diff --git a/HTS_etf.py b/HTS_etf.py index 62d3bba..b3c0dcd 100644 --- a/HTS_etf.py +++ b/HTS_etf.py @@ -16,7 +16,6 @@ from stock.analysis.Stochastic import Stochastic from stock.analysis.RSI import RSI from stock.analysis.MACD import MACD from stock.analysis.IchimokuCloud import IchimokuCloud -from statsmodels.tsa.seasonal import seasonal_decompose from hts.BuySellChecker import BuySellChecker from stock.analysis.MovingAverage import MovingAverage diff --git a/JSDPattern.py b/JSDPattern.py index 95082a0..284f852 100644 --- a/JSDPattern.py +++ b/JSDPattern.py @@ -88,26 +88,9 @@ class JSDPattern: return data - def getDBData(self, stock_code, day, mins, get_days=14): + def getDBData(self, stock_code, day, get_days=14): - if mins == 3: - table = 'minute3' - elif mins == 5: - table = 'minute5' - elif mins == 10: - table = 'minute10' - elif mins == 20: - table = 'minute20' - elif mins == 30: - table = 'minute30' - elif mins == 60: - table = 'minute60' - elif mins == 200: - table = 'minute200' - elif mins == 1440: - table = 'daily' - else: - table = 'minutely' + table = 'stock' conn = sqlite3.connect(os.path.join(self.RESOURCE_PATH, 'coins.db')) cursor = conn.cursor() @@ -142,35 +125,8 @@ class JSDPattern: return result def getCoinData(self, ticker, mins=None, to=None, ymd=None, get_days=14): - result = None - - if ymd is not None and datetime.now() < datetime.strptime(ymd, '%Y%m%d'): - ymd = None - - if ymd is None: - if to is None: - if mins is None: - df = pyupbit.get_ohlcv(ticker=ticker['ticker_code']) - else: - if mins == 1440: - df = pyupbit.get_ohlcv(ticker=ticker['ticker_code'], interval='minute1', count=1) - else: - df = pyupbit.get_ohlcv(ticker=ticker['ticker_code'], interval='minute' + str(mins)) - else: - df = pyupbit.get_ohlcv(ticker=ticker['ticker_code'], interval='minute' + str(mins), to=to) - - if df is not None: - df["datetime"] = df.index - df = df[['open', 'high', 'low', 'close', 'volume']].astype(float) - - if mins is not None: - result = self.getDBData(ticker['ticker_code'], datetime.today().strftime('%Y%m%d'), mins=mins, get_days=get_days) - - data = self.append(df, result) - - else: - result = self.getDBData(ticker['ticker_code'], ymd, mins=mins, get_days=get_days) - data = self.append(df=None, result=result) + result = self.getDBData(ticker['ticker_code'], ymd, mins=mins, get_days=get_days) + data = self.append(df=None, result=result) return data diff --git a/Simulation_daily.py b/Simulation_daily.py new file mode 100644 index 0000000..9986fa5 --- /dev/null +++ b/Simulation_daily.py @@ -0,0 +1,437 @@ +from math import nan +import pandas as pd +import plotly.graph_objects as go +from plotly import subplots +import math + +import os +import json + +from datetime import datetime, timedelta + +from hts.BuySell_Daily import BuySell_Daily +from JSDPattern_daily import JSDPattern_daily + + +class Simulation_daily: + + upbit = None + + def __init__(self, RESOURCE_PATH): + + self.buySell_Daily = BuySell_Daily() + self.jSDPattern = JSDPattern_daily(RESOURCE_PATH) + + return + + def cz(self, value): + if value is None or math.isnan(value): + return 0 + + return value + + + def clear_BSLINE(self, BUY_LIST, sell_type=None): + if sell_type is None or sell_type == '': + BUY_LIST['avg_buy_price'] = 0 + BUY_LIST['buy_count'] = 0 + BUY_LIST['buy_list'].clear() + else: + BUY_LIST['avg_buy_price'] = 0 + BUY_LIST['buy_count'] = 0 + + tmp_sell_type = sell_type.split(',') + for i, buy_list in reversed(list(enumerate(BUY_LIST['buy_list']))): + for t_sell_type in tmp_sell_type: + if buy_list['buy_type'].strip() == t_sell_type.strip(): + del BUY_LIST['buy_list'][i] + break + return + + def draw(self, stock_code, data, data_scaled, bsLine=None, show=False, info=None): + + # 어제 데이터는 지운다. + #data = data.loc[pd.DatetimeIndex(data.index).day == int(given_day[6:])] + buy_price_line, buy_count_line, buy_type, buy_count_line, sell_price_line, sell_count_line, sell_type = [], [], [], [], [], [], [] + buy_sell_size, buy_colors, sell_colors, buy_colors = [], [], [], [] + + if bsLine is not None: + buy_price_line = bsLine['buy_price'] + buy_count_line = bsLine['buy_count'] + sell_price_line = bsLine['sell_price'] + sell_count_line = bsLine['sell_count'] + buy_type = bsLine['buy_type'] + sell_type = bsLine['sell_type'] + + for i in range(len(data)): + if buy_price_line[i] < 1: + buy_colors.append("#ffffff") + buy_price_line[i] = nan + buy_sell_size.append(0) + else: + buy_colors.append("#0C752E") + buy_sell_size.append(14) + for i in range(len(data)): + if sell_price_line[i] < 1: + sell_colors.append("#ffffff") + sell_price_line[i] = nan + else: + sell_colors.append("#00ced1") + + volume_colors = [] + for i in range(len(data)): + if data['open'].iloc[i] > data['close'].iloc[i]: + volume_colors.append("#FF0000") + elif data['open'].iloc[i] < data['close'].iloc[i]: + volume_colors.append("#FF0000") + else: + volume_colors.append("#000000") + + # 그래프를 설정한다. + if bsLine is not None: + buy_text_list, sell_text_list = [], [] + for i in range(len(data)): + buy_text_list.append( + "[{}] {:,}
" + "{}, {:,} ({:,.2f})

" + "[BASIC]
" + " poly_5: {:.5f}, poly_10: {:.5f}, poly_20: {:.5f}, poly_60: {:.5f}, poly_120: {:.5f}, poly_240: {:.5f}, poly_480: {:.5f}
" + "[INFO]
" + " new_high_7: {:,.2f}, new_high_9: {:,.2f}, new_high_26: {:,.2f}, new_low_7: {:,.2f}, new_low_9: {:,.2f}, new_low_26: {:,.2f}
" + .format(data['ymd'].iloc[i].strftime('%Y-%m-%d %H:%M'), data["close"].iloc[i], + buy_type[i], buy_price_line[i], buy_price_line[i] * buy_count_line[i], + data_scaled['poly_5'].iloc[i], data_scaled['poly_10'].iloc[i], data_scaled['poly_20'].iloc[i], data_scaled['poly_60'].iloc[i], data_scaled['poly_120'].iloc[i], data_scaled['poly_240'].iloc[i], data_scaled['poly_480'].iloc[i], + data['new_high_7'].iloc[i], data['new_high_9'].iloc[i], data['new_high_26'].iloc[i], data['new_low_7'].iloc[i], data['new_low_9'].iloc[i], data['new_low_26'].iloc[i], + )) + sell_text_list.append( + "[{}] {:,}
" + "{}, {:,} ({:,.2f})

" + "[BASIC]
" + " poly_5: {:.5f}, poly_10: {:.5f}, poly_20: {:.5f}, poly_60: {:.5f}, poly_120: {:.5f}, poly_240: {:.5f}, poly_480: {:.5f}
" + "[INFO]
" + " new_high_7: {:,.2f}, new_high_9: {:,.2f}, new_high_26: {:,.2f}, new_low_7: {:,.2f}, new_low_9: {:,.2f}, new_low_26: {:,.2f}
" + .format( + data['ymd'].iloc[i].strftime('%Y-%m-%d %H:%M'), data["close"].iloc[i], + sell_type[i], sell_price_line[i], sell_price_line[i] * sell_count_line[i], + data_scaled['poly_5'].iloc[i], data_scaled['poly_10'].iloc[i], data_scaled['poly_20'].iloc[i], data_scaled['poly_60'].iloc[i], data_scaled['poly_120'].iloc[i], data_scaled['poly_240'].iloc[i], data_scaled['poly_480'].iloc[i], + data['new_high_7'].iloc[i], data['new_high_9'].iloc[i], data['new_high_26'].iloc[i], data['new_low_7'].iloc[i], data['new_low_9'].iloc[i], data['new_low_26'].iloc[i], + )) + buy_check = go.Scatter(x=data['ymd'], y=buy_price_line, mode='markers', name="buy_price", marker=dict(size=buy_sell_size, color=buy_colors, line_width=0), text=buy_text_list, hoverinfo="text") + sell_check = go.Scatter(x=data['ymd'], y=sell_price_line, mode='markers', name="sell_price", marker=dict(size=14, color=sell_colors, line_width=0), text=sell_text_list, hoverinfo="text") + + volume_line = go.Bar(x=data['ymd'], y=data["volume"], marker_color=volume_colors, name='volume') + + avg5 = go.Scatter(x=data['ymd'], y=data["avg5"], name="avg5", line_color='#079118') + avg10 = go.Scatter(x=data['ymd'], y=data["avg10"], name="avg10", line_color='grey') + avg20 = go.Scatter(x=data['ymd'], y=data["avg20"], name="avg20", line_color='#d755e8') + avg60 = go.Scatter(x=data['ymd'], y=data["avg60"], name="avg60", line_color='#099B92') + avg90 = go.Scatter(x=data['ymd'], y=data["avg90"], name="avg90", line_color='#2a9c0c') + avg120 = go.Scatter(x=data['ymd'], y=data["avg120"], name="avg120", line_color='#079118') + avg240 = go.Scatter(x=data['ymd'], y=data["avg240"], name="avg240", line_color='#e68456') + avg360 = go.Scatter(x=data['ymd'], y=data["avg360"], name="avg360", line_color='#e6b55c') + avg480 = go.Scatter(x=data['ymd'], y=data["avg480"], name="avg480", line_color='#2a9c0c') + avg720 = go.Scatter(x=data['ymd'], y=data["avg720"], name="avg720", line_color='#e75d53') + avg1440 = go.Scatter(x=data['ymd'], y=data["avg1440"], name="avg1440", line_color='#2a9c0c') + avg2880 = go.Scatter(x=data['ymd'], y=data["avg2880"], name="avg2880", line_color='#46406c') + + disparity_avg5 = go.Scatter(x=data['ymd'], y=data_scaled["disparity_avg5"], name="disparity_avg5", line_color='#079118') + disparity_avg20 = go.Scatter(x=data['ymd'], y=data_scaled["disparity_avg20"], name="disparity_avg20", line_color='grey') + disparity_avg60 = go.Scatter(x=data['ymd'], y=data_scaled["disparity_avg60"], name="disparity_avg60", line_color='#d755e8') + disparity_avg120 = go.Scatter(x=data['ymd'], y=data_scaled["disparity_avg120"], name="disparity_avg120", line_color='#099B92') + disparity_avg240 = go.Scatter(x=data['ymd'], y=data_scaled["disparity_avg240"], name="disparity_avg240", line_color='#2a9c0c') + disparity_avg480 = go.Scatter(x=data['ymd'], y=data_scaled["disparity_avg480"], name="disparity_avg480", line_color='#079118') + disparity_avg1440 = go.Scatter(x=data['ymd'], y=data_scaled["disparity_avg1440"], name="disparity_avg1440", line_color='#e68456') + disparity_480_loc = go.Scatter(x=data['ymd'], y=data_scaled["disparity_480_loc"], name="disparity_480_loc", line_color='#2a9c0c') + disparity_1440_loc = go.Scatter(x=data['ymd'], y=data_scaled["disparity_1440_loc"], name="disparity_1440_loc", line_color='#2a9c0c') + + + changeLine = go.Scatter(x=data['ymd'], y=data["changeLine"], name="changeLine", line_color='#0196ff') + baseLine = go.Scatter(x=data['ymd'], y=data["baseLine"], name="baseLine", line_color='#991515') + laggingSpan = go.Scatter(x=data['ymd'], y=data["laggingSpan"], name="laggingSpan", line_color='#12A524') + leadingSpan1 = go.Scatter(x=data['ymd'], y=data["leadingSpan1"], name="leadingSpan1", line_color='#008001') + leadingSpan2 = go.Scatter(x=data['ymd'], y=data["leadingSpan2"], name="leadingSpan2", line_color='#830fd4') + + upper_20_Line = go.Scatter(x=data['ymd'], y=data["upper_20"], name="upper_20", line_color='#0196ff') + lower_20_Line = go.Scatter(x=data['ymd'], y=data["lower_20"], name="lower_20", line_color='#991515') + middle_20_line = go.Scatter(x=data['ymd'], y=data["middle_20"], name="middle_20", line_color='#12A524') + bb_pb = go.Scatter(x=data['ymd'], y=data["bb_pb"], name="bb_pb", line_color='#0196ff') + bb_width = go.Scatter(x=data['ymd'], y=data["bb_width"], name="bb_width", line_color='#991515') + + loc_240_k = go.Scatter(x=data['ymd'], y=data["loc_240_k"], name="loc_240_k", line_color='#0196ff') + loc_240_d = go.Scatter(x=data['ymd'], y=data["loc_240_d"], name="loc_240_d", line_color='#991515') + loc_240_s = go.Scatter(x=data['ymd'], y=data["loc_240_s"], name="loc_240_s", line_color='#12A524') + + new_high_9 = go.Scatter(x=data['ymd'], y=data["new_high_9"], name="new_high_9", line_color='#0196ff') + new_high_26 = go.Scatter(x=data['ymd'], y=data["new_high_26"], name="new_high_26", line_color='#991515') + new_high_33 = go.Scatter(x=data['ymd'], y=data["new_high_33"], name="new_high_33", line_color='#12A524') + new_high_52 = go.Scatter(x=data['ymd'], y=data["new_high_52"], name="new_high_52", line_color='#099B92') + new_low_9 = go.Scatter(x=data['ymd'], y=data["new_low_9"], name="new_low_9", line_color='#0196ff') + new_low_26 = go.Scatter(x=data['ymd'], y=data["new_low_26"], name="new_low_26", line_color='#991515') + new_low_33 = go.Scatter(x=data['ymd'], y=data["new_low_33"], name="new_low_33", line_color='#12A524') + new_low_52 = go.Scatter(x=data['ymd'], y=data["new_low_52"], name="new_low_52", line_color='#099B92') + + poly_5 = go.Scatter(x=data['ymd'], y=data_scaled["poly_5"], name="poly_5", line_color='#D27144') + poly_10 = go.Scatter(x=data['ymd'], y=data_scaled["poly_10"], name="poly_10", line_color='#BBBEC3') + poly_20 = go.Scatter(x=data['ymd'], y=data_scaled["poly_20"], name="poly_20", line_color='#d755e8') + poly_60 = go.Scatter(x=data['ymd'], y=data_scaled["poly_60"], name="poly_60", line_color='#099B92') + poly_120 = go.Scatter(x=data['ymd'], y=data_scaled["poly_120"], name="poly_120", line_color='#e68456') + poly_240 = go.Scatter(x=data['ymd'], y=data_scaled["poly_240"], name="poly_240", line_color='#E8DD26') + poly_480 = go.Scatter(x=data['ymd'], y=data_scaled["poly_480"], name="poly_480", line_color='#EF3644') + + disparity_diff_20_5 = go.Scatter(x=data['ymd'], y=data_scaled["disparity_diff_20_5"], name="disparity_diff_20_5", line_color='#D27144') + disparity_diff_60_20 = go.Scatter(x=data['ymd'], y=data_scaled["disparity_diff_60_20"], name="disparity_diff_60_20", line_color='#BBBEC3') + disparity_diff_120_20 = go.Scatter(x=data['ymd'], y=data_scaled["disparity_diff_120_20"], name="disparity_diff_120_20", line_color='#d755e8') + disparity_diff_240_20 = go.Scatter(x=data['ymd'], y=data_scaled["disparity_diff_240_20"], name="disparity_diff_240_20", line_color='#099B92') + disparity_diff_480_20 = go.Scatter(x=data['ymd'], y=data_scaled["disparity_diff_480_20"], name="disparity_diff_480_20", line_color='#e68456') + disparity_diff_1440_20 = go.Scatter(x=data['ymd'], y=data_scaled["disparity_diff_1440_20"], name="disparity_diff_1440_20", line_color='#0196ff') + + disparity_diff_20_5_rate = go.Scatter(x=data['ymd'], y=data_scaled["disparity_diff_20_5_rate"], name="disparity_diff_20_5_rate", line_color='#D27144') + disparity_diff_60_20_rate = go.Scatter(x=data['ymd'], y=data_scaled["disparity_diff_60_20_rate"], name="disparity_diff_60_20_rate", line_color='#BBBEC3') + disparity_diff_120_20_rate = go.Scatter(x=data['ymd'], y=data_scaled["disparity_diff_120_20_rate"], name="disparity_diff_120_20_rate", line_color='#d755e8') + disparity_diff_240_20_rate = go.Scatter(x=data['ymd'], y=data_scaled["disparity_diff_240_20_rate"], name="disparity_diff_240_20_rate", line_color='#099B92') + disparity_diff_480_20_rate = go.Scatter(x=data['ymd'], y=data_scaled["disparity_diff_480_20_rate"], name="disparity_diff_480_20_rate", line_color='#e68456') + disparity_diff_1440_20_rate = go.Scatter(x=data['ymd'], y=data_scaled["disparity_diff_1440_20_rate"], name="disparity_diff_1440_20_rate", line_color='#0196ff') + + slowk_up_limit = [80 for i in data['ymd']] + slowk_middle_limit = [50 for i in data['ymd']] + slowk_down_limit = [20 for i in data['ymd']] + slowk_up_limit = go.Scatter(x=data['ymd'], y=slowk_up_limit, line=dict(color='grey', width=1), name='slowk_up_limit') + slowk_middle_limit = go.Scatter(x=data['ymd'], y=slowk_middle_limit, line=dict(color='grey', width=1), name='slowk_middle_limit') + slowk_down_limit = go.Scatter(x=data['ymd'], y=slowk_down_limit, line=dict(color='grey', width=1), name='slowk_down_limit') + + slowk_5 = go.Scatter(x=data['ymd'], y=data["slowk_5"], line=dict(color='#D27144', width=2), name='slowk_5') + slowd_5 = go.Scatter(x=data['ymd'], y=data["slowd_5"], line=dict(dash='dashdot', color='grey', width=2), name='slowd_5') + slowk_10 = go.Scatter(x=data['ymd'], y=data["slowk_10"], line=dict(color='#BBBEC3', width=2), name='slowk_10') + slowd_10 = go.Scatter(x=data['ymd'], y=data["slowd_10"], line=dict(dash='dashdot', color='grey', width=2), name='slowd_10') + slowk_20 = go.Scatter(x=data['ymd'], y=data["slowk_20"], line=dict(color='#d755e8', width=2), name='slowk_20') + slowd_20 = go.Scatter(x=data['ymd'], y=data["slowd_20"], line=dict(dash='dashdot', color='grey', width=2), name='slowd_20') + slowk_60 = go.Scatter(x=data['ymd'], y=data["slowk_60"], line=dict(color='#099B92', width=2), name='slowk_60') + slowd_60 = go.Scatter(x=data['ymd'], y=data["slowd_60"], line=dict(dash='dashdot', color='grey', width=2), name='slowd_60') + slowk_120 = go.Scatter(x=data['ymd'], y=data["slowk_120"], line=dict(color='#640745', width=2), name='slowk_120') + slowd_120 = go.Scatter(x=data['ymd'], y=data["slowd_120"], line=dict(dash='dashdot', color='grey', width=2), name='slowd_120') + slowk_240 = go.Scatter(x=data['ymd'], y=data["slowk_240"], line=dict(color='#e68456', width=2), name='slowk_240') + slowd_240 = go.Scatter(x=data['ymd'], y=data["slowd_240"], line=dict(dash='dashdot', color='grey', width=2), name='slowd_240') + slowk_480 = go.Scatter(x=data['ymd'], y=data["slowk_480"], line=dict(color='#E8DD26', width=2), name='slowk_480') + slowd_480 = go.Scatter(x=data['ymd'], y=data["slowd_480"], line=dict(dash='dashdot', color='grey', width=2), name='slowd_480') + + min_price = go.Scatter(x=data['ymd'], y=data["min_price"], name="min_price", line_color='#0196ff') + max_price = go.Scatter(x=data['ymd'], y=data["max_price"], name="max_price", line_color='#991515') + + text_list = [] + for i in range(len(data['ymd'])): + text_list.append( + "{}

" + " o: {}, c: {}, h: {}, l: {}

" + " poly_5: {:.5f}, poly_10: {:.5f}, poly_20: {:.5f}, poly_60: {:.5f}, poly_120: {:.5f}, poly_240: {:.5f}, poly_480: {:.5f}
" + " new_high_9: {}, new_high_26: {}
" + " avg5: {:.2f}, avg10: {:.2f}, avg20: {:.2f}, avg60: {:.2f}, avg90: {:.2f}, avg120: {:.2f}, avg240: {:.2f}
" + " avg360: {:.2f}, avg480: {:.2f}, avg720: {:.2f}, avg1440: {:.2f}, avg2880: {:.2f}

" + " loc_k: {:.2f}, loc_d: {:.2f}, loc_s: {:.2f}

" + .format( + data['ymd'].iloc[i].strftime('%Y-%m-%d %H:%M'), + self.cz(data["open"].iloc[i]), self.cz(data["close"].iloc[i]), self.cz(data["high"].iloc[i]), self.cz(data["low"].iloc[i]), + data_scaled['poly_5'].iloc[i], data_scaled['poly_10'].iloc[i], data_scaled['poly_20'].iloc[i], data_scaled['poly_60'].iloc[i], data_scaled['poly_120'].iloc[i], data_scaled['poly_240'].iloc[i], data_scaled['poly_480'].iloc[i], + data["new_high_9"].iloc[i], data["new_high_26"].iloc[i], + self.cz(data["avg5"].iloc[i]), self.cz(data["avg10"].iloc[i]), self.cz(data["avg20"].iloc[i]), self.cz(data["avg60"].iloc[i]), self.cz(data["avg90"].iloc[i]), self.cz(data["avg120"].iloc[i]), self.cz(data["avg240"].iloc[i]), self.cz(data["avg360"].iloc[i]), self.cz(data["avg480"].iloc[i]), self.cz(data["avg720"].iloc[i]), self.cz(data["avg1440"].iloc[i]), self.cz(data["avg2880"].iloc[i]), + self.cz(data['loc_240_k'].iloc[i]), self.cz(data['loc_240_d'].iloc[i]), self.cz(data['loc_240_s'].iloc[i]), + )) + + 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', + name='candle', text=text_list, hoverinfo="text" + ) + + if bsLine is not None: + candle_data = [avg5, avg10, avg20, avg60, avg90, avg120, avg240, avg360, avg480, avg720, avg1440, avg2880, min_price, max_price, buy_check, sell_check, candle_stick, changeLine, baseLine, laggingSpan, leadingSpan1, leadingSpan2, upper_20_Line, lower_20_Line, middle_20_line] + else: + candle_data = [avg5, avg10, avg20, avg60, avg90, avg120, avg240, avg360, avg480, avg720, avg1440, avg2880, min_price, max_price, candle_stick,changeLine, baseLine, laggingSpan, leadingSpan1, leadingSpan2] + + volume_data = [volume_line] + disparity_data = [disparity_avg5, disparity_avg20, disparity_avg60, disparity_avg120, disparity_avg240, disparity_avg480, disparity_avg1440, disparity_480_loc, disparity_1440_loc, bb_width, + disparity_diff_20_5, disparity_diff_60_20, disparity_diff_120_20, disparity_diff_240_20, disparity_diff_480_20, disparity_diff_1440_20] + loc_disparity_data = [loc_240_k, loc_240_d, loc_240_s, + new_high_9 ,new_high_26, new_high_33, new_high_52,new_low_9 ,new_low_26, new_low_33, new_low_52, + poly_5, poly_10, poly_20, poly_60, poly_120, poly_240, poly_480, + disparity_diff_20_5_rate, disparity_diff_60_20_rate, disparity_diff_120_20_rate, disparity_diff_240_20_rate, disparity_diff_480_20_rate, disparity_diff_1440_20_rate + ] + stochastic_data = [ + slowk_up_limit, slowk_middle_limit, slowk_down_limit, + slowk_5, slowd_5, + slowk_10, slowd_10, + slowk_20, slowd_20, + slowk_60, slowd_60, + slowk_120, slowd_120, + slowk_240, slowd_240, + slowk_480, slowd_480, + bb_pb + ] + # 그래프를 그린다. + """ + fig = go.Figure(data=candle_data) + fig.update_layout(title=stock_code) + fig.show() + """ + fig = subplots.make_subplots( + rows=5, cols=1, + subplot_titles=("이격도", "이격도 위치", "slowkd", "캔들", "거래량"), + shared_xaxes=True, horizontal_spacing=0.03, vertical_spacing=0.01, + row_heights=[200, 200, 200, 700, 200] + ) + for trace in disparity_data: + fig.append_trace(trace, 1, 1) + for trace in loc_disparity_data: + fig.append_trace(trace, 2, 1) + for trace in stochastic_data: + fig.append_trace(trace, 3, 1) + for trace in candle_data: + fig.append_trace(trace, 4, 1) + for trace in volume_data: + fig.append_trace(trace, 5, 1) + + #fig.update_xaxes(nticks=5) + #fig.update_layout(height=2400, title=stock_code, xaxis_rangeslider_visible=False) + + df = pd.DataFrame(bsLine) + #df = df.fillna(-1) + + if info is not None: + buy_count, sell_count = 0, 0 + if bsLine is not None: + buy_count = len(df.loc[df["buy_price"] > 0]) + sell_count = len(df.loc[df["sell_price"] > 0]) + fig.update_layout(height=1400, + title="{}, buy: {} ({:,.2f}원), sell: {} ({:,.2f}원), profit: {:,.2f}원 ({:.2f}%), holding_amt: {:.2f}".format(stock_code, buy_count, info['buy_amt'], sell_count, info['sell_amt'], info['profit'], info['rate'], info['holding_amt']), + xaxis_rangeslider_visible=False, + xaxis2_rangeslider_visible=False, + xaxis3_rangeslider_visible=False, + xaxis4_rangeslider_visible=False + ) + else: + buy_count = 0 + if bsLine is not None: + buy_count = len(df.loc[df["buy_price"] > 0]) + fig.update_layout(height=1400, + title="{}, buy: {}번 ".format(stock_code, buy_count), + xaxis_rangeslider_visible=False, + xaxis2_rangeslider_visible=False, + xaxis3_rangeslider_visible=False, + xaxis4_rangeslider_visible=False + ) + #fig.update_layout(title=stock_code + "_" + str(buy_count) + "," + str(sell_count)) + # 파일로 작성함 + ###fileName = os.path.join(self.RESOURCE_PATH, 'analysis', stock_code + '.html') + ###po.write_html(fig, file=fileName, auto_open=False) + + # 화면으로 출력함 + if show: + fig.show() + + return + + + def checkTransaction(self, ticker, data, data_scaled, ci): + + # 어제 오늘 데이터로 분석 + bsLine = {} + + if data is not None and 'close' in data.columns: + size = len(data["close"]) + bsLine['buy_ymd'] = [None for i in range(size)] + bsLine['buy_price'] = [0 for i in range(size)] + bsLine['buy_count'] = [0 for i in range(size)] + bsLine['buy_type'] = ['' for i in range(size)] + bsLine['buy_cut'] = [None for i in range(size)] + bsLine['sell_price'] = [0 for i in range(size)] + bsLine['sell_count'] = [0 for i in range(size)] + bsLine['sell_type'] = ['' for i in range(size)] + bsLine['sell_cut'] = [0 for i in range(size)] + + size = ci + start = 0 + for i in range(start, size): + + # 매도 확인 + sell_price, sell_count, sell_type = self.buySell_Daily.getSellPrice(ticker, data, data_scaled, i, bsLine) + bsLine['sell_price'][i] = sell_price + bsLine['sell_count'][i] = sell_count + bsLine['sell_type'][i] = sell_type + bsLine['sell_cut'][i] = 0 + + if sell_price < 1: + buy_ymd, buy_price, buy_count, buy_type, buy_cut = self.buySell_Daily.getBuyPrice(ticker, data, data_scaled, i, bsLine) + + bsLine['buy_ymd'][i] = buy_ymd + bsLine['buy_price'][i] = buy_price + bsLine['buy_count'][i] = buy_count + bsLine['buy_type'][i] = buy_type + bsLine['buy_cut'][i] = buy_cut + + return bsLine + + + def simulate(self, ticker, get_days=720): + + data, data_scaled, ci = self.jSDPattern.getData(ticker, mins=720, ymd=ticker['ymd'], get_days=get_days) + if data is None: + return + + with open("config.json", "r", encoding="utf-8") as f: + config = json.load(f) + BUY_INFO = config['BUY_INFO'] + ticker['BUY_INFO'] = BUY_INFO + ticker['INIT'] = True + ticker['unit'] = self.upbit.checkUnit(data['close'].iloc[-1]) + ticker['MAX_BUY'] = self.upbit.getMaxPrice(data['close'].iloc[-1]) + + bsLine = self.checkTransaction(ticker, data, data_scaled, ci) + + self.draw(ticker['ticker_code'], data, data_scaled, bsLine, show=True, info=None) + + if bsLine['buy_ymd'][ci-1] is not None: + return True + + return False + + +if __name__ == "__main__": + + PROJECT_HOME = os.getcwd() + RESOURCE_PATH = os.path.join(PROJECT_HOME, "resources") + + # 1000원 이하: 0.1 + # 1000원 이상: 1 + # 1만원 이상 10 + # 10만원 이상: 50 + # 100만원 이상: 1000 + #day_list = [(datetime.now()+timedelta(days=1)).strftime('%Y%m%d')] + + """ + all_tickers = pyupbit.get_tickers("KRW") + tickers = [] + for ticker in all_tickers: + #tickers.append({'ticker_code': ticker, 'ticker_name': '', 'BUY_INFO': {}, 'ymd': (datetime.now()+timedelta(days=1)).strftime('%Y%m%d')},) + tickers.append({'ticker_code': ticker, 'ticker_name': '', 'BUY_INFO': {}, 'ymd': '20240418'},) + + TODAY_BUY_ticket_list = [] + for ticker in tickers: + simulation = Simulation_daily(RESOURCE_PATH) + buy = simulation.simulate(ticker, get_days=1500) + if buy: + TODAY_BUY_ticket_list.append(ticker) + + print ('TODAY: {}개\n{}'.format (len(TODAY_BUY_ticket_list), TODAY_BUY_ticket_list)) + + """ + simulation = Simulation_daily(RESOURCE_PATH) + tickers = [ + {"ticker_code": "252670", "ticker_name": "KODEX 200선물인버스2X", 'BUY_INFO': {}, 'ymd': (datetime.now()+timedelta(days=1)).strftime('%Y%m%d')}, + {"ticker_code": "122630", "ticker_name": "KODEX 레버리지", 'BUY_INFO': {}, 'ymd': (datetime.now()+timedelta(days=1)).strftime('%Y%m%d')}, + {"ticker_code": "251340", "ticker_name": "KODEX 코스닥150선물인버스", 'BUY_INFO': {}, 'ymd': (datetime.now()+timedelta(days=1)).strftime('%Y%m%d')}, + {"ticker_code": "233740", "ticker_name": "KODEX 코스닥150레버리지", 'BUY_INFO': {}, 'ymd': (datetime.now()+timedelta(days=1)).strftime('%Y%m%d')} + ] + for ticker in tickers: + simulation.simulate(ticker, get_days=1500) + + + print ("done...") diff --git a/Simulation_minutely.py b/Simulation_minutely.py new file mode 100644 index 0000000..d1f8430 --- /dev/null +++ b/Simulation_minutely.py @@ -0,0 +1,571 @@ +import numpy as np +from math import nan +import pandas as pd +import plotly.graph_objects as go +from plotly import subplots +import math + +import os +import json + +from datetime import datetime, timedelta +from Upbit import Upbit + +from hts.BuySell_Minutely import BuySell_Minutely +from JSDPattern_minutely import JSDPattern_minutely + +class Simulation_minutely: + + test = None + upbit = None + buySell_Minutely = None + + def __init__(self, RESOURCE_PATH): + + self.test = [] + self.upbit = Upbit(RESOURCE_PATH) + self.buySell_Minutely = BuySell_Minutely(RESOURCE_PATH) + self.jSDPattern = JSDPattern_minutely(RESOURCE_PATH) + + return + + def clear_BSLINE(self, BUY_LIST, sell_type=None): + if sell_type is None or sell_type == '': + BUY_LIST['avg_buy_price'] = 0 + BUY_LIST['buy_count'] = 0 + BUY_LIST["buy_amount"] = 0 + + BUY_LIST['buy_list'].clear() + else: + BUY_LIST['avg_buy_price'] = 0 + BUY_LIST['buy_count'] = 0 + BUY_LIST["buy_amount"] = 0 + + tmp_sell_type = sell_type.split(',') + for i, buy_list in reversed(list(enumerate(BUY_LIST['buy_list']))): + for t_sell_type in tmp_sell_type: + if buy_list['buy_type'].strip() == t_sell_type.strip(): + del BUY_LIST['buy_list'][i] + break + return + + def draw(self, ticker, data, data_scaled, bsLine=None, show=False, info=None): + stock_code = ticker['ticker_code'] + + # 어제 데이터는 지운다. + #data = data.loc[pd.DatetimeIndex(data.index).day == int(given_day[6:])] + buy_price_line, buy_count_line, buy_type, buy_count_line, sell_price_line, sell_count_line, sell_type = [], [], [], [], [], [], [] + buy_sell_size, buy_colors, sell_colors, buy_colors = [], [], [], [] + + if bsLine is not None: + buy_price_line = bsLine['buy_price'] + buy_count_line = bsLine['buy_count'] + sell_price_line = bsLine['sell_price'] + sell_count_line = bsLine['sell_count'] + buy_type = bsLine['buy_type'] + sell_type = bsLine['sell_type'] + + for i in range(len(data)): + if buy_price_line[i] < 1: + buy_colors.append("#ffffff") + buy_price_line[i] = nan + buy_sell_size.append(0) + else: + buy_colors.append("#0C752E") + buy_sell_size.append(14) + for i in range(len(data)): + if sell_price_line[i] < 1: + sell_colors.append("#ffffff") + sell_price_line[i] = nan + else: + sell_colors.append("#00ced1") + + volume_colors = [] + for i in range(len(data)): + if data['open'].iloc[i] > data['close'].iloc[i]: + volume_colors.append("#FF0000") + elif data['open'].iloc[i] < data['close'].iloc[i]: + volume_colors.append("#FF0000") + else: + volume_colors.append("#000000") + + # 그래프를 설정한다. + buy_check, sell_check = None, None + if bsLine is not None: + buy_text_list, sell_text_list = [], [] + for i in range(len(data)): + buy_text_list.append( + "[{}] {:,}
" + "{}, {:,} ({:,.2f})

" + "[BASIC]
" + " support: {:.2f}, resistance: {:.2f}
" + " poly_5: {:.5f}, poly_10: {:.5f}, poly_20: {:.5f}, 6: {:.5f}, poly_120: {:.5f}, poly_240: {:.5f}, poly_480: {:.5f}, poly_720: {:.5f}, poly_1440: {:.5f}
" + "[INFO]
" + " new_high_7: {:,.2f}, new_high_9: {:,.2f}, new_high_26: {:,.2f}, new_low_7: {:,.2f}, new_low_9: {:,.2f}, new_low_26: {:,.2f}
" + .format(data['ymd'].iloc[i].strftime('%Y-%m-%d %H:%M'), data["close"].iloc[i], + buy_type[i], buy_price_line[i], buy_price_line[i]*buy_count_line[i], + data['support'].iloc[i], data['resistance'].iloc[i], + data_scaled['poly_5'].iloc[i], data_scaled['poly_10'].iloc[i], data_scaled['poly_20'].iloc[i], data_scaled['poly_60'].iloc[i], data_scaled['poly_120'].iloc[i], data_scaled['poly_240'].iloc[i], data_scaled['poly_480'].iloc[i], data_scaled['poly_720'].iloc[i], data_scaled['poly_1440'].iloc[i], + data['new_high_7'].iloc[i], data['new_high_9'].iloc[i], data['new_high_26'].iloc[i], data['new_low_7'].iloc[i], data['new_low_9'].iloc[i], data['new_low_26'].iloc[i], + )) + sell_text_list.append( + "[{}] {:,}
" + "{}, {:,} ({:,.2f})

" + "[BASIC]
" + " support: {:.2f}, resistance: {:.2f}
" + " poly_5: {:.5f}, poly_10: {:.5f}, poly_20: {:.5f}, 6: {:.5f}, poly_120: {:.5f}, poly_240: {:.5f}, poly_480: {:.5f}, poly_720: {:.5f}, poly_1440: {:.5f}
" + "[INFO]
" + " new_high_7: {:,.2f}, new_high_9: {:,.2f}, new_high_26: {:,.2f}, new_low_7: {:,.2f}, new_low_9: {:,.2f}, new_low_26: {:,.2f}
" + .format( + data['ymd'].iloc[i].strftime('%Y-%m-%d %H:%M'), data["close"].iloc[i], + sell_type[i], sell_price_line[i], sell_price_line[i]*sell_count_line[i], + data['support'].iloc[i], data['resistance'].iloc[i], + data_scaled['poly_5'].iloc[i], data_scaled['poly_10'].iloc[i], data_scaled['poly_20'].iloc[i], data_scaled['poly_60'].iloc[i], data_scaled['poly_120'].iloc[i], data_scaled['poly_240'].iloc[i], data_scaled['poly_480'].iloc[i], data_scaled['poly_720'].iloc[i], data_scaled['poly_1440'].iloc[i], + data['new_high_7'].iloc[i], data['new_high_9'].iloc[i], data['new_high_26'].iloc[i], data['new_low_7'].iloc[i], data['new_low_9'].iloc[i], data['new_low_26'].iloc[i], + )) + buy_check = go.Scatter(x=data['ymd'], y=buy_price_line, mode='markers', name="buy_price", marker=dict(size=buy_sell_size, color=buy_colors, line_width=0), text=buy_text_list, hoverinfo="text") + sell_check = go.Scatter(x=data['ymd'], y=sell_price_line, mode='markers', name="sell_price", marker=dict(size=14, color=sell_colors, line_width=0), text=sell_text_list, hoverinfo="text") + + volume_line = go.Bar(x=data['ymd'], y=data["volume"], marker_color=volume_colors, name='volume') + + avg5 = go.Scatter(x=data['ymd'], y=data["avg5"], name="avg5", line_color='#D27144') + avg10 = go.Scatter(x=data['ymd'], y=data["avg10"], name="avg10", line_color='#BBBEC3') + avg20 = go.Scatter(x=data['ymd'], y=data["avg20"], name="avg20", line_color='#d755e8') + avg60 = go.Scatter(x=data['ymd'], y=data["avg60"], name="avg60", line_color='#099B92') + avg120 = go.Scatter(x=data['ymd'], y=data["avg120"], name="avg120", line_color='#640745') + avg240 = go.Scatter(x=data['ymd'], y=data["avg240"], name="avg240", line_color='#e68456') + avg480 = go.Scatter(x=data['ymd'], y=data["avg480"], name="avg480", line_color='#A18A0D') + avg720 = go.Scatter(x=data['ymd'], y=data["avg720"], name="avg720", line_color='#EF3644') + avg1440 = go.Scatter(x=data['ymd'], y=data["avg1440"], name="avg1440", line_color='#4479D2') + + poly_5 = go.Scatter(x=data['ymd'], y=data_scaled["poly_5"], name="poly_5", line_color='#D27144') + poly_10 = go.Scatter(x=data['ymd'], y=data_scaled["poly_10"], name="poly_10", line_color='#BBBEC3') + poly_20 = go.Scatter(x=data['ymd'], y=data_scaled["poly_20"], name="poly_20", line_color='#d755e8') + poly_60 = go.Scatter(x=data['ymd'], y=data_scaled["poly_60"], name="poly_60", line_color='#099B92') + poly_120 = go.Scatter(x=data['ymd'], y=data_scaled["poly_120"], name="poly_120", line_color='#e68456') + poly_240 = go.Scatter(x=data['ymd'], y=data_scaled["poly_240"], name="poly_240", line_color='#A18A0D') + poly_480 = go.Scatter(x=data['ymd'], y=data_scaled["poly_480"], name="poly_480", line_color='#0196ff') + poly_720 = go.Scatter(x=data['ymd'], y=data_scaled["poly_720"], name="poly_720", line_color='#EF3644') + poly_1440 = go.Scatter(x=data['ymd'], y=data_scaled["poly_1440"], name="poly_1440", line_color='#4479D2') + + disparity_diff_20_5 = go.Scatter(x=data['ymd'], y=data_scaled["disparity_diff_20_5"], name="disparity_diff_20_5", line_color='#D27144') + disparity_diff_60_20 = go.Scatter(x=data['ymd'], y=data_scaled["disparity_diff_60_20"], name="disparity_diff_60_20", line_color='#BBBEC3') + disparity_diff_120_20 = go.Scatter(x=data['ymd'], y=data_scaled["disparity_diff_120_20"], name="disparity_diff_120_20", line_color='#d755e8') + disparity_diff_240_20 = go.Scatter(x=data['ymd'], y=data_scaled["disparity_diff_240_20"], name="disparity_diff_240_20", line_color='#099B92') + disparity_diff_480_20 = go.Scatter(x=data['ymd'], y=data_scaled["disparity_diff_480_20"], name="disparity_diff_480_20", line_color='#e68456') + disparity_diff_720_20 = go.Scatter(x=data['ymd'], y=data_scaled["disparity_diff_720_20"], name="disparity_diff_720_20", line_color='#A18A0D') + disparity_diff_1440_20 = go.Scatter(x=data['ymd'], y=data_scaled["disparity_diff_1440_20"], name="disparity_diff_1440_20", line_color='#0196ff') + + disparity_diff_20_5_rate = go.Scatter(x=data['ymd'], y=data_scaled["disparity_diff_20_5_rate"], name="disparity_diff_20_5_rate", line_color='#D27144') + disparity_diff_60_20_rate = go.Scatter(x=data['ymd'], y=data_scaled["disparity_diff_60_20_rate"], name="disparity_diff_60_20_rate", line_color='#BBBEC3') + disparity_diff_120_20_rate = go.Scatter(x=data['ymd'], y=data_scaled["disparity_diff_120_20_rate"], name="disparity_diff_120_20_rate", line_color='#d755e8') + disparity_diff_240_20_rate = go.Scatter(x=data['ymd'], y=data_scaled["disparity_diff_240_20_rate"], name="disparity_diff_240_20_rate", line_color='#099B92') + disparity_diff_480_20_rate = go.Scatter(x=data['ymd'], y=data_scaled["disparity_diff_480_20_rate"], name="disparity_diff_480_20_rate", line_color='#e68456') + disparity_diff_720_20_rate = go.Scatter(x=data['ymd'], y=data_scaled["disparity_diff_720_20_rate"], name="disparity_diff_720_20_rate", line_color='#A18A0D') + disparity_diff_1440_20_rate = go.Scatter(x=data['ymd'], y=data_scaled["disparity_diff_1440_20_rate"], name="disparity_diff_1440_20_rate", line_color='#0196ff') + + new_high_7 = go.Scatter(x=data['ymd'], y=data["new_high_7"], name="new_high_7", line_color='#EA62A2') + new_high_9 = go.Scatter(x=data['ymd'], y=data["new_high_9"], name="new_high_9", line_color='#0196ff') + new_high_26 = go.Scatter(x=data['ymd'], y=data["new_high_26"], name="new_high_26", line_color='#991515') + new_low_7 = go.Scatter(x=data['ymd'], y=data["new_low_7"], name="new_low_7", line_color='#EA62A2') + new_low_9 = go.Scatter(x=data['ymd'], y=data["new_low_9"], name="new_low_9", line_color='#0196ff') + new_low_26 = go.Scatter(x=data['ymd'], y=data["new_low_26"], name="new_low_26", line_color='#991515') + + info_p_up_limit = [0.8 for i in data['ymd']] + info_p_middle_limit = [0.5 for i in data['ymd']] + info_p_down_limit = [0.2 for i in data['ymd']] + info_n_up_limit = [0.5 for i in data['ymd']] + info_n_middle_limit = [0 for i in data['ymd']] + info_n_down_limit = [-0.5 for i in data['ymd']] + info_p_up_limit = go.Scatter(x=data['ymd'], y=info_p_up_limit, line=dict(color='grey', width=1), name='info_p_up_limit') + info_p_middle_limit = go.Scatter(x=data['ymd'], y=info_p_middle_limit, line=dict(color='grey', width=1), name='info_p_middle_limit') + info_p_down_limit = go.Scatter(x=data['ymd'], y=info_p_down_limit, line=dict(color='grey', width=1), name='info_p_down_limit') + info_n_up_limit = go.Scatter(x=data['ymd'], y=info_n_up_limit, line=dict(color='grey', width=1), name='info_n_up_limit') + info_n_middle_limit = go.Scatter(x=data['ymd'], y=info_n_middle_limit, line=dict(color='grey', width=1), name='info_n_middle_limit') + info_n_down_limit = go.Scatter(x=data['ymd'], y=info_n_down_limit, line=dict(color='grey', width=1), name='info_n_down_limit') + + rsi = go.Scatter(x=data['ymd'], y=data_scaled["rsi"], line=dict(color='#239507', width=2), name='rsi') + rsi_720 = go.Scatter(x=data['ymd'], y=data_scaled["rsi_720"], line=dict(color='#239507', width=2), name='rsi_720') + rsi_1440 = go.Scatter(x=data['ymd'], y=data_scaled["rsi_1440"], line=dict(color='#239507', width=2), name='rsi_1440') + + macd = go.Scatter(x=data['ymd'], y=data_scaled["macd"], line=dict(color='#079118', width=2), name='macd') + macds = go.Scatter(x=data['ymd'], y=data_scaled["macds"], line=dict(dash='dashdot', color='#991515', width=2), name='macds') + macdo = go.Bar(x=data['ymd'], y=data_scaled["macdo"], marker_color='#7343e8', name='macdo') + macd_720 = go.Scatter(x=data['ymd'], y=data_scaled["macd_720"], line=dict(color='#079118', width=2), name='macd_720') + macd_1440 = go.Scatter(x=data['ymd'], y=data_scaled["macd_1440"], line=dict(color='#079118', width=2), name='macd_1440') + + slowk_up_limit = [80 for i in data['ymd']] + slowk_middle_limit = [50 for i in data['ymd']] + slowk_down_limit = [20 for i in data['ymd']] + slowk_up_limit = go.Scatter(x=data['ymd'], y=slowk_up_limit, line=dict(color='grey', width=1), name='slowk_up_limit') + slowk_middle_limit = go.Scatter(x=data['ymd'], y=slowk_middle_limit, line=dict(color='grey', width=1), name='slowk_middle_limit') + slowk_down_limit = go.Scatter(x=data['ymd'], y=slowk_down_limit, line=dict(color='grey', width=1), name='slowk_down_limit') + + slowk_5 = go.Scatter(x=data['ymd'], y=data["slowk_5"], line=dict(color='#D27144', width=2), name='slowk_5') + slowd_5 = go.Scatter(x=data['ymd'], y=data["slowd_5"], line=dict(dash='dashdot', color='grey', width=2), name='slowd_5') + slowk_10 = go.Scatter(x=data['ymd'], y=data["slowk_10"], line=dict(color='#BBBEC3', width=2), name='slowk_10') + slowd_10 = go.Scatter(x=data['ymd'], y=data["slowd_10"], line=dict(dash='dashdot', color='grey', width=2), name='slowd_10') + slowk_20 = go.Scatter(x=data['ymd'], y=data["slowk_20"], line=dict(color='#d755e8', width=2), name='slowk_20') + slowd_20 = go.Scatter(x=data['ymd'], y=data["slowd_20"], line=dict(dash='dashdot', color='grey', width=2), name='slowd_20') + slowk_60 = go.Scatter(x=data['ymd'], y=data["slowk_60"], line=dict(color='#099B92', width=2), name='slowk_60') + slowd_60 = go.Scatter(x=data['ymd'], y=data["slowd_60"], line=dict(dash='dashdot', color='grey', width=2), name='slowd_60') + slowk_120 = go.Scatter(x=data['ymd'], y=data["slowk_120"], line=dict(color='#640745', width=2), name='slowk_120') + slowd_120 = go.Scatter(x=data['ymd'], y=data["slowd_120"], line=dict(dash='dashdot', color='grey', width=2), name='slowd_120') + slowk_240 = go.Scatter(x=data['ymd'], y=data["slowk_240"], line=dict(color='#e68456', width=2), name='slowk_240') + slowd_240 = go.Scatter(x=data['ymd'], y=data["slowd_240"], line=dict(dash='dashdot', color='grey', width=2), name='slowd_240') + slowk_480 = go.Scatter(x=data['ymd'], y=data["slowk_480"], line=dict(color='#A18A0D', width=2), name='slowk_480') + slowd_480 = go.Scatter(x=data['ymd'], y=data["slowd_480"], line=dict(dash='dashdot', color='grey', width=2), name='slowd_480') + slowk_720 = go.Scatter(x=data['ymd'], y=data["slowk_720"], line=dict(color='#EF3644', width=2), name='slowk_720') + slowd_720 = go.Scatter(x=data['ymd'], y=data["slowd_720"], line=dict(dash='dashdot', color='grey', width=2), name='slowd_720') + slowk_1440 = go.Scatter(x=data['ymd'], y=data["slowk_1440"], line=dict(color='#4479D2', width=2), name='slowk_1440') + slowd_1440 = go.Scatter(x=data['ymd'], y=data["slowd_1440"], line=dict(dash='dashdot', color='grey', width=2), name='slowd_1440') + + text_list = [] + for i in range(len(data)): + text_list.append( + "[{}] {:,}

" + "[BASIC]
" + " support: {:.2f}, resistance: {:.2f}
" + " poly_5: {:.5f}, poly_10: {:.5f}, poly_20: {:.5f}, 6: {:.5f}, poly_120: {:.5f}, poly_240: {:.5f}, poly_480: {:.5f}, poly_720: {:.5f}, poly_1440: {:.5f}
" + "[INFO]
" + " new_high_7: {:,.2f}, new_high_9: {:,.2f}, new_high_26: {:,.2f}, new_low_7: {:,.2f}, new_low_9: {:,.2f}, new_low_26: {:,.2f}
" + .format( + data['ymd'].iloc[i].strftime('%Y-%m-%d %H:%M'), data["close"].iloc[i], + data['support'].iloc[i], data['resistance'].iloc[i], + data_scaled['poly_5'].iloc[i], data_scaled['poly_10'].iloc[i], data_scaled['poly_20'].iloc[i], data_scaled['poly_60'].iloc[i], data_scaled['poly_120'].iloc[i], data_scaled['poly_240'].iloc[i], data_scaled['poly_480'].iloc[i], data_scaled['poly_720'].iloc[i], data_scaled['poly_1440'].iloc[i], + data['new_high_7'].iloc[i], data['new_high_9'].iloc[i], data['new_high_26'].iloc[i], data['new_low_7'].iloc[i], data['new_low_9'].iloc[i], data['new_low_26'].iloc[i], + )) + + support = go.Scatter(x=data['ymd'], y=data["support"], name="support", line_color='#192BB1') + resistance = go.Scatter(x=data['ymd'], y=data["resistance"], name="resistance", line_color='#E31313') + + 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', + name='candle', text=text_list, hoverinfo="text" + ) + + if bsLine is not None: + candle_data = [avg5, avg10, avg20, avg60, avg120, avg240, avg480, avg720, avg1440, support, resistance, buy_check, sell_check, candle_stick] + else: + candle_data = [avg5, avg10, avg20, avg60, avg120, avg240, avg480, avg720, avg1440, support, resistance, candle_stick] + + volume_data = [volume_line] + # 절대정보 + indicator1 = [ + info_p_up_limit, info_p_middle_limit, info_p_down_limit, info_n_up_limit, info_n_middle_limit, + info_n_down_limit, + new_high_7, new_high_9, new_high_26, new_low_7, new_low_9, new_low_26, + disparity_diff_20_5_rate, disparity_diff_60_20_rate, disparity_diff_120_20_rate, disparity_diff_240_20_rate, disparity_diff_480_20_rate, disparity_diff_720_20_rate, disparity_diff_1440_20_rate, + ] + # 상대정보 + info_data = [ + disparity_diff_20_5, disparity_diff_60_20, disparity_diff_120_20, disparity_diff_240_20, disparity_diff_480_20, disparity_diff_720_20, disparity_diff_1440_20, + poly_5, poly_10, poly_20, poly_60, poly_120, poly_240, poly_480, poly_720, poly_1440 + ] + slow_data = [ + slowk_up_limit, slowk_middle_limit, slowk_down_limit, + slowk_5, slowd_5, + slowk_10, slowd_10, + slowk_20, slowd_20, + slowk_60, slowd_60, + slowk_120, slowd_120, + slowk_240, slowd_240, + slowk_480, slowd_480, + slowk_720, slowd_720, + slowk_1440, slowd_1440, + ] + # macd + macd_data = [ + macd, macds, macdo, macd_720, macd_1440 + ] + # rsi + rsi_data = [ + rsi, rsi_720, rsi_1440 + ] + # 그래프를 그린다. + """ + fig = go.Figure(data=candle_data) + fig.update_layout(title=stock_code) + fig.show() + """ + fig = subplots.make_subplots( + rows=7, cols=1, + subplot_titles=("기술지표#1", "통계정보", "캔들", "slow", "macd", "rsi", "거래량"), + shared_xaxes=True, horizontal_spacing=0.03, vertical_spacing=0.01, + row_heights=[200, 200, 800, 200, 200, 200, 200] + ) + for trace in indicator1: + fig.append_trace(trace, 1, 1) + for trace in info_data: + fig.append_trace(trace, 2, 1) + for trace in candle_data: + fig.append_trace(trace, 3, 1) + for trace in slow_data: + fig.append_trace(trace, 4, 1) + for trace in macd_data: + fig.append_trace(trace, 5, 1) + for trace in rsi_data: + fig.append_trace(trace, 6, 1) + for trace in volume_data: + fig.append_trace(trace, 7, 1) + + #fig.update_xaxes(nticks=5) + #fig.update_layout(height=2400, title=stock_code, xaxis_rangeslider_visible=False) + + df = pd.DataFrame(bsLine) + df = df.fillna(-1) + + if info is not None: + buy_count, sell_count = 0, 0 + if bsLine is not None: + buy_count = len(df.loc[df["buy_price"] > 0]) + sell_count = len(df.loc[df["sell_price"] > 0]) + fig.update_layout( + height=2000, + title="{}, buy: {} ({:,.2f}원), sell: {} ({:,.2f}원), profit: {:,.2f}원 ({:.2f}%), holding_amt: {:,.2f}".format(stock_code, buy_count, info['buy_amt'], sell_count, info['sell_amt'], info['profit'], info['rate'], info['holding_amt']), + xaxis_rangeslider_visible=False, + xaxis1_rangeslider_visible=False, + xaxis2_rangeslider_visible = False, + xaxis3_rangeslider_visible = False, + xaxis4_rangeslider_visible = False + ) + else: + buy_count = 0 + if bsLine is not None: + buy_count = len(df.loc[df["buy_price"] > 0]) + fig.update_layout( + height=2700, + title="{}, buy: {}번 ".format(stock_code, buy_count), + xaxis_rangeslider_visible=False, + xaxis1_rangeslider_visible=False, + xaxis2_rangeslider_visible=False, + xaxis3_rangeslider_visible=False, + xaxis4_rangeslider_visible=False + ) + #fig.update_layout(title=stock_code + "_" + str(buy_count) + "," + str(sell_count)) + # 파일로 작성함 + ###fileName = os.path.join(self.RESOURCE_PATH, 'analysis', stock_code + '.html') + ###po.write_html(fig, file=fileName, auto_open=False) + + # 화면으로 출력함 + if show: + fig.show() + + return + + + def checkTransaction(self, ticker, data, data_scaled, ci): + + # 어제 오늘 데이터로 분석 + bsLine = {} + + if data is not None and 'close' in data.columns: + size = len(data["close"]) + bsLine['buy_ymd'] = [None for i in range(size)] + bsLine['buy_price'] = [0 for i in range(size)] + bsLine['buy_count'] = [0 for i in range(size)] + bsLine['buy_type'] = ['' for i in range(size)] + bsLine['buy_cut'] = [None for i in range(size)] + bsLine['sell_price'] = [0 for i in range(size)] + bsLine['sell_count'] = [0 for i in range(size)] + bsLine['sell_type'] = ['' for i in range(size)] + bsLine['sell_cut'] = [-1 for i in range(size)] + + size = ci + start = 0 + for i in range(start, size): + + bsLine['buy_ymd'][i] = data['ymd'].iloc[i] + + """ + if 0 < len(ticker['BUY_INFO']['buy_list']): + count = sum([price['buy_count'] for price in ticker['BUY_INFO']['buy_list']]) + prices = [price['buy_price'] for price in ticker['BUY_INFO']['buy_list']] + + ticker['BUY_INFO']['buy_count'] = count + ticker['BUY_INFO']['avg_buy_price'] = (sum(prices) / len(prices)) + + # loss cut 체크 + if 0 < len(ticker['BUY_INFO']['buy_list']): + sell_count = 0 + + for c, buy_list in reversed(list(enumerate(ticker['BUY_INFO']['buy_list']))): + # 만약 장기가 아니라면 1일전 가격 아래로 떨어지면 loss cut + if buy_list['buy_price'] < np.min(data['close'][i-4320:i]): + if data['close'][i] < np.min(data['close'][i-4320:i]): + del ticker['BUY_INFO']['buy_list'][c] + sell_count += buy_list['buy_count'] + + if 0 < sell_count: + bsLine['sell_price'][i] = data['close'][i] + bsLine['sell_count'][i] = sell_count + bsLine['sell_type'][i] = 'loss_cut' + bsLine['sell_cut'][i] = -1 + + self.test.append({'type': 'SELL', 'ymd': data['ymd'].iloc[i], 'price': data['close'][i] - ticker['unit'], 'count': count, 'amt': count*(data['close'][i] - ticker['unit'])}) + continue + """ + + # 매도 확인 + sell_price, sell_count, sell_type = self.buySell_Minutely.getSellPrice(ticker, data, data_scaled, i, bsLine) + bsLine['sell_price'][i] = sell_price + bsLine['sell_count'][i] = sell_count + bsLine['sell_type'][i] = sell_type + bsLine['sell_cut'][i] = -1 + + + # buy_cut 체크 + check = False + if 0 < len(ticker['BUY_INFO']['buy_list']): + + current_price = data['close'].iloc[i] + + for c in range(len(ticker['BUY_INFO']['buy_list'])-1, -1, -1): + buy_list = ticker['BUY_INFO']['buy_list'][c] + + buy_cut = ticker['BUY_INFO']['buy_list'][c]['buy_cut'] + if buy_cut is not None and 0 < buy_cut and current_price < buy_cut: + self.test.append({'type': 'SELL', 'ymd': data['ymd'].iloc[i], 'price': current_price - ticker['unit'], 'count': buy_list['buy_count'], 'amt': buy_list['buy_count']*(current_price - ticker['unit'])}) + del ticker['BUY_INFO']['buy_list'][c] + + bsLine['sell_price'][i] = current_price + bsLine['sell_count'][i] = buy_list['buy_count'] + bsLine['sell_type'][i] = "buy_cut" + bsLine['sell_cut'][i] = c + check = True + continue + + if check: + continue + + + if 0 < sell_price: + self.test.append({'type': 'SELL', 'ymd': data['ymd'].iloc[i], 'price': sell_price-ticker['unit'], 'count': sell_count, 'amt': sell_count*(sell_price - ticker['unit'])}) + self.clear_BSLINE(ticker['BUY_INFO'], sell_type) + else: + # 매도가 아니면 매수 확인 + buy_ymd, buy_price, buy_count, buy_type, buy_cut = self.buySell_Minutely.getBuyPrice(ticker, data, data_scaled, i, bsLine) + + bsLine['buy_price'][i] = buy_price + bsLine['buy_count'][i] = buy_count + bsLine['buy_type'][i] = buy_type + bsLine['buy_cut'][i] = buy_cut + + if 0 < buy_price: + self.test.append({'type': 'BUY', 'ymd': data['ymd'].iloc[i], 'price': buy_price+ticker['unit'], 'count': buy_count, 'amt': buy_count*(buy_price+ticker['unit'])}) + ticker['BUY_INFO']['buy_list'].append({'buy_ymd': buy_ymd, 'buy_price': buy_price, 'buy_count': buy_count, 'buy_type': buy_type, 'buy_cut': buy_cut}) + ticker['BUY_INFO']["avg_buy_price"] = np.average([buy_list['buy_price'] for buy_list in ticker['BUY_INFO']['buy_list']]) + ticker['BUY_INFO']["buy_count"] = np.sum([buy_list['buy_count'] for buy_list in ticker['BUY_INFO']['buy_list']]) + ticker['BUY_INFO']["buy_amount"] = ticker['BUY_INFO']["avg_buy_price"] * ticker['BUY_INFO']["buy_count"] + + return bsLine + + + def simulate(self, ticker, get_days=30, mins=1): + + total_buy_amount, profit, buy_amt = 0, 0, 0 + + #data, ci = self.jSDPattern.getData(ticker, mins=1440, ymd=ymd, get_days=1500) + data, data_scaled, ci = self.jSDPattern.getData(ticker, mins=mins, ymd=ticker['ymd'], get_days=get_days) + if data is None: + return + + with open("config.json", "r", encoding="utf-8") as f: + config = json.load(f) + BUY_INFO = config['BUY_INFO'] + ticker['BUY_INFO'] = BUY_INFO + ticker['INIT'] = True + ticker['unit'] = self.upbit.checkUnit(data['close'].iloc[-1]) + ticker['MAX_BUY'] = self.upbit.getMaxPrice(data['close'].iloc[-1]) + + bsLine = self.checkTransaction(ticker, data, data_scaled, ci) + + for item in self.test: + if item['type'] == 'BUY': + buy_amt += item['amt']*0.9995 + else: + profit += item['amt'] - buy_amt + buy_amt = 0 + + holding_amt = sum([buy_list['buy_price']*buy_list['buy_count'] for buy_list in ticker['BUY_INFO']['buy_list']]) + buy_test = [item['price']*item['count']*0.9995 for item in self.test if item['type'] == 'BUY'] + sell_test = [item['price']*item['count']*1.0005 for item in self.test if item['type'] == 'SELL'] + if 0 < sum(buy_test): + rate = 100 * profit / sum(buy_test) + else: + rate = 0 + print("\n시도 ({}): {}회, 이익: {:,.0f}원 ({:.2f}%)".format(ticker['ticker_code'], len(self.test), profit, rate)) + print("\t- 매수: {}회, 금액: {:,.0f}원".format(len(buy_test), sum(buy_test))) + print("\t- 매도: {}회, 금액: {:,.0f}원".format(len(sell_test), sum(sell_test))) + print("\t- 보유: 금액: {:,.0f}원".format(holding_amt)) + total_buy_amount += sum(buy_test) + + info = {'profit': profit, 'rate': rate, 'buy_count': len(buy_test), 'buy_amt': sum(buy_test), 'sell_count': len(sell_test), 'sell_amt': sum(sell_test), 'holding_amt': holding_amt} + self.draw(ticker, data, data_scaled, bsLine, show=True, info=info) + + return total_buy_amount, profit + + +if __name__ == "__main__": + + PROJECT_HOME = os.getcwd() + RESOURCE_PATH = os.path.join(PROJECT_HOME, "resources") + + # 1000원 이하: 0.1 + # 1000원 이상: 1 + # 1만원 이상 10 + # 10만원 이상: 50 + # 100만원 이상: 1000 + day_list = (datetime.now()+timedelta(days=1)).strftime('%Y%m%d') + + """ + tickers = [ + {'ticker_code': 'KRW-ADA', 'ticker_name': '에이다', 'BUY_INFO': {}, 'rise_rate': 0.0, 'INIT': True, 'volume_check': False, 'ymd': day_list}, + {'ticker_code': 'KRW-AVAX', 'ticker_name': '아발란체', 'BUY_INFO': {}, 'rise_rate': 0.0, 'INIT': True, 'volume_check': False, 'ymd': day_list}, + {'ticker_code': 'KRW-BLUR', 'ticker_name': '블러', 'BUY_INFO': {}, 'rise_rate': 0.0, 'INIT': True, 'volume_check': False, 'ymd': day_list}, + {'ticker_code': 'KRW-BSV', 'ticker_name': '비트코인에스브이', 'BUY_INFO': {}, 'rise_rate': 0.0, 'INIT': True, 'volume_check': False, 'ymd': day_list}, + {'ticker_code': 'KRW-BTC', 'ticker_name': '비트코인', 'BUY_INFO': {}, 'rise_rate': 0.0, 'INIT': True, 'volume_check': False, 'ymd': day_list}, + {'ticker_code': 'KRW-BTG', 'ticker_name': '비트코인골드', 'BUY_INFO': {}, 'rise_rate': 0.0, 'INIT': True, 'volume_check': False, 'ymd': day_list}, + {'ticker_code': 'KRW-CTC', 'ticker_name': '크레딧코인', 'BUY_INFO': {}, 'rise_rate': 0.0, 'INIT': True, 'volume_check': False, 'ymd': day_list}, + {'ticker_code': 'KRW-DOGE', 'ticker_name': '도지코인', 'BUY_INFO': {}, 'rise_rate': 0.0, 'INIT': True, 'volume_check': False, 'ymd': day_list}, + {'ticker_code': 'KRW-DOT', 'ticker_name': '폴카닷', 'BUY_INFO': {}, 'rise_rate': 0.0, 'INIT': True, 'volume_check': False, 'ymd': day_list}, + {'ticker_code': 'KRW-ETC', 'ticker_name': '이더리움클래식', 'BUY_INFO': {}, 'rise_rate': 0.0, 'INIT': True, 'volume_check': False, 'ymd': day_list}, + {'ticker_code': 'KRW-ETH', 'ticker_name': '이더리움', 'BUY_INFO': {}, 'rise_rate': 0.0, 'INIT': True, 'volume_check': False, 'ymd': day_list}, + {'ticker_code': 'KRW-FLOW', 'ticker_name': '플로우', 'BUY_INFO': {}, 'rise_rate': 0.0, 'INIT': True, 'volume_check': False, 'ymd': day_list}, + {'ticker_code': 'KRW-GAS', 'ticker_name': '가스', 'BUY_INFO': {}, 'rise_rate': 0.0, 'INIT': True, 'volume_check': False, 'ymd': day_list}, + {'ticker_code': 'KRW-GLM', 'ticker_name': '골렘', 'BUY_INFO': {}, 'rise_rate': 0.0, 'INIT': True, 'volume_check': False, 'ymd': day_list}, + {'ticker_code': 'KRW-HIFI', 'ticker_name': '하이파이', 'BUY_INFO': {}, 'rise_rate': 0.0, 'INIT': True, 'volume_check': False, 'ymd': day_list}, + {'ticker_code': 'KRW-IQ', 'ticker_name': '아이큐', 'BUY_INFO': {}, 'rise_rate': 0.0, 'INIT': True, 'volume_check': False, 'ymd': day_list}, + {'ticker_code': 'KRW-LINK', 'ticker_name': '체인링크', 'BUY_INFO': {}, 'rise_rate': 0.0, 'INIT': True, 'volume_check': False, 'ymd': day_list}, + {'ticker_code': 'KRW-MATIC', 'ticker_name': '폴리곤', 'BUY_INFO': {}, 'rise_rate': 0.0, 'INIT': True, 'volume_check': False, 'ymd': day_list}, + {'ticker_code': 'KRW-MINA', 'ticker_name': '미나', 'BUY_INFO': {}, 'rise_rate': 0.0, 'INIT': True, 'volume_check': False, 'ymd': day_list}, + {'ticker_code': 'KRW-NEAR', 'ticker_name': '니어프로토콜', 'BUY_INFO': {}, 'rise_rate': 0.0, 'INIT': True, 'volume_check': False, 'ymd': day_list}, + {'ticker_code': 'KRW-SAND', 'ticker_name': '샌드박스', 'BUY_INFO': {}, 'rise_rate': 0.0, 'INIT': True, 'volume_check': False, 'ymd': day_list}, + {'ticker_code': 'KRW-SC', 'ticker_name': '시아코인', 'BUY_INFO': {}, 'rise_rate': 0.0, 'INIT': True, 'volume_check': False, 'ymd': day_list}, + {'ticker_code': 'KRW-SEI', 'ticker_name': '세이', 'BUY_INFO': {}, 'rise_rate': 0.0, 'INIT': True, 'volume_check': False, 'ymd': day_list}, + {'ticker_code': 'KRW-SOL', 'ticker_name': '솔라나', 'BUY_INFO': {}, 'rise_rate': 0.0, 'INIT': True, 'volume_check': False, 'ymd': day_list}, + {'ticker_code': 'KRW-STORJ', 'ticker_name': '스토리지', 'BUY_INFO': {}, 'rise_rate': 0.0, 'INIT': True, 'volume_check': False, 'ymd': day_list}, + {'ticker_code': 'KRW-STRAX', 'ticker_name': '스트라티스', 'BUY_INFO': {}, 'rise_rate': 0.0, 'INIT': True, 'volume_check': False, 'ymd': day_list}, + {'ticker_code': 'KRW-STX', 'ticker_name': '스택스', 'BUY_INFO': {}, 'rise_rate': 0.0, 'INIT': True, 'volume_check': False, 'ymd': day_list}, + {'ticker_code': 'KRW-SUI', 'ticker_name': '수이', 'BUY_INFO': {}, 'rise_rate': 0.0, 'INIT': True, 'volume_check': False, 'ymd': day_list}, + {'ticker_code': 'KRW-THETA', 'ticker_name': '쎄타토큰', 'BUY_INFO': {}, 'rise_rate': 0.0, 'INIT': True, 'volume_check': False, 'ymd': day_list}, + {'ticker_code': 'KRW-XRP', 'ticker_name': '리플', 'BUY_INFO': {}, 'rise_rate': 0.0, 'INIT': True, 'volume_check': False, 'ymd': day_list} + ] + + total_profit, total_buy = 0, 0 + for ticker in tickers: + simulation = Simulation_minutely(RESOURCE_PATH) + total_buy_amount, profit = simulation.simulate(ticker, get_days=14) + total_profit += profit + total_buy += total_buy_amount + print("\nticker: {}개: 총이익: {:,.0f}원 ({:.2f})%".format(len(tickers), total_profit, 100*total_profit/total_buy)) + """ + + simulation = Simulation_minutely(RESOURCE_PATH) + ticker = {'ticker_code': 'KRW-ONT', 'ticker_name': '체인링크', 'BUY_INFO': {}, 'ymd': (datetime.now()+timedelta(days=1)).strftime('%Y%m%d')} + #ticker = {'ticker_code': 'KRW-BCH', 'ticker_name': '체인링크', 'BUY_INFO': {}, 'ymd': '20240324'} + simulation.simulate(ticker, get_days=7) + + + print ("done...") diff --git a/hts/BuySell_Daily.py b/hts/BuySell_Daily.py index ed79db0..a0b1de9 100644 --- a/hts/BuySell_Daily.py +++ b/hts/BuySell_Daily.py @@ -10,229 +10,83 @@ class BuySell_Daily: count += 1 return count - def getBuyPrice(self, ticker, data, i, BS=None): + def getBuyPrice(self, ticker, data, data_scaled, i, BS=None): buy_ymd, buy_price, buy_count, buy_weight, buy_type, buy_cut = None, 0, 0, 1, '', None - point = None - for c in range(i-1, 0, -1): - if data['close'][c] < data['changeLine'][c]: - point = c + sub_i = None + for c in range(i-1, i-5, -1): + if 0 < BS['buy_count'][c] and 0 < BS['buy_price'][c]: + sub_i = c break - if point is not None: - if 3 < sum([1 if 0 < BS['buy_price'][k] else 0 for k in range(point, i)]): - return buy_ymd, 0, 0, '', None - - tmp_buy_ymd, tmp_buy_price, tmp_buy_count, tmp_buy_type, tmp_buy_cut = self.getBuyPrice_ichimok_changeLine(ticker, data, i, BS) - if 0 < tmp_buy_count: - buy_ymd = tmp_buy_ymd; buy_price = tmp_buy_price; buy_count = tmp_buy_count; buy_type = tmp_buy_type; buy_cut = tmp_buy_cut - tmp_buy_ymd, tmp_buy_price, tmp_buy_count, tmp_buy_type, tmp_buy_cut = self.getBuyPrice_ichimok_baseLine(ticker, data, i, BS) - if 0 < tmp_buy_count: - buy_ymd = tmp_buy_ymd; buy_price = tmp_buy_price; buy_count = tmp_buy_count; buy_type = tmp_buy_type; buy_cut = tmp_buy_cut - tmp_buy_ymd, tmp_buy_price, tmp_buy_count, tmp_buy_type, tmp_buy_cut = self.getBuyPrice_ichimok_laggingSpan(ticker, data, i, BS) - if 0 < tmp_buy_count: - buy_ymd = tmp_buy_ymd; buy_price = tmp_buy_price; buy_count = tmp_buy_count; buy_type = tmp_buy_type; buy_cut = tmp_buy_cut - tmp_buy_ymd, tmp_buy_price, tmp_buy_count, tmp_buy_type, tmp_buy_cut = self.getBuyPrice_ichimok_avg(ticker, data, i, BS) - if 0 < tmp_buy_count: - buy_ymd = tmp_buy_ymd; buy_price = tmp_buy_price; buy_count = tmp_buy_count; buy_type = tmp_buy_type; buy_cut = tmp_buy_cut - - if 0 < len(ticker['BUY_INFO']['buy_list']): - diff = (datetime.strptime(str(data['ymd'][i]), '%Y-%m-%d %H:%M:%S') - ticker['BUY_INFO']['buy_list'][-1]['buy_ymd']) - d = diff.days - s = diff.seconds - - # 해당 종목에 대해서 1분 이내 매수 금지 - if s < 3 * 60: - return buy_ymd, 0, 0, '', None + sub_check = False + if sub_i is not None: + sub_check = True + for c in range(sub_i, i+1): + if data['close'].iloc[c+1] < BS['buy_price'][c] * 0.99: + sub_check = False + break + if sub_check: + buy_price = data['close'].iloc[i] - 2 * ticker['unit'] + buy_count = (buy_weight * ticker['MAX_BUY']) / data['close'].iloc[i] + buy_type = '' + else: + tmp_buy_ymd, tmp_buy_price, tmp_buy_count, tmp_buy_type, tmp_buy_cut = self.getBuyPrice_BBAND(ticker, data, data_scaled, i, BS) + if 0 < tmp_buy_count: + buy_ymd = tmp_buy_ymd; buy_price = tmp_buy_price; buy_count = tmp_buy_count; buy_type = tmp_buy_type; buy_cut = tmp_buy_cut + tmp_buy_ymd, tmp_buy_price, tmp_buy_count, tmp_buy_type, tmp_buy_cut = self.getBuyPrice_PolyLine(ticker, data, data_scaled, i, BS) + if 0 < tmp_buy_count: + buy_ymd = tmp_buy_ymd; buy_price = tmp_buy_price; buy_count = tmp_buy_count; buy_type = tmp_buy_type; buy_cut = tmp_buy_cut + tmp_buy_ymd, tmp_buy_price, tmp_buy_count, tmp_buy_type, tmp_buy_cut = self.getBuyPrice_Slow(ticker, data, data_scaled, i, BS) + if 0 < tmp_buy_count: + buy_ymd = tmp_buy_ymd; buy_price = tmp_buy_price; buy_count = tmp_buy_count; buy_type = tmp_buy_type; buy_cut = tmp_buy_cut return buy_ymd, buy_price, buy_count, buy_type, buy_cut - def getSellPrice(self, ticker, data, i, BS=None): + def getSellPrice(self, ticker, data, data_scaled, i, BS=None): sell_price, sell_count, sell_type = 0, 1, '' sell_type_list = [] - tmp_sell_price, tmp_sell_type_list = self.getSellPrice_ichimok_baseLine(ticker, data, i, BS) - sell_type_list += tmp_sell_type_list + #tmp_sell_price, tmp_sell_type_list = self.getSellPrice_Umbong(ticker, data, data_scaled, i, BS) + #sell_type_list += tmp_sell_type_list - tmp_sell_price, tmp_sell_type_list = self.getSellPrice_candle(ticker, data, i, BS) - sell_type_list += tmp_sell_type_list - - sell_price = tmp_sell_price + #sell_price = tmp_sell_price if 0 < len(sell_type_list) or 0 < sell_price: sell_type = ','.join(list(set(sell_type_list))) return sell_price, sell_count, sell_type - def getBuyPrice_ichimok_changeLine(self, ticker, data, i, BS=None): + """""""""""""""""" + """""""""""""""""" + + def getBuyPrice_BBAND(self, ticker, data, data_scaled, i, BS): buy_ymd, buy_price, buy_count, buy_weight, buy_type, buy_cut = None, 0, 0, 1, '', None check = False - id9, id26, id33, id52 = 8, 25, 32, 51 - if 5 < i: - # 신저가를 갱신하지 않으면서 전환선이 떨어질 때 주가는 올라감 (기준선은 횡보, 현재 봉이 신저가가 이닐 때) - # --> 기준선이 계속 횡보하거나 떨어지면 상승하지는 않는다. - # https://www.youtube.com/watch?v=KZMP0Ssv8WI&t=432s (8:45) - if data['new_low_9'][i] == 0: - if data['changeLine'][i] < data['baseLine'][i]: - if data['changeLine'][i] < data['changeLine'][i-1] and np.min(data['close'][i-8:i]) < data['close'][i]: - if data['baseLine'][i-1] == data['baseLine'][i] < data['baseLine'][i-2]: - if 3 < self.countYangBong(data, i): - check = True - buy_type = "ichimok_changeLine_1" - buy_weight = 5 - buy_cut = min(np.min(data['open'][i - 60:i]), np.min(data['close'][i - 60:i])) + if 60 < i: - if data['new_high_9'][i] == 1: - if data['changeLine'][i-1] < data['changeLine'][i] and data['baseLine'][i-1] < data['baseLine'][i]: - if data['baseLine'][i - 1] != data['baseLine'][i]: - if 0.2 < data['leadingSpan1_leadingSpan2_diff_rate'][i+id52]: - check = True - buy_type = "ichimok_changeLine_2" - buy_weight = 10 - buy_cut = min(np.min(data['open'][i - 60:i]), np.min(data['close'][i - 60:i])) + sub_check1, sub_check2 = False, False + for c in range(i-20, i): + if not sub_check1 and data['bb_width'].iloc[i-1] < data['bb_width'].iloc[i] and data['bb_width'].iloc[i] < 5: + sub_check1 = True + if sub_check1 and not sub_check2 and data['upper_20'].iloc[i] < data['high'].iloc[i]: + sub_check2 = True + break - if data['new_high_26'][i] == 1: - for c in range(i-15, i): - if data['changeLine'][c-1] < data['baseLine'][c] and data['baseLine'][i-1] < data['changeLine'][i]: - if 0.2 < data['leadingSpan1_leadingSpan2_diff_rate'][i + id52]: - check = True - buy_type = "ichimok_changeLine_3" - buy_weight = 10 - buy_cut = min(np.min(data['open'][i - 60:i]), np.min(data['close'][i - 60:i])) - break - - if check: - buy_ymd = data['ymd'][i] - buy_price = data['close'][i] - 2 * ticker['unit'] - buy_count = (buy_weight * ticker['MAX_BUY']) / data['close'][i] - - return buy_ymd, buy_price, buy_count, buy_type, buy_cut - - - """""""""""""""""" - """""""""""""""""" - - def getBuyPrice_ichimok_baseLine(self, ticker, data, i, BS=None): - - buy_ymd, buy_price, buy_count, buy_weight, buy_type, buy_cut = None, 0, 0, 1, '', None - - check = False - - id9, id26, id33, id52 = 9, 26, 33, 52 - if 5 < i: - # 기준선이 하락할 때, 전환선이 상승하는 경우를 중기 추세의 변곡이라 한다 - if data['changeLine'][i-1] < data['changeLine'][i] and data['baseLine'][i] < data['baseLine'][i-1]: - if data['changeLine'][i - 1] < data['baseLine'][i-1] and data['baseLine'][i] < data['changeLine'][i]: - if data['open'][i] < data['close'][i]: - if 3 < self.countYangBong(data, i): - check = True - buy_type = "ichimok_baseLine_1" - buy_weight = 5 - buy_cut = min(np.min(data['open'][i - 60:i]), np.min(data['close'][i - 60:i])) - - # 기준선이 평행 때, 전환선이 상승하는 경우를 중기 추세의 변곡이라 한다 - if data['changeLine'][i-1] < data['changeLine'][i] and data['baseLine'][i-3] == data['baseLine'][i-2] == data['baseLine'][i-1] == data['baseLine'][i]: - if data['changeLine'][i - 1] < data['baseLine'][i-1] and data['baseLine'][i] < data['changeLine'][i]: - if data['open'][i] < data['close'][i]: - if 3 < self.countYangBong(data, i): - check = True - buy_type = "ichimok_baseLine_1" - buy_weight = 5 - buy_cut = min(np.min(data['open'][i - 60:i]), np.min(data['close'][i - 60:i])) - - if check: - buy_ymd = data['ymd'][i] - buy_price = data['close'][i] - 2 * ticker['unit'] - buy_count = (buy_weight * ticker['MAX_BUY']) / data['close'][i] - - return buy_ymd, buy_price, buy_count, buy_type, buy_cut - - """""""""""""""""" - """""""""""""""""" - - def getBuyPrice_ichimok_laggingSpan(self, ticker, data, i, BS): - - buy_ymd, buy_price, buy_count, buy_weight, buy_type, buy_cut = None, 0, 0, 1, '', None - - check = False - - if 5 < i: - - if data['laggingSpan_close_diff_rate'][i-1] <= 0 and 0 < data['laggingSpan_close_diff_rate'][i]: - check = True - buy_price = data['close'][i] - 2 * ticker['unit'] - buy_count = (buy_weight * ticker['MAX_BUY']) / data['close'][i] - buy_weight = 2 - buy_type = 'laggingSpan1' - - if 0 <= data['laggingSpan_avg60_diff_rate'][i-1] and data['laggingSpan_avg60_diff_rate'][i] < 0: - check = True - buy_price = data['close'][i] - 2 * ticker['unit'] - buy_count = (buy_weight * ticker['MAX_BUY']) / data['close'][i] - buy_weight = 2 - buy_type = 'laggingSpan2' - - if check: - buy_ymd = data['ymd'][i] - buy_price = data['close'][i] - 2 * ticker['unit'] - buy_count = (buy_weight * ticker['MAX_BUY']) / data['close'][i] - - return buy_ymd, buy_price, buy_count, buy_type, buy_cut - - """""""""""""""""" - """""""""""""""""" - - def getSellPrice_ichimok_baseLine(self, ticker, data, i, BS=None): - sell_price = 0 - sell_type_list = [] - - check = False - - id26, id52 = 26, 52 - - if data['new_high_9'][i] == 0: - if data['baseLine'][i-1] < data['baseLine'][i] and data['changeLine'][i] < data['changeLine'][i-1]: - check = True - sell_type_list.append('ichimok_baseLine') - - if check: - sell_price = data['close'][i] + 2 * ticker['unit'] - - return sell_price, sell_type_list - - """""""""""""""""" - """""""""""""""""" - - def getBuyPrice_ichimok_avg(self, ticker, data, i, BS): - - buy_ymd, buy_price, buy_count, buy_weight, buy_type, buy_cut = None, 0, 0, 1, '', None - - check = False - - if 5 < i: - - if data['avg5'][i] < data['avg20'][i] < data['baseLine'][i] < data['changeLine'][i] < data['close'][i]: - if data['avg5'][i-1] price and 1000 < profit: + buy_count = 2 * last_buy_count + elif last_buy_price < price and 1000 > profit: + buy_count = 1.5 * last_buy_count + else: + buy_count = 1 * last_buy_count + + if 'today_buy_type' in ticker and ticker['today_buy_type'] == 3: + buy_count *= 2 + else: + buy_count = 1.5 * ticker['MAX_BUY'] / price + + if 200000 < price * buy_count: + buy_count = 200000 / price + + return buy_count + + def getBuyPrice(self, ticker, data, data_scaled, i, BS=None): + buy_ymd, buy_price, buy_count, buy_weight, buy_type, buy_cut = None, 0, 0, 1, '', None + + # buy_ymd, buy_price, buy_count, buy_type, buy_cut = self.getBuyPrice_PolyLine(ticker, data, data_scaled, i, BS) + #tmp_buy_ymd, tmp_buy_price, tmp_buy_count, tmp_buy_type, tmp_buy_cut = self.getBuyPrice_Candle(ticker, data, data_scaled, i, BS) + tmp_buy_ymd, tmp_buy_price, tmp_buy_count, tmp_buy_type, tmp_buy_cut = self.getBuyPrice_Slow(ticker, data, data_scaled, i,BS) + if 0 < tmp_buy_count: + buy_ymd = tmp_buy_ymd; + buy_price = tmp_buy_price; + buy_count = tmp_buy_count; + buy_type = tmp_buy_type; + buy_cut = tmp_buy_cut + + if 0 < len(ticker['BUY_INFO']['buy_list']): + diff = (datetime.strptime(str(data['ymd'].iloc[i]), '%Y-%m-%d %H:%M:%S') - ticker['BUY_INFO']['buy_list'][-1]['buy_ymd']) + d = diff.days + s = diff.seconds + + # 해당 종목에 대해서 10분 이내 매수 금지 + if s < 15 * 60: + return None, 0, 0, '', None + + return buy_ymd, buy_price, buy_count, buy_type, buy_cut + + def getSellPrice(self, ticker, data, data_scaled, i, BS=None): + sell_price, sell_count, sell_type = 0, 0, '' + sell_type_list = [] + + """ + tmp_sell_price, tmp_sell_count, tmp_sell_type_list = self.getSelllPrice_Umbong(ticker, data, data_scaled, i, BS) + sell_count += tmp_sell_count + sell_type_list += tmp_sell_type_list + sell_price += tmp_sell_price + """ + + if 0 < len(sell_type_list) or 0 < sell_price: + sell_type = ','.join(list(set(sell_type_list))) + + return sell_price, sell_count, sell_type + + """""""""""""""""" + """""""""""""""""" + + def getBuyPrice_Slow(self, ticker, data, data_scaled, i, BS): + + buy_ymd, buy_price, buy_count, buy_weight, buy_type, buy_cut = None, 0, 0, 1, '', None + + check = False + + if 5 < i: + """ + if data['poly_20'].iloc[i - 1] < data['poly_20'].iloc[i] and data_scaled['disparity_diff_60_20_rate'].iloc[i] < -0.5: + if data_scaled['macd_720'].iloc[i - 1] < data_scaled['macd_720'].iloc[i] and data_scaled['macd'].iloc[i - 1] < data_scaled['macd'].iloc[i]: + if data['avg10'].iloc[i] < data['avg5'].iloc[i]: + check = True + buy_price = data['close'][i] - 2 * ticker['unit'] + buy_count = (buy_weight * ticker['MAX_BUY']) / data['close'][i] + buy_type = 'slowk_10' + # buy_cut = data['support'].iloc[i] + """ + + if data["slowk_10"].iloc[i-1] < data["slowk_10"].iloc[i] < 20: + if data["slowk_10"].iloc[i-1] < data["slowd_10"].iloc[i-1] and data["slowd_10"].iloc[i] < data["slowk_10"].iloc[i]: + check = True + buy_price = data['close'][i] - 2 * ticker['unit'] + buy_count = (buy_weight * ticker['MAX_BUY']) / data['close'][i] + buy_type = 'slowk_10' + # buy_cut = data['support'].iloc[i] + + if check: + buy_ymd = data['ymd'].iloc[i] + buy_price = data['close'][i] - 2 * ticker['unit'] + buy_count = (buy_weight * ticker['MAX_BUY']) / data['close'][i] + + return buy_ymd, buy_price, buy_count, buy_type, buy_cut + + + """""""""""""""""" + """""""""""""""""" + + def getBuyPrice_Candle(self, ticker, data, data_scaled, i, BS): + + buy_ymd, buy_price, buy_count, buy_weight, buy_type, buy_cut = None, 0, 0, 1, '', None + + check = False + + if 60 < i: + if data_scaled['disparity_diff_20_5_rate'].iloc[i] < 1 and data_scaled['disparity_diff_60_20_rate'].iloc[i] < 1 and data_scaled['disparity_diff_120_20_rate'].iloc[i] < 1: + + if data['slowk_1440'].iloc[i - 1] < data['slowd_1440'].iloc[i - 1]: + if data['slowd_1440'].iloc[i] < data['slowk_1440'].iloc[i] < 40: + check = True + buy_price = data['close'].iloc[i] - 2 * ticker['unit'] + buy_count = self.getBuy_Count(ticker, data['close'].iloc[i]) + buy_type = 'slowk_1440' + #buy_cut = data['support'].iloc[i] + + if data['avg240'].iloc[i - 1] < data['avg240'].iloc[i]: + if data_scaled['poly_480'].iloc[i - 1] <= 0 and 0 < data_scaled['poly_480'].iloc[i]: + check = True + buy_price = data['close'].iloc[i] - 2 * ticker['unit'] + buy_count = self.getBuy_Count(ticker, data['close'].iloc[i]) + buy_type = 'poly_480' + #buy_cut = data['support'].iloc[i] + + if data_scaled['poly_720'].iloc[i - 1] < data_scaled['poly_720'].iloc[i] and data['slowk_720'].iloc[i] < 50: + if data['close'].iloc[i - 1] < data['avg720'].iloc[i-1] and data['avg720'].iloc[i] < data['close'].iloc[i]: + check = True + buy_price = data['close'].iloc[i] - 2 * ticker['unit'] + buy_count = self.getBuy_Count(ticker, data['close'].iloc[i]) + buy_type = 'poly_720' + #buy_cut = data['support'].iloc[i] + + if data_scaled['poly_1440'].iloc[i - 1] < data_scaled['poly_1440'].iloc[i] and data['slowk_1440'].iloc[i] < 50: + if data['close'].iloc[i - 1] < data['avg1440'].iloc[i-1] and data['avg1440'].iloc[i] < data['close'].iloc[i]: + check = True + buy_price = data['close'].iloc[i] - 2 * ticker['unit'] + buy_count = self.getBuy_Count(ticker, data['close'].iloc[i]) + buy_type = 'poly_1440' + #buy_cut = data['support'].iloc[i] + + if check: + buy_ymd = data['ymd'].iloc[i] + buy_price = data['close'][i] - 2 * ticker['unit'] + buy_count = (buy_weight * ticker['MAX_BUY']) / data['close'][i] + + return buy_ymd, buy_price, buy_count, buy_type, buy_cut + + """""""""""""""""" + """""""""""""""""" + + def getSelllPrice_Umbong(self, ticker, data, data_scaled, i, BS): + sell_price, sell_count = 0, 0 + sell_type_list = [] + + if 0 < len(ticker['BUY_INFO']['buy_list']): + check = False + sell_count = 0 + + if data['close'].iloc[i] < data['open'].iloc[i]: + for c in range(i - 1, i - 10, -1): + if data['open'].iloc[c] < data['close'].iloc[c] == data['high'].iloc[c]: + if data['close'].iloc[i] < data['open'].iloc[c]: + check = True + sell_count_1 = sum([price['buy_count'] for price in ticker['BUY_INFO']['buy_list'] if price['buy_type'] == "slowk_1440"]) + if 0 < sell_count_1: + sell_type_list.append('slowk_1440') + + sell_count_2 = sum([price['buy_count'] for price in ticker['BUY_INFO']['buy_list'] if price['buy_type'] == "poly_480"]) + if 0 < sell_count_2: + sell_type_list.append('poly_480') + + if "buy_amount" in ticker['BUY_INFO'] and ticker['BUY_INFO']["buy_amount"] < 50000: + sell_count = sell_count_1 + sell_count_2 + else: + sell_count = (sell_count_1 + sell_count_2) * 0.8 + + if check and 0 < sell_count: + sell_price = data['close'].iloc[i] + 2 * ticker['unit'] + + return sell_price, sell_count, sell_type_list