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| import pandas as pd | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import yfinance as yf | |
| from keras.models import Sequential | |
| from sklearn.preprocessing import MinMaxScaler | |
| from keras.layers.core import Dense,Dropout,Activation | |
| from tensorflow.keras.layers import LSTM | |
| #from keras.layers.recurrent import LSTM | |
| from datetime import date | |
| import gradio as gr | |
| def create_dataset(dataset,time_step=15): | |
| x_ind,y_dep =[],[] | |
| for i in range(len(dataset)-time_step-1): | |
| a=dataset[i:(i+time_step),0] | |
| x_ind.append(a) | |
| y_dep.append(dataset[i+time_step,0]) | |
| return np.array(x_ind),np.array(y_dep) | |
| def stockprice(stockname,number_of_samples): | |
| df_yahoo = yf.download(stockname,start='2020-09-15',end=date.today(),interval = "1h",progress=False,auto_adjust=True) | |
| df=df_yahoo | |
| df.index.rename('Date', inplace=True) | |
| df=df.sort_values(by=['Date'],ignore_index=True) | |
| min_max_scaler=MinMaxScaler(feature_range=(0,1)) | |
| dataset=min_max_scaler.fit_transform(df['Close'].values.reshape(-1,1)) | |
| train_size=int(len(df)*0.8) | |
| test_size=len(df)-train_size | |
| Train=dataset[0:train_size,:] | |
| Test=dataset[train_size:len(dataset),:] | |
| x_train,y_train=create_dataset(Train,time_step=15) | |
| x_test,y_test=create_dataset(Test,time_step=15) | |
| x_train=np.reshape(x_train,(x_train.shape[0],1,x_train.shape[1])) | |
| x_test=np.reshape(x_test,(x_test.shape[0],1,x_test.shape[1])) | |
| time_step=15 | |
| model=Sequential() | |
| model.add(LSTM(20,input_shape=(1,time_step))) | |
| model.add(Dense(1)) | |
| model.compile(loss="mean_squared_error",optimizer='adam') | |
| model.fit(x_train,y_train,epochs=100,verbose=0) | |
| y_pred=model.predict(x_test) | |
| y_pred_RNN=min_max_scaler.inverse_transform(y_pred) | |
| y_test=np.expand_dims(y_test,axis=1) | |
| y_test=min_max_scaler.inverse_transform(y_test) | |
| df1=df.drop(["Volume","Open","High","Low"],axis=1) | |
| a= int(number_of_samples)*15 | |
| new_data = df1[-(a+1):-1] | |
| last60prices=np.array(new_data) | |
| last60prices=last60prices.reshape(-1, 1) | |
| X=min_max_scaler.transform(last60prices) | |
| TimeSteps=int(15) | |
| NumFeatures=int(1) | |
| number_of_samples=int(number_of_samples) | |
| X=X.reshape(number_of_samples, NumFeatures, TimeSteps) | |
| predicted_Price = model.predict(X) | |
| predicted_Price = min_max_scaler.inverse_transform(predicted_Price) | |
| pred_df=pd.DataFrame(list(map(lambda x: x[0], predicted_Price)),columns=["PREDICTIONS"]) | |
| pred_df.reset_index(inplace=True) | |
| pred_df = pred_df.rename(columns = {'index':'HOURS'}) | |
| pred_df['HOURS'] = np.arange(1, len(pred_df) + 1) | |
| plt.figure(figsize=(15, 6)) | |
| range_history = len(new_data) | |
| range_future = list(range(range_history, range_history +len(predicted_Price))) | |
| plt.plot(np.arange(range_history), np.array(new_data),label='History') | |
| plt.plot(range_future, np.array(predicted_Price),label='Forecasted for RNN') | |
| plt.legend(loc='upper right') | |
| plt.xlabel('Time step (hour)') | |
| plt.ylabel('Stock Price') | |
| return pred_df,plt.gcf() | |
| interface = gr.Interface(fn = stockprice, | |
| inputs = [gr.inputs.Textbox(lines=1, placeholder="Enter STOCK-TICKER", default="FB", label="STOCKNAME"), | |
| gr.inputs.Slider(minimum=0, maximum=150, step=1, default=5, label="Number of Sample to Predict")], | |
| outputs = ["dataframe","plot"], | |
| description="LSTM STOCK PREDICTION") | |
| interface.launch() |