diff --git a/Bithumb.py b/Bithumb_daily.py similarity index 99% rename from Bithumb.py rename to Bithumb_daily.py index 85b955b..1349df5 100644 --- a/Bithumb.py +++ b/Bithumb_daily.py @@ -15,7 +15,7 @@ from stock.analysis.AnalyzerSqlite import AnalyzerSqlite from hts.BuySellChecker import BuySellChecker from hts.HTS import HTS -class Bithumb(HTS): +class Bithumb_daily(HTS): RESOURCE_PATH = None buySellChecker = None @@ -653,7 +653,7 @@ if __name__ == "__main__": if not os.path.exists(dirName): os.mkdir(dirName) - bithumb = Bithumb(RESOURCE_PATH) + bithumb = Bithumb_daily(RESOURCE_PATH) tickers = ['XRP', 'BTC', 'SOL'] isRealTime = False diff --git a/Bithumb_minute.py b/Bithumb_minute.py new file mode 100644 index 0000000..90bdbd4 --- /dev/null +++ b/Bithumb_minute.py @@ -0,0 +1,740 @@ +import os +import csv +import time +import requests +import json +import ccxt +import pybithumb +import pandas as pd +from math import nan +import plotly.io as po +from plotly import subplots +import plotly.graph_objects as go +from datetime import datetime +from sklearn.linear_model import LinearRegression +from sklearn.preprocessing import StandardScaler, MinMaxScaler + +from stock.analysis.AnalyzerSqlite import AnalyzerSqlite +from hts.BuySellChecker import BuySellChecker +from hts.HTS import HTS + + +class Bithumb_minute(HTS): + RESOURCE_PATH = None + buySellChecker = None + analyzerSqlite = None + bithumb = None + binance = None + + def __init__(self, RESOURCE_PATH): + super().__init__(RESOURCE_PATH) + self.RESOURCE_PATH = RESOURCE_PATH + + con_key = "946dd0b0e6f8ad411144cd33f09518d3" # 본인의 Connect Key를 입력한다. + sec_key = "56b2a3cdd9fe3a82aa3f38c97c161125" # 본인의 Secret Key를 입력한다. + + self.buySellChecker = BuySellChecker() + self.analyzerSqlite = AnalyzerSqlite() + + # bithumb api에 연결한 클라스 객체를 선언한다. + self.bithumb = pybithumb.Bithumb(con_key, sec_key) + self.binance = ccxt.binance() + + return + + def bull_market(self, df, ticker): + m5 = df['close'].rolling(5).mean() + last_m5 = m5[-2] + + price = pybithumb.get_current_price(ticker) + + if price > last_m5: + return True + + return False + + def append(self, df, stock): + for i in range(len(df)): + stock['PRICE'].append( + { + "ymd": df.index[i], + "close": df['close'][i], + "diff": 0, + "open": df['open'][i], + "high": df['high'][i], + "low": df['low'][i], + "volume": df['volume'][i], + "avg3": -1, + "avg4": -1, + "avg5": -1, + "avg6": -1, + "avg10": -1, + "avg12": -1, + "avg20": -1, + "avg36": -1, + "avg40": -1, + "avg48": -1, + "avg60": -1, + "avg120": -1, + "avg200": -1, + "avg240": -1, + "avg300": -1, + "disparity_avg5": -1, + "disparity_avg10": -1, + "disparity_avg20": -1, + "disparity_avg60": -1, + "disparity_avg120": -1, + "bolingerband_upper": -1, + "bolingerband_lower": -1, + "bolingerband_middle": -1, + "envelope_upper": -1, + "envelope_lower": -1, + "envelope_middle": -1, + "ichimokucloud_changeLine": -1, + "ichimokucloud_baseLine": -1, + "ichimokucloud_leadingSpan1": -1, + "ichimokucloud_leadingSpan2": -1, + "stochastic_fast_k": -1, + "stochastic_slow_k": -1, + "stochastic_slow_d": -1, + "rsi": -1, + "rsis": -1, + "macd": -1, + "macds": -1, + "macdo": -1, + }) + return + + def analyze(self, stock, days=120): + stock["PRICE"] = sorted(stock["PRICE"], key=lambda x: x['ymd']) + self.analyzerSqlite.get_moving_average(stock["PRICE"]) + + # 이동 평균을 이용한 이격도 계산 + self.analyzerSqlite.get_disparity(stock["PRICE"]) + + self.analyzerSqlite.ichimokuCloud.analyze(stock) + self.analyzerSqlite.stochastic.analyze(stock) + self.analyzerSqlite.bolingerBand.analyze(stock) + self.analyzerSqlite.envelope.analyze(stock) + self.analyzerSqlite.rsi.analyze(stock) + self.analyzerSqlite.macd.analyze(stock) + + result = { + "ymd": [], + "open": [], + "close": [], + "high": [], + "low": [], + "avg3": [], + "avg4": [], + "avg5": [], + "avg6": [], + "avg10": [], + "avg12": [], + "avg20": [], + "avg36": [], + "avg40": [], + "avg48": [], + "avg60": [], + "avg120": [], + "avg200": [], + "avg240": [], + "avg300": [], + "disparity_avg5": [], + "disparity_avg20": [], + "disparity_avg60": [], + "disparity_avg120": [], + "disparity": [], + "disparity_type": [], + "envelope_upper": [], + "envelope_lower": [], + "envelope_middle": [], + "rsi": [], + "rsis": [], + "macd": [], + "macds": [], + "slow_k": [], + "slow_d": [], + "buy": [], + "sell": [], + } + for item in stock['PRICE']: + result["ymd"].append(item['ymd']) + result["open"].append(item['open']) + result["close"].append(item['close']) + result["high"].append(item['high']) + result["low"].append(item['low']) + result["avg3"].append(item['avg3']) + result["avg4"].append(item['avg4']) + result["avg5"].append(item['avg5']) + result["avg6"].append(item['avg6']) + result["avg10"].append(item['avg10']) + result["avg12"].append(item['avg12']) + result["avg20"].append(item['avg20']) + result["avg36"].append(item['avg36']) + result["avg40"].append(item['avg40']) + result["avg48"].append(item['avg48']) + result["avg60"].append(item['avg60']) + result["avg120"].append(item['avg120']) + result["avg200"].append(item['avg200']) + result["avg240"].append(item['avg240']) + result["avg300"].append(item['avg300']) + result["disparity_avg5"].append(item['disparity_avg5']) + result["disparity_avg20"].append(item['disparity_avg20']) + result["disparity_avg60"].append(item['disparity_avg60']) + result["disparity_avg120"].append(item['disparity_avg120']) + result['disparity'].append( + max(item['disparity_avg5'], item['disparity_avg20'], item['disparity_avg60']) - min( + item['disparity_avg5'], item['disparity_avg20'], item['disparity_avg60'])) + if item['disparity_avg60'] < item['disparity_avg20'] < item['disparity_avg5']: + result['disparity_type'].append(1) + elif item['disparity_avg5'] < item['disparity_avg20'] < item['disparity_avg60']: + result['disparity_type'].append(-1) + else: + result['disparity_type'].append(0) + result["envelope_upper"].append(item['envelope_upper']) + result["envelope_lower"].append(item['envelope_lower']) + result["envelope_middle"].append(item['envelope_middle']) + result["rsi"].append(item['rsi']) + result["rsis"].append(item['rsis']) + result["macd"].append(item['macd']) + result["macds"].append(item['macds']) + result["slow_k"].append(item['stochastic_slow_k']) + result["slow_d"].append(item['stochastic_slow_d']) + result["buy"].append(-1) + result["sell"].append(-1) + + data = pd.DataFrame(result) + df_final_time = pd.DatetimeIndex(result['ymd']) + data.index = df_final_time + + data = data.astype( + { + 'open': 'int', + 'high': 'int', + 'low': 'int', + 'close': 'int', + 'avg3': 'float', + 'avg4': 'float', + 'avg5': 'float', + 'avg6': 'float', + 'avg10': 'float', + 'avg12': 'float', + 'avg20': 'float', + 'avg36': 'float', + 'avg40': 'float', + 'avg48': 'float', + 'avg60': 'float', + 'avg120': 'float', + 'avg200': 'float', + 'avg240': 'float', + 'avg300': 'float', + 'disparity_avg5': 'float', + 'disparity_avg20': 'float', + 'disparity_avg60': 'float', + 'disparity_avg120': 'float', + 'buy': 'int', + 'sell': 'int', + 'slow_k': 'float', + 'slow_d': 'float', + 'macd': 'float', + 'macds': 'float', + 'envelope_upper': 'float', + 'envelope_lower': 'float', + 'envelope_middle': 'float', + 'rsi': 'float', + 'rsis': 'float' + } + ) + + scaler = StandardScaler() + low_df = pd.DataFrame(data['low']) + low_df.index = [c for c in range(len(low_df))] + low_std = scaler.fit_transform(data['low'].values.reshape(-1, 1)) + low_std = pd.DataFrame(low_std, columns=['low_std']) + + min_df = pd.DataFrame({'open': data['open'].to_list(), 'close': data['close'].to_list()}) + min_df['min_std'] = min_df.min(axis=1) + min_df.index = [c for c in range(len(min_df))] + min_std = scaler.fit_transform(min_df['min_std'].values.reshape(-1, 1)) + min_std = pd.DataFrame(min_std, columns=['min_std']) + + line_fitter = LinearRegression() + size = len(data["close"]) + gradients_low = [] + gradients_avg5 = [] + gradients_avg20 = [] + gradients_avg60 = [] + + for i in range(size): + coef_low = -999 + coef_avg5 = -999 + coef_avg20 = -999 + coef_avg60 = -999 + + if i > 0: + l = days if i >= days else i + x = pd.DataFrame([c for c in range(i - l, i + 1)]) + y = pd.DataFrame(low_std.values.tolist()[i - l:i + 1]) + line_fitter.fit(x.values.reshape(-1, 1), y) + coef_low = line_fitter.coef_[0][0] + + l = 5 if i >= 5 else i + x = pd.DataFrame([c for c in range(i - l, i + 1)]) + y = pd.DataFrame(min_std.values.tolist()[i - l:i + 1]) + line_fitter.fit(x.values.reshape(-1, 1), y) + coef_avg5 = line_fitter.coef_[0][0] + + l = 20 if i >= 20 else i + x = pd.DataFrame([c for c in range(i - l, i + 1)]) + y = pd.DataFrame(min_std.values.tolist()[i - l:i + 1]) + line_fitter.fit(x.values.reshape(-1, 1), y) + coef_avg20 = line_fitter.coef_[0][0] + + l = 60 if i >= 60 else i + x = pd.DataFrame([c for c in range(i - l, i + 1)]) + y = pd.DataFrame(min_std.values.tolist()[i - l:i + 1]) + line_fitter.fit(x.values.reshape(-1, 1), y) + coef_avg60 = line_fitter.coef_[0][0] + + gradients_low.append(coef_low) + gradients_avg5.append(coef_avg5) + gradients_avg20.append(coef_avg20) + gradients_avg60.append(coef_avg60) + + gradients_low_df = pd.DataFrame(gradients_low, columns=['gradients_low']) + gradients_avg5_df = pd.DataFrame(gradients_avg5, columns=['gradients_avg5']) + gradients_avg20_df = pd.DataFrame(gradients_avg20, columns=['gradients_avg20']) + gradients_avg60_df = pd.DataFrame(gradients_avg60, columns=['gradients_avg60']) + + gradients_low_df.index = df_final_time + gradients_avg5_df.index = df_final_time + gradients_avg20_df.index = df_final_time + gradients_avg60_df.index = df_final_time + + data = data.merge(gradients_low_df, left_index=True, right_index=True) + data = data.merge(gradients_avg5_df, left_index=True, right_index=True) + data = data.merge(gradients_avg20_df, left_index=True, right_index=True) + data = data.merge(gradients_avg60_df, left_index=True, right_index=True) + + return data + + def writeFile(self, dirName, ticker, data, bsLine, today): + if bsLine is None: + return + + # 어제 데이터는 지운다. + buy_line = bsLine['buy'] + buy_weight_line = bsLine['buy_weight'] + sell_line = bsLine['sell'] + + buy_size = [] + buy_colors = [] + for i in range(len(buy_line)): + if buy_line[i] < 0: + buy_colors.append("#ffffff") + buy_line[i] = nan + buy_size.append(0) + else: + buy_colors.append("#B2028C") + buy_size.append(10 + (0.1 * buy_weight_line[i])) + + sell_colors = [] + for i in range(len(sell_line)): + if sell_line[i] < 0: + sell_colors.append("#ffffff") + sell_line[i] = nan + else: + sell_colors.append("#00ced1") + + # 그래프를 설정한다. + buy_check = go.Scatter(x=data['ymd'], y=buy_line, mode='markers', name="buy", + marker=dict(size=buy_size, color=buy_colors, line_width=0)) + sell_check = go.Scatter(x=data['ymd'], y=sell_line, mode='markers', name="sell", + marker=dict(size=14, color=sell_colors, line_width=0)) + envelope_upper = go.Scatter(x=data['ymd'], y=data["envelope_upper"], name="upper", line_color='#000000') + envelope_middle = go.Scatter(x=data['ymd'], y=data["envelope_middle"], name="upper", line_color='#927786') + envelope_lower = go.Scatter(x=data['ymd'], y=data["envelope_lower"], name="lower", line_color='#000000') + + avg5 = go.Scatter(x=data['ymd'], y=data["avg5"], name="avg5", line_color='#6C2507') + avg20 = go.Scatter(x=data['ymd'], y=data["avg20"], name="avg20", line_color='#f84c43') + avg60 = go.Scatter(x=data['ymd'], y=data["avg60"], name="avg60", line_color='#f89543') + candle_stick = go.Candlestick(x=data['ymd'], open=data['open'], high=data['high'], low=data['low'], + close=data['close'], increasing_line_color='red', decreasing_line_color='blue', + showlegend=False) + + macd_line = go.Scatter(x=data['ymd'], y=data["macd"], line=dict(color='red', width=2), name='macd') + macd_s_line = go.Scatter(x=data['ymd'], y=data["macds"], line=dict(dash='dashdot', color='black', width=2), + name='macds') + + # fast_k_line = go.Scatter(x=hts['date'], y=hts["fast_k"], mode='lines', name='fast_k') + slow_k_line = go.Scatter(x=data['ymd'], y=data["slow_k"], line=dict(color='red', width=2), name='slow_k') + slow_d_line = go.Scatter(x=data['ymd'], y=data["slow_d"], line=dict(dash='dashdot', color='black', width=2), + name='slow_d') + + rsi_line = go.Scatter(x=data['ymd'], y=data["rsi"], line=dict(color='red', width=2), name='rsi') + rsis_line = go.Scatter(x=data['ymd'], y=data["rsis"], line=dict(dash='dashdot', color='black', width=2), + name='rsis') + + disparity_avg5 = go.Scatter(x=data['ymd'], y=data["disparity_avg5"], name="disparity_avg5", + line_color='#8F8203') + disparity_avg20 = go.Scatter(x=data['ymd'], y=data["disparity_avg20"], name="disparity_avg20", + line_color='#ff00ff') + disparity_avg60 = go.Scatter(x=data['ymd'], y=data["disparity_avg60"], name="disparity_avg60", + line_color='#1469F4') + + candle_data = [candle_stick, avg5, avg20, avg60, envelope_upper, envelope_middle, envelope_lower, buy_check, + sell_check] + disparity_data = [disparity_avg5, disparity_avg20, disparity_avg60] + macd_data = [macd_line, macd_s_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=5, cols=1, + subplot_titles=("MACD", "RSI", "스토캐스틱", '이격도', '캔들'), + # specs=[[{}], [{}], [{}], [{}], [{}], [{}]], + shared_xaxes=True, horizontal_spacing=0.03, vertical_spacing=0.01, + row_heights=[200, 200, 200, 200, 750] + ) + for trace in macd_data: + fig.append_trace(trace, 1, 1) + for trace in rsi_data: + fig.append_trace(trace, 2, 1) + for trace in stochastic_data: + fig.append_trace(trace, 3, 1) + for trace in disparity_data: + fig.append_trace(trace, 4, 1) + for trace in candle_data: + fig.append_trace(trace, 5, 1) + + 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=1700, title="_" + str(buy_count) + "," + str(sell_count)) + fig['layout'].update() + + fileName = "%s/%s_%s.html" % (dirName, ticker, today) + po.write_html(fig, file=fileName, auto_open=False) + + return + + def getBalance(self, ticker): + tmp = self.bithumb.get_balance(ticker) + return tmp[2] + + def getCount(self, ticker): + tmp = self.bithumb.get_balance(ticker) + return tmp[0] + + def exist_buy(self, ticker, log_filename): + if os.path.exists(log_filename): + log_file = open(log_filename, 'r', ) + reader = csv.reader(log_file) + for line in reader: + if line[2] == ticker: + log_file.close() + return True + log_file.close() + return False + + def notBuy(self, data, i): + if i > 5: + check = True + for l in range(i - 4, i + 1): + if ( + data['gradients_avg60'][l - 1] > data['gradients_avg60'][l] or + data['gradients_avg20'][l - 1] > data['gradients_avg20'][l] or + data['gradients_low'][l - 1] > data['gradients_low'][l] + ): + check = False + break + if not check: + return False + return True + + def checkWithEnvelope(self, data, analyzed_day=120, isRealTime=False): + + bsLine = {} + size = len(data["close"]) + + bsLine['buy'] = [-1 for i in range(size)] + bsLine['buy_weight'] = [-1 for i in range(size)] + bsLine['sell'] = [-1 for i in range(size)] + bsLine['sell_weight'] = [-1 for i in range(size)] + + gap_interval = analyzed_day + gap_state = False + for i in range(size): + if isRealTime: + if i < size - 1: + continue + + if i > 10: + # 만약 전일 저가와 오늘 종의 차이가 1만원이 넘으면 향후 60일은 분석하지 않는다. + if data['high'][i] < int(data['low'][i - 1] * 0.7): + gap_state = True + gap_interval -= 1 + continue + if gap_state: + if gap_interval <= 0: + gap_state = False + gap_interval = 60 + else: + gap_interval -= 1 + continue + + if data['disparity'][i] < 2: + check = True + for l in range(i - 3, i): + if ( + data['gradients_avg60'][l - 1] > data['gradients_avg60'][l] or + data['gradients_avg20'][l - 1] > data['gradients_avg20'][l] or + data['gradients_low'][l - 1] > data['gradients_low'][l] or + data['disparity_avg5'][l - 1] > data['disparity_avg5'][l] or + data['disparity'][l - 1] < data['disparity'][l] + ): + check = False + break + if check and 99 < sum(data['disparity_avg5'][i - 4:i + 1]) / 5 < 100 and 99 < sum( + data['disparity_avg60'][i - 4:i + 1]) / 5 < 100: + buy = data['low'][i] + data['buy'][i] = buy + bsLine['buy'][i] = buy + bsLine['buy_weight'][i] = 1 + + check = True + for l in range(i - 2, i): + if ( + data['gradients_avg60'][l - 1] > data['gradients_avg60'][l] or + data['gradients_low'][l - 1] > data['gradients_low'][l] + ): + check = False + break + if ( + check and + -0.0011 < data['gradients_low'][i] < 0 and -0.007 < data['gradients_avg5'][i] < 0.001 and + -0.0012 < data['gradients_avg60'][i] < 0 and + 98.90 < data['disparity_avg5'][i] < 101 + ): + buy = data['low'][i] + data['buy'][i] = buy + bsLine['buy'][i] = buy + bsLine['buy_weight'][i] = 1 + + check = True + for l in range(i - 6, i): + if ( + data['gradients_avg60'][l - 1] < data['gradients_avg60'][l] or + data['gradients_avg20'][l - 1] < data['gradients_avg20'][l] or + data['gradients_low'][l - 1] < data['gradients_low'][l] or + -0.039 < data['gradients_low'][l - 1] < -0.35 or + -0.05 < data['gradients_avg20'][l - 1] < -0.30 or + -0.40 < data['gradients_avg60'][l - 1] < -0.30 + ): + check = False + break + if check and 99 < min(data['disparity_avg5'][i - 6:i]) < max(data['disparity_avg5'][i - 6:i]) < 101: + buy = data['low'][i] + data['buy'][i] = buy + bsLine['buy'][i] = buy + bsLine['buy_weight'][i] = 1 + + check = True + for l in range(i - 3, i): + if ( + data['gradients_low'][l - 1] < data['gradients_low'][l] or + data['gradients_avg60'][l - 1] < data['gradients_avg60'][l] or + data['gradients_avg20'][l - 1] < data['gradients_avg20'][l] or + 0.01 < data['gradients_low'][l - 1] < 0.21 or + -0.09 < data['gradients_avg20'][l - 1] < -0.002 or + 0.01 < data['gradients_avg60'][l - 1] < 0.021 + ): + check = False + break + if check: + buy = data['low'][i] + data['buy'][i] = buy + bsLine['buy'][i] = buy + bsLine['buy_weight'][i] = 1 + + if (data['disparity'][i] < 5 and 99.0 < data['disparity_avg60'][i] < 99.1 and + -0.009 < data['gradients_avg60'][i] < -0.008 and 0.015 < data['gradients_avg20'][i] < 0.016 and + -0.006 < data['gradients_avg5'][i] < -0.005 and -0.009 < data['gradients_low'][i] < -0.008): + check = True + for l in range(i - 5, i): + if ( + data['gradients_avg60'][l - 1] > data['gradients_avg60'][l] or + data['gradients_low'][l - 1] > data['gradients_low'][l] or + data['disparity'][l - 1] < data['disparity'][l] + ): + check = False + break + if check: + buy = data['low'][i] + data['buy'][i] = buy + bsLine['buy'][i] = buy + bsLine['buy_weight'][i] = 1 + + if data['macd'][i] < -4000: + if data['macd'][i - 1] < data['macd'][i]: + if not self.notBuy(data, i) and data['slow_k'][i] < 30: + buy = data['low'][i] + data['buy'][i] = buy + bsLine['buy'][i] = buy + bsLine['buy_weight'][i] = 1 + + # macd 이전에 없던 바닥인 경우 상승할 찰나 매수 + if data['macds'][i - 1] < min(data['macds'][:i - 1]): + if data['macds'][i - 1] < data['macds'][i]: + if not self.notBuy(data, i) and data['slow_k'][i] < 30: + buy = data['low'][i] + data['buy'][i] = buy + bsLine['buy'][i] = buy + bsLine['buy_weight'][i] = 1 + + if ( + 98 < data['disparity_avg5'][i] < 100 and data['disparity_avg20'][i] < 93.5 and + data['disparity_avg60'][i] < 89 and + -0.014 < data['gradients_avg60'][i] < -0.013 and -0.03 < data['gradients_avg20'][ + i] < -0.02 and -0.014 < data['gradients_low'][i] < -0.013 and + data['slow_k'][i] < 11 + ): + if not self.notBuy(data, i): + buy = data['low'][i] + data['buy'][i] = buy + bsLine['buy'][i] = buy + bsLine['buy_weight'][i] = 1 + + if data['slow_k'][i] < 20 and data['slow_k'][i - 1] < data['slow_d'][i - 1] and data['slow_d'][i] < data['slow_k'][i]: + buy = data['low'][i] + data['buy'][i] = buy + bsLine['buy'][i] = buy + bsLine['buy_weight'][i] = 3 + + if data['slow_k'][i-1] < 40 and data['slow_k'][i - 1] < data['slow_d'][i - 1] and data['slow_d'][i] < data['slow_k'][i]: + buy = data['low'][i] + data['buy'][i] = buy + bsLine['buy'][i] = buy + bsLine['buy_weight'][i] = 2 + + + if data['slow_k'][i] > 80 and data['slow_d'][i-1] < data['slow_k'][i-1] and data['slow_k'][i] < data['slow_d'][i]: + buy = data['high'][i] + data['sell'][i] = buy + bsLine['sell'][i] = buy + bsLine['sell_weight'][i] = 100 + + return bsLine, data + + def get_ohlcv(self, ticker): + url = "https://api.upbit.com/v1/candles/minutes/5" + querystring = {"market": "KRW-"+ticker, "count": "300"} + response = requests.request("GET", url, params=querystring) + json_response = json.loads(response.text) + + btc_ohlcv = [] + for json_data in json_response: + btc_ohlcv.append({'datetime': datetime.strptime(json_data['candle_date_time_kst'], '%Y-%m-%dT%H:%M:%S'), 'open': json_data['opening_price'], 'high': json_data['high_price'], 'low': json_data['low_price'], 'close': json_data['trade_price'], 'volume': json_data['candle_acc_trade_volume']}) + btc_ohlcv = sorted(btc_ohlcv, key=lambda item: (item['datetime'])) + + df = pd.DataFrame(btc_ohlcv, columns=['datetime', 'open', 'high', 'low', 'close', 'volume']) + df['datetime'] = pd.to_datetime(df['datetime'], unit='ms') + df.set_index('datetime', inplace=True) + return df + + def buyRealTime(self, ticker, isRealTime=False): + + stock = {"CODE": ticker, "NAME": ticker, "PRICE": []} + + """ + # binance + btc_ohlcv = self.binance.fetch_ohlcv(ticker + "/BKRW") + df = pd.DataFrame(btc_ohlcv, columns=['datetime', 'open', 'high', 'low', 'close', 'volume']) + df['datetime'] = pd.to_datetime(df['datetime'], unit='ms') + df.set_index('datetime', inplace=True) + """ + + """ + # bithumb + df_ = pybithumb.get_ohlcv(ticker) + """ + + df = self.get_ohlcv(ticker) + close = pybithumb.get_current_price(ticker) + + size = len(df) + df['close'][size - 1] = close + if close < df['low'][size - 1]: + df['low'][size - 1] = close + if df['high'][size - 1] < close: + df['high'][size - 1] = close + self.append(df, stock) + + analyzed_day = 120 + data = self.analyze(stock, analyzed_day) + # 분석일 데이터만 활용한다 (이전 데이터는 제거) + data.drop(data.index[:len(data) - analyzed_day], inplace=True) + + bsLine, data = self.checkWithEnvelope(data, analyzed_day, isRealTime=isRealTime) + + # 그래프를 그린다. + if len(data.index) > 10: + today = datetime.today().strftime('%Y%m%d') + log_filename = os.path.join(RESOURCE_PATH, 'analysis', 'bithumb', today + '.log') + if isRealTime: + if not self.exist_buy(ticker, log_filename): + if max(bsLine['buy'][len(bsLine['buy']) - 2:]) > 100: + balance = self.getBalance(ticker) + count = round((balance * (bsLine['buy_weight'][len(bsLine['buy_weight']) - 1] / 100)) / bsLine['buy'][len(bsLine['buy']) - 1], 2) + order = self.bithumb.buy_limit_order(ticker, bsLine['buy'][len(bsLine['buy']) - 1], count) + # order: ('bid', 'BTC', 'C0101000000322993432', 'KRW') + + with open(log_filename, 'a', newline='', encoding='utf-8') as log_file: + wr = csv.writer(log_file) + wr.writerow([datetime.now().strftime('%Y-%m-%d %H:%M:%S'), order[0], order[1], order[2], order[3]]) + + if max(bsLine['sell'][len(bsLine['sell']) - 2:]) > 100: + count = self.getCount(ticker) + order = self.bithumb.sell_limit_order(ticker, bsLine['sell'][len(bsLine['sell'])-1], count) + else: + dirName = os.path.join(RESOURCE_PATH, 'analysis', 'bithumb') + self.writeFile(dirName, ticker, data, bsLine, datetime.now().strftime('%Y%m%d %H%M%S')) + + return + + +if __name__ == "__main__": + + PROJECT_HOME = os.getcwd() + RESOURCE_PATH = os.path.join(PROJECT_HOME, "resources") + + if not os.path.exists(os.path.join(RESOURCE_PATH, 'analysis', 'bithumb')): + os.mkdir(os.path.join(RESOURCE_PATH, 'analysis', 'bithumb')) + dirName = os.path.join(RESOURCE_PATH, 'analysis', 'bithumb') + if not os.path.exists(dirName): + os.mkdir(dirName) + + bithumb = Bithumb_minute(RESOURCE_PATH) + + tickers = ['XRP', 'BTC'] + isRealTime = False + if isRealTime: + while True: + for ticker in tickers: + print(ticker, datetime.now().strftime('%Y-%m-%d %H:%M:%S')) + bithumb.buyRealTime(ticker, isRealTime) + time.sleep(5) + else: + for ticker in tickers: + print(ticker, datetime.now().strftime('%Y-%m-%d %H:%M:%S')) + bithumb.buyRealTime(ticker, isRealTime)