init

StockTrainer.py

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+import os
+import keras
+import numpy as np
+from stock.util.Stock2Vector import Stock2Vector
+from classification_models.keras import Classifiers
+
+class StockTrainer:
+
+    RESOURCE_PATH = None
+    stock2Vector = None
+
+    def __init__(self, RESOURCE_PATH):
+        self.RESOURCE_PATH = RESOURCE_PATH
+        self.stock2Vector = Stock2Vector(RESOURCE_PATH)
+        return
+
+    def getDataset(self, stock_code):
+        df, minmax_df = self.stock2Vector.makeTrainData(stock_code)
+
+        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())
+
+        X, Y = [], []
+        for i in range(299, len(TOTAL_X[0])):
+            temp_X, temp_Y = np.zeros((299, 299)), np.zeros(0)
+            idx = 0
+            for j in range(i-299, i):
+                for k in range(len(TOTAL_X)):
+                    temp_X[k][idx] = TOTAL_X[k][j]
+                idx += 1
+            X.append(temp_X)
+            Y.append(TOTAL_Y[i])
+
+
+
+
+        return X, Y
+
+    def train(self, stock_code):
+        ResNet18, preprocess_input = Classifiers.get('inceptionresnetv2')
+
+        X, Y = self.getDataset(stock_code)
+
+        X = preprocess_input(X)
+
+        n_classes = 3
+
+        # build model
+        base_model = ResNet18(input_shape=(299, 299, 3), weights='imagenet', include_top=False)
+        x = keras.layers.GlobalAveragePooling2D()(base_model.output)
+        output = 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'])
+        model.fit(X, Y)
+
+        return
+
+
+if __name__ == "__main__":
+    PROJECT_HOME = os.path.join(os.path.dirname(__file__))
+    RESOURCE_PATH = os.path.join(PROJECT_HOME, "resources")
+
+    # to check bying
+    stock_codes = {
+        # 252670
+        # 122630
+        "252670": ['20220729'],
+    }
+
+    for stock_code in stock_codes:
+        stockTrainer = StockTrainer(RESOURCE_PATH)
+        stockTrainer.train(stock_code)
+
+
+    print ("done...")