init

stock/util/Stock2Vector.py

@@ -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": []}
 

stock/util/StockPredictor.py

@@ -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