init

stockpredictor/train/PricePredictor.py

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+import os
+import json
+import sqlite3
+import numpy as np
+from keras.models import Sequential
+from keras.layers import Dense
+from keras.callbacks import EarlyStopping, ModelCheckpoint
+from keras.layers import LSTM
+
+class PricePredictor:
+
+    trainingRate = 0.7
+    TYPE = 0 # OPEN = 0,  CLOSE = 1, HIGH = 2, LOW = 3, VOLUME = 4
+    MAX_VALUE = 500000
+    MAX_VOLUME_VALUE = 300000000
+
+    def getNormalPrice(self, value):
+        value = value / self.MAX_VALUE
+        if value>1:
+            value = 1
+        return value
+
+    def getNormalVolume(self, value):
+        value = value / self.MAX_VOLUME_VALUE
+        if value > 1:
+            value = 1
+        return value
+
+    def read (self, inFileName):
+        conn = sqlite3.connect(inFileName)
+        cursor = conn.cursor()
+
+        stocks = []
+        rowid = 1
+        cursor.execute('SELECT * FROM stock WHERE rowid=?', (rowid,))
+        result = cursor.fetchone()
+        while result != None:
+            prices = json.loads(result[2])
+            stock = []
+            for price in prices:
+                stock.append([self.getNormalPrice(price['open']), self.getNormalPrice(price['close']), self.getNormalPrice(price['high']), self.getNormalPrice(price['low']), self.getNormalVolume(price['volume'])])
+            stocks.append(stock)
+
+            rowid += 1
+            cursor.execute('SELECT * FROM stock WHERE rowid=?', (rowid,))
+            result = cursor.fetchone()
+
+        cursor.close()
+        conn.close()
+
+        return stocks
+
+    def make_dataset(self, stocks, window_size=5):
+        feature = []
+        label = []
+        test_feature = []
+        test_label = []
+
+        for i in range(len(stocks)):
+            stock = stocks[i]
+            for j in range(len(stock) - window_size):
+                feature.append(stock[j:j+window_size])
+                label.append([stock[j+window_size][self.TYPE]])
+
+            test_feature.append(stock[len(stock)-window_size-1:len(stock)-1])
+            test_label.append(stock[len(stock)-1][self.TYPE])
+
+        index = int(len(feature) * self.trainingRate)
+        train_feature = feature[:index]
+        train_label = label[:index]
+        valid_feature = feature[index:]
+        valid_label = label[index:]
+
+        test_feature = feature[len(feature) - 1]
+        test_label = label[len(feature) - 1]
+
+        return np.array(train_feature), np.array(train_label), np.array(valid_feature), np.array(valid_label), np.array(test_feature), np.array(test_label)
+
+    def train(self, inFileName, model_path):
+        stocks = self.read(inFileName)
+        train_feature, train_label, valid_feature, valid_label, test_feature, test_label = self.make_dataset(stocks)
+        print (train_feature.shape)
+        print (train_label.shape)
+
+        model = Sequential()
+        model.add(LSTM(32,
+                       input_shape=(train_feature.shape[1], train_feature.shape[2]),
+                       activation='relu',
+                       return_sequences=False))
+
+        model.add(Dense(1))
+
+        model.compile(loss='mean_squared_error', optimizer='adam')
+        early_stop = EarlyStopping(monitor='val_loss', patience=5)
+
+        filename = ''
+        if self.TYPE==0 :
+            filename = os.path.join(model_path, 'open_checkpoint.h5')
+        elif self.TYPE==1 :
+            filename = os.path.join(model_path, 'close_checkpoint.h5')
+        elif self.TYPE==2 :
+            filename = os.path.join(model_path, 'high_checkpoint.h5')
+        elif self.TYPE==3 :
+            filename = os.path.join(model_path, 'low_checkpoint.h5')
+
+        checkpoint = ModelCheckpoint(filename, monitor='val_loss', verbose=1, save_best_only=True, mode='auto')
+
+        history = model.fit(train_feature, train_label,
+                            epochs=1000,
+                            batch_size=100000,
+                            validation_data=(valid_feature, valid_label),
+                            callbacks=[early_stop, checkpoint])
+
+        test_feature = np.array([test_feature])
+        predition = model.predict(test_feature)
+        print(predition[0], test_label)
+        print(predition[0] * self.MAX_VALUE, test_label * self.MAX_VALUE)
+        return
+
+    def predict(self, inFileName, model_path):
+        stocks = self.read(inFileName)
+        train_feature, train_label, valid_feature, valid_label, test_feature, test_label = self.make_dataset(stocks)
+
+        filename = ''
+        if self.TYPE==0 :
+            filename = os.path.join(model_path, 'open_checkpoint.h5')
+        elif self.TYPE==1 :
+            filename = os.path.join(model_path, 'close_checkpoint.h5')
+        elif self.TYPE==2 :
+            filename = os.path.join(model_path, 'high_checkpoint.h5')
+        elif self.TYPE==3 :
+            filename = os.path.join(model_path, 'low_checkpoint.h5')
+
+        model = Sequential()
+        model.add(LSTM(32,
+                       input_shape=(train_feature.shape[1], train_feature.shape[2]),
+                       activation='relu',
+                       return_sequences=False))
+        model.add(Dense(1))
+        model.compile(loss='mean_squared_error', optimizer='adam')
+        model.load_weights(filename)
+
+        test_feature = np.array([test_feature])
+        predition = model.predict(test_feature)
+        print(predition[0], test_label)
+        print(predition[0] * self.MAX_VALUE, test_label * self.MAX_VALUE)
+
+        return
+
+if __name__ == "__main__":
+    PROJECT_HOME = "../.."
+    inFileName = PROJECT_HOME + '/resources/stock.db'
+    model_path = PROJECT_HOME + '/resources/model'
+
+    pricePredictor = PricePredictor()
+    pricePredictor.train(inFileName, model_path)
+    #pricePredictor.predict(inFileName, model_path)