Rust算子库架构设计

1. 类型系统设计

rust

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// 定义数据类型
#[derive(Debug, Clone)]
enum DataType {
    Scalar(f64),
    Vector(Vec<f64>),
    Matrix(Vec<Vec<f64>>),
    TimeSeries(TimeSeriesData),
}

// 时间序列数据
struct TimeSeriesData {
    timestamps: Vec<i64>,
    values: Vec<f64>,
    frequency: Frequency,
}

// 算子特征（Trait）
pub trait Operator: Send + Sync {
    /// 算子名称
    fn name(&self) -> &str;
    
    /// 计算算子
    fn compute(&self, inputs: &[DataType]) -> Result<DataType, OperatorError>;
    
    /// 获取梯度（用于自动微分）
    fn gradient(&self, inputs: &[DataType]) -> Result<Vec<DataType>, OperatorError> {
        // 默认实现：数值微分
        self.numerical_gradient(inputs)
    }
    
    /// 获取算子签名
    fn signature(&self) -> OperatorSignature;
}

2. 高性能算子实现

rust

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// 使用SIMD指令优化的移动平均算子
use std::simd::{f64x4, Simd};

pub struct MovingAverage {
    window: usize,
}

impl MovingAverage {
    pub fn new(window: usize) -> Self {
        assert!(window > 0, "Window must be positive");
        MovingAverage { window }
    }
    
    // SIMD版本（比普通版本快4-8倍）
    pub fn compute_simd(&self, data: &[f64]) -> Vec<f64> {
        let n = data.len();
        if n < self.window {
            return Vec::new();
        }
        
        let mut result = Vec::with_capacity(n - self.window + 1);
        
        // 使用SIMD处理
        let simd_width = 4; // f64x4处理4个元素
        for i in 0..=(n - self.window) {
            let mut sum = 0.0;
            
            // SIMD求和
            let mut j = 0;
            while j + simd_width <= self.window {
                let chunk = &data[i + j..i + j + simd_width];
                let simd_vec = f64x4::from_slice(chunk);
                sum += simd_vec.reduce_sum();
                j += simd_width;
            }
            
            // 处理剩余元素
            for k in j..self.window {
                sum += data[i + k];
            }
            
            result.push(sum / self.window as f64);
        }
        
        result
    }
}

// 实现Operator trait
impl Operator for MovingAverage {
    fn name(&self) -> &str {
        "MovingAverage"
    }
    
    fn compute(&self, inputs: &[DataType]) -> Result<DataType, OperatorError> {
        match &inputs[0] {
            DataType::Vector(data) => {
                let result = self.compute_simd(data);
                Ok(DataType::Vector(result))
            }
            _ => Err(OperatorError::InvalidInputType),
        }
    }
    
    fn signature(&self) -> OperatorSignature {
        OperatorSignature {
            name: "MovingAverage".to_string(),
            inputs: vec![ParamType::Vector],
            outputs: vec![ParamType::Vector],
            params: vec![("window".to_string(), ParamType::Integer)],
        }
    }
}

3. 并行计算支持

rust

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use rayon::prelude::*;
use std::sync::Arc;

// 并行算子执行器
pub struct ParallelExecutor {
    num_threads: usize,
}

impl ParallelExecutor {
    pub fn new(num_threads: usize) -> Self {
        ParallelExecutor { num_threads }
    }
    
    pub fn execute_batch(
        &self,
        operator: Arc<dyn Operator>,
        batch_data: Vec<Vec<f64>>,
    ) -> Vec<Vec<f64>> {
        // 使用Rayon进行并行计算
        batch_data
            .into_par_iter()
            .with_min_len(self.num_threads)
            .map(|data| {
                let result = operator.compute(&[DataType::Vector(data)]);
                match result {
                    Ok(DataType::Vector(vec)) => vec,
                    _ => vec![],
                }
            })
            .collect()
    }
}

// 横截面排名的并行实现
pub struct CrossSectionalRank;

impl Operator for CrossSectionalRank {
    fn compute(&self, inputs: &[DataType]) -> Result<DataType, OperatorError> {
        match &inputs[0] {
            DataType::Matrix(matrix) => {
                // 对每一行（时间点）进行横截面排名
                let ranked: Vec<Vec<f64>> = matrix
                    .par_iter()  // 并行迭代行
                    .map(|row| {
                        // 计算每个元素在行中的排名
                        let n = row.len();
                        let mut indices: Vec<usize> = (0..n).collect();
                        
                        // 按值排序索引
                        indices.sort_by(|&i, &j| {
                            row[i].partial_cmp(&row[j]).unwrap_or(std::cmp::Ordering::Equal)
                        });
                        
                        // 创建排名结果
                        let mut ranks = vec![0.0; n];
                        for (rank, &idx) in indices.iter().enumerate() {
                            ranks[idx] = (rank as f64) / (n as f64 - 1.0); // 归一化到[0,1]
                        }
                        
                        ranks
                    })
                    .collect();
                
                Ok(DataType::Matrix(ranked))
            }
            _ => Err(OperatorError::InvalidInputType),
        }
    }
}

4. GPU加速算子

rust

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// 使用wgpu进行GPU计算
use wgpu::{Buffer, Device, Queue};
use bytemuck::{Pod, Zeroable};

#[repr(C)]
#[derive(Clone, Copy, Pod, Zeroable)]
struct GPUVector {
    data: [f32; 4], // GPU使用f32更快
}

pub struct GPUOperator {
    device: Device,
    queue: Queue,
}

impl GPUOperator {
    pub async fn new() -> Self {
        // 初始化GPU设备
        let instance = wgpu::Instance::default();
        let adapter = instance
            .request_adapter(&wgpu::RequestAdapterOptions::default())
            .await
            .unwrap();
        
        let (device, queue) = adapter
            .request_device(&wgpu::DeviceDescriptor::default(), None)
            .await
            .unwrap();
        
        GPUOperator { device, queue }
    }
    
    // GPU加速的矩阵乘法（用于因子组合）
    pub async fn matrix_multiply_gpu(&self, a: &[f32], b: &[f32], m: usize, n: usize, p: usize) -> Vec<f32> {
        // 创建GPU缓冲区
        let buffer_a = self.device.create_buffer_with_data(
            bytemuck::cast_slice(a),
            wgpu::BufferUsage::STORAGE,
        );
        
        let buffer_b = self.device.create_buffer_with_data(
            bytemuck::cast_slice(b),
            wgpu::BufferUsage::STORAGE,
        );
        
        let buffer_result = self.device.create_buffer(
            &wgpu::BufferDescriptor {
                label: Some("Result Buffer"),
                size: (m * p * std::mem::size_of::<f32>()) as u64,
                usage: wgpu::BufferUsage::STORAGE | wgpu::BufferUsage::COPY_SRC,
                mapped_at_creation: false,
            },
        );
        
        // 创建计算管道
        let compute_pipeline = self.create_matrix_multiply_pipeline();
        
        // 执行计算
        let mut encoder = self.device.create_command_encoder(&wgpu::CommandEncoderDescriptor {
            label: Some("Matrix Multiply Encoder"),
        });
        
        {
            let mut compute_pass = encoder.begin_compute_pass();
            compute_pass.set_pipeline(&compute_pipeline);
            compute_pass.set_bind_group(0, &bind_group, &[]);
            compute_pass.dispatch(m as u32, p as u32, 1);
        }
        
        // 复制结果回CPU
        let staging_buffer = self.device.create_buffer(
            &wgpu::BufferDescriptor {
                label: Some("Staging Buffer"),
                size: buffer_result.size(),
                usage: wgpu::BufferUsage::MAP_READ | wgpu::BufferUsage::COPY_DST,
                mapped_at_creation: false,
            },
        );
        
        encoder.copy_buffer_to_buffer(&buffer_result, 0, &staging_buffer, 0, buffer_result.size());
        self.queue.submit(Some(encoder.finish()));
        
        // 映射缓冲区并读取结果
        let buffer_slice = staging_buffer.slice(..);
        let (tx, rx) = futures::channel::oneshot::channel();
        buffer_slice.map_async(wgpu::MapMode::Read, move |result| {
            tx.send(result).unwrap();
        });
        
        self.device.poll(wgpu::Maintain::Wait);
        rx.await.unwrap().unwrap();
        
        let data = buffer_slice.get_mapped_range();
        let result = bytemuck::cast_slice(&data).to_vec();
        
        result
    }
}

5. 自动微分算子

rust

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// 支持自动微分的算子
use autodiff::*;

#[derive(Differentiable)]
struct LinearOperator {
    weight: f64,
    bias: f64,
}

impl LinearOperator {
    pub fn new(weight: f64, bias: f64) -> Self {
        LinearOperator { weight, bias }
    }
    
    #[differentiable]
    pub fn forward(&self, x: f64) -> f64 {
        self.weight * x + self.bias
    }
}

// 在量化中的应用：策略梯度的自动微分
pub struct StrategyGradient {
    operators: Vec<Box<dyn DifferentiableOperator>>,
}

pub trait DifferentiableOperator: Operator {
    fn forward(&self, inputs: &[f64]) -> Vec<f64>;
    fn backward(&self, grad_output: &[f64]) -> Vec<f64>;
}

// 使用自动微分计算因子对收益的梯度
impl StrategyGradient {
    pub fn compute_gradient(&self, factors: &[Vec<f64>], returns: &[f64]) -> Vec<f64> {
        // 将数据转换为可微分的变量
        let factor_vars: Vec<_> = factors
            .iter()
            .map(|f| f.iter().map(|&x| x.into_var()).collect::<Vec<_>>())
            .collect();
        
        let return_vars: Vec<_> = returns.iter().map(|&r| r.into_var()).collect();
        
        // 构建计算图
        let mut loss = 0.0.into_var();
        for (factor, ret) in factor_vars.iter().zip(return_vars.iter()) {
            // 计算每个因子的贡献
            let prediction = self.predict(factor);
            let diff = prediction - ret;
            loss = loss + diff * diff; // MSE损失
        }
        
        // 反向传播
        loss.backward();
        
        // 收集梯度
        let gradients: Vec<_> = factor_vars
            .iter()
            .flat_map(|fv| fv.iter().map(|v| v.grad()))
            .collect();
        
        gradients
    }
}

6. 与Python的互操作

rust

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// 使用PyO3暴露Rust算子给Python
use pyo3::prelude::*;
use pyo3::wrap_pyfunction;
use numpy::{PyArray1, PyArray2, IntoPyArray};
use numpy::PyArrayDyn;

#[pyclass]
struct RustOperatorLibrary {
    registry: OperatorRegistry,
}

#[pymethods]
impl RustOperatorLibrary {
    #[new]
    fn new() -> Self {
        RustOperatorLibrary {
            registry: OperatorRegistry::new(),
        }
    }
    
    fn register_operator(&mut self, name: String, operator: PyObject) -> PyResult<()> {
        // 将Python算子包装为Rust算子
        let rust_operator = PythonOperator::new(operator);
        self.registry.register(name, Box::new(rust_operator));
        Ok(())
    }
    
    fn moving_average(&self, data: &PyArray1<f64>, window: usize) -> PyResult<PyArray1<f64>> {
        let data_vec = data.as_slice()?.to_vec();
        let ma = MovingAverage::new(window);
        let result = ma.compute(&[DataType::Vector(data_vec)])?;
        
        match result {
            DataType::Vector(vec) => {
                Python::with_gil(|py| {
                    Ok(vec.into_pyarray(py).to_owned())
                })
            }
            _ => Err(PyErr::new::<pyo3::exceptions::PyValueError, _>(
                "Unexpected result type",
            )),
        }
    }
    
    fn cross_sectional_rank(&self, matrix: &PyArray2<f64>) -> PyResult<PyArray2<f64>> {
        // 获取数据
        let matrix_data = matrix.as_array();
        let nrows = matrix_data.shape()[0];
        let ncols = matrix_data.shape()[1];
        
        let mut matrix_vec = Vec::with_capacity(nrows);
        for i in 0..nrows {
            let mut row = Vec::with_capacity(ncols);
            for j in 0..ncols {
                row.push(matrix_data[[i, j]]);
            }
            matrix_vec.push(row);
        }
        
        // 执行算子
        let rank_op = CrossSectionalRank;
        let result = rank_op.compute(&[DataType::Matrix(matrix_vec)])?;
        
        match result {
            DataType::Matrix(mat) => {
                Python::with_gil(|py| {
                    let array = PyArray2::new(py, [mat.len(), mat[0].len()], false);
                    for (i, row) in mat.iter().enumerate() {
                        for (j, &val) in row.iter().enumerate() {
                            array[[i, j]] = val;
                        }
                    }
                    Ok(array.to_owned())
                })
            }
            _ => Err(PyErr::new::<pyo3::exceptions::PyValueError, _>(
                "Unexpected result type",
            )),
        }
    }
}

// Python中调用
"""
import numpy as np
from rust_operators import RustOperatorLibrary

lib = RustOperatorLibrary()

# 计算移动平均
prices = np.random.randn(1000)
ma_result = lib.moving_average(prices, window=20)

# 计算横截面排名
matrix = np.random.randn(100, 50)  # 100个时间点，50只股票
rank_result = lib.cross_sectional_rank(matrix)
"""

7. 完整的Rust算子库示例

rust

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// 完整的算子库架构
mod operators {
    pub mod base;
    pub mod timeseries;
    pub mod cross_section;
    pub mod statistical;
    pub mod technical;
    pub mod machine_learning;
}

mod engine {
    pub mod executor;
    pub mod optimizer;
    pub mod scheduler;
}

mod data {
    pub mod types;
    pub mod provider;
    pub mod cache;
}

mod utils {
    pub mod validation;
    pub mod logging;
    pub mod profiling;
}

// 主库接口
pub struct QuantLib {
    registry: OperatorRegistry,
    executor: ExecutionEngine,
    data_provider: DataProvider,
    cache: FactorCache,
}

impl QuantLib {
    pub fn new() -> Self {
        let mut registry = OperatorRegistry::new();
        
        // 注册内置算子
        registry.register("MA", Box::new(timeseries::MovingAverage::default()));
        registry.register("EMA", Box::new(timeseries::ExponentialMovingAverage::default()));
        registry.register("STD", Box::new(timeseries::RollingStd::default()));
        registry.register("ZSCORE", Box::new(cross_section::ZScore::default()));
        registry.register("RANK", Box::new(cross_section::Rank::default()));
        registry.register("CORR", Box::new(statistical::Correlation::default()));
        registry.register("REGRESSION", Box::new(statistical::LinearRegression::default()));
        
        let executor = ExecutionEngine::new();
        let data_provider = DataProvider::new();
        let cache = FactorCache::new();
        
        QuantLib {
            registry,
            executor,
            data_provider,
            cache,
        }
    }
    
    pub fn compute_factor(
        &self,
        factor_name: &str,
        symbols: &[String],
        start_date: &str,
        end_date: &str,
    ) -> Result<HashMap<String, Vec<f64>>, LibError> {
        // 1. 检查缓存
        if let Some(cached) = self.cache.get(factor_name, symbols, start_date, end_date) {
            return Ok(cached);
        }
        
        // 2. 获取数据
        let price_data = self.data_provider.get_price_data(symbols, start_date, end_date)?;
        
        // 3. 获取算子
        let operator = self.registry.get_operator(factor_name)
            .ok_or_else(|| LibError::OperatorNotFound(factor_name.to_string()))?;
        
        // 4. 执行计算
        let mut results = HashMap::new();
        for (symbol, prices) in price_data {
            let input = DataType::TimeSeries(TimeSeriesData::new(prices));
            let result = self.executor.execute(&*operator, &[input])?;
            
            if let DataType::Vector(values) = result {
                results.insert(symbol, values);
            }
        }
        
        // 5. 缓存结果
        self.cache.set(factor_name, symbols, start_date, end_date, &results);
        
        Ok(results)
    }
}