arrow(c++)改写empyrical系列1---用arrow读取基金净值数据并计算夏普率-CSDN博客

用arrow c++版本读取了csv中的基金净值数据然后计算了夏普率比较尴尬的是arrow c++版本计算耗费的时间却比python的empyrical版本耗费时间多。。。

arrow新手上路第一次自己去实现功能实现的大概率并不是最高效的方式但是我也踏出了用arrow c++改写backtrader的第一步。

• 用arrow改写empyrical就当练手了目标是做成两个文件empyrical.h和empyrical.cpp
• 用arrow和qt改写pyfolio, 实现更美观的界面做成两个文件pyfolio.h 和pyfolio.cpp
• 改写backtrader

c++版本的文件

my_example.cc

#include <arrow/api.h>
#include <arrow/io/api.h>
#include "arrow/csv/api.h"
#include <arrow/compute/api.h>
#include <iostream>
#include <chrono>
//#include "../empyrical/empyrical.h"

arrow::Status RunMain(){
    auto start_time = std::chrono::high_resolution_clock::now();
    // 首先我们需要设置一个可读文件对象它允许我们将读取器指向磁盘上的正确数据。我们将重复使用这个对象并将其重新绑定到多个文件中。
    std::shared_ptr<arrow::io::ReadableFile> infile;
    // 绑定输入文件到 "test_in.csv"
    ARROW_ASSIGN_OR_RAISE(infile, arrow::io::ReadableFile::Open("/home/yun/Documents/fund_nav.csv"));
    // (文档部分CSV 表格声明)
    std::shared_ptr<arrow::Table> csv_table;
    // CSV 读取器有多个对象用于不同选项。现在我们将使用默认值。
    ARROW_ASSIGN_OR_RAISE(
        auto csv_reader,
        arrow::csv::TableReader::Make(
            arrow::io::default_io_context(), infile, arrow::csv::ReadOptions::Defaults(),
            arrow::csv::ParseOptions::Defaults(), arrow::csv::ConvertOptions::Defaults()));
    // 读取表格。
    ARROW_ASSIGN_OR_RAISE(csv_table, csv_reader->Read());

    // 输出显示Table的元数据信息
    // std::cout << "Table Metadata:" << std::endl;
    // std::cout << "Number of columns: " << csv_table->num_columns() << std::endl;
    // std::cout << "Number of rows: " << csv_table->num_rows() << std::endl;
    // std::cout << "Schema: " << csv_table->schema()->ToString() << std::endl;

    // 输出显示Table的数据
    // for (int i = 0; i < csv_table->num_columns(); ++i) {
    //   std::shared_ptr<arrow::Array> column = csv_table->column(i);
    //   std::cout << "Column " << i << ": " << column->ToString() << std::endl;
    // }

    // 1. 显示table信息到std::cout的方法
    // std::shared_ptr<arrow::RecordBatch> record_batch;
    // arrow::Result<std::shared_ptr<arrow::RecordBatch>> result = csv_table->CombineChunksToBatch(); // 执行某个操作返回Result
    // if (result.ok()) {
    //   record_batch = result.ValueOrDie();
    //   // 在这里使用 record_batch
    // } else {
    //   // 处理错误
    //   std::cerr << "Error: " << result.status().ToString() << std::endl;
    // }
    // //arrow::PrettyPrint(*record_batch, 2, &std::cout);
    // arrow::Status status = arrow::PrettyPrint(*record_batch, 2, &std::cout);
    // if (!status.ok()) {
    //   // 处理错误例如打印错误信息
    //   std::cerr << "Error: " << status.ToString() << std::endl;
    // }
    // 2. 显示table信息到std::cout的方法
    // std::cout << csv_table->ToString() << std::endl;
    // 3. 显示table信息到std::cout的方法
    // arrow::Status status = arrow::PrettyPrint(*csv_table, 2, &std::cout);
    // if (!status.ok()) {
    //   // 处理错误例如打印错误信息
    //   std::cerr << "Error: " << status.ToString() << std::endl;
    // }
    // 开始计算夏普率
    // std::cout << "一年的交易日有" << AnnualizationFactors::DAILY << "天" << std::endl;
    // std::cout << DAILY << std::endl;

    // 计算收益率
    arrow::Datum fund_returns;
    arrow::Datum fund_diff;
    std::shared_ptr<arrow::ChunkedArray> cum_nav = csv_table->GetColumnByName("复权净值");
    std::shared_ptr<arrow::ChunkedArray> now_cum_nav = cum_nav->Slice(1,cum_nav->length()-1);
    std::shared_ptr<arrow::ChunkedArray> pre_cum_nav = cum_nav->Slice(0,cum_nav->length()-1);
    ARROW_ASSIGN_OR_RAISE(fund_diff, arrow::compute::CallFunction(
                                          "subtract", {now_cum_nav,pre_cum_nav}));
    ARROW_ASSIGN_OR_RAISE(fund_returns, arrow::compute::CallFunction(
                                          "divide", {fund_diff,pre_cum_nav}));
    // // 获取结果数组
    // std::cout << "Datum kind: " << fund_returns.ToString()
    //           << " content type: " << fund_returns.type()->ToString() << std::endl;

    // // std::cout << fund_returns.scalar_as<arrow::DoubleScalar>().value << std::endl;
    // std::cout << fund_returns.chunked_array()->ToString() << std::endl;
    // 计算夏普率
    arrow::Datum avg_return;
    arrow::Datum avg_std;
    arrow::Datum daily_sharpe_ratio;
    arrow::Datum sharpe_ratio;
    arrow::Datum sqrt_year;
    // 创建 Arrow Double 标量
    double days_of_year_double = 252.0;
    std::shared_ptr<arrow::Scalar> days_of_year = arrow::MakeScalar(days_of_year_double);
    ARROW_ASSIGN_OR_RAISE(sqrt_year, arrow::compute::CallFunction(
                                          "sqrt", {days_of_year}));
    ARROW_ASSIGN_OR_RAISE(avg_return, arrow::compute::CallFunction(
                                          "mean", {fund_returns}));
    arrow::compute::VarianceOptions variance_options;
    variance_options.ddof = 1;
    ARROW_ASSIGN_OR_RAISE(avg_std, arrow::compute::CallFunction(
                                          "stddev", {fund_returns},&variance_options));
    ARROW_ASSIGN_OR_RAISE(daily_sharpe_ratio, arrow::compute::CallFunction(
                                          "divide", {avg_return,avg_std}));
    ARROW_ASSIGN_OR_RAISE(sharpe_ratio, arrow::compute::CallFunction(
                                          "multiply", {daily_sharpe_ratio,sqrt_year}));
    
    std::cout << "计算得到的夏普率为 : " << sharpe_ratio.scalar_as<arrow::DoubleScalar>().value << std::endl;

    auto end_time = std::chrono::high_resolution_clock::now();
    auto duration = std::chrono::duration_cast<std::chrono::microseconds>(end_time - start_time);

    std::cout << "c++读取数据,然后计算夏普率一共耗费时间为: " << duration.count()/1000.0 << " ms" << std::endl;

    return arrow::Status::OK();
  }

// (文档部分: 主函数)
int main() {
  arrow::Status st = RunMain();
  if (!st.ok()) {
    std::cerr << st << std::endl;
    return 1;
  }
  
  return 0;
}

CMakeLists.txt

cmake_minimum_required(VERSION 3.16)

project(MyExample)

find_package(Arrow REQUIRED)
find_package(Parquet REQUIRED)
find_package(ArrowDataset REQUIRED)

add_executable(my_example my_example.cc)
target_link_libraries(my_example PRIVATE Arrow::arrow_shared Parquet::parquet_shared ArrowDataset::arrow_dataset_shared)

在同一个文件夹下运行

cmake -B build
cmake --build build
./build/my_example

python 运行代码如下

import pandas as pd 
import empyrical as ep
import time 
a = time.perf_counter()
data = pd.read_csv("/home/yun/Documents/fund_nav.csv")
returns = data['复权净值'].pct_change().dropna()
sharpe_ratio = ep.sharpe_ratio(returns)
print("计算得到的sharpe_ratio : ", sharpe_ratio)
b = time.perf_counter()
print(f"python读取数据,然后计算夏普率一共耗费时间为: {(b-a)*1000.0} ms")