やりたいこと

yahoo_finance_api2の関数(引数、output)の確認
- pandas_datareaderとの違い
- コピペだけじゃなくて何をしているのかをできるだけ見てみる
可視化の方法
- グラフ系のライブラリ・関数の確認

参考URL

【日本株対応】Pythonで株価のローソク足データを取得する方法まとめ【CSV、ライブラリ、スクレイピング】
- https://myfrankblog.com/how_to_get_stock_price_with_python/
GitHub - pkout_yahoo_finance_api2_ API to interact with Yahoo Finance
- https://github.com/pkout/yahoo_finance_api2
【Pythonコード解説】yahoo_finance_api2で日本株の株価データを取得する
- https://myfrankblog.com/stock_price_with_yahoo_finance_api2/
Pythonのmpl_financeで株価のローソク足チャートを描く方法【コード解説】
- https://myfrankblog.com/stock_price_chart_with_python_mpl_finance/
【Matplotlibの基礎】Pythonでグラフ、図を表示する方法｜株チャートを表示 _ こがたぶろぐ
- https://kgt-blog.com/tech-32/3342/

yahoo_finance_api2の特徴

メリット

細かいデータ(分足・時間足・日足・週足・月足)の取得が可能
リアルタイムに近いデータが取得可能　★ただし20分以上遅延あり
日本株に対応

デメリット

pandas_datareaderに比べると複雑
調整終値がない　←調整終値とは？？
短期間で乱発すると制限がかけられる可能性あり　←どれくらいが限度なのか確認必要

コーディング

株価の取得(1976：明星工)

とりあえず株価を取得してみる

import sys
#sys: Pythonのインタプリタや実行環境に関する情報を扱うためのライブラリ

import pandas as pd

from yahoo_finance_api2 import share
from yahoo_finance_api2.exceptions import YahooFinanceError

my_share = share.Share('1976.T')
symbol_data = None

try:
    symbol_data = my_share.get_historical(
        share.PERIOD_TYPE_DAY, 10,            #期間の指定 →10日間
        share.FREQUENCY_TYPE_MINUTE, 5)       #頻度の指定　→5分足
except YahooFinanceError as e:
    print(e.message)
    sys.exit(1)

print(symbol_data)

結果

{'timestamp': [1695081600000, 1695081900000, 1695082200000, 1695082500000, 1695082800000, 1695083100000, 1695083400000, 1695083700000, 1695084000000, 1695084300000, 1695084600000, 1695084900000, 1695085200000, 1695085500000, 1695085800000, 1695086100000, 1695086400000, 1695086700000, 1695087000000, 1695087300000, 1695087600000, 1695087900000, 1695088200000, 1695088500000, 1695088800000, 1695089100000, 1695089400000, 1695089700000, 1695090000000, 1695090300000, 1695090600000, 1695090900000, 1695091200000, 1695091500000, 1695091800000, 1695092100000, 1695092400000, 1695092700000, 1695093000000, 1695093300000, 1695093600000, 1695093900000, 1695094200000, 1695094500000, 1695094800000, 1695095100000, 1695095400000, 1695095700000, 1695096000000, 1695096300000, 1695096600000, 1695096900000, 1695097200000, 1695097500000, 1695097800000, 1695098100000, 1695098400000, 1695098700000, 1695099000000, 1695099300000, 1695099600000, 1695099900000, 1695100200000, 1695100500000, 1695100800000, 1695101100000, 1695101400000, 1695101700000, 1695102000000, 1695102300000, 1695102600000, 1695102900000, 1695168000000, 1695168300000, 1695168600000, 1695168900000, 1695169200000, 1695169500000, 1695169800000, 1695170100000, 1695170400000, 1695170700000, 1695171000000, 1695171300000, 1695171600000, 1695171900000, 1695172200000, 1695172500000, 1695172800000, 1695173100000, 1695173400000, 1695173700000, 1695174000000, 1695174300000, 1695174600000, 1695174900000, 1695175200000, 1695175500000, 1695175800000, 1695176100000, 1695176400000, 1695176700000, 1695177000000, 1695177300000, 1695177600000, 1695177900000, 1695178200000, 1695178500000, 1695178800000, 1695179100000, 1695179400000, 1695179700000, 1695180000000, 1695180300000, 1695180600000, 1695180900000, 1695181200000, 1695181500000, 1695181800000, 1695182100000, 1695182400000, 1695182700000, 1695183000000, 1695183300000, 1695183600000, 1695183900000, 1695184200000, 1695184500000, 1695184800000, 1695185100000, 1695185400000, 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1029.0, 1030.0, 1032.0, 1031.0, 1031.0, 1031.0, 1015.0, 1029.0, 1032.0, 1033.0, 1035.0, 1032.0, 1032.0, 1033.0, 1035.0, 1034.0, None, None, 1035.0, 1037.0, 1036.0, 1036.0, None, None, None, 1033.0, 1033.0, None, 1033.0, None, None, 1031.0, 1031.0, None, 1029.0, 1029.0, 1029.0, 1029.0, None, None, None, None, None, None, None, None, None, None, 1029.0, 1033.0, 1035.0, 1034.0, 1034.0, 1033.0, 1034.0, 1034.0, 1033.0, 1034.0, 1033.0, None, 1032.0, 1031.0, None, 1032.0, 1029.0, 1027.0, None, 1029.0, 1031.0, 1031.0, 1030.0, 1030.0, 1030.0, 1031.0, 1033.0, 1034.0, 1032.0, 1031.0, 1016.0, 1012.0, 1015.0, 1015.0, 1013.0, 1014.0, 1015.0, 1014.0, 1016.0, 1013.0, 1015.0, 1016.0, 1018.0, 1019.0, 1019.0, 1019.0, 1019.0, 1019.0, 1020.0, 1022.0, 1022.0, 1021.0, 1020.0, 1019.0, 1019.0, 1017.0, 1018.0, 1022.0, 1021.0, 1018.0, 1018.0, 1018.0, None, None, None, None, None, None, None, None, None, None, 1018.0, 1022.0, 1023.0, 1021.0, 1021.0, 1022.0, 1022.0, 1025.0, 1023.0, 1026.0, 1024.0, 1025.0, 1023.0, None, 1022.0, 1024.0, 1025.0, 1026.0, 1026.0, 1027.0, 1029.0, 1028.0, 1028.0, 1028.0, 1028.0, 1029.0, 1030.0, 1029.0, 1027.0, 1025.0, 1021.0, 1024.0, 1023.0, 1020.0, 1026.0, 1026.0, 1027.0, 1028.0, 1027.0, 1026.0, 1027.0, 1028.0, None, 1028.0, 1029.0, 1029.0, 1027.0, 1029.0, 1028.0, None, 1028.0, 1030.0, 1028.0, 1028.0, None, 1028.0, 1028.0, None, 1027.0, 1026.0, 1025.0, 1025.0, None, None, None, None, None, None, None, None, None, None, 1025.0, 1026.0, 1026.0, None, 1026.0, None, 1027.0, None, 1027.0, None, 1027.0, None, 1026.0, 1026.0, 1024.0, 1023.0, 1023.0, None, 1023.0, 1024.0, None, 1025.0, None, 1025.0, 1026.0, 1027.0, 1027.0, 1028.0, 1028.0, 1029.0], 'close': [1055.0, 1050.0, 1051.0, 1046.0, 1046.0, 1049.0, 1049.0, 1048.0, 1048.0, 1047.0, 1049.0, 1045.0, 1047.0, 1047.0, 1047.0, 1046.0, None, 1045.0, 1043.0, 1044.0, 1045.0, 1044.0, 1045.0, 1044.0, 1044.0, 1047.0, 1047.0, 1047.0, 1047.0, 1046.0, 1045.0, 1045.0, None, None, None, None, None, None, None, None, None, None, 1049.0, 1051.0, 1049.0, 1049.0, 1048.0, 1048.0, 1048.0, 1048.0, 1047.0, 1047.0, 1050.0, 1051.0, 1051.0, 1052.0, 1051.0, None, 1052.0, 1053.0, 1052.0, None, 1053.0, 1053.0, 1053.0, 1054.0, 1055.0, 1055.0, 1056.0, 1058.0, 1058.0, 1060.0, 1052.0, 1051.0, 1049.0, 1046.0, 1042.0, 1040.0, 1040.0, 1039.0, 1040.0, 1042.0, 1041.0, 1041.0, 1041.0, 1042.0, 1041.0, 1040.0, 1039.0, 1039.0, 1037.0, 1039.0, 1039.0, 1039.0, None, 1038.0, 1037.0, 1036.0, 1035.0, 1034.0, 1033.0, 1032.0, 1031.0, 1031.0, None, None, None, None, None, None, None, None, None, None, 1029.0, 1031.0, 1030.0, 1029.0, 1029.0, 1028.0, 1028.0, 1030.0, 1030.0, 1029.0, 1030.0, 1030.0, 1031.0, 1029.0, 1029.0, 1030.0, 1029.0, 1029.0, 1029.0, 1030.0, 1029.0, 1028.0, 1028.0, 1029.0, 1030.0, 1032.0, 1032.0, 1031.0, 1033.0, 1031.0, 1033.0, 1029.0, 1035.0, 1035.0, 1036.0, 1032.0, 1035.0, 1035.0, 1035.0, 1036.0, None, None, 1037.0, 1038.0, 1036.0, 1037.0, None, None, None, 1033.0, 1035.0, None, 1033.0, None, None, 1031.0, 1031.0, None, 1029.0, 1029.0, 1029.0, 1029.0, None, None, None, None, None, None, None, None, None, None, 1034.0, 1035.0, 1035.0, 1034.0, 1034.0, 1033.0, 1034.0, 1034.0, 1034.0, 1034.0, 1033.0, None, 1032.0, 1031.0, None, 1032.0, 1029.0, 1031.0, None, 1029.0, 1031.0, 1031.0, 1030.0, 1031.0, 1031.0, 1032.0, 1033.0, 1034.0, 1032.0, 1031.0, 1016.0, 1017.0, 1017.0, 1016.0, 1017.0, 1015.0, 1015.0, 1014.0, 1016.0, 1013.0, 1015.0, 1017.0, 1019.0, 1020.0, 1019.0, 1019.0, 1019.0, 1019.0, 1022.0, 1022.0, 1022.0, 1021.0, 1020.0, 1019.0, 1020.0, 1019.0, 1021.0, 1022.0, 1022.0, 1019.0, 1018.0, 1018.0, None, None, None, None, None, None, None, None, None, None, 1025.0, 1024.0, 1023.0, 1021.0, 1022.0, 1022.0, 1024.0, 1026.0, 1024.0, 1026.0, 1024.0, 1025.0, 1023.0, None, 1024.0, 1024.0, 1025.0, 1027.0, 1026.0, 1030.0, 1029.0, 1028.0, 1028.0, 1029.0, 1029.0, 1030.0, 1031.0, 1030.0, 1027.0, 1026.0, 1021.0, 1024.0, 1023.0, 1026.0, 1027.0, 1026.0, 1028.0, 1028.0, 1027.0, 1028.0, 1027.0, 1028.0, None, 1029.0, 1029.0, 1029.0, 1030.0, 1029.0, 1028.0, None, 1029.0, 1030.0, 1029.0, 1029.0, None, 1028.0, 1028.0, None, 1027.0, 1026.0, 1025.0, 1025.0, None, None, None, None, None, None, None, None, None, None, 1026.0, 1026.0, 1026.0, None, 1026.0, None, 1027.0, None, 1027.0, None, 1027.0, None, 1026.0, 1026.0, 1024.0, 1023.0, 1025.0, None, 1023.0, 1025.0, None, 1025.0, None, 1026.0, 1026.0, 1027.0, 1028.0, 1029.0, 1029.0, 1029.0], 'volume': [0, 1800, 2900, 2500, 1400, 1100, 2100, 800, 200, 1000, 1700, 1400, 2100, 100, 1100, 900, None, 1100, 1000, 1300, 700, 500, 900, 500, 2100, 1500, 100, 100, 1200, 1200, 100, 0, None, None, None, None, None, None, None, None, None, None, 49600, 700, 2000, 100, 500, 300, 700, 100, 1300, 1700, 1100, 200, 1300, 300, 1300, None, 800, 700, 1100, None, 1000, 300, 1100, 500, 3100, 3900, 1600, 1600, 1900, 3800, 4900, 3000, 1900, 600, 3000, 2800, 1600, 300, 1500, 3000, 300, 1400, 900, 200, 500, 1400, 1400, 500, 700, 2600, 300, 2500, None, 600, 300, 1200, 400, 500, 1900, 900, 500, 0, None, None, None, None, None, None, None, None, None, None, 45200, 0, 46800, 900, 1500, 200, 4500, 1000, 100, 1500, 1000, 100, 1200, 600, 200, 1100, 200, 600, 200, 3700, 400, 700, 500, 1600, 1200, 1200, 200, 200, 4000, 3400, 3900, 100, 300, 700, 1100, 1100, 1000, 3500, 100, 600, None, None, 700, 700, 100, 700, None, None, None, 500, 900, None, 500, None, None, 1000, 200, None, 2000, 300, 100, 0, None, None, None, None, None, None, None, None, None, None, 22300, 700, 100, 300, 100, 1200, 100, 700, 300, 400, 700, None, 2800, 300, None, 100, 600, 2600, None, 100, 500, 400, 300, 1900, 2400, 800, 500, 1800, 1700, 1000, 4400, 6100, 1200, 1100, 2100, 600, 600, 700, 500, 700, 0, 19900, 300, 700, 300, 100, 200, 1100, 200, 200, 800, 300, 300, 800, 500, 900, 3300, 200, 300, 1500, 600, 0, None, None, None, None, None, None, None, None, None, None, 37000, 900, 700, 800, 500, 500, 1200, 300, 1500, 100, 200, 200, 800, None, 1300, 400, 900, 200, 300, 5300, 900, 600, 200, 2100, 1200, 1700, 4800, 1800, 2800, 2300, 7700, 200, 700, 3500, 1100, 700, 300, 1900, 100, 300, 1300, 300, None, 0, 20600, 100, 1400, 300, 500, None, 300, 800, 800, 200, None, 300, 100, None, 300, 500, 700, 0, None, None, None, None, None, None, None, None, None, None, 28500, 900, 200, None, 100, None, 900, None, 2200, None, 300, None, 400, 100, 2400, 1900, 1400, None, 1000, 800, None, 700, None, 300, 1500, 100, 1100, 1200, 4300, 2300]}

データはjson形式
pandas_datareaderに比べると確かに複雑

表示形式を変換(DataFrame、Datatime、日本時間)

#取得データの形式を変換(json→DataFrame)
df = pd.DataFrame(symbol_data)

#Datetimeの列を追加(timestamp→datetime変換し追加)
df["datetime"] = pd.to_datetime(df.timestamp, unit="ms")

#日本時間へ変換
import datetime 
df["datetime_JST"] = df["datetime"] + datetime.timedelta(hours=9)

df

結果

	timestamp	open	high	low	close	volume	datetime	datetime_JST
0	1695081600000	1060.0	1060.0	1051.0	1055.0	0.0	2023-09-19 00:00:00	2023-09-19 09:00:00
1	1695081900000	1055.0	1056.0	1050.0	1050.0	1800.0	2023-09-19 00:05:00	2023-09-19 09:05:00
2	1695082200000	1050.0	1051.0	1049.0	1051.0	2900.0	2023-09-19 00:10:00	2023-09-19 09:10:00
3	1695082500000	1049.0	1049.0	1046.0	1046.0	2500.0	2023-09-19 00:15:00	2023-09-19 09:15:00
4	1695082800000	1045.0	1047.0	1044.0	1046.0	1400.0	2023-09-19 00:20:00	2023-09-19 09:20:00
...	...	...	...	...	...	...	...	...
355	1695620100000	1027.0	1027.0	1027.0	1027.0	100.0	2023-09-25 05:35:00	2023-09-25 14:35:00
356	1695620400000	1027.0	1028.0	1027.0	1028.0	1100.0	2023-09-25 05:40:00	2023-09-25 14:40:00
357	1695620700000	1029.0	1029.0	1028.0	1029.0	1200.0	2023-09-25 05:45:00	2023-09-25 14:45:00
358	1695621000000	1028.0	1029.0	1028.0	1029.0	4300.0	2023-09-25 05:50:00	2023-09-25 14:50:00
359	1695621300000	1029.0	1030.0	1029.0	1029.0	2300.0	2023-09-25 05:55:00	2023-09-25 14:55:00
360 rows × 8 columns

日付情報はtimestampなのでdatatimeに変換が必要
日時情報はUTC。日本時間で利用するには変換が必要

取得単位を変えてみる(期間、頻度)


try:
    symbol_data2 = my_share.get_historical(
        share.PERIOD_TYPE_MONTH, 1,            #期間の指定 →1か月
        share.FREQUENCY_TYPE_DAY, 1)       #頻度の指定　→日足
except YahooFinanceError as e:
    print(e.message)
    sys.exit(1)

df2 = pd.DataFrame(symbol_data)
df2["datetime"] = pd.to_datetime(df.timestamp, unit="ms")
df2["datetime_JST"] = df["datetime"] + datetime.timedelta(hours=9)

df2

結果

timestamp	open	high	low	close	volume	datetime	datetime_JST
0	1695081600000	1060.0	1060.0	1051.0	1055.0	0.0	2023-09-19 00:00:00	2023-09-19 09:00:00
1	1695081900000	1055.0	1056.0	1050.0	1050.0	1800.0	2023-09-19 00:05:00	2023-09-19 09:05:00
2	1695082200000	1050.0	1051.0	1049.0	1051.0	2900.0	2023-09-19 00:10:00	2023-09-19 09:10:00
3	1695082500000	1049.0	1049.0	1046.0	1046.0	2500.0	2023-09-19 00:15:00	2023-09-19 09:15:00
4	1695082800000	1045.0	1047.0	1044.0	1046.0	1400.0	2023-09-19 00:20:00	2023-09-19 09:20:00
...	...	...	...	...	...	...	...	...
355	1695620100000	1027.0	1027.0	1027.0	1027.0	100.0	2023-09-25 05:35:00	2023-09-25 14:35:00
356	1695620400000	1027.0	1028.0	1027.0	1028.0	1100.0	2023-09-25 05:40:00	2023-09-25 14:40:00
357	1695620700000	1029.0	1029.0	1028.0	1029.0	1200.0	2023-09-25 05:45:00	2023-09-25 14:45:00
358	1695621000000	1028.0	1029.0	1028.0	1029.0	4300.0	2023-09-25 05:50:00	2023-09-25 14:50:00
359	1695621300000	1029.0	1030.0	1029.0	1029.0	2300.0	2023-09-25 05:55:00	2023-09-25 14:55:00
360 rows × 8 columns

期間指定には上限あり。100日指定→4か月指定など大きい単位で指定する

グラフで表示する

グラフ表示のためのDataFrame調整

import matplotlib.pyplot as plt
import mplfinance as mpf
import pandas as pd
from datetime import datetime
from datetime import timedelta

df2 = pd.DataFrame(symbol_data.values(), index=symbol_data.keys()).T    #★ここは要確認
df2.timestamp = pd.to_datetime(df2.timestamp, unit='ms')
df2.index = pd.DatetimeIndex(df2.timestamp, name='timestamp').tz_localize('UTC').tz_convert('Asia/Tokyo')

# データの形を調整(mplfinanceモジュールに合わせた列名)
df2 = df2.rename(columns = {
    'open' : 'Open',
    'high' : 'High',
    'low' : 'Low',
    'close' : 'Close',
    'volume' : 'Volume'
})

df2

結果

	timestamp	Open	High	Low	Close	Volume
timestamp						
2023-09-19 09:00:00+09:00	2023-09-19 00:00:00	1060.0	1060.0	1051.0	1055.0	0.0
2023-09-19 09:05:00+09:00	2023-09-19 00:05:00	1055.0	1056.0	1050.0	1050.0	1800.0
2023-09-19 09:10:00+09:00	2023-09-19 00:10:00	1050.0	1051.0	1049.0	1051.0	2900.0
2023-09-19 09:15:00+09:00	2023-09-19 00:15:00	1049.0	1049.0	1046.0	1046.0	2500.0
2023-09-19 09:20:00+09:00	2023-09-19 00:20:00	1045.0	1047.0	1044.0	1046.0	1400.0
...	...	...	...	...	...	...
2023-09-25 14:35:00+09:00	2023-09-25 05:35:00	1027.0	1027.0	1027.0	1027.0	100.0
2023-09-25 14:40:00+09:00	2023-09-25 05:40:00	1027.0	1028.0	1027.0	1028.0	1100.0
2023-09-25 14:45:00+09:00	2023-09-25 05:45:00	1029.0	1029.0	1028.0	1029.0	1200.0
2023-09-25 14:50:00+09:00	2023-09-25 05:50:00	1028.0	1029.0	1028.0	1029.0	4300.0
2023-09-25 14:55:00+09:00	2023-09-25 05:55:00	1029.0	1030.0	1029.0	1029.0	2300.0
360 rows × 6 columns

単純にプロットする

mpf.plot(df2)

～とりあえず今日はここまで～

次はローソク足チャートの表示
移動平均などの指標の入れ方(および指標の意味理解)