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dsyoon
2024-04-26 09:19:28 +09:00
parent d5e5316fce
commit 0d18c61fa2
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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(
"[{}] {:,}<br>"
"{}, {:,} ({:,.2f})<br><br>"
"[BASIC]<br>"
" poly_5: {:.5f}, poly_10: {:.5f}, poly_20: {:.5f}, poly_60: {:.5f}, poly_120: {:.5f}, poly_240: {:.5f}, poly_480: {:.5f}<br>"
"[INFO] <br>"
" new_high_7: {:,.2f}, new_high_9: {:,.2f}, new_high_26: {:,.2f}, new_low_7: {:,.2f}, new_low_9: {:,.2f}, new_low_26: {:,.2f}<br>"
.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(
"[{}] {:,}<br>"
"{}, {:,} ({:,.2f})<br><br>"
"[BASIC]<br>"
" poly_5: {:.5f}, poly_10: {:.5f}, poly_20: {:.5f}, poly_60: {:.5f}, poly_120: {:.5f}, poly_240: {:.5f}, poly_480: {:.5f}<br>"
"[INFO] <br>"
" new_high_7: {:,.2f}, new_high_9: {:,.2f}, new_high_26: {:,.2f}, new_low_7: {:,.2f}, new_low_9: {:,.2f}, new_low_26: {:,.2f}<br>"
.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(
"{}<br><br>"
" o: {}, c: {}, h: {}, l: {}<br><br>"
" poly_5: {:.5f}, poly_10: {:.5f}, poly_20: {:.5f}, poly_60: {:.5f}, poly_120: {:.5f}, poly_240: {:.5f}, poly_480: {:.5f}<br>"
" new_high_9: {}, new_high_26: {}<br>"
" avg5: {:.2f}, avg10: {:.2f}, avg20: {:.2f}, avg60: {:.2f}, avg90: {:.2f}, avg120: {:.2f}, avg240: {:.2f}<br>"
" avg360: {:.2f}, avg480: {:.2f}, avg720: {:.2f}, avg1440: {:.2f}, avg2880: {:.2f}<br><br>"
" loc_k: {:.2f}, loc_d: {:.2f}, loc_s: {:.2f}<br><br>"
.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...")