# -*- coding: utf-8 -*-
import os import pandas as pd # ========== 遍历数据文件夹中所有股票文件的文件名，得到股票代码列表stock_code_list stock_code_list = [] for root, dirs, files in os.walk(\'stock data\'):# 注意：这里请填写数据文件在您电脑中的路径 if files: for f in files: if \'.csv\' in f: stock_code_list.append(f.split(\'.csv\')[0]) # ========== 根据上一步得到的代码列表，遍历所有股票，将这些股票合并到一张表格all_stock中 all_stock = pd.DataFrame() # 遍历每个创业板的股票 for code in stock_code_list: print(code) # 从csv文件中读取该股票数据 stock_data = pd.read_csv(\'stock data/\' + code + \'.csv\', parse_dates=[1])# 注意：这里请填写数据文件在您电脑中的路径 stock_data.sort(\'date\', inplace=True)# 对数据按照【date】交易日期从小到大排序 # 计算KDJ指标 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\'] # 计算KDJ指标金叉、死叉情况 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\' # 通过复权价格计算接下来几个交易日的收益率 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)# 删除所有有空值的数据行 # 筛选出KDJ金叉的数据，并将这些数据合并到all_stock中 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) # ========== 根据上一步得到的所有股票KDJ金叉数据all_stock，统计这些股票在未来交易日中的收益情况 #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
以上是pandas0.17版本。
以下是pandas0.19版本。
import os import pandas as pd # 从csv文件中读取该股票数据 stock_data = pd.read_csv(\'000078.csv\',parse_dates=[1],encoding=\'gbk\') stock_data.sort_values(\'日期\', 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\'] # 计算KDJ指标金叉、死叉情况 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\' # 计算接下来几个交易日的收益率 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[\'收盘价\'].shift(-1*n) / stock_data[\'收盘价\'] - 1.0 # 删除所有有空值的数据行 stock_data.dropna(how=\'any\', inplace=True) # 筛选出KDJ金叉的数据，并将这些数据合并到all_stock中 stock_data = stock_data[(stock_data[\'KDJ_金叉死叉\'] == \'金叉\')] print(\'历史上股票出现KDJ金叉的次数为%d，这些股票在：\' %stock_data.shape[0]) 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%%，\' % (stock_data[\'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%%。\' % \ (stock_data[stock_data[\'接下来\'+str(n)+\'个交易日涨跌幅\'] > 0].shape[0]/float(stock_data.shape[0]) * 100))