使用Python分析股价波动周期

本文主要是介绍使用Python分析股价波动周期，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

基本思路是获取股价收盘信息后，使用希尔伯特黄变换将股价波动数据拆解为不同周期的波动曲线。再本别利用频谱分析计算每一个曲线的频率。目标是将股价波动数据拆解为不同周期波动的叠加态。

1.获取收盘价

富途有很好的API接口，给我这种小散送了每个月的使用次数也够了。

富途openAPI官网

2.希尔伯特黄变换

利用pyhht包，官方的文档磕磕绊绊看懂。

合起来

import pyhht
from pyhht.visualization import plot_imfs
import numpy as np
import random
from futu import *
import pandas as pd
import sys
import re
def likaiHHT_savefig_imfs(filepath,xlabel,title,signal, imfs, time_samples=None, fignum=None):if time_samples is None:time_samples = np.arange(signal.shape[0])n_imfs = imfs.shape[0]plt.figure(num=fignum)axis_extent = max(np.max(np.abs(imfs[:-1, :]), axis=0))# Plot original signalax = plt.subplot(n_imfs + 1, 1, 1)ax.plot(time_samples, signal)ax.axis([time_samples[0], time_samples[-1], signal.min(), signal.max()])ax.tick_params(which='both', left=True, bottom=False, labelleft=True,labelbottom=False)ax.grid(False)ax.set_ylabel('signal')ax.set_title(title)# Plot the IMFsfor i in range(n_imfs - 1):print(i + 2)ax = plt.subplot(n_imfs + 1, 1, i + 2)ax.plot(time_samples, imfs[i, :])ax.axis([time_samples[0], time_samples[-1], -axis_extent, axis_extent])ax.tick_params(which='both', left=True, bottom=False, labelleft=True,labelbottom=False)ax.grid(False)ax.set_ylabel('imf' + str(i + 1))# Plot the residueax = plt.subplot(n_imfs + 1, 1, n_imfs + 1)ax.plot(time_samples, imfs[-1, :], 'r')ax.axis('auto')#ax.tick_params(which='both', left=False, bottom=False, labelleft=False,labelbottom=False)ax.grid(False)ax.set_ylabel('res.')ax.set_xlabel(xlabel)plt.savefig(filepath)return 
def imfs_max_freq(imfs,sample_rate,fft_size):
#计算每一个imfs频谱中最高的那个频率n_imfs=imfs.shape[0]max_freq=[]for i in range(n_imfs-1):xs=imfs[i,:][:fft_size]xf=np.fft.rfft(xs)/fft_sizefreqs=np.linspace(0,sample_rate/2,fft_size//2+1)xfp=20*np.log10(np.clip(np.abs(xf), 1e-20, 1e100))max_freq.append(freqs[np.argmax(xfp)])return max_freq
def HHTstock(stockid,begindate,enddate):closelist=[]quote_ctx = OpenQuoteContext(host='127.0.0.1', port=11111)  # 创建行情对象ret, data, page_req_key = quote_ctx.request_history_kline(stockid, start=begindate, end=enddate, max_count=5)  # 每页5个，请求第一页if ret == RET_OK:#print(data)#print(data['code'][0])    # 取第一条的股票代码#print(data['close'].values.tolist())   # 第一页收盘价转为listcloselist=data['close'].values.tolist()else:print('error:', data)while page_req_key != None:  # 请求后面的所有结果#print('*************************************')ret, data, page_req_key = quote_ctx.request_history_kline(stockid, start=begindate, end=enddate, max_count=5, page_req_key=page_req_key) # 请求翻页后的数据if ret == RET_OK:#print(data)closelist.extend(data['close'].values.tolist())else:print('error:', data)print('All pages are finished!')quote_ctx.close() # 关闭对象，防止连接条数用尽trading_day_num=len(closelist)t=np.linspace(0,trading_day_num,trading_day_num)np_close=np.array(closelist)decomposer=pyhht.EMD(np_close)imfs=decomposer.decompose()#plot_imfs(np_close,imfs,t)likaiHHT_savefig_imfs('./'+stockid+'.png','t/day',stockid,np_close,imfs,t)ls=imfs_max_freq(imfs,1,1000)#算每一段曲线的频率print(ls)return;
def main():HHTstock('HK.01816','2012-9-11','2020-9-18')
if __name__ == '__main__':main()

计算结果