Python北交所股指【北证50】构造

code • 2022-5-5 • python • 阅读 27

为北交所构造一个股票指数，【北证50】,包含50个成分股。

文章目录

文章目录

前言

一、股指构建流程

二、Python实现

1.引入库

2.绘制K线图函数

3.从含有多个股票数据的面板中抽取出某个股票的数据函数

4.判断调整股本系数函数

5.成分股筛选函数

6.股指计算函数（加权计算公式）

7.调用函数实现功能

总结

前言

北交所目前没有一个成熟的指数，咱给他编一个。

一、股指构建流程

一、基期和基点的确定：

基期：20211231

大部分股指是以某一固定日为基日。基于北交所的情况，选取其开市当年的年尾作为基日

基点：1000

基点参考沪深300指数

二、样本股的确定：

考虑到北交所上市不到180天，上市公司不到一百家，故根据日均成交金额剔除排名后25%的股票，再根据日均总市值对剩余股票进行排名，选取前50只股票作为样本股。

三、计算公式的确定

二、Python实现 1.引入库

代码如下（示例）：

import pandas as pd
import mplfinance as mpf
from datetime import datetime,timedelta
base_index = 1000 #基期的基点数确定为1000

2.绘制K线图函数

def draw_candlepic(data, filename):
    # 设置marketcolors
    mc = mpf.make_marketcolors(
        up='red',
        down='green',
        edge='i',
        wick='i',
        volume='in',
        inherit=True)

    # 设置图形风格
    s = mpf.make_mpf_style(
        gridaxis='both',
        gridstyle='-.',
        y_on_right=False,
        marketcolors=mc)

    kwargs = dict(
        type='candle',
        mav=(5, 10),
        volume=True,
        title="BJ stock exchange stock index",
        ylabel='Stock Index',
        ylabel_lower='Traded Volume',
        # figratio=(15, 10),
        # figscale=5
        )
    pic = mpf.plot(data,
             **kwargs,
             style=s,
             show_nontrading=False,
             savefig=filename
             )
    return pic

3.从含有多个股票数据的面板中抽取出某个股票的数据函数

def pro_daily_stock(code_val,data,start_val ,end_val):
    """
    从含有多个股票数据的面板中抽取出某个股票的数据。

    Parameters:
     code_val - 要抽取的股票带代码
     data - 含有股指成分股票的open、close、high、low、volume、流通股本、总股本数据的dataframe
     start_val - 开始的时间
     end_val - 结束的时间

    Returns:
     股指的open、close、high、low、volume、equity_all 、equity_mkt，dataframe。

    Raises:
     KeyError - raises an exception
    """
    df = data.loc[data['Code'] == code_val]
    df = df[(df['Date'] < end_val) & (df['Date'] > start_val)]

    #返回
    return  df

4.判断调整股本系数函数

def adjusted_pro(proposition_all):
    #print(proposition_all)
    result = []
    for proposition in proposition_all:
        if(proposition <= 0.1):
            result.append(proposition)
        elif(proposition > 0.1 and proposition <= 0.2):
            result.append(0.2)
        elif(proposition > 0.2 and proposition <= 0.3):
            result.append(0.3)
        elif(proposition > 0.3 and proposition <= 0.4):
            result.append(0.4)
        elif(proposition > 0.4 and proposition <= 0.5):
            result.append(0.5)
        elif(proposition > 0.5 and proposition <= 0.6):
            result.append(0.6)
        elif(proposition > 0.6 and proposition <= 0.7):
            result.append(0.7)
        elif(proposition > 0.7 and proposition <= 0.8):
            result.append(0.8)
        elif(proposition > 0.8):
            result.append(1.0)
    #print(result)
    return result

5.成分股筛选函数

def component_select( stock_list , volum , value):
    """
    对选入北交所股指的成分股进行筛选.

    Parameters:
     stock_list - 所有股票的代码列表
     volum - 用于筛选的成交量列表
     value - 用于筛选的市值列表

    Returns:
     被筛选进入股指的股票代码列表

    Raises:
     KeyError - raises an exception
    """
    table = []
    for stock, vol, val in zip(stock_list , volum , value):
        table.append([stock, vol, val])
    table.sort(key=lambda x : x[1],reverse=True)
    table = table[0:int(3*(len(table)/4))]
    
    table.sort(key=lambda x : x[2],reverse=True)
    table = table[0:20]
    return [i[0] for i in table]

6.股指计算函数（加权计算公式）

#股指计算函数（加权计算公式）
def stockindex_cal(list_code, data , start_val, end_val):
  """
    计算股指的open、close、high、low、volume数值，并返回dataframe。

    Parameters:
     list_code - 所有股票的代码列表
     data - 含有股指成分股票的open、close、high、low、volume、流通股本、总股本数据的dataframe
     start_val - 开始的时间
     end_val - 结束的时间

    Returns:
     股指的open、close、high、low、volume，dataframe。

    Raises:
     KeyError - raises an exception
    """
    dfopen_list = pd.DataFrame()
    dfclose_list = pd.DataFrame()
    dfhigh_list = pd.DataFrame()
    dflow_list = pd.DataFrame()
    dfvolumn_list = pd.DataFrame()
    dfequity_adjusted_list = pd.DataFrame()
    dfdatetime_list = pd.DataFrame()

    i = 0
    j = []
    stock_dati = pro_daily_stock(list_code[0],data, start_val, end_val)
    dfdatetime_list = stock_dati['Date']
    for code in list_code:
        print(i,"股票代码：",code)
        stock_dati = pro_daily_stock(code,data, start_val, end_val)
        #dfdatetime_list[i] = stock_dati['Date']
        #stock_dati.info()
        ##更新数值，叠加序列
        proposition_all = []
        for a,b in zip(stock_dati['equity_mkt'] , stock_dati['equity_all']):
            proposition_all.append(a/b)
        dfequity_adjusted_list[i] = (np.multiply(np.array(stock_dati['equity_all']),np.array(adjusted_pro(proposition_all)))).tolist()
        dfopen_list[i] = (np.multiply(np.array(stock_dati['Open']),np.array(dfequity_adjusted_list[i]))).tolist()
        dfclose_list[i] = (np.multiply(np.array(stock_dati['Close']),np.array(dfequity_adjusted_list[i]))).tolist()
        dfhigh_list[i] = (np.multiply(np.array(stock_dati['High']),np.array(dfequity_adjusted_list[i]))).tolist()
        dflow_list[i] = (np.multiply(np.array(stock_dati['Low']),np.array(dfequity_adjusted_list[i]))).tolist()
        dfvolumn_list[i] = stock_dati['Volume'].tolist()
        j.append(dfequity_adjusted_list[i])
        i = i + 1
    new_j = []
    
    for column in range(len(j[0])):
        total = 0
        # print("column = ",column)
        for i in range(len(j)):
            total += j[i][column]
        new_j.append(total)
    df_new_j = pd.DataFrame(new_j)
    # 
    print(pd.concat([dfopen_list,df_new_j],axis=1,ignore_index=True).index)
    stockindex = pd.DataFrame()
    stockindex['Open'] = pd.concat([dfopen_list,df_new_j],axis=1,ignore_index=True).apply(lambda x: x[0:len(x)-2].sum()/x[len(x)-1], axis=1)
    stockindex['Close'] = pd.concat([dfclose_list,df_new_j],axis=1,ignore_index=True).apply(lambda x: x[0:len(x)-2].sum()/x[len(x)-1], axis=1)
    stockindex['High'] = pd.concat([dfhigh_list,df_new_j],axis=1,ignore_index=True).apply(lambda x: x[0:len(x)-2].sum()/x[len(x)-1], axis=1)
    stockindex['Low'] = pd.concat([dflow_list,df_new_j],axis=1,ignore_index=True).apply(lambda x: x[0:len(x)-2].sum()/x[len(x)-1], axis=1)
    stockindex['Volume'] = dfvolumn_list.apply(lambda x: x.sum(), axis=1)
    coefficient = 1000 / stockindex['Open'][0]
    stockindex['Open'] = stockindex['Open'] *coefficient
    stockindex['Close'] = stockindex['Close'] *coefficient
    stockindex['High'] = stockindex['High'] *coefficient
    stockindex['Low'] = stockindex['Low'] *coefficient  
    stockindex.index = dfdatetime_list
    return stockindex

7.调用函数实现功能

#获取数据
filepath = r''#北交所股票数据文件位置
df_data = pd.read_excel(filepath)
df_data=df_data[~df_data.isin(['--'])]
df_data=df_data[~df_data.isin(['#VALUE!'])]
df_data['Date'] = pd.to_datetime(df_data['Date'])
df_data.dropna(inplace = True)
name = ['Code','Date']
for (columnName, columnData) in df_data.iteritems():
    if(columnName not in name):
        df_data[columnName] = df_data[columnName].astype('float')
benchemarkday = '2021-12-31'
df_data_benchemarkday = df_data[df_data['Date'] == benchemarkday]
#print(df_data_benchemarkday)
#对应代码 选取市值前十的编制指数
list_code = component_select(list(df_data_benchemarkday['Code']),list(df_data_benchemarkday['Volume']),list(df_data_benchemarkday['Value']))
#print(list_code)
BJstockindex = stockindex_cal(list_code,df_data,datetime(2021,12,31), datetime(2022,4,29))
print(BJstockindex)
BJstockindex.info()
BJstockindex.to_excel(r'')#股指行情数据存储位置
#绘制版块K线图
filename = r''#k线图存储位置
pic = draw_candlepic(BJstockindex, filename)

mpf.plot(BJstockindex)
print("成分股包括",len(list_code),"个:",list_code)

总结

结果

北交所【北证50】行情数据：

Date	Open	Close	High	Low	Volume
2022-01-04 00:00:00	1000	986.5408	1022.178	953.5873	80598800
2022-01-05 00:00:00	989.2613	954.2665	1006.629	936.4114	1.03E+08
2022-01-06 00:00:00	946.8143	945.2824	969.7085	911.8832	77924100
2022-01-07 00:00:00	942.4053	915.9837	958.7197	911.758	60408800
2022-01-10 00:00:00	912.706	929.5972	946.6833	901.8682	52655000
2022-01-11 00:00:00	938.3407	934.82	960.3012	922.3229	55217800
2022-01-12 00:00:00	942.1217	939.1366	951.3516	916.4603	45396100
2022-01-13 00:00:00	939.2697	927.4674	963.4592	905.494	83014200
2022-01-14 00:00:00	914.5249	927.7141	943.0316	899.8858	60246600
2022-01-17 00:00:00	925.4504	955.1867	977.8098	912.5262	75765800
2022-01-18 00:00:00	960.5596	940.2313	980.2006	921.9308	76349300
2022-01-19 00:00:00	944.1635	935.6306	971.2086	911.5943	61660000
2022-01-20 00:00:00	934.9683	938.9257	978.9594	908.132	95134700
2022-01-21 00:00:00	1028.6	1023.601	1050.957	1001.908	72205400
2022-01-24 00:00:00	1010.445	1024.021	1051.696	981.6646	54250600
2022-01-25 00:00:00	1019.272	981.6793	1036.821	974.0316	51871600
2022-01-26 00:00:00	983.0435	988.1808	1014.898	965.7074	50068600
2022-01-27 00:00:00	992.7041	974.8998	1011.379	963.4773	46187500
2022-01-28 00:00:00	977.2909	987.1041	1005.311	949.9261	40265700
2022-02-07 00:00:00	999.698	994.0131	1014.872	983.5998	32720800
2022-02-08 00:00:00	990.8865	987.0471	1002.264	960.0484	29712700
2022-02-09 00:00:00	1450.09	1466.217	1483.445	1423.855	37139700
2022-02-10 00:00:00	1428.835	1378.052	1442.228	1373.028	33087300
2022-02-11 00:00:00	1368.167	1286.253	1385.1	1262.891	40672700
2022-02-14 00:00:00	1295.337	1247.96	1323.363	1212.197	44993000
2022-02-15 00:00:00	1250.372	1273.998	1299.66	1225.309	39029600
2022-02-16 00:00:00	1276.899	1263.539	1307.824	1241.128	29390200
2022-02-17 00:00:00	1268.484	1305.274	1367.227	1249.467	38378700
2022-02-18 00:00:00	1300.815	1304.83	1336.585	1266.32	34889700
2022-02-21 00:00:00	1351.581	1323.68	1366.376	1291.069	46562100
2022-02-22 00:00:00	1294.972	1284.721	1333.129	1272.243	40648000
2022-02-23 00:00:00	1274.881	1329.058	1345.19	1271.517	31787500
2022-02-24 00:00:00	1313.74	1278.999	1341.771	1249.304	47591400
2022-02-25 00:00:00	1295.946	1299.368	1330.629	1280.885	28000000
2022-02-28 00:00:00	1283.309	1294.169	1314.167	1267.874	26582000
2022-03-01 00:00:00	1312.017	1332.495	1354.694	1289.717	23780100
2022-03-02 00:00:00	1340.972	1340.7	1353.752	1300.461	23910000
2022-03-03 00:00:00	1352.794	1329.664	1385.332	1310.594	42113000
2022-03-04 00:00:00	1306.006	1279.347	1321.759	1266.478	30435200
2022-03-07 00:00:00	1283.316	1223.638	1292.16	1204.202	24980100
2022-03-08 00:00:00	1220.691	1145.845	1229.332	1138.984	30382200
2022-03-09 00:00:00	1167.263	1143.331	1178.242	1085.955	27911700
2022-03-10 00:00:00	1194.332	1178.875	1205.584	1158.722	25295200
2022-03-11 00:00:00	1148.767	1186.108	1198.658	1128.707	32567800
2022-03-14 00:00:00	1175.973	1145.295	1191.162	1125.298	30073600
2022-03-15 00:00:00	1119.607	1101.183	1148.451	1083.695	29185900
2022-03-16 00:00:00	1125.492	1142.168	1159.565	1070.374	40329000
2022-03-17 00:00:00	1170.873	1201.55	1272.452	1151.441	64867900
2022-03-18 00:00:00	1190.577	1226.283	1249.543	1167.585	39864200
2022-03-21 00:00:00	1226.993	1242.745	1261.102	1209.557	34461500
2022-03-22 00:00:00	1239.708	1253.67	1277.65	1188.961	35634000
2022-03-23 00:00:00	1245.819	1229.006	1292.028	1217.96	33735700
2022-03-24 00:00:00	1219.059	1231.971	1252.869	1194.718	41297500
2022-03-25 00:00:00	1232.595	1165.315	1243.135	1156.427	33959000
2022-03-28 00:00:00	1162.159	1136.051	1178.955	1114.905	24276300
2022-03-29 00:00:00	1146.782	1139.965	1170.548	1123.653	18767600
2022-03-30 00:00:00	1125.18	1176.721	1193.722	1116.72	18174700
2022-03-31 00:00:00	1184.614	1159.233	1202.292	1138.03	26247300
2022-04-01 00:00:00	1150.849	1148.865	1175.49	1129.803	31485500
2022-04-06 00:00:00	1152.652	1142.254	1168.794	1134.1	18058400
2022-04-07 00:00:00	1143.542	1109.45	1148.069	1098.292	27544100
2022-04-08 00:00:00	1104.86	1091.087	1116.109	1067.896	16668200
2022-04-11 00:00:00	1087.215	1038.793	1088.858	1020.6	16673700
2022-04-12 00:00:00	1032.911	1049.588	1060.75	1026.167	16275600
2022-04-13 00:00:00	1035.516	1022.78	1048.966	1010.46	13224300
2022-04-14 00:00:00	1035.141	998.5331	1046.152	987.6233	19209800
2022-04-15 00:00:00	992.672	960.7639	999.4594	953.5972	18066900
2022-04-18 00:00:00	965.4588	1036.804	1051.604	945.1813	16440500
2022-04-19 00:00:00	1028.708	1011.64	1045.359	1000.62	14490700
2022-04-20 00:00:00	1013.811	989.1011	1019.696	973.8115	15203200
2022-04-21 00:00:00	988.0004	953.9597	996.0003	944.6672	17631900
2022-04-22 00:00:00	941.2076	953.4744	972.9929	925.416	16280500
2022-04-25 00:00:00	945.9288	899.1527	955.1544	883.6141	20358500
2022-04-26 00:00:00	914.9489	862.8075	928.0836	851.0499	18024600
2022-04-27 00:00:00	844.578	920.8308	932.3685	823.7524	21012500
2022-04-28 00:00:00	913.7189	899.2154	935.7863	885.6944	17545400

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