import os import time import shutil import matplotlib.pyplot as plt import datetime import sqlite3 from datetime import datetime from dateutil.relativedelta import relativedelta from matplotlib import rc import pandas as pd rc('font', family='AppleGothic') plt.rcParams['axes.unicode_minus'] = False import plotly.graph_objs as go from plotly import subplots import plotly.io as po from stock.analysis.Common import Common from stock.analysis.RelativePerformance import RelativePerformance from stock.analysis.Stochastic import Stochastic from stock.analysis.BolingerBand import BolingerBand from stock.analysis.IchimokuCloud import IchimokuCloud from stock.analysis.RSI import RSI from stock.analysis.MACD import MACD from stock.analysis.Envelope import Envelope from stock.crawler.MovingAverage import MovingAverage from hts.BuySellChecker import BuySellChecker class AnalyzerSqlite: relativePerformance = None stochastic = None bolingerBand = None ichimokuCloud = None rsi = None macd = None envelope = None topCompany = None fnguide = None common = None stockFileName = None analyzedFileName = None moving_avg = None buySellChecker = None def __init__(self, stockFileName=None): self.common = Common() self.relativePerformance = RelativePerformance() self.stochastic = Stochastic() self.bolingerBand = BolingerBand() self.ichimokuCloud = IchimokuCloud() self.rsi = RSI() self.macd = MACD() self.envelope = Envelope() self.buySellChecker = BuySellChecker() if stockFileName is not None: self.stockFileName = stockFileName self.topCompany = self.getTopCompany(stockFileName, 2000) self.fnguide = self.readFnguide(stockFileName) return def getTopCompany(self, fnguideFileName, top): conn = sqlite3.connect(fnguideFileName) cursor = conn.cursor() sql = "select DISTINCT CODE, NAME from fnguide order by total_ownership_interest desc limit " + str(top) cursor.execute(sql) result = cursor.fetchall() top_company = {} for idx, item in enumerate(result): top_company[item[0]] = (idx+1, item[1]) cursor.close() conn.close() return top_company def readFnguide(self, fnguideFileName): conn = sqlite3.connect(fnguideFileName) cursor = conn.cursor() today = datetime.today() year1 = str(today.year - 1) + ".12.01" year2 = str(today.year - 2) + ".12.01" year3 = str(today.year - 3) + ".12.01" sql = "SELECT CODE, NAME, ymd, business_profits, business_profits_ratio, debt_ratio, ROA, ROE, EPS, BPS, DPS, PER, PBR FROM fnguide " sql += " WHERE (ymd=? or ymd=? or ymd=?) and type=''" sql += " order by code, ymd desc" cursor.execute(sql, (year1,year2,year3)) result = cursor.fetchall() fnguide = {} for item in result: if item[0] not in fnguide: fnguide[item[0]] = [] fnguide[item[0]].append( {'NAME': item[1], 'ymd': item[2], 'business_profits': item[3], 'business_profits_ratio': item[4], 'debt_ratio': item[5], 'ROA': item[6], 'ROE': item[7], 'EPS': item[8], 'BPS': item[9], 'DPS': item[10], 'PER': item[11], 'PBR': item[12]}) cursor.close() conn.close() return fnguide def draw(self, stock): # 참고) https://sjblog1.tistory.com/45 ymd = list(reversed(stock['ymd'])) open = list(reversed(stock['open'])) close = list(reversed(stock['close'])) high = list(reversed(stock['high'])) low = list(reversed(stock['low'])) volume = list(reversed(stock['volume'])) avg3 = list(reversed(stock['avg3'])) avg4 = list(reversed(stock['avg4'])) avg5 = list(reversed(stock['avg5'])) avg6 = list(reversed(stock['avg6'])) avg10 = list(reversed(stock['avg10'])) avg12 = list(reversed(stock['avg12'])) avg20 = list(reversed(stock['avg20'])) avg36 = list(reversed(stock['avg36'])) avg40 = list(reversed(stock['avg40'])) avg48 = list(reversed(stock['avg48'])) avg60 = list(reversed(stock['avg60'])) avg120 = list(reversed(stock['avg120'])) avg240 = list(reversed(stock['avg240'])) avg300 = list(reversed(stock['avg300'])) disparity_avg5 = list(reversed(stock['disparity_avg5'])) disparity_avg10 = list(reversed(stock['disparity_avg10'])) disparity_avg20 = list(reversed(stock['disparity_avg20'])) disparity_avg60 = list(reversed(stock['disparity_avg60'])) disparity_avg120 = list(reversed(stock['disparity_avg120'])) macd = list(reversed(stock['macd'])) macdo = list(reversed(stock['macdo'])) macds = list(reversed(stock['macds'])) rsi = list(reversed(stock['rsi'])) rsis = list(reversed(stock['rsis'])) stochastic_slow_k = list(reversed(stock['stochastic_slow_k'])) stochastic_slow_d = list(reversed(stock['stochastic_slow_d'])) bolingerband_upper = list(reversed(stock['bolingerband_upper'])) bolingerband_lower = list(reversed(stock['bolingerband_lower'])) envelope_upper = list(reversed(stock['envelope_upper'])) envelope_lower = list(reversed(stock['envelope_lower'])) ichimokucloud_changeLine = list(reversed(stock['ichimokucloud_changeLine'])) ichimokucloud_baseLine = list(reversed(stock['ichimokucloud_baseLine'])) # general candle_stick = go.Candlestick(x=ymd, open=open, high=high, low=low, close=close, increasing_line_color='red', decreasing_line_color='blue') #avg3 = go.Scatter(x=ymd, y=avg3, name="avg3", line_color='#085F1B') #avg4 = go.Scatter(x=ymd, y=avg4, name="avg4", line_color='#085F1B') avg5 = go.Scatter(x=ymd, y=avg5, name="avg5", line_color='#6C2507') #avg6 = go.Scatter(x=ymd, y=avg6, name="avg6", line_color='#698D09') avg10 = go.Scatter(x=ymd, y=avg10, name="avg10", line_color='#8013ED') #avg12 = go.Scatter(x=ymd, y=avg12, name="avg12", line_color='#000000') avg20 = go.Scatter(x=ymd, y=avg20, name="avg20", line_color='#f84c43') #avg36 = go.Scatter(x=ymd, y=avg36, name="avg36", line_color='#370557') #avg40 = go.Scatter(x=ymd, y=avg40, name="avg40", line_color='#041366') #avg48 = go.Scatter(x=ymd, y=avg48, name="avg48", line_color='#7A1E66') avg60 = go.Scatter(x=ymd, y=avg60, name="avg60", line_color='#f89543') avg120 = go.Scatter(x=ymd, y=avg120, name="avg120", line_color='#0ed604') avg240 = go.Scatter(x=ymd, y=avg240, name="avg240", line_color='#FF00F7') #avg300 = go.Scatter(x=ymd, y=avg300, name="avg300", line_color='#00FF49') bolinger_upper = go.Scatter(x=ymd, y=bolingerband_upper, name="bol_upper", line_color='#8B4513') bolinger_lower = go.Scatter(x=ymd, y=bolingerband_lower, name="bol_lower", line_color='#8B4513') env_upper = go.Scatter(x=ymd, y=envelope_upper, name="env_upper", line_color='#FF33A2') env_lower = go.Scatter(x=ymd, y=envelope_lower, name="env_lower", line_color='#FF33A2') changeLine = go.Scatter(x=ymd, y=ichimokucloud_changeLine, name="changeLine", line_color='#000000') baseLine = go.Scatter(x=ymd, y=ichimokucloud_baseLine, name="baseLine", line_color='#FF0000') #candle_data = [candle_stick, avg3, avg4, avg5, avg6, avg10, avg12, avg20, avg36, avg40, avg48, avg60, avg120, avg240, avg300, bolinger_upper, bolinger_lower, changeLine, baseLine] candle_data = [candle_stick, avg5, avg10, avg20, avg60, avg120, avg240, bolinger_upper, bolinger_lower, env_upper, env_lower, changeLine, baseLine] #candle_data = [candle_stick, bolinger_upper, bolinger_lower, changeLine, baseLine] volume = go.Bar(x=ymd, y=volume, marker_color='red', name="volume") volume_data = [volume] disparity_avg5 = go.Scatter(x=ymd, y=disparity_avg5, name="disparity_avg5", line_color='#8F8203') disparity_avg10 = go.Scatter(x=ymd, y=disparity_avg10, name="disparity_avg10", line_color='#089B5B') disparity_avg20 = go.Scatter(x=ymd, y=disparity_avg20, name="disparity_avg20", line_color='#ff00ff') disparity_avg60 = go.Scatter(x=ymd, y=disparity_avg60, name="disparity_avg60", line_color='#1469F4') disparity_avg120 = go.Scatter(x=ymd, y=disparity_avg120, name="disparity_avg120", line_color='#000000') disparity_data = [disparity_avg5, disparity_avg10, disparity_avg20, disparity_avg60, disparity_avg120] # macd macd_line = go.Scatter(x=ymd, y=macd, line=dict(color='red', width=2), name='macd') macd_s_line = go.Scatter(x=ymd, y=macds, line=dict(dash='dashdot', color='black', width=2), name='macds') macd_o_line = go.Bar(x=ymd, y=macdo, marker_color='purple', name='macdo') macd_data = [macd_line, macd_s_line, macd_o_line] # stochastic rsi_line = go.Scatter(x=ymd, y=rsi, line=dict(color='red', width=2), name='rsi') rsis_line = go.Scatter(x=ymd, y=rsis, line=dict(dash='dashdot', color='black', width=2), name='rsis') rsi_data = [rsi_line, rsis_line] # stochastic stochastic_slow_k_line = go.Scatter(x=ymd, y=stochastic_slow_k, line=dict(color='red', width=2), name='slow_k') stochastic_slow_d_line = go.Scatter(x=ymd, y=stochastic_slow_d, line=dict(dash='dashdot', color='black', width=2), name='slow_d') stochastic_data = [stochastic_slow_k_line, stochastic_slow_d_line] fig = subplots.make_subplots( rows=6, cols=1, subplot_titles=("MACD", "스토캐스틱", "RSI", "거래량", "이격도", '캔들'), # specs=[[{}], [{}], [{}], [{}], [{}], [{}]], shared_xaxes=True, horizontal_spacing=0.03, vertical_spacing=0.01, row_heights=[200, 200, 200, 200, 200, 1200] ) for trace in macd_data: fig.append_trace(trace, 1, 1) for trace in stochastic_data: fig.append_trace(trace, 2, 1) for trace in rsi_data: fig.append_trace(trace, 3, 1) for trace in volume_data: fig.append_trace(trace, 4, 1) for trace in disparity_data: fig.append_trace(trace, 5, 1) for trace in candle_data: fig.append_trace(trace, 6, 1) fig.update_layout(height=2200, xaxis_rangeslider_visible=False) return fig def getPositionalEnergy(self, close): # 260 (= 52 * 5)일 중 가장 찾은 금액과 가장 높았던 금액 중 현재가의 위치 계산 top = close[0] bottom = close[0] for i in range(1, 260): if i >= len(close): break if top < close[i]: top = close[i] if bottom > close[i]: bottom = close[i] if top-close[0] == 0: energy1 = 100.0 else: energy1 = round((close[0]-bottom) / (top-close[0]), 2) energy2 = round((close[0] / top), 2) return energy1, energy2 def makeDir(self, dir_code, dir_name): if os.path.isdir(self.outPath + "/" + dir_code+"_"+dir_name): os.rmdir(self.outPath + "/" + dir_code+"_"+dir_name) os.mkdir(self.outPath + "/" + dir_code+"_"+dir_name) return def makeDirectory(self, outPath): self.outPath = outPath if os.path.isdir(outPath): shutil.rmtree(outPath) os.mkdir(outPath) self.makeDir("0", "final") self.makeDir("1", "monthly_macd_-300이하") self.makeDir("2", "monthly_rsi_20이하") self.makeDir("3", "monthly_EV하단위로_올라옴") self.makeDir("11", "daily_macd_-1000이하") self.makeDir("12", "daily_weekly_monthly_rsi_10_20_30이하") self.makeDir("13", "daily_이전에_없던_거래량") self.makeDir("14", "daily_이격도") self.makeDir("15", "daily_낙폭과대") self.makeDir("16", "daily_EV하단_내려옴") self.makeDir("17", "daily_볼린저하단_내려옴") return def writeFile(self, dir_code, dir_name, CODE, NAME, top, stock, state, final_status_count=-1): # 3년 이내 한번이라도 영업이익이 났는지 체크를 함 fnguide = None if CODE in self.fnguide: fnguide = self.fnguide[CODE] check = True if fnguide: check = False for item in fnguide: if item['business_profits'] > 0: check = True if check: fig = self.draw(stock) title = "%s (%s), %d, %s 차트 (URL1, URL2)" % (NAME, CODE, stock['close'][0], dir_code+"_"+dir_name, CODE, CODE) fig['layout'].update(title=title) fileName = self.outPath + "/" + dir_code+"_"+dir_name if dir_code in ("0", "6", "26"): fileName = "%s/%s_%s_%s_%s_%s.html" % (fileName, str(final_status_count), top, NAME.replace(" ", ""), CODE, state) else: fileName = "%s/%s_%s_%s_%s.html" % (fileName, state, NAME.replace(" ", ""), CODE, top) po.write_html(fig, file=fileName, auto_open=False) return def checkVolume(self, p_volume, volume): if 0 < p_volume <= 10000 and p_volume * 700 < volume: return True if 10000 < p_volume <= 50000 and p_volume * 40 < volume: return True if 50000 < p_volume <= 100000 and p_volume * 25 < volume: return True if 100000 < p_volume <= 200000 and p_volume * 15 < volume: return True if 200000 < p_volume <= 700000 and p_volume * 13 < volume: return True if 700000 < p_volume <= 1000000 and p_volume * 10 < volume: return True if 5000000 < p_volume <= 5000000 and p_volume * 5 < volume: return True if 5000000 < p_volume and p_volume * 4 < volume: return True return False def getStockData(self, TableName, CODE): conn = sqlite3.connect(self.stockFileName) cursor = conn.cursor() sql = 'SELECT ymd, close, open, high, low, volume, ' sql += ' avg3, avg4, avg5, avg6, avg10, avg12, avg20, avg36, avg40, avg48, avg60, avg120, avg200, avg240, avg300, ' sql += ' disparity_avg5, disparity_avg10, disparity_avg20, disparity_avg60, disparity_avg120, ' sql += ' bolingerband_upper, bolingerband_lower, bolingerband_middle, ' sql += ' envelope_upper, envelope_lower, envelope_middle, ' sql += ' ichimokucloud_changeLine, ichimokucloud_baseLine, ichimokucloud_leadingSpan1, ichimokucloud_leadingSpan2, ' sql += ' stochastic_fast_k, stochastic_slow_k, stochastic_slow_d, ' sql += ' rsi, rsis, ' sql += ' macd, macds, macdo ' sql += ' FROM ' + TableName + ' where CODE=? order by ymd desc limit 512 ' cursor.execute(sql, (CODE,)) prices = cursor.fetchall() cursor.close() conn.close() ymd = [] close, open, high, low, volume = [], [], [], [], [] avg3, avg4, avg5, avg6, avg10, avg12, avg20, avg36, avg40, avg48, avg60, avg120, avg200, avg240, avg300 = [], [], [], [], [], [], [], [], [], [], [], [], [], [], [] disparity_avg5, disparity_avg10, disparity_avg20, disparity_avg60, disparity_avg120 = [], [], [], [], [] bolingerband_upper, bolingerband_lower, bolingerband_middle = [], [], [] envelope_upper, envelope_lower, envelope_middle = [], [], [] ichimokucloud_changeLine, ichimokucloud_baseLine, ichimokucloud_leadingSpan1, ichimokucloud_leadingSpan2 = [], [], [], [] stochastic_fast_k, stochastic_slow_k, stochastic_slow_d = [], [], [] rsi, rsis = [], [] macd, macds, macdo = [], [], [] for price in prices: ymd.append(price[0]) close.append(price[1]) open.append(price[2]) high.append(price[3]) low.append(price[4]) volume.append(price[5]) avg3.append(price[6]) avg4.append(price[7]) avg5.append(price[8]) avg6.append(price[9]) avg10.append(price[10]) avg12.append(price[11]) avg20.append(price[12]) avg36.append(price[13]) avg40.append(price[14]) avg48.append(price[15]) avg60.append(price[16]) avg120.append(price[17]) avg200.append(price[18]) avg240.append(price[19]) avg300.append(price[20]) disparity_avg5.append(price[21]) disparity_avg10.append(price[22]) disparity_avg20.append(price[23]) disparity_avg60.append(price[24]) disparity_avg120.append(price[25]) bolingerband_upper.append(price[26]) bolingerband_lower.append(price[27]) bolingerband_middle.append(price[28]) envelope_upper.append(price[29]) envelope_lower.append(price[30]) envelope_middle.append(price[31]) ichimokucloud_changeLine.append(price[32]) ichimokucloud_baseLine.append(price[33]) ichimokucloud_leadingSpan1.append(price[34]) ichimokucloud_leadingSpan2.append(price[35]) stochastic_fast_k.append(price[36]) stochastic_slow_k.append(price[37]) stochastic_slow_d.append(price[38]) rsi.append(price[39]) rsis.append(price[40]) macd.append(price[41]) macds.append(price[42]) macdo.append(price[43]) stock = { "ymd": ymd, "close": close, "open": open, "high": high, "low": low, "volume": volume, "avg3": avg3, "avg4": avg4, "avg5": avg5, "avg6": avg6, "avg10": avg10, "avg12": avg12, "avg20": avg20, "avg36": avg36, "avg40": avg40, "avg48": avg48, "avg60": avg60, "avg120": avg120, "avg200": avg200, "avg240": avg240, "avg300": avg300, "disparity_avg5": disparity_avg5, "disparity_avg10": disparity_avg10, "disparity_avg20": disparity_avg20, "disparity_avg60": disparity_avg60, "disparity_avg120": disparity_avg120, "bolingerband_upper": bolingerband_upper, "bolingerband_lower": bolingerband_lower, "bolingerband_middle": bolingerband_middle, "envelope_upper": envelope_upper, "envelope_lower": envelope_lower, "envelope_middle": envelope_middle, "ichimokucloud_changeLine": ichimokucloud_changeLine, "ichimokucloud_baseLine": ichimokucloud_baseLine, "ichimokucloud_leadingSpan1": ichimokucloud_leadingSpan1, "ichimokucloud_leadingSpan2": ichimokucloud_leadingSpan2, "stochastic_fast_k": stochastic_fast_k, "stochastic_slow_k": stochastic_slow_k, "stochastic_slow_d": stochastic_slow_d, "rsi": rsi, "rsis": rsis, "macd": macd, "macds": macds, "macdo": macdo } return stock # 후보 찾기 def findCandidates(self, outPath): self.makeDirectory(outPath) stockTableName = 'stock' fnguideTableName = 'fnguide' stockAnalysisTableName = 'stock_analysis' stockAnalysisWeeklyTableName = 'stock_analysis_weekly' stockAnalysisMonthlyTableName = 'stock_analysis_monthly' conn = sqlite3.connect(self.stockFileName) cursor = conn.cursor() cursor.execute('SELECT distinct code, name FROM ' + stockTableName + ' order by code') #cursor.execute('select CODE, NAME, max(ymd) as ymd from ' + fnguideTableName + ' where type != "E" group by 1 order by total_assets desc') items = cursor.fetchall() cursor.close() conn.close() for idx, item in enumerate(items): CODE = item[0] NAME = item[1] print("#", idx, ", CODE: ", CODE, ", NAME: ", NAME) top = "0" if CODE in self.topCompany: top = str(self.topCompany[CODE][0]) stock_daily = self.getStockData(stockAnalysisTableName, CODE) stock_weekly = self.getStockData(stockAnalysisWeeklyTableName, CODE) stock_monthly = self.getStockData(stockAnalysisMonthlyTableName, CODE) status = "" final_status = "" final_status_count = 0 # 거래량이 100만 이상이고, 종가가 1천원 이상인지 체크 (https://happpy-rich.tistory.com/94) if stock_weekly['volume'][0] > 100000 and stock_weekly['close'][0] > 1000: # 종목 상태 체크 분석 # MACD가 -300 이하에서 macd가 macds 위로 올라온 경우 if len(stock_monthly['close']) > 1: if stock_monthly['macd'][1] is not None and stock_monthly['macds'][1] is not None and stock_monthly['macd'][0] is not None and stock_monthly['macds'][0] is not None: if stock_monthly['macd'][0] <= -300: if stock_monthly['macd'][1] < stock_monthly['macds'][1] and stock_monthly['macds'][0] < stock_monthly['macds'][0]: dir_code = "1" dir_name = "monthly_macd_-300이하" final_status_count += 1 status = "{:.2f}".format(stock_monthly['macd'][0]) + "_" + status self.writeFile(dir_code, dir_name, CODE, NAME, top, stock_monthly, status) # RSI가 20 이하인 경우, rsi가 rsis위로 올라온 경우 if len(stock_monthly['close']) > 1: if stock_monthly['rsi'][0] is not None and stock_monthly['rsi'][1] is not None and stock_monthly['rsis'][0] is not None and stock_monthly['rsis'][1] is not None: if stock_monthly['rsi'][0] <= 20: if stock_monthly['rsi'][1] < stock_monthly['rsis'][1] and stock_monthly['rsis'][0] < stock_monthly['rsi'][0]: dir_code = "2" dir_name = "monthly_rsi_20이하" final_status_count += 1 status = "{:.2f}".format(stock_monthly['rsi'][0]) + "_" + status self.writeFile(dir_code, dir_name, CODE, NAME, top, stock_monthly, status) if len(stock_monthly['volume']) > 5: if stock_monthly['envelope_lower'][0] is not None and stock_monthly['envelope_lower'][1] is not None: # env 하단에 부딪힘 if stock_monthly['close'][1] < stock_monthly['envelope_lower'][1] and stock_monthly['envelope_lower'][0] < stock_monthly['close'][0]: if stock_daily['close'][1] < stock_daily['avg5'][1] and stock_daily['avg5'][0] < stock_daily['close'][0]: dir_code = "3" dir_name = "monthly_EV하단위로_올라옴" final_status_count += 1 self.writeFile(dir_code, dir_name, CODE, NAME, top, stock_monthly, status) # 2) daily if len(stock_daily['volume']) > 100: # MSCD -500 이하인 경우 if stock_daily['macd'][1] is not None and stock_daily['macds'][1] is not None and stock_daily['macd'][0] is not None and stock_daily['macds'][0] is not None: if stock_daily['macd'][0] <= -1000: if stock_daily['macd'][1] < stock_daily['macds'][1] and stock_daily['macds'][0] < stock_daily['macds'][0]: dir_code = "11" dir_name = "daily_macd_-1000이하" final_status_count += 1 status = "{:.2f}".format(stock_daily['macd'][0]) + "_" + status self.writeFile(dir_code, dir_name, CODE, NAME, top, stock_daily, status) # daily_weekly_monthly_rsi_10_20_30이하 if len(stock_monthly['close']) > 1: if stock_monthly['rsi'][0] is not None and stock_weekly['rsi'][0] is not None and stock_daily['rsi'][0] is not None: if stock_monthly['rsi'][0] < 30 and stock_weekly['rsi'][0] < 20 and stock_daily['rsi'][0] < 10: dir_code = "12" dir_name = "daily_weekly_monthly_rsi_10_20_30이하" final_status_count += 1 self.writeFile(dir_code, dir_name, CODE, NAME, top, stock_daily, status) # 52주 200일 기준 평균 + 50% 보다 높은 거래량의 경우 c_index = 200 if len(stock_daily['volume']) < c_index: c_index = len(stock_daily['volume']) avg = sum(stock_daily['volume'][1:c_index]) / len(stock_daily['volume'][1:c_index]) if avg + avg/2 < stock_daily['volume'][0]: dir_code = "13" dir_name = "daily_이전에_없던_거래량" final_status_count += 1 self.writeFile(dir_code, dir_name, CODE, NAME, top, stock_daily, status) # daily_이격도 if (98 0.6: if stock_daily['stochastic_slow_k'][0] is not None and stock_daily['stochastic_slow_k'][1] is not None and stock_daily['stochastic_slow_d'][1] is not None and stock_daily['stochastic_slow_d'][0] is not None and stock_monthly['stochastic_slow_k'][0] is not None: if stock_daily['stochastic_slow_k'][0] < 20 and (stock_daily['stochastic_slow_k'][1] < stock_daily['stochastic_slow_d'][1] and stock_daily['stochastic_slow_d'][0] < stock_monthly['stochastic_slow_k'][0]): dir_code = "15" dir_name = "daily_낙폭과대" final_status_count += 1 status = "{:.2f}".format(location) + "_" + status self.writeFile(dir_code, dir_name, CODE, NAME, top, stock_daily, status) if len(stock_daily['volume']) > 5: if stock_daily['envelope_lower'][0] is not None and stock_daily['envelope_lower'][1] is not None: # ev 하단에 부딪힘 if stock_daily['close'][1] < stock_daily['avg5'][1] and stock_daily['avg5'][0] < stock_daily['close'][0]: check = False for c in range(1, 4): if stock_daily['close'][c] < stock_daily['envelope_lower'][c]: check = True break if check: dir_code = "16" dir_name = "daily_EV하단_내려옴" final_status_count += 1 self.writeFile(dir_code, dir_name, CODE, NAME, top, stock_daily, status) if len(stock_daily['volume']) > 5: if stock_daily['bolingerband_lower'][0] is not None and stock_daily['bolingerband_lower'][1] is not None: # bb 하단에 부딪힘 if stock_daily['close'][1] < stock_daily['avg5'][1] and stock_daily['avg5'][0] < stock_daily['close'][0]: check = False for c in range(1, 4): if stock_daily['close'][c] < stock_daily['bolingerband_lower'][c]: check = True break if check: dir_code = "17" dir_name = "daily_EV하단_내려옴" final_status_count += 1 self.writeFile(dir_code, dir_name, CODE, NAME, top, stock_daily, status) if final_status_count >= 5: dir_code = "0" dir_name = "final" self.writeFile(dir_code, dir_name, CODE, NAME, top, stock_daily, final_status, final_status_count) return def get_moving_average(self, stock): q_3 = MovingAverage(3) q_4 = MovingAverage(4) q_5 = MovingAverage(5) q_6 = MovingAverage(6) q_10 = MovingAverage(10) q_12 = MovingAverage(12) q_20 = MovingAverage(20) q_36 = MovingAverage(36) q_40 = MovingAverage(40) q_48 = MovingAverage(48) q_60 = MovingAverage(60) q_120 = MovingAverage(120) q_200 = MovingAverage(200) q_240 = MovingAverage(240) q_300 = MovingAverage(300) for i in range(len(stock)): q_3.enqueue(stock[i]['close']) q_4.enqueue(stock[i]['close']) q_5.enqueue(stock[i]['close']) q_6.enqueue(stock[i]['close']) q_10.enqueue(stock[i]['close']) q_12.enqueue(stock[i]['close']) q_20.enqueue(stock[i]['close']) q_36.enqueue(stock[i]['close']) q_40.enqueue(stock[i]['close']) q_48.enqueue(stock[i]['close']) q_60.enqueue(stock[i]['close']) q_120.enqueue(stock[i]['close']) q_200.enqueue(stock[i]['close']) q_240.enqueue(stock[i]['close']) q_300.enqueue(stock[i]['close']) stock[i]['avg3'] = q_3.avg() stock[i]['avg4'] = q_4.avg() stock[i]['avg5'] = q_5.avg() stock[i]['avg6'] = q_6.avg() stock[i]['avg10'] = q_10.avg() stock[i]['avg12'] = q_12.avg() stock[i]['avg20'] = q_20.avg() stock[i]['avg36'] = q_36.avg() stock[i]['avg40'] = q_40.avg() stock[i]['avg48'] = q_48.avg() stock[i]['avg60'] = q_60.avg() stock[i]['avg120'] = q_120.avg() stock[i]['avg200'] = q_200.avg() stock[i]['avg240'] = q_240.avg() stock[i]['avg300'] = q_300.avg() return def get_disparity(self, stock): for i in range(len(stock)): stock[i]['disparity_avg5'] = 100 * (stock[i]["open"] / stock[i]["avg5"]) stock[i]['disparity_avg10'] = 100 * (stock[i]["open"] / stock[i]["avg10"]) stock[i]['disparity_avg20'] = 100 * (stock[i]["open"] / stock[i]["avg20"]) stock[i]['disparity_avg60'] = 100 * (stock[i]["open"] / stock[i]["avg60"]) stock[i]['disparity_avg120'] = 100 * (stock[i]["open"] / stock[i]["avg120"]) return def convertFormat(self, weekDict): previous_close = 0 stock_price = [] for ts in weekDict['open']: stock_price.append( { "ymd": ts.strftime("%Y.%m.%d"), "close": weekDict['close'][ts], "diff": weekDict['close'][ts] - previous_close, "open": weekDict['open'][ts], "high": weekDict['high'][ts], "low": weekDict['low'][ts], "volume": weekDict['volume'][ts], "avg3": -1, "avg4": -1, "avg5": -1, "avg6": -1, "avg10": -1, "avg12": -1, "avg20": -1, "avg36": -1, "avg40": -1, "avg48": -1, "avg60": -1, "avg120": -1, "avg200": -1, "avg240": -1, "avg300": -1, "disparity_avg5": -1, "disparity_avg10": -1, "disparity_avg20": -1, "disparity_avg60": -1, "disparity_avg120": -1, "bolingerband_upper": -1, "bolingerband_lower": -1, "bolingerband_middle": -1, "ichimokucloud_changeLine": -1, "ichimokucloud_baseLine": -1, "ichimokucloud_leadingSpan1": -1, "ichimokucloud_leadingSpan2": -1, "stochastic_fast_k": -1, "stochastic_slow_k": -1, "stochastic_slow_d": -1, "rsi": -1, "rsis": -1, "macd": -1, "macds": -1, "macdo": -1 } ) previous_close = weekDict['close'][ts] return stock_price def analyzeAdditionalInfo(self, stock, cursor, type=None): if type==None: stockAnalysisTableName = 'stock_analysis' else: stockAnalysisTableName = 'stock_analysis_' + type # 테이블 생성 cursor.execute("CREATE TABLE IF NOT EXISTS " + stockAnalysisTableName + " (CODE text, NAME text, ymd text, close REAL, diff REAL, open REAL, high REAL, low REAL, volume REAL, avg3 REAL, avg4 REAL, avg5 REAL, avg6 REAL, avg10 REAL, avg12 REAL, avg20 REAL, avg36 REAL, avg40 REAL, avg48 REAL, avg60 REAL, avg120 REAL, avg200 REAL, avg240 REAL, avg300 REAL, disparity_avg5 REAL, disparity_avg10 REAL, disparity_avg20 REAL, disparity_avg60 REAL, disparity_avg120, bolingerband_upper REAL, bolingerband_lower REAL, bolingerband_middle REAL, envelope_upper REAL, envelope_lower REAL, envelope_middle REAL, ichimokucloud_changeLine REAL, ichimokucloud_baseLine REAL, ichimokucloud_leadingSpan1 REAL, ichimokucloud_leadingSpan2 REAL, stochastic_fast_k REAL, stochastic_slow_k REAL, stochastic_slow_d REAL, rsi REAL, rsis REAL, macd REAL, macds REAL, macdo REAL, rp REAL)") # 키 생성 create_key = "CREATE INDEX IF NOT EXISTS " + stockAnalysisTableName + "_idx on " + stockAnalysisTableName + " (CODE, ymd) " cursor.execute(create_key) # 이동 평균 계산 stock["PRICE"] = sorted(stock["PRICE"], key=lambda x: x['ymd']) self.get_moving_average(stock["PRICE"]) # 이동 평균을 이용한 이격도 계산 self.get_disparity(stock["PRICE"]) self.ichimokuCloud.analyze(stock) self.stochastic.analyze(stock) self.bolingerBand.analyze(stock) self.envelope.analyze(stock) self.rsi.analyze(stock) self.macd.analyze(stock) sorted_stock = sorted(stock["PRICE"], key=lambda x: x['ymd'], reverse=True) for price in sorted_stock: cursor.execute('SELECT * FROM ' + stockAnalysisTableName + ' WHERE CODE=? and ymd=?', (stock['CODE'], price['ymd'],)) result = cursor.fetchone() if result == None: sql = "INSERT INTO " + stockAnalysisTableName + "(CODE, NAME, ymd, close, diff, open, high, low, volume, " sql += " avg3, avg4, avg5, avg6, avg10, avg12, avg20, avg36, avg40, avg48, avg60, avg120, avg200, avg240, avg300, " sql += " disparity_avg5, disparity_avg10, disparity_avg20, disparity_avg60, disparity_avg120, " sql += " bolingerband_upper, bolingerband_lower, bolingerband_middle, " sql += " envelope_upper, envelope_lower, envelope_middle, " sql += " ichimokucloud_changeLine, ichimokucloud_baseLine, ichimokucloud_leadingSpan1, ichimokucloud_leadingSpan2, " sql += " stochastic_fast_k, stochastic_slow_k, stochastic_slow_d, " sql += " rsi, rsis, macd, macds, macdo) " sql += " VALUES(?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)" cursor.execute(sql, ( stock["CODE"], stock["NAME"], price['ymd'], price['close'], price['diff'], price['open'], price['high'], price['low'], price['volume'], price['avg3'], price['avg4'], price['avg5'], price['avg6'], price['avg10'], price['avg12'], price['avg20'], price['avg36'], price['avg40'], price['avg48'], price['avg60'], price['avg120'], price['avg200'], price['avg240'], price['avg300'], price['disparity_avg5'], price['disparity_avg10'], price['disparity_avg20'], price['disparity_avg60'], price['disparity_avg120'], price['bolingerband_upper'], price['bolingerband_lower'], price['bolingerband_middle'], price['envelope_upper'], price['envelope_lower'], price['envelope_middle'], price['ichimokucloud_changeLine'], price['ichimokucloud_baseLine'], price['ichimokucloud_leadingSpan1'], price['ichimokucloud_leadingSpan2'], price['stochastic_fast_k'], price['stochastic_slow_k'], price['stochastic_slow_d'], price['rsi'], price['rsis'], price['macd'], price['macds'], price['macdo'],)) else: sql = "UPDATE " + stockAnalysisTableName + " SET close=?, diff=?, open=?, high=?, low=?, volume=?, " sql += " avg3=?, avg4=?, avg5=?, avg6=?, avg10=?, avg12=?, avg20=?, avg36=?, avg40=?, avg48=?, avg60=?, avg120=?, avg200=?, avg240=?, avg300=?, " sql += " disparity_avg5=?, disparity_avg10=?, disparity_avg20=?, disparity_avg60=?, disparity_avg120=?, " sql += " bolingerband_upper=?, bolingerband_lower=?, bolingerband_middle=?, " sql += " envelope_upper=?, envelope_lower=?, envelope_middle=?, " sql += " ichimokucloud_changeLine=?, ichimokucloud_baseLine=?, ichimokucloud_leadingSpan1=?, ichimokucloud_leadingSpan2=?, " sql += " stochastic_fast_k=?, stochastic_slow_k=?, stochastic_slow_d=?, " sql += " rsi=?, rsis=?, " sql += " macd=?, macds=?, macdo=? " sql += " WHERE CODE=? and ymd=?" cursor.execute(sql, (price['close'], price['diff'], price['open'], price['high'], price['low'], price['volume'], price['avg3'], price['avg4'], price['avg5'], price['avg6'], price['avg10'], price['avg12'], price['avg20'], price['avg36'], price['avg40'], price['avg48'], price['avg60'], price['avg120'], price['avg200'], price['avg240'], price['avg300'], price['disparity_avg5'], price['disparity_avg10'], price['disparity_avg20'], price['disparity_avg60'], price['disparity_avg120'], price['bolingerband_upper'], price['bolingerband_lower'], price['bolingerband_middle'], price['envelope_upper'], price['envelope_lower'], price['envelope_middle'], price['ichimokucloud_changeLine'], price['ichimokucloud_baseLine'], price['ichimokucloud_leadingSpan1'], price['ichimokucloud_leadingSpan2'], price['stochastic_fast_k'], price['stochastic_slow_k'], price['stochastic_slow_d'], price['rsi'], price['rsis'], price['macd'], price['macds'], price['macdo'], stock["CODE"], price['ymd'],)) break cursor.execute("commit",) return def setItem(self, item): return { "ymd": item[0], "close": item[1], "diff": item[2], "open": item[3], "high": item[4], "low": item[5], "volume": item[6], "avg3": -1, "avg4": -1, "avg5": -1, "avg6": -1, "avg10": -1, "avg12": -1, "avg20": -1, "avg36": -1, "avg40": -1, "avg48": -1, "avg60": -1, "avg120": -1, "avg200": -1, "avg240": -1, "avg300": -1, "disparity_avg5": -1, "disparity_avg10": -1, "disparity_avg20": -1, "disparity_avg60": -1, "disparity_avg120": -1, "bolingerband_upper": -1, "bolingerband_lower": -1, "bolingerband_middle": -1, "envelope_upper": -1, "envelope_lower": -1, "envelope_middle": -1, "ichimokucloud_changeLine": -1, "ichimokucloud_baseLine": -1, "ichimokucloud_leadingSpan1": -1, "ichimokucloud_leadingSpan2": -1, "stochastic_fast_k": -1, "stochastic_slow_k": -1, "stochastic_slow_d": -1, "rsi": -1, "rsis": -1, "macd": -1, "macds": -1, "macdo": -1, "rp": -1 } def analyzeDaily(self): stockTableName = 'stock' conn = sqlite3.connect(self.stockFileName) cursor = conn.cursor() cursor.execute('SELECT distinct code, name FROM ' + stockTableName + ' order by code') items = cursor.fetchall() for rowid, item in enumerate(items): stock = {"CODE": item[0], "NAME": item[1], "PRICE":[]} print("Daily # :", rowid, ", CODE: ", stock['CODE'], ", NAME: ", stock['NAME']) sql = 'SELECT ymd, close, diff, open, high, low, volume FROM ' + stockTableName + ' where CODE=? order by ymd desc ' sql += ' limit 350' cursor.execute(sql, (stock['CODE'],)) items = cursor.fetchall() items_reverse = reversed(items) for item in items_reverse: stock['PRICE'].append( self.setItem(item) ) self.analyzeAdditionalInfo(stock, cursor) conn.commit() cursor.close() conn.close() return def analyzeGrouping(self, type): stockTableName = 'stock' conn = sqlite3.connect(self.stockFileName) cursor = conn.cursor() cursor.execute('SELECT distinct code, name FROM ' + stockTableName + ' order by code') items = cursor.fetchall() for rowid, item in enumerate(items): stock = {"CODE": item[0], "NAME": item[1], "PRICE": []} print(type, "# :", rowid, ", CODE: ", stock['CODE'], ", NAME: ", stock['NAME']) sql = 'SELECT ymd, close, diff, open, high, low, volume FROM ' + stockTableName + ' where CODE=? order by ymd desc ' sql += ' limit 350' cursor.execute(sql, (stock['CODE'],)) items = cursor.fetchall() items_reverse = reversed(items) for item in items_reverse: stock['PRICE'].append( self.setItem(item) ) agg_dict = {'open': 'first', 'high': 'max', 'low': 'min', 'close': 'last', 'volume': 'sum'} df = pd.DataFrame(stock['PRICE']) df['ymd'] = pd.to_datetime(df['ymd']) df.set_index('ymd', inplace=True) if type == "weekly": condition="W" else: condition='M' df_group = df.resample(condition).agg(agg_dict) df_group = df_group.dropna() df_group.merge(df, ) stock['PRICE'] = self.convertFormat(df_group.to_dict()) self.analyzeAdditionalInfo(stock, cursor, type) conn.commit() cursor.close() conn.close() return def analyzeRP (self): type = {200:None, 52: "weekly", 12: "monthly"} limits = [200, 52, 12] for limit in limits: if type[limit]==None: stockAnalysisTableName = 'stock_analysis' else: stockAnalysisTableName = 'stock_analysis_' + type[limit] conn = sqlite3.connect(self.stockFileName) cursor = conn.cursor() # S: Kospi 구하기 (RP 계산을 위함) kospi = {"CODE": "^KS11", "NAME": "Kospi", "PRICE": []} sql = "SELECT CODE, NAME, ymd, close, diff, open, high, low, volume, avg200 FROM " + stockAnalysisTableName + " where CODE=? order by ymd desc " sql += ' limit ' + str(limit) cursor.execute(sql, (kospi['CODE'],)) items = cursor.fetchall() items_reverse = reversed(items) for item in items_reverse: kospi['PRICE'].append(self.setItem(item)) # E: Kospi 구하기 (RP 계산을 위함) # S: 종목 구하기 cursor.execute('SELECT distinct code, name FROM ' + stockAnalysisTableName + ' order by code') items = cursor.fetchall() for rowid, item in enumerate(items): stock = {"CODE": item[0], "NAME": item[1], "PRICE": []} print("Daily # :", rowid, ", CODE: ", stock['CODE'], ", NAME: ", stock['NAME']) sql = "SELECT CODE, NAME, ymd, close, diff, open, high, low, volume, avg200 FROM " + stockAnalysisTableName + " where CODE=? order by ymd desc " sql += ' limit ' + str(limit) cursor.execute(sql, (stock['CODE'],)) items = cursor.fetchall() items_reverse = reversed(items) for item in items_reverse: stock['PRICE'].append(self.setItem(item)) self.relativePerformance.analyze(stock, kospi) sql = "UPDATE " + stockAnalysisTableName + " SET rp=? WHERE CODE=? and ymd=?" cursor.execute(sql, (price['rp'], stock["CODE"], price['ymd'],)) cursor.execute("commit",) return if __name__ == "__main__": start = time.time() PROJECT_HOME = os.path.join(os.path.dirname(os.path.join(os.path.dirname(os.path.join(os.path.dirname(__file__)))))) RESOURCE_PATH = os.path.join(PROJECT_HOME, 'resources') stockFileName = os.path.join(RESOURCE_PATH, 'stock.db') analyzer = AnalyzerSqlite(stockFileName) #analyzer.analyzeDaily() #analyzer.analyzeGrouping("weekly") #analyzer.analyzeGrouping("monthly") #analyzer.analyzeRP() # HTML 출력 outPath = os.path.join(PROJECT_HOME, "resources", "analysis") if not os.path.isdir(outPath): os.mkdir(outPath) day = datetime.today().strftime("%Y%m%d") before_7_day = datetime.today() + relativedelta(days=-7) dayList = os.listdir(outPath) for dayDir in dayList: if dayDir[0] != '.' and dayDir < before_7_day.strftime("%Y%m%d"): if os.path.exists(os.path.join(outPath, dayDir)): shutil.rmtree(os.path.join(outPath, dayDir)) outPath = os.path.join(outPath, day) if os.path.isdir(outPath): shutil.rmtree(outPath) os.mkdir(outPath) print("print to Html...") analyzer.findCandidates(outPath) print("time : %6.2f 초" % (time.time() - start)) print("done...")