def main():
start = "2003-01-01"
end = "2018-01-01"
hist.get_stock_data("AAPL", start_date=start, end_date=end)
process = DataProcessing("stock_prices.csv", 0.9)
process.gen_test(10)
process.gen_train(10)
X_train = process.X_train / 200
Y_train = process.Y_train / 200
X_test = process.X_test / 200
Y_test = process.Y_test / 200
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Dense(100, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(100, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(1, activation=tf.nn.relu))
model.compile(optimizer="adam", loss="mean_squared_error")
model.fit(X_train, Y_train, epochs=100)
print(model.evaluate(X_test, Y_test))
import get_prices as hist
import tensorflow as tf
from preprocessing import DataProcessing
import pandas_datareader.data as pdr
import yfinance as fix
import matplotlib.pyplot as plt
import WXBizSendMsg
fix.pdr_override()
start = "2000-01-01"
end = "2019-08-22"
stock = "000001.SS"
hist.get_stock_data(stock, start_date=start, end_date=end)
process = DataProcessing("stock_prices.csv", 0.9)
process.gen_test(10)
process.gen_train(10)
X_train = process.X_train / np.array([process.value_max, process.volume_max
]) # 归一化， 包括Adj Close 和 Volume
Y_train = process.Y_train / process.value_max
X_test = process.X_test / np.array([process.value_max, process.volume_max])
Y_test = process.Y_test / process.value_max
model = tf.keras.Sequential()
model.add(tf.keras.layers.LSTM(20, input_shape=(10, 2), return_sequences=True))
model.add(tf.keras.layers.LSTM(20))
import get_prices as hist
import tensorflow as tf
from preprocessing import DataProcessing
# import pandas_datareader.data as pdr if using the single test below
import yfinance as fix
fix.pdr_override()
start = "2003-01-01"
end = "2018-01-01"
hist.get_stock_data("AAPL", start_date=start, end_date=end)
process = DataProcessing("stock_prices.csv", 0.9)
process.gen_test(10)
process.gen_train(10)
X_train = process.X_train / 200
Y_train = process.Y_train / 200
X_test = process.X_test / 200
Y_test = process.Y_test / 200
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Dense(100, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(100, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(1, activation=tf.nn.relu))
model.compile(optimizer="adam", loss="mean_squared_error")
model.fit(X_train, Y_train, epochs=100)
print(model.evaluate(X_test, Y_test))