import pandas as pd import numpy as np from stock.analysis.Common import Common from stock.analysis.Stochastic import Stochastic from stock.analysis.RSI import RSI from stock.analysis.MACD import MACD from stock.analysis.IchimokuCloud import IchimokuCloud class BuySellChecker: common = None stochastic = None rsi = None macd = None ichimokuCloud = None def __init__(self): self.common = Common() self.stochastic = Stochastic() self.rsi = RSI() self.macd = MACD() self.ichimokuCloud = IchimokuCloud() return def getPriceAndWeight1(self, data, i): buy, weight, sell = -1, -1, -1 START_TIME_INDEX = 0 for c in range(370, len(data.index)): if data.index[c].strftime("%H:%M:%S") == "09:01:00": START_TIME_INDEX = c break if i >= START_TIME_INDEX: ################ ### sell 분석 ### ################ # 1. 볼린져밴드 상단이 최고와 종가 사이 아래에 있는 경우 매도한다. #if (hts["high"][i] - hts["close"][i]) / 2 + hts["close"][i] > hts["upper"][i]: # sell = hts["high"][i] # 2. slow_k가 90이 넘으면 매도한다. if data["slow_k"][i] > 90: sell = data["high"][i] #if hts["slow_k"][i] >= 85: # if hts["slow_d"][i-1] < hts["slow_k"][i-1] and hts["slow_k"][i] < hts["slow_d"][i]: # sell = hts["high"][i] # 3. 2시 이후에는 최고가가 볼린져밴드 상단 위에 있으면 매도한다. if i > 300 and data["high"][i] > data["upper"][i]: sell = data["high"][i] ########################## ### buy 분석 ### ########################## if data["low"][i] < data["lower"][i] + 5 and data["open"][i] <= data["close"][i]: if data["slow_k"][i-1] < 30 and data["slow_k"][i] < 30: if data["slow_k"][i-1] < data["slow_k"][i]: buy = data["low"][i] if data["rsi"][i] < 25: if data["rsi"][i - 2] < data["rsis"][i - 2] and data["rsi"][i - 1] < data["rsis"][i - 1] and data["rsis"][i] < data["rsi"][i]: if data["close"][i] < data["avg5"][i]: buy = data["close"][i] else: buy = data["low"][i] weight = 1 ############################# ### STOCHASTIC weight 분석 ### ############################# if data["slow_k"][i] in (0, 1, 2, 3): weight = 1 if data["slow_k"][i] in (4, 5, 6, 7, 8): weight = 1 elif data["slow_k"][i] in (9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20): weight = 1 elif data["slow_k"][i] in (21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35): weight = 1 return buy, weight, sell def getPriceAndWeight2(self, data, i): buy, weight, sell = -1, -1, -1 START_TIME_INDEX = 0 for c in range(370, len(data.index)): if data.index[c].strftime("%H:%M:%S") == "09:01:00": START_TIME_INDEX = c break if i >= START_TIME_INDEX: ################ ### sell 분석 ### ################ # 1. 볼린져밴드 상단이 최고와 종가 사이 아래에 있는 경우 매도한다. if (data["high"][i] - data["close"][i]) / 2 + data["close"][i] > data["upper"][i]: sell = data["high"][i] if data["slow_k"][i] >= 85: if data["slow_d"][i - 1] < data["slow_k"][i - 1] and data["slow_k"][i] < data["slow_d"][i]: sell = data["high"][i] # 3. 2시 이후에는 최고가가 볼린져밴드 상단 위에 있으면 매도한다. if i > 300 and data["high"][i] > data["upper"][i]: sell = data["high"][i] ########################## ### STOCHASTIC buy 분석 ### ########################## if i < 40: pre_slow = data["slow_k"][i - 1] / data["slow_d"][i - 1] - 1 now_slow = data["slow_k"][i] / data["slow_d"][i] - 1 if pre_slow < 0 and 0 < now_slow: if data["slow_k"][i] <= 35: if (data["close"][i] - data["lower"][i]) / (data["upper"][i] - data["lower"][i]) < 0.35: if data["slow_k"][i - 1] < data["slow_d"][i - 1] and data["slow_d"][i] < data["slow_k"][i]: if data['avg10'][i] < data['avg5'][i]: if data["open"][i] < data["close"][i]: buy = data["close"][i] else: buy = data["low"][i] else: pre_slow = data["slow_k"][i - 1] / data["slow_d"][i - 1] - 1 now_slow = data["slow_k"][i] / data["slow_d"][i] - 1 if pre_slow < 0 and pre_slow < now_slow and -0.15 < now_slow: if data["slow_k"][i] <= 30: if (data["close"][i] - data["lower"][i]) / (data["upper"][i] - data["lower"][i]) < 0.35: if data["slow_k"][i - 1] < data["slow_d"][i - 1] and data["slow_d"][i] < data["slow_k"][i]: if data['avg10'][i] < data['avg5'][i]: if data["close"][i] < data["avg5"][i]: buy = data["close"][i] else: buy = data["low"][i] ############################# ### STOCHASTIC weight 분석 ### ############################# if data["slow_k"][i] in (0, 1, 2, 3): weight = 1 if data["slow_k"][i] in (4, 5, 6, 7, 8): weight = 1 elif data["slow_k"][i] in (9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20): weight = 1 elif data["slow_k"][i] in (21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35): weight = 1 return buy, weight, sell def getBuyCheck(self, data, i, buy, weight): if data['close'][i] 40: buy, weight, sell = -1, -1, -1 return buy, weight def isYangbong(self, data, i): if data['close'][i] > data['open'][i]: return True else: if data['low'][i] < data['close'][i] == data['open'][i] == data['high'][i]: return True if data['low'][i] < data['open'][i] == data['close'][i] < data['high'][i]: return True return False def isUmbong(self, data, i): if data['close'][i] < data['open'][i]: return True else: if data['low'][i] == data['close'][i] == data['open'][i] < data['high'][i]: return True if data['low'][i] < data['open'][i] == data['close'][i] < data['high'][i]: return True return False # 곱버스에 해당함 def getBuyPriceAndWeight_252670(self, data, i): buy, weight = -1, -1 START_TIME_INDEX = 0 for c in range(370, len(data.index)): if data.index[c].strftime("%H:%M:%S") == "09:01:00": START_TIME_INDEX = c break if i > START_TIME_INDEX: # 매수 분석 param = 1 if (data["macd"][i] < -3.110935149 and data["macds"][i] < -2.370579802 and data["diff_avg27"][i] < -0.51457476*param and data["diff_avg3_avg54"][i] < -11.04578189 * param and data["diff_avg6_avg27"][i] < -6.53755144 * param and data["diff_avg6_avg54"][i] < -9.518004115*param and data["diff_avg9_avg27"][i] < -5.21244856 * param and data["diff_avg9_avg54"][i] < -8.192901235*param and data["diff_open_lead1"][i] < -13.26157407*param and data["diff_close_lead1"][i] < -13.71064815*param and data["diff_high_lead1"][i] < -10.08564815*param and data["diff_low_lead1"][i] < -17.8912037*param and data["abs_avg_1"][i] > 15.72655178 * param and 29.0499289 - 22.02727828/3 < data["diff_upper_lower"][i] < 29.0499289*param + 22.02727828 ): buy = int((data["open"][i] + data["close"][i]) / 2) weight = 1 return buy, weight return buy, weight def getSellPriceAndWeight_252670(self, data, i): sell, weight = -1, -1 START_TIME_INDEX = 0 for c in range(370, len(data.index)): if data.index[c].strftime("%H:%M:%S") == "09:01:00": START_TIME_INDEX = c break if i > START_TIME_INDEX: # 매도 분석 # 3분선이 10분 이상 6분선 위에 있다가 5분선 아래로 내려옴 if i >= 381 + 10: vaild = True count = 0 for c in range(1, 11): if data["avg3"][i - c] == data["avg6"][i - c]: count += 1 if data["avg3"][i - c] < data["avg6"][i - c]: vaild = False break if vaild and count < 3: if data["avg3"][i] < data["avg6"][i]: sell = int(data["avg3"][i] - data["avg3"][i] % 5) weight = 1 return sell, weight # 3분선이 5분 이상 12분선 위에 있다가 12분선 아래로 내려옴 if i >= 381 + 5: vaild = True for c in range(1, 6): if not (data["avg3"][i - c] >= data["avg6"][i - c] >= data["avg9"][i - c] >= data["avg12"][i - c]): vaild = False break if vaild: if data["avg3"][i] < data["avg12"][i]: sell = data["close"][i] weight = 1 return sell, weight return sell, weight def getBuyPriceAndWeight_122630(self, data, i): buy, weight = -1, -1 START_TIME_INDEX = 0 for c in range(370, len(data.index)): if data.index[c].strftime("%H:%M:%S") == "09:01:00": START_TIME_INDEX = c break if i > START_TIME_INDEX: # 매수 분석 param = 1 if (data["macd"][i] < -8.532976905 and data["macds"][i] < -5.679850674 and data["abs_avg_1"][i] > 70.48701299 * param ): """ if (data["diff_avg27"][i] < -1.637205387 * param and data["diff_avg3_avg27"][i] < -25.4455267 * param and data["diff_avg3_avg54"][i] < -31.55964406 * param and data["diff_avg6_avg27"][i] < -17.45039683 * param and data["diff_avg6_avg54"][i] < -23.56451419 * param and data["diff_avg9_avg27"][i] < -13.27020202 * param and data["diff_avg9_avg54"][i] < -19.38431938 * param and data["diff_avg12_avg27"][i] < -10.42388167 * param and data["diff_avg12_avg54"][i] < -16.53799904 * param and data["diff_change_lead1"][i] < -25.68993506 * param and data["diff_open_lead1"][i] < -37.53246753 * param and data["diff_close_lead1"][i] < -45.1461039 * param and data["diff_high_lead1"][i] < -30.03246753 * param and data["diff_low_lead1"][i] < -53.08441558 * param ): buy = int((data["open"][i] + data["close"][i]) / 2) weight = 1 return buy, weight """ if -30 < data["macd"][i] < -25: valid = True for c in range(1, 20): if data["macd"][i-c] < -30: valid = False break if valid: buy = int((data["open"][i] + data["close"][i]) / 2) weight = 1 return buy, weight previous_lowest_close = 99999999 for c in range(10, 30): if data["close"][i-c] < previous_lowest_close: previous_lowest_close = data["close"][i-c] if data["close"][i] > previous_lowest_close: valid = True for c in range(1, 20): if data["macd"][i-c] < -30: valid = False break if valid: buy = int((data["open"][i] + data["close"][i]) / 2) weight = 1 return buy, weight if (data["avg54"][i-4] < data["avg54"][i-3] < data["avg54"][i-2] < data["avg54"][i-1] < data["avg54"][i] and data["avg54"][i] < min(data["avg3"][i], data["avg6"][i], data["avg9"][i], data["avg12"][i], data["avg27"][i]) and data["avg54"][i] < data["avg27"][i] < data["avg12"][i] < data["avg9"][i] < data["avg6"][i] < data["avg3"][i] and max(data["avg3"][i], data["avg6"][i], data["avg9"][i], data["avg12"][i], data["avg27"][i]) - min(data["avg3"][i], data["avg6"][i], data["avg9"][i], data["avg12"][i], data["avg27"][i]) < 5 ): buy = data["close"][i] weight = 1 return buy, weight if (data["avg54"][i-4] < data["avg54"][i-3] < data["avg54"][i-2] < data["avg54"][i-1] < data["avg54"][i] and data["avg54"][i-7] < data["avg3"][i-7] < data["avg6"][i-7] < data["avg9"][i-7] < data["avg12"][i-7] and data["avg54"][i] < data["avg12"][i] < data["avg9"][i] < data["avg6"][i] < data["avg3"][i] ): if data['macd'][i] < -5: buy = data["close"][i] weight = 1 return buy, weight return buy, weight def getSellPriceAndWeight_122630(self, data, i): sell, weight = -1, -1 START_TIME_INDEX = 0 for c in range(370, len(data.index)): if data.index[c].strftime("%H:%M:%S") == "09:01:00": START_TIME_INDEX = c break if i > START_TIME_INDEX: # 매수 분석 # 3분선이 10분 이상 6분선 위에 있다가 5분선 아래로 내려옴 if i >= 381 + 10: vaild = True count = 0 for c in range(1, 11): if data["avg3"][i - c] == data["avg6"][i - c]: count += 1 if data["avg3"][i - c] < data["avg6"][i - c]: vaild = False break if vaild and count < 3: if data["avg3"][i] < data["avg6"][i]: sell = int(data["avg3"][i] - data["avg3"][i]%5) weight = 1 return sell, weight # 3분선이 5분 이상 12분선 위에 있다가 12분선 아래로 내려옴 if i >= 381 + 5: vaild = True for c in range(1, 6): if not (data["avg3"][i - c] >= data["avg6"][i - c] >= data["avg9"][i - c] >= data["avg12"][i - c]): vaild = False break if vaild: if data["avg3"][i] < data["avg12"][i]: sell = data["close"][i] weight = 1 return sell, weight param = 2 if (data["macd"][i] > 11.4590339 and data["diff_avg27"][i] > 2.261904762 * param and data["diff_avg3_avg27"][i] > 28.83730159 * param and data["diff_avg3_avg54"][i] > 40.84391534 * param and data["diff_avg6_avg27"][i] > 22.49801587 * param and data["diff_avg6_avg54"][i] > 34.50462963 * param and data["diff_avg9_avg27"][i] > 17.6984127 * param and data["diff_avg9_avg54"][i] > 29.70502646 * param and data["diff_avg12_avg27"][i] > 13.59920635 * param and data["diff_avg12_avg54"][i] > 25.60582011 * param and data["diff_change_lead1"][i] > 40.82142857 * param and data["diff_open_lead1"][i] > 53.48214286 * param and data["diff_close_lead1"][i] > 58.23214286 * param and data["diff_high_lead1"][i] > 63.125 * param and data["diff_low_lead1"][i] > 49.41071429 * param and data["diff_upper_lower"][i] < 70.63330362 * param + 124.7189534 / 3 and data["diff_change_base"][i] > 16.73214286 * param and data["diff_avg3"][i] > 4.714285714 * param and data["diff_avg6"][i] > 3.857142857 * param and data["diff_avg9"][i] > 3.373015873 * param and data["diff_avg12"][i] > 3.026785714 * param and data["diff_avg27"][i] > 2.261904762 * param and data["diff_avg54"][i] > 1.18452381 * param ): buy = int((data["open"][i] + data["close"][i]) / 2) weight = 1 return buy, weight return sell, weight def analyze(self, result): # 기본 캔들 정보 open = result["open"] close = result["close"] high = result["high"] low = result["low"] vol = result["vol"] label = result["label"] # 캔들 정보 연산 height = [close[i] - open[i] for i in range(0, len(close))] top_tail_height = [high[i] - max(open[i], close[i]) for i in range(0, len(close))] bottom_tail_height = [min(open[i], close[i]) - low[i] for i in range(0, len(close))] # 이동 평균 close_df = pd.DataFrame(close) avg3_list = close_df.rolling(window=3).mean().fillna(close[0]).values.tolist() avg3 = [item[0] for item in avg3_list] avg6_list = close_df.rolling(window=6).mean().fillna(close[0]).values.tolist() avg6 = [item[0] for item in avg6_list] avg9_list = close_df.rolling(window=9).mean().fillna(close[0]).values.tolist() avg9 = [item[0] for item in avg9_list] avg12_list = close_df.rolling(window=12).mean().fillna(close[0]).values.tolist() avg12 = [item[0] for item in avg12_list] avg27_list = close_df.rolling(window=27).mean().fillna(close[0]).values.tolist() avg27 = [item[0] for item in avg27_list] avg54_list = close_df.rolling(window=54).mean().fillna(close[0]).values.tolist() avg54 = [item[0] for item in avg54_list] abs_avg_1 = [max(avg3[i], avg6[i], avg9[i], avg12[i], avg27[i], avg54[i]) - min(avg3[i], avg6[i], avg9[i], avg12[i], avg27[i], avg54[i]) for i in range(0, len(close))] abs_avg_2 = [max(avg3[i], avg6[i], avg9[i], avg12[i], avg27[i]) - min(avg3[i], avg6[i], avg9[i], avg12[i], avg27[i]) for i in range(0, len(close))] abs_avg_3 = [max(avg3[i], avg6[i], avg9[i], avg12[i]) - min(avg3[i], avg6[i], avg9[i], avg12[i]) for i in range(0, len(close))] abs_avg_4 = [max(avg3[i], avg6[i], avg9[i]) - min(avg3[i], avg6[i], avg9[i]) for i in range(0, len(close))] abs_avg_5 = [max(avg3[i], avg6[i]) - min(avg3[i], avg6[i]) for i in range(0, len(close))] diff_open, diff_close, diff_low, diff_high = [], [], [], [] diff_open.append(0) for i in range(1, len(open)): diff_open.append(open[i] - open[i - 1]) diff_close.append(0) for i in range(1, len(close)): diff_close.append(close[i] - close[i - 1]) diff_low.append(0) for i in range(1, len(low)): diff_low.append(low[i] - low[i - 1]) diff_high.append(0) for i in range(1, len(high)): diff_high.append(high[i] - high[i - 1]) diff_avg3, diff_avg6, diff_avg9, diff_avg12, diff_avg27, diff_avg54 = [], [], [], [], [], [] diff_avg3.append(0) for i in range(1, len(avg3)): diff_avg3.append(avg3[i]-avg3[i-1]) diff_avg6.append(0) for i in range(1, len(avg6)): diff_avg6.append(avg6[i] - avg6[i - 1]) diff_avg9.append(0) for i in range(1, len(avg9)): diff_avg9.append(avg9[i] - avg9[i - 1]) diff_avg12.append(0) for i in range(1, len(avg12)): diff_avg12.append(avg12[i] - avg12[i - 1]) diff_avg27.append(0) for i in range(1, len(avg27)): diff_avg27.append(avg27[i] - avg27[i - 1]) diff_avg54.append(0) for i in range(1, len(avg54)): diff_avg54.append(avg54[i] - avg54[i - 1]) diff_avg3_avg6 = [avg3[i] - avg6[i] for i in range(0, len(close))] diff_avg3_avg9 = [avg3[i] - avg9[i] for i in range(0, len(close))] diff_avg3_avg12 = [avg3[i] - avg12[i] for i in range(0, len(close))] diff_avg3_avg27 = [avg3[i] - avg27[i] for i in range(0, len(close))] diff_avg3_avg54 = [avg3[i] - avg54[i] for i in range(0, len(close))] diff_avg6_avg9 = [avg6[i] - avg9[i] for i in range(0, len(close))] diff_avg6_avg12 = [avg6[i] - avg12[i] for i in range(0, len(close))] diff_avg6_avg27 = [avg6[i] - avg27[i] for i in range(0, len(close))] diff_avg6_avg54 = [avg6[i] - avg54[i] for i in range(0, len(close))] diff_avg9_avg12 = [avg9[i] - avg12[i] for i in range(0, len(close))] diff_avg9_avg27 = [avg9[i] - avg27[i] for i in range(0, len(close))] diff_avg9_avg54 = [avg9[i] - avg54[i] for i in range(0, len(close))] diff_avg12_avg27 = [avg12[i] - avg27[i] for i in range(0, len(close))] diff_avg12_avg54 = [avg12[i] - avg54[i] for i in range(0, len(close))] diff_avg27_avg54 = [avg27[i] - avg54[i] for i in range(0, len(close))] # 볼린져 밴드 df = pd.DataFrame(close) max20 = df.rolling(window=20).mean() stddev20 = df.rolling(window=20).std() upper_df = max20 + (stddev20 * 2) # 상단 볼린저 밴드 lower_df = max20 - (stddev20 * 2) # 하단 볼린저 밴드 upper, lower = [], [] for i in range(len(upper_df)): if i < 10: upper.append(upper_df.values[0][0]) lower.append(lower_df.values[0][0]) else: upper.append(upper_df.values[i][0]) lower.append(lower_df.values[i][0]) point_temp = result["time"] STOCK = [] for i in range(len(open)): STOCK.append({'volume': vol[i], 'close': close[i], 'open': open[i], 'high': high[i], 'low': low[i], 'avg3': avg3[i], 'avg6': avg6[i],'avg9': avg9[i],'avg12': avg12[i],'avg27': avg27[i],'avg54': avg54[i]}) # stochastic stochastic_df = self.stochastic.apply(STOCK, n=30, m=5, t=5) fast_k = stochastic_df['fast_k'].values.tolist() slow_k = stochastic_df['slow_k'].values.tolist() slow_d = stochastic_df['slow_d'].values.tolist() # macd macd_df = self.macd.apply(STOCK, short=12, long=26, t=9) macd = macd_df['macd'].values.tolist() macds = macd_df['macds'].values.tolist() macdo = macd_df['macdo'].values.tolist() # rsi rsi_df = self.rsi.apply(STOCK, period=30, window=5) rsi = rsi_df['rsi'].values.tolist() rsis = rsi_df['rsis'].values.tolist() # ichimokuCloud ichimokuCloud_df = self.ichimokuCloud.apply(STOCK, c=9, b=26, l=52) ichimokuCloud_df = ichimokuCloud_df[:len(ichimokuCloud_df) - 51] changeLine = ichimokuCloud_df['changeLine'].values.tolist() baseLine = ichimokuCloud_df['baseLine'].values.tolist() leadingSpan1 = ichimokuCloud_df['leadingSpan1'].values.tolist() leadingSpan2 = ichimokuCloud_df['leadingSpan2'].values.tolist() # 간격 ##### 볼린져 밴드 diff_upper_lower = [upper[i] - lower[i] for i in range(0, len(upper))] diff_open_lower = [open[i] - lower[i] for i in range(0, len(open))] diff_open_upper = [open[i] - upper[i] for i in range(0, len(open))] diff_close_lower = [close[i] - lower[i] for i in range(0, len(close))] diff_close_upper = [close[i] - upper[i] for i in range(0, len(close))] diff_high_lower = [high[i] - lower[i] for i in range(0, len(high))] diff_high_upper = [high[i] - upper[i] for i in range(0, len(high))] diff_low_lower = [low[i] - lower[i] for i in range(0, len(low))] diff_low_upper = [low[i] - upper[i] for i in range(0, len(low))] ##### 일목균형표 diff_lead1_lead2 = [leadingSpan1[i] - leadingSpan2[i] for i in range(0, len(leadingSpan1))] diff_change_base = [changeLine[i] - baseLine[i] for i in range(0, len(baseLine))] diff_base_lead1 = [baseLine[i] - leadingSpan1[i] for i in range(0, len(baseLine))] diff_base_lead2 = [baseLine[i] - leadingSpan2[i] for i in range(0, len(baseLine))] diff_change_lead1 = [changeLine[i] - leadingSpan1[i] for i in range(0, len(changeLine))] diff_change_lead2 = [changeLine[i] - leadingSpan2[i] for i in range(0, len(changeLine))] diff_open_lead2 = [open[i] - leadingSpan2[i] for i in range(0, len(open))] diff_open_lead1 = [open[i] - leadingSpan1[i] for i in range(0, len(open))] diff_open_change = [open[i] - changeLine[i] for i in range(0, len(open))] diff_open_base = [open[i] - baseLine[i] for i in range(0, len(open))] diff_close_lead2 = [close[i] - leadingSpan2[i] for i in range(0, len(close))] diff_close_lead1 = [close[i] - leadingSpan1[i] for i in range(0, len(close))] diff_close_change = [close[i] - changeLine[i] for i in range(0, len(close))] diff_close_base = [close[i] - baseLine[i] for i in range(0, len(close))] diff_high_lead2 = [high[i] - leadingSpan2[i] for i in range(0, len(high))] diff_high_lead1 = [high[i] - leadingSpan1[i] for i in range(0, len(high))] diff_high_change = [high[i] - changeLine[i] for i in range(0, len(high))] diff_high_base = [high[i] - baseLine[i] for i in range(0, len(high))] diff_low_lead2 = [low[i] - leadingSpan2[i] for i in range(0, len(low))] diff_low_lead1 = [low[i] - leadingSpan1[i] for i in range(0, len(low))] diff_low_change = [low[i] - changeLine[i] for i in range(0, len(low))] diff_low_base = [low[i] - baseLine[i] for i in range(0, len(low))] diff_macd_macds = [macd[i] - macds[i] for i in range(0, len(macd))] diff_slowk_slowd = [slow_k[i] - slow_d[i] for i in range(0, len(slow_k))] diff_rsi_rsis = [rsi[i] - rsis[i] for i in range(0, len(rsi))] diff_macd, diff_macdo, diff_macds = [], [], [] diff_macd.append(0) for i in range(1, len(macd)): diff_macd.append(macd[i] - macd[i - 1]) diff_macdo.append(0) for i in range(1, len(macdo)): diff_macdo.append(macdo[i] - macdo[i - 1]) diff_macds.append(0) for i in range(1, len(macds)): diff_macds.append(macds[i] - macds[i - 1]) diff_fast_k, diff_slow_k, diff_slow_d = [], [], [] diff_fast_k.append(0) for i in range(1, len(fast_k)): diff_fast_k.append(fast_k[i] - fast_k[i - 1]) diff_slow_k.append(0) for i in range(1, len(slow_k)): diff_slow_k.append(slow_k[i] - slow_k[i - 1]) diff_slow_d.append(0) for i in range(1, len(slow_d)): diff_slow_d.append(slow_d[i] - slow_d[i - 1]) diff_rsi, diff_rsis = [], [] diff_rsi.append(0) for i in range(1, len(rsi)): diff_rsi.append(rsi[i] - rsi[i - 1]) diff_rsis.append(0) for i in range(1, len(rsis)): diff_rsis.append(rsis[i] - rsis[i - 1]) diff_changeLine, diff_baseLine = [], [] diff_changeLine.append(0) for i in range(1, len(changeLine)): diff_changeLine.append(changeLine[i] - changeLine[i - 1]) diff_baseLine.append(0) for i in range(1, len(baseLine)): diff_baseLine.append(baseLine[i] - baseLine[i - 1]) diff_upper, diff_lower = [], [] diff_upper.append(0) for i in range(1, len(upper)): diff_upper.append(upper[i] - upper[i - 1]) diff_lower.append(0) for i in range(1, len(lower)): diff_lower.append(lower[i] - lower[i - 1]) # 결과 temp = { "date": point_temp, "open": open, "high": high, "low": low, "close": close, "volume": vol, "avg3": avg3, "avg6": avg6, "avg9": avg9, "avg12": avg12, "avg27": avg27, "avg54": avg54, "upper": upper, "lower": lower, "macd": macd, "macds": macds, "macdo": macdo, "fast_k": fast_k, "slow_k": slow_k, "slow_d": slow_d, "rsi": rsi, "rsis": rsis, "changeLine": changeLine, "baseLine": baseLine, "leadingSpan1": leadingSpan1, "leadingSpan2": leadingSpan2, "height": height, "top_tail_height": top_tail_height, "bottom_tail_height": bottom_tail_height, "abs_avg_1": abs_avg_1, "abs_avg_2": abs_avg_2, "abs_avg_3": abs_avg_3, "abs_avg_4": abs_avg_4, "abs_avg_5": abs_avg_5, "diff_open": diff_open, "diff_close": diff_close, "diff_low": diff_low, "diff_high": diff_high, "diff_avg3":diff_avg3, "diff_avg6":diff_avg6, "diff_avg9":diff_avg9, "diff_avg12":diff_avg12, "diff_avg27":diff_avg27, "diff_avg54":diff_avg54, "diff_macd":diff_macd, "diff_macdo":diff_macdo, "diff_macds":diff_macds, "diff_fast_k":diff_fast_k, "diff_slow_k":diff_slow_k, "diff_slow_d":diff_slow_d, "diff_rsi":diff_rsi, "diff_rsis":diff_rsis, "diff_changeLine":diff_changeLine, "diff_baseLine":diff_baseLine, "diff_upper":diff_upper, "diff_lower":diff_lower, "diff_avg3_avg6": diff_avg3_avg6, "diff_avg3_avg9": diff_avg3_avg9, "diff_avg3_avg12": diff_avg3_avg12, "diff_avg3_avg27": diff_avg3_avg27, "diff_avg3_avg54": diff_avg3_avg54, "diff_avg6_avg9": diff_avg6_avg9, "diff_avg6_avg12": diff_avg6_avg12, "diff_avg6_avg27": diff_avg6_avg27, "diff_avg6_avg54": diff_avg6_avg54, "diff_avg9_avg12": diff_avg9_avg12, "diff_avg9_avg27": diff_avg9_avg27, "diff_avg9_avg54": diff_avg9_avg54, "diff_avg12_avg27": diff_avg12_avg27, "diff_avg12_avg54": diff_avg12_avg54, "diff_avg27_avg54": diff_avg27_avg54, "diff_upper_lower": diff_upper_lower, "diff_open_lower": diff_open_lower, "diff_open_upper": diff_open_upper, "diff_close_lower": diff_close_lower, "diff_close_upper": diff_close_upper, "diff_high_lower": diff_high_lower, "diff_high_upper": diff_high_upper, "diff_low_lower": diff_low_lower, "diff_low_upper": diff_low_upper, "diff_lead1_lead2": diff_lead1_lead2, "diff_change_base": diff_change_base, "diff_base_lead1": diff_base_lead1, "diff_base_lead2": diff_base_lead2, "diff_change_lead1": diff_change_lead1, "diff_change_lead2": diff_change_lead2, "diff_open_lead2": diff_open_lead2, "diff_open_lead1": diff_open_lead1, "diff_open_change": diff_open_change, "diff_open_base": diff_open_base, "diff_close_lead2": diff_close_lead2, "diff_close_lead1": diff_close_lead1, "diff_close_change": diff_close_change, "diff_close_base": diff_close_base, "diff_high_lead2": diff_high_lead2, "diff_high_lead1": diff_high_lead1, "diff_high_change": diff_high_change, "diff_high_base": diff_high_base, "diff_low_lead2": diff_low_lead2, "diff_low_lead1": diff_low_lead1, "diff_low_change": diff_low_change, "diff_low_base": diff_low_base, "diff_macd_macds": diff_macd_macds, "diff_slowk_slowd": diff_slowk_slowd, "diff_rsi_rsis": diff_rsi_rsis, "label": label } data = pd.DataFrame(temp) df_final_time = pd.DatetimeIndex(point_temp) data.index = df_final_time data = data.fillna(close[0]) return data def write(self, outFp, df, i): outFp.write(str(df["macd"][i]) + "\t") outFp.write(str(df["macds"][i]) + "\t") outFp.write(str(df["macdo"][i]) + "\t") outFp.write(str(df["fast_k"][i]) + "\t") outFp.write(str(df["slow_k"][i]) + "\t") outFp.write(str(df["slow_d"][i]) + "\t") outFp.write(str(df["rsi"][i]) + "\t") outFp.write(str(df["rsis"][i]) + "\t") outFp.write(str(df["height"][i]) + "\t") outFp.write(str(df["top_tail_height"][i]) + "\t") outFp.write(str(df["bottom_tail_height"][i]) + "\t") outFp.write(str(df["abs_avg_1"][i]) + "\t") outFp.write(str(df["abs_avg_2"][i]) + "\t") outFp.write(str(df["abs_avg_3"][i]) + "\t") outFp.write(str(df["abs_avg_4"][i]) + "\t") outFp.write(str(df["abs_avg_5"][i]) + "\t") outFp.write(str(df["diff_open"][i]) + "\t") outFp.write(str(df["diff_close"][i]) + "\t") outFp.write(str(df["diff_low"][i]) + "\t") outFp.write(str(df["diff_high"][i]) + "\t") outFp.write(str(df["diff_avg3"][i]) + "\t") outFp.write(str(df["diff_avg6"][i]) + "\t") outFp.write(str(df["diff_avg9"][i]) + "\t") outFp.write(str(df["diff_avg12"][i]) + "\t") outFp.write(str(df["diff_avg27"][i]) + "\t") outFp.write(str(df["diff_avg54"][i]) + "\t") outFp.write(str(df["diff_macd"][i]) + "\t") outFp.write(str(df["diff_macdo"][i]) + "\t") outFp.write(str(df["diff_macds"][i]) + "\t") outFp.write(str(df["diff_fast_k"][i]) + "\t") outFp.write(str(df["diff_slow_k"][i]) + "\t") outFp.write(str(df["diff_slow_d"][i]) + "\t") outFp.write(str(df["diff_rsi"][i]) + "\t") outFp.write(str(df["diff_rsis"][i]) + "\t") outFp.write(str(df["diff_changeLine"][i]) + "\t") outFp.write(str(df["diff_baseLine"][i]) + "\t") outFp.write(str(df["diff_upper"][i]) + "\t") outFp.write(str(df["diff_lower"][i]) + "\t") outFp.write(str(df["diff_avg3_avg6"][i]) + "\t") outFp.write(str(df["diff_avg3_avg9"][i]) + "\t") outFp.write(str(df["diff_avg3_avg12"][i]) + "\t") outFp.write(str(df["diff_avg3_avg27"][i]) + "\t") outFp.write(str(df["diff_avg3_avg54"][i]) + "\t") outFp.write(str(df["diff_avg6_avg9"][i]) + "\t") outFp.write(str(df["diff_avg6_avg12"][i]) + "\t") outFp.write(str(df["diff_avg6_avg27"][i]) + "\t") outFp.write(str(df["diff_avg6_avg54"][i]) + "\t") outFp.write(str(df["diff_avg9_avg12"][i]) + "\t") outFp.write(str(df["diff_avg9_avg27"][i]) + "\t") outFp.write(str(df["diff_avg9_avg54"][i]) + "\t") outFp.write(str(df["diff_avg12_avg27"][i]) + "\t") outFp.write(str(df["diff_avg12_avg54"][i]) + "\t") outFp.write(str(df["diff_avg27_avg54"][i]) + "\t") outFp.write(str(df["diff_upper_lower"][i]) + "\t") outFp.write(str(df["diff_open_lower"][i]) + "\t") outFp.write(str(df["diff_open_upper"][i]) + "\t") outFp.write(str(df["diff_close_lower"][i]) + "\t") outFp.write(str(df["diff_close_upper"][i]) + "\t") outFp.write(str(df["diff_high_lower"][i]) + "\t") outFp.write(str(df["diff_high_upper"][i]) + "\t") outFp.write(str(df["diff_low_lower"][i]) + "\t") outFp.write(str(df["diff_low_upper"][i]) + "\t") outFp.write(str(df["diff_lead1_lead2"][i]) + "\t") outFp.write(str(df["diff_change_base"][i]) + "\t") outFp.write(str(df["diff_base_lead1"][i]) + "\t") outFp.write(str(df["diff_base_lead2"][i]) + "\t") outFp.write(str(df["diff_change_lead1"][i]) + "\t") outFp.write(str(df["diff_change_lead2"][i]) + "\t") outFp.write(str(df["diff_open_lead2"][i]) + "\t") outFp.write(str(df["diff_open_lead1"][i]) + "\t") outFp.write(str(df["diff_open_change"][i]) + "\t") outFp.write(str(df["diff_open_base"][i]) + "\t") outFp.write(str(df["diff_close_lead2"][i]) + "\t") outFp.write(str(df["diff_close_lead1"][i]) + "\t") outFp.write(str(df["diff_close_change"][i]) + "\t") outFp.write(str(df["diff_close_base"][i]) + "\t") outFp.write(str(df["diff_high_lead2"][i]) + "\t") outFp.write(str(df["diff_high_lead1"][i]) + "\t") outFp.write(str(df["diff_high_change"][i]) + "\t") outFp.write(str(df["diff_high_base"][i]) + "\t") outFp.write(str(df["diff_low_lead2"][i]) + "\t") outFp.write(str(df["diff_low_lead1"][i]) + "\t") outFp.write(str(df["diff_low_change"][i]) + "\t") outFp.write(str(df["diff_low_base"][i]) + "\t") outFp.write(str(df["diff_macd_macds"][i]) + "\t") outFp.write(str(df["diff_slowk_slowd"][i]) + "\t") outFp.write(str(df["diff_rsi_rsis"][i]) + "\t") outFp.write(str(df["label"][i]) + "\n") return def checkTransaction(self, data, stock_code, isRealTime=True): # 4일치 중에서 앞에 2일은 제거한다. date = data['date'].dt.date.unique().tolist() data = data[data['date'].dt.date != date[0]] data = data[data['date'].dt.date != date[1]] # 어제 오늘 데이터로 분석 bsLine = {} size = len(data["close"]) if isRealTime: # isRealTime=True, 실시간 적용 last_index = size - 1 if stock_code == "252670": sell, sell_weight = self.getSellPriceAndWeight_252670(data, last_index) buy, buy_weight = self.getBuyPriceAndWeight_252670(data, last_index) else: sell, sell_weight = self.getSellPriceAndWeight_122630(data, last_index) buy, buy_weight = self.getBuyPriceAndWeight_122630(data, last_index) bsLine['buy'] = [buy] bsLine['buy_weight'] = [buy_weight] bsLine['sell'] = [sell] bsLine['sell_weight'] = [sell_weight] else: # Type=False, 시뮬레이션 적용 bsLine['buy'] = [-1 for i in range(size)] bsLine['buy_weight'] = [-1 for i in range(size)] bsLine['sell'] = [-1 for i in range(size)] bsLine['sell_weight'] = [-1 for i in range(size)] for i in range(size): if stock_code == "252670": sell, sell_weight = self.getSellPriceAndWeight_252670(data, i) buy, buy_weight = self.getBuyPriceAndWeight_252670(data, i) else: sell, sell_weight = self.getSellPriceAndWeight_122630(data, i) buy, buy_weight = self.getBuyPriceAndWeight_122630(data, i) bsLine['buy'][i] = buy bsLine['buy_weight'][i] = buy_weight bsLine['sell'][i] = sell bsLine['sell_weight'][i] = sell_weight return bsLine, data def checkTransactionML(self, data, stock_code, predY, isRealTime=True): # 4일치 중에서 앞에 2일은 제거한다. date = data['date'].dt.date.unique().tolist() data = data[data['date'].dt.date != date[0]] data = data[data['date'].dt.date != date[1]] # 어제 오늘 데이터로 분석 bsLine = {} size = len(data["close"]) if isRealTime: # isRealTime=True, 실시간 적용 last_index = size - 1 # Type=False, 시뮬레이션 적용 bsLine['buy'] = [-1 for i in range(size)] bsLine['buy_weight'] = [-1 for i in range(size)] bsLine['sell'] = [-1 for i in range(size)] bsLine['sell_weight'] = [-1 for i in range(size)] sell, sell_weight, buy, buy_weight = -1, -1, -1, -1 if predY[last_index] == 1: sell = int((data["open"][last_index] + data["close"][last_index]) / 2) sell_weight = 1 elif predY[last_index] == 2: buy = int((data["open"][last_index] + data["close"][last_index]) / 2) buy_weight = 1 bsLine['buy'] = [buy] bsLine['buy_weight'] = [buy_weight] bsLine['sell'] = [sell] bsLine['sell_weight'] = [sell_weight] else: # Type=False, 시뮬레이션 적용 bsLine['buy'] = [-1 for i in range(size)] bsLine['buy_weight'] = [-1 for i in range(size)] bsLine['sell'] = [-1 for i in range(size)] bsLine['sell_weight'] = [-1 for i in range(size)] for i in range(size): if predY[i] == 1: bsLine['sell'][i] = int((data["open"][i] + data["close"][i]) / 2) bsLine['sell_weight'][i] = 1 elif predY[i] == 2: bsLine['buy'][i] = int((data["open"][i] + data["close"][i]) / 2) bsLine['buy_weight'][i] = 1 return bsLine, data