[ADDED]: Stock-Price-Prediction

Stock-Price-Prediction/README.md

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+
+<h2 align=center> 📑 Stock Price Prediction </h2>
+
+Machine learning has significant applications in the stock price prediction. In this machine learning project, we will be talking about predicting the returns on stocks. This is a very complex task and has uncertainties. We will develop this project into two parts:
+
+First, we will predict stock price using the LSTM neural network.<br>
+Then we will build a dashboard using Plotly dash for stock analysis.
+
+<h3>Datasets</h3>
+
+To build the stock price prediction model, we will use the NSE TATA GLOBAL dataset. This is a dataset of Tata Beverages from Tata Global Beverages Limited, National Stock Exchange of India: Tata Global Dataset
+To develop the dashboard for stock analysis we will use another stock dataset with multiple stocks like Apple, Microsoft, Facebook: Stocks Dataset
+
+<video src="Stock_demo%20-%20Made%20with%20Clipchamp.mp4" controls title="Stock Demo"></video>

Stock-Price-Prediction/stock_app.py

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+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)