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

init

Browse Source
This commit is contained in:
dosangyoon
2022-08-06 10:59:27 +09:00
parent 57c5cc5638
commit 3b6b33f030
2 changed files with 63 additions and 21 deletions
Download Patch File Download Diff File Expand all files Collapse all files

35
stock/util/Stock2Vector.py
View File

@@ -1,4 +1,5 @@
import os
import csv
import copy
import sqlite3
import numpy as np
@@ -158,8 +159,8 @@ class Stock2Vector(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"))
cursor.execute('SELECT ymd, hms, open, high, low, close, volume, label FROM ' + tableName + ' WHERE CODE=? order by ymd desc, hms ', (stock_code,))
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, "20220701", "20220731"))
#cursor.execute('SELECT ymd, hms, open, high, low, close, volume, label FROM ' + tableName + ' WHERE CODE=? order by ymd desc, hms ', (stock_code,))
db_result = cursor.fetchall()
temp_result = []
for rows in db_result:
@@ -245,7 +246,7 @@ class Stock2Vector(HTS):
return np.asarray(vector)
def getDataset2D(self, stock_code, VECTOR_SIZE = 224):
def getDataset2D(self, stock_code, VECTOR_SIZE = 381):
result = self.getTrainData(stock_code)
df, minmax_df = self.preprocessData(result)
@@ -266,17 +267,30 @@ class Stock2Vector(HTS):
for j in range(SIZE_HEIGHT):
temp_X[j][0:VECTOR_SIZE] = TOTAL_X[j][i-VECTOR_SIZE:i]
X.append(temp_X)
if int(TOTAL_Y[0][i]) == 0:
Y.append([1, 0, 0])
elif int(TOTAL_Y[0][i]) == 0.5:
Y.append([0, 1, 0])
if TOTAL_Y[0][i] == 0:
#Y.append([1, 0, 0])
Y.append([0])
elif TOTAL_Y[0][i] == 0.5:
#Y.append([0, 1, 0])
Y.append([1])
else:
Y.append([0, 0, 1])
#Y.append([0, 0, 1])
Y.append([2])
X = np.asarray(X)
Y = np.asarray(Y)
return X, Y
def makeDataset2D(self, stock_code, outFileName=None):
X, Y = self.getDataset2D(stock_code)
#reX = X.reshape(X.shape[0], (X.shape[1] * X.shape[2]))
#df = pd.DataFrame(np.hstack((reX, Y)))
#df.to_csv(outFileName, index=False, header=False)
return X, Y
def getDataset3D(self, stock_code, VECTOR_SIZE = 299):
result = self.getTrainData(stock_code)
df, minmax_df = self.preprocessData(result)
@@ -324,9 +338,10 @@ if __name__ == "__main__":
for stock_code in stock_codes:
stock2Vector = Stock2Vector(RESOURCE_PATH)
for given_day in stock_codes[stock_code]:
X, Y = stock2Vector.getDataset2D(stock_code)
# X, Y = stock2Vector.getDataset2D(stock_code)
stock2Vector.makeDataset2D(stock_code, outFileName=os.path.join(RESOURCE_PATH, "tmp", "stock_features.csv"))
for given_day in stock_codes[stock_code]:
data, minmax_data = stock2Vector.makeData(given_day, stock_code)
vector = stock2Vector.vectorize(data)
minmax_vector = stock2Vector.vectorize(minmax_data)