363 lines
16 KiB
Python
363 lines
16 KiB
Python
import math
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import pandas as pd
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from stockpredictor.analysis.Common import Common
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from stockpredictor.analysis.Stochastic import Stochastic
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from stockpredictor.analysis.RSI import RSI
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from stockpredictor.analysis.MACD import MACD
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from stockpredictor.analysis.IchimokuCloud import IchimokuCloud
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class BuySellChecker:
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common = None
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stochastic = None
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rsi = None
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ichimokuCloud = None
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def __init__(self):
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self.common = Common()
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self.stochastic = Stochastic()
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self.rsi = RSI()
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self.macd = MACD()
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self.ichimokuCloud = IchimokuCloud()
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return
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def getPriceAndWeight1(self, data, i):
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buy, weight, sell = -1, -1, -1
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if i >= 3:
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################
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### sell 분석 ###
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################
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# 1. 볼린져밴드 상단이 최고와 종가 사이 아래에 있는 경우 매도한다.
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#if (data["high"][i] - data["close"][i]) / 2 + data["close"][i] > data["upper"][i]:
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# sell = data["high"][i]
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# 2. slow_k가 90이 넘으면 매도한다.
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if data["slow_k"][i] > 90:
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sell = data["high"][i]
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#if data["slow_k"][i] >= 85:
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# if data["slow_d"][i-1] < data["slow_k"][i-1] and data["slow_k"][i] < data["slow_d"][i]:
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# sell = data["high"][i]
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# 3. 2시 이후에는 최고가가 볼린져밴드 상단 위에 있으면 매도한다.
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if i > 300 and data["high"][i] > data["upper"][i]:
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sell = data["high"][i]
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##########################
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### buy 분석 ###
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##########################
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if data["low"][i] < data["lower"][i] + 5 and data["open"][i] <= data["close"][i]:
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if data["slow_k"][i-1] < 30 and data["slow_k"][i] < 30:
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if data["slow_k"][i-1] < data["slow_k"][i]:
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buy = data["low"][i]
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if data["rsi"][i] < 25:
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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]:
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if data["close"][i] < data["avg5"][i]:
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buy = data["close"][i]
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else:
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buy = data["low"][i]
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weight = 1
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#############################
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### STOCHASTIC weight 분석 ###
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#############################
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if data["slow_k"][i] in (0, 1, 2, 3):
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weight = 1
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if data["slow_k"][i] in (4, 5, 6, 7, 8):
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weight = 1
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elif data["slow_k"][i] in (9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20):
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weight = 1
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elif data["slow_k"][i] in (21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35):
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weight = 1
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return buy, weight, sell
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def getPriceAndWeight2(self, data, i):
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buy, weight, sell = -1, -1, -1
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################
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### sell 분석 ###
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################
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# 1. 볼린져밴드 상단이 최고와 종가 사이 아래에 있는 경우 매도한다.
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if (data["high"][i] - data["close"][i]) / 2 + data["close"][i] > data["upper"][i]:
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sell = data["high"][i]
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if data["slow_k"][i] >= 85:
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if data["slow_d"][i - 1] < data["slow_k"][i - 1] and data["slow_k"][i] < data["slow_d"][i]:
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sell = data["high"][i]
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# 3. 2시 이후에는 최고가가 볼린져밴드 상단 위에 있으면 매도한다.
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if i > 300 and data["high"][i] > data["upper"][i]:
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sell = data["high"][i]
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##########################
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### STOCHASTIC buy 분석 ###
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##########################
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if i < 40:
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pre_slow = data["slow_k"][i - 1] / data["slow_d"][i - 1] - 1
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now_slow = data["slow_k"][i] / data["slow_d"][i] - 1
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if pre_slow < 0 and 0 < now_slow:
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if data["slow_k"][i] <= 35:
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if (data["close"][i] - data["lower"][i]) / (data["upper"][i] - data["lower"][i]) < 0.35:
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if data["slow_k"][i - 1] < data["slow_d"][i - 1] and data["slow_d"][i] < data["slow_k"][i]:
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if data['avg10'][i] < data['avg5'][i]:
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if data["open"][i] < data["close"][i]:
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buy = data["close"][i]
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else:
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buy = data["low"][i]
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else:
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pre_slow = data["slow_k"][i - 1] / data["slow_d"][i - 1] - 1
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now_slow = data["slow_k"][i] / data["slow_d"][i] - 1
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if pre_slow < 0 and pre_slow < now_slow and -0.15 < now_slow:
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if data["slow_k"][i] <= 30:
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if (data["close"][i] - data["lower"][i]) / (data["upper"][i] - data["lower"][i]) < 0.35:
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if data["slow_k"][i - 1] < data["slow_d"][i - 1] and data["slow_d"][i] < data["slow_k"][i]:
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if data['avg10'][i] < data['avg5'][i]:
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if data["close"][i] < data["avg5"][i]:
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buy = data["close"][i]
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else:
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buy = data["low"][i]
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#############################
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### STOCHASTIC weight 분석 ###
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#############################
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if data["slow_k"][i] in (0, 1, 2, 3):
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weight = 1
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if data["slow_k"][i] in (4, 5, 6, 7, 8):
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weight = 1
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elif data["slow_k"][i] in (9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20):
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weight = 1
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elif data["slow_k"][i] in (21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35):
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weight = 1
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return buy, weight, sell
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# 곱버스에 해당함
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def getPriceAndWeight3(self, data, i):
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buy, weight, sell = -1, -1, -1
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# 381: 어제 날짜 데이터 개수
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if i >= 381:
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# 매수 분석
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# 이동선을 이용한 매매
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# 3분선과 5분선이 10분 이상 내려오다가 3분선이 5분선을 넘어 서는 순간 매수
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if int(data["avg3"][i]) > int(data["avg5"][i]):
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valid = True
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same_count = 0
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for c in range(1, 10):
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if int(data["avg3"][i-c]) == int(data["avg5"][i-c]):
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same_count += 1
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if int(data["avg3"][i-c]) > int(data["avg5"][i-c]):
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valid = False
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break
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if int(data["avg3"][i-c]) > int(data["avg3"][i-c-1]) or int(data["avg5"][i-c]) > int(data["avg5"][i-c-1]):
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valid = False
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break
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if valid and same_count < 3:
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buy = data["close"][i] - 5
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weight = 3
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# 이동선을 이용한 매매
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# 3분선이 10분선에 돌파 후 지지하는지 확인하고 slow_k < 40일 때 매수함
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# 현재 단계:
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# - avg3[i]이 avg10[i]보다 커야함
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# - avg3[i]가 avg3[i-1]보다 커야함
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if data['avg10'][i] < data['avg3'][i] and data['avg3'][i-1] < data['avg3'][i] and abs(data['avg10'][i] - data['avg3'][i]) > 2:
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# 첫 이전 단계:
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# - avg3[i-1]과 avg10[i-1]의 abs가 3이내여야 함
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if abs(data['avg3'][i-1] - data['avg10'][i-1]) < 3 and data['avg3'][i-1] < data['avg3'][i-2]:
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index1 = -1
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valid = False
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for j in range(2, 20):
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# 두 번째 이전 단계:
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# - avg3[i-2]가 avg10[i-2]보다 커야 함
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# - avg3[i-2]가 avg3[i-3]보다 작아야함
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if data['avg10'][i-j] < data['avg3'][i-j] and data['avg3'][i-j] > data['avg3'][i-j-1]:
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index1 = j
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break
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for j in range(index1 + 1, 20):
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# 세 번째 이전 단계:
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# - avg3[i-3]가 avg3[i-4]보다 커야 함
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if data['avg3'][i-j] > data['avg3'][i-j-1]:
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valid = True
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index1 = j
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else:
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break
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# 마지막 체크:
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# 만약 avg[3]이 avg[10]보다 작다면 매수함
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if valid:
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if data['avg3'][i-index1-1] < data['avg10'][i-index1-1]:
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if data["slow_k"][i] < 40:
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buy = data["close"][i]
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weight = 3
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# 만약 30원 이상 장대 양봉이 나온 경우, 다음이나 다다음 중간 값에서 매수를 한다.
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if (data["close"][i] - data["low"][i]) >= 30:
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middle = int((data["close"][i] + data["low"][i])/2)
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buy = middle
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# 장 초기 (시작 7분 이내), 볼린져 하단에서 시작하여 이병선을 모두 상승하여 마감한 경우 low 값에서 매수한다.
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if i < 381 + 8:
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if data["open"][i] == data["low"][i]:
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if data["close"][i] > max(data["avg3"][i], data["avg5"][i], data["avg10"][i], data["avg20"][i], data["avg30"][i]):
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buy = data["low"][i]
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"""
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## macd를 이용한 매매
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#if data["macdo"][i] < 0 and data["macd"][i] < -5:
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# 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]:
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# buy = data["close"][i] - 5
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"""
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# 표준편차를 이용한 매매
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"""
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#mean = (data["avg3"][i] + data["avg5"][i] + data["avg10"][i] + data["avg20"][i] + data["avg30"][i])/5
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#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
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#variance = vsum / 5
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#std = math.sqrt(variance)
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#if std < 1:
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# sell = data["close"][i] - 5
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"""
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"""
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# 매도 분석
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# 3일 선이 10분 전부터 게속 10분선 위에 있다가 아래로 내려오면 매도함
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valid = False
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if data["avg3"][i] < data["avg5"][i]:
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if (data["avg3"][i-1] > data["avg5"][i-1] and data["avg3"][i-2] > data["avg5"][i-2] and
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data["avg3"][i-3] > data["avg5"][i-3] and data["avg3"][i-4] > data["avg5"][i-4] and
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data["avg3"][i-5] > data["avg5"][i-5] and data["avg3"][i-6] > data["avg5"][i-6] and
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data["avg3"][i-7] > data["avg5"][i-7] and data["avg3"][i-8] > data["avg5"][i-8] and
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data["avg3"][i-9] > data["avg5"][i-8] and data["avg3"][i-10] > data["avg5"][i-10]):
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valid = True
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if valid:
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sell = data["close"][i]
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"""
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# slow_k와 slow_d가 90이상에서 slow_k가 slow_d 아래롸 내려온 경우
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if data["rsi"][i] >= 75 and data["rsis"][i] >= 75:
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if data["rsi"][i-1] > data["rsis"][i-1] and data["rsi"][i] < data["rsis"][i]:
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sell = data["close"][i] - 5
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#if data["slow_d"][i] > 90 and data["rsi"][i] > 65:
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# if data["upper"][i] <= data["high"][i]:
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# sell = data["close"][i] - 5
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#if data['avg3'][i-1] > data['avg10'][i-1] and data['avg3'][i] <= data['avg10'][i]:
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# if abs(data['avg3'][i - 1] - data['avg30'][i - 1]) > 10:
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# sell = data["close"][i]
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return buy, weight, sell
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def analyze(self, result):
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open = result["open"]
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close = result["close"]
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high = result["high"]
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low = result["low"]
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vol = result["vol"]
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close_df = pd.DataFrame(close)
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avg3_list = close_df.rolling(window=3).mean().fillna(close[0]).values.tolist()
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avg3 = [item[0] for item in avg3_list]
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avg5_list = close_df.rolling(window=5).mean().fillna(close[0]).values.tolist()
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avg5 = [item[0] for item in avg5_list]
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avg10_list = close_df.rolling(window=10).mean().fillna(close[0]).values.tolist()
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avg10 = [item[0] for item in avg10_list]
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avg20_list = close_df.rolling(window=20).mean().fillna(close[0]).values.tolist()
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avg20 = [item[0] for item in avg20_list]
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avg30_list = close_df.rolling(window=30).mean().fillna(close[0]).values.tolist()
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avg30 = [item[0] for item in avg30_list]
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avg60_list = close_df.rolling(window=60).mean().fillna(close[0]).values.tolist()
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avg60 = [item[0] for item in avg60_list]
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df = pd.DataFrame(close)
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max20 = df.rolling(window=20).mean()
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stddev20 = df.rolling(window=20).std()
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upper_df = max20 + (stddev20 * 2) # 상단 볼린저 밴드
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lower_df = max20 - (stddev20 * 2) # 하단 볼린저 밴드
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upper, lower = [], []
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for i in range(len(upper_df)):
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if i < 10:
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upper.append(upper_df.values[0][0])
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lower.append(lower_df.values[0][0])
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else:
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upper.append(upper_df.values[i][0])
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lower.append(lower_df.values[i][0])
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point_temp = result["time"]
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STOCK = []
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for i in range(len(open)):
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STOCK.append({'volume': vol[i], 'close': close[i], 'open': open[i], 'high': high[i], 'low': low[i],
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'avg3': avg3[i], 'avg5': avg5[i],'avg10': avg10[i],'avg20': avg20[i],'avg30': avg30[i],'avg60': avg60[i]})
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# stochastic 계산
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stochastic_df = self.stochastic.apply(STOCK, n=30, m=5, t=5)
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stochastic_df = stochastic_df.fillna(100)
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fast_k = stochastic_df['fast_k'].values.tolist()
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slow_k = stochastic_df['slow_k'].values.tolist()
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slow_d = stochastic_df['slow_d'].values.tolist()
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# macd 계산
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macd_df = self.macd.apply(STOCK, short=12, long=26, t=9)
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macd_df = macd_df.fillna(100)
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macd = macd_df['macd'].values.tolist()
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macds = macd_df['macds'].values.tolist()
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macdo = macd_df['macdo'].values.tolist()
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# rsi 계산
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rsi_df = self.rsi.apply(STOCK, period=30, window=5)
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rsi_df = rsi_df.fillna(100)
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rsi = rsi_df['rsi'].values.tolist()
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rsis = rsi_df['rsis'].values.tolist()
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# ichimokuCloud 계산
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# ichimokuCloud_df = self.ichimokuCloud.apply(STOCK, c=9, b=26, l=52)
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# ichimokuCloud_df = rsi_df.fillna(100)
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# changeLine = rsi_df['changeLine'].values.tolist()
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# baseLine = rsi_df['baseLine'].values.tolist()
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# leadingSpan1 = rsi_df['leadingSpan1'].values.tolist()
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# leadingSpan2 = rsi_df['leadingSpan2'].values.tolist()
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temp = {"date": point_temp,
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"open": open, "high": high, "low": low, "close": close, "volume": vol, "upper": upper, "lower": lower,
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"avg3": avg3, "avg5": avg5, "avg10": avg10, "avg20": avg20, "avg30": avg30, "avg60": avg60,
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"macd": macd, "macds": macds, "macdo": macdo,
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"fast_k": fast_k, "slow_k": slow_k, "slow_d": slow_d,
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"rsi": rsi, "rsis": rsis}
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data = pd.DataFrame(temp)
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df_final_time = pd.DatetimeIndex(point_temp)
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data.index = df_final_time
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return data
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def checkTransaction(self, data, stock_code):
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size = len(data["close"])
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bsLine = {}
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bsLine['buy'] = [-1 for i in range(size)]
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bsLine['weight'] = [-1 for i in range(size)]
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bsLine['sell'] = [-1 for i in range(size)]
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for i in range(size):
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if stock_code == "252670":
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buy, weight, sell = self.getPriceAndWeight3(data, i)
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else:
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buy, weight, sell = self.getPriceAndWeight4(data, i)
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bsLine['buy'][i] = buy
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bsLine['weight'][i] = weight
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bsLine['sell'][i] = sell
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return bsLine
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