import os from datetime import datetime, timedelta import pandas as pd import plotly.graph_objects as go from plotly import subplots from hts.BuySellChecker import BuySellChecker class Simulation: buySellChecker = None stock_code = None def __init__(self, stock_code): self.buySellChecker = BuySellChecker() self.stock_code = stock_code #self.connect() return def getCSV(self, fileName, given_day, result): data = pd.read_csv(fileName) days = data.날짜 time = data.시간 open = data.시가 close = data.종가 high = data.고가 low = data.저가 vol = data.거래량 start_time = datetime.strptime(given_day + " 090000", '%Y%m%d %H%M%S') for i in range(len(data)): temp = datetime.strptime(str(days[i]) + " " + str(time[i]).zfill(4)+"00", '%Y%m%d %H%M%S') if temp < start_time: continue if temp not in result["check"]: result["check"].add(temp) result["time"].append(temp) result["open"].append(open[i]) result["close"].append(close[i]) result["high"].append(high[i]) result["low"].append(low[i]) result["vol"].append(vol[i]) return def checkTransaction(self, data): size = len(data["close"]) bsLine = {} bsLine['buy'] = [-1 for i in range(size)] bsLine['weight'] = [-1 for i in range(size)] bsLine['sell'] = [-1 for i in range(size)] for i in range(6, size-5): if self.stock_code == "252670": buy, weight, sell = self.buySellChecker.getPriceAndWeight1(data, i) else: buy, weight, sell = self.buySellChecker.getPriceAndWeight2(data, i) bsLine['buy'][i] = buy bsLine['weight'][i] = weight bsLine['sell'][i] = sell return bsLine def draw(self, stock_code, given_day, data, bsLine): buy_line = bsLine['buy'] sell_line = bsLine['sell'] # 그래프 설정을 위한 변수를 생성한다. data['open'] = pd.to_numeric(data['open']) data['high'] = pd.to_numeric(data['high']) data['low'] = pd.to_numeric(data['low']) data['close'] = pd.to_numeric(data['close']) data['volume'] = pd.to_numeric(data['volume']) data['avg5'] = pd.to_numeric(data['avg5']) data["fast_k"] = pd.to_numeric(data['fast_k']) data["slow_k"] = pd.to_numeric(data['slow_k']) data["slow_d"] = pd.to_numeric(data['slow_d']) data["rsi"] = pd.to_numeric(data['rsi']) data["rsis"] = pd.to_numeric(data['rsis']) buy_colors = [] for i in range(len(buy_line)): if buy_line[i] < 0: buy_colors.append("#ffffff") buy_line[i] = data["lower"][0] else: buy_colors.append("#ff00ff") sell_colors = [] for i in range(len(sell_line)): if sell_line[i] < 0: sell_colors.append("#ffffff") sell_line[i] = data["lower"][0] else: sell_colors.append("#00ced1") # 그래프를 설정한다. buy_check = go.Scatter(x=data['date'], y=buy_line, mode='markers', name="buy", marker=dict(size=14, color=buy_colors, line_width=0)) sell_check = go.Scatter(x=data['date'], y=sell_line, mode='markers', name="sell", marker=dict(size=14, color=sell_colors, line_width=0)) bolinger_upper = go.Scatter(x=data['date'], y=data["upper"], name="upper", line_color='#8B4513') bolinger_lower = go.Scatter(x=data['date'], y=data["lower"], name="lower", line_color='#8B4513') avg5 = go.Scatter(x=data['date'], y=data["avg5"], name="avg5", line_color='#800080') avg10 = go.Scatter(x=data['date'], y=data["avg10"], name="avg10", line_color='#ff00ff') avg30 = go.Scatter(x=data['date'], y=data["avg30"], name="avg30", line_color='#00ffff') avg60 = go.Scatter(x=data['date'], y=data["avg60"], name="avg60", line_color='#008000') 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') volume_line = go.Scatter(x=data['date'], y=data["volume"], mode='lines', name='volume') fast_k_line = go.Scatter(x=data['date'], y=data["fast_k"], mode='lines', name='fast_k') slow_k_line = go.Scatter(x=data['date'], y=data["slow_k"], mode='lines', name='slow_k') slow_d_line = go.Scatter(x=data['date'], y=data["slow_d"], mode='lines', name='slow_d') rsi_line = go.Scatter(x=data['date'], y=data["rsi"], mode='lines', name='rsi') rsis_line = go.Scatter(x=data['date'], y=data["rsis"], mode='lines', name='rsis') #candle_data = [candle_stick, bolinger_upper, bolinger_lower, buy_check, sell_check, avg1, avg2, avg5, avg10, avg20, avg30, avg40, avg50, avg60] candle_data = [candle_stick, bolinger_upper, bolinger_lower, avg5, avg10, avg30, avg60, buy_check, sell_check] volume_data = [volume_line] stochastic_data = [slow_k_line, slow_d_line] rsi_data = [rsi_line, rsis_line] # 그래프를 그린다. """ fig = go.Figure(data=candle_data) fig.update_layout(title=stock_code + "_" + given_day) fig.show() """ fig = subplots.make_subplots(rows=4, cols=1, subplot_titles=('캔들', "거래량", "스토캐스틱", "RSI")) for trace in candle_data: fig.append_trace(trace, 1, 1) for trace in volume_data: fig.append_trace(trace, 2, 1) for trace in stochastic_data: fig.append_trace(trace, 3, 1) for trace in rsi_data: fig.append_trace(trace, 4, 1) #fig.update_xaxes(nticks=5) #fig.update_layout(height=1800, title=stock_code + "_" + given_day, xaxis_rangeslider_visible=False) df = pd.DataFrame(bsLine) df = df.fillna(-1) buy_count = len(df.loc[df["buy"] > 0]) sell_count = len(df.loc[df["sell"] > 0]) fig.update_layout(height=1800, title=stock_code + "_" + given_day + "_" + str(buy_count)+","+str(sell_count)) fig.show() return def simulate(self, GIVEN_DAY): result = {"check": set(), "time": [], "open": [], "close": [], "high": [], "low": [], "vol": []} # 데이터를 가지고 온다. self.getCSV("./data/"+self.stock_code+"_"+GIVEN_DAY+".csv", GIVEN_DAY, result) # 분석을 통해서 볼린저밴드 상/하단을 계산한다. data = self.buySellChecker.analyze(result) # 사야 할 시점과 팔아야 할 시점을 체크한다. bsLine = self.checkTransaction(data) # 그래프를 그린다. self.draw(self.stock_code, GIVEN_DAY, data, bsLine) return if __name__ == "__main__": today = datetime.today() PROJECT_HOME = os.path.join(os.path.dirname(os.path.join(os.path.dirname(__file__)))) RESOURCE_DIR = PROJECT_HOME + "/resources/analysis/"+today.strftime("%Y%m%d") stock_codes = ["252670", "122630"] given_days = ['20210901', '20211020', '20220128', '20220520'] simulation = Simulation(stock_codes[0]) given_days = sorted(given_days, reverse=True) for given_day in given_days: simulation.simulate(given_day) print ("done...")