57 lines
2.1 KiB
Python
57 lines
2.1 KiB
Python
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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from matplotlib.pylab import rcParams
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from keras.models import Sequential
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from keras.layers import LSTM, Dropout, Dense
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from sklearn.preprocessing import MinMaxScaler
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df = pd.read_csv('static/HistoricalData_APL.csv')
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df = df[['Date', 'Close/Last']]
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df = df.replace({'\$': ''}, regex=True)
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print(df.head())
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df = df.astype({"Close/Last": float})
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df["Date"] = pd.to_datetime(df.Date, format="%m/%d/%Y")
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print(df.dtypes)
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df.index = df['Date']
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plt.plot(df["Close/Last"], label='AAPL Close Price history')
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plt.show()
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df = df.sort_index(ascending=True,axis=0)
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data = pd.DataFrame(index=range(0,len(df)),columns=['Date','Close/Last'])
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for i in range(0,len(data)):
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data["Date"][i]=df['Date'][i]
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data["Close/Last"][i]=df["Close/Last"][i]
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scaler=MinMaxScaler(feature_range=(0,1))
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data.index=data.Date
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data.drop("Date",axis=1,inplace=True)
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final_data = data.values
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train_data=final_data[0:200,:]
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valid_data=final_data[200:,:]
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scaler=MinMaxScaler(feature_range=(0,1))
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scaled_data=scaler.fit_transform(final_data)
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x_train_data,y_train_data=[],[]
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for i in range(60,len(train_data)):
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x_train_data.append(scaled_data[i-60:i,0])
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y_train_data.append(scaled_data[i,0])
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lstm_model=Sequential()
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lstm_model.add(LSTM(units=50,return_sequences=True,input_shape=(np.shape(x_train_data)[1],1)))
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lstm_model.add(LSTM(units=50))
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lstm_model.add(Dense(1))
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model_data=data[len(data)-len(valid_data)-60:].values
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model_data=model_data.reshape(-1,1)
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model_data=scaler.transform(model_data)
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lstm_model.compile(loss='mean_squared_error',optimizer='adam')
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lstm_model.fit(x_train_data,y_train_data,epochs=1,batch_size=1,verbose=2)
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X_test=[]
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for i in range(60,model_data.shape[0]):
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X_test.append(model_data[i-60:i,0])
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X_test=np.array(X_test)
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X_test=np.reshape(X_test,(X_test.shape[0],X_test.shape[1],1))
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predicted_stock_price=lstm_model.predict(X_test)
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predicted_stock_price=scaler.inverse_transform(predicted_stock_price)
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train_data=data[:200]
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valid_data=data[200:]
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valid_data['Predictions']=predicted_stock_price
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plt.plot(train_data["Close/Last"])
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plt.plot(valid_data[['Close/Last',"Predictions"]])
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plt.show()
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