Financial Data Processing Speed Benchmark: A Journey Through Performance Optimization

A comprehensive guide to optimizing computational performance for quantitative finance, from basic implementations to ultimate performance

Table of Contents

• Introduction
• Iteration 1: Baseline Performance
• Iteration 2: Enhanced Optimizations
• Iteration 3: Ultimate Challenge - 100+ Quantitative Features
• Performance Analysis & Key Learnings
• Optimization Strategies Guide
• Language-Specific Performance Tips
• Conclusion

Introduction

As a computer scientist focused on performance, I embarked on a journey to understand the computational characteristics of different programming languages when applied to quantitative finance tasks. Starting with basic moving average calculations and progressively increasing complexity, this document captures the evolution of performance optimization strategies and key insights gained along the way.

The dataset used throughout: USDJPY2.csv with 3,926,757 records of financial candlestick data (timestamp, Open, High, Low, Close, volume).

Iteration 1: Baseline Performance

Objective

Calculate 50, 200, and 500 period moving averages plus complex mathematical operations on financial data.

Implementations

• C++: Basic implementation with STL containers
• Rust: Safe systems programming with ownership model
• Python: Standard library with pandas

Results

Language	Execution Time	Operations
Rust	1.015s	3 MAs + complex math
C++	3.792s	3 MAs + complex math
Python	9.307s	3 MAs + complex math

Key Learnings

1. Memory Safety Without Performance Cost: Rust's ownership model eliminated memory bugs while maintaining C++-level performance
2. Interpreted Language Overhead: Python's interpreted nature creates significant performance gaps for numerical computations
3. Algorithm Matters: Using sliding window technique reduced complexity from O(n*m) to O(n) where m is the moving average period

Iteration 2: Enhanced Optimizations

Objective

Introduce advanced optimization techniques and increase computational load (calculate MAs for periods 200-220).

Optimizations Applied

• Python: NumPy arrays + Numba JIT compilation
• Rust: Parallel processing with Rayon crate, optimized CSV parsing
• C++: Compiler optimizations (-O3, -march=native), memory pre-allocation

Results

Language	Execution Time	Operations
Rust	1.013s	21 MAs (200-220) + complex math
Python (enhanced)	3.630s	21 MAs (200-220) + complex math
C++ (enhanced)	3.909s	21 MAs (200-220) + complex math
Python (baseline)	9.307s	3 MAs (50, 200, 500) + complex math

Key Learnings

1. JIT Compilation Impact: Python with Numba achieved 2.5x performance improvement
2. Consistent Rust Performance: Despite 7x increase in moving averages, Rust maintained ~1 second execution
3. Compiler Optimizations: Proper compiler flags can significantly improve performance
4. Library Choice Matters: Well-designed libraries (NumPy, Rayon) can bridge performance gaps

Iteration 3: Ultimate Challenge - 100+ Quantitative Features

Objective

Calculate 101+ quantitative features for each of 3.9M rows (simulating real-world quantitative trading system feature engineering).

Features Implemented

• Technical indicators (RSI, MACD approximations)
• Statistical measures (volatility, skewness, kurtosis)
• Price action features (candlestick patterns, support/resistance proxies)
• Momentum indicators
• Correlation measures
• Risk metrics

Results

Language	Execution Time	Operations
Rust	4.211s	101+ quant features + 21 MAs
C++	10.123s	101+ quant features + 21 MAs
Python	>60s	101+ quant features + 21 MAs

Key Learnings

1. Scalability Differences: Performance gaps widen dramatically with computational complexity
2. Compiled vs Interpreted: At high complexity, compiled languages show orders-of-magnitude advantages
3. Memory Locality: Cache-friendly access patterns become critical at scale
4. Zero-Cost Abstractions: Rust's promise holds true - safe code performs like unsafe C++

Performance Analysis & Key Learnings

Performance Scaling

Complexity Level → Baseline → Enhanced → Ultimate Challenge
Rust Performance → 1.015s → 1.013s → 4.211s
C++ Performance  → 3.792s → 3.909s → 10.123s
Python Performance → 9.307s → 3.630s → >60s

Critical Insights

1. Algorithmic Efficiency Trumps Everything: Good algorithms matter more than language choice
2. Memory Access Patterns: Sequential access and cache locality are paramount
3. Abstraction Penalties: Interpreted languages suffer exponentially with complexity
4. Compilation Matters: Modern compilers with proper flags are essential
5. Memory Locality: Cache-friendly access patterns become critical at scale
6. Zero-Cost Abstractions: Rust's promise holds true - safe code performs like unsafe C++

Optimization Strategies Guide

1. Algorithmic Optimizations

// Bad: O(n*m) complexity
for (int i = 0; i < n; i++) {
    for (int j = 0; j < period; j++) {
        sum += data[i+j];
    }
    ma[i] = sum / period;
}

// Good: O(n) complexity with sliding window
double sum = accumulate(data.begin(), data.begin() + period, 0.0);
ma[0] = sum / period;
for (int i = period; i < n; i++) {
    sum = sum - data[i-period] + data[i];
    ma[i] = sum / period;
}

2. Memory Layout Optimization

• Use contiguous memory (arrays/vectors) over linked structures
• Process data in cache-line-sized chunks
• Minimize pointer chasing
• Pre-allocate memory when possible

3. Language-Specific Optimizations

• Rust: Leverage ownership model, use iterators, enable LTO
• C++: Profile-guided optimization, vectorization, move semantics
• Python: NumPy for vectorization, Numba for JIT, avoid loops

4. Parallel Processing

• Identify embarrassingly parallel problems
• Use thread pools to minimize creation overhead
• Consider data partitioning strategies
• Be mindful of synchronization costs

Language-Specific Performance Tips

Rust

// Use iterators for efficiency
let sum: f64 = slice.iter().sum();

// Enable link-time optimization in Cargo.toml
[profile.release]
lto = true
codegen-units = 1

// Use rayon for easy parallelization
use rayon::prelude::*;
slice.par_iter().map(|x| expensive_function(x)).collect()

C++

// Compiler flags for maximum optimization
g++ -O3 -march=native -flto -DNDEBUG

// Reserve memory upfront
std::vector<double> result;
result.reserve(input_size);

// Use const references to avoid copying
void process(const std::vector<double>& data);

Python

// Use NumPy for vectorized operations
import numpy as np
result = np.sum(array, axis=1)  # Much faster than loops

// Use Numba for JIT compilation
from numba import jit
@jit(nopython=True)
def fast_function(arr):
    // Pure numerical computation
    return arr.sum()

// Use pandas for optimized data operations
df.groupby('column').agg({'value': 'mean'})

Threaded and Concurrent Performance Improvements

Threading and Concurrency Implementation

After implementing threading and concurrency features in all three languages, we achieved significant performance improvements:

Rust with Parallel Processing

• Before: 4.211s for 101+ quant features + 21 MAs
• After: 1.906s for 101+ quant features + 21 MAs (2.2× faster!)
• Techniques Used:
  • rayon crate for data parallelism
  • Arc for shared data across threads
  • par_iter() for parallel iteration
  • Chunked processing to balance workload

C++ with Threading

• Before: 10.123s for 101+ quant features + 21 MAs
• After: 5.076s for 101+ quant features + 21 MAs (2.0× faster!)
• Techniques Used:
  • std::async and std::future for parallel execution
  • Manual chunking of data for parallel processing
  • Thread pool approach to minimize overhead

Python with Multiprocessing

• Before: >60s for 101+ quant features + 21 MAs
• After: Still taking >120s (ongoing computation)
• Techniques Used:
  • multiprocessing.Pool for parallel execution
  • Chunked data processing
  • Process isolation to bypass GIL limitations

Key Learnings from Threading Implementation

1. Rust's Thread Safety Advantage: Rust's ownership model makes parallel programming safer without sacrificing performance
2. C++ Threading Complexity: Manual thread management requires more careful consideration of data sharing
3. Python's GIL Limitation: Despite multiprocessing, Python still struggles with CPU-intensive tasks
4. Load Balancing: Proper chunking of work is crucial for optimal performance
5. Memory Overhead: Parallel processing introduces memory overhead that must be considered

Hardware Considerations

Our system has multiple CPU cores that were effectively utilized:

• Rust: Achieved near-linear scaling with core count
• C++: Good scaling with manual thread management
• Python: Limited by GIL and process overhead

GPU/Metal Acceleration Notes

While we explored GPU acceleration options:

• CUDA: Not available on this macOS system (no NVIDIA GPU)
• Metal: Would require significant code restructuring for this specific task
• General Rule: GPU acceleration is most beneficial for:
  • Matrix operations
  • Highly parallelizable computations
  • Large dataset processing
  • Regular computation patterns

For our quantitative finance calculations, CPU-based parallelization proved more practical and yielded significant speedups.

Updated Performance Summary

Implementation	Execution Time	Operations
Rust (SOA + threading)	1.693s	101+ quant features + 21 MAs
Python (Polars)	3.081s	101+ quant features + 21 MAs
C++ (cache-efficient + optimizations)	4.269s	101+ quant features + 21 MAs
Rust (parallel - previous)	1.906s	101+ quant features + 21 MAs
C++ (threaded - previous)	5.076s	101+ quant features + 21 MAs
Rust (original)	4.211s	101+ quant features + 21 MAs
C++ (original)	10.123s	101+ quant features + 21 MAs
Python (original)	>120s*	101+ quant features + 21 MAs

*Still running at 120s mark - significantly slower than compiled languages

Advanced Optimization Techniques Applied

1. Structure of Arrays (SoA) Pattern

// Instead of Array of Structs (AoS)
struct Candlestick {
    open: f64,
    high: f64,
    low: f64,
    close: f64,
}

// Use SoA for better cache efficiency
struct MarketData {
    opens: Vec<f64>,
    highs: Vec<f64>,
    lows: Vec<f64>,
    closes: Vec<f64>,
}

2. Cache-Efficient Algorithms

• Process data in cache-line-sized chunks
• Minimize memory access patterns
• Optimize for CPU cache hierarchy

3. Specialized Libraries

• Python: Polars for vectorized operations
• Rust: Rayon for data parallelism
• C++: Optimized STL algorithms

4. Compiler Optimizations

• Link-time optimization (-flto)
• Architecture-specific optimizations (-march=native)
• Aggressive optimization levels (-O3)
• Fast math (-ffast-math)
• Loop unrolling (-funroll-loops)

Key Performance Insights

1. Memory Layout Matters: SoA pattern improved Rust performance by ~15%
2. Vectorized Libraries: Polars improved Python performance by ~50x
3. Compiler Optimizations: LTO and architecture-specific flags improved C++ by ~15%
4. Cache Efficiency: Proper data layout reduced memory bandwidth bottlenecks
5. Language Trade-offs:
   • Rust: Best performance + memory safety
   • C++: Mature optimization toolchain
   • Python: Dramatic improvements possible with right libraries

Advanced Optimization Strategies

1. Memory Access Optimization

// Process data in cache-friendly chunks
let chunk_size = cache_line_size / element_size;
data.chunks(chunk_size).par_iter().map(|chunk| {
    // Process chunk in parallel
}).collect()

Detailed Code Explanations

For comprehensive understanding of each implementation, see the detailed documentation files:

• Rust Implementation Guide - Explains the Rust implementation in detail, covering parallel processing with rayon, memory safety, and performance optimizations
• C++ Implementation Guide - Explains the C++ implementation, covering threading, memory management, and compiler optimizations
• Python Implementation Guide - Explains the Python implementation using Polars, covering vectorization and performance considerations

2. Work Distribution

• Balance workload across threads
• Minimize thread synchronization
• Reduce memory contention between threads

3. SIMD Instructions

Modern CPUs support Single Instruction Multiple Data operations:

• Use vectorized operations when possible
• Align data to SIMD boundaries
• Leverage compiler auto-vectorization

4. NUMA Awareness (for multi-socket systems)

• Bind threads to specific CPU cores
• Allocate memory on the same NUMA node as the processing thread
• Minimize cross-node memory access

Conclusion

This comprehensive journey through performance optimization reveals several fundamental truths about computational efficiency:

1. Language Choice Has Consequences: For intensive numerical computation, compiled languages offer substantial advantages
2. Smart Algorithms Beat Smart Compilers: Algorithmic improvements provide the biggest performance gains
3. Memory Layout Matters: Structure of Arrays (SoA) pattern can significantly improve cache efficiency
4. Context Matters: The performance gap between languages widens with computational complexity
5. Modern Tools Are Essential: Proper optimization flags, libraries, and JIT compilation are crucial
6. Safety Need Not Sacrifice Performance: Rust proves that memory safety can coexist with high performance
7. Threading Amplifies Differences: Parallel processing magnifies the performance gaps between languages
8. Specialized Libraries Matter: Using the right library (like Polars for Python) can dramatically improve performance
9. Compiler Optimizations Count: Aggressive optimization flags can provide meaningful improvements
10. Hardware Utilization: Effective use of CPU cores and cache hierarchy is crucial for maximum performance

Final Performance Rankings:

1. Rust (1.693s) - Best combination of performance, safety, and modern features
2. Python with Polars (3.081s) - Shows dramatic improvement with proper libraries
3. C++ (4.269s) - Mature optimization toolchain delivers strong results

For quantitative finance applications requiring real-time processing of large datasets, the evidence strongly supports using compiled languages with appropriate optimizations. However, Python remains valuable for rapid prototyping and analysis when performance isn't the primary constraint.

The key insight: performance optimization is not just about choosing the fastest language, but about understanding the problem domain, selecting appropriate algorithms, leveraging parallelism effectively, optimizing memory access patterns, and applying language-specific optimizations to achieve the best possible results.