464 lines
16 KiB
Python
464 lines
16 KiB
Python
# https://bigdata-sk.tistory.com/10
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import pandas as pd
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import re
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import json
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import sqlite3
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import requests
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import math
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import time
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from time import sleep
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class Queue(object):
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def __init__(self, max):
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self.queue = []
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self.max = max
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def dequeue(self):
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length = len(self.queue)
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if length == 0 or length < self.max:
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return -1
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return self.queue.pop(0)
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def enqueue(self, n):
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length = len(self.queue)
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if length == self.max:
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self.dequeue()
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self.queue.append(n)
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pass
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def sum(self):
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sum = 0
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for item in self.queue:
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sum += item
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return sum
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def avg(self):
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length = len(self.queue)
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total = self.sum()
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return round(total / length)
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def print(self):
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print(self.sum(), self.queue)
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# 닐짜 형식으로 바뀐 this_date값을 확인 가능
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# 읽어온 날짜 정보를 date형식으로 바꿀 일이 계속 생기므로 이 기능을 함수로 정의해줌.
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# 함수명은 date_format()
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class StockCrawler:
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header = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36'}
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historical_prices = None
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special_pattern = None
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fnGuideCrawler = None
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limit_page_count = 10000
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def __init__(self):
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self.historical_prices = dict()
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self.special_pattern = (
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'[', '!', '@', '#', '$', '%', '^', '&', '*', '(', ')', ',', '.', '?', '"', ':', ';', '{', '}', '|', '<', '>',
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']', '+', '-', '/', '=', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9')
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return
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def clean_str(self, string):
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string = re.sub(r"\\", " ", string)
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string = re.sub(r"\'", " ", string)
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string = re.sub(r"\"", " ", string)
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string = re.sub(r"`", " ", string)
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string = re.sub(r"-", " ", string)
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string = re.sub(r"\(.*?\)", " ", string)
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string = re.sub(r" ", " ", string)
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return string.strip().lower()
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def getStockInfo(self):
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#code_df = pd.read_html('http://kind.krx.co.kr/corpgeneral/corpList.do?method=download&searchType=13', header=0)[0]
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code_df = pd.read_html(requests.get('http://kind.krx.co.kr/corpgeneral/corpList.do?method=download&searchType=13', headers=self.header, timeout=30).text)[0]
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# code_df = pd.read_excel('../resources/stock/상장법인목록.xls')
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# 종목코드가 6자리이기 때문에 6자리를 맞춰주기 위해 설정해줌
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code_df.종목코드 = code_df.종목코드.map('{:06d}'.format)
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# 우리가 필요한 것은 회사명과 종목코드이기 때문에 필요없는 column들은 제외해준다.
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code_df = code_df[['회사명', '종목코드']]
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# 한글로된 컬럼명을 영어로 바꿔준다.
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code_df = code_df.rename(columns={'회사명': 'name', '종목코드': 'code'})
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###print (code_df.head())
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return code_df
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# 종목 이름을 입력하면 종목에 해당하는 코드를 불러와
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# 네이버 금융(http://finance.naver.com)에 넣어줌
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def get_url(self, item_name, code_df):
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code = code_df.query("name=='{}'".format(item_name))['code'].to_string(index=False).strip()
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url = 'http://finance.naver.com/item/sise_day.nhn?code={code}'.format(code=code.strip())
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return code, url
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def date_format(slef, d):
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d = str(d).replace('-', '.')
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#yyyy = int(d.split('.')[0])
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#mm = int(d.split('.')[1])
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#dd = int(d.split('.')[2])
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#this_date = dt.date(yyyy, mm, dd)
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return d
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def getCodeIndex(self, stocks, item_code):
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for i, stock in enumerate(stocks):
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if item_code == stock['CODE']:
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return i
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return -1
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def crawl_etf_stocks(self, inFileName):
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tableName = 'stock'
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conn = sqlite3.connect(inFileName)
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cursor = conn.cursor()
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cursor.execute("CREATE TABLE IF NOT EXISTS " + tableName + " (CODE text PRIMARY KEY, NAME text, PRICE text, MACD text, STOCHASTIC text, ICHIMOKU text, RSI text, BOLINGERBAND text)")
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stocks = []
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stocks.append({"NAME": 'KODEX 코스닥150선물인버스', "CODE": "251340", "PRICE": []})
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stocks.append({"NAME": 'KODEX 코스닥150 레버리지', "CODE": "233740", "PRICE": []})
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stocks.append({"NAME": 'KODEX 200선물인버스2X', "CODE": "252670", "PRICE": []})
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stocks.append({"NAME": 'KODEX 레버리지', "CODE": "122630", "PRICE": []})
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stocks.append({"NAME": 'KODEX 인버스', "CODE": "114800", "PRICE": []})
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stocks.append({"NAME": 'KODEX 중국본토CSI300', "CODE": "283580", "PRICE": []})
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stocks.append({"NAME": 'KODEX 심천ChiNext(합성)', "CODE": "256750", "PRICE": []})
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stocks.append({"NAME": 'KINDEX 블룸버그베트남VN30선물레버리지(H)', "CODE": "371130", "PRICE": []})
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stocks.append({"NAME": 'KODEX 미국S&P바이오(합성)', "CODE": "185680", "PRICE": []})
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stocks.append({"NAME": 'KODEX 미국S&P에너지(합성)', "CODE": "218420", "PRICE": []})
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stocks.append({"NAME": 'KODEX 골드선물(H)', "CODE": "132030", "PRICE": []})
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stocks.append({"NAME": 'KODEX 콩선물(H)', "CODE": "138920", "PRICE": []})
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stocks.append({"NAME": 'KODEX 3대농산물선물(H)', "CODE": "271060", "PRICE": []})
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stocks.append({"NAME": 'KODEX 건설', "CODE": "117700", "PRICE": []})
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stocks.append({"NAME": 'KODEX 헬스케어', "CODE": "266420", "PRICE": []})
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stocks.append({"NAME": 'KODEX 글로벌4차산업로보틱스(합성)', "CODE": "276990", "PRICE": []})
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stocks.append({"NAME": 'KODEX 바이오', "CODE": "244580", "PRICE": []})
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stocks.append({"NAME": 'KODEX 반도체', "CODE": "091160", "PRICE": []})
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stocks.append({"NAME": 'KODEX 보험', "CODE": "140700", "PRICE": []})
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stocks.append({"NAME": 'KODEX 필수소비재', "CODE": "266410", "PRICE": []})
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stocks.append({"NAME": 'KODEX 2차전지산업', "CODE": "305720", "PRICE": []})
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stocks.append({"NAME": 'KODEX 경기소비재', "CODE": "266390", "PRICE": []})
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stocks.append({"NAME": 'KODEX 철강', "CODE": "117680", "PRICE": []})
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stocks.append({"NAME": 'KODEX 에너지화학', "CODE": "117460", "PRICE": []})
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stocks.append({"NAME": 'KODEX 은행', "CODE": "091170", "PRICE": []})
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stocks.append({"NAME": 'TIGER 탄소효율그린뉴딜', "CODE": "376410", "PRICE": []})
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start_time = time.time()
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for i, stock in enumerate(stocks):
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print (i, stock["NAME"], stock["CODE"], (time.time()-start_time), "s")
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start_time = time.time()
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cursor.execute('SELECT * FROM ' + tableName + ' WHERE CODE=?', (stock["CODE"],))
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result = cursor.fetchone()
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if result is not None:
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stock["PRICE"] = json.loads(result[2])
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self.crawl_specific_stock(stock)
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text = json.dumps(stock['PRICE'], ensure_ascii=False)
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cursor.execute('SELECT * FROM ' + tableName + ' WHERE CODE=?', (stock["CODE"],))
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result = cursor.fetchone()
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if result == None:
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cursor.execute("INSERT INTO " + tableName + "(CODE, NAME, PRICE) VALUES(?, ?, ?)", (stock["CODE"], stock["NAME"], text))
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else:
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cursor.execute("UPDATE " + tableName + " SET PRICE=? WHERE CODE=?", (text, stock["CODE"]))
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conn.commit()
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cursor.close()
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conn.close()
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return
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def crawl_stocks(self, inFileName):
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tableName = 'stock'
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conn = sqlite3.connect(inFileName)
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cursor = conn.cursor()
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cursor.execute("CREATE TABLE IF NOT EXISTS " + tableName + " (CODE text PRIMARY KEY, NAME text, PRICE text, MACD text, STOCHASTIC text, ICHIMOKU text, RSI text, BOLINGERBAND text)")
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code_df = self.getStockInfo()
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items = code_df.values
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start_time = time.time()
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idx = 0
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for item in items:
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idx += 1
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item_name = item[0]
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item_code = item[1]
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cursor.execute('SELECT * FROM ' + tableName + ' WHERE CODE=?', (item_code,))
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result = cursor.fetchone()
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stock = {"CODE": item_code, "NAME": item_name, "PRICE": []}
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if result is not None:
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stock["PRICE"] = json.loads(result[2])
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self.crawl_specific_stock(stock)
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text = json.dumps(stock['PRICE'], ensure_ascii=False)
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print(idx, item_name, item_code, (time.time()-start_time),"s")
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start_time = time.time()
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cursor.execute('SELECT * FROM ' + tableName + ' WHERE CODE=?', (stock["CODE"],))
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result = cursor.fetchone()
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if result == None:
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cursor.execute("INSERT INTO " + tableName + "(CODE, NAME, PRICE) VALUES(?, ?, ?)", (stock["CODE"], stock["NAME"], text))
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else:
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cursor.execute("UPDATE " + tableName + " SET PRICE=? WHERE CODE=?", (text, stock["CODE"]))
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conn.commit()
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cursor.close()
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conn.close()
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return
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def get_data(self, stock):
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url = 'http://finance.naver.com/item/sise_day.nhn?code={code}'.format(code=stock['CODE'].strip())
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# 일자 데이터를 담을 df라는 DataFrame 정의
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df = pd.DataFrame()
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lastDay = ""
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if len(stock) > 0 and len(stock["PRICE"]) - 1 > 0:
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lastDay = stock["PRICE"][len(stock["PRICE"]) - 1]["DATE"].replace("-", ".")
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date_set = set()
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lastPage = False
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# 1페이지에서 1000페이지의 데이터만 가져오기
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for page in range(1, self.limit_page_count):
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# 최근 상장 기업의 마지막 반복되는 페이지를 제외시킨다.
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pg_url = '{url}&page={page}'.format(url=url, page=page)
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#html = pd.read_html(pg_url, header=0)
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html = None
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while True:
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try:
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html = pd.read_html(requests.get(pg_url, headers=self.header, timeout=30).text)
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sleep(0.5)
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break
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except:
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print(pg_url)
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if page > 200:
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break
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continue
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for date in html[0].날짜.values:
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if type(date) is str:
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if date in date_set:
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lastPage = True
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break
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date_set.add(date)
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if date == lastDay:
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lastPage = True
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df = df.append(html[0], ignore_index=True)
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break
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df = df.append(html[0], ignore_index=True)
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if lastPage:
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print("\t- lastpage:", page)
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break
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"""
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if count == 10:
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df = df.append(html[0], ignore_index=True)
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if lastPage:
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break
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else:
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if lastPage == False:
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df = df.append(html[0], ignore_index=True)
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lastPage = True
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else:
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break
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"""
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# df.dropna()를 이용해 결측값 있는 행 제거
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df = df.dropna()
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# 상위 5개 데이터 확인하기
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###print (df.head())
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# 한글로 된 컬럼명을 영어로 바꿔줌
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df = df.rename(columns={'날짜': 'date', '종가': 'close', '전일비': 'diff', '시가': 'open', '고가': 'high', '저가': 'low', '거래량': 'volume'})
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# 데이터의 타입을 int형으로 바꿔줌
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df[['close', 'diff', 'open', 'high', 'low', 'volume']] = df[['close', 'diff', 'open', 'high', 'low', 'volume']].astype(int)
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# 컬럼명 'date'의 타입을 date로 바꿔줌
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df['date'] = pd.to_datetime(df['date'])
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# 일자(date)를 기준으로 오름차순 정렬
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# df = df.sort_values(by=['date'], ascending=True)
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# 상위 5개 데이터 확인
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###print (df.head())
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if len(stock) > 0 and len(stock["PRICE"]) - 1 > 0:
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lastDay = stock["PRICE"][len(stock["PRICE"]) - 1]["DATE"]
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for values in df.values:
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day = str(values[0]).split(' ')[0]
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if lastDay == day:
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break
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stock["PRICE"].append({
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"DATE": day,
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df.columns[1]: values[1],
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df.columns[2]: values[2],
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df.columns[3]: values[3],
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df.columns[4]: values[4],
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df.columns[5]: values[5],
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df.columns[6]: values[6],
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})
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# stock["PRICE"] = sorted(stock["PRICE"], key=lambda x: x['DATE'], reverse=True)
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stock["PRICE"] = sorted(stock["PRICE"], key=lambda x: x['DATE'])
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return
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def get_moving_avg(self, stock):
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q_3 = Queue(3)
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q_5 = Queue(5)
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q_7 = Queue(7)
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q_10 = Queue(10)
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q_20 = Queue(20)
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q_30 = Queue(30)
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q_60 = Queue(60)
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q_90 = Queue(90)
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q_100 = Queue(100)
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q_120 = Queue(120)
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q_150 = Queue(150)
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q_180 = Queue(180)
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q_200 = Queue(200)
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q_240 = Queue(240)
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for i in range(len(stock['PRICE'])):
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q_3.enqueue(stock['PRICE'][i]['close'])
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q_5.enqueue(stock['PRICE'][i]['close'])
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q_7.enqueue(stock['PRICE'][i]['close'])
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q_10.enqueue(stock['PRICE'][i]['close'])
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q_20.enqueue(stock['PRICE'][i]['close'])
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q_30.enqueue(stock['PRICE'][i]['close'])
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q_60.enqueue(stock['PRICE'][i]['close'])
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q_90.enqueue(stock['PRICE'][i]['close'])
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q_100.enqueue(stock['PRICE'][i]['close'])
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q_120.enqueue(stock['PRICE'][i]['close'])
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q_150.enqueue(stock['PRICE'][i]['close'])
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q_180.enqueue(stock['PRICE'][i]['close'])
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q_200.enqueue(stock['PRICE'][i]['close'])
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q_240.enqueue(stock['PRICE'][i]['close'])
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stock['PRICE'][i]['avg3'] = q_3.avg()
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stock['PRICE'][i]['avg5'] = q_5.avg()
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stock['PRICE'][i]['avg7'] = q_7.avg()
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stock['PRICE'][i]['avg10'] = q_10.avg()
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stock['PRICE'][i]['avg20'] = q_20.avg()
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stock['PRICE'][i]['avg30'] = q_30.avg()
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stock['PRICE'][i]['avg60'] = q_60.avg()
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stock['PRICE'][i]['avg90'] = q_90.avg()
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stock['PRICE'][i]['avg100'] = q_100.avg()
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stock['PRICE'][i]['avg120'] = q_120.avg()
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stock['PRICE'][i]['avg150'] = q_150.avg()
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stock['PRICE'][i]['avg180'] = q_180.avg()
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stock['PRICE'][i]['avg200'] = q_200.avg()
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stock['PRICE'][i]['avg240'] = q_240.avg()
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return
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def crawl_specific_stock(self, stock):
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# 데이터 수집
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self.get_data(stock)
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# 이동 평균 계산
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self.get_moving_avg(stock)
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return
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def update(self, inFileName, outFileName):
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"""
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Full json 데이터를 db에 import 시킴
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inFileName = PROJECT_HOME + '/resources/stock.json.full'
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outFileName = PROJECT_HOME + '/resources/stock.db'
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crawler = StockCrawler()
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crawler.update(inFileName, outFileName)
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:param inFileName:
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:param outFileName:
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:return:
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"""
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tableName = 'stock'
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conn = sqlite3.connect(outFileName, isolation_level=None)
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cursor = conn.cursor()
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cursor.execute("CREATE TABLE IF NOT EXISTS " + tableName + " (CODE text PRIMARY KEY, NAME text, PRICE text, MACD text, STOCHASTIC text, ICHIMOKU text, RSI text, BOLINGERBAND text)")
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idx = 0
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inFp = open(inFileName, 'r')
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for line in inFp.readlines():
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if line:
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idx += 1
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stock = json.loads(line)
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print(idx, stock["CODE"], stock["NAME"])
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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) VALUES(?, ?, ?)", (stock["CODE"], stock["NAME"], text))
|
|
else:
|
|
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, MACD text, STOCHASTIC text, ICHIMOKU text, RSI text, BOLINGERBAND text)")
|
|
|
|
stock = {"NAME": code, "CODE": code, "PRICE": []}
|
|
|
|
lastDay = ""
|
|
cursor.execute('SELECT * FROM ' + tableName + ' WHERE CODE=?', (stock["CODE"],))
|
|
result = cursor.fetchone()
|
|
if result is not 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:
|
|
break
|
|
|
|
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 |