import dash import dash_core_components as dcc import dash_html_components as html import pandas as pd import plotly.graph_objs as go from dash.dependencies import Input, Output from keras.models import load_model from sklearn.preprocessing import MinMaxScaler import numpy as np app = dash.Dash() server = app.server scaler=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"][i]=data['Date'][i] new_data["Close"][i]=data["Close"][i] new_data.index=new_data.Date new_data.drop("Date",axis=1,inplace=True) dataset=new_data.values train=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[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)) model=load_model("saved_model.h5") 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) train=new_data[:987] valid=new_data[987:] valid['Predictions']=closing_price df= 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("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("Stocks 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'), [Input('my-dropdown', 'value')]) 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[df["Stock"] == stock]["Date"], y=df[df["Stock"] == stock]["High"], mode='lines', opacity=0.7, name=f'High {dropdown[stock]}',textposition='bottom center')) trace2.append( go.Scatter(x=df[df["Stock"] == stock]["Date"], y=df[df["Stock"] == stock]["Low"], mode='lines', opacity=0.6, name=f'Low {dropdown[stock]}',textposition='bottom center')) traces = [trace1, trace2] data = [val for sublist in traces for val in sublist] 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[i]) 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'), [Input('my-dropdown2', 'value')]) 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[df["Stock"] == stock]["Date"], y=df[df["Stock"] == stock]["Volume"], mode='lines', opacity=0.7, name=f'Volume {dropdown[stock]}', textposition='bottom center')) traces = [trace1] data = [val for sublist in traces for val in sublist] figure = {'data': data, 'layout': go.Layout(colorway=["#5E0DAC", '#FF4F00', '#375CB1', '#FF7400', '#FFF400', '#FF0056'], height=600, title=f"Market Volume for {', '.join(str(dropdown[i]) 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 figure if __name__=='__main__': app.run_server(debug=True)