日経225の全銘柄で、配当日の一ヶ月前に買って、直前に売ったら儲かるか、過去5年分で検証してみた。
日経225の全銘柄で、配当日の一ヶ月前に買って、直前に売ったら儲かるか、過去5年分で検証してみた。
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import yfinance as yf import pandas as pd import sqlite3 import matplotlib.pyplot as plt import time # データベースファイルのパス db_path = 'profit_loss.db' # 日経225銘柄リスト ticker_symbols = ['7272','2914','4452','4911','6326','3659','4704','5108','1605','2502','3405','3436','4631','8804','2503','4004','4324','4519','5101','5201','5301','6302','6361','6723','7751','2501','4578','5214','9147','1928','6506','8233','9602','3086','3382','8267','7741','8697','4689','6861','8795','8411','9064','4568','5831','7832','2768','3099','6301','6758','6902','7733','8035','9104','3401','4005','4523','4902','5406','5801','6501','6594','8031','8253','9101','9434','9613','9984','4061','4503','4507','5019','6103','6178','6473','6701','6762','7004','7186','7270','8316','9020','9433','4042','5401','5711','5713','6471','6645','6770','6857','6988','7013','7211','7267','7269','8001','8002','8015','8053','8058','8304','8630','8766','9001','9022','9107','9432','9532','9843','2432','4043','5333','6702','7735','7752','7974','8309','8591','8725','9021','9201','2802','2871','3402','3407','4183','4188','4506','5332','5411','5541','5631','6098','6305','6504','6752','6841','6971','7205','8252','8306','8308','8750','9735','1332','1801','2002','2282','3289','4021','4502','4543','5020','5232','5802','6113','6472','6724','6753','7012','7202','7261','8331','8604','9202','1721','1802','1803','1808','1812','1925','1963','2269','2413','2531','2801','3861','3863','4041','4208','4661','5706','5714','6273','6367','6479','6503','6674','6952','6981','7011','7731','7762','7911','7912','8354','8601','8801','8802','9005','9007','9008','9009','9301','9501','9502','9503','9531','9766','4063','4901','5233','6954','6976','7201','7203','7951','8830','5803','4385','6920','9983','4751','4151'] # SQLiteデータベースに接続 conn = sqlite3.connect(db_path) # 各銘柄について損益を計算 for ticker_symbol in ticker_symbols: ticker = yf.Ticker(ticker_symbol + '.T') # 過去5年間の配当データと株価データを取得 dividends = ticker.dividends.last('5YE') for ex_date in dividends.index: buy_date = ex_date - pd.Timedelta(days=30) sell_date = ex_date - pd.Timedelta(days=1) # 株価データを取得 hist = ticker.history(start=buy_date, end=sell_date) time.sleep(1) # 購入日と売却日の株価を取得 if not hist.empty and len(hist) > 1: buy_price = hist.iloc[0]['Close'] sell_price = hist.iloc[-1]['Close'] profit = (sell_price - buy_price) / buy_price * 100 # 損益率(%) # 損益率と配当支払日をデータベースに保存 pd.DataFrame({ 'Ticker': [ticker_symbol], 'AvgProfit': [profit], 'Date': [ex_date.strftime('%Y-%m-%d')] # 日付を文字列形式で保存 }).to_sql('profit_loss', conn, if_exists='append', index=False) # データベース接続を閉じる conn.close() # データベースから損益データを読み込むための関数 def load_data(db_path): conn = sqlite3.connect(db_path) df = pd.read_sql('SELECT * FROM profit_loss', conn) conn.close() df['Year'] = pd.to_datetime(df['Date']).dt.year return df # データベースから損益データを読み込む profit_loss_df = load_data(db_path) # 銘柄ごとにプロット plt.figure(figsize=(12, 8)) for ticker in profit_loss_df['Ticker'].unique(): data = profit_loss_df[profit_loss_df['Ticker'] == ticker] years = data['Year'].unique() profits_by_year = data.groupby('Year')['AvgProfit'].mean().reindex(years).fillna(0) # 年ごとの損益を折れ線グラフでプロット plt.plot(profits_by_year.index, profits_by_year.values, '-o', label=f'{ticker} Yearly') plt.xlabel('Year / Ticker Symbol') plt.ylabel('Profit (%)') plt.title('Yearly Profit/Loss for Each Ticker Over 5 Years') plt.xticks(rotation=45) # X軸のラベルを45度回転 plt.legend() plt.tight_layout() plt.show() |
過去5年で、日経２２５の全銘柄を配当日の一ヶ月前に買っ 続きを読む 日経225の全銘柄で、配当日の一ヶ月前に買って、直前に売ったら儲かるか、過去5年分で検証してみた。