中国股市历史价格理性的简单探讨

Author: YE KAIWEN
 Copyright (c) 2022 YE KAIWEN. All Rights Reserved.

import pandas as pd
import numpy as np
import datetime
import valuequant as vq
import matplotlib.pyplot as plt

循环证券列表获取各证券历史价格兑现率数据
def stocklistloop():
    vq.login(,<账号密码>)valuequant账号可登陆www.boomevolve.com注册
    stocklist=<获取证券列表信息的函数>
    for inx,row in stocklist.iterrows():
        getstockhistory(row[<六位证券代码字段>],row[<证券上市日期字段>])

获取某证券各历史报告期模型下的历史价格兑现率数据
def getstockhistory(stockcode,listdate):
    dateinx=pd.date_range(start=max(pd.to_datetime(listdate),pd.to_datetime(2006-01-01)),end=pd.to_datetime(datetime.datetime.now()),freq=A-DEC).strftime(%Y-%m-%d)
    for item in dateinx:
        以移动平均严格状态更新模型证券时间序列集分析模板框架为例建立模型
        modelset=vq.StockFrameSets.ma_strict_latest_status_frameset(stockcode=stockcode,reportdate=item,mawindow=4,minstep=8,seasonal=True,conservative=True)
        if hasattr(modelset,_modelset):
            try:
                分别计算贴现率为3%，6%，9%的兑现率数据
                acrdata=pd.DataFrame()
                acrdata[acr3]=modelset.batchachieverate_trade(dimension=综合收益总额,pricemode=close,startdate=listdate,enddate=item,r=0.03)
                acrdata[acr6]=modelset.batchachieverate_trade(dimension=综合收益总额,pricemode=close,startdate=listdate,enddate=item,r=0.06)
                acrdata[acr9]=modelset.batchachieverate_trade(dimension=综合收益总额, pricemode=close, startdate=listdate,enddate=item,r=0.09)
                acrdata.to_csv(<存储路径>)
                print(stockcode,报告,item,已计算)
            except Exception as e:
                print(stockcode,报告,item,acr计算出错,e)
        else:
            print(stockcode, 报告, item, 模型未计算)

获取某年市场各证券年平均价格在此后第N年业绩模型的兑现率
def getmarketpriceacr(priceyear,pvgap):
    pvyear=priceyear+pvgap
    reportdate=str(pvyear)+-12-31
    marketstart=str(priceyear)+-01-01
    marketend=str(priceyear)+-12-31
    acrstats=pd.DataFrame()
    stocklist=<获取证券列表信息的函数>
    for inx, row in stocklist.iterrows():
        if (pd.to_datetime(marketend)>=pd.to_datetime(row[<证券上市日期字段>])):
            try:
                df=pd.read_csv(<某证券reportdate报告期的兑现率数据存储路径>,index_col=[0])
                df.index=pd.to_datetime(df.index)
                df=df.sort_index(ascending=True)
                if len(df.loc[marketstart:marketend])>0:
                    yearacr=df.loc[marketstart:marketend].mean()
                    yearacr.name=row[stockcode]
                    acrstats=acrstats.append(yearacr)
            except Exception as e:
                pass
    acrstats.to_csv(<存储路径>)
    print(价格%s现值%s已统计 % (str(priceyear), str(pvyear)))

循环统计市场各年各证券年平均价格在此后各年业绩模型的兑现率
def statisticsloop():
    for priceyear in range(2000,2022):
        for pvgap in range(max(0,2007-priceyear),min(10,2021-priceyear)+1):
            getmarketpriceacr(priceyear,pvgap)

计算对市场证券某年平均价格在此N年后业绩模型的兑现率数据进行区间统计
def histstatistics(priceyear,pvgap):
    pvyear=priceyear+pvgap
    df=pd.read_csv(,index_col=[0])
    bins=[(-oo,0),[0,0.5),[0.5,1),[1,5),[5,10),[10,oo)]
    histdata=pd.DataFrame(index=bins,columns=df.columns)
    for col in df.columns:
        histdata.loc[(-oo,0),col]=df[col][df[col]<0].count()
        histdata.loc[[0,0.5), col] = df[col][(df[col]>=0)&(df[col]<0.5)].count()
        histdata.loc[[0.5,1), col] = df[col][(df[col] >=0.5) & (df[col] <1)].count()
        histdata.loc[[1,5), col] = df[col][(df[col] >= 1) & (df[col] < 5)].count()
        histdata.loc[[5,10), col] = df[col][(df[col] >= 5) & (df[col] < 10)].count()
        histdata.loc[[10,oo), col] = df[col][(df[col] >= 10)].count()
    return histdata

循环计算存储histstatistics
def histloop():
    for priceyear in range(2000,2022):
        for pvgap in range(max(0,2007-priceyear),min(10,2021-priceyear)+1):
            histdata=histstatistics(priceyear,pvgap)
            pvyear = priceyear + pvgap
            histdata.to_csv(<存储路径>)

histdata的可视化图表分析
def histvisual(priceyear,pvgap):
    pvyear = priceyear + pvgap
    path=
    df=pd.read_csv(path,index_col=[0])
    rdf=df/(df.sum())
    x=np.arange(len(df))
    plt.cla()
    plt.grid(linestyle=:)
    for colnum in [3,6,9]:
        col=acr+str(colnum)
        plt.bar(x=x+colnum/12-1/2,height=df[col],width=1/4)
        for i in x:
            plt.text(i + 1.8*(colnum / 12 - 5 / 8), df[col].iloc[i],str(int(np.round(rdf[col].iloc[i], 2) * 100)) + %, fontsize=8)
    plt.xticks(x,df.index)
    if pvgap>0:
        plt.title(str(priceyear)+年平均价格+str(pvgap)+年后业绩模型的兑现率)
    else:
        plt.title(str(priceyear) + 年平均价格当年后业绩模型的兑现率)
    plt.show()