189 lines
7.9 KiB
Python
189 lines
7.9 KiB
Python
import os
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import csv
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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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from hts.HTS import HTS
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from hts.BuySellChecker import BuySellChecker
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class LabelMaker (HTS):
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buySellChecker = None
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def __init__(self):
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super().__init__(RESOURCE_PATH)
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self.buySellChecker = BuySellChecker()
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return
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def checkTransaction(self, data):
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bsLine = {}
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size = len(data["close"])
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# Type=False, 시뮬레이션 적용
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bsLine['buy'] = [-1 for i in range(size)]
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bsLine['buy_weight'] = [-1 for i in range(size)]
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bsLine['sell'] = [-1 for i in range(size)]
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bsLine['sell_weight'] = [-1 for i in range(size)]
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for i in range(size-60):
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min_price, min_price_c, max_price, max_price_c = 9999999, -1, 0, -1
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for c in range(i, i+60):
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if data["close"][c] > max_price:
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max_price = data["close"][c]
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max_price_c = c
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if data["close"][c] < min_price:
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min_price = data["close"][c]
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min_price_c = c
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if min_price_c > 0:
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bsLine['buy'][min_price_c] = min_price
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bsLine['buy_weight'][min_price_c] = 1
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if max_price_c > 0:
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bsLine['sell'][max_price_c] = max_price
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bsLine['sell_weight'][min_price_c] = 1
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return bsLine, data
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def draw(self, stock_code, given_day, data, bsLine):
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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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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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"""
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fig = go.Figure(data=candle_data)
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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=1000, title=stock_code + "_" + given_day + "_" + str(buy_count)+","+str(sell_count))
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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=3000, 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 writeLabelFile(self, bsLine, data, ymd):
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outFileName = os.path.join(self.RESOURCE_PATH, "tmp", ymd+".sell.csv")
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with open(outFileName, "w", encoding="utf-8") as outFp:
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writer = csv.writer(outFp)
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for i, price in enumerate(bsLine["sell"]):
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if price != -1:
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writer.writerow([data['date'][i], bsLine["sell"][i]])
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outFileName = os.path.join(self.RESOURCE_PATH, "tmp", ymd + ".buy.csv")
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with open(outFileName, "w", encoding="utf-8") as outFp:
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writer = csv.writer(outFp)
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for i, price in enumerate(bsLine["buy"]):
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if price != -1:
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writer.writerow([data['date'][i], bsLine["buy"][i]])
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return
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def makeCandidate(self, stock_code, ymd="20220727"):
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result = {"check": set(), "time": [], "open": [], "close": [], "high": [], "low": [], "vol": [], "label": []}
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self.getDBData(stock_code, ymd, result)
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data = self.buySellChecker.analyze(result)
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bsLine, data = self.checkTransaction(data)
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self.writeLabelFile(bsLine, data, ymd)
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self.draw(stock_code, ymd, 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(os.path.join(os.path.dirname(os.path.join(os.path.dirname(__file__))))))
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RESOURCE_PATH = os.path.join(PROJECT_HOME, "resources")
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labelMaker = LabelMaker()
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stock_code = "252670"
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#stock_code = "122630"
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labelMaker.makeCandidate(stock_code, "20220727") |