180 lines
7.5 KiB
Python
180 lines
7.5 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.StockPredictor import StockPredictor
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from stock.util.LabelMaker import LabelMaker
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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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def __init__(self, RESOURCE_PATH):
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super().__init__(RESOURCE_PATH)
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self.stock2Vector = Stock2Vector(RESOURCE_PATH)
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self.labelMaker = LabelMaker(RESOURCE_PATH)
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self.stockPredictor = StockPredictor()
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self.buySellChecker = BuySellChecker()
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self.RESOURCE_PATH = RESOURCE_PATH
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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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# 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(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(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, stock_code, today, method="rule"):
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if method == "answer":
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bsLine, data = self.labelMaker.makeCandidate(stock_code, today, view=True)
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else:
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if method == "ml":
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LAST_DATA = self.stock2Vector.getLastData(stock_code, today, n=10)
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result = self.stock2Vector.getRealTime(stock_code, today, LAST_DATA)
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df, minmax_df = self.stock2Vector.preprocessData(result)
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bsLine, data = self.stockPredictor.predict(df, minmax_df, isRealTime=False)
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else:
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LAST_DATA = self.stock2Vector.getLastData(stock_code, today)
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result = self.stock2Vector.getRealTime(stock_code, today, 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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if data is not None:
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# 그래프를 그린다.
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self.draw(stock_code, today, data, bsLine)
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return
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if __name__ == "__main__":
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PROJECT_HOME = os.path.join(os.path.dirname(__file__))
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RESOURCE_PATH = os.path.join(PROJECT_HOME, "resources")
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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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"252670": ['20220804'],
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}
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method = "answer" # "ml", "answer"
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for stock_code in stock_codes:
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simulation = Simulation(RESOURCE_PATH)
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for given_day in stock_codes[stock_code]:
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simulation.simulate(stock_code, given_day, method=method)
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print ("done...")
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