import os from dtw import dtw import json import sqlite3 import numpy as np from datetime import datetime, timedelta class BuySellChecker(): PATTERNS = None RESOURCE_PATH = None def __init__(self, RESOURCE_PATH, s): self.RESOURCE_PATH = RESOURCE_PATH return def nearDisparity(self, data, i): if (0.998 < data['disparity_avg5'][i] < 1.002 and 0.998 < data['disparity_avg5'][i] < 1.002 and 0.998 < data['disparity_avg5'][i] < 1.002 and 0.998 < data['disparity_avg5'][i] < 1.002 and 0.998 < data['disparity_avg5'][i] < 1.002): return True return False def cosine_similarity(self, x, y): return np.dot(x, y) / (np.sqrt(np.dot(x, x)) * np.sqrt(np.dot(y, y))) """ def findBuyPoint(self, data, data_signal, i): # 코사인 유사도(cosine similarity)로 과거 주가의 유사 패턴을 찾아 미래 예측하기 # https://teddylee777.github.io/pandas/cos-sim-stock/ buy_target = data['close'].iloc[i-179:i+1] window_size = len(buy_target) if window_size == 180: buy_target = (buy_target - buy_target.min()) / (buy_target.max() - buy_target.min()) for pattern in self.PATTERNS: cos_similarity = self.cosine_similarity(pattern, buy_target) if 0.995 < cos_similarity: return True return False """ def findBuyPoint(self, data, i): # DTW (Dynamic Time Warping) # 시계열 유사도: https://m.blog.naver.com/happyrachy/221693939341 if i < 24: return False for p in range(len(self.PATTERNS['min_max'])): size = len(self.PATTERNS['stndardization'][p]) if i - size + 1 < 0: continue close = data['close'].iloc[i-size+1:i+1] #min_max = np.array(self.PATTERNS['min_max'][p]).reshape(-1, 1) stndardization = np.array(self.PATTERNS['stndardization'][p]).reshape(-1, 1) #min_max_y = np.array((close - close.min()) / (close.max() - close.min())).reshape(-1, 1) stndardization_y = np.array((close - close.mean()) / close.std()).reshape(-1, 1) #manhattan_distance = lambda min_max, min_max_y: np.abs(min_max - min_max_y) #min_max_d, cost_matrix, acc_cost_matrix, path = dtw(min_max, min_max_y, dist=manhattan_distance) manhattan_distance = lambda stndardization, stndardization_y: np.abs(stndardization - stndardization_y) stndardization_d, cost_matrix, acc_cost_matrix, path = dtw(stndardization, stndardization_y, dist=manhattan_distance) if stndardization_d < 2: #print(i, data['ymd'].iloc[i], stndardization_d) return True return False def getMacd(self, ticker_code, day, mins=1): table = 'minutely_max_macd_' + str(mins) conn = sqlite3.connect(os.path.join(self.RESOURCE_PATH, 'coins.db')) cursor = conn.cursor() day1 = (datetime.strptime(day, '%Y%m%d') - timedelta(1)).strftime('%Y%m%d') cursor.execute('SELECT ymd, hms, macd, close FROM '+table+' WHERE (CODE=? or CODE=?) and (ymd=? or ymd=?) order by macd desc', (ticker_code, ticker_code.replace('KRW-', ''), day, day1, )) db_result1 = cursor.fetchall() cursor.close() conn.close() macd_limit = [(datetime.strptime(rows[0]+" "+rows[1], '%Y%m%d %H%M%S'), rows[2], rows[3]) for rows in db_result1] macd_dup = list(set(macd_limit)) return macd_dup def is_Support(self, df, i, observation_time=300): # c1 = df.Low[i] < df.Low[i - 1] < df.Low[i - 2] < df.Low[i - 3] # c2 = df.Low[i] < df.Low[i + 1] < df.Low[i + 2] < df.Low[i + 3] # return c1 & c2 #if df['low'][i] == np.min(df['low'][i - self.observation_time:i + self.observation_time + 1]): if df['low'][i] == np.min(df['low'][i - observation_time:i+1]): return True else: return False def is_Resistance(self, df, i, observation_time = 300): # c1 = df.High[i] > df.High[i - 1] > df.High[i - 2] > df.High[i - 3] # c2 = df.High[i] > df.High[i + 1] > df.High[i + 2] > df.High[i + 3] # return c1 & c2 #if df['high'][i] == np.max(df['high'][i - self.observation_time:i + self.observation_time + 1]): if df['high'][i] == np.max(df['high'][i - observation_time:i+1]): return True else: return False def getBuyPriceAndWeight1(self, ticker, MAX_BUY_PRICE, i, data, data_signal, BUY_LIST, isRealTime=True): buy_ymd, buy_price, buy_count, buy_cut, buy_type = None, -1, -1, -1, '' df_tmp = data_signal['ymd'] <= data['ymd'][i] df_signal = data_signal.loc[df_tmp] si = len(df_signal) - 1 """ duration = 5 if duration < i: if sum(data['avg20'][i - duration:i])/len(data['avg20'][i - duration:i]) < data['avg20'][i]: min_value1 = min(data['close'][i - 1], data['close'][i - 1]) min_value2 = min(data['close'][i - 2], data['close'][i - 2]) min_value3 = min(data['close'][i - 3], data['close'][i - 3]) min_sum = min_value1 + min_value2 + min_value3 if min_sum / 3 < data['close'][i] and data['close'][i] == data['high'][i]: if data['avg60'][i] < data['avg20'][i] and data['avg5'][i-1] < data['avg5'][i]: if data['middle'][i-1] < data['middle'][i]: if 0 < len(BUY_LIST['buy_list']): if BUY_LIST['buy_list'][-1]['buy_price'] < data['close'][i]: buy_price = data['close'][i] buy_type = 'avg20_close_up' buy_ymd = data['ymd'][i] buy_cut = -1 if data['slow_k'][si] < 30: buy_count = MAX_BUY_PRICE / (1 * data['close'][i]) elif data['slow_k'][si] < 50: buy_count = MAX_BUY_PRICE / (1.5 * data['close'][i]) else: buy_count = MAX_BUY_PRICE / (2 * data['close'][i]) return buy_ymd, buy_price, buy_count, buy_cut, buy_type else: buy_price = data['close'][i] buy_type = 'avg20_close_up' buy_ymd = data['ymd'][i] buy_cut = -1 if data['slow_k'][si] < 30: buy_count = MAX_BUY_PRICE / (1 * data['close'][i]) elif data['slow_k'][si] < 50: buy_count = MAX_BUY_PRICE / (1.5 * data['close'][i]) else: buy_count = MAX_BUY_PRICE / (2 * data['close'][i]) return buy_ymd, buy_price, buy_count, buy_cut, buy_type """ duration = 5 if duration < i: if np.average(data['trend_avg'][i - duration:i]) < data['trend_avg'][i]: if self.is_Support(data, i-10, observation_time = 300): if data['open'][i] < data['close'][i]: if np.max(data['high'][i-2:i]) < data['close'][i]: buy_price = data['close'][i] buy_type = 'support_300' buy_ymd = data['ymd'][i] buy_cut = data['close'][i] * 0.995 BUY_LIST['buy_limit'] = 0 if data['slow_k'][si] < 30: buy_count = MAX_BUY_PRICE*5 / (data['close'][i]) elif data['slow_k'][si] < 50: buy_count = MAX_BUY_PRICE*4 / (data['close'][i]) else: buy_count = MAX_BUY_PRICE*3 / (data['close'][i]) return buy_ymd, buy_price, buy_count, buy_cut, buy_type if data['slow_k'][i] < 15: if data['slow_k'][i-1] < data['slow_d'][i-1] and data['slow_d'][i] < data['slow_k'][i]: buy_price = data['close'][i] buy_type = 'slow_k' buy_ymd = data['ymd'][i] buy_cut = data['close'][i] * 0.995 BUY_LIST['buy_limit'] = 0 buy_count = MAX_BUY_PRICE * 2 / (data['close'][i]) return buy_ymd, buy_price, buy_count, buy_cut, buy_type return buy_ymd, buy_price, buy_count, buy_cut, buy_type def getSellPriceAndWeight1(self, ticker, i, data, data_signal, BUY_LIST=None): sell_price, sell_count, sell_type = -1, -1, '' df_tmp = data_signal['ymd'] <= data['ymd'][i] df_signal = data_signal.loc[df_tmp] si = len(df_signal) - 1 if 0 < len(BUY_LIST['buy_list']): duration = 5 if duration < i: if data['trend_avg'][i] < np.average(data['trend_avg'][i - duration:i]): if self.is_Resistance(data, i - 10, observation_time=300): sell_price = data['close'][i] sell_count = sum([price['buy_count'] for price in BUY_LIST['buy_list']]) if 75 < np.max(data_signal['rsi'][si-5:si]): if self.is_Resistance(data, i - 10, observation_time=300): sell_price = data['close'][i] sell_count = sum([price['buy_count'] for price in BUY_LIST['buy_list']]) if 70 < data['slow_k'][i]: if data['slow_d'][i-1] < data['slow_k'][i-1] and data['slow_k'][i] <= data['slow_d'][i]: sell_price = data['close'][i] sell_count = sum([price['buy_count'] for price in BUY_LIST['buy_list'] if price['buy_type'] == 'slow_k']) sell_type = 'slow_k' return sell_price, sell_count, sell_type def checkTransaction1(self, ticker, MAX_BUY_PRICE, data, data_signal, BUY_LIST=None, isRealTime=True): # 어제 오늘 데이터로 분석 bsLine = {} if data is not None and 'close' in data.columns: size = len(data["close"]) if isRealTime: # isRealTime=True, 실시간 적용 last_index = size - 1 sell_price, sell_count, sell_type = self.getSellPriceAndWeight1(ticker, last_index, data, data_signal, BUY_LIST) bsLine['sell_price'] = [sell_price] bsLine['sell_count'] = [sell_count] bsLine['sell_type'] = [sell_type] if 0 < sell_price: BUY_LIST['buy_limit'] = 0 BUY_LIST['buy_list'].clear() else: buy_ymd, buy_price, buy_count, buy_cut, buy_type = self.getBuyPriceAndWeight1(ticker, MAX_BUY_PRICE, last_index, data, data_signal, BUY_LIST, isRealTime) bsLine['buy_ymd'] = [buy_ymd] bsLine['buy_price'] = [buy_price] bsLine['buy_count'] = [buy_count] bsLine['buy_cut'] = [buy_cut] bsLine['buy_type'] = [buy_type] if 0 < buy_price: BUY_LIST['buy_list'].append({'buy_ymd': buy_ymd, 'buy_price': buy_price, 'buy_count': buy_count, 'buy_cut': buy_cut, 'buy_type': buy_type}) else: # Type=False, 시뮬레이션 적용 bsLine['buy_ymd'] = [-1 for i in range(size)] bsLine['buy_price'] = [-1 for i in range(size)] bsLine['buy_count'] = [-1 for i in range(size)] bsLine['buy_cut'] = [-1 for i in range(size)] bsLine['buy_type'] = ['' for i in range(size)] bsLine['sell_price'] = [-1 for i in range(size)] bsLine['sell_count'] = [-1 for i in range(size)] bsLine['sell_type'] = ['' for i in range(size)] for last_index in range(size): sell_price, sell_count, sell_type = self.getSellPriceAndWeight1(ticker, last_index, data, data_signal, BUY_LIST) bsLine['sell_price'][last_index] = sell_price bsLine['sell_count'][last_index] = sell_count bsLine['sell_type'] = [sell_type] if 0 < sell_price: BUY_LIST['buy_limit'] = 0 BUY_LIST['buy_list'].clear() else: buy_ymd, buy_price, buy_count, buy_cut, buy_type = self.getBuyPriceAndWeight1(ticker, MAX_BUY_PRICE, last_index, data, data_signal, BUY_LIST, isRealTime) bsLine['buy_price'][last_index] = buy_price bsLine['buy_count'][last_index] = buy_count bsLine['buy_cut'][last_index] = buy_cut bsLine['buy_type'][last_index] = buy_type if 0 < buy_price: BUY_LIST['buy_list'].append({'buy_ymd': buy_ymd, 'buy_price': buy_price, 'buy_count': buy_count, 'buy_cut': buy_cut, 'buy_type': buy_type}) else: bsLine['buy_price'] = [-1] bsLine['buy_count'] = [-1] bsLine['buy_cut'] = [-1] bsLine['buy_type'] = [''] bsLine['sell_price'] = [-1] bsLine['sell_count'] = [-1] return bsLine