init

stockpredictor/crawler/toSQLite/Crawler.py

@@ -19,8 +19,6 @@ print("[KOSPI 상장기업 재무제표 다운로드]")
 crawler.crawl_fnguide(inFnguideFileName)
 """
 
-
-
 crawler = MetaCrawler()
 print("\n[환율 (USD, JPY, EUR, CNY), 원유 (WTI), 국제금]")
 inFileName = PROJECT_HOME + '/resources/meta_1.db'
@@ -44,6 +42,14 @@ crawler = StockCrawler()
 crawler.crawl_etf_stocks(inFileName)
 crawler.crawl_stocks(inFileName)
 
+print("\n[지수 저장]")
+kospiFileName = PROJECT_HOME + '/resources/kospi.tsv'
+kosdakFileName = PROJECT_HOME + '/resources/kosdak.tsv'
+outFileName = PROJECT_HOME + '/resources/stock.db'
+crawler = StockCrawler()
+crawler.saveIndex("KOSPI", kospiFileName, outFileName)
+crawler.saveIndex("KOSDAK", kosdakFileName, outFileName)
+
 print("\n[종목 분석]")
 # S: 분석까지 진행
 inFileName = PROJECT_HOME + '/resources/stock.db'

stockpredictor/crawler/toSQLite/StockCrawler.py

@@ -223,12 +223,10 @@ class StockCrawler:
 		###print (df.head())
 
 		# 한글로 된 컬럼명을 영어로 바꿔줌
-		df = df.rename(columns={'날짜': 'date', '종가': 'close', '전일비': 'diff', '시가': 'open', '고가': 'high', '저가': 'low',
-								'거래량': 'volume'})
+		df = df.rename(columns={'날짜': 'date', '종가': 'close', '전일비': 'diff', '시가': 'open', '고가': 'high', '저가': 'low', '거래량': 'volume'})
 
 		# 데이터의 타입을 int형으로 바꿔줌
-		df[['close', 'diff', 'open', 'high', 'low', 'volume']] = df[
-			['close', 'diff', 'open', 'high', 'low', 'volume']].astype(int)
+		df[['close', 'diff', 'open', 'high', 'low', 'volume']] = df[['close', 'diff', 'open', 'high', 'low', 'volume']].astype(int)
 
 		# 컬럼명 'date'의 타입을 date로 바꿔줌
 		df['date'] = pd.to_datetime(df['date'])
@@ -350,3 +348,65 @@ class StockCrawler:
 					cursor.execute("UPDATE " + tableName + " SET PRICE=? WHERE CODE=?", (text, stock["CODE"]))
 
 		return
+
+
+	def saveIndex(self, code, inFileName, outFileName):
+		tableName = 'stock'
+		conn = sqlite3.connect(outFileName)
+		cursor = conn.cursor()
+		cursor.execute("CREATE TABLE IF NOT EXISTS " + tableName + " (CODE text PRIMARY KEY, NAME text, PRICE text)")
+
+		stock = {"NAME": code, "CODE": code, "PRICE": []}
+
+		lastDay = ""
+		cursor.execute('SELECT * FROM ' + tableName + ' WHERE CODE=?', (stock["CODE"],))
+		result = cursor.fetchone()
+		if result != None:
+			stock["PRICE"] = json.loads(result[2])
+			lastDay = stock["PRICE"][len(stock["PRICE"]) - 1]["DATE"]
+
+		with open(inFileName, "r", encoding="utf-8") as inFp:
+			for line in inFp:
+				line = line.strip()
+				if line[0] == "#":
+					continue
+
+				arr = line.split("\t")
+				if arr[0] == lastDay:
+					cursor.close()
+					conn.close()
+					return
+
+				price = {"DATE": arr[0], "close": float(arr[1]), "diff": float(arr[6].replace("%", "")), "open": float(arr[2]), "high": float(arr[3]), "low": float(arr[4]), "volume": 0}
+				price['avg3'] = 0
+				price['avg5'] = 0
+				price['avg7'] = 0
+				price['avg10'] = 0
+				price['avg20'] = 0
+				price['avg30'] = 0
+				price['avg60'] = 0
+				price['avg90'] = 0
+				price['avg100'] = 0
+				price['avg120'] = 0
+				price['avg150'] = 0
+				price['avg180'] = 0
+				price['avg200'] = 0
+				price['avg240'] = 0
+				stock["PRICE"].append(price)
+
+		stock["PRICE"] = sorted(stock["PRICE"], key=lambda x: x['DATE'])
+		self.get_moving_avg(stock)
+
+		text = json.dumps(stock['PRICE'], ensure_ascii=False)
+
+		cursor.execute('SELECT * FROM ' + tableName + ' WHERE CODE=?', (stock["CODE"],))
+		result = cursor.fetchone()
+		if result == None:
+			cursor.execute("INSERT INTO " + tableName + "(CODE, NAME, PRICE, MACD, STOCHASTIC, ICHIMOKU, RSI) VALUES(?, ?, ?, ?, ?, ?, ?)", (stock["CODE"], stock["NAME"], text, "[{}]", "[{}]", "[{}]", "[{}]"))
+		else:
+			cursor.execute("UPDATE " + tableName + " SET PRICE=?, MACD=?, STOCHASTIC=?, ICHIMOKU=?, RSI=? WHERE CODE=?", (text, "[{}]", "[{}]", "[{}]", "[{}]", stock["CODE"]))
+
+		conn.commit()
+		cursor.close()
+		conn.close()
+		return