Python で米国株をデータ分析

本のソースコード: https://gist.github.com/beavis28/5244d2340761b45f490a240eb7d54f30#file-python-usstockdataanalytics-ipyn

Twitter: https://twitter.com/beavis28

Youtube: https://www.youtube.com/watch?v=L8J9YiWuv0A

yahoofin ライブラリのドキュメント http:// theautomatic. net/ yahoo fin-documentation/

[FinTech API:]
Yahoo Finance
Alpha Vantage
MorningSTAR
OECD API
Trading Economics API
Quandl API
TradeStation Web API
Alpha Wave Data API
Rapid API

! pip install yahoo_fin

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import yahoo_fin.stock_info as si 
import pandas as pd
import matplotlib.pyplot as plt
from datetime import date
from dateutil.relativedelta import relativedelta
import numpy as np

plt.style.use('fivethirtyeight')
plt.rcParams['figure.figsize'] = [24, 8]

# show Zoom price as DataFrame 

symbol = 'ZM'
hist_data = si.get_data(symbol)
hist_data

	open	high	low	close	adjclose	volume	ticker
2019-04-18	65.000000	66.000000	60.320999	62.000000	62.000000	25764700	ZM
2019-04-22	61.000000	68.900002	59.939999	65.699997	65.699997	9949700	ZM
2019-04-23	66.870003	74.168999	65.550003	69.000000	69.000000	6786500	ZM
2019-04-24	71.400002	71.500000	63.160000	63.200001	63.200001	4973500	ZM
2019-04-25	64.739998	66.849998	62.599998	65.000000	65.000000	3863300	ZM
...	...	...	...	...	...	...	...
2022-02-28	126.790001	135.089996	125.099998	132.600006	132.600006	12033100	ZM
2022-03-01	128.570007	136.000000	122.129997	122.779999	122.779999	11541600	ZM
2022-03-02	122.250000	122.889999	114.519997	121.610001	121.610001	7224300	ZM
2022-03-03	122.000000	122.059998	111.949997	113.110001	113.110001	6194800	ZM
2022-03-04	114.099998	116.190002	108.440002	108.940002	108.940002	6178377	ZM

726 rows × 7 columns

# extract 'close' (終値) column data ans plot graph.
chart = hist_data.plot(kind='line', y='close', color='blue')

# extract 'volume' (取引量) data and plot graph.
volume = hist_data.plot(kind='line', y='volume', color='green')

# 財務諸表
income_statement = si.get_income_statement(symbol, yearly=False)   # yearly=False 直近の4四半期データを見る
income_statement

endDate	2022-01-31	2021-10-31	2021-07-31	2021-04-30
Breakdown
researchDevelopment	116996000	98508000	82311000	65175000
effectOfAccountingCharges	None	None	None	None
incomeBeforeTax	138534000	410283000	323884000	228931000
minorityInterest	None	None	None	None
netIncome	490641000	340383000	317084000	227531000
sellingGeneralAdministrative	445214000	390434000	383325000	332856000
grossProfit	814029000	779799000	760239000	691243000
ebit	251819000	290857000	294603000	293212000
operatingIncome	251819000	290857000	294603000	293212000
otherOperatingExpenses	None	None	None	None
interestExpense	None	None	None	None
extraordinaryItems	None	None	None	None
nonRecurring	None	None	None	None
otherItems	None	None	None	None
incomeTaxExpense	-352107000	69900000	6800000	1400000
totalRevenue	1071376000	1050756000	1021495000	956237000
totalOperatingExpenses	819557000	759899000	726892000	663025000
costOfRevenue	257347000	270957000	261256000	264994000
totalOtherIncomeExpenseNet	-113285000	119426000	29281000	-64281000
discontinuedOperations	None	None	None	None
netIncomeFromContinuingOps	490641000	340383000	317084000	227531000
netIncomeApplicableToCommonShares	490525000	340271000	316930000	227383000

# 売上グラフ
income_statement_for_graph = si.get_income_statement(symbol, yearly=False).transpose()
income_statement_for_graph.plot(kind='line', y='totalRevenue', color='blue')

<matplotlib.axes._subplots.AxesSubplot at 0x7f589ab49e90>

# 利益ぐラフ
income_statement_for_graph.plot(kind='line', y='netIncome', color='blue')

<matplotlib.axes._subplots.AxesSubplot at 0x7f589ab72b50>

PSR を調べる

PSR = Price-Sales Ratio

$\displaystyle \mbox{P/S Ratio} = \frac{\mbox{Market Capitalization}}{\mbox{Revenue}} = \frac{\mbox{Per-Share Stock Price}}{\mbox{Per-Share Revenue}}$

$\mbox{Revenue} = \mbox{年間売り上げ}$

$\mbox{Market Capitalization} = \mbox{現在の株価} \times \mbox{発行株式数}$

過去の4半期の売り上げ(totalRevenue)を足し合わせたもので、Market Capitalization を割って PSRを計算してみる。

income_statement_yearly = si.get_income_statement(symbol, yearly = True)
market_cap = si.get_quote_table(symbol)['Market Cap']
market_cap_char = market_cap[-1]
unit = 0
if market_cap_char == 'B':
    unit = 1000000000
elif market_cap_char == 'T':
    unit = 1000000000000
elif market_cap_char == 'M':
    unit = 1000000
                
psr = (float(market_cap[:-1]) * unit) / (income_statement.loc['totalRevenue'][0]+income_statement.loc['totalRevenue'][1]+income_statement.loc['totalRevenue'][2]+income_statement.loc['totalRevenue'][3])
print(psr)

7.918311436672045

高成長企業を評価するときに使われる「40%ルール」

「売り上げ成長率｜利益成長率」が40% を超えているかで、 ボートフォリオに入れる企業を選ぶことがある。

fourty_rule = (income_statement.loc['grossProfit'][0]/income_statement.loc['grossProfit'][1] - 1) + (income_statement.loc['totalRevenue'][0]/income_statement.loc['totalRevenue'][1] - 1)
"{0:.0%}".format(fourty_rule)

'6%'

企業同士を比較する

GAFAM を含んだ多くの成長企業を比較してみる。

fourty_rule と 0.4 を比較することで、40%ルールに満たない会社を省略している。

# quaterly base growth rate

nasdaq_list = ["AAPL","ADBE","AMD","AMZN","ATVI","AVGO","BA","BABA","BOX","CCI","CHGG","CRM","CRWD","CSCO","CTXS","CVNA",
               "DBX","DDOG","DIS","DLR","DOCU","EA","EBAY","EQIX","ETSY","FB","FSLY","FVRR","GDS","GILD","GOOG","HON","IBM",
               "INTC","ISRG","KMX","LOGI","LYFT","MA","MCK","MDB","MSFT","MTCH","MU","NDX","NET","NFLX","NUE",
               "NVDA","OKTA","ORCL","PANW","PINS", "PYPL","QCOM","RNG","PTON","ROKU","SHOP","SNAP","SNOW",
               "SPLK","SPOT","SQ","SWKS","T","TEAM","TMO","TMUS","TSLA","TTD","TTWO","TW","TWLO","TWTR","U",
               "V","VMW","VZ","WDAY","WIX","WORK","XLNX","ZI", "ZM","ZS"]  # si.tickers_nasdaq()

symbol_list = []
gross_profit_list = []
total_revenue_list = []
gross_growth_list = []
revenue_growth_list = []
fourty_list = []
psr_list = []
for symbol in nasdaq_list:
    try:
        income_statement = si.get_income_statement(symbol, yearly = False)
        fourty_rule = (income_statement.loc['grossProfit'][0]/income_statement.loc['grossProfit'][1] - 1) + (income_statement.loc['totalRevenue'][0]/income_statement.loc['totalRevenue'][1] - 1)
        if fourty_rule > 0.4:
            gross_profit = income_statement.loc['grossProfit'][0]
            total_revenue = income_statement.loc['totalRevenue'][0]
            gross_growth = (income_statement.loc['grossProfit'][0]/income_statement.loc['grossProfit'][1] - 1)
            total_revenue_growth = (income_statement.loc['totalRevenue'][0]/income_statement.loc['totalRevenue'][1] - 1)

            market_cap = si.get_quote_table(symbol)['Market Cap']
            market_cap_char = market_cap[-1]
            unit = 0
            if market_cap_char == 'B':
                unit = 1000000000
            elif market_cap_char == 'T':
                unit = 1000000000000
            elif market_cap_char == 'M':
                unit = 1000000
                
            psr = (float(market_cap[:-1]) * unit) / (income_statement.loc['totalRevenue'][0]+income_statement.loc['totalRevenue'][1]+income_statement.loc['totalRevenue'][2]+income_statement.loc['totalRevenue'][3])

                
            symbol_list.append(symbol)
            #fourty_list.append("{0:.0%}".format(fourty_rule))
            gross_profit_list.append(gross_profit)
            total_revenue_list.append(total_revenue)
            gross_growth_list.append(gross_growth)
            revenue_growth_list.append(total_revenue_growth)
            fourty_list.append(round(fourty_rule,2)*100)
            psr_list.append(psr)
            
    except Exception as e:
        str(e)
        
# result
result_df = pd.DataFrame(list(zip(gross_profit_list, total_revenue_list, gross_growth_list, revenue_growth_list, fourty_list, psr_list)), columns=['grossProfit','totalRevenue','GrossGrowth','RevenueGrowth','Percentage', 'PSR'], index=symbol_list)
result_df

/usr/local/lib/python3.7/dist-packages/yahoo_fin/stock_info.py:116: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  frame['ticker'] = ticker.upper()

	grossProfit	totalRevenue	GrossGrowth	RevenueGrowth	Percentage	PSR
AAPL	54243000000	123945000000	0.542133	0.486864	103.0	7.038959
BABA	95922000000	242580000000	0.352157	0.208730	56.0	0.325586
CHGG	151757000	207467000	0.447510	0.206610	65.0	5.339671
DDOG	259141000	326198000	0.250572	0.205961	46.0	43.386172
ETSY	508173000	717139000	0.341644	0.346919	69.0	8.266663
LOGI	661136000	1632782000	0.210874	0.249960	46.0	2.115938
PINS	705407000	846655000	0.393675	0.337671	73.0	6.172938
PTON	280100000	1133900000	0.066235	0.408396	47.0	1.882219
SNAP	848734000	1297885000	0.360155	0.215850	58.0	13.054985
SPLK	710753000	901119000	0.491734	0.355574	85.0	7.664763
TSLA	4847000000	17719000000	0.324317	0.287999	61.0	16.096855
TTD	328753000	395598000	0.327271	0.313882	64.0	30.658597
TWTR	1052129000	1567220000	0.316250	0.220750	54.0	5.265011

# 売り上げを棒グラフで見る

result_df.plot(kind='bar', y='totalRevenue',color='blue')

<matplotlib.axes._subplots.AxesSubplot at 0x7f589a9a5e90>

# 利益を棒グラフで見る

result_df.plot(kind='bar', y='grossProfit',color='blue')

<matplotlib.axes._subplots.AxesSubplot at 0x7f589ca13110>

比較した企業をグラフで描く

横軸を「利益成長率(IncomeGrowth)」、縦軸を「売上成長率(RevenueGrowth)」でグラフに表示してみる。

右上に表示される会社が最も優れていることになる。

# 比較した企業をグラフで描く

p = result_df.plot(kind='scatter',x='GrossGrowth',y='RevenueGrowth',color='red', marker='x')
p.set_xlabel('IncomeGrowth', fontsize=16)
p.set_ylabel('RevenueGrowth', fontsize=14)
p.set_title('Income Statement', fontsize=18)
plt.rcParams['figure.figsize'] = [20, 8]

for i in range(0,result_df.shape[0]):
     p.text(result_df.GrossGrowth[i], result_df.RevenueGrowth[i], 
     result_df.index[i], horizontalalignment='left', 
     size='medium', color='blue', weight='semibold')

横軸を「成長率」、縦軸をPSRとして「40%ルール」のグラフを作る。

PSR が低くて、成長率が高い(＝グラフの右下に位置する)会社 の株を買うとよい。

p = result_df.plot(kind='scatter',x='Percentage',y='PSR',color='red', marker='x')
p.set_xlabel('Growth Rate', fontsize=16)
p.set_ylabel('PSR', fontsize=14)
p.set_title('Rule of 40% Graph', fontsize=18)

for i in range(0,result_df.shape[0]):
     p.text(result_df.Percentage[i], result_df.PSR[i], 
     result_df.index[i], horizontalalignment='left', 
     size='medium', color='blue', weight='semibold')

株価の成長率を見る

新しいカラムを作り、そこに3か月間の株価の成長率を入れてみる

new_column = []
for symbol in result_df.index: 
    try:
        three_months_ago = date.today() + relativedelta(months=-3)
        three_months_ago_yyyy_mm_dd = three_months_ago.strftime('%Y-%m-%d')
        hist_data = si.get_data(symbol, start_date=three_months_ago_yyyy_mm_dd)
        price_growth = hist_data.iloc[-1, hist_data.columns.get_loc('close')]/hist_data.iloc[0, hist_data.columns.get_loc('close')] - 1
        new_column.append(price_growth*100) 
    except Exception as e:
        print(str(e))
        
new_column

[-1.3005135857857342,
 -18.608414469208633,
 4.451238370228605,
 -11.907565073189442,
 -32.60559740433675,
 -6.893041279751166,
 -36.06557244767803,
 -44.716406582251345,
 -30.947409396956203,
 13.6691884075403,
 -16.919557792884177,
 -15.58629255762457,
 -24.91567692131379]

result_df['Price_growth'] = new_column 

result_df = result_df.sort_values(by=['Price_growth'], ascending=False)
result_df

	grossProfit	totalRevenue	GrossGrowth	RevenueGrowth	Percentage	PSR	Price_growth
SPLK	710753000	901119000	0.491734	0.355574	85.0	7.664763	13.669188
CHGG	151757000	207467000	0.447510	0.206610	65.0	5.339671	4.451238
AAPL	54243000000	123945000000	0.542133	0.486864	103.0	7.038959	-1.300514
LOGI	661136000	1632782000	0.210874	0.249960	46.0	2.115938	-6.893041
DDOG	259141000	326198000	0.250572	0.205961	46.0	43.386172	-11.907565
TTD	328753000	395598000	0.327271	0.313882	64.0	30.658597	-15.586293
TSLA	4847000000	17719000000	0.324317	0.287999	61.0	16.096855	-16.919558
BABA	95922000000	242580000000	0.352157	0.208730	56.0	0.325586	-18.608414
TWTR	1052129000	1567220000	0.316250	0.220750	54.0	5.265011	-24.915677
SNAP	848734000	1297885000	0.360155	0.215850	58.0	13.054985	-30.947409
ETSY	508173000	717139000	0.341644	0.346919	69.0	8.266663	-32.605597
PINS	705407000	846655000	0.393675	0.337671	73.0	6.172938	-36.065572
PTON	280100000	1133900000	0.066235	0.408396	47.0	1.882219	-44.716407

利上げ観測と、ロシアによるウクライナ侵攻でグロース株は軒並み下落しているところであることがわかる。

横軸を「売上＋利益成長率」、縦軸を「株価成長率」としたグラフを描く。

右下に位置する会社が、成長率が高いが、株価があまり上がっていないことを表すので、ねらい目となる。

p = result_df.plot(kind='scatter',x='Percentage',y='Price_growth',color='red', marker='x')
p.set_xlabel('Growth Rate', fontsize=16)
p.set_ylabel('Price_growth', fontsize=14)
p.set_title('Price Growth vs Income Growth', fontsize=18)

for i in range(0,result_df.shape[0]):
     p.text(result_df.Percentage[i], result_df.Price_growth[i], 
     result_df.index[i], horizontalalignment='left', 
     size='medium', color='blue', weight='semibold')

割安感を知る

割安な株価を知るために、成長率をPSRで割ってみる。

new_column = []
for index, row in result_df.iterrows():
    try:
        new_column.append(row['Percentage'] / row['PSR'])
    except Exception as e:
        print(str(e))
        
result_df['percentage/PSR'] = new_column
result_df = result_df.sort_values(by=['percentage/PSR'], ascending=False)
result_df

	grossProfit	totalRevenue	GrossGrowth	RevenueGrowth	Percentage	PSR	Price_growth	percentage/PSR
BABA	95922000000	242580000000	0.352157	0.208730	56.0	0.325586	-18.608414	171.997415
PTON	280100000	1133900000	0.066235	0.408396	47.0	1.882219	-44.716407	24.970523
LOGI	661136000	1632782000	0.210874	0.249960	46.0	2.115938	-6.893041	21.739769
AAPL	54243000000	123945000000	0.542133	0.486864	103.0	7.038959	-1.300514	14.632846
CHGG	151757000	207467000	0.447510	0.206610	65.0	5.339671	4.451238	12.173034
PINS	705407000	846655000	0.393675	0.337671	73.0	6.172938	-36.065572	11.825812
SPLK	710753000	901119000	0.491734	0.355574	85.0	7.664763	13.669188	11.089711
TWTR	1052129000	1567220000	0.316250	0.220750	54.0	5.265011	-24.915677	10.256388
ETSY	508173000	717139000	0.341644	0.346919	69.0	8.266663	-32.605597	8.346778
SNAP	848734000	1297885000	0.360155	0.215850	58.0	13.054985	-30.947409	4.442747
TSLA	4847000000	17719000000	0.324317	0.287999	61.0	16.096855	-16.919558	3.789560
TTD	328753000	395598000	0.327271	0.313882	64.0	30.658597	-15.586293	2.087506
DDOG	259141000	326198000	0.250572	0.205961	46.0	43.386172	-11.907565	1.060246

Market Capitalization をグラフ化する

Market Capitalization は、$\mbox{株価} \times \mbox{発行株式数}$ なので、企業の規模がわかる。

new_column = []
for index, row in result_df.iterrows():
    try:
        market_cap = si.get_quote_table(index)['Market Cap']
        market_cap_char = market_cap[-1]
        unit = 0
        if market_cap_char == 'B':
            unit = 1000000000
        elif market_cap_char == 'T':
            unit = 1000000000000
        elif market_cap_char == 'M':
            unit = 1000000
                       
        marketCap = (float(market_cap[:-1]) * unit)
        new_column.append(marketCap)
    except Exception as e:
        print(str(e))
        
result_df['marketCap'] = new_column
result_df

	grossProfit	totalRevenue	GrossGrowth	RevenueGrowth	Percentage	PSR	Price_growth	percentage/PSR	marketCap
BABA	95922000000	242580000000	0.352157	0.208730	56.0	0.325586	-18.608414	171.997415	2.723220e+11
PTON	280100000	1133900000	0.066235	0.408396	47.0	1.882219	-44.716407	24.970523	7.789000e+09
LOGI	661136000	1632782000	0.210874	0.249960	46.0	2.115938	-6.893041	21.739769	1.224500e+10
AAPL	54243000000	123945000000	0.542133	0.486864	103.0	7.038959	-1.300514	14.632846	2.663000e+12
CHGG	151757000	207467000	0.447510	0.206610	65.0	5.339671	4.451238	12.173034	4.145000e+09
PINS	705407000	846655000	0.393675	0.337671	73.0	6.172938	-36.065572	11.825812	1.591400e+10
SPLK	710753000	901119000	0.491734	0.355574	85.0	7.664763	13.669188	11.089711	2.049300e+10
TWTR	1052129000	1567220000	0.316250	0.220750	54.0	5.265011	-24.915677	10.256388	2.673300e+10
ETSY	508173000	717139000	0.341644	0.346919	69.0	8.266663	-32.605597	8.346778	1.925400e+10
SNAP	848734000	1297885000	0.360155	0.215850	58.0	13.054985	-30.947409	4.442747	5.374800e+10
TSLA	4847000000	17719000000	0.324317	0.287999	61.0	16.096855	-16.919558	3.789560	8.663810e+11
TTD	328753000	395598000	0.327271	0.313882	64.0	30.658597	-15.586293	2.087506	3.668200e+10
DDOG	259141000	326198000	0.250572	0.205961	46.0	43.386172	-11.907565	1.060246	4.463500e+10

result_df.plot(kind='bar', y='marketCap',color='blue')

<matplotlib.axes._subplots.AxesSubplot at 0x7f589aa93a90>

estimate_column = []
estimate_price_column = []
current_price_column = []

for index, row in result_df.iterrows():
    try:
        estimate = si.get_analysts_info(index)['Growth Estimates'].iloc[1,1]
        if type(estimate) == str:
            estimate_float = float(estimate[:-1].replace(",", ""))
            four_months_ago = date.today() + relativedelta(months=-4)
            hist_data = si.get_data(index, start_date=four_months_ago)
            four_months_ago_price = hist_data.iloc[0, hist_data.columns.get_loc('close')]
            cur_price = hist_data.iloc[-1, hist_data.columns.get_loc('close')]
            abs_value = abs(cur_price - four_months_ago_price)
            future_price = (abs_value * (estimate_float/100)) + four_months_ago_price
        else:
            estimate_float = 0
            future_price = 0
            cur_price = 0
        current_price_column.append(cur_price)    
        estimate_column.append(estimate_float)
        estimate_price_column.append(future_price)
    except Exception as e:
        print(str(e))
        
result_df['nextQuarterlyEstimate'] = estimate_column
result_df['current price'] = current_price_column
result_df['estimated price'] = estimate_price_column
result_df

	grossProfit	totalRevenue	GrossGrowth	RevenueGrowth	Percentage	PSR	Price_growth	percentage/PSR	marketCap	nextQuarterlyEstimate	current price	estimated price
BABA	95922000000	242580000000	0.352157	0.208730	56.0	0.325586	-18.608414	171.997415	2.723220e+11	-15.2	100.599998	152.802883
PTON	280100000	1133900000	0.066235	0.408396	47.0	1.882219	-44.716407	24.970523	7.789000e+09	35.2	23.490000	61.021520
LOGI	661136000	1632782000	0.210874	0.249960	46.0	2.115938	-6.893041	21.739769	1.224500e+10	-46.5	73.209999	76.537700
AAPL	54243000000	123945000000	0.542133	0.486864	103.0	7.038959	-1.300514	14.632846	2.663000e+12	-3.8	163.169998	149.956263
CHGG	151757000	207467000	0.447510	0.206610	65.0	5.339671	4.451238	12.173034	4.145000e+09	-18.6	30.740000	30.772561
PINS	705407000	846655000	0.393675	0.337671	73.0	6.172938	-36.065572	11.825812	1.591400e+10	-32.0	24.180000	39.398401
SPLK	710753000	901119000	0.491734	0.355574	85.0	7.664763	13.669188	11.089711	2.049300e+10	16.1	129.059998	175.070433
TWTR	1052129000	1567220000	0.316250	0.220750	54.0	5.265011	-24.915677	10.256388	2.673300e+10	-20.0	33.389999	49.942001
ETSY	508173000	717139000	0.341644	0.346919	69.0	8.266663	-32.605597	8.346778	1.925400e+10	14.7	151.570007	276.925612
SNAP	848734000	1297885000	0.360155	0.215850	58.0	13.054985	-30.947409	4.442747	5.374800e+10	-20.0	33.090000	50.538001
TSLA	4847000000	17719000000	0.324317	0.287999	61.0	16.096855	-16.919558	3.789560	8.663810e+11	70.3	838.289978	1391.168866
TTD	328753000	395598000	0.327271	0.313882	64.0	30.658597	-15.586293	2.087506	3.668200e+10	5.6	75.660004	89.483040
DDOG	259141000	326198000	0.250572	0.205961	46.0	43.386172	-11.907565	1.060246	4.463500e+10	20.0	142.190002	201.182007

result_df.plot(kind='bar', y='nextQuarterlyEstimate',color='blue')

<matplotlib.axes._subplots.AxesSubplot at 0x7f589c8d3dd0>

p = result_df.plot(kind='scatter',x='Percentage',y='Price_growth',color='red', marker='x')
p.set_xlabel('Growth Rate', fontsize=16)
p.set_ylabel('Price_growth', fontsize=14)
p.set_title('Price Growth vs Income Growth', fontsize=18)

for i in range(0,result_df.shape[0]):
     p.text(result_df.Percentage[i], result_df.Price_growth[i], 
     result_df.index[i], horizontalalignment='left', 
     size='medium', color='blue', weight='semibold')