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dosangyoon
212
Simulation.py
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212
Simulation.py
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from math import nan
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from datetime import datetime
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import csv
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import pandas as pd
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import plotly.graph_objects as go
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from plotly import subplots
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from glob import glob
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from hts.BuySellChecker import BuySellChecker
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class Simulation:
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buySellChecker = None
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stock_code = None
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def __init__(self, stock_code):
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self.buySellChecker = BuySellChecker()
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self.stock_code = stock_code
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#self.connect()
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return
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def getCSV(self, fileName, given_day, result):
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with open(fileName, 'r', encoding='utf-8') as infp:
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reader = csv.reader(infp)
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next(reader)
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for rows in reader:
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days = rows[0] # hts.날짜
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time = rows[1] # hts.시간
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open_v = rows[2] # hts.시가
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high = rows[3] # hts.고가
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low = rows[4] # hts.저가
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close = rows[5] # hts.종가
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vol = rows[6] # hts.거래량
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start_time = datetime.strptime(given_day + " 090000", '%Y%m%d %H%M%S')
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temp = datetime.strptime(str(days) + " " + str(time).zfill(4)+"00", '%Y%m%d %H%M%S')
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#if temp < start_time:
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# continue
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result["time"].append(temp)
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result["open"].append(int(open_v))
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result["close"].append(int(close))
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result["high"].append(int(high))
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result["low"].append(int(low))
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result["vol"].append(int(vol))
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return
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def draw(self, stock_code, given_day, data, bsLine):
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# 어제 데이터는 지운다.
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data = data.loc[pd.DatetimeIndex(data.index).day == int(given_day[6:])]
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buy_line = bsLine['buy'][381:]
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sell_line = bsLine['sell'][381:]
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#buy_line = bsLine['buy']
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#sell_line = bsLine['sell']
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# 그래프 설정을 위한 변수를 생성한다.
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data = data.astype({'open': 'int',
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'high': 'int',
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'low': 'int',
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'close': 'int',
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'volume': 'int',
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'avg3': 'float',
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'avg5': 'float',
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'avg10': 'float',
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'avg20': 'float',
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'avg30': 'float',
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'avg60': 'float',
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'fast_k': 'float',
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'slow_k': 'float',
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'slow_d': 'float',
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'rsi': 'float',
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'rsis': 'float'
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})
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buy_colors = []
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for i in range(len(buy_line)):
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if buy_line[i] < 0:
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buy_colors.append("#ffffff")
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buy_line[i] = nan
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else:
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buy_colors.append("#ff00ff")
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sell_colors = []
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for i in range(len(sell_line)):
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if sell_line[i] < 0:
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sell_colors.append("#ffffff")
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sell_line[i] = nan
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else:
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sell_colors.append("#00ced1")
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# 그래프를 설정한다.
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buy_check = go.Scatter(x=data['date'], y=buy_line, mode='markers', name="buy", marker=dict(size=14, color=buy_colors, line_width=0))
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sell_check = go.Scatter(x=data['date'], y=sell_line, mode='markers', name="sell", marker=dict(size=14, color=sell_colors, line_width=0))
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upper = go.Scatter(x=data['date'], y=data["upper"], name="upper", line_color='#000000')
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lower = go.Scatter(x=data['date'], y=data["lower"], name="lower", line_color='#000000')
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avg3 = go.Scatter(x=data['date'], y=data["avg3"], name="avg3", line_color='#1469F4')
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avg5 = go.Scatter(x=data['date'], y=data["avg5"], name="avg5", line_color='#089B5B')
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avg10 = go.Scatter(x=data['date'], y=data["avg10"], name="avg10", line_color='#ff00ff')
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avg20 = go.Scatter(x=data['date'], y=data["avg20"], name="avg20", line_color='#8F8203')
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avg30 = go.Scatter(x=data['date'], y=data["avg30"], name="avg30", line_color='#000000')
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#avg60 = go.Scatter(x=hts['date'], y=hts["avg60"], name="avg60", line_color='#008000')
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candle_stick = go.Candlestick(x=data['date'], open=data['open'], high=data['high'], low=data['low'], close=data['close'], increasing_line_color='red', decreasing_line_color='blue')
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volume_line = go.Scatter(x=data['date'], y=data["volume"], mode='lines', name='volume')
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#fast_k_line = go.Scatter(x=hts['date'], y=hts["fast_k"], mode='lines', name='fast_k')
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macd_line = go.Scatter(x=data['date'], y=data["macd"], mode='lines', name='macd')
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macd_s_line = go.Scatter(x=data['date'], y=data["macds"], mode='lines', name='macds')
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macd_o_line = go.Scatter(x=data['date'], y=data["macdo"], mode='lines', name='macdo')
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slow_k_line = go.Scatter(x=data['date'], y=data["slow_k"], mode='lines', name='slow_k')
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slow_d_line = go.Scatter(x=data['date'], y=data["slow_d"], mode='lines', name='slow_d')
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rsi_line = go.Scatter(x=data['date'], y=data["rsi"], mode='lines', name='rsi')
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rsis_line = go.Scatter(x=data['date'], y=data["rsis"], mode='lines', name='rsis')
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candle_data = [candle_stick, upper, lower, avg3, avg5, avg10, avg20, avg30, buy_check, sell_check]
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volume_data = [volume_line]
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macd_data = [macd_line, macd_s_line, macd_o_line]
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stochastic_data = [slow_k_line, slow_d_line]
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rsi_data = [rsi_line, rsis_line]
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# 그래프를 그린다.
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"""
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fig = go.Figure(hts=candle_data)
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fig.update_layout(title=stock_code + "_" + given_day)
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fig.show()
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"""
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fig = subplots.make_subplots(rows=5, cols=1, subplot_titles=('캔들', "거래량", "MACD", "스토캐스틱", "RSI"))
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for trace in candle_data:
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fig.append_trace(trace, 1, 1)
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for trace in volume_data:
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fig.append_trace(trace, 2, 1)
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for trace in macd_data:
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fig.append_trace(trace, 3, 1)
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for trace in stochastic_data:
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fig.append_trace(trace, 4, 1)
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for trace in rsi_data:
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fig.append_trace(trace, 5, 1)
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#fig.update_xaxes(nticks=5)
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#fig.update_layout(height=1800, title=stock_code + "_" + given_day, xaxis_rangeslider_visible=False)
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df = pd.DataFrame(bsLine)
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df = df.fillna(-1)
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buy_count = len(df.loc[df["buy"] > 0])
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sell_count = len(df.loc[df["sell"] > 0])
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fig.update_layout(height=5000, title=stock_code + "_" + given_day + "_" + str(buy_count)+","+str(sell_count))
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fig.show()
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return
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def simulate(self, days, path=None):
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result = {"check": set(),
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"time": [],
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"open": [],
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"close": [],
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"high": [],
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"low": [],
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"vol": []}
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last_day = days[0]
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today = days[1]
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if path == None:
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path = "resources/hts"
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# 데이터를 가지고 온다.
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self.getCSV(path + "/" + self.stock_code + "_" + last_day + ".csv", last_day, result)
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self.getCSV(path + "/" + self.stock_code + "_" + today + ".csv", today, result)
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# 분석을 통해서 볼린저밴드 상/하단을 계산한다.
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data = self.buySellChecker.analyze(result)
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# 사야 할 시점과 팔아야 할 시점을 체크한다.
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bsLine = self.buySellChecker.checkTransaction(data, self.stock_code)
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# 그래프를 그린다.
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self.draw(self.stock_code, today, data, bsLine)
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return
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if __name__ == "__main__":
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# to check bying
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stock_codes = {
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# 252670
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# 122630
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"122630": [
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('20220722', '20220725')
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],
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}
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path = 'resources/hts'
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"""
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path = './hts/backup'
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fileList = glob(path + '/122630*.csv')
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fileList = sorted(fileList, reverse=True)
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stock_codes = {'122630':[]}
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for i in range(11, 21):
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stock_codes['122630'].append((fileList[i][20:28], fileList[i-1][20:28]))
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"""
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for stock_code in stock_codes:
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simulation = Simulation(stock_code)
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for given_day in stock_codes[stock_code]:
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simulation.simulate(given_day, path)
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print ("done...")
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