208 lines
9.3 KiB
Python
208 lines
9.3 KiB
Python
from math import nan
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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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import os
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from hts.HTS import HTS
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from stock.util.Stock2Vector import Stock2Vector
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from stock.util.LabelChecker import LabelChecker
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from stock.util.StockPredictor import StockPredictor
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from stock.analysis.DailyStatus import DailyStatus
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from hts.BuySellChecker import BuySellChecker
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class Simulation (HTS):
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stock2Vector = None
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buySellChecker = None
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stockPredictor = None
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def __init__(self, RESOURCE_PATH):
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super().__init__(RESOURCE_PATH)
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self.RESOURCE_PATH = RESOURCE_PATH
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self.buySellChecker = BuySellChecker()
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try:
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self.stock2Vector = Stock2Vector(RESOURCE_PATH)
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self.labelChecker = LabelChecker(RESOURCE_PATH)
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self.stockPredictor = StockPredictor(RESOURCE_PATH)
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except:
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pass
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#self.connect()
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return
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def draw(self, stock_code, given_day, data, bsLine):
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if bsLine is None:
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return
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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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buy_weight_line = bsLine['buy_weight'][381:]
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sell_line = bsLine['sell'][381:]
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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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'avg6': 'float',
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'avg9': 'float',
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'avg12': 'float',
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'avg20': '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_size = []
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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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buy_size.append(0)
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else:
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buy_colors.append("#B2028C")
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buy_size.append(10 + (5 * buy_weight_line[i]))
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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=buy_size, 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='#8F8203')
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avg6 = go.Scatter(x=data['date'], y=data["avg6"], name="avg6", line_color='#089B5B')
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avg9 = go.Scatter(x=data['date'], y=data["avg9"], name="avg9", line_color='#ff00ff')
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avg12 = go.Scatter(x=data['date'], y=data["avg12"], name="avg12", line_color='#1469F4')
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avg20 = go.Scatter(x=data['date'], y=data["avg20"], name="avg20", line_color='#000000')
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laggingSpan = go.Scatter(x=data['date'], y=data["laggingSpan"], name='laggingSpan', line_color='#B50ABB')
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changeLine = go.Scatter(x=data['date'], y=data["changeLine"], name='changeLine', line_color='#14A200')
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baseLine = go.Scatter(x=data['date'], y=data["baseLine"], name='baseLine', line_color='#CF6E0D')
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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', showlegend=False)
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#volume_line = go.Scatter(x=data['date'], y=data["volume"], mode='lines', name='volume')
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volume_line = go.Bar(x=data['date'], y=data["volume"], marker_color='red', name='volume')
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disparity_avg5 = go.Scatter(x=data['date'], y=data["disparity_avg5"], name="disparity_avg5", line_color='#8F8203')
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disparity_avg10 = go.Scatter(x=data['date'], y=data["disparity_avg10"], name="disparity_avg10", line_color='#089B5B')
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disparity_avg20 = go.Scatter(x=data['date'], y=data["disparity_avg20"], name="disparity_avg20", line_color='#ff00ff')
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disparity_avg60 = go.Scatter(x=data['date'], y=data["disparity_avg60"], name="disparity_avg60", line_color='#1469F4')
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disparity_avg120 = go.Scatter(x=data['date'], y=data["disparity_avg120"], name="disparity_avg120", line_color='#000000')
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macd_line = go.Scatter(x=data['date'], y=data["macd"], line=dict(color='red', width=2), name='macd')
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macd_s_line = go.Scatter(x=data['date'], y=data["macds"], line=dict(dash='dashdot', color='black', width=2), name='macds')
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macd_o_line = go.Bar(x=data['date'], y=data["macdo"], marker_color='purple', name='macdo')
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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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slow_k_line = go.Scatter(x=data['date'], y=data["slow_k"], line=dict(color='red', width=2), name='slow_k')
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slow_d_line = go.Scatter(x=data['date'], y=data["slow_d"], line=dict(dash='dashdot', color='black', width=2), name='slow_d')
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rsi_line = go.Scatter(x=data['date'], y=data["rsi"], line=dict(color='red', width=2), name='rsi')
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rsis_line = go.Scatter(x=data['date'], y=data["rsis"], line=dict(dash='dashdot', color='black', width=2), name='rsis')
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candle_data = [candle_stick, upper, lower, avg3, avg6, avg9, avg12, avg20, buy_check, sell_check, laggingSpan, changeLine, baseLine]
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volume_data = [volume_line]
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disparity_data = [disparity_avg5, disparity_avg10, disparity_avg20, disparity_avg60, disparity_avg120]
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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(data=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(
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rows=6, cols=1,
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subplot_titles=("MACD", "스토캐스틱", "RSI", "거래량", "이격도", '캔들'),
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#specs=[[{}], [{}], [{}], [{}], [{}], [{}]],
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shared_xaxes=True, horizontal_spacing=0.03, vertical_spacing=0.01,
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row_heights=[200, 200, 200, 200, 200, 700]
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)
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for trace in macd_data:
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fig.append_trace(trace, 1, 1)
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for trace in stochastic_data:
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fig.append_trace(trace, 2, 1)
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for trace in rsi_data:
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fig.append_trace(trace, 3, 1)
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for trace in volume_data:
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fig.append_trace(trace, 4, 1)
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for trace in disparity_data:
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fig.append_trace(trace, 5, 1)
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for trace in candle_data:
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fig.append_trace(trace, 6, 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=1700, title=stock_code + "_" + given_day + "_" + str(buy_count)+","+str(sell_count))
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#fig.update_layout(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, stock_codes:dict=None):
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if stock_codes is not None:
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for stock_code in stock_codes:
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for given_day in stock_codes[stock_code]:
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LAST_DATA = self.stock2Vector.getLastData(stock_code, given_day)
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result = self.stock2Vector.getRealTime(stock_code, given_day, LAST_DATA)
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# 이동평균, RSI, MACD, 일목균형, 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, data = self.buySellChecker.checkTransaction(data, stock_code, isRealTime=False)
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analyzed_day = 120
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bsLine, data = self.buySellChecker.checkTransactionWithEnvelope(data, stock_code, analyzed_day, isRealTime=False)
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# 그래프를 그린다.
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self.draw(stock_code, given_day, data, bsLine)
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else:
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dailyStatus = DailyStatus(self.RESOURCE_PATH)
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dailyStatus.checkEnvelope()
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return
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if __name__ == "__main__":
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PROJECT_HOME = os.getcwd()
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RESOURCE_PATH = os.path.join(PROJECT_HOME, "resources")
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simulation = Simulation(RESOURCE_PATH)
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# to check bying
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stock_codes = {
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"252670": [
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'20220901', '20220902', '20220905', '20220906', '20220907',
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'20220908','20220913','20220914','20220915','20220916'
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]
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}
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#simulation.simulate(stock_codes)
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simulation.simulate()
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print ("done...")
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