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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")