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dsyoon
2022-08-03 22:40:23 +09:00
parent 41e23641a8
commit ce26ef5623
5 changed files with 139 additions and 32 deletions
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24
Simulation.py
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@@ -6,6 +6,7 @@ import os
from hts.HTS import HTS
from stock.util.Stock2Vector import Stock2Vector
from stock.util.StockPredictor import StockPredictor
from hts.BuySellChecker import BuySellChecker
class Simulation (HTS):
@@ -16,6 +17,7 @@ class Simulation (HTS):
super().__init__(RESOURCE_PATH)
self.stock2Vector = Stock2Vector(RESOURCE_PATH)
self.stockPredictor = StockPredictor()
self.buySellChecker = BuySellChecker()
self.RESOURCE_PATH = RESOURCE_PATH
#self.connect()
@@ -126,23 +128,23 @@ class Simulation (HTS):
return
def analyzeAutoMode(self, data):
return data, None
def simulate(self, stock_code, today, type="rule"):
LAST_DATA = self.stock2Vector.getLastData(stock_code, today)
def simulate(self, stock_code, today, method="rule"):
if method == "ml":
LAST_DATA = self.stock2Vector.getLastData(stock_code, today, n=10)
result = self.stock2Vector.getRealTime(stock_code, today, LAST_DATA)
df, minmax_df = self.stock2Vector.preprocessData(result)
bsLine, data = self.stockPredictor.predict(df, minmax_df, isRealTime=False)
else:
LAST_DATA = self.stock2Vector.getLastData(stock_code, today)
result = self.stock2Vector.getRealTime(stock_code, today, LAST_DATA)
if type == "rule":
# 이동평균, RSI, MACD, 일목균형, 볼린저밴드 상/하단을 계산한다.
data = self.buySellChecker.analyze(result)
# 사야 할 시점과 팔아야 할 시점을 체크한다.
bsLine, data = self.buySellChecker.checkTransaction(data, stock_code, isRealTime=False)
elif type == "auto":
data, bsLine = self.analyzeAutoMode(result)
else:
data, bsLine = None, None
if data is not None:
# 그래프를 그린다.
@@ -159,13 +161,13 @@ if __name__ == "__main__":
stock_codes = {
# 252670
# 122630
"122630": ['20220729'],
"122630": ['20220803'],
}
for stock_code in stock_codes:
simulation = Simulation(RESOURCE_PATH)
for given_day in stock_codes[stock_code]:
simulation.simulate(stock_code, given_day)
simulation.simulate(stock_code, given_day, method='ml')
print ("done...")

36
StockTrainer.py
View File

@@ -1,7 +1,6 @@
import os
import keras
import numpy as np
from numpy import zeros, newaxis
import tensorflow as tf
from stock.util.Stock2Vector import Stock2Vector
from classification_models.keras import Classifiers
@@ -18,16 +17,17 @@ class StockTrainer:
def getDataset(self, stock_code):
VECTOR_SIZE = 299
df, minmax_df = self.stock2Vector.makeTrainData(stock_code)
result = self.stock2Vector.getTrainData(stock_code)
df, minmax_df = self.stock2Vector.preprocessData(result)
TOTAL_X, TOTAL_Y = [], []
for key in df:
for key in minmax_df:
if key == "date":
continue
elif key == "label":
TOTAL_Y.append(df[key].tolist())
TOTAL_Y.append(minmax_df[key].tolist())
else:
TOTAL_X.append(df[key].tolist())
TOTAL_X.append(minmax_df[key].tolist())
SIZE_WIDTH = len(TOTAL_X[0])
SIZE_HEIGHT = len(TOTAL_X)
@@ -44,38 +44,37 @@ class StockTrainer:
Y.append([0, 1, 0])
else:
Y.append([0, 0, 1])
if i >= VECTOR_SIZE+10:
break
X = np.asarray(X)
Y = np.asarray(Y)
return X, Y
def train(self, stock_code):
ResNet18, preprocess_input = Classifiers.get('inceptionresnetv2')
X, Y = self.getDataset(stock_code)
# build model
n_classes = 3
Inceptionresnetv2, preprocess_input = Classifiers.get('inceptionresnetv2')
X = preprocess_input(X)
n_classes = 3
# build model
base_model = ResNet18(input_shape=(299, 299, 3), include_top=False)
# train
checkpoint_filename = os.path.join(self.RESOURCE_PATH, "model", "stock.ckpt")
base_model = Inceptionresnetv2(input_shape=(299, 299, 3), include_top=False)
x = tf.keras.layers.GlobalAveragePooling2D()(base_model.output)
output = tf.keras.layers.Dense(n_classes, activation='softmax')(x)
model = keras.models.Model(inputs=[base_model.input], outputs=[output])
# train
model.compile(optimizer='SGD', loss='categorical_crossentropy', metrics=['accuracy'])
checkpoint_filename = os.path.join(self.RESOURCE_PATH, "model", "stock.ckpt")
chekpoint = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_filename, save_weights_only=True, verbose=1)
earlystop = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=3, mode="auto")
earlystop = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=5, mode="auto")
if os.path.isfile(checkpoint_filename):
model.load_weights(checkpoint_filename)
model.fit(x=X,
y=Y,
epochs=10,
batch_size=10000,
epochs=10000,
callbacks=[chekpoint, earlystop])
return
@@ -95,5 +94,4 @@ if __name__ == "__main__":
stockTrainer = StockTrainer(RESOURCE_PATH)
stockTrainer.train(stock_code)
print ("done...")

3
hts/HTS.py
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@@ -634,7 +634,7 @@ class HTS:
"label": []}
days = []
for i in range(1, 10):
for i in range(1, 100):
last_day = (datetime.strptime(today, '%Y%m%d') - timedelta(i)).strftime('%Y%m%d')
isValid = self.isValidYMD(stock_code, last_day)
if isValid:
@@ -655,6 +655,7 @@ class HTS:
else:
result = {"check": set(), "time": [], "open": [], "close": [], "high": [], "low": [], "vol": [], "label": []}
#### real time에서 아직 저장된 것이 없기 때문에 result는 아무것도 채워지지 않는다.
self.getDBData(stock_code, today, result)

47
stock/util/Stock2Vector.py
View File

@@ -153,6 +153,53 @@ class Stock2Vector(HTS):
return df, minmax_df
def getTrainData(self, stock_code):
tableName = 'hts'
conn = sqlite3.connect(os.path.join(self.RESOURCE_PATH, "hts.db"))
cursor = conn.cursor()
cursor.execute(
'SELECT ymd, hms, open, high, low, close, volume, label FROM ' + tableName + ' WHERE CODE=? and (ymd >= ? and ymd <= ?) order by ymd desc, hms ',
(stock_code, "20220721", "20220731"))
db_result = cursor.fetchall()
temp_result = []
for rows in db_result:
temp_result.append(
[rows[0], rows[1], rows[2], rows[3], rows[4], rows[5], rows[6], 0 if rows[7] is None else rows[7]])
temp_result.sort(key=lambda x: (x[0], x[1]))
result = {"check": set(), "time": [], "open": [], "close": [], "high": [], "low": [], "vol": [], "label": []}
for rows in temp_result:
ymd = rows[0] # hts.날짜
hms = rows[1] # hts.시간
open = rows[2] # hts.시가
high = rows[3] # hts.고가
low = rows[4] # hts.저가
close = rows[5] # hts.종가
vol = rows[6] # hts.거래량
label = 0 if rows[7] is None else rows[7] # hts.매매구분
temp = datetime.strptime(str(ymd) + " " + str(hms).zfill(4) + "00", '%Y%m%d %H%M%S')
result["time"].append(temp)
result["open"].append(int(open))
result["close"].append(int(close))
result["high"].append(int(high))
result["low"].append(int(low))
result["vol"].append(int(vol))
result["label"].append(int(label))
return result
def preprocessData(self, result):
# 분석을 통해서 볼린저밴드 상/하단을 계산한다.
df = self.buySellChecker.getVectorFeature(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'])
minmax_df = minmax_df.fillna(0)
return df, minmax_df
def makeTrainData(self, stock_code):
result = {"check": set(), "time": [], "open": [], "close": [], "high": [], "low": [], "vol": [], "label": []}

59
stock/util/StockPredictor.py Normal file
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@@ -0,0 +1,59 @@
import os
import keras
import numpy as np
from keras.applications.imagenet_utils import decode_predictions
from classification_models.keras import Classifiers
class StockPredictor:
RESOURCE_PATH = None
stock2Vector = None
def __init__(self):
return
def getDataset(self, df):
VECTOR_SIZE = 299
TOTAL_X, TOTAL_Y = [], []
for key in df:
if key == "date":
continue
elif key == "label":
TOTAL_Y.append(df[key].tolist())
else:
TOTAL_X.append(df[key].tolist())
SIZE_WIDTH = len(TOTAL_X[0])
SIZE_HEIGHT = len(TOTAL_X)
X = []
for i in range(VECTOR_SIZE, SIZE_WIDTH):
temp_X, temp_Y = np.zeros((VECTOR_SIZE, VECTOR_SIZE)), np.zeros(0)
for j in range(SIZE_HEIGHT):
temp_X[j][0:VECTOR_SIZE] = TOTAL_X[j][i - VECTOR_SIZE:i]
temp_X = np.stack([temp_X, temp_X, temp_X], axis=-1)
X.append(temp_X)
X = np.asarray(X[len(X)-1])
return X
def predict(self, df, minmax_df, isRealTime=False):
X = self.getDataset(df)
# build model
n_classes = 3
Inceptionresnetv2, preprocess_input = Classifiers.get('inceptionresnetv2')
X = preprocess_input(X)
base_model = Inceptionresnetv2(input_shape=(299, 299, 3), include_top=False)
model = keras.models.Model(inputs=[base_model.input])
checkpoint_filename = os.path.join(self.RESOURCE_PATH, "model", "stock.ckpt")
model.load_weights(checkpoint_filename)
y = model.predict(X)
# result
print(decode_predictions(y))
return