LongShortTM.py

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense, Dropout, LSTM
import streamlit as st
from pandas import DataFrame

def LongShortTM(df, type, split):
    # creating dataframe
    data = df.sort_index(ascending=True, axis=0)
    new_data = pd.DataFrame(index=range(0, len(df)), columns=['Date', type])

    for i in range(0, len(data)):
        new_data['Date'][i] = data['Date'][i]
        new_data[type][i] = data[type][i]

    # setting index
    new_data.index = new_data.Date
    new_data.drop('Date', axis=1, inplace=True)

    # creating train and test sets
    dataset = new_data.values

    train = dataset[0:split, :]
    valid = dataset[split:, :]

    # converting dataset into x_train and y_train
    scaler = MinMaxScaler(feature_range=(0, 1))
    scaled_data = scaler.fit_transform(dataset)

    x_train, y_train = [], []
    for i in range(60, len(train)):
        x_train.append(scaled_data[i - 60:i, 0])
        y_train.append(scaled_data[i, 0])

    x_train, y_train = np.array(x_train), np.array(y_train)

    x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))

    # create and fit the LSTM network
    model = Sequential()  # 顺序模型，核心操作是添加layer（图层）
    model.add(LSTM(50, return_sequences=True, input_shape=(x_train.shape[1], 1)))
    model.add(LSTM(units=50))
    model.add(Dense(1))  # 全连接层

    model.compile(loss='mean_squared_error', optimizer='adam')  # 选择优化器，并指定损失函数
    model.fit(x_train, y_train, epochs=1, batch_size=1, verbose=2)

    # predicting 246 values, using past 60 from the train data
    inputs = new_data[len(new_data) - len(valid) - 60:].values
    inputs = inputs.reshape(-1, 1)
    inputs = scaler.transform(inputs)

    X_test = []
    for i in range(60, inputs.shape[0]):
        X_test.append(inputs[i - 60:i, 0])
    X_test = np.array(X_test)

    X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))

    closing_price = model.predict(X_test)
    closing_price = scaler.inverse_transform(closing_price)

    rmse = np.sqrt(np.mean(np.power((valid - closing_price), 2)))
    st.write('RMSE value on validation set:')
    st.write(rmse)

    # for plotting
    train = new_data[:split]
    valid = new_data[split:]
    valid['Predictions'] = closing_price

    append_data = DataFrame(data={type: [], 'Predictions': []})

    append_data[type] = train[type]
    append_data['Predictions'] = train[type]

    pic = pd.concat([append_data[[type, 'Predictions']], valid[[type, 'Predictions']]], axis=0)

    st.line_chart(pic)