGSL is often fast enough, especially if you're batching and pipelining work intelligently.

🔻 When WGSL Might Not Work for You:

  • If your backtesting framework is tightly coupled with a native GLSL-based pipeline or needs features not yet standardized in WGSL.
  • If you’re using specialized GLSL extensions or GPU interop features (e.g., shared memory with CPU in zero-copy mode).
  • If you're looking to replay real-time trade logs faster than real-time at maximum throughput, GLSL with SPIR-V might still be slightly more optimized due to better compiler maturity.

🛠️ Realistic Setup

If you wanted to build a backtester powered by WGSL, you could:

  • Load historical tick or order book data into GPU buffers.
  • Encode trading logic (e.g., simple market making, arbitrage models) as compute shaders.
  • Simulate the exchange matching logic in WGSL as well.
  • Use WGPU (Rust) or WebGPU (JS/TS) to orchestrate the pipeline.

🧠 Summary

Use CaseGLSLWGSL
Live HFT Trading✅ Preferred (ns-level)🚫 Not ideal
Offline Backtest✅ Possible✅ Great fit (safe + fast)

Let me know if you’d like a scaffold for a backtester in Rust + WGPU using WGSL — I can help with that architecture. Or if you want to simulate the order book itself on the GPU — also very doable!

You're asking the right questions — long-term efficiency and foundational sequencing matter more than tool hype. Let's break it down:

1. Is using wgpu + wgsl for backtesting a waste of time?

Short answer: Not necessarily, but it might be premature if you haven't nailed the core HFT stack yet.

Pros:

  • You get GPU-parallelism for heavy simulations (millions of ticks, multistrategy backtests).
  • WGSL is portable, modern, and integrates well with WebGPU.
  • You learn data-parallel thinking early, which is key for low-latency batch ops.

Cons:

  • GPU backtesting is overkill at early stages — CPU is more than enough until you hit scale.
  • Debuggability and iteration speed are lower on the GPU.
  • You might spend more time learning wgpu than improving your models or infrastructure.

Verdict: If you're early, prioritize breadth (core infra and domain modeling). Once you're solid, GPU is a killer optimization layer.

2. What should you focus on first instead?

Here’s a structured roadmap to build a real foundation for HFT/backtesting systems:

✅ Stage 1: Core Domain Knowledge

  • Market microstructure — LOBs, priority rules, maker/taker fees
  • Exchange protocols — e.g., NASDAQ ITCH, OUCH, FIX
  • Order matching algorithms — FIFO, pro-rata, price-time

Learn:

  • How orders are matched and queued
  • How latency and queue position affect fill probability
  • How exchanges broadcast state (ITCH/FIX feeds)

✅ Stage 2: Infrastructure and Systems

  • Rust systems programming — get fast, memory-safe code for LOBs and strategies
  • Protocol parsing — e.g., decoding binary feeds with nom, binrw, or handcrafted parsers
  • LOB simulator + matching engine — simulate exchange behavior and queue modeling

Build:

  • A real-time feed parser from ITCH or L3 data
  • A matching engine for limit/market/cancel orders
  • A log system that tracks fill events, PnL, latency

✅ Stage 3: Strategy Framework + Backtester

  • Build backtester loop that loads data, feeds it to LOB, accepts strategy output
  • Add metrics: PnL, Sharpe, latency histograms, order stats

Once you can backtest 1M+ events on CPU in <5 seconds, you’re ready for GPU offloading.

✅ Stage 4 (optional): GPU Acceleration

  • Move fill simulation or multi-strategy backtests to wgpu + wgsl
  • Use GPU for queue position estimation, multistrategy sweeps, or latency modeling
  • Later: Visualize order book replay in browser via WebGPU

Final Recommendation

If you're in your first 6–9 months of serious HFT/infra dev:

  • Focus on Rust + exchange protocols + matching engine + LOB sim
  • Build a CPU-based backtester that is clean and testable
  • Only then experiment with wgpu + wgsl as an optimization path, not a foundation

GPU is a bonus layer, not the first one. You’ll know when you need it — when CPU becomes the bottleneck.