lstm_model=Sequential()lstm_model.add(LSTM(units=50,return_sequences=True,input_shape=(x_train_data.shape<1>,1)))lstm_model.add(LSTM(units=50))lstm_model.add(Dense(1))inputs_data=new_dataset.valuesinputs_data=inputs_data.reshape(-1,1)inputs_data=scaler.transform(inputs_data)lstm_model.compile(loss='mean_squared_error',optimizer='adam')lstm_model.fit(x_train_data,y_train_data,epochs=1,batch_size=1,verbose=2)7.抽取数据集样本，以使用LSTM模型进行股票价格预测：

X_test=<>for i in range(60,inputs_data.shape<0>): X_test.append(inputs_data)X_test=np.array(X_test)X_test=np.reshape(X_test,(X_test.shape<0>,X_test.shape<1>,1))predicted_closing_price=lstm_model.predict(X_test)predicted_closing_price=scaler.inverse_transform(predicted_closing_price)8.保存LSTM模型：

lstm_model.save("saved_model.h5")9.用实际股价可视化预测的股价：

train_data=new_dataset<:987>valid_data=new_dataset<987:>valid_data<'Predictions'>=predicted_closing_priceplt.plot(train_data<"Close">)plt.plot(valid_data<<'Close',"Predictions">>)

您可以观察到LSTM预测的股价几乎与实际股价相似。

使用Plotly dash构建仪表板在本节中，我们将构建一个仪表板来分析股票。Dash是一个python框架，提供了flask和react.js的抽象，以构建分析型Web应用程序。
在继续之前，您需要安装dash。在终端中运行以下命令。

pip3 install dashpip3 install dash-html-componentspip3 install dash-core-components现在制作一个新的python文件stock_app.py并粘贴以下脚本：

import dashimport dash_core_components as dccimport dash_html_components as htmlimport pandas as pdimport plotly.graph_objs as gofrom dash.dependencies import Input, Outputfrom keras.models import load_modelfrom sklearn.preprocessing import MinMaxScalerimport numpy as npapp = dash.Dash()server = app.serverscaler=MinMaxScaler(feature_range=(0,1))df_nse = pd.read_csv("./NSE-TATA.csv")df_nse<"Date">=pd.to_datetime(df_nse.Date,format="%Y-%m-%d")df_nse.index=df_nse<'Date'>data=df_nse.sort_index(ascending=True,axis=0)new_data=pd.DataFrame(index=range(0,len(df_nse)),columns=<'Date','Close'>)for i in range(0,len(data)): new_data<"Date">=data<'Date'> new_data<"Close">=data<"Close">new_data.index=new_data.Datenew_data.drop("Date",axis=1,inplace=True)dataset=new_data.valuestrain=dataset<0:987,:>valid=dataset<987:,:>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) y_train.append(scaled_data) 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))model=load_model("saved_model.h5")inputs=new_data.valuesinputs=inputs.reshape(-1,1)inputs=scaler.transform(inputs)X_test=<>for i in range(60,inputs.shape<0>): X_test.append(inputs)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)train=new_data<:987>valid=new_data<987:>valid<'Predictions'>=closing_pricedf= pd.read_csv("./stock_data.csv")app.layout = html.Div(< html.H1("Stock Price Analysis Dashboard", style={"textAlign": "center"}), dcc.Tabs(id="tabs", children=< dcc.Tab(label='NSE-TATAGLOBAL Stock Data',children=< html.Div(< html.H2("Actual closing price",style={"textAlign": "center"}), dcc.Graph( id="Actual Data", figure={ "data":< go.Scatter( x=train.index, y=valid<"Close">, mode='markers' ) >, "layout":go.Layout( title='scatter plot', xaxis={'title':'Date'}, yaxis={'title':'Closing Rate'} ) } ), html.H2("LSTM Predicted closing price",style={"textAlign": "center"}), dcc.Graph( id="Predicted Data", figure={ "data":< go.Scatter( x=valid.index, y=valid<"Predictions">, mode='markers' ) >, "layout":go.Layout( title='scatter plot', xaxis={'title':'Date'}, yaxis={'title':'Closing Rate'} ) } ) >) >), dcc.Tab(label='Facebook Stock Data', children=< html.Div(< html.H1("Facebook Stocks High vs Lows", style={'textAlign': 'center'}), dcc.Dropdown(id='my-dropdown', options=<{'label': 'Tesla', 'value': 'TSLA'}, {'label': 'Apple','value': 'AAPL'}, {'label': 'Facebook', 'value': 'FB'}, {'label': 'Microsoft','value': 'MSFT'}>, multi=True,value=<'FB'>, style={"display": "block", "margin-left": "auto", "margin-right": "auto", "width": "60%"}), dcc.Graph(id='highlow'), html.H1("Facebook Market Volume", style={'textAlign': 'center'}), dcc.Dropdown(id='my-dropdown2', options=<{'label': 'Tesla', 'value': 'TSLA'}, {'label': 'Apple','value': 'AAPL'}, {'label': 'Facebook', 'value': 'FB'}, {'label': 'Microsoft','value': 'MSFT'}>, multi=True,value=<'FB'>, style={"display": "block", "margin-left": "auto", "margin-right": "auto", "width": "60%"}), dcc.Graph(id='volume') >, className="container"), >) >)>)@app.callback(Output('highlow', 'figure'), )def update_graph(selected_dropdown): dropdown = {"TSLA": "Tesla","AAPL": "Apple","FB": "Facebook","MSFT": "Microsoft",} trace1 = <> trace2 = <> for stock in selected_dropdown: trace1.append( go.Scatter(x=df == stock><"Date">, y=df == stock><"High">, mode='lines', opacity=0.7, name=f'High {dropdown}',textposition='bottom center')) trace2.append( go.Scatter(x=df == stock><"Date">, y=df == stock><"Low">, mode='lines', opacity=0.6, name=f'Low {dropdown}',textposition='bottom center')) traces = data = figure = {'data': data, 'layout': go.Layout(colorway=<"#5E0DAC", '#FF4F00', '#375CB1', '#FF7400', '#FFF400', '#FF0056'>, height=600, title=f"High and Low Prices for {', '.join(str(dropdown) for i in selected_dropdown)} Over Time", xaxis={"title":"Date", 'rangeselector': {'buttons': list(<{'count': 1, 'label': '1M', 'step': 'month', 'stepmode': 'backward'}, {'count': 6, 'label': '6M', 'step': 'month', 'stepmode': 'backward'}, {'step': 'all'}>)}, 'rangeslider': {'visible': True}, 'type': 'date'}, yaxis={"title":"Price (USD)"})} return figure@app.callback(Output('volume', 'figure'), )def update_graph(selected_dropdown_value): dropdown = {"TSLA": "Tesla","AAPL": "Apple","FB": "Facebook","MSFT": "Microsoft",} trace1 = <> for stock in selected_dropdown_value: trace1.append( go.Scatter(x=df == stock><"Date">, y=df == stock><"Volume">, mode='lines', opacity=0.7, name=f'Volume {dropdown}', textposition='bottom center')) traces = data = figure = {'data': data, 'layout': go.Layout(colorway=<"#5E0DAC", '#FF4F00', '#375CB1', '#FF7400', '#FFF400', '#FF0056'>, height=600, title=f"Market Volume for {', '.join(str(dropdown) for i in selected_dropdown_value)} Over Time", xaxis={"title":"Date", 'rangeselector': {'buttons': list(<{'count': 1, 'label': '1M', 'step': 'month', 'stepmode': 'backward'}, {'count': 6, 'label': '6M', 'step': 'month', 'stepmode': 'backward'}, {'step': 'all'}>)}, 'rangeslider': {'visible': True}, 'type': 'date'}, yaxis={"title":"Transactions Volume"})} return figureif __name__=='__main__': app.run_server(debug=True)现在运行此文件并在浏览器中打开应用程序：