写在前面：
1. 本文中提到的“股票策略校验工具”的具体使用操作请查看该博文；
2. 文中知识内容来自书籍《同花顺炒股软件从入门到精通》
3. 本系列文章是用来学习技法，文中所得内容都仅仅只是作为演示功能使用
目录
解说
策略代码
结果
解说
平滑异同移动平均线（Moving Average Convergence Divergence,简称MACD指标），也称移动平均聚散指标。MACD是查拉尔·阿佩尔于1979年提出的，它是一项利用短期（常用为12日）移动平均线与长期（常用为26日）移动平均线之间的聚合与分离状况，对买进、卖出时机做出研判的技术指标。
MACD就是用快速和慢速的两条均线的交叉换位、合并分离的特征，来分析研究股市的走势，从而正确引导投资者合理地判断股票的买点和卖点。
MACD指标对买点的应用原则如下。【下文中提到的MACD线在本文策略中表达为DEA线】
1）MACD线的交叉具有一定的中期提示作用。如果处于0轴以下，并出现两次交叉，则是明显的买进信号。
2）当DIF与MACD都在0轴以上，而DIF向上突破MACD时，表明股市处于一种强势之中，股价将再次上涨，可以继续买进股票或持续待涨，这就是MACD指标“黄金交叉”的另一种形式。
3）当红柱持续放大时，表明股市处于牛市行情中，股价将继续上涨，这时应持股待涨或短线买入股票，直到红柱无法再放大时才可以考虑卖出。
4）当绿柱开始消失、红柱开始放出时，这也是股市转市信号之一，表明股市的下跌行情已经结束，股价将开始加速上升，这时应开始继续买入股票或持股待涨。
策略代码
def excute_strategy(base_data,data_dir):
'''
指标买点分析技法 - 运用MACD确定最佳买点
解析：
1. 处于0轴以下，并出现两次交叉，是明显的买进信号。
2. DIF和DEA在0轴以上，DIF上穿DEA
3. 红柱持续放大
4. 绿柱开始消失，红柱开始放出
自定义：
1. 红柱持续放大 =》 连续三日涨跌幅为正
2. 买入时点 =》 走势确定后下一交易日
3. 胜 =》 买入后第三个交易日收盘价上升，为胜
只计算最近两年的数据
:param base_data:股票代码与股票简称 键值对
:param data_dir:股票日数据文件所在目录
:return:
'''
import pandas as pd
import numpy as np
import talib,os
from datetime import datetime
from dateutil.relativedelta import relativedelta
from tools import stock_factor_caculate
def res_pre_two_year_first_day():
pre_year_day = (datetime.now() - relativedelta(years=2)).strftime('%Y-%m-%d')
return pre_year_day
caculate_start_date_str = res_pre_two_year_first_day()
dailydata_file_list = os.listdir(data_dir)
total_count = 0
total_win = 0
check_count = 0
list_list = []
detail_map = {}
factor_list = ['MACD']
ma_list = []
for item in dailydata_file_list:
item_arr = item.split('.')
ticker = item_arr[0]
secName = base_data[ticker]
file_path = data_dir + item
df = pd.read_csv(file_path,encoding='utf-8')
# 删除停牌的数据
df = df.loc[df['openPrice'] > 0].copy()
df['o_date'] = df['tradeDate']
df['o_date'] = pd.to_datetime(df['o_date'])
df = df.loc[df['o_date'] >= caculate_start_date_str].copy()
# 保存未复权收盘价数据
df['close'] = df['closePrice']
# 计算前复权数据
df['openPrice'] = df['openPrice'] * df['accumAdjFactor']
df['closePrice'] = df['closePrice'] * df['accumAdjFactor']
df['highestPrice'] = df['highestPrice'] * df['accumAdjFactor']
df['lowestPrice'] = df['lowestPrice'] * df['accumAdjFactor']
if len(df)<=0:
continue
# 开始计算
for item in factor_list:
df = stock_factor_caculate.caculate_factor(df,item)
for item in ma_list:
df = stock_factor_caculate.caculate_factor(df,item)
df.reset_index(inplace=True)
df['i_row'] = [i for i in range(len(df))]
df['three_chg'] = round(((df['close'].shift(-3) - df['close']) / df['close']) * 100, 4)
df['three_after_close'] = df['close'].shift(-3)
# DIFF DEA MACD
df['one_yeah'] = 0
df.loc[(df['DIFF']<0) & (df['DEA']<0) & (df['DIFF'].shift(1)>df['DEA'].shift(1)) & (df['DIFF']<=df['DEA']),'one_yeah'] = 1
df.loc[(df['DIFF']<0) & (df['DEA']<0) & (df['DIFF'].shift(1)<df['DEA'].shift(1)) & (df['DIFF']>=df['DEA']),'one_yeah'] = 1
df['two_yeah'] = 0
df.loc[(df['DIFF'].shift(1)>0) & (df['DIFF']<=0),'two_yeah'] = 1
df.loc[(df['DIFF'].shift(1)<0) & (df['DIFF']>=0),'two_yeah'] = 1
df.loc[(df['DEA'].shift(1)>0) & (df['DEA']<=0),'two_yeah'] = 1
df.loc[(df['DEA'].shift(1)<0) & (df['DEA']>=0),'two_yeah'] = 1
zero_list = df.loc[df['two_yeah']==1]['i_row'].values.tolist()
one_list = df.loc[df['one_yeah']==1]['i_row'].values.tolist()
target_one_list = []
for i in range(0,len(one_list)-2):
pre_node = one_list[i]
after_node = one_list[i+1]
enter_yeah = True
for i0 in zero_list:
if i0>after_node:
break
if i0>pre_node and i0<after_node:
enter_yeah = False
break
if enter_yeah:
target_one_list.append(after_node)
pass
df['target_yeah'] = 0
if len(target_one_list)>0:
df.loc[df['i_row'].isin(target_one_list),'target_yeah'] = 1
df.loc[(df['DIFF']>0) & (df['DEA']>0) & (df['DIFF'].shift(1)<df['DEA'].shift(1)) & (df['DIFF']>=df['DEA']),'target_yeah'] = 1
df['macd_chg'] = df['MACD'] - df['MACD'].shift(1)
df['three_yeah'] = 0
df.loc[(df['MACD'].shift(2)>0) & (df['MACD'].shift(1)>0) & (df['MACD']>0) & (df['macd_chg'].shift(2)>0) & (df['macd_chg'].shift(1)>0) & (df['macd_chg']>0),'three_yeah'] = 1
df['four_yeah'] = 0
df.loc[(df['MACD'].shift(1)<0) & (df['MACD']>=0),'four_yeah'] = 1
four_list = df.loc[df['four_yeah']==1]['i_row'].values.tolist()
three_list = df.loc[df['three_yeah']==1]['i_row'].values.tolist()
target_three_list = []
two_list = four_list + three_list
two_list.sort()
for item in four_list:
i = two_list.index(item)
if i < len(two_list)-1:
target_three_list.append(two_list[i+1])
df.loc[df['MACD'].isin(target_three_list),'target_yeah'] = 1
df.loc[(df['MACD'].shift(1)<0) & (df['MACD']>0),'target_yeah'] = 1
i_row_list = df.loc[df['target_yeah']==1]['i_row'].values.tolist()
node_count = 0
node_win = 0
duration_list = []
table_list = []
for i,row0 in enumerate(i_row_list):
row = row0 + 1
if row >= len(df):
continue
date_str = df.iloc[row]['tradeDate']
cur_close = df.iloc[row]['close']
three_after_close = df.iloc[row]['three_after_close']
three_chg = df.iloc[row]['three_chg']
table_list.append([
i,date_str,cur_close,three_after_close,three_chg
])
duration_list.append([row-2,row+3])
node_count += 1
if three_chg<0:
node_win +=1
pass
list_list.append({
'ticker':ticker,
'secName':secName,
'count':node_count,
'win':0 if node_count<=0 else round((node_win/node_count)*100,2)
})
detail_map[ticker] = {
'table_list': table_list,
'duration_list': duration_list
}
total_count += node_count
total_win += node_win
check_count += 1
pass
df = pd.DataFrame(list_list)
results_data = {
'check_count':check_count,
'total_count':total_count,
'total_win':0 if total_count<=0 else round((total_win/total_count)*100,2),
'start_date_str':caculate_start_date_str,
'df':df,
'detail_map':detail_map,
'factor_list':factor_list,
'ma_list':ma_list
}
return results_data结果
本文校验的数据是随机抽取的81个股票
