KunQuant-rs

Rust bindings for the KunQuant financial factor computation library.

Overview

KunQuant-rs provides safe Rust bindings for KunQuant, a high-performance financial factor computation library. This crate wraps the C API with safe Rust abstractions while maintaining the performance characteristics of the underlying library.

Features

• Safe Rust API: All unsafe FFI calls are wrapped in safe abstractions
• RAII Resource Management: Automatic cleanup of resources using Rust's Drop trait
• Batch Computation: Efficient batch processing of financial factors
• Multi-threading Support: Both single-thread and multi-thread executors
• Streaming Computation: Real-time factor calculation with low latency
• Memory Safety: Proper lifetime management and buffer handling

Quick Start

Dependencies

This project depends on the KunRuntime library. Follow these steps to ensure proper linking:

Linking libKunRuntime.so

1. Compile with explicit library path:

   cargo rustc -- -L /path/to/KunQuant/runner
   

   Replace /path/to/KunQuant/runner with the actual path to libKunRuntime.so.

2. Permanent configuration via RUSTFLAGS:

   export RUSTFLAGS="-L /path/to/KunQuant/runner"
   cargo build
   

3. Runtime configuration: Ensure the library is available at runtime by setting LD_LIBRARY_PATH:

   export LD_LIBRARY_PATH=/path/to/KunQuant/runner:$LD_LIBRARY_PATH
   

Basic Usage

use kunquant_rs::{Executor, Library, BufferNameMap, BatchParams, run_graph};

// Create executor and load library
let executor = Executor::single_thread()?;
let library = Library::load("test_libs/simple_test_lib.so")?;
let module = library.get_module("simple_test")?;

// Prepare data
let mut input_data = vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0];
let mut output_data = vec![0.0; 8];

// Set up buffers
let mut buffers = BufferNameMap::new()?;
buffers.set_buffer_slice("input", &mut input_data)?;
buffers.set_buffer_slice("output", &mut output_data)?;

// Run computation
let params = BatchParams::full_range(8, 1)?;
run_graph(&executor, &module, &buffers, &params)?;

// Results are now in output_data
println!("Results: {:?}", output_data);

API Documentation

Core Types

• Executor: Manages computation execution (single-thread or multi-thread)
• Library: Represents a loaded factor library
• Module: A specific factor module within a library
• BufferNameMap: Maps buffer names to data slices
• BatchParams: Parameters for batch computation
• StreamContext: Context for streaming computation

Key Functions

• run_graph(): Execute a factor computation graph
• Executor::single_thread(): Create single-thread executor
• Executor::multi_thread(n): Create multi-thread executor with n threads
• Library::load(path): Load a factor library from file

Testing

Run tests with the provided script that sets up the correct library path:

./run_tests.sh

Architecture

The library follows a layered architecture:

1. FFI Layer (ffi.rs): Raw C bindings
2. Error Handling (error.rs): Rust error types
3. Core Types (executor.rs, library.rs): Safe wrappers
4. Buffer Management (buffer.rs): Memory-safe buffer handling
5. Computation APIs (batch.rs, stream.rs): High-level computation interfaces

Memory Management

• All C resources are automatically cleaned up using Rust's RAII pattern
• Buffer lifetimes are tracked to prevent use-after-free
• No manual memory management required

Performance

• Zero-cost abstractions over the C API
• Efficient buffer management with minimal copying
• Multi-threading support for parallel computation
• Stock count must be a multiple of 8 for optimal SIMD performance

Limitations

• Requires KunQuant C library to be installed and accessible
• Factor libraries must be pre-compiled using the Python interface
• Streaming factors require special compilation with output_layout="STREAM"

Contributing

1. Ensure all tests pass: ./run_tests.sh
2. Add tests for new functionality
3. Follow Rust naming conventions and safety guidelines
4. Document public APIs

License

MIT License - see LICENSE file for details.