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Created October 15, 2023 06:49
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stock_prediction_new_open_n50.ipynb
{
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"<a href=\"https://colab.research.google.com/gist/moneebullah25/8a03f2e09c11b2763e7f9daa58f6e678/stock_prediction_new_open_n50.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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"text": [
"Requirement already satisfied: yfinance in /usr/local/lib/python3.10/dist-packages (0.2.31)\n",
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"Collecting truedata_ws\n",
" Downloading truedata_ws-5.0.7-py3-none-any.whl (48 kB)\n",
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"Collecting colorama>=0.4.3 (from truedata_ws)\n",
" Downloading colorama-0.4.6-py2.py3-none-any.whl (25 kB)\n",
"Collecting lz4==3.1.3 (from truedata_ws)\n",
" Downloading lz4-3.1.3.tar.gz (159 kB)\n",
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"\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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"Successfully installed colorama-0.4.6 lz4-3.1.3 truedata_ws-5.0.7\n"
]
}
],
"source": [
"!pip install yfinance numpy pandas matplotlib seaborn truedata_ws"
]
},
{
"cell_type": "code",
"source": [
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"import yfinance as yf\n",
"from sklearn.preprocessing import StandardScaler\n",
"import torch\n",
"import torch.nn as nn\n",
"import time\n",
"import math, time\n",
"from sklearn.metrics import mean_squared_error\n",
"from dateutil.relativedelta import relativedelta\n",
"from datetime import datetime\n",
"from sklearn.preprocessing import MinMaxScaler\n",
"from truedata_ws.websocket.TD import TD\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import plotly.express as px\n",
"import plotly.graph_objects as go"
],
"metadata": {
"id": "SrzULvEmBbON"
},
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"source": [
"td_obj = TD('--', '--', live_port=8086)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "TsCSNYQvBbMV",
"outputId": "a4b4f366-f94c-45ad-a5b3-bd00de06e19b"
},
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"(2023-10-15 06:44:59,002) WARNING :: Connected successfully to TrueData Real Time Data Service... (PID:214 Thread:137299229021760)\n",
"WARNING:truedata_ws.websocket.TD:\u001b[22m\u001b[34mConnected successfully to TrueData Real Time Data Service... \u001b[0m\n",
"(2023-10-15 06:44:59,226) WARNING :: Connected successfully to TrueData Historical Data Service... (PID:214 Thread:137303218540544)\n",
"WARNING:truedata_ws.websocket.TD:\u001b[22m\u001b[34mConnected successfully to TrueData Historical Data Service... \u001b[0m\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"def prepare_model_input(data, lookback):\n",
" data_raw = data.to_numpy() # convert to numpy array\n",
" data_sequences = []\n",
"\n",
" # create all possible sequences of length lookback\n",
" for index in range(len(data_raw) - lookback):\n",
" data_sequences.append(data_raw[index: index + lookback])\n",
"\n",
" data_sequences = np.array(data_sequences)\n",
"\n",
" return data_sequences"
],
"metadata": {
"id": "M1UA28n_BbKV"
},
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"source": [
"lookback = 20\n",
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
],
"metadata": {
"id": "13PV80jjBbFN"
},
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
],
"metadata": {
"id": "83-5pX4F8c5C",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "a20669d7-961f-460d-ace6-ecbdaa686d2f"
},
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mounted at /content/drive\n"
]
}
]
},
{
"metadata": {
"trusted": true,
"id": "jXZYNVoK-V6c"
},
"cell_type": "code",
"source": [
"class LSTM(nn.Module):\n",
" def __init__(self, input_dim, hidden_dim, num_layers, output_dim):\n",
" super(LSTM, self).__init__()\n",
" self.hidden_dim = hidden_dim\n",
" self.num_layers = num_layers\n",
"\n",
" # Use torch.double here\n",
" self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True).to(torch.double)\n",
" self.fc = nn.Linear(hidden_dim, output_dim).to(torch.double)\n",
"\n",
" def forward(self, x):\n",
" # Explicitly set the data type for initial hidden and cell states\n",
" h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim, dtype=torch.double).requires_grad_().to(device)\n",
" c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim, dtype=torch.double).requires_grad_().to(device)\n",
" out, (hn, cn) = self.lstm(x, (h0.detach(), c0.detach()))\n",
" out = self.fc(out[:, -1, :])\n",
" return out\n"
],
"execution_count": 9,
"outputs": []
},
{
"cell_type": "code",
"source": [
"model = torch.load('./drive/MyDrive/Pretrained_Models/nsei_new_no.pt', map_location=torch.device('cpu'))\n",
"model.eval()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "UYwpYdxVjF9k",
"outputId": "38368d2b-cb98-4400-9a48-800856e252ee"
},
"execution_count": 10,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"LSTM(\n",
" (lstm): LSTM(6, 128, num_layers=4, batch_first=True)\n",
" (fc): Linear(in_features=128, out_features=1, bias=True)\n",
")"
]
},
"metadata": {},
"execution_count": 10
}
]
},
{
"cell_type": "code",
"source": [
"td_hist_data = td_obj.get_historic_data('NIFTY 50', duration='60 D', bar_size='eod') # NIFTY 50 # NIFTY BANK # NIFTY FIN SERVICE"
],
"metadata": {
"id": "a90lcGZUclEQ"
},
"execution_count": 15,
"outputs": []
},
{
"cell_type": "code",
"source": [
"td_df = pd.DataFrame(td_hist_data)\n",
"td_df.rename(columns={'time': 'Date', 'o': 'Open', 'h': 'High', 'l': 'Low', 'v': 'Volume', 'c': 'Close'}, inplace=True)\n",
"td_df.drop(columns=['oi'], inplace=True)\n",
"td_df['NextOpen'] = td_df['Open'].shift(-1)"
],
"metadata": {
"id": "7PPTr---ILPT"
},
"execution_count": 16,
"outputs": []
},
{
"cell_type": "code",
"source": [
"td_df.tail()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "8Z4JUunaXBDy",
"outputId": "897f019f-115d-471a-e5f4-afd666bb8db2"
},
"execution_count": 17,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Date Open High Low Close Volume NextOpen\n",
"35 2023-10-09 19539.45 19588.95 19480.50 19512.35 0 19565.60\n",
"36 2023-10-10 19565.60 19717.80 19565.45 19689.85 0 19767.00\n",
"37 2023-10-11 19767.00 19839.20 19756.95 19811.35 0 19822.70\n",
"38 2023-10-12 19822.70 19843.30 19772.65 19794.00 0 19654.55\n",
"39 2023-10-13 19654.55 19805.40 19635.30 19751.05 0 NaN"
],
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"\n",
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" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
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" try {\n",
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" 'suggestCharts', [key], {});\n",
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"</div>\n",
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]
},
"metadata": {},
"execution_count": 17
}
]
},
{
"cell_type": "code",
"source": [
"scaler_train = StandardScaler()\n",
"scaler_test = StandardScaler()\n",
"\n",
"features_td = td_df[['High', 'Low', 'Open', 'Volume', 'NextOpen', 'Close']]\n",
"scaler_test.fit_transform(td_df[['Close']])\n",
"features_td_scaled = scaler_train.fit_transform(features_td)"
],
"metadata": {
"id": "VA3YyHb80lcW"
},
"execution_count": 18,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Combine normalized features_td with other columns\n",
"data_scaled_td = pd.DataFrame(features_td_scaled, columns=['High', 'Low', 'Open', 'Close', 'NextOpen', 'Volume'])\n",
"data_scaled_td['Date'] = td_df['Date']\n",
"data_scaled_td = data_scaled_td.set_index('Date')\n",
"data_scaled_td = data_scaled_td.sort_values('Date')"
],
"metadata": {
"id": "7XVXzIuO0wew"
},
"execution_count": 19,
"outputs": []
},
{
"cell_type": "code",
"source": [
"print(data_scaled_td.shape)\n",
"data_scaled_td.tail()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 255
},
"id": "_V2S7q3Y1TMv",
"outputId": "cc7ad97c-f0ef-47e2-859c-b91091530f9a"
},
"execution_count": 20,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"(40, 6)\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
" High Low Open Close NextOpen Volume\n",
"Date \n",
"2023-10-09 -0.427539 -0.334074 -0.391331 0.0 -0.303101 -0.473706\n",
"2023-10-10 0.091636 0.010020 -0.285472 0.0 0.507832 0.232650\n",
"2023-10-11 0.580793 0.785700 0.529829 0.0 0.732107 0.716155\n",
"2023-10-12 0.597313 0.849293 0.755312 0.0 0.055054 0.647112\n",
"2023-10-13 0.444603 0.292951 0.074613 0.0 NaN 0.476193"
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},
"metadata": {},
"execution_count": 20
}
]
},
{
"cell_type": "code",
"source": [
"data_scaled_td = data_scaled_td.iloc[:-1 , :]"
],
"metadata": {
"id": "cABkkPh-1TIP"
},
"execution_count": 21,
"outputs": []
},
{
"cell_type": "code",
"source": [
"print(data_scaled_td.shape)\n",
"data_scaled_td.tail()"
],
"metadata": {
"id": "iwpQz72mKODf",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 255
},
"outputId": "a18e9f3b-72b7-4d0a-e855-d88d5dabc447"
},
"execution_count": 22,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"(39, 6)\n"
]
},
{
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"data": {
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" High Low Open Close NextOpen Volume\n",
"Date \n",
"2023-10-06 -0.077796 0.107031 -0.060394 0.0 -0.408394 0.087996\n",
"2023-10-09 -0.427539 -0.334074 -0.391331 0.0 -0.303101 -0.473706\n",
"2023-10-10 0.091636 0.010020 -0.285472 0.0 0.507832 0.232650\n",
"2023-10-11 0.580793 0.785700 0.529829 0.0 0.732107 0.716155\n",
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"\n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
" <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
" </svg>\n",
" </button>\n",
"\n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" .colab-df-buttons div {\n",
" margin-bottom: 4px;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-8402536a-878b-4f57-81a9-db33b44ad9e9 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-8402536a-878b-4f57-81a9-db33b44ad9e9');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
"\n",
"\n",
"<div id=\"df-d3cfc6b7-678a-4ec5-ab69-092672d097b9\">\n",
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d3cfc6b7-678a-4ec5-ab69-092672d097b9')\"\n",
" title=\"Suggest charts.\"\n",
" style=\"display:none;\">\n",
"\n",
"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <g>\n",
" <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
" </g>\n",
"</svg>\n",
" </button>\n",
"\n",
"<style>\n",
" .colab-df-quickchart {\n",
" --bg-color: #E8F0FE;\n",
" --fill-color: #1967D2;\n",
" --hover-bg-color: #E2EBFA;\n",
" --hover-fill-color: #174EA6;\n",
" --disabled-fill-color: #AAA;\n",
" --disabled-bg-color: #DDD;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" --bg-color: #3B4455;\n",
" --fill-color: #D2E3FC;\n",
" --hover-bg-color: #434B5C;\n",
" --hover-fill-color: #FFFFFF;\n",
" --disabled-bg-color: #3B4455;\n",
" --disabled-fill-color: #666;\n",
" }\n",
"\n",
" .colab-df-quickchart {\n",
" background-color: var(--bg-color);\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: var(--fill-color);\n",
" height: 32px;\n",
" padding: 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
" background-color: var(--hover-bg-color);\n",
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: var(--button-hover-fill-color);\n",
" }\n",
"\n",
" .colab-df-quickchart-complete:disabled,\n",
" .colab-df-quickchart-complete:disabled:hover {\n",
" background-color: var(--disabled-bg-color);\n",
" fill: var(--disabled-fill-color);\n",
" box-shadow: none;\n",
" }\n",
"\n",
" .colab-df-spinner {\n",
" border: 2px solid var(--fill-color);\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" animation:\n",
" spin 1s steps(1) infinite;\n",
" }\n",
"\n",
" @keyframes spin {\n",
" 0% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" border-left-color: var(--fill-color);\n",
" }\n",
" 20% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-d3cfc6b7-678a-4ec5-ab69-092672d097b9 button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
" </div>\n",
" </div>\n"
]
},
"metadata": {},
"execution_count": 22
}
]
},
{
"cell_type": "code",
"source": [
"x_td = prepare_model_input(data_scaled_td, lookback)"
],
"metadata": {
"id": "ykcKq52qW8Om"
},
"execution_count": 23,
"outputs": []
},
{
"cell_type": "code",
"source": [
"print(x_td.shape)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Bg5JBUFu8XXs",
"outputId": "820e93be-2d63-493f-b659-b4eafade5e9d"
},
"execution_count": 24,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"(19, 20, 6)\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"x_td = torch.from_numpy(x_td).type(torch.double).to(device)\n",
"x_td = x_td[-1, :, :].unsqueeze(0)"
],
"metadata": {
"id": "L98hxgk48ZAI"
},
"execution_count": 25,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Make predictions on TrueData API data\n",
"y_td_pred = model(x_td)\n",
"\n",
"# Invert predictions\n",
"y_td_pred = scaler_test.inverse_transform(y_td_pred.detach().cpu().numpy())\n",
"\n",
"# Print or use predictions\n",
"print('Predictions for TrueData API:')\n",
"print(y_td_pred)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "bR5ZdYQU9A33",
"outputId": "8f08658f-4f99-4bf2-ab43-00cc69816d9f"
},
"execution_count": 26,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Predictions for TrueData API:\n",
"[[19636.19590559]]\n"
]
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "F_WbX8nLDvAg"
},
"execution_count": null,
"outputs": []
}
]
}
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