class SimpleModel:
def __init__(self):
self.data = dict()
self.frame_len = 30
self.predict_dist = 5
self.scaler = dict()
def load_all_data(self, begin_date, end_date):
con = sqlite3.connect('../data/stock.db')
code_list = con.execute("SELECT name FROM sqlite_master WHERE type='table'").fetchall()
X_data_list, Y_data_list, DATA_list = [0]*10, [0]*10, [0]*10
idx = 0
split = int(len(code_list) / 9)
bar = ProgressBar(len(code_list), max_width=80)
for code in code_list:
data = self.load_data(code[0], begin_date, end_date)
data = data.dropna()
X, Y = self.make_x_y(data, code[0])
if len(X) <= 1: continue
code_array = [code[0]] * len(X)
assert len(X) == len(data.loc[29:len(data)-6, '일자'])
if idx%split == 0:
X_data_list[int(idx/split)] = list(X)
Y_data_list[int(idx/split)] = list(Y)
DATA_list[int(idx/split)] = np.array([data.loc[29:len(data)-6, '일자'].values.tolist(), code_array, data.loc[29:len(data)-6, '현재가'], data.loc[34:len(data), '현재가']]).T.tolist()
else:
X_data_list[int(idx/split)].extend(X)
Y_data_list[int(idx/split)].extend(Y)
DATA_list[int(idx/split)].extend(np.array([data.loc[29:len(data)-6, '일자'].values.tolist(), code_array, data.loc[29:len(data)-6, '현재가'], data.loc[34:len(data), '현재가']]).T.tolist())
bar.numerator += 1
print("%s | %d" % (bar, len(X_data_list[int(idx/split)])), end='\r')
sys.stdout.flush()
idx += 1
print("%s" % bar)
print("Merge splited data")
bar = ProgressBar(10, max_width=80)
for i in range(10):
if type(X_data_list[i]) == type(1):
continue
if i == 0:
X_data = X_data_list[i]
Y_data = Y_data_list[i]
DATA = DATA_list[i]
else:
X_data.extend(X_data_list[i])
Y_data.extend(Y_data_list[i])
DATA.extend(DATA_list[i])
bar.numerator = i+1
print("%s | %d" % (bar, len(DATA)), end='\r')
sys.stdout.flush()
print("%s | %d" % (bar, len(DATA)))
return np.array(X_data), np.array(Y_data), np.array(DATA)
def load_data(self, code, begin_date, end_date):
con = sqlite3.connect('../data/stock.db')
df = pd.read_sql("SELECT * from '%s'" % code, con, index_col='일자').sort_index()
data = df.loc[df.index > str(begin_date)]
data = data.loc[data.index < str(end_date)]
data = data.reset_index()
return data
def make_x_y(self, data, code):
data_x = []
data_y = []
for col in data.columns:
try:
data.loc[:, col] = data.loc[:, col].str.replace('--', '-')
data.loc[:, col] = data.loc[:, col].str.replace('+', '')
except AttributeError as e:
pass
print(e)
data.loc[:, 'month'] = data.loc[:, '일자'].str[4:6]
data = data.drop(['일자', '체결강도'], axis=1)
# normalization
data = np.array(data)
if len(data) <= 0 :
return np.array([]), np.array([])
if code not in self.scaler:
self.scaler[code] = StandardScaler()
data = self.scaler[code].fit_transform(data)
elif code not in self.scaler:
return np.array([]), np.array([])
else:
data = self.scaler[code].transform(data)
for i in range(self.frame_len, len(data)-self.predict_dist+1):
data_x.extend(np.array(data[i-self.frame_len:i, :]))
data_y.append(data[i+self.predict_dist-1][0])
np_x = np.array(data_x).reshape(-1, 23*30)
np_y = np.array(data_y)
return np_x, np_y
def train_model(self, X_train, Y_train):
print("training model %d_%d.pkl" % (self.frame_len, self.predict_dist))
model_name = "../model/simple_reg_model/%d_%d.pkl" % (self.frame_len, self.predict_dist)
self.estimator = RandomForestRegressor(random_state=0, n_estimators=100, n_jobs=-1)
self.estimator.fit(X_train, Y_train)
print("finish training model")
joblib.dump(self.estimator, model_name)
def set_config(self):
#Tensorflow GPU optimization
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
K.set_session(sess)
def train_model_keras(self, X_train, Y_train, date):
print("training model %d_%d.h5" % (self.frame_len, self.predict_dist))
model_name = "../model/reg_keras/%d_%d_%s.h5" % (self.frame_len, self.predict_dist, date)
self.estimator = KerasRegressor(build_fn=baseline_model, nb_epoch=200, batch_size=64, verbose=1)
self.estimator.fit(X_train, Y_train)
print("finish training model")
# saving model
json_model = self.estimator.model.to_json()
open(model_name.replace('h5', 'json'), 'w').write(json_model)
self.estimator.model.save_weights(model_name, overwrite=True)
def evaluate_model(self, X_test, Y_test, orig_data, s_date):
print("Evaluate model %d_%d.pkl" % (self.frame_len, self.predict_dist))
if MODEL_TYPE == 'random_forest':
model_name = "../model/simple_reg_model/%d_%d.pkl" % (self.frame_len, self.predict_dist)
self.estimator = joblib.load(model_name)
elif MODEL_TYPE == 'keras':
model_name = "../model/reg_keras/%d_%d_%s.h5" % (self.frame_len, self.predict_dist, s_date)
self.estimator = model_from_json(open(model_name.replace('h5', 'json')).read())
self.estimator.load_weights(model_name)
pred = self.estimator.predict(X_test)
res = 0
score = 0
assert(len(pred) == len(Y_test))
pred = np.array(pred).reshape(-1)
Y_test = np.array(Y_test).reshape(-1)
for i in range(len(pred)):
score += (float(pred[i]) - float(Y_test[i]))*(float(pred[i]) - float(Y_test[i]))
score = np.sqrt(score/len(pred))
print("score: %f" % score)
for idx in range(len(pred)):
buy_price = int(orig_data[idx][2])
future_price = int(orig_data[idx][3])
date = int(orig_data[idx][0])
pred_transform = self.scaler[orig_data[idx][1]].inverse_transform([pred[idx]] + [0]*22)[0]
cur_transform = self.scaler[orig_data[idx][1]].inverse_transform([X_test[idx][23*29]] + [0]*22)[0]
if pred_transform > buy_price * 1.01:
res += (future_price - buy_price*1.005)*(100000/buy_price+1)
print("[%s] buy: %6d, sell: %6d, earn: %6d" % (str(date), buy_price, future_price, (future_price - buy_price*1.005)*(100000/buy_price)))
print("result: %d" % res)
def load_current_data(self):
con = sqlite3.connect('../data/stock.db')
code_list = con.execute("SELECT name FROM sqlite_master WHERE type='table'").fetchall()
X_test = []
DATA = []
code_list = list(map(lambda x: x[0], code_list))
first = True
bar = ProgressBar(len(code_list), max_width=80)
for code in code_list:
bar.numerator += 1
print("%s | %d" % (bar, len(X_test)), end='\r')
sys.stdout.flush()
df = pd.read_sql("SELECT * from '%s'" % code, con, index_col='일자').sort_index()
data = df.iloc[-30:,:]
data = data.reset_index()
for col in data.columns:
try:
data.loc[:, col] = data.loc[:, col].str.replace('--', '-')
data.loc[:, col] = data.loc[:, col].str.replace('+', '')
except AttributeError as e:
pass
data.loc[:, 'month'] = data.loc[:, '일자'].str[4:6]
data = data.drop(['일자', '체결강도'], axis=1)
if len(data) < 30:
code_list.remove(code)
continue
DATA.append(int(data.loc[len(data)-1, '현재가']))
try:
data = self.scaler[code].transform(np.array(data))
except KeyError:
code_list.remove(code)
continue
X_test.extend(np.array(data))
X_test = np.array(X_test).reshape(-1, 23*30)
return X_test, code_list, DATA
def make_buy_list(self, X_test, code_list, orig_data, s_date):
BUY_UNIT = 10000
print("make buy_list")
if MODEL_TYPE == 'random_forest':
model_name = "../model/simple_reg_model/%d_%d.pkl" % (self.frame_len, self.predict_dist)
self.estimator = joblib.load(model_name)
elif MODEL_TYPE == 'keras':
model_name = "../model/reg_keras/%d_%d_%s.h5" % (self.frame_len, self.predict_dist, s_date)
self.estimator = model_from_json(open(model_name.replace('h5', 'json')).read())
self.estimator.load_weights(model_name)
pred = self.estimator.predict(X_test)
res = 0
score = 0
pred = np.array(pred).reshape(-1)
# load code list from account
set_account = set([])
with open('../data/stocks_in_account.txt') as f_stocks:
for line in f_stocks.readlines():
data = line.split(',')
set_account.add(data[6].replace('A', ''))
buy_item = ["매수", "", "시장가", 0, 0, "매수전"] # 매수/매도, code, 시장가/현재가, qty, price, "주문전/주문완료"
with open("../data/buy_list.txt", "wt") as f_buy:
for idx in range(len(pred)):
real_buy_price = int(orig_data[idx])
buy_price = float(X_test[idx][23*29])
try:
pred_transform = self.scaler[code_list[idx]].inverse_transform([pred[idx]] + [0]*22)[0]
except KeyError:
continue
print("[BUY PREDICT] code: %s, cur: %5d, predict: %5d" % (code_list[idx], real_buy_price, pred_transform))
if pred_transform > real_buy_price * 3 and code_list[idx] not in set_account:
print("add to buy_list %s" % code_list[idx])
buy_item[1] = code_list[idx]
buy_item[3] = int(BUY_UNIT / real_buy_price) + 1
for item in buy_item:
f_buy.write("%s;"%str(item))
f_buy.write('\n')
def load_data_in_account(self):
# load code list from account
DATA = []
with open('../data/stocks_in_account.txt') as f_stocks:
for line in f_stocks.readlines():
data = line.split(',')
DATA.append([data[6].replace('A', ''), data[1], data[0]])
# load data in DATA
con = sqlite3.connect('../data/stock.db')
X_test = []
idx_rm = []
first = True
bar = ProgressBar(len(DATA), max_width=80)
for idx, code in enumerate(DATA):
bar.numerator += 1
print("%s | %d" % (bar, len(X_test)), end='\r')
sys.stdout.flush()
try:
df = pd.read_sql("SELECT * from '%s'" % code[0], con, index_col='일자').sort_index()
except pd.io.sql.DatabaseError as e:
print(e)
idx_rm.append(idx)
continue
data = df.iloc[-30:,:]
data = data.reset_index()
for col in data.columns:
try:
data.loc[:, col] = data.loc[:, col].str.replace('--', '-')
data.loc[:, col] = data.loc[:, col].str.replace('+', '')
except AttributeError as e:
pass
print(e)
data.loc[:, 'month'] = data.loc[:, '일자'].str[4:6]
DATA[idx].append(int(data.loc[len(data)-1, '현재가']))
data = data.drop(['일자', '체결강도'], axis=1)
if len(data) < 30:
idx_rm.append(idx)
continue
try:
data = self.scaler[code[0]].transform(np.array(data))
except KeyError:
idx_rm.append(idx)
continue
X_test.extend(np.array(data))
for i in idx_rm[-1:0:-1]:
del DATA[i]
X_test = np.array(X_test).reshape(-1, 23*30)
return X_test, DATA
def make_sell_list(self, X_test, DATA, s_date):
print("make sell_list")
if MODEL_TYPE == 'random_forest':
model_name = "../model/simple_reg_model/%d_%d.pkl" % (self.frame_len, self.predict_dist)
self.estimator = joblib.load(model_name)
elif MODEL_TYPE == 'keras':
model_name = "../model/reg_keras/%d_%d_%s.h5" % (self.frame_len, self.predict_dist, s_date)
self.estimator = model_from_json(open(model_name.replace('h5', 'json')).read())
self.estimator.load_weights(model_name)
pred = self.estimator.predict(X_test)
res = 0
score = 0
pred = np.array(pred).reshape(-1)
sell_item = ["매도", "", "시장가", 0, 0, "매도전"] # 매수/매도, code, 시장가/현재가, qty, price, "주문전/주문완료"
with open("../data/sell_list.txt", "wt") as f_sell:
for idx in range(len(pred)):
current_price = float(X_test[idx][23*29])
current_real_price = int(DATA[idx][3])
name = DATA[idx][2]
print("[SELL PREDICT] name: %s, code: %s, cur: %f(%d), predict: %f" % (name, DATA[idx][0], current_price, current_real_price, pred[idx]))
if pred[idx] < current_price:
print("add to sell_list %s" % name)
sell_item[1] = DATA[idx][0]
sell_item[3] = DATA[idx][1]
for item in sell_item:
f_sell.write("%s;"%str(item))
f_sell.write('\n')
def save_scaler(self, s_date):
model_name = "../model/scaler_%s.pkl" % s_date
joblib.dump(self.scaler, model_name)
def load_scaler(self, s_date):
model_name = "../model/scaler_%s.pkl" % s_date
self.scaler = joblib.load(model_name)
class SimpleModel:
def __init__(self):
self.data = dict()
self.frame_len = 30
self.predict_dist = 5
def load_all_data(self, begin_date, end_date):
con = sqlite3.connect('stock.db')
code_list = con.execute("SELECT name FROM sqlite_master WHERE type='table'").fetchall()
X_data_list, Y_data_list, DATA_list = [0]*10, [0]*10, [0]*10
idx = 0
split = int(len(code_list) / 9)
for code in code_list:
data = self.load_data(code[0], begin_date, end_date)
data = data.dropna()
X, Y = self.make_x_y(data)
print(idx, split, len(code_list))
if idx%split == 0:
X_data_list[int(idx/split)] = list(X)
Y_data_list[int(idx/split)] = list(Y)
DATA_list[int(idx/split)] = data.values.tolist()
else:
X_data_list[int(idx/split)].extend(X)
Y_data_list[int(idx/split)].extend(Y)
DATA_list[int(idx/split)].extend(data.values.tolist())
print(idx, np.shape(X_data_list[int(idx/split)]), np.shape(Y_data_list[int(idx/split)]))
idx += 1
for i in range(10):
if i == 0:
X_data = X_data_list[i]
Y_data = Y_data_list[i]
DATA = DATA_list[i]
else:
X_data.extend(X_data_list[i])
Y_data.extend(Y_data_list[i])
DATA.extend(DATA_list[i])
return np.array(X_data), np.array(Y_data), np.array(DATA)
def load_data(self, code, begin_date, end_date):
con = sqlite3.connect('stock.db')
df = pd.read_sql("SELECT * from '%s'" % code, con, index_col='일자').sort_index()
data = df.loc[df.index > str(begin_date)]
data = data.loc[data.index < str(end_date)]
data = data.reset_index()
return data
def make_x_y(self, data):
data_x = []
data_y = []
for col in data.columns:
try:
data.loc[:, col] = data.loc[:, col].str.replace('--', '-')
data.loc[:, col] = data.loc[:, col].str.replace('+', '')
except AttributeError as e:
pass
print(e)
data.loc[:, 'month'] = data.loc[:, '일자'].str[4:6]
data = data.drop(['일자', '체결강도'], axis=1)
for i in range(self.frame_len, len(data)-self.predict_dist):
"""
for c in data.columns:
try:
test = float(data.loc[i,c])
except:
print("can not convert %s" % data.loc[i,c])
"""
#print("make data from %s to %s, %s" % (data['일자'][i-frame_len], data['일자'][i-1], data['일자'][i-frame_len-predict_dist]))
#print(np.array(data.iloc[i-frame_len:i, :]))
data_x.extend(np.array(data.iloc[i-self.frame_len:i, :]))
data_y.extend(data.loc[i+self.predict_dist, ['현재가']])
np_x = np.array(data_x).reshape(-1, 23*30)
np_y = np.array(data_y)
return np_x, np_y
def train_model(self, X_train, Y_train):
print("training model %d_%d.pkl" % (self.frame_len, self.predict_dist))
model_name = "simple_reg_model/%d_%d.pkl" % (self.frame_len, self.predict_dist)
self.estimator = RandomForestRegressor(random_state=0, n_estimators=100, n_jobs=-1)
self.estimator.fit(X_train, Y_train)
print("finish training model")
joblib.dump(self.estimator, model_name)
def train_model_keras(self, X_train, Y_train):
#Tensorflow GPU optimization
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
K.set_session(sess)
print("training model %d_%d.pkl" % (self.frame_len, self.predict_dist))
model_name = "reg_keras/%d_%d.pkl" % (self.frame_len, self.predict_dist)
self.estimators = KerasRegressor(build_fn=baseline_model, nb_epoch=20, batch_size=64, verbose=0)
self.estimators.fit(X_train, Y_train)
self.estimator.fit(X_train, Y_train)
print("finish training model")
# saving model
json_model = estimators.model.to_json()
open(model_name.replace('h5', 'json'), 'w').write(json_model)
self.estimators.model.save_weights(model_name, overwrite=True)
def evaluate_model(self, X_test, Y_test, orig_data):
print("Evaluate model %d_%d.pkl" % (self.frame_len, self.predict_dist))
if MODEL_TYPE == 'random_forest':
model_name = "simple_reg_model/%d_%d.pkl" % (self.frame_len, self.predict_dist)
self.estimator = joblib.load(model_name)
elif MODEL_TYPE == 'keras':
model_name = "reg_keras/%d_%d.h5" % (self.frame_len, self.predict_dist)
self.estimators = model_from_json(open(model_name.replace('h5', 'json')).read())
self.estimators.load_weights(model_name)
pred = self.estimator.predict(X_test)
res = 0
score = 0
assert(len(pred) == len(Y_test))
pred = np.array(pred).reshape(-1)
Y_test = np.array(Y_test).reshape(-1)
for i in range(len(pred)):
score += (float(pred[i]) - float(Y_test[i]))*(float(pred[i]) - float(Y_test[i]))
score = np.sqrt(score/len(pred))
print("score: %f" % score)
for idx in range(len(pred)):
buy_price = float(X_test[idx][23*29])
date = int(orig_data[idx][0])
if pred[idx] > buy_price*1.2:
res += (int(Y_test[idx]) - buy_price*1.005)*(100000/buy_price+1)
print("[%s] buy: %6d, sell: %6d, earn: %6d" % (str(date), buy_price, int(Y_test[idx]), (int(Y_test[idx]) - buy_price*1.005)*(100000/buy_price)))
print("result: %d" % res)
def load_current_data(self):
con = sqlite3.connect('stock.db')
code_list = con.execute("SELECT name FROM sqlite_master WHERE type='table'").fetchall()
X_test = []
code_list = list(map(lambda x: x[0], code_list))
first = True
for code in code_list:
df = pd.read_sql("SELECT * from '%s'" % code, con, index_col='일자').sort_index()
data = df.iloc[-30:,:]
data = data.reset_index()
for col in data.columns:
try:
data.loc[:, col] = data.loc[:, col].str.replace('--', '-')
data.loc[:, col] = data.loc[:, col].str.replace('+', '')
except AttributeError as e:
pass
print(e)
data.loc[:, 'month'] = data.loc[:, '일자'].str[4:6]
data = data.drop(['일자', '체결강도'], axis=1)
if len(data) < 30:
continue
X_test.extend(np.array(data))
print(np.shape(X_test))
X_test = np.array(X_test).reshape(-1, 23*30)
return X_test, code_list
def make_buy_list(self, X_test, code_list):
BUY_UNIT = 300000
print("make buy_list")
model_name = "simple_reg_model/%d_%d.pkl" % (self.frame_len, self.predict_dist)
self.estimator = joblib.load(model_name)
pred = self.estimator.predict(X_test)
res = 0
score = 0
pred = np.array(pred).reshape(-1)
buy_item = ["매수", "", "시장가", 0, 0, "매수전"] # 매수/매도, code, 시장가/현재가, qty, price, "주문전/주문완료"
with open("buy_list.txt", "wt", encoding='utf-8') as f_buy:
for idx in range(len(pred)):
buy_price = float(X_test[idx][23*29])
print("[BUY?] code: %s, cur: %d, predict: %d" % (code_list[idx], buy_price, pred[idx]))
if pred[idx] > buy_price*1.3:
print("add to buy_list %d")
buy_item[1] = code_list[idx]
buy_item[3] = int(BUY_UNIT / buy_price)
for item in buy_item:
f_buy.write("%s;"%str(item))
f_buy.write('\n')
def load_data_in_account(self):
# load code list from account
code_list = []
with open('stocks_in_account.txt', encoding='utf-8') as f_stocks:
for line in f_stocks.readlines():
data = line.split(',')
code_list.append([data[6].replace('A', ''), data[1], data[0]])
# load data in code_list
con = sqlite3.connect('stock.db')
X_test = []
first = True
for code in code_list:
df = pd.read_sql("SELECT * from '%s'" % code[0], con, index_col='일자').sort_index()
data = df.iloc[-30:,:]
data = data.reset_index()
for col in data.columns:
try:
data.loc[:, col] = data.loc[:, col].str.replace('--', '-')
data.loc[:, col] = data.loc[:, col].str.replace('+', '')
except AttributeError as e:
pass
print(e)
data.loc[:, 'month'] = data.loc[:, '일자'].str[4:6]
data = data.drop(['일자', '체결강도'], axis=1)
if len(data) < 30:
continue
X_test.extend(np.array(data))
print(np.shape(X_test))
X_test = np.array(X_test).reshape(-1, 23*30)
return X_test, code_list
def make_sell_list(self, X_test, code_list):
print("make sell_list")
model_name = "simple_reg_model/%d_%d.pkl" % (self.frame_len, self.predict_dist)
self.estimator = joblib.load(model_name)
pred = self.estimator.predict(X_test)
res = 0
score = 0
pred = np.array(pred).reshape(-1)
sell_item = ["매도", "", "시장가", 0, 0, "매도전"] # 매수/매도, code, 시장가/현재가, qty, price, "주문전/주문완료"
with open("sell_list.txt", "wt", encoding='utf-8') as f_sell:
for idx in range(len(pred)):
current_price = float(X_test[idx][23*29])
print("[SELL?] code: %s, cur: %d, predict: %d" % (code_list[idx], current_price, pred[idx]))
if pred[idx] < current_price:
print("add to sell_list %d")
sell_item[1] = code_list[idx][0]
sell_item[3] = code_list[idx][1]
for item in sell_item:
f_sell.write("%s;"%str(item))
f_sell.write('\n')
class SimpleModel:
def __init__(self):
self.data = dict()
self.frame_len = 30
self.predict_dist = 5
self.scaler = dict()
def load_all_data(self, begin_date, end_date):
con = sqlite3.connect('../data/stock.db')
code_list = con.execute(
"SELECT name FROM sqlite_master WHERE type='table'").fetchall()
X_data_list, Y_data_list, DATA_list = [0] * 10, [0] * 10, [0] * 10
idx = 0
split = int(len(code_list) / 9)
bar = ProgressBar(len(code_list), max_width=80)
for code in code_list:
data = self.load_data(code[0], begin_date, end_date)
data = data.dropna()
X, Y = self.make_x_y(data, code[0])
if len(X) <= 1: continue
code_array = [code[0]] * len(X)
assert len(X) == len(data.loc[29:len(data) - 6, '일자'])
if idx % split == 0:
X_data_list[int(idx / split)] = list(X)
Y_data_list[int(idx / split)] = list(Y)
DATA_list[int(idx / split)] = np.array([
data.loc[29:len(data) - 6, '일자'].values.tolist(),
code_array, data.loc[29:len(data) - 6,
'현재가'], data.loc[34:len(data), '현재가']
]).T.tolist()
else:
X_data_list[int(idx / split)].extend(X)
Y_data_list[int(idx / split)].extend(Y)
DATA_list[int(idx / split)].extend(
np.array([
data.loc[29:len(data) - 6, '일자'].values.tolist(),
code_array, data.loc[29:len(data) - 6, '현재가'],
data.loc[34:len(data), '현재가']
]).T.tolist())
bar.numerator += 1
print("%s | %d" % (bar, len(X_data_list[int(idx / split)])),
end='\r')
sys.stdout.flush()
idx += 1
print("%s" % bar)
print("Merge splited data")
bar = ProgressBar(10, max_width=80)
for i in range(10):
if type(X_data_list[i]) == type(1):
continue
if i == 0:
X_data = X_data_list[i]
Y_data = Y_data_list[i]
DATA = DATA_list[i]
else:
X_data.extend(X_data_list[i])
Y_data.extend(Y_data_list[i])
DATA.extend(DATA_list[i])
bar.numerator = i + 1
print("%s | %d" % (bar, len(DATA)), end='\r')
sys.stdout.flush()
print("%s | %d" % (bar, len(DATA)))
return np.array(X_data), np.array(Y_data), np.array(DATA)
def load_data(self, code, begin_date, end_date):
con = sqlite3.connect('../data/stock.db')
df = pd.read_sql("SELECT * from '%s'" % code, con,
index_col='일자').sort_index()
data = df.loc[df.index > str(begin_date)]
data = data.loc[data.index < str(end_date)]
data = data.reset_index()
return data
def make_x_y(self, data, code):
data_x = []
data_y = []
for col in data.columns:
try:
data.loc[:, col] = data.loc[:, col].str.replace('--', '-')
data.loc[:, col] = data.loc[:, col].str.replace('+', '')
except AttributeError as e:
pass
print(e)
data.loc[:, 'month'] = data.loc[:, '일자'].str[4:6]
data = data.drop(['일자', '체결강도'], axis=1)
# normalization
data = np.array(data)
if len(data) <= 0:
return np.array([]), np.array([])
if code not in self.scaler:
self.scaler[code] = StandardScaler()
data = self.scaler[code].fit_transform(data)
elif code not in self.scaler:
return np.array([]), np.array([])
else:
data = self.scaler[code].transform(data)
for i in range(self.frame_len, len(data) - self.predict_dist + 1):
data_x.extend(np.array(data[i - self.frame_len:i, :]))
data_y.append(data[i + self.predict_dist - 1][0])
np_x = np.array(data_x).reshape(-1, 23 * 30)
np_y = np.array(data_y)
return np_x, np_y
def train_model(self, X_train, Y_train):
print("training model %d_%d.pkl" % (self.frame_len, self.predict_dist))
model_name = "../model/simple_reg_model/%d_%d.pkl" % (
self.frame_len, self.predict_dist)
self.estimator = RandomForestRegressor(random_state=0,
n_estimators=100,
n_jobs=-1)
self.estimator.fit(X_train, Y_train)
print("finish training model")
joblib.dump(self.estimator, model_name)
def set_config(self):
#Tensorflow GPU optimization
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
K.set_session(sess)
def train_model_keras(self, X_train, Y_train, date):
print("training model %d_%d.h5" % (self.frame_len, self.predict_dist))
model_name = "../model/reg_keras/%d_%d_%s.h5" % (
self.frame_len, self.predict_dist, date)
self.estimator = KerasRegressor(build_fn=baseline_model,
nb_epoch=200,
batch_size=64,
verbose=1)
self.estimator.fit(X_train, Y_train)
print("finish training model")
# saving model
json_model = self.estimator.model.to_json()
open(model_name.replace('h5', 'json'), 'w').write(json_model)
self.estimator.model.save_weights(model_name, overwrite=True)
def evaluate_model(self, X_test, Y_test, orig_data, s_date):
print("Evaluate model %d_%d.pkl" % (self.frame_len, self.predict_dist))
if MODEL_TYPE == 'random_forest':
model_name = "../model/simple_reg_model/%d_%d.pkl" % (
self.frame_len, self.predict_dist)
self.estimator = joblib.load(model_name)
elif MODEL_TYPE == 'keras':
model_name = "../model/reg_keras/%d_%d_%s.h5" % (
self.frame_len, self.predict_dist, s_date)
self.estimator = model_from_json(
open(model_name.replace('h5', 'json')).read())
self.estimator.load_weights(model_name)
pred = self.estimator.predict(X_test)
res = 0
score = 0
assert (len(pred) == len(Y_test))
pred = np.array(pred).reshape(-1)
Y_test = np.array(Y_test).reshape(-1)
for i in range(len(pred)):
score += (float(pred[i]) - float(Y_test[i])) * (float(pred[i]) -
float(Y_test[i]))
score = np.sqrt(score / len(pred))
print("score: %f" % score)
for idx in range(len(pred)):
buy_price = int(orig_data[idx][2])
future_price = int(orig_data[idx][3])
date = int(orig_data[idx][0])
pred_transform = self.scaler[orig_data[idx][1]].inverse_transform(
[pred[idx]] + [0] * 22)[0]
cur_transform = self.scaler[orig_data[idx][1]].inverse_transform(
[X_test[idx][23 * 29]] + [0] * 22)[0]
if pred_transform > buy_price * 1.01:
res += (future_price -
buy_price * 1.005) * (100000 / buy_price + 1)
print("[%s] buy: %6d, sell: %6d, earn: %6d" %
(str(date), buy_price, future_price,
(future_price - buy_price * 1.005) *
(100000 / buy_price)))
print("result: %d" % res)
def load_current_data(self):
con = sqlite3.connect('../data/stock.db')
code_list = con.execute(
"SELECT name FROM sqlite_master WHERE type='table'").fetchall()
X_test = []
DATA = []
code_list = list(map(lambda x: x[0], code_list))
first = True
bar = ProgressBar(len(code_list), max_width=80)
for code in code_list:
bar.numerator += 1
print("%s | %d" % (bar, len(X_test)), end='\r')
sys.stdout.flush()
df = pd.read_sql("SELECT * from '%s'" % code, con,
index_col='일자').sort_index()
data = df.iloc[-30:, :]
data = data.reset_index()
for col in data.columns:
try:
data.loc[:, col] = data.loc[:, col].str.replace('--', '-')
data.loc[:, col] = data.loc[:, col].str.replace('+', '')
except AttributeError as e:
pass
data.loc[:, 'month'] = data.loc[:, '일자'].str[4:6]
data = data.drop(['일자', '체결강도'], axis=1)
if len(data) < 30:
code_list.remove(code)
continue
DATA.append(int(data.loc[len(data) - 1, '현재가']))
try:
data = self.scaler[code].transform(np.array(data))
except KeyError:
code_list.remove(code)
continue
X_test.extend(np.array(data))
X_test = np.array(X_test).reshape(-1, 23 * 30)
return X_test, code_list, DATA
def make_buy_list(self, X_test, code_list, orig_data, s_date):
BUY_UNIT = 10000
print("make buy_list")
if MODEL_TYPE == 'random_forest':
model_name = "../model/simple_reg_model/%d_%d.pkl" % (
self.frame_len, self.predict_dist)
self.estimator = joblib.load(model_name)
elif MODEL_TYPE == 'keras':
model_name = "../model/reg_keras/%d_%d_%s.h5" % (
self.frame_len, self.predict_dist, s_date)
self.estimator = model_from_json(
open(model_name.replace('h5', 'json')).read())
self.estimator.load_weights(model_name)
pred = self.estimator.predict(X_test)
res = 0
score = 0
pred = np.array(pred).reshape(-1)
# load code list from account
set_account = set([])
with open('../data/stocks_in_account.txt') as f_stocks:
for line in f_stocks.readlines():
data = line.split(',')
set_account.add(data[6].replace('A', ''))
buy_item = ["매수", "", "시장가", 0, 0,
"매수전"] # 매수/매도, code, 시장가/현재가, qty, price, "주문전/주문완료"
with open("../data/buy_list.txt", "wt") as f_buy:
for idx in range(len(pred)):
real_buy_price = int(orig_data[idx])
buy_price = float(X_test[idx][23 * 29])
try:
pred_transform = self.scaler[
code_list[idx]].inverse_transform([pred[idx]] +
[0] * 22)[0]
except KeyError:
continue
print("[BUY PREDICT] code: %s, cur: %5d, predict: %5d" %
(code_list[idx], real_buy_price, pred_transform))
if pred_transform > real_buy_price * 3 and code_list[
idx] not in set_account:
print("add to buy_list %s" % code_list[idx])
buy_item[1] = code_list[idx]
buy_item[3] = int(BUY_UNIT / real_buy_price) + 1
for item in buy_item:
f_buy.write("%s;" % str(item))
f_buy.write('\n')
def load_data_in_account(self):
# load code list from account
DATA = []
with open('../data/stocks_in_account.txt') as f_stocks:
for line in f_stocks.readlines():
data = line.split(',')
DATA.append([data[6].replace('A', ''), data[1], data[0]])
# load data in DATA
con = sqlite3.connect('../data/stock.db')
X_test = []
idx_rm = []
first = True
bar = ProgressBar(len(DATA), max_width=80)
for idx, code in enumerate(DATA):
bar.numerator += 1
print("%s | %d" % (bar, len(X_test)), end='\r')
sys.stdout.flush()
try:
df = pd.read_sql("SELECT * from '%s'" % code[0],
con,
index_col='일자').sort_index()
except pd.io.sql.DatabaseError as e:
print(e)
idx_rm.append(idx)
continue
data = df.iloc[-30:, :]
data = data.reset_index()
for col in data.columns:
try:
data.loc[:, col] = data.loc[:, col].str.replace('--', '-')
data.loc[:, col] = data.loc[:, col].str.replace('+', '')
except AttributeError as e:
pass
print(e)
data.loc[:, 'month'] = data.loc[:, '일자'].str[4:6]
DATA[idx].append(int(data.loc[len(data) - 1, '현재가']))
data = data.drop(['일자', '체결강도'], axis=1)
if len(data) < 30:
idx_rm.append(idx)
continue
try:
data = self.scaler[code[0]].transform(np.array(data))
except KeyError:
idx_rm.append(idx)
continue
X_test.extend(np.array(data))
for i in idx_rm[-1:0:-1]:
del DATA[i]
X_test = np.array(X_test).reshape(-1, 23 * 30)
return X_test, DATA
def make_sell_list(self, X_test, DATA, s_date):
print("make sell_list")
if MODEL_TYPE == 'random_forest':
model_name = "../model/simple_reg_model/%d_%d.pkl" % (
self.frame_len, self.predict_dist)
self.estimator = joblib.load(model_name)
elif MODEL_TYPE == 'keras':
model_name = "../model/reg_keras/%d_%d_%s.h5" % (
self.frame_len, self.predict_dist, s_date)
self.estimator = model_from_json(
open(model_name.replace('h5', 'json')).read())
self.estimator.load_weights(model_name)
pred = self.estimator.predict(X_test)
res = 0
score = 0
pred = np.array(pred).reshape(-1)
sell_item = ["매도", "", "시장가", 0, 0,
"매도전"] # 매수/매도, code, 시장가/현재가, qty, price, "주문전/주문완료"
with open("../data/sell_list.txt", "wt") as f_sell:
for idx in range(len(pred)):
current_price = float(X_test[idx][23 * 29])
current_real_price = int(DATA[idx][3])
name = DATA[idx][2]
print(
"[SELL PREDICT] name: %s, code: %s, cur: %f(%d), predict: %f"
% (name, DATA[idx][0], current_price, current_real_price,
pred[idx]))
if pred[idx] < current_price:
print("add to sell_list %s" % name)
sell_item[1] = DATA[idx][0]
sell_item[3] = DATA[idx][1]
for item in sell_item:
f_sell.write("%s;" % str(item))
f_sell.write('\n')
def save_scaler(self, s_date):
model_name = "../model/scaler_%s.pkl" % s_date
joblib.dump(self.scaler, model_name)
def load_scaler(self, s_date):
model_name = "../model/scaler_%s.pkl" % s_date
self.scaler = joblib.load(model_name)
