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Merge pull request #1878 from Killer2OP/stock
[ADDED]: Stock-Price-Prediction
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‎Stock-Price-Prediction/README.md

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<h2 align=center> 📑 Stock Price Prediction </h2>
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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:
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First, we will predict stock price using the LSTM neural network.<br>
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Then we will build a dashboard using Plotly dash for stock analysis.
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<h3>Datasets</h3>
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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
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To develop the dashboard for stock analysis we will use another stock dataset with multiple stocks like Apple, Microsoft, Facebook: Stocks Dataset
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![Alt text](Stock-Price-Prediction-project-dashboard-2.gif)

‎Stock-Price-Prediction/Stock-Price-Prediction-project-dashboard-2.gif

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‎Stock-Price-Prediction/stock_app.py

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

‎Stock-Price-Prediction/stock_pred.py

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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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%matplotlib inline
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from matplotlib.pylab import rcParams
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rcParams['figure.figsize']=20,10
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from sklearn.preprocessing import MinMaxScaler
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scaler=MinMaxScaler(feature_range=(0,1))
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df=pd.read_csv("NSE-TATA.csv")
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df.head()
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df["Date"]=pd.to_datetime(df.Date,format="%Y-%m-%d")
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df.index=df['Date']
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plt.figure(figsize=(16,8))
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plt.plot(df["Close"],label='Close Price history')
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from keras.models import Sequential
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from keras.layers import LSTM,Dropout,Dense
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data=df.sort_index(ascending=True,axis=0)
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new_dataset=pd.DataFrame(index=range(0,len(df)),columns=['Date','Close'])
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for i in range(0,len(data)):
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new_dataset["Date"][i]=data['Date'][i]
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new_dataset["Close"][i]=data["Close"][i]
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new_dataset.index=new_dataset.Date
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new_dataset.drop("Date",axis=1,inplace=True)
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final_dataset=new_dataset.values
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train_data=final_dataset[0:987,:]
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valid_data=final_dataset[987:,:]
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scaler=MinMaxScaler(feature_range=(0,1))
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scaled_data=scaler.fit_transform(final_dataset)
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x_train_data,y_train_data=[],[]
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for i in range(60,len(train_data)):
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x_train_data.append(scaled_data[i-60:i,0])
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y_train_data.append(scaled_data[i,0])
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x_train_data,y_train_data=np.array(x_train_data),np.array(y_train_data)
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x_train_data=np.reshape(x_train_data,(x_train_data.shape[0],x_train_data.shape[1],1))
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lstm_model=Sequential()
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lstm_model.add(LSTM(units=50,return_sequences=True,input_shape=(x_train_data.shape[1],1)))
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lstm_model.add(LSTM(units=50))
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lstm_model.add(Dense(1))
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lstm_model.compile(loss='mean_squared_error',optimizer='adam')
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lstm_model.fit(x_train_data,y_train_data,epochs=1,batch_size=1,verbose=2)
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inputs_data=new_dataset[len(new_dataset)-len(valid_data)-60:].values
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inputs_data=inputs_data.reshape(-1,1)
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inputs_data=scaler.transform(inputs_data)
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X_test=[]
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for i in range(60,inputs_data.shape[0]):
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X_test.append(inputs_data[i-60:i,0])
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X_test=np.array(X_test)
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X_test=np.reshape(X_test,(X_test.shape[0],X_test.shape[1],1))
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closing_price=model.predict(X_test)
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closing_price=scaler.inverse_transform(closing_price)
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lstm_model.save("saved_lstm_model.h5")
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train_data=new_dataset[:987]
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valid_data=new_dataset[987:]
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valid_data['Predictions']=prediction_closing
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plt.plot(train_data["Close"])
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plt.plot(valid_data[['Close',"Predictions"]])

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