294 lines
12 KiB
Python
294 lines
12 KiB
Python
import os
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from dtw import dtw
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import json
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import sqlite3
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import numpy as np
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from datetime import datetime, timedelta
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from stock.analysis.MovingAverage import MovingAverage
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class BuySellChecker():
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PATTERNS = None
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RESOURCE_PATH = None
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def __init__(self, RESOURCE_PATH):
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self.RESOURCE_PATH = RESOURCE_PATH
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return
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def nearDisparity(self, data, i):
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if (0.998 < data['disparity_avg5'][i] < 1.002 and
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0.998 < data['disparity_avg5'][i] < 1.002 and
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0.998 < data['disparity_avg5'][i] < 1.002 and
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0.998 < data['disparity_avg5'][i] < 1.002 and
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0.998 < data['disparity_avg5'][i] < 1.002):
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return True
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return False
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def cosine_similarity(self, x, y):
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return np.dot(x, y) / (np.sqrt(np.dot(x, x)) * np.sqrt(np.dot(y, y)))
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"""
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def findBuyPoint(self, data, data_signal, i):
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# 코사인 유사도(cosine similarity)로 과거 주가의 유사 패턴을 찾아 미래 예측하기
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# https://teddylee777.github.io/pandas/cos-sim-stock/
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buy_target = data['close'].iloc[i-179:i+1]
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window_size = len(buy_target)
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if window_size == 180:
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buy_target = (buy_target - buy_target.min()) / (buy_target.max() - buy_target.min())
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for pattern in self.PATTERNS:
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cos_similarity = self.cosine_similarity(pattern, buy_target)
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if 0.995 < cos_similarity:
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return True
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return False
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"""
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def findBuyPoint(self, data, i):
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# DTW (Dynamic Time Warping)
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# 시계열 유사도: https://m.blog.naver.com/happyrachy/221693939341
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if i < 24:
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return False
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for p in range(len(self.PATTERNS['min_max'])):
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size = len(self.PATTERNS['stndardization'][p])
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if i - size + 1 < 0:
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continue
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close = data['close'].iloc[i-size+1:i+1]
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#min_max = np.array(self.PATTERNS['min_max'][p]).reshape(-1, 1)
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stndardization = np.array(self.PATTERNS['stndardization'][p]).reshape(-1, 1)
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#min_max_y = np.array((close - close.min()) / (close.max() - close.min())).reshape(-1, 1)
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stndardization_y = np.array((close - close.mean()) / close.std()).reshape(-1, 1)
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#manhattan_distance = lambda min_max, min_max_y: np.abs(min_max - min_max_y)
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#min_max_d, cost_matrix, acc_cost_matrix, path = dtw(min_max, min_max_y, dist=manhattan_distance)
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manhattan_distance = lambda stndardization, stndardization_y: np.abs(stndardization - stndardization_y)
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stndardization_d, cost_matrix, acc_cost_matrix, path = dtw(stndardization, stndardization_y, dist=manhattan_distance)
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if stndardization_d < 2:
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#print(i, data['ymd'].iloc[i], stndardization_d)
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return True
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return False
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def getMacd(self, ticker_code, day, mins=1):
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table = 'minutely_max_macd_' + str(mins)
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conn = sqlite3.connect(os.path.join(self.RESOURCE_PATH, 'coins.db'))
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cursor = conn.cursor()
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day1 = (datetime.strptime(day, '%Y%m%d') - timedelta(1)).strftime('%Y%m%d')
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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, ))
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db_result1 = cursor.fetchall()
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cursor.close()
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conn.close()
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macd_limit = [(datetime.strptime(rows[0]+" "+rows[1], '%Y%m%d %H%M%S'), rows[2], rows[3]) for rows in db_result1]
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macd_dup = list(set(macd_limit))
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return macd_dup
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def is_Support(self, df, i, observation_time=300):
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# c1 = df.Low[i] < df.Low[i - 1] < df.Low[i - 2] < df.Low[i - 3]
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# c2 = df.Low[i] < df.Low[i + 1] < df.Low[i + 2] < df.Low[i + 3]
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# return c1 & c2
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#if df['low'][i] == np.min(df['low'][i - self.observation_time:i + self.observation_time + 1]):
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if df['low'][i] == np.min(df['low'][i - observation_time:i+1]):
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return True
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else:
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return False
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def is_Resistance(self, df, i, observation_time = 300):
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# c1 = df.High[i] > df.High[i - 1] > df.High[i - 2] > df.High[i - 3]
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# c2 = df.High[i] > df.High[i + 1] > df.High[i + 2] > df.High[i + 3]
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# return c1 & c2
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#if df['high'][i] == np.max(df['high'][i - self.observation_time:i + self.observation_time + 1]):
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if df['high'][i] == np.max(df['high'][i - observation_time:i+1]):
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return True
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else:
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return False
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def getBuyPriceAndWeight1(self, ticker, MAX_BUY_PRICE, i, data, data_signal, BUY_LIST, isRealTime=True):
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buy_ymd, buy_price, buy_count, buy_cut, buy_type = None, -1, -1, -1, ''
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df_tmp = data_signal['ymd'] <= data['ymd'][i]
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df_signal = data_signal.loc[df_tmp]
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si = len(df_signal) - 1
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check = False
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duration = 5 + 60
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if duration < i:
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if np.average(data['trend_avg'][i - duration:i]) < data['trend_avg'][i]:
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if np.average(data['avg480'][i - duration:i]) < data['avg480'][i]:
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if data['avg480'][i] < data['trend_avg'][i]:
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if data['avg20'][i] < data['avg480'][i] and data['avg20'][i - 1] < data['avg20'][i]:
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if len(BUY_LIST['buy_list']) == 0:
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check = True
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else:
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if BUY_LIST['buy_list'][-1]['buy_price'] < data['close'][i]:
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check = True
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if 2800 < len(data['close'][i - 2880:i]) and np.max(data['close'][i - 2880:i]) < data['close'][i]:
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if np.max(data['rsi'][i - 30:i]) < data['rsi'][i]:
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if data['disparity_avg1500'][i] < 1.1:
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if data['close'][i] < data['trend_avg'][i]:
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buy_type = 'upward'
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check = True
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if data['disparity_avg20'][i] < BUY_LIST['disparity']['limit_bottom_10']['avg20']:
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buy_type = 'disparity_avg20'
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check = True
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if data['disparity_avg60'][i] < BUY_LIST['disparity']['limit_bottom_10']['avg60']:
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buy_type = 'disparity_avg60'
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check = True
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if data['disparity_avg480'][i] < BUY_LIST['disparity']['limit_bottom_10']['avg480']:
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buy_type = 'disparity_avg1500'
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check = True
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if data['disparity_avg1500'][i] < BUY_LIST['disparity']['limit_bottom_10']['avg1500']:
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buy_type = 'disparity_avg1500'
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check = True
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if data['disparity_avg20'][i] < BUY_LIST['disparity']['limit_bottom_1']['avg20']:
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buy_type = 'disparity_avg20'
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check = True
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if data['disparity_avg60'][i] < BUY_LIST['disparity']['limit_bottom_1']['avg60']:
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buy_type = 'disparity_avg60'
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check = True
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if data['disparity_avg480'][i] < BUY_LIST['disparity']['limit_bottom_1']['avg480']:
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buy_type = 'disparity_avg1500'
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check = True
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if data['disparity_avg1500'][i] < BUY_LIST['disparity']['limit_bottom_1']['avg1500']:
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buy_type = 'disparity_avg1500'
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check = True
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if check:
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buy_price = data['close'][i]
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buy_ymd = data['ymd'][i]
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if data['slow_k'][si] < 30:
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buy_count = MAX_BUY_PRICE * 2 / (data['close'][i])
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elif data['slow_k'][si] < 50:
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buy_count = MAX_BUY_PRICE * 1.5 / (data['close'][i])
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else:
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buy_count = MAX_BUY_PRICE * 1 / (data['close'][i])
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return buy_ymd, buy_price, buy_count, buy_cut, buy_type
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return buy_ymd, buy_price, buy_count, buy_cut, buy_type
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def getSellPriceAndWeight1(self, ticker, i, data, data_signal, BUY_LIST=None):
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sell_price, sell_count, sell_type = -1, -1, ''
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check = False
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if 0 < len(BUY_LIST['buy_list']):
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"""
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if 1.05 < data['disparity_avg20'][i]:
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check = True
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if 1.10 < data['disparity_avg480'][i]:
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check = True
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if 1.15 < data['disparity_avg1500'][i]:
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check = True
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"""
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if BUY_LIST['disparity']['limit_top_5']['avg20'] < data['disparity_avg20'][i]:
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check = True
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if BUY_LIST['disparity']['limit_top_5']['avg480'] < data['disparity_avg480'][i]:
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check = True
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if BUY_LIST['disparity']['limit_top_5']['avg1500'] < data['disparity_avg1500'][i]:
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check = True
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# if data['avg1500'][i-1] < data['trend_avg'][i-1] and data['trend_avg'][i] <= data['avg1500'][i]:
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# check = True
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if check:
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sell_price = data['close'][i]
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sell_count = sum([price['buy_count'] for price in BUY_LIST['buy_list']])
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return sell_price, sell_count, sell_type
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def checkTransaction1(self, ticker, MAX_BUY_PRICE, data, data_signal, BUY_LIST=None, isRealTime=True):
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# 어제 오늘 데이터로 분석
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bsLine = {}
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if data is not None and 'close' in data.columns:
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size = len(data["close"])
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if isRealTime:
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# isRealTime=True, 실시간 적용
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last_index = size - 1
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sell_price, sell_count, sell_type = self.getSellPriceAndWeight1(ticker, last_index, data, data_signal, BUY_LIST)
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bsLine['sell_price'] = [sell_price]
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bsLine['sell_count'] = [sell_count]
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bsLine['sell_type'] = [sell_type]
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if 0 < sell_price:
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BUY_LIST['buy_limit'] = 0
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BUY_LIST['buy_list'].clear()
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else:
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buy_ymd, buy_price, buy_count, buy_cut, buy_type = self.getBuyPriceAndWeight1(ticker, MAX_BUY_PRICE, last_index, data, data_signal, BUY_LIST, isRealTime)
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bsLine['buy_ymd'] = [buy_ymd]
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bsLine['buy_price'] = [buy_price]
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bsLine['buy_count'] = [buy_count]
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bsLine['buy_cut'] = [buy_cut]
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bsLine['buy_type'] = [buy_type]
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if 0 < buy_price:
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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})
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else:
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# Type=False, 시뮬레이션 적용
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bsLine['buy_ymd'] = [-1 for i in range(size)]
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bsLine['buy_price'] = [-1 for i in range(size)]
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bsLine['buy_count'] = [-1 for i in range(size)]
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bsLine['buy_cut'] = [-1 for i in range(size)]
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bsLine['buy_type'] = ['' for i in range(size)]
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bsLine['sell_price'] = [-1 for i in range(size)]
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bsLine['sell_count'] = [-1 for i in range(size)]
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bsLine['sell_type'] = ['' for i in range(size)]
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for last_index in range(size):
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sell_price, sell_count, sell_type = self.getSellPriceAndWeight1(ticker, last_index, data, data_signal, BUY_LIST)
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bsLine['sell_price'][last_index] = sell_price
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bsLine['sell_count'][last_index] = sell_count
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bsLine['sell_type'][last_index] = sell_type
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if 0 < sell_price:
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BUY_LIST['buy_limit'] = 0
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BUY_LIST['buy_list'].clear()
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else:
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buy_ymd, buy_price, buy_count, buy_cut, buy_type = self.getBuyPriceAndWeight1(ticker, MAX_BUY_PRICE, last_index, data, data_signal, BUY_LIST, isRealTime)
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bsLine['buy_price'][last_index] = buy_price
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bsLine['buy_count'][last_index] = buy_count
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bsLine['buy_cut'][last_index] = buy_cut
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bsLine['buy_type'][last_index] = buy_type
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if 0 < buy_price:
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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})
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else:
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bsLine['buy_price'] = [-1]
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bsLine['buy_count'] = [-1]
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bsLine['buy_cut'] = [-1]
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bsLine['buy_type'] = ['']
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bsLine['sell_price'] = [-1]
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bsLine['sell_count'] = [-1]
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return bsLine |