dsyoon/DeepStock
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

init

Browse Source
This commit is contained in:
dsyoon
2023-10-15 10:50:57 +09:00
parent 4d1cc22b41
commit 4fa2d4ef6e
1 changed files with 0 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

247
Simulation.py
View File

@@ -1,247 +0,0 @@
from math import nan
import pandas as pd
import plotly.graph_objects as go
from plotly import subplots
import os
from hts.HTS import HTS
from stock.util.Stock2Vector import Stock2Vector
from stock.util.LabelChecker import LabelChecker
from hts.BuySellChecker import BuySellChecker
from stock.analysis.StockStatus import StockStatus
class Simulation (HTS):
stock2Vector = None
buySellChecker = None
def __init__(self, RESOURCE_PATH):
super().__init__(RESOURCE_PATH)
self.RESOURCE_PATH = RESOURCE_PATH
self.buySellChecker = BuySellChecker()
try:
self.stock2Vector = Stock2Vector(RESOURCE_PATH)
self.labelChecker = LabelChecker(RESOURCE_PATH)
except:
pass
#self.connect()
return
def draw(self, stock_code, given_day, data, bsLine):
if bsLine is None:
return
# 어제 데이터는 지운다.
data = data.loc[pd.DatetimeIndex(data.index).day == int(given_day[6:])]
buy_line = bsLine['buy'][len(bsLine['buy'])-len(data):]
buy_weight_line = bsLine['buy_weight'][len(bsLine['buy'])-len(data):]
sell_line = bsLine['sell'][len(bsLine['buy'])-len(data):]
# 그래프 설정을 위한 변수를 생성한다.
data = data.astype({'open': 'int',
'high': 'int',
'low': 'int',
'close': 'int',
'volume': 'int',
'avg5': 'float',
'avg20': 'float',
'avg60': 'float',
'avg120': 'float',
'avg200': 'float',
'disparity_avg5': 'float',
'disparity_avg20': 'float',
'disparity_avg60': 'float',
'disparity_avg120': 'float',
'disparity_avg200': 'float',
'fast_k': 'float',
'slow_k': 'float',
'slow_d': 'float',
'rsi': 'float',
'rsis': 'float'
})
buy_size = []
buy_colors = []
for i in range(len(buy_line)):
if buy_line[i] < 0:
buy_colors.append("#ffffff")
buy_line[i] = nan
buy_size.append(0)
else:
buy_colors.append("#0C752E")
buy_size.append(10 + (5 * buy_weight_line[i]))
sell_colors = []
for i in range(len(sell_line)):
if sell_line[i] < 0:
sell_colors.append("#ffffff")
sell_line[i] = nan
else:
sell_colors.append("#00ced1")
# 그래프를 설정한다.
buy_check = go.Scatter(x=data['date'], y=buy_line, mode='markers', name="buy", marker=dict(size=buy_size, color=buy_colors, line_width=0))
sell_check = go.Scatter(x=data['date'], y=sell_line, mode='markers', name="sell", marker=dict(size=14, color=sell_colors, line_width=0))
upper = go.Scatter(x=data['date'], y=data["upper"], name="upper", line_color='#000000')
lower = go.Scatter(x=data['date'], y=data["lower"], name="lower", line_color='#000000')
avg5 = go.Scatter(x=data['date'], y=data["avg5"], name="avg5", line_color='#F81191')
avg20 = go.Scatter(x=data['date'], y=data["avg20"], name="avg20", line_color='#097F19')
avg30 = go.Scatter(x=data['date'], y=data["avg30"], name="avg30", line_color='#097F19')
avg60 = go.Scatter(x=data['date'], y=data["avg60"], name="avg60", line_color='#671BEA')
avg120 = go.Scatter(x=data['date'], y=data["avg120"], name="avg120", line_color='#DFB809')
avg200 = go.Scatter(x=data['date'], y=data["avg200"], name="avg200", line_color='#000000')
laggingSpan = go.Scatter(x=data['date'], y=data["laggingSpan"], name='laggingSpan', line_color='#B50ABB')
changeLine = go.Scatter(x=data['date'], y=data["changeLine"], name='changeLine', line_color='#14A200')
baseLine = go.Scatter(x=data['date'], y=data["baseLine"], name='baseLine', line_color='#CF6E0D')
candle_stick = go.Candlestick(x=data['date'], open=data['open'], high=data['high'], low=data['low'], close=data['close'], increasing_line_color='red', decreasing_line_color='blue', showlegend=False)
#volume_line = go.Scatter(x=data['date'], y=data["volume"], mode='lines', name='volume')
volume_line = go.Bar(x=data['date'], y=data["volume"], marker_color='red', name='volume')
disparity_avg5 = go.Scatter(x=data['date'], y=data["disparity_avg5"], name="disparity_avg5", line_color='#F81191')
disparity_avg20 = go.Scatter(x=data['date'], y=data["disparity_avg20"], name="disparity_avg20", line_color='#097F19')
disparity_avg30 = go.Scatter(x=data['date'], y=data["disparity_avg30"], name="disparity_avg30", line_color='#097F19')
disparity_avg60 = go.Scatter(x=data['date'], y=data["disparity_avg60"], name="disparity_avg60", line_color='#671BEA')
disparity_avg120 = go.Scatter(x=data['date'], y=data["disparity_avg120"], name="disparity_avg120", line_color='#DFB809')
disparity_avg200 = go.Scatter(x=data['date'], y=data["disparity_avg200"], name="disparity_avg200", line_color='#000000')
macd_line = go.Scatter(x=data['date'], y=data["macd"], line=dict(color='red', width=2), name='macd')
macd_s_line = go.Scatter(x=data['date'], y=data["macds"], line=dict(dash='dashdot', color='black', width=2), name='macds')
macd_o_line = go.Bar(x=data['date'], y=data["macdo"], marker_color='purple', name='macdo')
# fast_k_line = go.Scatter(x=hts['date'], y=hts["fast_k"], mode='lines', name='fast_k')
slow_k_line = go.Scatter(x=data['date'], y=data["slow_k"], line=dict(color='red', width=2), name='slow_k')
slow_d_line = go.Scatter(x=data['date'], y=data["slow_d"], line=dict(dash='dashdot', color='black', width=2), name='slow_d')
rsi_line = go.Scatter(x=data['date'], y=data["rsi"], line=dict(color='red', width=2), name='rsi')
rsis_line = go.Scatter(x=data['date'], y=data["rsis"], line=dict(dash='dashdot', color='black', width=2), name='rsis')
candle_data = [candle_stick, upper, lower, avg5, avg20, avg30, avg60, avg120, avg200, buy_check, sell_check, laggingSpan, changeLine, baseLine]
candle_data = [candle_stick, avg5, avg20, avg30, avg60, avg200, buy_check, sell_check]
candle_data = [candle_stick, avg200, buy_check, sell_check]
volume_data = [volume_line]
disparity_data = [disparity_avg5, disparity_avg20, disparity_avg30, disparity_avg60, disparity_avg120, disparity_avg200]
macd_data = [macd_line, macd_s_line, macd_o_line]
stochastic_data = [slow_k_line, slow_d_line]
rsi_data = [rsi_line, rsis_line]
# 그래프를 그린다.
"""
fig = go.Figure(data=candle_data)
fig.update_layout(title=stock_code + "_" + given_day)
fig.show()
"""
fig = subplots.make_subplots(
rows=6, cols=1,
subplot_titles=("거래량", "이격도", "스토캐스틱", "RSI", "MACD", '캔들'),
#specs=[[{}], [{}], [{}], [{}], [{}], [{}]],
shared_xaxes=True, horizontal_spacing=0.03, vertical_spacing=0.01,
row_heights=[200, 200, 200, 200, 200, 700]
)
for trace in volume_data:
fig.append_trace(trace, 1, 1)
for trace in disparity_data:
fig.append_trace(trace, 2, 1)
for trace in stochastic_data:
fig.append_trace(trace, 3, 1)
for trace in rsi_data:
fig.append_trace(trace, 4, 1)
for trace in macd_data:
fig.append_trace(trace, 5, 1)
for trace in candle_data:
fig.append_trace(trace, 6, 1)
#fig.update_xaxes(nticks=5)
#fig.update_layout(height=1800, title=stock_code + "_" + given_day, xaxis_rangeslider_visible=False)
df = pd.DataFrame(bsLine)
df = df.fillna(-1)
buy_count = len(df.loc[df["buy"] > 0])
sell_count = len(df.loc[df["sell"] > 0])
fig.update_layout(height=1700, title=stock_code + "_" + given_day + "_" + str(buy_count)+","+str(sell_count))
#fig.update_layout(title=stock_code + "_" + given_day + "_" + str(buy_count) + "," + str(sell_count))
fig.show()
return
def makeTickData(self, data, mins=30):
result = {"check": set(),
"time": [],
"open": [],
"close": [],
"high": [],
"low": [],
"vol": [],
"label": []}
for i in range(mins, len(data['time'])+1):
result["check"].add(data['time'][i-1])
result["time"].append(data['time'][i-1])
result["open"].append(data['open'][i-mins])
result["close"].append(data['close'][i-1])
result["high"].append(max(data['high'][i - mins: i]))
result["low"].append(min(data['low'][i - mins: i]))
result["vol"].append(sum(data['vol'][i - mins: i]))
return result
def simulate(self, stock_codes:dict=None, analyzed_day=1000):
for stock_code in stock_codes:
for given_day in stock_codes[stock_code]:
LAST_DATA = self.stock2Vector.getLastData(stock_code, given_day)
# 1분봉
result = self.stock2Vector.getRealTime(stock_code, given_day, LAST_DATA)
# 5분봉
#result = self.makeTickData(result, mins=5)
# 30분봉
#result = self.makeTickData(result, mins=30)
data = self.buySellChecker.analyze(result)
data.drop(data.index[:len(data) - analyzed_day], inplace=True)
# 이동평균, RSI, MACD, 일목균형, 볼린저밴드 상/하단을 계산한다.
#data_5 = self.buySellChecker.analyze(result_5)
# 분석일 데이터만 활용한다 (이전 데이터는 제거)
#data_5.drop(data_5.index[:len(data_5) - analyzed_day], inplace=True)
#data_30 = self.buySellChecker.analyze(result_30)
# 분석일 데이터만 활용한다 (이전 데이터는 제거)
#data_30.drop(data_30.index[:len(data_30) - analyzed_day], inplace=True)
# 사야 할 시점과 팔아야 할 시점을 체크한다.
#bsLine = self.buySellChecker.checkTransaction(stock_code, data, data_5, data_30, isRealTime=False)
# 어제 데이터는 지운다.
#data = data.loc[pd.DatetimeIndex(data.index).day == int(given_day[6:])]
bsLine = self.buySellChecker.checkTransaction(stock_code, data, None, None, isRealTime=False)
# 그래프를 그린다.
self.draw(stock_code, given_day, data, bsLine)
return
if __name__ == "__main__":
PROJECT_HOME = os.getcwd()
RESOURCE_PATH = os.path.join(PROJECT_HOME, "resources")
simulation = Simulation(RESOURCE_PATH)
# to check bying
stock_codes = {
"252670": ['20231012'],
"122630": ['20231012'],
#"252670": ['20210901','20210902','20210903','20210906'],
#"252670": ['20210901', '20210902', '20210903', '20210906', '20210907', '20210908', '20210909', '20210910', '20210913', '20210914', '20210915', '20210916', '20210917', '20210923', '20210924', '20210927', '20210928', '20210929', '20210930', '20211001', '20211005'],
#"122630": ['20220901', '20220902', '20220905', '20220906']
#"122630": ['20210901', '20210902', '20210903', '20210906', '20210907', '20210908', '20210909', '20210910', '20210913', '20210914', '20210915', '20210916', '20210917', '20210923', '20210924', '20210927', '20210928', '20210929', '20210930', '20211001', '20211005'],
}
#simulation.simulate(stock_codes)
simulation.simulate(stock_codes)
print ("done...")