量化投资学习之【6】历史数据告诉你：KDJ指标选股有效吗？

文章来源: 量化小课堂

#!/usr/bin/env python
#coding:utf-8

import os
import pandas as pd 

stock_code_list = []
for root, dirs, files in os.walk('./all_trading_data/stock data'):
    if files:
        for f in files:
            if '.csv' in f:
                stock_code_list.append(f.split('.csv')[0])

all_stock = pd.DataFrame()

for code in stock_code_list:
    #下面这行注释掉，避免太多打印
    # print code

    stock_data = pd.read_csv('./all_trading_data/stock data' + code +'.csv',
        parse_dates=[1])

    stock_data.sort_values('date', inplace=True)

    low_list = pd.rolling_min(stock_data['low'], 9)
    low_list.fillna(value=pd.expanding_min(stock_data['low']), inplace=True)
    high_list = pd.rolling_max(stock_data['high'], 9)
    high_list.fillna(value=pd.expanding_max(stock_data['high']), inplace=True)
    rsv = (stock_data['close'] - low_list) / (high_list -low_list) *100
    stock_data['KDJ_K'] = pd.ewma(rsv, com=2)
    stock_data['KDJ_D'] = pd.ewma(stock_data['KDJ_K'], com=2)
    stock_data['KDJ_J'] = 3 * stock_data['KDJ_K'] - 2 * stock_data['KDJ_D']
    stock_data['KDJ_金叉死叉'] = ''
    kdj_position = stock_data['KDJ_K'] > stock_data['KDJ_D']
    stock_data.loc[kdj_position[(kdj_position == True)&(kdj_position.shift() == False)].index, 'KDJ_金叉死叉'] = '金叉'
    stock_data.loc[kdj_position[(kdj_position == False)&(kdj_position.shift() == True)].index, 'KDJ_金叉死叉'] = '死叉'

    for n in [1, 2, 3, 5, 10, 20]:
        stock_data['接下来'+str(n) + '个交易日涨跌幅'] = stock_data['adjust_price'].shift(-1*n) / \
            stock_data['adjust_price'] - 1.0
    stock_data.dropna(how='any', inplace=True)

    stock_data = stock_data[(stock_data['KDJ_金叉死叉'] == '金叉')]
    if stock_data.empty:
        continue
    all_stock = all_stock.append(stock_data, ignore_index=True)


##################################################################

print 
print '历史上所有股票出现KDJ金叉的次数为%d, 这些股票在: '%all_stock.shape[0]
print 

for n in [1,2,3,5,10,20]:
    print "金叉之后的%d个交易日内，" %n,
    print "平均涨幅为%.2f%%, " %(all_stock['接下来'+ str(n) + '个交易日涨跌幅'].mean()*100),
    print "其中上涨股票的比例是%.2f%%。" % \
          (all_stock[all_stock['接下来' + str(n) + '个交易日涨跌幅'] > 0].shape[0]/float(all_stock.shape[0]) *100)
    print