git_Stock_app.ipynb

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        "\"\"\"\n",
        "Stock Price Prediction using LSTM\n",
        "\n",
        "This project uses historical stock price data to predict future prices of five stocks:\n",
        "- Microsoft\n",
        "- Tesla\n",
        "- Apple\n",
        "- Tata Beverages (training data)\n",
        "- Facebook\n",
        "\n",
        "The project has two main parts:\n",
        "- Long Short-Term Memory (LSTM) model development\n",
        "- dashboard creation\n",
        "\n",
        "The dataset used includes historical stock prices, and the models are evaluated based on their prediction accuracy.\n",
        "\n",
        "Requirements:\n",
        "- Python 3.x\n",
        "- pandas\n",
        "- numpy\n",
        "- keras\n",
        "- flask\n",
        "- matplotlib\n",
        "\"\"\"\n",
        "#available for non-commercial use only\n",
        "\n",
        "import dash\n",
        "import dash_core_components as dcc\n",
        "import dash_html_components as html\n",
        "import pandas as pd\n",
        "import plotly.graph_objs as go\n",
        "from dash.dependencies import Input, Output\n",
        "from keras.models import load_model\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "app = dash.Dash()\n",
        "server = app.server\n",
        "\n",
        "scaler=MinMaxScaler(feature_range=(0,1))\n",
        "'''\n",
        "Upload training set below\n",
        "'''\n",
        "#df_nse = pd.read_csv(\"File/NSE-TATA.csv\")\n",
        "\n",
        "df_nse[\"Date\"]=pd.to_datetime(df_nse.Date,format=\"%Y-%m-%d\")\n",
        "df_nse.index=df_nse['Date']\n",
        "\n",
        "\n",
        "data=df_nse.sort_index(ascending=True,axis=0)\n",
        "new_data=pd.DataFrame(index=range(0,len(df_nse)),columns=['Date','Close'])\n",
        "\n",
        "for i in range(0,len(data)):\n",
        "    new_data[\"Date\"][i]=data['Date'][i]\n",
        "    new_data[\"Close\"][i]=data[\"Close\"][i]\n",
        "\n",
        "new_data.index=new_data.Date\n",
        "new_data.drop(\"Date\",axis=1,inplace=True)\n",
        "\n",
        "dataset=new_data.values\n",
        "\n",
        "train=dataset[0:987,:]\n",
        "valid=dataset[987:,:]\n",
        "\n",
        "scaler=MinMaxScaler(feature_range=(0,1))\n",
        "scaled_data=scaler.fit_transform(dataset)\n",
        "\n",
        "x_train,y_train=[],[]\n",
        "\n",
        "for i in range(60,len(train)):\n",
        "    x_train.append(scaled_data[i-60:i,0])\n",
        "    y_train.append(scaled_data[i,0])\n",
        "\n",
        "x_train,y_train=np.array(x_train),np.array(y_train)\n",
        "\n",
        "x_train=np.reshape(x_train,(x_train.shape[0],x_train.shape[1],1))\n",
        "\n",
        "model=load_model(\"saved_lstm_model.h5\")\n",
        "#model=load_model(\"saved_model.h5\")\n",
        "\n",
        "inputs=new_data[len(new_data)-len(valid)-60:].values\n",
        "inputs=inputs.reshape(-1,1)\n",
        "inputs=scaler.transform(inputs)\n",
        "\n",
        "X_test=[]\n",
        "for i in range(60,inputs.shape[0]):\n",
        "    X_test.append(inputs[i-60:i,0])\n",
        "X_test=np.array(X_test)\n",
        "\n",
        "X_test=np.reshape(X_test,(X_test.shape[0],X_test.shape[1],1))\n",
        "closing_price=model.predict(X_test)\n",
        "closing_price=scaler.inverse_transform(closing_price)\n",
        "\n",
        "train=new_data[:987]\n",
        "valid=new_data[987:]\n",
        "valid['Predictions']=closing_price\n",
        "\n",
        "\n",
        "\n",
        "df= pd.read_csv(\"/stock_data.csv\")\n",
        "\n",
        "app.layout = html.Div([\n",
        "\n",
        "    html.H1(\"Stock Price Analysis Dashboard\", style={\"textAlign\": \"center\"}),\n",
        "\n",
        "    dcc.Tabs(id=\"tabs\", children=[\n",
        "\n",
        "        dcc.Tab(label='NSE-TATAGLOBAL Stock Data',children=[\n",
        "            html.Div([\n",
        "                html.H2(\"Actual closing price\",style={\"textAlign\": \"center\"}),\n",
        "                dcc.Graph(\n",
        "                    id=\"Actual Data\",\n",
        "                    figure={\n",
        "                        \"data\":[\n",
        "                            go.Scatter(\n",
        "                                x=train.index,\n",
        "                                y=valid[\"Close\"],\n",
        "                                mode='markers'\n",
        "                            )\n",
        "\n",
        "                        ],\n",
        "                        \"layout\":go.Layout(\n",
        "                            title='scatter plot',\n",
        "                            xaxis={'title':'Date'},\n",
        "                            yaxis={'title':'Closing Rate'}\n",
        "                        )\n",
        "                    }\n",
        "\n",
        "                ),\n",
        "                html.H2(\"LSTM Predicted closing price\",style={\"textAlign\": \"center\"}),\n",
        "                dcc.Graph(\n",
        "                    id=\"Predicted Data\",\n",
        "                    figure={\n",
        "                        \"data\":[\n",
        "                            go.Scatter(\n",
        "                                x=valid.index,\n",
        "                                y=valid[\"Predictions\"],\n",
        "                                mode='markers'\n",
        "                            )\n",
        "\n",
        "                        ],\n",
        "                        \"layout\":go.Layout(\n",
        "                            title='scatter plot',\n",
        "                            xaxis={'title':'Date'},\n",
        "                            yaxis={'title':'Closing Rate'}\n",
        "                        )\n",
        "                    }\n",
        "\n",
        "                )\n",
        "            ])\n",
        "\n",
        "\n",
        "        ]),\n",
        "        dcc.Tab(label='Facebook Stock Data', children=[\n",
        "            html.Div([\n",
        "                html.H1(\"Facebook Stocks High vs Lows\",\n",
        "                        style={'textAlign': 'center'}),\n",
        "\n",
        "                dcc.Dropdown(id='my-dropdown',\n",
        "                             options=[{'label': 'Tesla', 'value': 'TSLA'},\n",
        "                                      {'label': 'Apple','value': 'AAPL'},\n",
        "                                      {'label': 'Facebook', 'value': 'FB'},\n",
        "                                      {'label': 'Microsoft','value': 'MSFT'}],\n",
        "                             multi=True,value=['FB'],\n",
        "                             style={\"display\": \"block\", \"margin-left\": \"auto\",\n",
        "                                    \"margin-right\": \"auto\", \"width\": \"60%\"}),\n",
        "                dcc.Graph(id='highlow'),\n",
        "                html.H1(\"Facebook Market Volume\", style={'textAlign': 'center'}),\n",
        "\n",
        "                dcc.Dropdown(id='my-dropdown2',\n",
        "                             options=[{'label': 'Tesla', 'value': 'TSLA'},\n",
        "                                      {'label': 'Apple','value': 'AAPL'},\n",
        "                                      {'label': 'Facebook', 'value': 'FB'},\n",
        "                                      {'label': 'Microsoft','value': 'MSFT'}],\n",
        "                             multi=True,value=['FB'],\n",
        "                             style={\"display\": \"block\", \"margin-left\": \"auto\",\n",
        "                                    \"margin-right\": \"auto\", \"width\": \"60%\"}),\n",
        "                dcc.Graph(id='volume')\n",
        "            ], className=\"container\"),\n",
        "        ])\n",
        "\n",
        "\n",
        "    ])\n",
        "])\n",
        "\n",
        "\n",
        "@app.callback(Output('highlow', 'figure'),\n",
        "              [Input('my-dropdown', 'value')])\n",
        "def update_graph(selected_dropdown):\n",
        "    dropdown = {\"TSLA\": \"Tesla\",\"AAPL\": \"Apple\",\"FB\": \"Facebook\",\"MSFT\": \"Microsoft\",}\n",
        "    trace1 = []\n",
        "    trace2 = []\n",
        "    for stock in selected_dropdown:\n",
        "        trace1.append(\n",
        "          go.Scatter(x=df[df[\"Stock\"] == stock][\"Date\"],\n",
        "                     y=df[df[\"Stock\"] == stock][\"High\"],\n",
        "                     mode='lines', opacity=0.7,\n",
        "                     name=f'High {dropdown[stock]}',textposition='bottom center'))\n",
        "        trace2.append(\n",
        "          go.Scatter(x=df[df[\"Stock\"] == stock][\"Date\"],\n",
        "                     y=df[df[\"Stock\"] == stock][\"Low\"],\n",
        "                     mode='lines', opacity=0.6,\n",
        "                     name=f'Low {dropdown[stock]}',textposition='bottom center'))\n",
        "    traces = [trace1, trace2]\n",
        "    data = [val for sublist in traces for val in sublist]\n",
        "    figure = {'data': data,\n",
        "              'layout': go.Layout(colorway=[\"#5E0DAC\", '#FF4F00', '#375CB1',\n",
        "                                            '#FF7400', '#FFF400', '#FF0056'],\n",
        "            height=600,\n",
        "            title=f\"High and Low Prices for {', '.join(str(dropdown[i]) for i in selected_dropdown)} Over Time\",\n",
        "            xaxis={\"title\":\"Date\",\n",
        "                   'rangeselector': {'buttons': list([{'count': 1, 'label': '1M',\n",
        "                                                       'step': 'month',\n",
        "                                                       'stepmode': 'backward'},\n",
        "                                                      {'count': 6, 'label': '6M',\n",
        "                                                       'step': 'month',\n",
        "                                                       'stepmode': 'backward'},\n",
        "                                                      {'step': 'all'}])},\n",
        "                   'rangeslider': {'visible': True}, 'type': 'date'},\n",
        "             yaxis={\"title\":\"Price (USD)\"})}\n",
        "    return figure\n",
        "\n",
        "\n",
        "@app.callback(Output('volume', 'figure'),\n",
        "              [Input('my-dropdown2', 'value')])\n",
        "def update_graph(selected_dropdown_value):\n",
        "    dropdown = {\"TSLA\": \"Tesla\",\"AAPL\": \"Apple\",\"FB\": \"Facebook\",\"MSFT\": \"Microsoft\",}\n",
        "    trace1 = []\n",
        "    for stock in selected_dropdown_value:\n",
        "        trace1.append(\n",
        "          go.Scatter(x=df[df[\"Stock\"] == stock][\"Date\"],\n",
        "                     y=df[df[\"Stock\"] == stock][\"Volume\"],\n",
        "                     mode='lines', opacity=0.7,\n",
        "                     name=f'Volume {dropdown[stock]}', textposition='bottom center'))\n",
        "    traces = [trace1]\n",
        "    data = [val for sublist in traces for val in sublist]\n",
        "    figure = {'data': data,\n",
        "              'layout': go.Layout(colorway=[\"#5E0DAC\", '#FF4F00', '#375CB1',\n",
        "                                            '#FF7400', '#FFF400', '#FF0056'],\n",
        "            height=600,\n",
        "            title=f\"Market Volume for {', '.join(str(dropdown[i]) for i in selected_dropdown_value)} Over Time\",\n",
        "            xaxis={\"title\":\"Date\",\n",
        "                   'rangeselector': {'buttons': list([{'count': 1, 'label': '1M',\n",
        "                                                       'step': 'month',\n",
        "                                                       'stepmode': 'backward'},\n",
        "                                                      {'count': 6, 'label': '6M',\n",
        "                                                       'step': 'month',\n",
        "                                                       'stepmode': 'backward'},\n",
        "                                                      {'step': 'all'}])},\n",
        "                   'rangeslider': {'visible': True}, 'type': 'date'},\n",
        "             yaxis={\"title\":\"Transactions Volume\"})}\n",
        "    return figure\n",
        "\n",
        "\n",
        "if __name__=='__main__':\n",
        "    #app.run_server(debug=True)\n",
        "    app.run_server()"
      ]
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