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import yfinance as yf
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import numpy as np
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from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
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import asyncio
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import time
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import functools
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####################################################################
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# 获取股票数据（I/O密集型任务）
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def fetch_stock_data(stock):
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print(f"Fetching data for {stock}")
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data = yf.download(stock, start="2020-01-01", end="2023-01-01",auto_adjust=False)
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return data['Close'].values
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# 异步获取股票数据
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async def async_fetch_stock_data(stock):
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print(f"Fetching data for {stock}")
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data = await asyncio.to_thread(yf.download, stock, start="2020-01-01", end="2023-01-01",auto_adjust=False)
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return data['Close'].values
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# 傅里叶变换（计算密集型任务）
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def fourier_transform(data):
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print("Performing Fourier Transform")
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fft_data = np.fft.fft(data)
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return np.abs(fft_data)
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def timeit(message):
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def decorator(func):
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@functools.wraps(func)
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def sync_wrapper(*args, **kwargs):
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start_time = time.time()
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result = func(*args, **kwargs)
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end_time = time.time()
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print(f"{message}: {end_time - start_time:.2f} seconds")
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return result
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async def async_wrapper(*args, **kwargs):
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start_time = time.time()
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result = await func(*args, **kwargs)
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end_time = time.time()
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print(f"{message}: {end_time - start_time:.2f} seconds")
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return result
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if asyncio.iscoroutinefunction(func):
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return async_wrapper
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else:
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return sync_wrapper
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return decorator
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####################################################################
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# 股票列表
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stocks = ['AAPL', 'MSFT', 'GOOG']
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# 串行执行
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@timeit("串行执行")
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def serial_execution():
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for stock in stocks:
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data = fetch_stock_data(stock)
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fft_data = fourier_transform(data)
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# 可视化（等等...）
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# 多线程执行（优化 I/O 密集型任务）
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@timeit("多线程执行")
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def threaded_execution():
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with ThreadPoolExecutor(max_workers=3) as executor:
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data_list = list(executor.map(fetch_stock_data, stocks))
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for data in data_list:
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fft_data = fourier_transform(data)
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# 可视化（等等...）
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# 多进程执行（优化计算密集型任务）
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@timeit("多进程执行")
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def multiprocessing_execution():
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with ProcessPoolExecutor(max_workers=3) as executor:
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data_list = list(executor.map(fetch_stock_data, stocks))
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with ProcessPoolExecutor(max_workers=3) as executor:
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fft_data_list = list(executor.map(fourier_transform, data_list))
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# 可视化（等等...）
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# 异步执行（优化 I/O 密集型任务）
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@timeit("异步执行")
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async def async_execution():
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tasks = [async_fetch_stock_data(stock) for stock in stocks]
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data_list = await asyncio.gather(*tasks)
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for data in data_list:
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fft_data = fourier_transform(data)
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# 可视化（等等...）
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@timeit("混合执行")
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async def mixed_execution():
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tasks = [async_fetch_stock_data(stock) for stock in stocks]
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data_list = await asyncio.gather(*tasks)
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with ProcessPoolExecutor(max_workers=3) as executor:
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fft_data_list = list(executor.map(fourier_transform, data_list))
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# 可视化（等等...）
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if __name__ == "__main__":
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print("串行执行:"); serial_execution()
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print("\n多线程执行:") ; threaded_execution()
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print("\n多进程执行:"); multiprocessing_execution()
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print("\n异步执行:"); asyncio.run(async_execution())
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print("\n混合执行:") ; asyncio.run(mixed_execution()) |
