import sys import tushare as ts import numpy as np data=ts.get_h_data('601066') print(data) #读出兴业银行7列数据 date   open    high   close     low     volume      amount    

data.to_csv('E:/csv statistic/day/601066.csv')  把读取的股票数据存入某个内存空间中

#开始读某列数据 c,v=np.loadtxt('E:/csv statistic/day/601066.csv', skiprows=1,delimiter=',',usecols=(3,5),unpack=True)#收盘价，交易量 vwap=np.average(c,weights=v)#成交量加权平均值， print('vwap=',vwap) vwap= 9.63994147552952 print(np.mean(c)) 9.0965625 #计算时间加权平均值 t=np.arange(len(c)) print(np.average(c,weights=t)) 9.534940476190476

#寻找最大值和最小值 h,l=np.loadtxt('E:/csv statistic/day/601066.csv', skiprows=1,delimiter=',',usecols=(2,4),unpack=True) print(np.max(h)) 11.98 print(np.min(l)) 6.5

#计算中程数和极差 print(np.max(h)+np.min(l)/2)#计算中程数 print(np.ptp(h))#计算最大值和最小值的差值 print(np.ptp(l))#计算交易量化的差值 15.254999999999999 4.19 4.92 #统计分析c=np.loadtxt('E:/csv statistic/day/601066.csv', skiprows=1,delimiter=',',usecols=(3,),unpack=Trueprint(np.median(c))#中间数 8.850000000000001 print('sorted:',np.msort(c))#从小到大排列

sorted: [ 7.77 7.83 7.87 7.92 7.95 8. 8.06 8.15 8.17 8.19 8.2 8.2 8.23 8.27 8.28 8.32 8.32 8.35 8.36 8.36 8.36 8.36 8.48 8.5 8.53 8.53 8.54 8.55 8.61 8.61 8.64 8.82 8.88 9.09 9.15 9.37 9.38 9.38 9.39 9.45 9.45 9.48 9.52 9.67 9.7 9.73 9.8 9.83 9.86 9.91 9.93 10.06 10.23 10.27 10.31 10.33 10.33 10.41 10.42 10.56 10.8 11.09 11.46 11.85]

print('方差:',np.var(c)) 方差: 0.9610756835937502 print('手动计算方差:',np.mean((c-c.mean())**2)) 手动计算方差: 0.9610756835937502

#股票收益率的计算 c=np.loadtxt('E:/csv statistic/day/601066.csv', skiprows=1,delimiter=',',usecols=(3,),unpack=True) returns=np.diff(c)/c[:-1]  #diff计算差分,总体是求股票收益率 print(np.std(returns)) #    求标准差 log_returns=np.diff(np.log(c)) #股票对数收益率 print(log_returns) posretindices=np.where(returns>0)#股票收益率大于0的那几天 print(posretindices) ann_vol=np.std(log_returns)/np.mean(log_returns)#日收益率 print(ann_vol) ann_vol=ann_vol/np.sqrt(1./252.)#年收益率 print(ann_vol) print(ann_vol*np.sqrt(1./12.))#月收益率

#定义一个 把日期转换为数字的函数 from datetime import datetime def date2num(s):     return datetime.strptime(s,'%Y-%m-%d').date().weekday() #读取收盘价 close=np.loadtxt('E:/csv statistic/day/601066.csv', skiprows=1,delimiter=',',usecols=(3,),unpack=True) #读取日期 dates=np.loadtxt('E:/csv statistic/day/601066.csv', skiprows=1,delimiter=',',usecols=(0,),unpack=True,dtype=bytes).astype(str) #读取的str数据转换为 数字 for i in range (dates.size):     dates[i]=date2num(dates[i]) dates=dates.astype(np.int8) print(dates.dtype) print(dates)

averages = np.zeros(5)  # 定义一个空数组 # 依次获取不同天的索引，然后根据索引求得close（收盘价）数组中所有的对应值，计算平均值，把平均值加入到averages数组中 for i in range(5):     indices = np.where(dates == i)     prices = np.take(close, indices)     avg = np.mean(prices)     print("Day", i, "prices", prices, "Average", avg)     averages[i] = avg top = np.max(averages)  # 计算周每日的收盘价平均值的最大值 np.argmax(averages)  # 获取周每日的收盘价平均值的最大值是哪一天 bottom = np.min(averages)   # 计算周每日的收盘价平均值的最小值 np.argmin(averages)  # 获取周每日的收盘价平均值的最小值是哪一天