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Created October 15, 2023 06:49
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stock_prediction_new_open_nb.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"authorship_tag": "ABX9TyNk9F1qGdaCSwMvGkyh2b/q",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/moneebullah25/6d7b23c4d31f97179d096917af7e288c/stock_prediction_new_open_nb.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "L4plaxiKBQ9P"
},
"outputs": [],
"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": "23431bff-37fe-425b-8800-267ec4459fcd"
},
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"(2023-10-15 06:32:44,243) WARNING :: Connected successfully to TrueData Real Time Data Service... (PID:224 Thread:132302802130496)\n",
"WARNING:truedata_ws.websocket.TD:\u001b[22m\u001b[34mConnected successfully to TrueData Real Time Data Service... \u001b[0m\n",
"(2023-10-15 06:32:44,334) WARNING :: Connected successfully to TrueData Historical Data Service... (PID:224 Thread:132306869886976)\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": 6,
"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": "3a7f77ff-e495-4dde-d280-adb80609f20f"
},
"execution_count": 7,
"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/nsebank_new_no.pt', map_location=torch.device('cpu'))\n",
"model.eval()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "UYwpYdxVjF9k",
"outputId": "19bbfff6-c035-48f3-b33c-c2fd0fc6c64a"
},
"execution_count": 28,
"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": 28
}
]
},
{
"cell_type": "code",
"source": [
"td_hist_data = td_obj.get_historic_data('NIFTY BANK', duration='60 D', bar_size='eod') # NIFTY 50 # NIFTY BANK # NIFTY FIN SERVICE"
],
"metadata": {
"id": "a90lcGZUclEQ"
},
"execution_count": 29,
"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": 30,
"outputs": []
},
{
"cell_type": "code",
"source": [
"td_df.tail()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "8Z4JUunaXBDy",
"outputId": "25ac5483-2a62-4c59-9f76-ae9d3bd3ed13"
},
"execution_count": 31,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Date Open High Low Close Volume NextOpen\n",
"35 2023-10-09 44057.80 44113.20 43796.75 43886.50 0 44027.55\n",
"36 2023-10-10 44027.55 44487.25 44004.55 44360.15 0 44554.90\n",
"37 2023-10-11 44554.90 44710.55 44411.40 44516.90 0 44571.55\n",
"38 2023-10-12 44571.55 44693.05 44530.05 44599.20 0 44322.05\n",
"39 2023-10-13 44322.05 44563.05 44203.70 44287.95 0 NaN"
],
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" <th></th>\n",
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" <th>High</th>\n",
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" <th>Close</th>\n",
" <th>Volume</th>\n",
" <th>NextOpen</th>\n",
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" <th>35</th>\n",
" <td>2023-10-09</td>\n",
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" <td>44113.20</td>\n",
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" <td>44004.55</td>\n",
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" <th>37</th>\n",
" <td>2023-10-11</td>\n",
" <td>44554.90</td>\n",
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" <th>39</th>\n",
" <td>2023-10-13</td>\n",
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"\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",
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" element.innerHTML = '';\n",
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},
"metadata": {},
"execution_count": 31
}
]
},
{
"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": 41,
"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": 42,
"outputs": []
},
{
"cell_type": "code",
"source": [
"print(data_scaled_td.shape)\n",
"data_scaled_td.tail()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 255
},
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"outputId": "b8514bbb-b520-46a4-a56d-78b03e1c5e97"
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"execution_count": 43,
"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 -1.184121 -1.079462 -0.961895 0.0 -1.050157 -1.224945\n",
"2023-10-10 -0.576613 -0.726399 -1.011465 0.0 -0.179975 -0.450382\n",
"2023-10-11 -0.213943 -0.035141 -0.147318 0.0 -0.152501 -0.194048\n",
"2023-10-12 -0.242366 0.166452 -0.120034 0.0 -0.564202 -0.059462\n",
"2023-10-13 -0.453503 -0.388033 -0.528879 0.0 NaN -0.568451"
],
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"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Open</th>\n",
" <th>Close</th>\n",
" <th>NextOpen</th>\n",
" <th>Volume</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2023-10-09</th>\n",
" <td>-1.184121</td>\n",
" <td>-1.079462</td>\n",
" <td>-0.961895</td>\n",
" <td>0.0</td>\n",
" <td>-1.050157</td>\n",
" <td>-1.224945</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-10-10</th>\n",
" <td>-0.576613</td>\n",
" <td>-0.726399</td>\n",
" <td>-1.011465</td>\n",
" <td>0.0</td>\n",
" <td>-0.179975</td>\n",
" <td>-0.450382</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-10-11</th>\n",
" <td>-0.213943</td>\n",
" <td>-0.035141</td>\n",
" <td>-0.147318</td>\n",
" <td>0.0</td>\n",
" <td>-0.152501</td>\n",
" <td>-0.194048</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-10-12</th>\n",
" <td>-0.242366</td>\n",
" <td>0.166452</td>\n",
" <td>-0.120034</td>\n",
" <td>0.0</td>\n",
" <td>-0.564202</td>\n",
" <td>-0.059462</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-10-13</th>\n",
" <td>-0.453503</td>\n",
" <td>-0.388033</td>\n",
" <td>-0.528879</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>-0.568451</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <div class=\"colab-df-buttons\">\n",
"\n",
" <div class=\"colab-df-container\">\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-59eca1b3-e3df-4d5a-ac31-82917404259a')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
" style=\"display:none;\">\n",
"\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-59eca1b3-e3df-4d5a-ac31-82917404259a 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-59eca1b3-e3df-4d5a-ac31-82917404259a');\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-b5bca3fa-4122-4e82-b282-6b09e794ed36\">\n",
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b5bca3fa-4122-4e82-b282-6b09e794ed36')\"\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-b5bca3fa-4122-4e82-b282-6b09e794ed36 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": 43
}
]
},
{
"cell_type": "code",
"source": [
"data_scaled_td = data_scaled_td.iloc[:-1 , :]"
],
"metadata": {
"id": "cABkkPh-1TIP"
},
"execution_count": 44,
"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": "37e3a89c-316b-47cc-a75c-259bd8942be4"
},
"execution_count": 45,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"(39, 6)\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
" High Low Open Close NextOpen Volume\n",
"Date \n",
"2023-10-06 -0.554768 -0.321346 -0.408684 0.0 -1.000242 -0.449646\n",
"2023-10-09 -1.184121 -1.079462 -0.961895 0.0 -1.050157 -1.224945\n",
"2023-10-10 -0.576613 -0.726399 -1.011465 0.0 -0.179975 -0.450382\n",
"2023-10-11 -0.213943 -0.035141 -0.147318 0.0 -0.152501 -0.194048\n",
"2023-10-12 -0.242366 0.166452 -0.120034 0.0 -0.564202 -0.059462"
],
"text/html": [
"\n",
" <div id=\"df-0300802c-d535-411a-a83a-80b793a2bd91\" class=\"colab-df-container\">\n",
" <div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Open</th>\n",
" <th>Close</th>\n",
" <th>NextOpen</th>\n",
" <th>Volume</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2023-10-06</th>\n",
" <td>-0.554768</td>\n",
" <td>-0.321346</td>\n",
" <td>-0.408684</td>\n",
" <td>0.0</td>\n",
" <td>-1.000242</td>\n",
" <td>-0.449646</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-10-09</th>\n",
" <td>-1.184121</td>\n",
" <td>-1.079462</td>\n",
" <td>-0.961895</td>\n",
" <td>0.0</td>\n",
" <td>-1.050157</td>\n",
" <td>-1.224945</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-10-10</th>\n",
" <td>-0.576613</td>\n",
" <td>-0.726399</td>\n",
" <td>-1.011465</td>\n",
" <td>0.0</td>\n",
" <td>-0.179975</td>\n",
" <td>-0.450382</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-10-11</th>\n",
" <td>-0.213943</td>\n",
" <td>-0.035141</td>\n",
" <td>-0.147318</td>\n",
" <td>0.0</td>\n",
" <td>-0.152501</td>\n",
" <td>-0.194048</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2023-10-12</th>\n",
" <td>-0.242366</td>\n",
" <td>0.166452</td>\n",
" <td>-0.120034</td>\n",
" <td>0.0</td>\n",
" <td>-0.564202</td>\n",
" <td>-0.059462</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <div class=\"colab-df-buttons\">\n",
"\n",
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" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0300802c-d535-411a-a83a-80b793a2bd91')\"\n",
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" style=\"display:none;\">\n",
"\n",
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" </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",
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" }\n",
"\n",
" .colab-df-buttons div {\n",
" margin-bottom: 4px;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
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" fill: #D2E3FC;\n",
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"\n",
" [theme=dark] .colab-df-convert:hover {\n",
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" 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",
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" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-0300802c-d535-411a-a83a-80b793a2bd91 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-0300802c-d535-411a-a83a-80b793a2bd91');\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-1c025464-8902-45f7-8da2-bb4195494820\">\n",
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1c025464-8902-45f7-8da2-bb4195494820')\"\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",
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" --disabled-fill-color: #666;\n",
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" 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-1c025464-8902-45f7-8da2-bb4195494820 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": 45
}
]
},
{
"cell_type": "code",
"source": [
"x_td = prepare_model_input(data_scaled_td, lookback)"
],
"metadata": {
"id": "ykcKq52qW8Om"
},
"execution_count": 46,
"outputs": []
},
{
"cell_type": "code",
"source": [
"print(x_td.shape)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Bg5JBUFu8XXs",
"outputId": "daeaaf38-c7dc-4c31-d92a-682a718f4557"
},
"execution_count": 47,
"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": 48,
"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": "871b8dec-bb65-4a8f-9011-4ee4c26594c0"
},
"execution_count": 49,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Predictions for TrueData API:\n",
"[[44341.87956096]]\n"
]
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "F_WbX8nLDvAg"
},
"execution_count": null,
"outputs": []
}
]
}
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