tushare+matplotlib 简单财务分析

Posted 2020-11-09 oumuamua

tushare是python的一个免费财经数据接口包，可以很方便的查询股票数据和公司财务数据。matplotlib是python数据可视化的有力工具。如果把tushare和matplotlib结合起来，从tushare调取股票和财务数据，用matplotlib实现数据可视化，效果应该不错。闲话少叙，上图上代码。

代码中还有需要再完善的地方，欢迎批评指正。谢谢！

效果图

 

代码：

#-*- coding:utf-8 -*-

#代码基于python2版本，请用python2执行
#输入scode时，请加引号，如‘600600’
#输入year时，请直接输入，如 2017


import tushare as ts
import matplotlib.pyplot as plt
import numpy as np


def Asset_And_Equity(scode):

        scode = scode
    stock_basics = ts.get_stock_basics()
    stock__basics = stock_basics[stock_basics.index == scode]
    
    name = str(stock__basics.name[stock__basics.index == scode])
    industry = str(stock__basics.industry[stock__basics.index == scode])
    area = str(stock__basics.area[stock__basics.index == scode])
    pe = float(stock__basics.pe[stock__basics.index == scode])
    pb = float(stock__basics.pb[stock__basics.index == scode])

    liquidasset = float(stock__basics.liquidAssets[stock__basics.index == scode])
    fixedasset = float(stock__basics.fixedAssets[stock__basics.index == scode])
    totalasset = float(stock__basics.totalAssets[stock__basics.index == scode])

    outstanding = float(stock__basics.outstanding[stock__basics.index == scode])
    totals = float(stock__basics.totals[stock__basics.index == scode])

    esp = float(stock__basics.esp[stock__basics.index == scode])
    bvps = float(stock__basics.bvps[stock__basics.index == scode])
    reservedpershare = float(stock__basics.reservedPerShare[stock__basics.index == scode])
    perundp = float(stock__basics.perundp[stock__basics.index == scode])

    plt.figure(figsize=(12,6))

    #资产状况饼图
    plt.subplot(131)
    labels_asset = \'liquid asset\',\'fixed asset\'
    sizes_asset = liquidasset, fixedasset
    colors_asset = \'yellowgreen\', \'gold\'
    plt.axis(\'equal\')
    plt.pie(sizes_asset, explode=None,labels=labels_asset,colors=colors_asset,autopct=\'%1.1f%%\',shadow=True,startangle=50)
    plt.title(\'Asset situation\')

    col_labels = [\'liquid(wanyuan)\', \'fixed(wanyuan)\',\'outstanding(yigu)\', \'totals(yigu)\']
    row_labels = [\'%s\'%scode]
    table_vals = [[liquidasset,fixedasset,outstanding,totals]]
    my_table = plt.table(cellText=table_vals,colWidths=[0.4]*4,\\
                         rowLabels=row_labels,colLabels=col_labels
                         )

    #股本状况饼图
    plt.subplot(132)
    labels_share = \'outstanding\', \'totals\'
    sizes_share = outstanding, totals
    colors_share = \'yellowgreen\', \'gold\'
    plt.axis(\'equal\')
    plt.pie(sizes_share, explode=None, labels=labels_share,colors=colors_share,autopct=\'%1.1f%%\',shadow=True,startangle=50)
    plt.title(\'Equity situation\')


    #每股净资，每股未分配利润
    plt.subplot(133)
    ind = np.arange(2)
    numlist = [bvps,perundp]
    plt.bar(ind, numlist)
    plt.xlabel(\'DATA PS\')
    plt.ylabel(\'YUAN\')
    plt.title(\'NA&UNDNI PS\')
    plt.xticks(ind, (\'NAPS\', \'UNDNIPS\'))
    for a, b in zip(ind, numlist):
        plt.text(a, b+0.05, \'%.2f\'%b, ha=\'center\', va=\'bottom\', fontsize=7)

        
    plt.show()

def get_profit_data(year1, year2, year3, year4, year5, scode):

        timelist = []
        timelist.append(year1)
        timelist.append(year2)
        timelist.append(year3)
        timelist.append(year4)
        timelist.append(year5)

    
    roe = []
    net_profit_ratio = []
    gross_profit_rate = []
    net_profits = []
    eps = []
    business_income = []
    bips = []

    for i in timelist:
    
        profit_data = ts.get_profit_data(i,4)
        profit_data.index = profit_data.code
        data = profit_data[profit_data.index == scode]
        
        roe.append(float(data.roe))
        net_profit_ratio.append(float(data.net_profit_ratio))
        gross_profit_rate.append(float(data.gross_profit_rate))
        net_profits.append(float(data.net_profits))
        eps.append(float(data.eps))
        business_income.append(float(data.business_income))
        bips.append(float(data.bips))
        
    plt.figure(figsize=(12,6))

    #营业收入柱状图
    plt.subplot(231)
    ind = np.arange(5)
    plt.bar(ind, business_income, color=\'yellowgreen\')
    plt.title(\'Business Income(BaiWan Yuan)\')
    plt.xticks(ind, (year1, year2, year3, year4, year5))
    for a, b in zip(ind, business_income):
        plt.text(a, b+0.05, \'%.2f\'%b, ha=\'center\', va=\'bottom\', fontsize=7)

    #净利润柱状图
    plt.subplot(232)
    plt.bar(ind, net_profits, color=\'gold\')
    plt.title(\'Net_profits(Wan Yuan)\')
    plt.xticks(ind, (year1, year2, year3, year4, year5))
    for a, b in zip(ind, net_profits):
        plt.text(a, b+0.05, \'%.2f\'%b, ha=\'center\', va=\'bottom\', fontsize=7)

    #每股收益柱状图
    plt.subplot(233)
    plt.bar(ind, eps, color=\'#FFA500\')
    plt.title(\'EPS\')
    plt.xticks(ind, (year1, year2, year3, year4, year5))
    for a, b in zip(ind, eps):
        plt.text(a, b+0.05, \'%.2f\'%b, ha=\'center\', va=\'bottom\', fontsize=7)

    #roe折线图
    plt.subplot(234)
    plt.title(\'ROE(%)\')
    plt.plot(roe, \'r\', label=\'ROE\')
    plt.xticks(ind, (year1, year2, year3, year4, year5))

    #净利润率折线图
    plt.subplot(235)
    plt.title(\'Net_Profit_Ratio(%)\')
    plt.plot(net_profit_ratio, \'b\', label=\'Net_Profit_Ratio\')
    plt.xticks(ind, (year1, year2, year3, year4, year5))

    #毛利率折线图
    plt.subplot(236)
    plt.title(\'Gross_Profit_Rate(%)\')
    plt.plot(gross_profit_rate, \'g\', label=\'Gross_Profit_Ratio\')
    plt.xticks(ind, (year1, year2, year3, year4, year5))


    plt.show()

def get_operation_data(year1, year2, year3, year4, year5, scode):

        timelist = []
        timelist.append(year1)
        timelist.append(year2)
        timelist.append(year3)
        timelist.append(year4)
        timelist.append(year5)

    arturnover = []
    arturndays = []
    inventory_turnover = []
    inventory_days = []
    currentasset_turnover = []
    currentasset_days = []


    for i in timelist:
        
        operation_data = ts.get_operation_data(i,4)
        operation_data.index = operation_data.code
        data = operation_data[operation_data.index == scode]
        
        arturnover.append(float(data.arturnover))
        arturndays.append(float(data.arturndays))
        inventory_turnover.append(float(data.inventory_turnover))
        inventory_days.append(float(data.inventory_days))
        currentasset_turnover.append(float(data.currentasset_turnover))
        currentasset_days.append(float(data.currentasset_days))
     


    plt.figure(figsize=(12,6))
    ind = np.arange(5)

    #应收账款折线图
    plt.subplot(231)
    plt.title(\'AR Turnover(ci)\')
    plt.plot(arturnover, \'r\', label=\'AR Turnover\')
    plt.xticks(ind, (year1, year2, year3, year4, year5))

    #存货折线图
    plt.subplot(232)
    plt.title(\'Inventory Turnover(ci)\')
    plt.plot(inventory_turnover, \'b\', label=\'Inventory Turnover\')
    plt.xticks(ind, (year1, year2, year3, year4, year5))

    #流动资产折线图
    plt.subplot(233)
    plt.title(\'CA Turnover(ci)\')
    plt.plot(currentasset_turnover, \'g\', label=\'CA Turnover\')
    plt.xticks(ind, (year1, year2, year3, year4, year5))


    #应收账款柱状图
    plt.subplot(234)
    plt.bar(ind, arturndays, color=\'yellowgreen\')
    plt.title(\'AR Turnover Days\')
    plt.xticks(ind, (year1, year2, year3, year4, year5))
    for a, b in zip(ind, arturndays):
        plt.text(a, b+0.05, \'%.2f\'%b, ha=\'center\', va=\'bottom\', fontsize=7)

    #存货柱状图
    plt.subplot(235)
    plt.bar(ind, inventory_days, color=\'gold\')
    plt.title(\'Inventory Turnover Days\')
    plt.xticks(ind, (year1, year2, year3, year4, year5))
    for a, b in zip(ind, inventory_days):
        plt.text(a, b+0.05, \'%.2f\'%b, ha=\'center\', va=\'bottom\', fontsize=7)

    #流动资产柱状图
    plt.subplot(236)
    plt.bar(ind, currentasset_days, color=\'#FFA500\')
    plt.title(\'CA Turnover Days\')
    plt.xticks(ind, (year1, year2, year3, year4, year5))
    for a, b in zip(ind, currentasset_days):
        plt.text(a, b+0.05, \'%.2f\'%b, ha=\'center\', va=\'bottom\', fontsize=7)


    plt.show()


def get_growth_data(year1, year2, year3, year4, year5, scode):
        
        timelist = []
        timelist.append(year1)
        timelist.append(year2)
        timelist.append(year3)
        timelist.append(year4)
        timelist.append(year5)

    mbrg = []
    nprg = []
    nav = []
    targ = []
    eps = []
    epsg = []
    seg = []

    for i in timelist:
        
        growth_data = ts.get_growth_data(i,4)
        growth_data.index = growth_data.code
        data = growth_data[growth_data.index == scode]
        
        mbrg.append(float(data.mbrg))
        nprg.append(float(data.nprg))
        nav.append(float(data.nav))
        targ.append(float(data.targ))
        epsg.append(float(data.epsg))
        seg.append(float(data.seg))

    plt.figure(figsize=(12,6))
    ind = np.arange(5)

    #收入增长率
    plt.subplot(231)
    plt.title(\'Income Growth(%)\')
    plt.plot(mbrg, \'r\', label=\'Income Growth\')
    plt.xticks(ind, (year1, year2, year3, year4, year5))

    #净利润增长率
    plt.subplot(232)
    plt.title(\'NI Growth(%)\')
    plt.plot(nprg, \'b\', label=\'NI Growth\')
    plt.xticks(ind, (year1, year2, year3, year4, year5))

    #净资产增长率
    plt.subplot(233)
    plt.title(\'NA Growth(%)\')
    plt.plot(nav, \'g\', label=\'NA Growth\')
    plt.xticks(ind, (year1, year2, year3, year4, year5))

    #总资产增长率
    plt.subplot(234)
    plt.title(\'TA Growth(%)\')
    plt.plot(targ, \'r\', label=\'TA Growth\')
    plt.xticks(ind, (year1, year2, year3, year4, year5))

    #EPS增长率
    plt.subplot(235)
    plt.title(\'EPS Growth(%)\')
    plt.plot(epsg, \'b\', label=\'EPS Growth\')
    plt.xticks(ind, (year1, year2, year3, year4, year5))

    #股东权益增长率
    plt.subplot(236)
    plt.title(\'Equity Growth(%)\')
    plt.plot(seg, \'g\', label=\'seg\')
    plt.xticks(ind, (year1, year2, year3, year4, year5))


    plt.show()

def get_debtpaying_data(year1, year2, year3, year4, year5, scode):

        timelist = []
        timelist.append(year1)
        timelist.append(year2)
        timelist.append(year3)
        timelist.append(year4)
        timelist.append(year5)

    currentratio = []         #流动比率
    quickratio = []         #速动比率
    cashratio = []          #现金比率
    icratio =[]          #利息支付倍数
    sheqratio = []         #股东权益比率
    adratio = []           #股东权益增长率


    for i in timelist:
        
        debtpaying_data = ts.get_debtpaying_data(i,4)
        debtpaying_data.index = debtpaying_data.code
        data = debtpaying_data[debtpaying_data.index == scode]
        
        currentratio.append(float(data.currentratio))
        quickratio.append(float(data.quickratio))
        cashratio.append(float(data.cashratio))
        icratio.append(float(data.icratio))
        sheqratio.append(float(data.sheqratio))
        adratio.append(float(data.adratio))
     


    plt.figure(figsize=(12,6))
    ind = np.arange(5)


    plt.subplot(231)
    plt.title(\'CurrentRatio\')
    plt.plot(currentratio, \'r\', label=\'CurrentRatio\')
    plt.xticks(ind, (year1, year2, year3, year4, year5))

    plt.subplot(232)
    plt.title(\'QuickRatio\')
    plt.plot(quickratio, \'g\', label=\'QuickRatio\')
    plt.xticks(ind, (year1, year2, year3, year4, year5))

    plt.subplot(233)
    plt.title(\'CashRatio\')
    plt.plot(cashratio, \'b\', label=\'CashRatio\')
    plt.xticks(ind, (year1, year2, year3, year4, year5))

    plt.subplot(234)
    plt.title(\'Interest Coverage Ratio\')
    plt.plot(icratio, \'r\', label=\'Interest Coverage Ratio\')
    plt.xticks(ind, (year1, year2, year3, year4, year5))

    plt.subplot(235)
    plt.title(\'EquityRatio\')
    plt.plot(sheqratio, \'g\', label=\'EquityRatio\')
    plt.xticks(ind, (year1, year2, year3, year4, year5))

    plt.subplot(236)
    plt.title(\'Equity Growth(%)\')
    plt.plot(adratio, \'b\', label=\'Equity Growth\')
    plt.xticks(ind, (year1, year2, year3, year4, year5))

    plt.show()
    
def get_cashflow_data(year1, year2, year3, year4, year5, scode):

        timelist = []
        timelist.append(year1)
        timelist.append(year2)
        timelist.append(year3)
        timelist.append(year4)
        timelist.append(year5)

    cf_sales = []         #经营现金净流量对销售收入比率
    rateofreturn = []         #资产的经营现金流量回报率
    cf_nm = []               #经营现金净流量与净利润的比率
    cf_liabilities =[]          #经营现金净流量对负债比率
    cashflowratio = []         #现金流量比率


    for i in timelist:
        
        cashflow_data = ts.get_cashflow_data(i,4)
        cashflow_data.index = cashflow_data.code
        data = cashflow_data[cashflow_data.index == scode]

        cf_sales.append(float(data.cf_sales))
        rateofreturn.append(float(data.rateofreturn))
        cf_nm.append(float(data.cf_nm))
        cf_liabilities.append(float(data.cf_liabilities))
        cashflowratio.append(float(data.cashflowratio)) 


    plt.figure(figsize=(12,6))
    ind = np.arange(5)


    plt.subplot(231)
    plt.title(\'OCF/Sales\')
    plt.plot(cf_sales, \'r\', label=\'OCF/Sales\')
    plt.xticks(ind, (year1, year2, year3, year4, year5))

    plt.subplot(232)
    plt.title(\'OCF/Asset\')
    plt.plot(rateofreturn, \'g\', label=\'OCF/Asset\')
    plt.xticks(ind, (year1, year2, year3, year4, year5))

    plt.subplot(233)
    plt.title(\'OCF/NI\')
    plt.plot(cf_nm, \'b\', label=\'OCF/NI\')
    plt.xticks(ind, (year1, year2, year3, year4, year5))

    plt.subplot(234)
    plt.title(\'OCF/Liabilities\')
    plt.plot(cf_liabilities, \'r\', label=\'OCF/Liabilities\')
    plt.xticks(ind, (year1, year2, year3, year4, year5))

    plt.subplot(235)
    plt.title(\'CashflowRatio\')
    plt.plot(cashflowratio, \'g\', label=\'CashflowRatio\')
    plt.xticks(ind, (year1, year2, year3, year4, year5))


    plt.show()