import math import pandas as pd from stockpredictor.analysis.Common import Common from stockpredictor.analysis.Stochastic import Stochastic from stockpredictor.analysis.RSI import RSI from stockpredictor.analysis.MACD import MACD from stockpredictor.analysis.IchimokuCloud import IchimokuCloud class BuySellChecker: common = None stochastic = None rsi = 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 if i >= 3: ################ ### 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] # 2. slow_k가 90이 넘으면 매도한다. if data["slow_k"][i] > 90: 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] ########################## ### 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 ################ ### 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 # 곱버스에 해당함 def getPriceAndWeight3(self, data, i): buy, weight, sell = -1, -1, -1 # 381: 어제 날짜 데이터 개수 if i >= 381: # 매수 분석 # 이동선을 이용한 매매 # 3분선과 5분선이 10분 이상 내려오다가 3분선이 5분선을 넘어 서는 순간 매수 if int(data["avg3"][i]) > int(data["avg5"][i]): valid = True same_count = 0 for c in range(1, 10): if int(data["avg3"][i-c]) == int(data["avg5"][i-c]): same_count += 1 if int(data["avg3"][i-c]) > int(data["avg5"][i-c]): valid = False break if int(data["avg3"][i-c]) > int(data["avg3"][i-c-1]) or int(data["avg5"][i-c]) > int(data["avg5"][i-c-1]): valid = False break if valid and same_count < 3: buy = data["close"][i] - 5 weight = 3 # 이동선을 이용한 매매 # 3분선이 10분선에 돌파 후 지지하는지 확인하고 slow_k < 40일 때 매수함 # 현재 단계: # - avg3[i]이 avg10[i]보다 커야함 # - avg3[i]가 avg3[i-1]보다 커야함 if data['avg10'][i] < data['avg3'][i] and data['avg3'][i-1] < data['avg3'][i] and abs(data['avg10'][i] - data['avg3'][i]) > 2: # 첫 이전 단계: # - avg3[i-1]과 avg10[i-1]의 abs가 3이내여야 함 if abs(data['avg3'][i-1] - data['avg10'][i-1]) < 3 and data['avg3'][i-1] < data['avg3'][i-2]: index1 = -1 valid = False for j in range(2, 20): # 두 번째 이전 단계: # - avg3[i-2]가 avg10[i-2]보다 커야 함 # - avg3[i-2]가 avg3[i-3]보다 작아야함 if data['avg10'][i-j] < data['avg3'][i-j] and data['avg3'][i-j] > data['avg3'][i-j-1]: index1 = j break for j in range(index1 + 1, 20): # 세 번째 이전 단계: # - avg3[i-3]가 avg3[i-4]보다 커야 함 if data['avg3'][i-j] > data['avg3'][i-j-1]: valid = True index1 = j else: break # 마지막 체크: # 만약 avg[3]이 avg[10]보다 작다면 매수함 if valid: if data['avg3'][i-index1-1] < data['avg10'][i-index1-1]: if data["slow_k"][i] < 40: buy = data["close"][i] weight = 3 # 만약 30원 이상 장대 양봉이 나온 경우, 다음이나 다다음 중간 값에서 매수를 한다. if (data["close"][i] - data["low"][i]) >= 30: middle = int((data["close"][i] + data["low"][i])/2) buy = middle # 장 초기 (시작 7분 이내), 볼린져 하단에서 시작하여 이병선을 모두 상승하여 마감한 경우 low 값에서 매수한다. if i < 381 + 8: if data["open"][i] == data["low"][i]: if data["close"][i] > max(data["avg3"][i], data["avg5"][i], data["avg10"][i], data["avg20"][i], data["avg30"][i]): buy = data["low"][i] """ ## macd를 이용한 매매 #if data["macdo"][i] < 0 and data["macd"][i] < -5: # if data["macd"][i-3] > data["macd"][i-2] and data["macd"][i-2] > data["macd"][i-1] and data["macd"][i-1] < data["macd"][i]: # buy = data["close"][i] - 5 """ # 표준편차를 이용한 매매 """ #mean = (data["avg3"][i] + data["avg5"][i] + data["avg10"][i] + data["avg20"][i] + data["avg30"][i])/5 #vsum = (data["avg3"][i] - mean) ** 2 + (data["avg5"][i] - mean) ** 2 + (data["avg10"][i] - mean) ** 2 + (data["avg20"][i] - mean) ** 2 + (data["avg30"][i] - mean) ** 2 #variance = vsum / 5 #std = math.sqrt(variance) #if std < 1: # sell = data["close"][i] - 5 """ """ # 매도 분석 # 3일 선이 10분 전부터 게속 10분선 위에 있다가 아래로 내려오면 매도함 valid = False if data["avg3"][i] < data["avg5"][i]: if (data["avg3"][i-1] > data["avg5"][i-1] and data["avg3"][i-2] > data["avg5"][i-2] and data["avg3"][i-3] > data["avg5"][i-3] and data["avg3"][i-4] > data["avg5"][i-4] and data["avg3"][i-5] > data["avg5"][i-5] and data["avg3"][i-6] > data["avg5"][i-6] and data["avg3"][i-7] > data["avg5"][i-7] and data["avg3"][i-8] > data["avg5"][i-8] and data["avg3"][i-9] > data["avg5"][i-8] and data["avg3"][i-10] > data["avg5"][i-10]): valid = True if valid: sell = data["close"][i] """ # slow_k와 slow_d가 90이상에서 slow_k가 slow_d 아래롸 내려온 경우 if data["rsi"][i] >= 75 and data["rsis"][i] >= 75: if data["rsi"][i-1] > data["rsis"][i-1] and data["rsi"][i] < data["rsis"][i]: sell = data["close"][i] - 5 #if data["slow_d"][i] > 90 and data["rsi"][i] > 65: # if data["upper"][i] <= data["high"][i]: # sell = data["close"][i] - 5 #if data['avg3'][i-1] > data['avg10'][i-1] and data['avg3'][i] <= data['avg10'][i]: # if abs(data['avg3'][i - 1] - data['avg30'][i - 1]) > 10: # sell = data["close"][i] return buy, weight, sell def analyze(self, result): open = result["open"] close = result["close"] high = result["high"] low = result["low"] vol = result["vol"] 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] avg5_list = close_df.rolling(window=5).mean().fillna(close[0]).values.tolist() avg5 = [item[0] for item in avg5_list] avg10_list = close_df.rolling(window=10).mean().fillna(close[0]).values.tolist() avg10 = [item[0] for item in avg10_list] avg20_list = close_df.rolling(window=20).mean().fillna(close[0]).values.tolist() avg20 = [item[0] for item in avg20_list] avg30_list = close_df.rolling(window=30).mean().fillna(close[0]).values.tolist() avg30 = [item[0] for item in avg30_list] avg60_list = close_df.rolling(window=60).mean().fillna(close[0]).values.tolist() avg60 = [item[0] for item in avg60_list] 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], 'avg5': avg5[i],'avg10': avg10[i],'avg20': avg20[i],'avg30': avg30[i],'avg60': avg60[i]}) # stochastic 계산 stochastic_df = self.stochastic.apply(STOCK, n=30, m=5, t=5) stochastic_df = stochastic_df.fillna(100) 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 계산 macd_df = self.macd.apply(STOCK, short=12, long=26, t=9) macd_df = macd_df.fillna(100) macd = macd_df['macd'].values.tolist() macds = macd_df['macds'].values.tolist() macdo = macd_df['macdo'].values.tolist() # rsi 계산 rsi_df = self.rsi.apply(STOCK, period=30, window=5) rsi_df = rsi_df.fillna(100) rsi = rsi_df['rsi'].values.tolist() rsis = rsi_df['rsis'].values.tolist() # ichimokuCloud 계산 # ichimokuCloud_df = self.ichimokuCloud.apply(STOCK, c=9, b=26, l=52) # ichimokuCloud_df = rsi_df.fillna(100) # changeLine = rsi_df['changeLine'].values.tolist() # baseLine = rsi_df['baseLine'].values.tolist() # leadingSpan1 = rsi_df['leadingSpan1'].values.tolist() # leadingSpan2 = rsi_df['leadingSpan2'].values.tolist() temp = {"date": point_temp, "open": open, "high": high, "low": low, "close": close, "volume": vol, "upper": upper, "lower": lower, "avg3": avg3, "avg5": avg5, "avg10": avg10, "avg20": avg20, "avg30": avg30, "avg60": avg60, "macd": macd, "macds": macds, "macdo": macdo, "fast_k": fast_k, "slow_k": slow_k, "slow_d": slow_d, "rsi": rsi, "rsis": rsis} data = pd.DataFrame(temp) df_final_time = pd.DatetimeIndex(point_temp) data.index = df_final_time return data def checkTransaction(self, data, stock_code): size = len(data["close"]) bsLine = {} bsLine['buy'] = [-1 for i in range(size)] bsLine['weight'] = [-1 for i in range(size)] bsLine['sell'] = [-1 for i in range(size)] for i in range(size): if stock_code == "252670": buy, weight, sell = self.getPriceAndWeight3(data, i) else: buy, weight, sell = self.getPriceAndWeight4(data, i) bsLine['buy'][i] = buy bsLine['weight'][i] = weight bsLine['sell'][i] = sell return bsLine