股票研究

import pandas as pd
import numpy as np
import os
import matplotlib.pyplot as plt 

all_stock_data = pd.DataFrame()

stock_code_list = []

t = os.listdir('all_stock_data1')
for f in t:
    stock_code_list.append(f.split('.csv')[0])
    
for code in stock_code_list:
    print('正在计算%s'%code)
    stock_data = pd.read_csv('all_stock_data1\'+code+'.csv',encoding='gbk')
    stock_data = stock_data[['日期','最低价','开盘价','最高价','收盘价','成交量']]#.ix[:300]
    stock_data.replace(0,np.nan,inplace=True)
    stock_data.sort_values('日期',inplace=True)
    stock_data['本日长阴'] = ((stock_data['收盘价']/stock_data['开盘价'] - 1)*100)<-3
    stock_data['买入点'] = ''
    #print(stock_data)
    stock_data.dropna(how='any', inplace=True)
    stock_data.replace(np.inf,np.nan,inplace=True)
    # 计算KDJ指标
    low_list=stock_data['最低价'].rolling(window=9).min()
    low_list.fillna(value=stock_data['最低价'].expanding().min(), inplace=True)
    high_list = stock_data['最高价'].rolling(window=9).max()
    high_list.fillna(value=stock_data['最高价'].expanding().max(), inplace=True)

    rsv = (stock_data['收盘价'] - low_list) / (high_list - low_list) * 100
    stock_data['KDJ_K'] = rsv.ewm(com=2).mean()
    stock_data['KDJ_D'] = stock_data['KDJ_K'].ewm(com=2).mean()
    stock_data['KDJ_J'] = 3 * stock_data['KDJ_K'] - 2 * stock_data['KDJ_D']
    #print(stock_data)
    
    tiao1 = stock_data['成交量'].shift() == stock_data['成交量'].shift().rolling(window=100).min()
    tiao2 = stock_data['成交量']>(stock_data['成交量'].shift())*2
    tiao3 = stock_data['本日长阴'].rolling(window=5).sum()>=1
    tiao4 = stock_data['收盘价']==stock_data['最高价']
    tiao5 = stock_data['收盘价']>stock_data['开盘价']*1.2
    tiao6 = (stock_data['KDJ_J']>0) & (stock_data['KDJ_J'].shift()<0)
    tiao7 = stock_data['收盘价']>(stock_data['收盘价'].shift().rolling(window=30).max())
    tiaojian = tiao2&tiao7
    for i in [1,2,3,5,10,20]:
        stock_data[str(i)+'日涨幅'] = (stock_data['收盘价'].shift(-i)/stock_data['收盘价'] - 1)*100

    stock_data.ix[stock_data[tiaojian==True].index,'买入点'] = '是'
    
    stock_data.replace(np.inf,np.nan,inplace=True)
    stock_data.dropna(how='any', inplace=True)
    stock_data = stock_data[(stock_data['买入点'] == '是')]
    all_stock_data = all_stock_data.append(stock_data, ignore_index=True)
    #print(stock_data)

print('-------------------------------')        
print('收益测试：')
print('共发出%d次买出提示'%all_stock_data.shape[0])
print('-------------------------------')

for i in [1,2,3,5,10,20]:
    print('买入'+str(i)+'日后平均涨幅%.2f%%' % all_stock_data[str(i)+'日涨幅'].mean())
    print("其中上涨股票的比例是%.2f%%。" % 
          (all_stock_data[all_stock_data[str(i)+'日涨幅'] > 0].shape[0]/float(all_stock_data.shape[0]) * 100))
    print('------------------------------------')