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dosangyoon
2022-08-16 21:23:40 +09:00
parent f9ffa363fa
commit 1eed2ea40e
6 changed files with 435 additions and 951 deletions
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19
Simulation.py
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@@ -52,9 +52,8 @@ class Simulation (HTS):
'avg6': 'float',
'avg9': 'float',
'avg12': 'float',
'avg24': 'float',
'avg30': 'float',
'avg60': 'float',
'avg27': 'float',
'avg54': 'float',
'fast_k': 'float',
'slow_k': 'float',
'slow_d': 'float',
@@ -70,7 +69,7 @@ class Simulation (HTS):
buy_line[i] = nan
buy_size.append(0)
else:
buy_colors.append("#D87D0F")
buy_colors.append("#B2028C")
buy_size.append(10 + (5 * buy_weight_line[i]))
sell_colors = []
@@ -90,8 +89,8 @@ class Simulation (HTS):
avg6 = go.Scatter(x=data['date'], y=data["avg6"], name="avg6", line_color='#089B5B')
avg9 = go.Scatter(x=data['date'], y=data["avg9"], name="avg9", line_color='#ff00ff')
avg12 = go.Scatter(x=data['date'], y=data["avg12"], name="avg12", line_color='#1469F4')
avg24 = go.Scatter(x=data['date'], y=data["avg24"], name="avg24", line_color='#000000')
avg30 = go.Scatter(x=data['date'], y=data["avg30"], name="avg30", line_color='#008000')
avg27 = go.Scatter(x=data['date'], y=data["avg27"], name="avg27", line_color='#000000')
avg54 = go.Scatter(x=data['date'], y=data["avg54"], name="avg54", line_color='#008000')
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')
volume_line = go.Scatter(x=data['date'], y=data["volume"], mode='lines', name='volume')
@@ -107,7 +106,7 @@ class Simulation (HTS):
rsi_line = go.Scatter(x=data['date'], y=data["rsi"], mode='lines', name='rsi')
rsis_line = go.Scatter(x=data['date'], y=data["rsis"], mode='lines', name='rsis')
candle_data = [candle_stick, upper, lower, avg3, avg6, avg9, avg12, avg24, avg30, buy_check, sell_check]
candle_data = [candle_stick, upper, lower, avg3, avg6, avg9, avg12, avg27, avg54, buy_check, sell_check]
volume_data = [volume_line]
macd_data = [macd_line, macd_s_line, macd_o_line]
stochastic_data = [slow_k_line, slow_d_line]
@@ -180,11 +179,11 @@ if __name__ == "__main__":
# to check bying
stock_codes = {
"252670": ['20220805', '20220808', '20220809', '20220810', '20220811'],
"122630": ['20220805', '20220808', '20220809', '20220810', '20220811'],
#"252670": ['20220801', '20220802', '20220803', '20220804', '20220805', '20220808', '20220809', '20220810', '20220811', '20220812'],
"122630": ['20220801', '20220802', '20220803', '20220804', '20220805', '20220808', '20220809', '20220810', '20220811', '20220812'],
}
method = "ml" # "rule", "ml", "answer"
method = "rule" # "rule", "ml", "answer"
for stock_code in stock_codes:
simulation = Simulation(RESOURCE_PATH)

1
VitTrainer.py
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@@ -191,6 +191,7 @@ class VitTrainer:
return
def getData(self, stock_code, sDate, eDate):
# Instance Normalization를 NumPy 및 PyTorch로 구현하는 방법! (https://ndb796.tistory.com/653)
data = self.stock2Vector.getTrainData(stock_code, sDate, eDate)
#X, Y = self.stock2Vector.getDataset2D(data)
X, Y = self.stock2Vector.getVectorData(data)

1212
hts/BuySellChecker.py
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resources/hts.db
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140
stock/util/LabelMaker.py
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@@ -8,14 +8,16 @@ from plotly import subplots
from hts.HTS import HTS
from hts.BuySellChecker import BuySellChecker
from stock.util.Stock2Vector import Stock2Vector
class LabelMaker (HTS):
buySellChecker = None
stock2Vector = None
def __init__(self, RESOURCE_PATH):
super().__init__(RESOURCE_PATH)
self.buySellChecker = BuySellChecker()
self.stock2Vector = Stock2Vector(RESOURCE_PATH)
return
def checkTransaction(self, data, duration=60):
@@ -91,8 +93,9 @@ class LabelMaker (HTS):
return bsLine, data
def draw_simple(self, stock_code, given_day, data, bsLine):
buy_line = bsLine['buy']
sell_line = bsLine['sell']
data = data.loc[pd.DatetimeIndex(data.index).day == int(given_day[6:])]
buy_line = bsLine['buy'][381:]
sell_line = bsLine['sell'][381:]
buy_colors = []
for i in range(len(buy_line)):
@@ -138,11 +141,11 @@ class LabelMaker (HTS):
'close': 'int',
'volume': 'int',
'avg3': 'float',
'avg5': 'float',
'avg10': 'float',
'avg20': 'float',
'avg30': 'float',
'avg60': 'float',
'avg6': 'float',
'avg9': 'float',
'avg12': 'float',
'avg27': 'float',
'avg54': 'float',
'fast_k': 'float',
'slow_k': 'float',
'slow_d': 'float',
@@ -150,13 +153,17 @@ class LabelMaker (HTS):
'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("#ff00ff")
buy_colors.append("#D87D0F")
buy_size.append(10 + (5 * 1))
sell_colors = []
for i in range(len(sell_line)):
if sell_line[i] < 0:
@@ -166,19 +173,20 @@ class LabelMaker (HTS):
sell_colors.append("#00ced1")
# 그래프를 설정한다.
buy_check = go.Scatter(x=data['date'], y=buy_line, mode='markers', name="buy", marker=dict(size=14, color=buy_colors, line_width=0))
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')
avg3 = go.Scatter(x=data['date'], y=data["avg3"], name="avg3", line_color='#1469F4')
avg5 = go.Scatter(x=data['date'], y=data["avg5"], name="avg5", line_color='#089B5B')
avg10 = go.Scatter(x=data['date'], y=data["avg10"], name="avg10", line_color='#ff00ff')
avg20 = go.Scatter(x=data['date'], y=data["avg20"], name="avg20", line_color='#8F8203')
avg30 = go.Scatter(x=data['date'], y=data["avg30"], name="avg30", line_color='#000000')
avg3 = go.Scatter(x=data['date'], y=data["avg3"], name="avg3", line_color='#8F8203')
avg6 = go.Scatter(x=data['date'], y=data["avg6"], name="avg6", line_color='#089B5B')
avg9 = go.Scatter(x=data['date'], y=data["avg9"], name="avg9", line_color='#ff00ff')
avg12 = go.Scatter(x=data['date'], y=data["avg12"], name="avg12", line_color='#1469F4')
avg27 = go.Scatter(x=data['date'], y=data["avg27"], name="avg27", line_color='#000000')
avg54 = go.Scatter(x=data['date'], y=data["avg54"], name="avg54", line_color='#008000')
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')
volume_line = go.Scatter(x=data['date'], y=data["volume"], mode='lines', name='volume')
#fast_k_line = go.Scatter(x=hts['date'], y=hts["fast_k"], mode='lines', name='fast_k')
# fast_k_line = go.Scatter(x=hts['date'], y=hts["fast_k"], mode='lines', name='fast_k')
macd_line = go.Scatter(x=data['date'], y=data["macd"], mode='lines', name='macd')
macd_s_line = go.Scatter(x=data['date'], y=data["macds"], mode='lines', name='macds')
@@ -190,12 +198,19 @@ class LabelMaker (HTS):
rsi_line = go.Scatter(x=data['date'], y=data["rsi"], mode='lines', name='rsi')
rsis_line = go.Scatter(x=data['date'], y=data["rsis"], mode='lines', name='rsis')
candle_data = [candle_stick, upper, lower, avg3, avg5, avg10, avg20, avg30, buy_check, sell_check]
candle_data = [candle_stick, upper, lower, avg3, avg6, avg9, avg12, avg27, avg54, buy_check, sell_check]
volume_data = [volume_line]
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=5, cols=1, subplot_titles=('캔들', "거래량", "MACD", "스토캐스틱", "RSI"))
for trace in candle_data:
fig.append_trace(trace, 1, 1)
@@ -207,15 +222,15 @@ class LabelMaker (HTS):
fig.append_trace(trace, 4, 1)
for trace in rsi_data:
fig.append_trace(trace, 5, 1)
#fig.update_xaxes(nticks=5)
#fig.update_layout(height=1800, title=stock_code + "_" + given_day, xaxis_rangeslider_visible=False)
# 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=3000, title=stock_code + "_" + given_day + "_" + str(buy_count)+","+str(sell_count))
fig.update_layout(height=5000, title=stock_code + "_" + given_day + "_" + str(buy_count) + "," + str(sell_count))
fig.show()
return
@@ -251,30 +266,38 @@ class LabelMaker (HTS):
#self.draw_detail(stock_code, ymd, data, bsLine)
return bsLine, data
def showLabels(self, stock_code, ymd):
result = {"time": [], "open": [], "close": [], "high": [], "low": [], "vol": [], "label": []}
self.getDBData(stock_code, ymd, result)
def showLabels(self, stock_code, ymd, outFp=None):
LAST_DATA = self.stock2Vector.getLastData(stock_code, ymd)
result = self.stock2Vector.getRealTime(stock_code, ymd, LAST_DATA)
result["date"] = result["time"]
data = pd.DataFrame(result)
df_final_time = pd.DatetimeIndex(result["date"])
data.index = df_final_time
# 이동평균, RSI, MACD, 일목균형, 볼린저밴드 상/하단을 계산한다.
data = self.buySellChecker.analyze(result)
# 4일치 중에서 앞에 3일은 제거한다.
date = data['date'].dt.date.unique().tolist()
data = data[data['date'].dt.date != date[0]]
data = data[data['date'].dt.date != date[1]]
data = data[data['date'].dt.date != date[2]]
bsLine = {}
size = len(result["close"])
lows = data["low"].tolist()
closes = data["close"].tolist()
labels = data["label"].tolist()
size = len(lows)
# Type=False, 시뮬레이션 적용
bsLine['buy'] = [-1 for i in range(size)]
for i in range(size):
if result["label"][i] == 2:
bsLine['buy'][i] = result["low"][i]
bsLine['sell'] = [-1 for i in range(size)]
for i in range(size):
if result["label"][i] == 1:
bsLine['sell'][i] = result["close"][i]
self.draw_simple(stock_code, ymd, data, bsLine)
self.buySellChecker.write(outFp, data, i)
outFp.flush()
if labels[i] == 1:
bsLine['sell'][i] = closes[i]
if labels[i] == 2:
bsLine['buy'][i] = lows[i]
#self.draw_simple(stock_code, ymd, data, bsLine)
self.draw_detail(stock_code, ymd, data, bsLine)
return
def getDate(self, stock_code):
@@ -288,11 +311,11 @@ if __name__ == "__main__":
labelMaker = LabelMaker(RESOURCE_PATH)
UPDATE_MODE = True
UPDATE_MODE = False
if UPDATE_MODE:
stock_code = "122630"
ymd = '20220812'
"""
labelMaker.clearLabel(db_filename, stock_code, ymd)
labelMaker.makeLabel(db_filename, stock_code, ymd, "0910", 2)
labelMaker.makeLabel(db_filename, stock_code, ymd, "0911", 2)
@@ -300,45 +323,24 @@ if __name__ == "__main__":
labelMaker.makeLabel(db_filename, stock_code, ymd, "0915", 1)
labelMaker.makeLabel(db_filename, stock_code, ymd, "0916", 1)
labelMaker.makeLabel(db_filename, stock_code, ymd, "0948", 2)
labelMaker.makeLabel(db_filename, stock_code, ymd, "0949", 2)
labelMaker.makeLabel(db_filename, stock_code, ymd, "0950", 2)
labelMaker.makeLabel(db_filename, stock_code, ymd, "0951", 2)
labelMaker.makeLabel(db_filename, stock_code, ymd, "0952", 2)
labelMaker.makeLabel(db_filename, stock_code, ymd, "0953", 2)
labelMaker.makeLabel(db_filename, stock_code, ymd, "1023", 1)
labelMaker.makeLabel(db_filename, stock_code, ymd, "1024", 1)
labelMaker.makeLabel(db_filename, stock_code, ymd, "1025", 1)
labelMaker.makeLabel(db_filename, stock_code, ymd, "1026", 1)
labelMaker.makeLabel(db_filename, stock_code, ymd, "1056", 2)
labelMaker.makeLabel(db_filename, stock_code, ymd, "1057", 2)
labelMaker.makeLabel(db_filename, stock_code, ymd, "1058", 2)
labelMaker.makeLabel(db_filename, stock_code, ymd, "1059", 2)
labelMaker.makeLabel(db_filename, stock_code, ymd, "1116", 1)
labelMaker.makeLabel(db_filename, stock_code, ymd, "1117", 1)
labelMaker.makeLabel(db_filename, stock_code, ymd, "1118", 1)
labelMaker.makeLabel(db_filename, stock_code, ymd, "1207", 2)
labelMaker.makeLabel(db_filename, stock_code, ymd, "1208", 2)
labelMaker.makeLabel(db_filename, stock_code, ymd, "1209", 2)
labelMaker.makeLabel(db_filename, stock_code, ymd, "1210", 2)
labelMaker.makeLabel(db_filename, stock_code, ymd, "1242", 1)
labelMaker.makeLabel(db_filename, stock_code, ymd, "1243", 1)
labelMaker.makeLabel(db_filename, stock_code, ymd, "1244", 1)
labelMaker.makeLabel(db_filename, stock_code, ymd, "1245", 1)
"""
labelMaker.showLabels(stock_code, ymd)
else:
stock_codes = {
"252670": ['20220801', '20220802', '20220803', '20220804', '20220805', '20220808', '20220809', '20220810', '20220811'],
"122630": ['20220801', '20220802', '20220803', '20220804', '20220805', '20220808', '20220809', '20220810', '20220811'],
"252670": ['20220801', '20220802', '20220803', '20220804', '20220805', '20220808', '20220809', '20220810', '20220811', '20220812'],
"122630": ['20220801', '20220802', '20220803', '20220804', '20220805', '20220808', '20220809', '20220810', '20220811', '20220812'],
}
view = True
outFp = None
for stock_code in stock_codes:
if view:
if outFp is None:
outFp = open("check_"+stock_code+".txt", "w")
for ymd in stock_codes[stock_code]:
if view:
labelMaker.showLabels(stock_code, ymd)
labelMaker.showLabels(stock_code, ymd, outFp)
else:
stock_codes = ["252670", "122630"]
for stock_code in stock_codes:
@@ -346,4 +348,8 @@ if __name__ == "__main__":
for ymd in ymds:
logging.info(stock_code, ymd)
bsLine, data = labelMaker.makeCandidate(stock_code, ymd)
labelMaker.updateLabel(db_filename, stock_code, bsLine, data, ymd)
labelMaker.updateLabel(db_filename, stock_code, bsLine, data, ymd)
if view:
if outFp is not None:
outFp.close()
outFp = None

14
stock/util/Stock2Vector.py
View File

@@ -140,7 +140,7 @@ class Stock2Vector(HTS):
result = self.getRealTime(stock_code, today, LAST_DATA)
# 분석을 통해서 볼린저밴드 상/하단을 계산한다.
df = self.buySellChecker.getVectorFeature(result)
df = self.buySellChecker.analyze(result)
minmax_df1 = (df - df.min()) / (df.max() - df.min())
minmax_df2 = minmax_df1.drop(["date"], axis="columns")
minmax_df = minmax_df2.join(df['date'])
@@ -198,7 +198,7 @@ class Stock2Vector(HTS):
def preprocessData(self, result):
# 분석을 통해서 볼린저밴드 상/하단을 계산한다.
df = self.buySellChecker.getVectorFeature(result)
df = self.buySellChecker.analyze(result)
minmax_df1 = (df - df.min()) / (df.max() - df.min())
minmax_df2 = minmax_df1.drop(["date"], axis="columns")
minmax_df = minmax_df2.join(df['date'])
@@ -236,7 +236,7 @@ class Stock2Vector(HTS):
result["label"].append(int(label))
# 분석을 통해서 볼린저밴드 상/하단을 계산한다.
df = self.buySellChecker.getVectorFeature(result)
df = self.buySellChecker.analyze(result)
minmax_df1 = (df - df.min()) / (df.max() - df.min())
minmax_df2 = minmax_df1.drop(["date"], axis="columns")
minmax_df = minmax_df2.join(df['date'])
@@ -289,7 +289,7 @@ class Stock2Vector(HTS):
return self.getVectorData_2(data, VECTOR_SIZE)
def getVectorData_1(self, data, VECTOR_SIZE):
df = self.buySellChecker.getVectorFeature(data)
df = self.buySellChecker.analyze(data)
# avg10, 볼린져밴드 상단과 하단의 차이, rsi, avg3만 이용한다.
# channel1: avg10, channel2: diff_upper_lower, channel3: abs_avg_2, channel4: abs_avg_3
@@ -330,14 +330,14 @@ class Stock2Vector(HTS):
return batch_X, batch_Y
def getVectorData_2(self, data, VECTOR_SIZE = 32):
df = self.buySellChecker.getVectorFeature(data)
df = self.buySellChecker.analyze(data)
# avg10, 볼린져밴드 상단과 하단의 차이, rsi, avg3만 이용한다.
# channel1: avg10, channel2: diff_upper_lower, channel3: abs_avg_2, channel4: abs_avg_3
avg3 = df['avg3'].tolist()
avg6 = df['avg6'].tolist()
avg9 = df['avg9'].tolist()
avg12 = df['avg9'].tolist()
avg24 = df['avg9'].tolist()
diff_upper_lower = df['diff_upper_lower'].tolist()
rsi = df['rsi'].tolist()