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dsyoon
2024-04-26 09:19:28 +09:00
parent d5e5316fce
commit 0d18c61fa2
6 changed files with 1392 additions and 256 deletions
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1
HTS_etf.py
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@@ -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

48
JSDPattern.py
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@@ -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()
@@ -142,33 +125,6 @@ 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)

437
Simulation_daily.py Normal file
View File

@@ -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(
"[{}] {:,}<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...")

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Simulation_minutely.py Normal file
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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(
"[{}] {:,}<br>"
"{}, {:,} ({:,.2f})<br><br>"
"[BASIC]<br>"
" support: {:.2f}, resistance: {:.2f}<br>"
" 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}<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['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(
"[{}] {:,}<br>"
"{}, {:,} ({:,.2f})<br><br>"
"[BASIC]<br>"
" support: {:.2f}, resistance: {:.2f}<br>"
" 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}<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['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(
"[{}] {:,}<br><br>"
"[BASIC]<BR>"
" support: {:.2f}, resistance: {:.2f}<br>"
" 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}<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],
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...")

335
hts/BuySell_Daily.py
View File

@@ -10,103 +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)
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_ichimok_baseLine(ticker, data, i, BS)
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_ichimok_laggingSpan(ticker, data, 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
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
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]))
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]))
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 sub_check1 and sub_check2:
if data_scaled['poly_120'].iloc[i-1] < data_scaled['poly_120'].iloc[i]:
check = True
buy_price = data['close'].iloc[i] - 2 * ticker['unit']
buy_count = (buy_weight * ticker['MAX_BUY']) / data['close'].iloc[i]
buy_weight = 30
buy_type = 'bband'
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]
buy_ymd = data['ymd'].iloc[i]
buy_price = data['close'].iloc[i] - 2 * ticker['unit']
buy_count = (buy_weight * ticker['MAX_BUY']) / data['close'].iloc[i]
return buy_ymd, buy_price, buy_count, buy_type, buy_cut
@@ -114,45 +94,49 @@ class BuySell_Daily:
""""""""""""""""""
""""""""""""""""""
def getBuyPrice_ichimok_baseLine(self, ticker, data, i, BS=None):
def getBuyPrice_PolyLine(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 = 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]))
if 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):
if data_scaled['poly_60'].iloc[i-1] < 0 and data_scaled['poly_120'].iloc[i-1] < -0.003:
if data_scaled['poly_60'].iloc[i-1] <= data_scaled['poly_120'].iloc[i-1] and data_scaled['poly_120'].iloc[i] < data_scaled['poly_60'].iloc[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]))
buy_price = data['close'].iloc[i] - 2 * ticker['unit']
buy_count = (buy_weight * ticker['MAX_BUY']) / data['close'].iloc[i]
buy_weight = 30
buy_type = 'poly'
if data_scaled['poly_240'].iloc[i - 1] < data_scaled['poly_240'].iloc[i] and data['slowk_240'].iloc[i] < 50:
if data['close'].iloc[i - 1] < data['avg240'].iloc[i-1] and data['avg240'].iloc[i] < data['close'].iloc[i]:
check = True
buy_price = data['close'].iloc[i] - 2 * ticker['unit']
buy_count = (buy_weight * ticker['MAX_BUY']) / data['close'].iloc[i]
buy_weight = 30
buy_type = 'poly_240'
#buy_cut = data['support'].iloc[i]
if data_scaled['poly_480'].iloc[i - 1] < data_scaled['poly_480'].iloc[i] and data['slowk_480'].iloc[i] < 50:
if data['close'].iloc[i - 1] < data['avg480'].iloc[i-1] and data['avg480'].iloc[i] < data['close'].iloc[i]:
check = True
buy_price = data['close'].iloc[i] - 2 * ticker['unit']
buy_count = (buy_weight * ticker['MAX_BUY']) / data['close'].iloc[i]
buy_weight = 30
buy_type = 'poly_480'
#buy_cut = data['support'].iloc[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]
buy_ymd = data['ymd'].iloc[i]
buy_price = data['close'].iloc[i] - 2 * ticker['unit']
buy_count = (buy_weight * ticker['MAX_BUY']) / data['close'].iloc[i]
return buy_ymd, buy_price, buy_count, buy_type, buy_cut
""""""""""""""""""
""""""""""""""""""
def getBuyPrice_ichimok_laggingSpan(self, ticker, data, i, BS):
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
@@ -160,99 +144,84 @@ class BuySell_Daily:
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 data_scaled["disparity_diff_60_20_rate"].iloc[i] < -0.5:
if 0 <= data['laggingSpan_avg60_diff_rate'][i-1] and data['laggingSpan_avg60_diff_rate'][i] < 0:
if data_scaled["disparity_diff_60_20_rate"].iloc[i] < -0.5 and data_scaled["disparity_diff_60_20_rate"].iloc[i-1] < data_scaled["disparity_diff_60_20_rate"].iloc[i]:
if data_scaled['slowk_20'].iloc[i-1] <= data_scaled['slowd_20'].iloc[i-1] < 0:
if data_scaled['slowd_20'].iloc[i] < data_scaled['slowk_20'].iloc[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'
buy_price = data['close'].iloc[i] - 2 * ticker['unit']
buy_count = (buy_weight * ticker['MAX_BUY']) / data['close'].iloc[i]
buy_type = 'slowk_20'
#buy_cut = data['support'].iloc[i]
if data_scaled['slowk_60'].iloc[i-1] <= data_scaled['slowd_60'].iloc[i-1] < 0:
if data_scaled['slowd_60'].iloc[i] < data_scaled['slowk_60'].iloc[i] < 0:
check = True
buy_price = data['close'].iloc[i] - 2 * ticker['unit']
buy_count = (buy_weight * ticker['MAX_BUY']) / data['close'].iloc[i]
buy_type = 'slowk_60'
#buy_cut = data['support'].iloc[i]
if data_scaled['slowk_120'].iloc[i-1] <= data_scaled['slowd_120'].iloc[i-1] < 0:
if data_scaled['slowd_120'].iloc[i] < data_scaled['slowk_120'].iloc[i] < 0:
check = True
buy_price = data['close'].iloc[i] - 2 * ticker['unit']
buy_count = (buy_weight * ticker['MAX_BUY']) / data['close'].iloc[i]
buy_type = 'slowk_120'
#buy_cut = data['support'].iloc[i]
if data_scaled['slowk_240'].iloc[i-1] <= data_scaled['slowd_240'].iloc[i-1] < 0:
if data_scaled['slowd_240'].iloc[i] < data_scaled['slowk_240'].iloc[i] < 0:
check = True
buy_price = data['close'].iloc[i] - 2 * ticker['unit']
buy_count = (buy_weight * ticker['MAX_BUY']) / data['close'].iloc[i]
buy_type = 'slowk_240'
#buy_cut = data['support'].iloc[i]
if data['slowk_480'].iloc[i - 1] < data['slowd_480'].iloc[i - 1] < 40:
if data['slowd_480'].iloc[i] < data['slowk_480'].iloc[i]:
check = True
buy_price = data['close'].iloc[i] - 2 * ticker['unit']
buy_count = (buy_weight * ticker['MAX_BUY']) / 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 = (buy_weight * ticker['MAX_BUY']) / data['close'].iloc[i]
buy_type = 'poly_480'
#buy_cut = data['support'].iloc[i]
if data_scaled['poly_240'].iloc[i - 1] < data_scaled['poly_240'].iloc[i] and data['slowk_240'].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 = (buy_weight * ticker['MAX_BUY']) / data['close'].iloc[i]
buy_type = 'poly_240'
#buy_cut = data['support'].iloc[i]
if data_scaled['poly_480'].iloc[i - 1] < data_scaled['poly_480'].iloc[i] and data['slowk_480'].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 = (buy_weight * ticker['MAX_BUY']) / data['close'].iloc[i]
buy_type = 'poly_480'
#buy_cut = data['support'].iloc[i]
if data["slowk_10"].iloc[i-1] < data["slowk_10"].iloc[i] < 20:
check = True
buy_price = data['close'].iloc[i] - 2 * ticker['unit']
buy_count = (buy_weight * ticker['MAX_BUY']) / data['close'].iloc[i]
buy_type = 'slowk_10'
# buy_cut = data['support'].iloc[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]
buy_ymd = data['ymd'].iloc[i]
buy_price = data['close'].iloc[i] - 2 * ticker['unit']
buy_count = (buy_weight * ticker['MAX_BUY']) / data['close'].iloc[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]<data['avg5'][i] and data['avg20'][i-1]<data['avg20'][i] and data['baseLine'][i-1]<=data['baseLine'][i] and data['changeLine'][i-1]<data['changeLine'][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 = 'ichimok_avg1'
if data['avg20'][i] < data['baseLine'][i] < data['changeLine'][i] < data['avg5'][i] < data['close'][i]:
if data['avg20'][i-1]<data['avg20'][i] and data['baseLine'][i]<data['baseLine'][i-1] and data['changeLine'][i-1]<data['changeLine'][i] and data['avg5'][i-1]<data['avg5'][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 = 'ichimok_avg2'
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_candle(self, ticker, data, i, BS=None):
sell_price = 0
sell_type_list = []
check = False
high = max(data['close'][i], data['open'][i])
low = min(data['close'][i], data['open'][i])
if low - data['low'][i] < data['high'][i] - high:
check = True
sell_type_list.append('candle_tail')
if check:
sell_price = data['close'][i] + 2 * ticker['unit']
return sell_price, sell_type_list

204
hts/BuySell_Minutely.py Normal file
View File

@@ -0,0 +1,204 @@
import numpy as np
from datetime import datetime
from stock.util.DBManager import DBManager
class BuySell_Minutely:
dBManager = None
def __init__(self, RESOURCE_PATH):
self.dBManager = DBManager(RESOURCE_PATH)
return
def getBuy_Count(self, ticker, price):
buy_count = ticker['MAX_BUY'] / price
if 'BUY_INFO' in ticker and "buy_amount" in ticker['BUY_INFO']:
amount = ticker['BUY_INFO']["buy_amount"]
if 1000000 < amount:
return 0
profit = (price * ticker['BUY_INFO']["buy_count"]) - amount
last_buy_count, last_buy_price = self.dBManager.getLastBuyInfo(ticker["ticker_code"])
if last_buy_count is not None and last_buy_price is not None:
if last_buy_price < price and 1000 < profit:
buy_count = 3 * last_buy_count
elif last_buy_price > 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
""""""""""""""""""
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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
""""""""""""""""""
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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
""""""""""""""""""
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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