init

stockpredictor/crawler/sQLite/Crawler.py

@@ -16,7 +16,7 @@ PROJECT_HOME = os.path.join(os.path.dirname(os.path.join(os.path.dirname(os.path
 
 start = time.time()
 stockFileName = PROJECT_HOME + '/resources/stock.db'
-
+"""
 # 재무제표는 3개월마다 다운로드를 한다.
 fnGuideCrawler = FnGuideCrawler()
 print("[KOSPI 상장기업 재무제표 다운로드]")
@@ -49,7 +49,9 @@ print("\n[종목 분석]")
 inFileName = PROJECT_HOME + '/resources/stock.db'
 analyzerSqlite = AnalyzerSqlite(PROJECT_HOME, stockFileName)
 analyzerSqlite.analyze()
+"""
 
+analyzerSqlite = AnalyzerSqlite(PROJECT_HOME, stockFileName)
 print("\n[종목 결정]")
 day = datetime.datetime.today().strftime("%Y%m%d")
 outPath = PROJECT_HOME + "/resources/analysis/" + day

stockpredictor/crawler/sQLite/StockCrawler.py

@@ -1,5 +1,5 @@
 # https://bigdata-sk.tistory.com/10
-
+from datetime import datetime, timedelta
 import os
 import pandas as pd
 import re
@@ -223,7 +223,6 @@ class StockCrawler:
 
 		start_time = time.time()
 
-
 		pd.options.display.float_format = '{:.4f}'.format
 		pd.set_option('display.max_columns', None)
 
@@ -236,25 +235,51 @@ class StockCrawler:
 					 'AAPL': 'Apple Inc.','MSFT': 'Microsoft Corporation','GOOG': 'Alphabet Inc.','NVDA': 'NVIDIA Corporation',
 					 'FB': 'Meta Platforms, Inc.', 'ASML': 'ASML Holding','AVGO': 'Broadcom Inc.','COST': 'Costco Wholesale Corporation',
 					 'INTC': 'Intel Corporation','AMD': 'Advanced Micro Devices, Inc.','QCOM': 'QUALCOMM Incorporated',
-					 'HON': 'Honeywell International Inc.','VZ': 'Verizon Communications Inc.','EBAY': 'eBay Inc.',
-					 'TSM': 'Taiwan Semiconductor Manufacturing','V': 'Visa Inc.','WMT': 'Walmart Inc.','BABA': 'Alibaba Group Holding',
-					 'KO': 'Coca-Cola','DIS': 'Walt Disney','T': 'AT&T Inc.'}
-		df_price = pd.DataFrame(columns=usa_stock_code)
-		for ticker in usa_stock_code:
-			df_price[ticker] = pdr.get_data_yahoo(ticker)['Adj Close']
-		df_usa_stock = df_price.dropna(axis=0)
+					 'HON': 'Honeywell International Inc.','TSM': 'Taiwan Semiconductor Manufacturing','V': 'Visa Inc.',
+					 'WMT': 'Walmart Inc.','BABA': 'Alibaba Group Holding','KO': 'Coca-Cola','DIS': 'Walt Disney','T': 'AT&T Inc.'}
 
-		for idx, item_code in enumerate(df_usa_stock):
-			stock_data = []
-			stock = df_usa_stock[item_code]
-			stockDict = stock.to_dict()
-			stockList = list(stock.to_dict())
-			for i, ymd in enumerate(stockDict):
-				if i > 0:
-					diff = stockDict[ymd] - stockDict[stockList[i-1]]
+		conn = sqlite3.connect(inFileName)
+		cursor = conn.cursor()
+
+		usa_stock = {}
+		for ticker in usa_stock_code:
+
+			cursor.execute('SELECT * FROM ' + tableName + ' WHERE CODE=?', (ticker,))
+
+			result = cursor.fetchone()
+			if result == None:
+				start = pd.to_datetime('2017-01-01')
+				end = pd.to_datetime(datetime.today().strftime('%Y-%m-%d'))
 			else:
-					diff = stockDict[ymd]
-				stock_data.append({'CODE':item_code, 'NAME':item_name[item_code], 'ymd': ymd.strftime('%Y.%m.%d'), 'close': round(stockDict[ymd], 2), 'diff': round(diff, 2), 'open': round(stockDict[ymd], 2), 'high': round(stockDict[ymd], 2), 'low': round(stockDict[ymd], 2), 'volume': 0})
+				start = (datetime.today() - timedelta(days=300)).strftime('%Y-%m-%d')
+				end = pd.to_datetime(datetime.today().strftime('%Y-%m-%d'))
+
+			data = pdr.get_data_yahoo(ticker, start, end)
+			usa_stock[ticker] = {
+				'close': data['Close'].to_dict(),
+				'open': data['Open'].to_dict(),
+				'high': data['High'].to_dict(),
+				'low': data['Low'].to_dict(),
+				'volume': data['Volume'].to_dict()
+			}
+		cursor.close()
+		conn.close()
+
+		dateList = list(usa_stock['SQQQ']['close'])
+		for idx, item_code in enumerate(usa_stock):
+			stock_data = []
+			stock = usa_stock[item_code]
+			for i, ymd in enumerate(dateList):
+				if i > 0:
+					diff = stock['close'][ymd] - stock['close'][dateList[i-1]]
+				else:
+					diff = stock['close'][ymd]
+
+				stock_data.append({
+					'CODE':item_code, 'NAME':item_name[item_code], 'ymd': ymd.strftime('%Y.%m.%d'),
+					'close': round(stock['close'][ymd], 2), 'diff': round(diff, 2), 'open': round(stock['open'][ymd], 2),
+					'high': round(stock['high'][ymd], 2), 'low': round(stock['low'][ymd], 2), 'volume': stock['volume'][ymd]
+				})
 
 			stock_data = reversed(stock_data)