Prediccion_Precios_Acciones_LSTM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "03df03a6-48f2-465b-b5ed-5520f46f053b",
   "metadata": {},
   "source": [
    "# **Predicting Stock Prices with Deep Neural Networks**\n",
    "\n",
    "This project walks you through the end-to-end data science lifecycle of developing a predictive model for stock price movements with Alpha Vantage APIs and a powerful machine learning algorithm called Long Short-Term Memory (LSTM). By completing this project, you will learn the key concepts of machine learning / deep learning and build a fully functional predictive model for the stock market, all in a single Python file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8602d72a-ed8d-4de5-b311-aa7e164abfd7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "! pip install alpha_vantage -q\n",
    "\n",
    "# pip install numpy\n",
    "import numpy as np\n",
    "\n",
    "# pip install torch\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import Dataset\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "# pip install matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import figure\n",
    "\n",
    "# pip install alpha_vantage\n",
    "from alpha_vantage.foreignexchange import ForeignExchange\n",
    "from alpha_vantage.timeseries import TimeSeries\n",
    "from alpha_vantage.techindicators import TechIndicators\n",
    "from alpha_vantage.sectorperformance import SectorPerformances\n",
    "from alpha_vantage.cryptocurrencies import CryptoCurrencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b6da22ac-02d8-4b85-8c58-1588c48d7b32",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "config = {\n",
    "    \"alpha_vantage\": {\n",
    "        \"key\": \"QDVUL9L3I646J5IR\", # Claim your free API key here: https://www.alphavantage.co/support/#api-key\n",
    "        \"symbol\": \"AAPL\",\n",
    "        \"outputsize\": \"full\",\n",
    "        \"key_close\": \"4. close\",\n",
    "    },\n",
    "    \"data\": {\n",
    "        \"window_size\": 20,\n",
    "        \"train_split_size\": 0.80,\n",
    "    }, \n",
    "    \"plots\": {\n",
    "        \"show_plots\": True,\n",
    "        \"xticks_interval\": 90,\n",
    "        \"color_actual\": \"#001f3f\",\n",
    "        \"color_train\": \"#3D9970\",\n",
    "        \"color_val\": \"#0074D9\",\n",
    "        \"color_pred_train\": \"#3D9970\",\n",
    "        \"color_pred_val\": \"#0074D9\",\n",
    "        \"color_pred_test\": \"#FF4136\",\n",
    "    },\n",
    "    \"model\": {\n",
    "        \"input_size\": 1, # since we are only using 1 feature, close price\n",
    "        \"num_lstm_layers\": 2,\n",
    "        \"lstm_size\": 32,\n",
    "        \"dropout\": 0.2,\n",
    "    },\n",
    "    \"training\": {\n",
    "        \"device\": \"cpu\", # \"cuda\" or \"cpu\"\n",
    "        \"batch_size\": 64,\n",
    "        \"num_epoch\": 100,\n",
    "        \"learning_rate\": 0.01,\n",
    "        \"scheduler_step_size\": 40,\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "524aa988-f6b8-4bcc-88b6-471d4938c078",
   "metadata": {},
   "source": [
    "## Data preparation: acquiring financial market data from Alpha Vantage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3c3032f3-f262-4d93-88d7-bfc3eb256139",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def download_data(config, plot=False):\n",
    "    # get the data from alpha vantage\n",
    "\n",
    "    ts = TimeSeries(key=config[\"alpha_vantage\"][\"key\"])\n",
    "    data, meta_data = ts.get_daily(config[\"alpha_vantage\"][\"symbol\"], outputsize=config[\"alpha_vantage\"][\"outputsize\"])\n",
    "\n",
    "    data_date = [date for date in data.keys()]\n",
    "    data_date.reverse()\n",
    "\n",
    "    data_close_price = [float(data[date][config[\"alpha_vantage\"][\"key_close\"]]) for date in data.keys()]\n",
    "    data_close_price.reverse()\n",
    "    data_close_price = np.array(data_close_price)\n",
    "\n",
    "    num_data_points = len(data_date)\n",
    "    display_date_range = \"from \" + data_date[0] + \" to \" + data_date[num_data_points-1]\n",
    "    print(\"Number data points:\", num_data_points, display_date_range)\n",
    "\n",
    "    if plot:\n",
    "        fig = figure(figsize=(25, 5), dpi=80)\n",
    "        fig.patch.set_facecolor((1.0, 1.0, 1.0))\n",
    "        plt.plot(data_date, data_close_price, color=config[\"plots\"][\"color_actual\"])\n",
    "        xticks = [data_date[i] if ((i%config[\"plots\"][\"xticks_interval\"]==0 and (num_data_points-i) > config[\"plots\"][\"xticks_interval\"]) or i==num_data_points-1) else None for i in range(num_data_points)] # make x ticks nice\n",
    "        x = np.arange(0,len(xticks))\n",
    "        plt.xticks(x, xticks, rotation='vertical')\n",
    "        plt.title(\"Daily close price for \" + config[\"alpha_vantage\"][\"symbol\"] + \", \" + display_date_range)\n",
    "        plt.grid(True, which='major', axis='y', linestyle='--')\n",
    "        plt.show()\n",
    "\n",
    "    return data_date, data_close_price, num_data_points, display_date_range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b0a2e391-8e8e-4ed6-b378-d9ecd7387e15",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number data points: 6166 from 1999-11-01 to 2024-05-03\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_date, data_close_price, num_data_points, display_date_range = download_data(config, plot=config[\"plots\"][\"show_plots\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41df4455-888f-4ae8-b03f-ed68665a3a2e",
   "metadata": {},
   "source": [
    "## Data preparation: normalizing raw financial data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d08d67bd-200e-4176-9a54-a64b2ce9afea",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "class Normalizer():\n",
    "    def __init__(self):\n",
    "        self.mu = None\n",
    "        self.sd = None\n",
    "\n",
    "    def fit_transform(self, x):\n",
    "        self.mu = np.mean(x, axis=(0), keepdims=True)\n",
    "        self.sd = np.std(x, axis=(0), keepdims=True)\n",
    "        normalized_x = (x - self.mu)/self.sd\n",
    "        return normalized_x\n",
    "\n",
    "    def inverse_transform(self, x):\n",
    "        return (x*self.sd) + self.mu\n",
    "\n",
    "# normalize\n",
    "scaler = Normalizer()\n",
    "normalized_data_close_price = scaler.fit_transform(data_close_price)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4fea3dd-58ee-4b9f-a6bc-b001e9f487cf",
   "metadata": {},
   "source": [
    "## Data preparation: generating training and validation datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "242481ec-978d-49b6-9ee0-5bd796e3f163",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def prepare_data_x(x, window_size):\n",
    "    # perform windowing\n",
    "    n_row = x.shape[0] - window_size + 1\n",
    "    output = np.lib.stride_tricks.as_strided(x, shape=(n_row,window_size), strides=(x.strides[0],x.strides[0]))\n",
    "    return output[:-1], output[-1]\n",
    "\n",
    "def prepare_data_y(x, window_size):\n",
    "    # # perform simple moving average\n",
    "    # output = np.convolve(x, np.ones(window_size), 'valid') / window_size\n",
    "\n",
    "    # use the next day as label\n",
    "    output = x[window_size:]\n",
    "    return output\n",
    "\n",
    "def prepare_data(normalized_data_close_price, config, plot=False):\n",
    "    data_x, data_x_unseen = prepare_data_x(normalized_data_close_price, window_size=config[\"data\"][\"window_size\"])\n",
    "    data_y = prepare_data_y(normalized_data_close_price, window_size=config[\"data\"][\"window_size\"])\n",
    "\n",
    "    # split dataset\n",
    "\n",
    "    split_index = int(data_y.shape[0]*config[\"data\"][\"train_split_size\"])\n",
    "    data_x_train = data_x[:split_index]\n",
    "    data_x_val = data_x[split_index:]\n",
    "    data_y_train = data_y[:split_index]\n",
    "    data_y_val = data_y[split_index:]\n",
    "\n",
    "    if plot:\n",
    "        # prepare data for plotting\n",
    "\n",
    "        to_plot_data_y_train = np.zeros(num_data_points)\n",
    "        to_plot_data_y_val = np.zeros(num_data_points)\n",
    "\n",
    "        to_plot_data_y_train[config[\"data\"][\"window_size\"]:split_index+config[\"data\"][\"window_size\"]] = scaler.inverse_transform(data_y_train)\n",
    "        to_plot_data_y_val[split_index+config[\"data\"][\"window_size\"]:] = scaler.inverse_transform(data_y_val)\n",
    "\n",
    "        to_plot_data_y_train = np.where(to_plot_data_y_train == 0, None, to_plot_data_y_train)\n",
    "        to_plot_data_y_val = np.where(to_plot_data_y_val == 0, None, to_plot_data_y_val)\n",
    "\n",
    "        ## plots\n",
    "\n",
    "        fig = figure(figsize=(25, 5), dpi=80)\n",
    "        fig.patch.set_facecolor((1.0, 1.0, 1.0))\n",
    "        plt.plot(data_date, to_plot_data_y_train, label=\"Prices (train)\", color=config[\"plots\"][\"color_train\"])\n",
    "        plt.plot(data_date, to_plot_data_y_val, label=\"Prices (validation)\", color=config[\"plots\"][\"color_val\"])\n",
    "        xticks = [data_date[i] if ((i%config[\"plots\"][\"xticks_interval\"]==0 and (num_data_points-i) > config[\"plots\"][\"xticks_interval\"]) or i==num_data_points-1) else None for i in range(num_data_points)] # make x ticks nice\n",
    "        x = np.arange(0,len(xticks))\n",
    "        plt.xticks(x, xticks, rotation='vertical')\n",
    "        plt.title(\"Daily close prices for \" + config[\"alpha_vantage\"][\"symbol\"] + \" - showing training and validation data\")\n",
    "        plt.grid(True, which='major', axis='y', linestyle='--')\n",
    "        plt.legend()\n",
    "        plt.show()\n",
    "\n",
    "    return split_index, data_x_train, data_y_train, data_x_val, data_y_val, data_x_unseen\n",
    "\n",
    "split_index, data_x_train, data_y_train, data_x_val, data_y_val, data_x_unseen = prepare_data(normalized_data_close_price, config, plot=config[\"plots\"][\"show_plots\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "165b4ad8-50d9-48a6-a2b3-a412eff53b2c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape (4916, 20, 1) (4916,)\n",
      "Validation data shape (1230, 20, 1) (1230,)\n"
     ]
    }
   ],
   "source": [
    "class TimeSeriesDataset(Dataset):\n",
    "    def __init__(self, x, y):\n",
    "        x = np.expand_dims(x, 2) # in our case, we have only 1 feature, so we need to convert `x` into [batch, sequence, features] for LSTM\n",
    "        self.x = x.astype(np.float32)\n",
    "        self.y = y.astype(np.float32)\n",
    "        \n",
    "    def __len__(self):\n",
    "        return len(self.x)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return (self.x[idx], self.y[idx])\n",
    "\n",
    "dataset_train = TimeSeriesDataset(data_x_train, data_y_train)\n",
    "dataset_val = TimeSeriesDataset(data_x_val, data_y_val)\n",
    "\n",
    "print(\"Train data shape\", dataset_train.x.shape, dataset_train.y.shape)\n",
    "print(\"Validation data shape\", dataset_val.x.shape, dataset_val.y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05de1794-e49a-4d19-bbdc-257ebea81f52",
   "metadata": {},
   "source": [
    "## Defining the LSTM model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "51e9873e-836a-46f3-8517-da940dc22cde",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "class LSTMModel(nn.Module):\n",
    "    def __init__(self, input_size=1, hidden_layer_size=32, num_layers=2, output_size=1, dropout=0.2):\n",
    "        super().__init__()\n",
    "        self.hidden_layer_size = hidden_layer_size\n",
    "\n",
    "        self.linear_1 = nn.Linear(input_size, hidden_layer_size)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.lstm = nn.LSTM(hidden_layer_size, hidden_size=self.hidden_layer_size, num_layers=num_layers, batch_first=True)\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        self.linear_2 = nn.Linear(num_layers*hidden_layer_size, output_size)\n",
    "        \n",
    "        self.init_weights()\n",
    "\n",
    "    def init_weights(self):\n",
    "        for name, param in self.lstm.named_parameters():\n",
    "            if 'bias' in name:\n",
    "                 nn.init.constant_(param, 0.0)\n",
    "            elif 'weight_ih' in name:\n",
    "                 nn.init.kaiming_normal_(param)\n",
    "            elif 'weight_hh' in name:\n",
    "                 nn.init.orthogonal_(param)\n",
    "\n",
    "    def forward(self, x):\n",
    "        batchsize = x.shape[0]\n",
    "\n",
    "        # layer 1\n",
    "        x = self.linear_1(x)\n",
    "        x = self.relu(x)\n",
    "        \n",
    "        # LSTM layer\n",
    "        lstm_out, (h_n, c_n) = self.lstm(x)\n",
    "\n",
    "        # reshape output from hidden cell into [batch, features] for `linear_2`\n",
    "        x = h_n.permute(1, 0, 2).reshape(batchsize, -1) \n",
    "        \n",
    "        # layer 2\n",
    "        x = self.dropout(x)\n",
    "        predictions = self.linear_2(x)\n",
    "        return predictions[:,-1]\n",
    "\n",
    "model = LSTMModel(input_size=config[\"model\"][\"input_size\"], hidden_layer_size=config[\"model\"][\"lstm_size\"], num_layers=config[\"model\"][\"num_lstm_layers\"], output_size=1, dropout=config[\"model\"][\"dropout\"])\n",
    "model = model.to(config[\"training\"][\"device\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09d749e8-7ee3-40c1-ad38-a83cc71b8205",
   "metadata": {},
   "source": [
    "## Model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "bb431b7e-9dd3-45f6-ac43-a90035249e1d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def run_epoch(dataloader, is_training=False):\n",
    "    epoch_loss = 0\n",
    "\n",
    "    if is_training:\n",
    "        model.train()\n",
    "    else:\n",
    "        model.eval()\n",
    "\n",
    "    for idx, (x, y) in enumerate(dataloader):\n",
    "        if is_training:\n",
    "            optimizer.zero_grad()\n",
    "\n",
    "        batchsize = x.shape[0]\n",
    "\n",
    "        x = x.to(config[\"training\"][\"device\"])\n",
    "        y = y.to(config[\"training\"][\"device\"])\n",
    "\n",
    "        out = model(x)\n",
    "        loss = criterion(out.contiguous(), y.contiguous())\n",
    "\n",
    "        if is_training:\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "        epoch_loss += (loss.detach().item() / batchsize)\n",
    "\n",
    "    lr = scheduler.get_last_lr()[0]\n",
    "\n",
    "    return epoch_loss, lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "8b0c8389-350f-405e-b150-42f7dbb65525",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch[1/100] | loss train:0.080630, test:0.002548 | lr:0.010000\n",
      "Epoch[2/100] | loss train:0.020364, test:0.002140 | lr:0.010000\n",
      "Epoch[3/100] | loss train:0.014674, test:0.003171 | lr:0.010000\n",
      "Epoch[4/100] | loss train:0.018258, test:0.002449 | lr:0.010000\n",
      "Epoch[5/100] | loss train:0.016643, test:0.001892 | lr:0.010000\n",
      "Epoch[6/100] | loss train:0.012675, test:0.002247 | lr:0.010000\n",
      "Epoch[7/100] | loss train:0.015618, test:0.002080 | lr:0.010000\n",
      "Epoch[8/100] | loss train:0.014372, test:0.003632 | lr:0.010000\n",
      "Epoch[9/100] | loss train:0.015077, test:0.001966 | lr:0.010000\n",
      "Epoch[10/100] | loss train:0.018324, test:0.005745 | lr:0.010000\n",
      "Epoch[11/100] | loss train:0.014775, test:0.003210 | lr:0.010000\n",
      "Epoch[12/100] | loss train:0.013640, test:0.002602 | lr:0.010000\n",
      "Epoch[13/100] | loss train:0.015529, test:0.002283 | lr:0.010000\n",
      "Epoch[14/100] | loss train:0.014730, test:0.002396 | lr:0.010000\n",
      "Epoch[15/100] | loss train:0.013858, test:0.002327 | lr:0.010000\n",
      "Epoch[16/100] | loss train:0.013453, test:0.002162 | lr:0.010000\n",
      "Epoch[17/100] | loss train:0.016523, test:0.001905 | lr:0.010000\n",
      "Epoch[18/100] | loss train:0.012912, test:0.002117 | lr:0.010000\n",
      "Epoch[19/100] | loss train:0.015763, test:0.002725 | lr:0.010000\n",
      "Epoch[20/100] | loss train:0.015143, test:0.002090 | lr:0.010000\n",
      "Epoch[21/100] | loss train:0.013012, test:0.003444 | lr:0.010000\n",
      "Epoch[22/100] | loss train:0.014621, test:0.002767 | lr:0.010000\n",
      "Epoch[23/100] | loss train:0.015683, test:0.002799 | lr:0.010000\n",
      "Epoch[24/100] | loss train:0.014397, test:0.002584 | lr:0.010000\n",
      "Epoch[25/100] | loss train:0.013580, test:0.002493 | lr:0.010000\n",
      "Epoch[26/100] | loss train:0.013064, test:0.002305 | lr:0.010000\n",
      "Epoch[27/100] | loss train:0.012919, test:0.002268 | lr:0.010000\n",
      "Epoch[28/100] | loss train:0.012544, test:0.002037 | lr:0.010000\n",
      "Epoch[29/100] | loss train:0.012933, test:0.004263 | lr:0.010000\n",
      "Epoch[30/100] | loss train:0.013367, test:0.001923 | lr:0.010000\n",
      "Epoch[31/100] | loss train:0.013598, test:0.001897 | lr:0.010000\n",
      "Epoch[32/100] | loss train:0.014138, test:0.033351 | lr:0.010000\n",
      "Epoch[33/100] | loss train:0.013368, test:0.002111 | lr:0.010000\n",
      "Epoch[34/100] | loss train:0.012992, test:0.002813 | lr:0.010000\n",
      "Epoch[35/100] | loss train:0.013436, test:0.002115 | lr:0.010000\n",
      "Epoch[36/100] | loss train:0.013482, test:0.002025 | lr:0.010000\n",
      "Epoch[37/100] | loss train:0.012755, test:0.002042 | lr:0.010000\n",
      "Epoch[38/100] | loss train:0.011429, test:0.001974 | lr:0.010000\n",
      "Epoch[39/100] | loss train:0.015908, test:0.002111 | lr:0.010000\n",
      "Epoch[40/100] | loss train:0.013175, test:0.001969 | lr:0.010000\n",
      "Epoch[41/100] | loss train:0.011519, test:0.001949 | lr:0.001000\n",
      "Epoch[42/100] | loss train:0.011082, test:0.001833 | lr:0.001000\n",
      "Epoch[43/100] | loss train:0.011158, test:0.001855 | lr:0.001000\n",
      "Epoch[44/100] | loss train:0.010790, test:0.001860 | lr:0.001000\n",
      "Epoch[45/100] | loss train:0.011181, test:0.002107 | lr:0.001000\n",
      "Epoch[46/100] | loss train:0.011371, test:0.001820 | lr:0.001000\n",
      "Epoch[47/100] | loss train:0.011047, test:0.001843 | lr:0.001000\n",
      "Epoch[48/100] | loss train:0.010920, test:0.001874 | lr:0.001000\n",
      "Epoch[49/100] | loss train:0.011010, test:0.001847 | lr:0.001000\n",
      "Epoch[50/100] | loss train:0.011418, test:0.001873 | lr:0.001000\n",
      "Epoch[51/100] | loss train:0.010778, test:0.001970 | lr:0.001000\n",
      "Epoch[52/100] | loss train:0.010565, test:0.001834 | lr:0.001000\n",
      "Epoch[53/100] | loss train:0.010782, test:0.002129 | lr:0.001000\n",
      "Epoch[54/100] | loss train:0.010574, test:0.001857 | lr:0.001000\n",
      "Epoch[55/100] | loss train:0.011087, test:0.002103 | lr:0.001000\n",
      "Epoch[56/100] | loss train:0.010723, test:0.001887 | lr:0.001000\n",
      "Epoch[57/100] | loss train:0.010645, test:0.001928 | lr:0.001000\n",
      "Epoch[58/100] | loss train:0.010889, test:0.001799 | lr:0.001000\n",
      "Epoch[59/100] | loss train:0.011251, test:0.001931 | lr:0.001000\n",
      "Epoch[60/100] | loss train:0.010137, test:0.001880 | lr:0.001000\n",
      "Epoch[61/100] | loss train:0.010679, test:0.001828 | lr:0.001000\n",
      "Epoch[62/100] | loss train:0.009651, test:0.001917 | lr:0.001000\n",
      "Epoch[63/100] | loss train:0.011071, test:0.001907 | lr:0.001000\n",
      "Epoch[64/100] | loss train:0.009820, test:0.001838 | lr:0.001000\n",
      "Epoch[65/100] | loss train:0.010772, test:0.001879 | lr:0.001000\n",
      "Epoch[66/100] | loss train:0.011279, test:0.001855 | lr:0.001000\n",
      "Epoch[67/100] | loss train:0.010160, test:0.001810 | lr:0.001000\n",
      "Epoch[68/100] | loss train:0.010418, test:0.001808 | lr:0.001000\n",
      "Epoch[69/100] | loss train:0.010432, test:0.001804 | lr:0.001000\n",
      "Epoch[70/100] | loss train:0.010989, test:0.001929 | lr:0.001000\n",
      "Epoch[71/100] | loss train:0.010439, test:0.001820 | lr:0.001000\n",
      "Epoch[72/100] | loss train:0.010419, test:0.001823 | lr:0.001000\n",
      "Epoch[73/100] | loss train:0.010882, test:0.001893 | lr:0.001000\n",
      "Epoch[74/100] | loss train:0.010209, test:0.001816 | lr:0.001000\n",
      "Epoch[75/100] | loss train:0.009375, test:0.001855 | lr:0.001000\n",
      "Epoch[76/100] | loss train:0.010265, test:0.001931 | lr:0.001000\n",
      "Epoch[77/100] | loss train:0.009518, test:0.001854 | lr:0.001000\n",
      "Epoch[78/100] | loss train:0.009472, test:0.001841 | lr:0.001000\n",
      "Epoch[79/100] | loss train:0.010737, test:0.001833 | lr:0.001000\n",
      "Epoch[80/100] | loss train:0.010412, test:0.002081 | lr:0.001000\n",
      "Epoch[81/100] | loss train:0.010452, test:0.001813 | lr:0.000100\n",
      "Epoch[82/100] | loss train:0.009987, test:0.001805 | lr:0.000100\n",
      "Epoch[83/100] | loss train:0.009606, test:0.001832 | lr:0.000100\n",
      "Epoch[84/100] | loss train:0.010522, test:0.001901 | lr:0.000100\n",
      "Epoch[85/100] | loss train:0.009939, test:0.001812 | lr:0.000100\n",
      "Epoch[86/100] | loss train:0.010293, test:0.001931 | lr:0.000100\n",
      "Epoch[87/100] | loss train:0.010450, test:0.001811 | lr:0.000100\n",
      "Epoch[88/100] | loss train:0.010128, test:0.001837 | lr:0.000100\n",
      "Epoch[89/100] | loss train:0.009878, test:0.001849 | lr:0.000100\n",
      "Epoch[90/100] | loss train:0.009834, test:0.001826 | lr:0.000100\n",
      "Epoch[91/100] | loss train:0.010882, test:0.001826 | lr:0.000100\n",
      "Epoch[92/100] | loss train:0.009808, test:0.001860 | lr:0.000100\n",
      "Epoch[93/100] | loss train:0.010051, test:0.001820 | lr:0.000100\n",
      "Epoch[94/100] | loss train:0.010401, test:0.001794 | lr:0.000100\n",
      "Epoch[95/100] | loss train:0.010148, test:0.001822 | lr:0.000100\n",
      "Epoch[96/100] | loss train:0.010087, test:0.001808 | lr:0.000100\n",
      "Epoch[97/100] | loss train:0.009486, test:0.001851 | lr:0.000100\n",
      "Epoch[98/100] | loss train:0.010519, test:0.001817 | lr:0.000100\n",
      "Epoch[99/100] | loss train:0.009880, test:0.001841 | lr:0.000100\n",
      "Epoch[100/100] | loss train:0.010043, test:0.001792 | lr:0.000100\n"
     ]
    }
   ],
   "source": [
    "# create `DataLoader`\n",
    "train_dataloader = DataLoader(dataset_train, batch_size=config[\"training\"][\"batch_size\"], shuffle=True)\n",
    "val_dataloader = DataLoader(dataset_val, batch_size=config[\"training\"][\"batch_size\"], shuffle=True)\n",
    "\n",
    "# define optimizer, scheduler and loss function\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=config[\"training\"][\"learning_rate\"], betas=(0.9, 0.98), eps=1e-9)\n",
    "scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=config[\"training\"][\"scheduler_step_size\"], gamma=0.1)\n",
    "\n",
    "# begin training\n",
    "for epoch in range(config[\"training\"][\"num_epoch\"]):\n",
    "    loss_train, lr_train = run_epoch(train_dataloader, is_training=True)\n",
    "    loss_val, lr_val = run_epoch(val_dataloader)\n",
    "    scheduler.step()\n",
    "    \n",
    "    print('Epoch[{}/{}] | loss train:{:.6f}, test:{:.6f} | lr:{:.6f}'\n",
    "              .format(epoch+1, config[\"training\"][\"num_epoch\"], loss_train, loss_val, lr_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98eebb09-19b3-47d0-912a-45515fcbab2e",
   "metadata": {},
   "source": [
    "## Model evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "428b1983-a3cb-4036-8768-0c631272d58e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# here we re-initialize dataloader so the data doesn't shuffled, so we can plot the values by date\n",
    "\n",
    "train_dataloader = DataLoader(dataset_train, batch_size=config[\"training\"][\"batch_size\"], shuffle=False)\n",
    "val_dataloader = DataLoader(dataset_val, batch_size=config[\"training\"][\"batch_size\"], shuffle=False)\n",
    "\n",
    "model.eval()\n",
    "\n",
    "# predict on the training data, to see how well the model managed to learn and memorize\n",
    "\n",
    "predicted_train = np.array([])\n",
    "\n",
    "for idx, (x, y) in enumerate(train_dataloader):\n",
    "    x = x.to(config[\"training\"][\"device\"])\n",
    "    out = model(x)\n",
    "    out = out.cpu().detach().numpy()\n",
    "    predicted_train = np.concatenate((predicted_train, out))\n",
    "\n",
    "# predict on the validation data, to see how the model does\n",
    "\n",
    "predicted_val = np.array([])\n",
    "\n",
    "for idx, (x, y) in enumerate(val_dataloader):\n",
    "    x = x.to(config[\"training\"][\"device\"])\n",
    "    out = model(x)\n",
    "    out = out.cpu().detach().numpy()\n",
    "    predicted_val = np.concatenate((predicted_val, out))\n",
    "\n",
    "if config[\"plots\"][\"show_plots\"]:\n",
    "\n",
    "    # prepare data for plotting, show predicted prices\n",
    "\n",
    "    to_plot_data_y_train_pred = np.zeros(num_data_points)\n",
    "    to_plot_data_y_val_pred = np.zeros(num_data_points)\n",
    "\n",
    "    to_plot_data_y_train_pred[config[\"data\"][\"window_size\"]:split_index+config[\"data\"][\"window_size\"]] = scaler.inverse_transform(predicted_train)\n",
    "    to_plot_data_y_val_pred[split_index+config[\"data\"][\"window_size\"]:] = scaler.inverse_transform(predicted_val)\n",
    "\n",
    "    to_plot_data_y_train_pred = np.where(to_plot_data_y_train_pred == 0, None, to_plot_data_y_train_pred)\n",
    "    to_plot_data_y_val_pred = np.where(to_plot_data_y_val_pred == 0, None, to_plot_data_y_val_pred)\n",
    "\n",
    "    # plots\n",
    "\n",
    "    fig = figure(figsize=(25, 5), dpi=80)\n",
    "    fig.patch.set_facecolor((1.0, 1.0, 1.0))\n",
    "    plt.plot(data_date, data_close_price, label=\"Actual prices\", color=config[\"plots\"][\"color_actual\"])\n",
    "    plt.plot(data_date, to_plot_data_y_train_pred, label=\"Predicted prices (train)\", color=config[\"plots\"][\"color_pred_train\"])\n",
    "    plt.plot(data_date, to_plot_data_y_val_pred, label=\"Predicted prices (validation)\", color=config[\"plots\"][\"color_pred_val\"])\n",
    "    plt.title(\"Compare predicted prices to actual prices\")\n",
    "    xticks = [data_date[i] if ((i%config[\"plots\"][\"xticks_interval\"]==0 and (num_data_points-i) > config[\"plots\"][\"xticks_interval\"]) or i==num_data_points-1) else None for i in range(num_data_points)] # make x ticks nice\n",
    "    x = np.arange(0,len(xticks))\n",
    "    plt.xticks(x, xticks, rotation='vertical')\n",
    "    plt.grid(True, which='major', axis='y', linestyle='--')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "    # prepare data for plotting, zoom in validation\n",
    "\n",
    "    to_plot_data_y_val_subset = scaler.inverse_transform(data_y_val)\n",
    "    to_plot_predicted_val = scaler.inverse_transform(predicted_val)\n",
    "    to_plot_data_date = data_date[split_index+config[\"data\"][\"window_size\"]:]\n",
    "\n",
    "    # plots\n",
    "\n",
    "    fig = figure(figsize=(25, 5), dpi=80)\n",
    "    fig.patch.set_facecolor((1.0, 1.0, 1.0))\n",
    "    plt.plot(to_plot_data_date, to_plot_data_y_val_subset, label=\"Actual prices\", color=config[\"plots\"][\"color_actual\"])\n",
    "    plt.plot(to_plot_data_date, to_plot_predicted_val, label=\"Predicted prices (validation)\", color=config[\"plots\"][\"color_pred_val\"])\n",
    "    plt.title(\"Zoom in to examine predicted price on validation data portion\")\n",
    "    xticks = [to_plot_data_date[i] if ((i%int(config[\"plots\"][\"xticks_interval\"]/5)==0 and (len(to_plot_data_date)-i) > config[\"plots\"][\"xticks_interval\"]/6) or i==len(to_plot_data_date)-1) else None for i in range(len(to_plot_data_date))] # make x ticks nice\n",
    "    xs = np.arange(0,len(xticks))\n",
    "    plt.xticks(xs, xticks, rotation='vertical')\n",
    "    plt.grid(True, which='major', axis='y', linestyle='--')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfc7cf77-da44-49f2-b089-32fed05eedfc",
   "metadata": {},
   "source": [
    "## Predicting future stock prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f50cae6f-6899-4c32-8234-d5e6dec3a3ef",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted close price of the next trading day: 184.38\n"
     ]
    }
   ],
   "source": [
    "# predict on the unseen data, tomorrow's price \n",
    "\n",
    "model.eval()\n",
    "\n",
    "x = torch.tensor(data_x_unseen).float().to(config[\"training\"][\"device\"]).unsqueeze(0).unsqueeze(2) # this is the data type and shape required, [batch, sequence, feature]\n",
    "prediction = model(x)\n",
    "prediction = prediction.cpu().detach().numpy()\n",
    "prediction = scaler.inverse_transform(prediction)[0]\n",
    "\n",
    "if config[\"plots\"][\"show_plots\"]:\n",
    "        \n",
    "    # prepare plots\n",
    "\n",
    "    plot_range = 10\n",
    "    to_plot_data_y_val = np.zeros(plot_range)\n",
    "    to_plot_data_y_val_pred = np.zeros(plot_range)\n",
    "    to_plot_data_y_test_pred = np.zeros(plot_range)\n",
    "\n",
    "    to_plot_data_y_val[:plot_range-1] = scaler.inverse_transform(data_y_val)[-plot_range+1:]\n",
    "    to_plot_data_y_val_pred[:plot_range-1] = scaler.inverse_transform(predicted_val)[-plot_range+1:]\n",
    "\n",
    "    to_plot_data_y_test_pred[plot_range-1] = prediction\n",
    "\n",
    "    to_plot_data_y_val = np.where(to_plot_data_y_val == 0, None, to_plot_data_y_val)\n",
    "    to_plot_data_y_val_pred = np.where(to_plot_data_y_val_pred == 0, None, to_plot_data_y_val_pred)\n",
    "    to_plot_data_y_test_pred = np.where(to_plot_data_y_test_pred == 0, None, to_plot_data_y_test_pred)\n",
    "\n",
    "    # plot\n",
    "\n",
    "    plot_date_test = data_date[-plot_range+1:]\n",
    "    plot_date_test.append(\"next trading day\")\n",
    "\n",
    "    fig = figure(figsize=(25, 5), dpi=80)\n",
    "    fig.patch.set_facecolor((1.0, 1.0, 1.0))\n",
    "    plt.plot(plot_date_test, to_plot_data_y_val, label=\"Actual prices\", marker=\".\", markersize=10, color=config[\"plots\"][\"color_actual\"])\n",
    "    plt.plot(plot_date_test, to_plot_data_y_val_pred, label=\"Past predicted prices\", marker=\".\", markersize=10, color=config[\"plots\"][\"color_pred_val\"])\n",
    "    plt.plot(plot_date_test, to_plot_data_y_test_pred, label=\"Predicted price for next day\", marker=\".\", markersize=20, color=config[\"plots\"][\"color_pred_test\"])\n",
    "    plt.title(\"Predicted close price of the next trading day\")\n",
    "    plt.grid(True, which='major', axis='y', linestyle='--')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    \n",
    "    print(\"Predicted close price of the next trading day:\", round(prediction, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "69f71816-15b6-4aa1-b9d2-5abc97d6c2bd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'items': '50', 'sentiment_score_definition': 'x <= -0.35: Bearish; -0.35 < x <= -0.15: Somewhat-Bearish; -0.15 < x < 0.15: Neutral; 0.15 <= x < 0.35: Somewhat_Bullish; x >= 0.35: Bullish', 'relevance_score_definition': '0 < x <= 1, with a higher score indicating higher relevance.', 'feed': [{'title': \"Warren Buffett Discusses Apple, Cash, Insurance, Artificial Intelligence  ( AI ) , and More at Berkshire Hathaway's Annual Meeting\", 'url': 'https://www.fool.com/investing/2024/05/04/buffett-2024-meeting-berkshire-hathaway-stock/', 'time_published': '20240504T221900', 'authors': ['Daniel Foelber'], 'summary': 'Berkshire is bolstering its cash reserves and passing on riskier bets.', 'banner_image': 'https://g.foolcdn.com/image/?url=https%3A%2F%2Fg.foolcdn.com%2Feditorial%2Fimages%2F775878%2Fbuffett-fool-image.jpg&op=resize&w=700', 'source': 'Motley Fool', 'category_within_source': 'n/a', 'source_domain': 'www.fool.com', 'topics': [{'topic': 'Economy - Monetary', 'relevance_score': '0.158519'}, {'topic': 'Financial Markets', 'relevance_score': '0.999355'}, {'topic': 'Manufacturing', 'relevance_score': '0.333333'}, {'topic': 'Technology', 'relevance_score': '0.333333'}, {'topic': 'Finance', 'relevance_score': '0.333333'}], 'overall_sentiment_score': 0.185069, 'overall_sentiment_label': 'Somewhat-Bullish', 'ticker_sentiment': [{'ticker': 'AXP', 'relevance_score': '0.061159', 'ticker_sentiment_score': '0.326508', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'AAPL', 'relevance_score': '0.270108', 'ticker_sentiment_score': '0.113063', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'KO', 'relevance_score': '0.061159', 'ticker_sentiment_score': '0.326508', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'BRK-A', 'relevance_score': '0.061159', 'ticker_sentiment_score': '0.071209', 'ticker_sentiment_label': 'Neutral'}]}, {'title': \"Warren Buffett Confidence In Apple Wanes? Berkshire Cuts Stake By 18% Amid Tech Giant's Troubles - Apple  ( NASDAQ:AAPL ) , American Express  ( NYSE:AXP ) \", 'url': 'https://www.benzinga.com/markets/equities/24/05/38630421/warren-buffett-confidence-in-apple-wanes-berkshire-cuts-stake-by-18-amid-tech-giants-troubles', 'time_published': '20240504T160317', 'authors': ['Shanthi Rexaline'], 'summary': \"Warren Buffett's Berkshire, Inc. BRK BRK trimmed its position in its key core holding, Apple, Inc. AAPL, a 10-Q report for the first quarter filed with the SEC on Saturday showed. Paring Equity Bets: Berkshire's equity securities investment dropped from $353.84 billion in Q1 2023 to $335.86 ...\", 'banner_image': 'https://cdn.benzinga.com/files/images/story/2024/05/04/Apple-Shutterstock.jpeg?width=1200&height=800&fit=crop', 'source': 'Benzinga', 'category_within_source': 'Trading', 'source_domain': 'www.benzinga.com', 'topics': [{'topic': 'Financial Markets', 'relevance_score': '0.266143'}, {'topic': 'Manufacturing', 'relevance_score': '0.25'}, {'topic': 'Earnings', 'relevance_score': '0.360215'}, {'topic': 'Energy & Transportation', 'relevance_score': '0.25'}, {'topic': 'Technology', 'relevance_score': '0.25'}, {'topic': 'Finance', 'relevance_score': '0.25'}], 'overall_sentiment_score': 0.055317, 'overall_sentiment_label': 'Neutral', 'ticker_sentiment': [{'ticker': 'AXP', 'relevance_score': '0.194242', 'ticker_sentiment_score': '-0.142127', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'AAPL', 'relevance_score': '0.859898', 'ticker_sentiment_score': '-0.02087', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'BAC', 'relevance_score': '0.194242', 'ticker_sentiment_score': '-0.142127', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'CVX', 'relevance_score': '0.194242', 'ticker_sentiment_score': '-0.142127', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'KO', 'relevance_score': '0.194242', 'ticker_sentiment_score': '-0.142127', 'ticker_sentiment_label': 'Neutral'}]}, {'title': 'Berkshire Hathaway Trims Its Apple Investment and Has $189 Billion in Cash', 'url': 'https://www.fool.com/investing/2024/05/04/berkshire-hathaway-trims-its-apple-investment-and/', 'time_published': '20240504T151558', 'authors': ['Matt Frankel'], 'summary': \"The conglomerate just reported its first-quarter results, so here's a rundown of the key figures.\", 'banner_image': 'https://g.foolcdn.com/editorial/images/775861/buffett8-tmf.jpg', 'source': 'Motley Fool', 'category_within_source': 'n/a', 'source_domain': 'www.fool.com', 'topics': [{'topic': 'Economy - Monetary', 'relevance_score': '0.158519'}, {'topic': 'Financial Markets', 'relevance_score': '0.996023'}, {'topic': 'Earnings', 'relevance_score': '0.999759'}, {'topic': 'Technology', 'relevance_score': '0.5'}, {'topic': 'Finance', 'relevance_score': '0.5'}], 'overall_sentiment_score': 0.220681, 'overall_sentiment_label': 'Somewhat-Bullish', 'ticker_sentiment': [{'ticker': 'AAPL', 'relevance_score': '0.162802', 'ticker_sentiment_score': '0.212585', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'BRK-A', 'relevance_score': '0.108958', 'ticker_sentiment_score': '0.113996', 'ticker_sentiment_label': 'Neutral'}]}, {'title': 'Should You Buy 1 Share of Each \"Magnificent Seven\" Stock?', 'url': 'https://www.fool.com/investing/2024/05/04/should-you-buy-1-share-of-each-magnificent-seven-s/', 'time_published': '20240504T130900', 'authors': ['Daniel Foelber'], 'summary': 'For less than $2,500, you could own one share of each \"Magnificent Seven\" stock.', 'banner_image': 'https://g.foolcdn.com/image/?url=https%3A%2F%2Fg.foolcdn.com%2Feditorial%2Fimages%2F774738%2Fgettyimages-1400278972-1201x823-4f47a75.jpg&op=resize&w=700', 'source': 'Motley Fool', 'category_within_source': 'n/a', 'source_domain': 'www.fool.com', 'topics': [{'topic': 'Retail & Wholesale', 'relevance_score': '0.333333'}, {'topic': 'Financial Markets', 'relevance_score': '1.0'}, {'topic': 'Manufacturing', 'relevance_score': '0.333333'}, {'topic': 'Earnings', 'relevance_score': '0.838487'}, {'topic': 'Technology', 'relevance_score': '0.333333'}], 'overall_sentiment_score': 0.334331, 'overall_sentiment_label': 'Somewhat-Bullish', 'ticker_sentiment': [{'ticker': 'MSFT', 'relevance_score': '0.197816', 'ticker_sentiment_score': '0.161443', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'META', 'relevance_score': '0.197816', 'ticker_sentiment_score': '0.161443', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'NVDA', 'relevance_score': '0.338913', 'ticker_sentiment_score': '0.169299', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'AAPL', 'relevance_score': '0.197816', 'ticker_sentiment_score': '0.207218', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'TSLA', 'relevance_score': '0.149038', 'ticker_sentiment_score': '0.146866', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'AMZN', 'relevance_score': '0.099683', 'ticker_sentiment_score': '0.133984', 'ticker_sentiment_label': 'Neutral'}]}, {'title': \"Bulls And Bears: Apple, Tesla, AMC, Bitcoin And Shiba Inu's Chart Forms Powerful Technical Patterns\", 'url': 'https://www.benzinga.com/markets/equities/24/05/38629592/benzinga-bulls-and-bears-apple-tesla-amc-bitcoin-and-shiba-inus-chart-forms-powerful-technical-p', 'time_published': '20240504T125316', 'authors': ['Michael Cohen'], 'summary': \"Benzinga examined the prospects for many investors' favorite stocks over the last week - here's a look at some of our top stories. Stock markets ended the week on a high note following reassuring comments from Federal Reserve Chair Jerome Powell.\", 'banner_image': 'https://cdn.benzinga.com/files/images/story/2024/05/04/bulls-and-bears-ai.png?width=1200&height=800&fit=crop', 'source': 'Benzinga', 'category_within_source': 'Trading', 'source_domain': 'www.benzinga.com', 'topics': [{'topic': 'Economy - Monetary', 'relevance_score': '0.682689'}, {'topic': 'Retail & Wholesale', 'relevance_score': '0.333333'}, {'topic': 'Financial Markets', 'relevance_score': '0.929393'}, {'topic': 'Manufacturing', 'relevance_score': '0.333333'}, {'topic': 'Technology', 'relevance_score': '0.333333'}], 'overall_sentiment_score': 0.19216, 'overall_sentiment_label': 'Somewhat-Bullish', 'ticker_sentiment': [{'ticker': 'AAPL', 'relevance_score': '0.191675', 'ticker_sentiment_score': '0.338206', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'TSLA', 'relevance_score': '0.191675', 'ticker_sentiment_score': '0.295398', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'SBUX', 'relevance_score': '0.191675', 'ticker_sentiment_score': '-0.008351', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'AMC', 'relevance_score': '0.191675', 'ticker_sentiment_score': '-0.443466', 'ticker_sentiment_label': 'Bearish'}, {'ticker': 'CRYPTO:BTC', 'relevance_score': '0.128478', 'ticker_sentiment_score': '-0.079', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'CRYPTO:SHIB', 'relevance_score': '0.191675', 'ticker_sentiment_score': '0.207957', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'CRYPTO:DOGE', 'relevance_score': '0.191675', 'ticker_sentiment_score': '0.207957', 'ticker_sentiment_label': 'Somewhat-Bullish'}]}, {'title': 'How Dividend Investors Can Profit As AI Changes Everything', 'url': 'https://www.forbes.com/sites/michaelfoster/2024/05/04/how-dividend-investors-can-profit--as-ai-changes-everything/', 'time_published': '20240504T120000', 'authors': ['Michael Foster'], 'summary': \"If you've been following the AI space lately ( and honestly, who hasn't? ) , you've probably seen stories about tech investors feeling a bit shortchanged on the profits they're getting. That's actually good news for the rest of us-a sign the market is maturing and ripe to be tapped for income.\", 'banner_image': 'https://imageio.forbes.com/specials-images/imageserve/60803617d7b099e7fee76763/0x0.jpg?format=jpg&height=900&width=1600&fit=bounds', 'source': 'Forbes', 'category_within_source': 'n/a', 'source_domain': 'www.forbes.com', 'topics': [{'topic': 'Life Sciences', 'relevance_score': '0.2'}, {'topic': 'Technology', 'relevance_score': '0.2'}, {'topic': 'Finance', 'relevance_score': '0.2'}, {'topic': 'Retail & Wholesale', 'relevance_score': '0.2'}, {'topic': 'Financial Markets', 'relevance_score': '0.77141'}, {'topic': 'Manufacturing', 'relevance_score': '0.2'}, {'topic': 'Earnings', 'relevance_score': '0.495866'}], 'overall_sentiment_score': 0.252136, 'overall_sentiment_label': 'Somewhat-Bullish', 'ticker_sentiment': [{'ticker': 'MSFT', 'relevance_score': '0.043342', 'ticker_sentiment_score': '0.242391', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'META', 'relevance_score': '0.129516', 'ticker_sentiment_score': '-0.034987', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'NVDA', 'relevance_score': '0.255641', 'ticker_sentiment_score': '0.256317', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'AAPL', 'relevance_score': '0.172097', 'ticker_sentiment_score': '0.028219', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'MA', 'relevance_score': '0.043342', 'ticker_sentiment_score': '0.0', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'HON', 'relevance_score': '0.086556', 'ticker_sentiment_score': '0.0', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'LLY', 'relevance_score': '0.086556', 'ticker_sentiment_score': '0.0', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'AXP', 'relevance_score': '0.086556', 'ticker_sentiment_score': '0.0', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'AMZN', 'relevance_score': '0.086556', 'ticker_sentiment_score': '-0.030778', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'FOREX:USD', 'relevance_score': '0.043342', 'ticker_sentiment_score': '0.204341', 'ticker_sentiment_label': 'Somewhat-Bullish'}]}, {'title': 'Buy, Sell, or Hold: Apple', 'url': 'https://www.fool.com/investing/2024/05/04/buy-sell-or-hold-apple/', 'time_published': '20240504T071400', 'authors': ['CFA', 'Lawrence Rothman'], 'summary': 'What should you do with your Apple shares?', 'banner_image': 'https://g.foolcdn.com/image/?url=https%3A%2F%2Fg.foolcdn.com%2Feditorial%2Fimages%2F774393%2Fgettyimages-1207318396-1201x800-5b2df79.jpg&op=resize&w=700', 'source': 'Motley Fool', 'category_within_source': 'n/a', 'source_domain': 'www.fool.com', 'topics': [{'topic': 'Earnings', 'relevance_score': '0.158519'}, {'topic': 'Technology', 'relevance_score': '1.0'}, {'topic': 'Financial Markets', 'relevance_score': '0.214378'}], 'overall_sentiment_score': 0.118611, 'overall_sentiment_label': 'Neutral', 'ticker_sentiment': [{'ticker': 'META', 'relevance_score': '0.128822', 'ticker_sentiment_score': '-0.160494', 'ticker_sentiment_label': 'Somewhat-Bearish'}, {'ticker': 'AAPL', 'relevance_score': '0.483434', 'ticker_sentiment_score': '0.396755', 'ticker_sentiment_label': 'Bullish'}]}, {'title': \"'AI Revolution Now Coming To Cupertino:' Tech Bull Says Betting Against Apple's Tim Cook Has Been 'Proven Wrong Again And Again' - Apple  ( NASDAQ:AAPL ) \", 'url': 'https://www.benzinga.com/analyst-ratings/analyst-color/24/05/38628664/ai-revolution-now-coming-to-cupertino-tech-bull-says-betting-against-apples-tim-coo', 'time_published': '20240504T062300', 'authors': ['Rounak Jain'], 'summary': 'Apple Inc.\\'s AAPL highly-anticipated AI strategy, set to be announced at the Worldwide Developers Conference ( WWDC ) in June, has Wedbush\\'s Dan Ives excited. What Happened: Ives thinks that the \"AI revolution\" is headed for Cupertino next, given that it is the only company in the \"Mag 7\" ...', 'banner_image': 'https://cdn.benzinga.com/files/images/story/2024/05/04/Apple-iPhone.jpeg?width=1200&height=800&fit=crop', 'source': 'Benzinga', 'category_within_source': 'Markets', 'source_domain': 'www.benzinga.com', 'topics': [{'topic': 'Technology', 'relevance_score': '0.5'}, {'topic': 'Financial Markets', 'relevance_score': '0.108179'}, {'topic': 'Manufacturing', 'relevance_score': '0.5'}], 'overall_sentiment_score': 0.199469, 'overall_sentiment_label': 'Somewhat-Bullish', 'ticker_sentiment': [{'ticker': 'MSFT', 'relevance_score': '0.091162', 'ticker_sentiment_score': '0.094535', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'GOOG', 'relevance_score': '0.091162', 'ticker_sentiment_score': '0.094535', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'AAPL', 'relevance_score': '0.577173', 'ticker_sentiment_score': '0.303155', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'TSLA', 'relevance_score': '0.091162', 'ticker_sentiment_score': '0.086192', 'ticker_sentiment_label': 'Neutral'}]}, {'title': 'Market Rally Makes Bullish Move, Nvidia A Buy', 'url': 'https://www.investors.com/market-trend/stock-market-today/dow-jones-futures-bullish-move-nvidia-in-buy-area-palantir-ai-plays/', 'time_published': '20240503T211500', 'authors': [\"Investor's Business Daily\", 'ED CARSON'], 'summary': \"Dow Jones Futures: Stock Market Makes Bullish Move, Nvidia In Buy Area. Palantir, These AI Plays On Tap. Investor's Business Daily ...\", 'banner_image': 'https://www.investors.com/wp-content/uploads/2021/08/Stock-BullPaintedRushing-adobe.jpg', 'source': 'Investors Business Daily', 'category_within_source': 'n/a', 'source_domain': 'www.investors.com', 'topics': [{'topic': 'IPO', 'relevance_score': '0.158519'}, {'topic': 'Financial Markets', 'relevance_score': '1.0'}, {'topic': 'Manufacturing', 'relevance_score': '0.333333'}, {'topic': 'Earnings', 'relevance_score': '0.999953'}, {'topic': 'Technology', 'relevance_score': '0.333333'}, {'topic': 'Finance', 'relevance_score': '0.333333'}], 'overall_sentiment_score': 0.13465, 'overall_sentiment_label': 'Neutral', 'ticker_sentiment': [{'ticker': 'META', 'relevance_score': '0.061081', 'ticker_sentiment_score': '-0.054022', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'NVDA', 'relevance_score': '0.211454', 'ticker_sentiment_score': '0.08398', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'AAPL', 'relevance_score': '0.061081', 'ticker_sentiment_score': '0.212124', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'ANET', 'relevance_score': '0.121804', 'ticker_sentiment_score': '-0.026608', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'CEG', 'relevance_score': '0.151922', 'ticker_sentiment_score': '0.097217', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'SMCI', 'relevance_score': '0.061081', 'ticker_sentiment_score': '0.005037', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'BRK-A', 'relevance_score': '0.030563', 'ticker_sentiment_score': '0.0', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'DDOG', 'relevance_score': '0.211454', 'ticker_sentiment_score': '0.0', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'MSFT', 'relevance_score': '0.061081', 'ticker_sentiment_score': '-0.054022', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'ALAB', 'relevance_score': '0.151922', 'ticker_sentiment_score': '0.055478', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'AMD', 'relevance_score': '0.061081', 'ticker_sentiment_score': '0.028061', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'NET', 'relevance_score': '0.061081', 'ticker_sentiment_score': '-0.021165', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'PLTR', 'relevance_score': '0.091509', 'ticker_sentiment_score': '0.16686', 'ticker_sentiment_label': 'Somewhat-Bullish'}]}, {'title': 'Is Apple Stock Going to $230? 1 Wall Street Analyst Firm Thinks So.', 'url': 'https://www.fool.com/investing/2024/05/03/apple-stock-230-wall-street-analyst/', 'time_published': '20240503T210400', 'authors': ['John Ballard'], 'summary': 'Apple is bullish on its AI opportunity and says it plans to announce some \"exciting\" things soon.', 'banner_image': 'https://g.foolcdn.com/editorial/images/775705/apple-iphone.png', 'source': 'Motley Fool', 'category_within_source': 'n/a', 'source_domain': 'www.fool.com', 'topics': [{'topic': 'Financial Markets', 'relevance_score': '0.266143'}, {'topic': 'Earnings', 'relevance_score': '0.947132'}, {'topic': 'Technology', 'relevance_score': '0.5'}, {'topic': 'Finance', 'relevance_score': '0.5'}], 'overall_sentiment_score': 0.38845, 'overall_sentiment_label': 'Bullish', 'ticker_sentiment': [{'ticker': 'BAC', 'relevance_score': '0.116348', 'ticker_sentiment_score': '0.008064', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'AAPL', 'relevance_score': '0.620083', 'ticker_sentiment_score': '0.650701', 'ticker_sentiment_label': 'Bullish'}]}, {'title': 'Summary Notice of Pendency and Proposed Settlement of Shareholder Derivative Actions - Apple  ( NASDAQ:AAPL ) ', 'url': 'https://www.benzinga.com/pressreleases/24/05/g38625497/summary-notice-of-pendency-and-proposed-settlement-of-shareholder-derivative-actions', 'time_published': '20240503T203916', 'authors': ['Globe Newswire'], 'summary': 'CUPERTINO, Calif., May 03, 2024 ( GLOBE NEWSWIRE ) -- Apple Inc. has released the following notice: The purpose of this Notice is to inform you of: ( i ) the pendency of the shareholder derivative action brought on behalf of Apple Inc. ( \"Apple\" or the \"Company\" ) , styled as In re Apple Inc.', 'banner_image': 'https://www.benzinga.com/next-assets/images/schema-image-default.png', 'source': 'Benzinga', 'category_within_source': 'News', 'source_domain': 'www.benzinga.com', 'topics': [{'topic': 'Technology', 'relevance_score': '1.0'}, {'topic': 'Financial Markets', 'relevance_score': '0.996718'}], 'overall_sentiment_score': 0.215921, 'overall_sentiment_label': 'Somewhat-Bullish', 'ticker_sentiment': [{'ticker': 'DSSMY', 'relevance_score': '0.030178', 'ticker_sentiment_score': '0.064876', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'AAPL', 'relevance_score': '0.150029', 'ticker_sentiment_score': '0.062158', 'ticker_sentiment_label': 'Neutral'}]}, {'title': 'Summary Notice of Pendency and Proposed Settlement of Shareholder Derivative Actions', 'url': 'https://www.globenewswire.com/news-release/2024/05/03/2875372/0/en/Summary-Notice-of-Pendency-and-Proposed-Settlement-of-Shareholder-Derivative-Actions.html', 'time_published': '20240503T203900', 'authors': ['Apple Inc.'], 'summary': 'CUPERTINO, Calif., May 03, 2024 ( GLOBE NEWSWIRE ) -- Apple Inc. has released the following notice: ...', 'banner_image': '', 'source': 'GlobeNewswire', 'category_within_source': 'n/a', 'source_domain': 'www.globenewswire.com', 'topics': [{'topic': 'Economy - Monetary', 'relevance_score': '0.158519'}, {'topic': 'Technology', 'relevance_score': '1.0'}, {'topic': 'Financial Markets', 'relevance_score': '0.999691'}], 'overall_sentiment_score': 0.217838, 'overall_sentiment_label': 'Somewhat-Bullish', 'ticker_sentiment': [{'ticker': 'DSSMY', 'relevance_score': '0.028155', 'ticker_sentiment_score': '0.065115', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'AAPL', 'relevance_score': '0.167709', 'ticker_sentiment_score': '0.087381', 'ticker_sentiment_label': 'Neutral'}]}, {'title': 'Three Stocks: Apple, Alphabet, and Viking Holdings', 'url': 'https://moneymorning.com/2024/05/03/three-stocks-apple-alphabet-and-viking-holdings/', 'time_published': '20240503T200903', 'authors': ['Chris Johnson'], 'summary': \"It was a wild ride for Apple ( AAPL ) shares today as the company traded almost 6% higher after last night's earnings report.\", 'banner_image': 'https://moneymorning.com/wp-content/blogs.dir/1/files/2024/05/apple.png', 'source': 'Money Morning', 'category_within_source': 'GoogleRSS', 'source_domain': 'moneymorning.com', 'topics': [{'topic': 'IPO', 'relevance_score': '0.576289'}, {'topic': 'Financial Markets', 'relevance_score': '0.54554'}, {'topic': 'Earnings', 'relevance_score': '0.769861'}, {'topic': 'Energy & Transportation', 'relevance_score': '0.5'}, {'topic': 'Technology', 'relevance_score': '0.5'}], 'overall_sentiment_score': 0.162742, 'overall_sentiment_label': 'Somewhat-Bullish', 'ticker_sentiment': [{'ticker': 'RDDT', 'relevance_score': '0.268294', 'ticker_sentiment_score': '0.178649', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'AAPL', 'relevance_score': '0.451494', 'ticker_sentiment_score': '0.178458', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'RCL', 'relevance_score': '0.136113', 'ticker_sentiment_score': '0.124047', 'ticker_sentiment_label': 'Neutral'}]}, {'title': 'Stock Market Today: Stocks Soar on Apple Buyback News, Jobs Data', 'url': 'https://www.kiplinger.com/investing/stocks/stock-market-today-stocks-soar-on-apple-buyback-news-jobs-data', 'time_published': '20240503T200830', 'authors': ['Karee Venema'], 'summary': \"Stocks jumped out of the gate Friday and never looked back. Sentiment got a major lift thanks to a positive reaction to Apple's ( AAPL ) quarterly earnings report and a bad-news-is-good-news jobs report.\", 'banner_image': 'https://cdn.mos.cms.futurecdn.net/hwgJ7osrMtUWhk5koeVme7-320-80.png', 'source': 'Kiplinger', 'category_within_source': 'n/a', 'source_domain': 'www.kiplinger.com', 'topics': [{'topic': 'Economy - Monetary', 'relevance_score': '0.576289'}, {'topic': 'Life Sciences', 'relevance_score': '0.5'}, {'topic': 'Financial Markets', 'relevance_score': '0.87644'}, {'topic': 'Earnings', 'relevance_score': '0.996675'}, {'topic': 'Technology', 'relevance_score': '0.5'}], 'overall_sentiment_score': 0.219764, 'overall_sentiment_label': 'Somewhat-Bullish', 'ticker_sentiment': [{'ticker': 'AMGN', 'relevance_score': '0.361802', 'ticker_sentiment_score': '0.225809', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'AAPL', 'relevance_score': '0.427425', 'ticker_sentiment_score': '0.334369', 'ticker_sentiment_label': 'Somewhat-Bullish'}]}, {'title': 'Apple Eyes Best Session Since Late 2022, Breaks Above 200-Day Average For First Time In 4 Months', 'url': 'https://markets.businessinsider.com/news/stocks/apple-eyes-best-session-since-late-2022-breaks-above-200-day-average-for-first-time-in-4-months-1033328259', 'time_published': '20240503T200415', 'authors': ['Piero Cingari'], 'summary': \"Apple Inc. ( NASDAQ:AAPL ) shares are currently riding a spectacular rally, signaling the market's enthusiastic response to the quarterly report from the Cupertino-based tech giant. Friday's surge amounted to over 7%, marking its strongest single-day performance since November 2022, when Apple ...\", 'banner_image': 'https://cdn.benzinga.com/files/imagecache/1024x768xUP/images/story/2024/05/03/Apple-Shutterstock.jpeg', 'source': 'Business Insider', 'category_within_source': 'GoogleRSS', 'source_domain': 'markets.businessinsider.com', 'topics': [{'topic': 'Retail & Wholesale', 'relevance_score': '0.25'}, {'topic': 'Financial Markets', 'relevance_score': '0.108179'}, {'topic': 'Manufacturing', 'relevance_score': '0.25'}, {'topic': 'Earnings', 'relevance_score': '0.495866'}, {'topic': 'Technology', 'relevance_score': '0.25'}, {'topic': 'Finance', 'relevance_score': '0.25'}], 'overall_sentiment_score': 0.303268, 'overall_sentiment_label': 'Somewhat-Bullish', 'ticker_sentiment': [{'ticker': 'AAPL', 'relevance_score': '0.092213', 'ticker_sentiment_score': '0.347635', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'BCS', 'relevance_score': '0.092213', 'ticker_sentiment_score': '0.144022', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'SBUX', 'relevance_score': '0.092213', 'ticker_sentiment_score': '-0.070735', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'EBAY', 'relevance_score': '0.092213', 'ticker_sentiment_score': '-0.070735', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'BA', 'relevance_score': '0.092213', 'ticker_sentiment_score': '-0.070735', 'ticker_sentiment_label': 'Neutral'}]}, {'title': 'Big Quarterly Beats After the Bell: AAPL, SQ, COIN & More - Apple  ( NASDAQ:AAPL ) , Automatic Data Processing  ( NASDAQ:ADP ) ', 'url': 'https://www.benzinga.com/news/24/05/38603132/big-quarterly-beats-after-the-bell-aapl-sq-coin-more', 'time_published': '20240503T200106', 'authors': ['Zacks'], 'summary': 'Market indices put up healthy numbers this Thursday.', 'banner_image': 'https://cdn.benzinga.com/files/images/story/2024/05/03/sumudu-mohottige-bigpii04uig-unsplash.jpg?width=1200&height=800&fit=crop', 'source': 'Benzinga', 'category_within_source': 'Trading', 'source_domain': 'www.benzinga.com', 'topics': [{'topic': 'Economy - Monetary', 'relevance_score': '0.158519'}, {'topic': 'Financial Markets', 'relevance_score': '0.459462'}, {'topic': 'Earnings', 'relevance_score': '0.682689'}, {'topic': 'Energy & Transportation', 'relevance_score': '0.333333'}, {'topic': 'Technology', 'relevance_score': '0.333333'}, {'topic': 'Finance', 'relevance_score': '0.333333'}], 'overall_sentiment_score': 0.206938, 'overall_sentiment_label': 'Somewhat-Bullish', 'ticker_sentiment': [{'ticker': 'EXPE', 'relevance_score': '0.31006', 'ticker_sentiment_score': '0.225297', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'AAPL', 'relevance_score': '0.250386', 'ticker_sentiment_score': '0.075036', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'ADP', 'relevance_score': '0.126784', 'ticker_sentiment_score': '0.059758', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'BKNG', 'relevance_score': '0.189175', 'ticker_sentiment_score': '0.15381', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'COIN', 'relevance_score': '0.126784', 'ticker_sentiment_score': '0.167486', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'SQ', 'relevance_score': '0.126784', 'ticker_sentiment_score': '0.190902', 'ticker_sentiment_label': 'Somewhat-Bullish'}]}, {'title': 'These 3 Companies Shattered Quarterly Records', 'url': 'https://www.zacks.com/commentary/2268286/these-3-companies-shattered-quarterly-records', 'time_published': '20240503T194200', 'authors': ['Derek Lewis'], 'summary': \"We've seen many positive quarterly prints so far into the Q1 earnings cycle, including record-breaking numbers from these three companies.\", 'banner_image': 'https://staticx-tuner.zacks.com/images/articles/main/b2/56.jpg', 'source': 'Zacks Commentary', 'category_within_source': 'n/a', 'source_domain': 'www.zacks.com', 'topics': [{'topic': 'Earnings', 'relevance_score': '0.999491'}, {'topic': 'Technology', 'relevance_score': '1.0'}, {'topic': 'Financial Markets', 'relevance_score': '0.360215'}], 'overall_sentiment_score': 0.360633, 'overall_sentiment_label': 'Bullish', 'ticker_sentiment': [{'ticker': 'SPOT', 'relevance_score': '0.379435', 'ticker_sentiment_score': '0.221142', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'AAPL', 'relevance_score': '0.447528', 'ticker_sentiment_score': '0.180153', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'ETN', 'relevance_score': '0.447528', 'ticker_sentiment_score': '0.274054', 'ticker_sentiment_label': 'Somewhat-Bullish'}]}, {'title': '3 Intriguing Stocks to Buy After Beating Q1 Earnings Expectations', 'url': 'https://www.zacks.com/commentary/2268260/3-intriguing-stocks-to-buy-after-beating-q1-earnings-expectations', 'time_published': '20240503T193200', 'authors': ['Shaun Pruitt'], 'summary': 'Several top-rated Zacks stocks are standing out after exceeding their first quarter earnings expectations earlier in the week and could certainly move higher.', 'banner_image': 'https://staticx-tuner.zacks.com/images/articles/main/b6/2622.jpg', 'source': 'Zacks Commentary', 'category_within_source': 'n/a', 'source_domain': 'www.zacks.com', 'topics': [{'topic': 'Retail & Wholesale', 'relevance_score': '0.25'}, {'topic': 'Financial Markets', 'relevance_score': '0.108179'}, {'topic': 'Earnings', 'relevance_score': '0.997874'}, {'topic': 'Energy & Transportation', 'relevance_score': '0.25'}, {'topic': 'Technology', 'relevance_score': '0.25'}, {'topic': 'Finance', 'relevance_score': '0.25'}], 'overall_sentiment_score': 0.306228, 'overall_sentiment_label': 'Somewhat-Bullish', 'ticker_sentiment': [{'ticker': 'MPC', 'relevance_score': '0.264897', 'ticker_sentiment_score': '0.371296', 'ticker_sentiment_label': 'Bullish'}, {'ticker': 'TREE', 'relevance_score': '0.348103', 'ticker_sentiment_score': '0.434075', 'ticker_sentiment_label': 'Bullish'}, {'ticker': 'AAPL', 'relevance_score': '0.178459', 'ticker_sentiment_score': '0.307179', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'TKR', 'relevance_score': '0.427184', 'ticker_sentiment_score': '0.452947', 'ticker_sentiment_label': 'Bullish'}, {'ticker': 'AMZN', 'relevance_score': '0.178459', 'ticker_sentiment_score': '0.307179', 'ticker_sentiment_label': 'Somewhat-Bullish'}]}, {'title': 'Apple Eyes Best Session Since Late 2022, Breaks Above 200-Day Average For First Time In 4 Months - Apple  ( NASDAQ:AAPL ) ', 'url': 'https://www.benzinga.com/analyst-ratings/analyst-color/24/05/38623135/apple-eyes-best-session-since-late-2022-breaks-above-200-day-average-for-first-time', 'time_published': '20240503T192701', 'authors': ['Piero Cingari'], 'summary': \"Apple Inc. AAPL shares are currently riding a spectacular rally, signaling the market's enthusiastic response to the quarterly report from the Cupertino-based tech giant. Friday's surge amounted to over 7%, marking its strongest single-day performance since November 2022, when Apple saw an ...\", 'banner_image': 'https://cdn.benzinga.com/files/images/story/2024/05/03/Apple-Shutterstock.jpeg?width=1200&height=800&fit=crop', 'source': 'Benzinga', 'category_within_source': 'Trading', 'source_domain': 'www.benzinga.com', 'topics': [{'topic': 'Financial Markets', 'relevance_score': '0.108179'}, {'topic': 'Earnings', 'relevance_score': '0.495866'}, {'topic': 'Technology', 'relevance_score': '0.5'}, {'topic': 'Finance', 'relevance_score': '0.5'}], 'overall_sentiment_score': 0.32591, 'overall_sentiment_label': 'Somewhat-Bullish', 'ticker_sentiment': [{'ticker': 'AAPL', 'relevance_score': '0.628473', 'ticker_sentiment_score': '0.390342', 'ticker_sentiment_label': 'Bullish'}, {'ticker': 'BCS', 'relevance_score': '0.101582', 'ticker_sentiment_score': '0.147618', 'ticker_sentiment_label': 'Neutral'}]}, {'title': \"Stocks Rally As Jobs Report Rekindles Rate Cut Hopes, Apple Rockets, Bitcoin Soars Above $61,000: What's Driving Markets Friday? - Amgen  ( NASDAQ:AMGN ) \", 'url': 'https://www.benzinga.com/markets/cryptocurrency/24/05/38621297/stocks-rally-as-jobs-report-rekindles-rate-cut-hopes-apple-rockets-bitcoin-soars-above-61-', 'time_published': '20240503T175102', 'authors': ['Piero Cingari'], 'summary': 'A weaker-than-expected jobs report sparked a widespread market rally in the last session of the week, as traders increased expectations on potential interest rate cuts later this year.', 'banner_image': 'https://cdn.benzinga.com/files/images/story/2024/05/03/Wall-street-sign-in-New-York.jpeg?width=1200&height=800&fit=crop', 'source': 'Benzinga', 'category_within_source': 'Markets', 'source_domain': 'www.benzinga.com', 'topics': [{'topic': 'Life Sciences', 'relevance_score': '0.166667'}, {'topic': 'Energy & Transportation', 'relevance_score': '0.166667'}, {'topic': 'Technology', 'relevance_score': '0.166667'}, {'topic': 'Finance', 'relevance_score': '0.166667'}, {'topic': 'Blockchain', 'relevance_score': '0.158519'}, {'topic': 'Economy - Monetary', 'relevance_score': '0.310843'}, {'topic': 'Financial Markets', 'relevance_score': '0.650727'}, {'topic': 'Manufacturing', 'relevance_score': '0.166667'}, {'topic': 'Earnings', 'relevance_score': '0.614606'}, {'topic': 'Real Estate & Construction', 'relevance_score': '0.166667'}], 'overall_sentiment_score': 0.253494, 'overall_sentiment_label': 'Somewhat-Bullish', 'ticker_sentiment': [{'ticker': 'AAPL', 'relevance_score': '0.073321', 'ticker_sentiment_score': '0.145258', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'MNST', 'relevance_score': '0.146025', 'ticker_sentiment_score': '0.041029', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'EXPE', 'relevance_score': '0.146025', 'ticker_sentiment_score': '0.041029', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'CRBG', 'relevance_score': '0.146025', 'ticker_sentiment_score': '0.041029', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'IVZ', 'relevance_score': '0.073321', 'ticker_sentiment_score': '0.198972', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'AMGN', 'relevance_score': '0.146025', 'ticker_sentiment_score': '0.341047', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'CBRE', 'relevance_score': '0.146025', 'ticker_sentiment_score': '0.041029', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'COIN', 'relevance_score': '0.146025', 'ticker_sentiment_score': '0.041029', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'HSY', 'relevance_score': '0.146025', 'ticker_sentiment_score': '0.041029', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'MELI', 'relevance_score': '0.146025', 'ticker_sentiment_score': '0.041029', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'FTNT', 'relevance_score': '0.146025', 'ticker_sentiment_score': '0.041029', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'EOG', 'relevance_score': '0.146025', 'ticker_sentiment_score': '0.041029', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'BKNG', 'relevance_score': '0.146025', 'ticker_sentiment_score': '0.041029', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'SQ', 'relevance_score': '0.146025', 'ticker_sentiment_score': '0.041029', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'CTRA', 'relevance_score': '0.146025', 'ticker_sentiment_score': '0.041029', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'CRYPTO:BTC', 'relevance_score': '0.146025', 'ticker_sentiment_score': '0.217961', 'ticker_sentiment_label': 'Somewhat-Bullish'}]}, {'title': \"Dow Soars After Jobs Surprise; Apple Rockets While Warren Buffett's Berkshire Hathaway Heads Into Earnings\", 'url': 'https://www.investors.com/market-trend/stock-market-today/dow-jones-nasdaq-sp500-warren-buffett-berkshire-hathaway-apple-jobs/', 'time_published': '20240503T174900', 'authors': [\"Investor's Business Daily\", 'VIDYA RAMAKRISHNAN'], 'summary': \"Dow Soars After Jobs Surprise. Apple Rockets While Warren Buffett's Berkshire Hathaway Heads Into Earnings Investor's Business Daily ...\", 'banner_image': 'https://www.investors.com/wp-content/uploads/2019/05/stock-Buffett-Warren-02-shutter.jpg', 'source': 'Investors Business Daily', 'category_within_source': 'n/a', 'source_domain': 'www.investors.com', 'topics': [{'topic': 'Financial Markets', 'relevance_score': '0.99994'}, {'topic': 'Earnings', 'relevance_score': '0.904684'}, {'topic': 'Real Estate & Construction', 'relevance_score': '0.333333'}, {'topic': 'Technology', 'relevance_score': '0.333333'}, {'topic': 'Finance', 'relevance_score': '0.333333'}, {'topic': 'Blockchain', 'relevance_score': '0.158519'}], 'overall_sentiment_score': 0.141633, 'overall_sentiment_label': 'Neutral', 'ticker_sentiment': [{'ticker': 'DKNG', 'relevance_score': '0.131284', 'ticker_sentiment_score': '0.277737', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'MSFT', 'relevance_score': '0.131284', 'ticker_sentiment_score': '0.153648', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'MTH', 'relevance_score': '0.131284', 'ticker_sentiment_score': '0.049923', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'TOL', 'relevance_score': '0.131284', 'ticker_sentiment_score': '0.049923', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'MELI', 'relevance_score': '0.131284', 'ticker_sentiment_score': '-0.021764', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'AAPL', 'relevance_score': '0.195814', 'ticker_sentiment_score': '-0.085227', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'BRK-A', 'relevance_score': '0.131284', 'ticker_sentiment_score': '-0.047596', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'COIN', 'relevance_score': '0.131284', 'ticker_sentiment_score': '-0.129349', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'SQ', 'relevance_score': '0.131284', 'ticker_sentiment_score': '-0.129349', 'ticker_sentiment_label': 'Neutral'}]}, {'title': \"7 Apple Analysts Size Up Q2 Results: Artificial Intelligence 'A Major Upgrade Cycle Within Product Categories Such As The iPhone' - Apple  ( NASDAQ:AAPL ) \", 'url': 'https://www.benzinga.com/analyst-ratings/analyst-color/24/05/38621037/7-apple-analysts-size-up-q2-results-artificial-intelligence-a-major-upgrade-cycle-w', 'time_published': '20240503T173757', 'authors': ['Chris Katje'], 'summary': \"Technology giant Apple Inc AAPL reported second-quarter financial results that saw revenue and earnings per share beat Street estimates. Analysts have responded to Apple's results, updated guidance, increased dividend, and the announcement of a $110 billion buyback.\", 'banner_image': 'https://cdn.benzinga.com/files/images/story/2024/05/03/apple-cupertino.jpeg?width=1200&height=800&fit=crop', 'source': 'Benzinga', 'category_within_source': 'Trading', 'source_domain': 'www.benzinga.com', 'topics': [{'topic': 'Financial Markets', 'relevance_score': '0.316726'}, {'topic': 'Earnings', 'relevance_score': '0.988915'}, {'topic': 'Technology', 'relevance_score': '0.5'}, {'topic': 'Finance', 'relevance_score': '0.5'}], 'overall_sentiment_score': 0.243053, 'overall_sentiment_label': 'Somewhat-Bullish', 'ticker_sentiment': [{'ticker': 'AAPL', 'relevance_score': '0.866957', 'ticker_sentiment_score': '0.463999', 'ticker_sentiment_label': 'Bullish'}, {'ticker': 'BAC', 'relevance_score': '0.070414', 'ticker_sentiment_score': '0.052203', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'GS', 'relevance_score': '0.070414', 'ticker_sentiment_score': '0.103498', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'NXTT', 'relevance_score': '0.035241', 'ticker_sentiment_score': '-0.187486', 'ticker_sentiment_label': 'Somewhat-Bearish'}]}, {'title': 'Stock Market Rallies On Fed, Jobs Report, Apple: Weekly Review', 'url': 'https://www.investors.com/news/stock-market-rallies-fed-jobs-report-apple/', 'time_published': '20240503T171800', 'authors': ['IBD STAFF', \"Investor's Business Daily\"], 'summary': \"The stock market rallied for the week, with Friday's weaker-than-expected jobs report bolstering a relatively dovish Federal Reserve and Fed chief Jerome Powell on Wednesday. The S&P 500 and Nasdaq tested key levels. Apple ( AAPL ) soared on a record buyback and earnings that were better than ...\", 'banner_image': 'https://www.investors.com/wp-content/uploads/2018/12/Stock-NYSE-09-adobe.jpg', 'source': 'Investors Business Daily', 'category_within_source': 'n/a', 'source_domain': 'www.investors.com', 'topics': [{'topic': 'Life Sciences', 'relevance_score': '0.25'}, {'topic': 'Technology', 'relevance_score': '0.25'}, {'topic': 'Economy - Monetary', 'relevance_score': '0.838487'}, {'topic': 'Retail & Wholesale', 'relevance_score': '0.25'}, {'topic': 'Financial Markets', 'relevance_score': '0.996023'}, {'topic': 'Manufacturing', 'relevance_score': '0.25'}, {'topic': 'Earnings', 'relevance_score': '1.0'}], 'overall_sentiment_score': 0.019613, 'overall_sentiment_label': 'Neutral', 'ticker_sentiment': [{'ticker': 'XPEV', 'relevance_score': '0.041004', 'ticker_sentiment_score': '-0.021579', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'WEN', 'relevance_score': '0.041004', 'ticker_sentiment_score': '-0.029769', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'AAPL', 'relevance_score': '0.0819', 'ticker_sentiment_score': '0.106072', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'MDNDF', 'relevance_score': '0.020509', 'ticker_sentiment_score': '-0.027632', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'NVO', 'relevance_score': '0.061473', 'ticker_sentiment_score': '-0.015047', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'NBIX', 'relevance_score': '0.041004', 'ticker_sentiment_score': '-0.122221', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'AWON', 'relevance_score': '0.020509', 'ticker_sentiment_score': '-0.013582', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'AMGN', 'relevance_score': '0.0819', 'ticker_sentiment_score': '-0.015523', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'DASH', 'relevance_score': '0.041004', 'ticker_sentiment_score': '-0.05416', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'PFE', 'relevance_score': '0.061473', 'ticker_sentiment_score': '-0.092699', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'DTST', 'relevance_score': '0.020509', 'ticker_sentiment_score': '0.009351', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'TSLA', 'relevance_score': '0.102274', 'ticker_sentiment_score': '0.10427', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'MA', 'relevance_score': '0.020509', 'ticker_sentiment_score': '-0.059575', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'SBUX', 'relevance_score': '0.041004', 'ticker_sentiment_score': '-0.029769', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'QCOM', 'relevance_score': '0.102274', 'ticker_sentiment_score': '0.056179', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'LLY', 'relevance_score': '0.102274', 'ticker_sentiment_score': '-0.016434', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'SHAK', 'relevance_score': '0.061473', 'ticker_sentiment_score': '-0.030166', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'SFM', 'relevance_score': '0.0819', 'ticker_sentiment_score': '0.15427', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'TXRH', 'relevance_score': '0.061473', 'ticker_sentiment_score': '-0.030166', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'SQ', 'relevance_score': '0.041004', 'ticker_sentiment_score': '-0.028699', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'DKNG', 'relevance_score': '0.041004', 'ticker_sentiment_score': '-0.056596', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'PYPL', 'relevance_score': '0.041004', 'ticker_sentiment_score': '-0.028699', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'WING', 'relevance_score': '0.061473', 'ticker_sentiment_score': '-0.030166', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'SMCI', 'relevance_score': '0.0819', 'ticker_sentiment_score': '0.125874', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'NXPI', 'relevance_score': '0.041004', 'ticker_sentiment_score': '0.009968', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'QSR', 'relevance_score': '0.041004', 'ticker_sentiment_score': '-0.029769', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'NIO', 'relevance_score': '0.041004', 'ticker_sentiment_score': '-0.021579', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'CVNA', 'relevance_score': '0.0819', 'ticker_sentiment_score': '0.058097', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'GNRC', 'relevance_score': '0.041004', 'ticker_sentiment_score': '-0.010997', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'QRVO', 'relevance_score': '0.061473', 'ticker_sentiment_score': '0.010096', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'REGN', 'relevance_score': '0.041004', 'ticker_sentiment_score': '-0.122221', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'LI', 'relevance_score': '0.041004', 'ticker_sentiment_score': '-0.021579', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'AMZN', 'relevance_score': '0.0819', 'ticker_sentiment_score': '0.087864', 'ticker_sentiment_label': 'Neutral'}]}, {'title': \"Warren Buffett's Berkshire Earnings, Annual Meeting On Tap\", 'url': 'https://www.investors.com/news/buffett-berkshire-earnings-shareholder-meeting/', 'time_published': '20240503T165100', 'authors': [\"Investor's Business Daily\", 'ED CARSON'], 'summary': \"Buffett's Berkshire Earnings, Shareholder Meet Investor's Business Daily ...\", 'banner_image': 'https://www.investors.com/wp-content/uploads/2024/03/Stock-WarrenBuffett-smile-shut.jpg', 'source': 'Investors Business Daily', 'category_within_source': 'n/a', 'source_domain': 'www.investors.com', 'topics': [{'topic': 'Financial Markets', 'relevance_score': '0.999365'}, {'topic': 'Earnings', 'relevance_score': '0.714479'}, {'topic': 'Technology', 'relevance_score': '0.5'}, {'topic': 'Finance', 'relevance_score': '0.5'}], 'overall_sentiment_score': 0.130759, 'overall_sentiment_label': 'Neutral', 'ticker_sentiment': [{'ticker': 'AAPL', 'relevance_score': '0.245339', 'ticker_sentiment_score': '0.220675', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'BRK-A', 'relevance_score': '0.245339', 'ticker_sentiment_score': '0.157875', 'ticker_sentiment_label': 'Somewhat-Bullish'}]}, {'title': \"Warren Buffett's Berkshire Earnings, Annual Meeting On Tap\", 'url': 'https://www.investors.com/news/warren-buffett-berkshire-earnings-shareholder-meeting/', 'time_published': '20240503T165100', 'authors': ['ED CARSON', \"Investor's Business Daily\"], 'summary': \"Warren Buffett's Berkshire Hathaway Earnings, Shareholder Meeting On Tap: What To Expect Investor's Business Daily ...\", 'banner_image': 'https://www.investors.com/wp-content/uploads/2024/03/Stock-WarrenBuffett-smile-shut.jpg', 'source': 'Investors Business Daily', 'category_within_source': 'n/a', 'source_domain': 'www.investors.com', 'topics': [{'topic': 'Financial Markets', 'relevance_score': '0.999365'}, {'topic': 'Earnings', 'relevance_score': '0.714479'}, {'topic': 'Technology', 'relevance_score': '0.5'}, {'topic': 'Finance', 'relevance_score': '0.5'}], 'overall_sentiment_score': 0.130759, 'overall_sentiment_label': 'Neutral', 'ticker_sentiment': [{'ticker': 'AAPL', 'relevance_score': '0.245339', 'ticker_sentiment_score': '0.220675', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'BRK-A', 'relevance_score': '0.245339', 'ticker_sentiment_score': '0.157875', 'ticker_sentiment_label': 'Somewhat-Bullish'}]}, {'title': 'Nasdaq Surges 2%; Apple Posts Upbeat Results - Apple  ( NASDAQ:AAPL ) , AEON Biopharma  ( AMEX:AEON ) ', 'url': 'https://www.benzinga.com/news/earnings/24/05/38619882/nasdaq-surges-2-apple-posts-upbeat-results', 'time_published': '20240503T164934', 'authors': ['Avi Kapoor'], 'summary': 'U.S. stocks traded higher midway through trading, with the Nasdaq Composite gaining 2% on Friday. The Dow traded up 1.16% to 38,670.87 while the NASDAQ rose 2% to 16,157.26. The S&P 500 also rose, gaining, 1.24% to 5,127.07 . Information technology shares rose by 3.2% on Friday.', 'banner_image': 'https://cdn.benzinga.com/files/images/story/2024/05/03/Apple_1.jpeg?width=1200&height=800&fit=crop', 'source': 'Benzinga', 'category_within_source': 'Trading', 'source_domain': 'www.benzinga.com', 'topics': [{'topic': 'Life Sciences', 'relevance_score': '0.333333'}, {'topic': 'Economy - Fiscal', 'relevance_score': '0.158519'}, {'topic': 'Retail & Wholesale', 'relevance_score': '0.333333'}, {'topic': 'Financial Markets', 'relevance_score': '0.214378'}, {'topic': 'Earnings', 'relevance_score': '0.714479'}, {'topic': 'Technology', 'relevance_score': '0.333333'}], 'overall_sentiment_score': 0.124491, 'overall_sentiment_label': 'Neutral', 'ticker_sentiment': [{'ticker': 'SPGI', 'relevance_score': '0.147366', 'ticker_sentiment_score': '-0.206403', 'ticker_sentiment_label': 'Somewhat-Bearish'}, {'ticker': 'AAPL', 'relevance_score': '0.147366', 'ticker_sentiment_score': '0.416015', 'ticker_sentiment_label': 'Bullish'}, {'ticker': 'OMI', 'relevance_score': '0.147366', 'ticker_sentiment_score': '-0.173585', 'ticker_sentiment_label': 'Somewhat-Bearish'}, {'ticker': 'SGBX', 'relevance_score': '0.147366', 'ticker_sentiment_score': '0.239154', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'TNDM', 'relevance_score': '0.147366', 'ticker_sentiment_score': '0.466166', 'ticker_sentiment_label': 'Bullish'}, {'ticker': 'UPLD', 'relevance_score': '0.147366', 'ticker_sentiment_score': '0.444805', 'ticker_sentiment_label': 'Bullish'}]}, {'title': 'Apple  ( AAPL )  Q2 Earnings Beat Estimates, Shares Up on Solid View', 'url': 'https://www.zacks.com/stock/news/2268159/apple-aapl-q2-earnings-beat-estimates-shares-up-on-solid-view', 'time_published': '20240503T160800', 'authors': ['Zacks Equity Research'], 'summary': \"Apple's (AAPL) second-quarter fiscal 2024 results benefit from strong services growth and expanding gross margin.\", 'banner_image': 'https://staticx-tuner.zacks.com/images/articles/main/ad/429.jpg', 'source': 'Zacks Commentary', 'category_within_source': 'n/a', 'source_domain': 'www.zacks.com', 'topics': [{'topic': 'Financial Markets', 'relevance_score': '0.495866'}, {'topic': 'Manufacturing', 'relevance_score': '0.5'}, {'topic': 'Earnings', 'relevance_score': '0.495866'}, {'topic': 'Technology', 'relevance_score': '0.5'}], 'overall_sentiment_score': 0.06913, 'overall_sentiment_label': 'Neutral', 'ticker_sentiment': [{'ticker': 'NVDA', 'relevance_score': '0.179788', 'ticker_sentiment_score': '0.089917', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'AAPL', 'relevance_score': '0.430088', 'ticker_sentiment_score': '0.171682', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'ANET', 'relevance_score': '0.179788', 'ticker_sentiment_score': '0.132902', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'DELL', 'relevance_score': '0.179788', 'ticker_sentiment_score': '0.088484', 'ticker_sentiment_label': 'Neutral'}]}, {'title': 'How to Win in Business: Be Right, Be Positive', 'url': 'https://www.fool.com/investing/2024/05/03/how-to-win-in-business-be-right-be-positive/', 'time_published': '20240503T160400', 'authors': ['Motley Fool Staff'], 'summary': 'Author Elliott Parker helps us think about how to spot the difference between world-changing innovations and corporate theater.', 'banner_image': 'https://g.foolcdn.com/editorial/images/775217/mfm_28.jpg', 'source': 'Motley Fool', 'category_within_source': 'n/a', 'source_domain': 'www.fool.com', 'topics': [{'topic': 'Retail & Wholesale', 'relevance_score': '0.333333'}, {'topic': 'Financial Markets', 'relevance_score': '0.997902'}, {'topic': 'Earnings', 'relevance_score': '0.684621'}, {'topic': 'Mergers & Acquisitions', 'relevance_score': '0.360215'}, {'topic': 'Technology', 'relevance_score': '0.333333'}, {'topic': 'Finance', 'relevance_score': '0.333333'}], 'overall_sentiment_score': 0.175286, 'overall_sentiment_label': 'Somewhat-Bullish', 'ticker_sentiment': [{'ticker': 'MSFT', 'relevance_score': '0.008167', 'ticker_sentiment_score': '0.121829', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'NFLX', 'relevance_score': '0.024496', 'ticker_sentiment_score': '0.021736', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'AAPL', 'relevance_score': '0.008167', 'ticker_sentiment_score': '-0.004094', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'C', 'relevance_score': '0.008167', 'ticker_sentiment_score': '0.006467', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'BRK-A', 'relevance_score': '0.008167', 'ticker_sentiment_score': '0.057943', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'HD', 'relevance_score': '0.008167', 'ticker_sentiment_score': '0.009125', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'AMZN', 'relevance_score': '0.06526', 'ticker_sentiment_score': '0.068368', 'ticker_sentiment_label': 'Neutral'}]}, {'title': \"Here's what's driving Friday's stock action in Apple, Coterra and Eli Lilly\", 'url': 'https://www.cnbc.com/2024/05/03/heres-whats-driving-fridays-moves-in-apple-coterra-and-eli-lilly.html', 'time_published': '20240503T153158', 'authors': ['Morgan Chittum'], 'summary': 'The Investing Club holds its \"Morning Meeting\" every weekday at 10:20 a.m. ET.', 'banner_image': 'https://image.cnbcfm.com/api/v1/image/107374404-1708024054818-gettyimages-2013247027-js2_0524_hk5bxrx4.jpeg?v=1714748643&w=600&h=300&vtcrop=y', 'source': 'CNBC', 'category_within_source': 'Earnings', 'source_domain': 'www.cnbc.com', 'topics': [{'topic': 'Economy - Monetary', 'relevance_score': '0.158519'}, {'topic': 'Life Sciences', 'relevance_score': '0.333333'}, {'topic': 'Financial Markets', 'relevance_score': '0.980716'}, {'topic': 'Earnings', 'relevance_score': '0.310843'}, {'topic': 'Energy & Transportation', 'relevance_score': '0.333333'}, {'topic': 'Technology', 'relevance_score': '0.333333'}], 'overall_sentiment_score': 0.242832, 'overall_sentiment_label': 'Somewhat-Bullish', 'ticker_sentiment': [{'ticker': 'LLY', 'relevance_score': '0.283323', 'ticker_sentiment_score': '0.216765', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'AAPL', 'relevance_score': '0.19117', 'ticker_sentiment_score': '0.160125', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'CTRA', 'relevance_score': '0.19117', 'ticker_sentiment_score': '0.160125', 'ticker_sentiment_label': 'Somewhat-Bullish'}]}, {'title': 'Why Apple Stock Jumped on Friday', 'url': 'https://www.fool.com/investing/2024/05/03/why-apple-stock-jumped-on-friday/', 'time_published': '20240503T153000', 'authors': ['Demitri Kalogeropoulos'], 'summary': \"The tech giant's iPhone sales are declining.\", 'banner_image': 'https://g.foolcdn.com/editorial/images/775725/people-look-at-cell-phones.jpg', 'source': 'Motley Fool', 'category_within_source': 'n/a', 'source_domain': 'www.fool.com', 'topics': [{'topic': 'Earnings', 'relevance_score': '0.714479'}, {'topic': 'Technology', 'relevance_score': '1.0'}, {'topic': 'Financial Markets', 'relevance_score': '0.214378'}], 'overall_sentiment_score': 0.113899, 'overall_sentiment_label': 'Neutral', 'ticker_sentiment': [{'ticker': 'AAPL', 'relevance_score': '0.735697', 'ticker_sentiment_score': '0.199698', 'ticker_sentiment_label': 'Somewhat-Bullish'}]}, {'title': 'Wage Growth Slows - Stock Market Celebrates, Five Real Reasons Apple Is Running Up - Apple  ( NASDAQ:AAPL ) ', 'url': 'https://www.benzinga.com/news/24/05/38616598/wage-growth-slows-stock-market-celebrates-five-real-reasons-apple-is-running-up', 'time_published': '20240503T145859', 'authors': ['The Arora Report'], 'summary': 'To gain an edge, this is what you need to know today. Hedges are profitable. It is time to take more partial profits. It is time to deploy slightly more cash. Please click here for an enlarged chart of SPDR S&P 500 ETF Trust SPY which represents the benchmark stock market index S&P 500 ( SPX ) .', 'banner_image': 'https://cdn.benzinga.com/files/nicholas-cappello-wb63zqj5gne-unsplash_12_14.jpg?width=1200&height=800&fit=crop', 'source': 'Benzinga', 'category_within_source': 'Markets', 'source_domain': 'www.benzinga.com', 'topics': [{'topic': 'Economy - Monetary', 'relevance_score': '0.310843'}, {'topic': 'Financial Markets', 'relevance_score': '0.999346'}, {'topic': 'Manufacturing', 'relevance_score': '0.333333'}, {'topic': 'Earnings', 'relevance_score': '0.360215'}, {'topic': 'Technology', 'relevance_score': '0.333333'}, {'topic': 'Finance', 'relevance_score': '0.333333'}], 'overall_sentiment_score': 0.285383, 'overall_sentiment_label': 'Somewhat-Bullish', 'ticker_sentiment': [{'ticker': 'MSFT', 'relevance_score': '0.112008', 'ticker_sentiment_score': '0.139079', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'GOOG', 'relevance_score': '0.112008', 'ticker_sentiment_score': '0.139079', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'META', 'relevance_score': '0.112008', 'ticker_sentiment_score': '0.139079', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'NVDA', 'relevance_score': '0.112008', 'ticker_sentiment_score': '0.139079', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'AAPL', 'relevance_score': '0.327366', 'ticker_sentiment_score': '0.150691', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'TSLA', 'relevance_score': '0.112008', 'ticker_sentiment_score': '0.139079', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'IVZ', 'relevance_score': '0.056143', 'ticker_sentiment_score': '0.256999', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'CRYPTO:BTC', 'relevance_score': '0.112008', 'ticker_sentiment_score': '-0.249365', 'ticker_sentiment_label': 'Somewhat-Bearish'}]}, {'title': \"April Jobs Report Sparks Market Optimism; Apple's Earnings Release...\", 'url': 'https://moneymorning.com/2024/05/03/two-things-that-will-move-the-market-today/', 'time_published': '20240503T144141', 'authors': ['Chris Johnson'], 'summary': 'Two Things That Will Move the Market TodayApril Jobs Report Stocks are reacting well to the April Jobs report. Investors and traders are looking at the number of jobs created in April - lower than expected - as a welcome complication to the economy.', 'banner_image': 'https://moneymorning.com/wp-content/blogs.dir/1/files/2024/05/apple-revenue.png', 'source': 'Money Morning', 'category_within_source': 'RSS', 'source_domain': 'moneymorning.com', 'topics': [{'topic': 'Economy - Monetary', 'relevance_score': '0.310843'}, {'topic': 'Financial Markets', 'relevance_score': '0.266143'}, {'topic': 'Earnings', 'relevance_score': '0.986564'}, {'topic': 'Technology', 'relevance_score': '1.0'}], 'overall_sentiment_score': 0.041411, 'overall_sentiment_label': 'Neutral', 'ticker_sentiment': [{'ticker': 'AAPL', 'relevance_score': '0.154455', 'ticker_sentiment_score': '0.087472', 'ticker_sentiment_label': 'Neutral'}]}, {'title': 'Dow Jones Soars 400 Points As Amgen, Apple Surge; Nvidia Jumps Again, Tempts New Buyers', 'url': 'https://www.investors.com/market-trend/stock-market-today/dow-jones-sp500-nasdaq-apple-amgen-aapl-amgn-stock/', 'time_published': '20240503T144100', 'authors': ['KEN SHREVE', \"Investor's Business Daily\"], 'summary': \"Stock Market Today: Dow Jones Surges, Led By Amgen, Apple. Nvidia Jumps Above Key Level Investor's Business Daily ...\", 'banner_image': 'https://www.investors.com/wp-content/uploads/2019/01/stock-bull-20-adobe.jpg', 'source': 'Investors Business Daily', 'category_within_source': 'n/a', 'source_domain': 'www.investors.com', 'topics': [{'topic': 'Life Sciences', 'relevance_score': '0.25'}, {'topic': 'Energy & Transportation', 'relevance_score': '0.25'}, {'topic': 'Technology', 'relevance_score': '0.25'}, {'topic': 'Economy - Monetary', 'relevance_score': '0.158519'}, {'topic': 'Financial Markets', 'relevance_score': '0.999999'}, {'topic': 'Earnings', 'relevance_score': '0.999981'}, {'topic': 'Real Estate & Construction', 'relevance_score': '0.25'}], 'overall_sentiment_score': 0.232682, 'overall_sentiment_label': 'Somewhat-Bullish', 'ticker_sentiment': [{'ticker': 'DKNG', 'relevance_score': '0.170075', 'ticker_sentiment_score': '0.026809', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'TOL', 'relevance_score': '0.113866', 'ticker_sentiment_score': '0.319204', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'MELI', 'relevance_score': '0.113866', 'ticker_sentiment_score': '0.074693', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'AAPL', 'relevance_score': '0.279653', 'ticker_sentiment_score': '0.283304', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'CEG', 'relevance_score': '0.113866', 'ticker_sentiment_score': '0.043073', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'PHM', 'relevance_score': '0.113866', 'ticker_sentiment_score': '0.238609', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'NVO', 'relevance_score': '0.113866', 'ticker_sentiment_score': '-0.155187', 'ticker_sentiment_label': 'Somewhat-Bearish'}, {'ticker': 'EXPE', 'relevance_score': '0.113866', 'ticker_sentiment_score': '-0.088545', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'LLY', 'relevance_score': '0.113866', 'ticker_sentiment_score': '-0.155187', 'ticker_sentiment_label': 'Somewhat-Bearish'}, {'ticker': 'AMGN', 'relevance_score': '0.170075', 'ticker_sentiment_score': '0.250183', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'BKNG', 'relevance_score': '0.113866', 'ticker_sentiment_score': '0.228899', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'SQ', 'relevance_score': '0.113866', 'ticker_sentiment_score': '0.140678', 'ticker_sentiment_label': 'Neutral'}]}, {'title': 'All Eyes On Palantir Stock Ahead Of Q1 Results; AI Players Arm, Arista Networks Set To Report', 'url': 'https://www.investors.com/research/earnings-preview/palantir-stock-q1-earnings-arm-arista-stock/', 'time_published': '20240503T143500', 'authors': ['KEN SHREVE', \"Investor's Business Daily\"], 'summary': \"All Eyes On Palantir Stock Ahead Of Q1 Results. AI Players Arm, Arista Networks Set To Report Investor's Business Daily ...\", 'banner_image': 'https://www.investors.com/wp-content/uploads/2024/05/EARNwatch050624.jpg', 'source': 'Investors Business Daily', 'category_within_source': 'n/a', 'source_domain': 'www.investors.com', 'topics': [{'topic': 'Financial Markets', 'relevance_score': '1.0'}, {'topic': 'Manufacturing', 'relevance_score': '0.25'}, {'topic': 'Earnings', 'relevance_score': '1.0'}, {'topic': 'Energy & Transportation', 'relevance_score': '0.25'}, {'topic': 'Technology', 'relevance_score': '0.25'}, {'topic': 'Finance', 'relevance_score': '0.25'}], 'overall_sentiment_score': 0.196887, 'overall_sentiment_label': 'Somewhat-Bullish', 'ticker_sentiment': [{'ticker': 'APP', 'relevance_score': '0.139064', 'ticker_sentiment_score': '0.109446', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'META', 'relevance_score': '0.069798', 'ticker_sentiment_score': '0.064232', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'NVDA', 'relevance_score': '0.069798', 'ticker_sentiment_score': '0.122567', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'AAPL', 'relevance_score': '0.069798', 'ticker_sentiment_score': '0.122567', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'ANET', 'relevance_score': '0.069798', 'ticker_sentiment_score': '0.0', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'FWRG', 'relevance_score': '0.069798', 'ticker_sentiment_score': '0.0572', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'AXON', 'relevance_score': '0.069798', 'ticker_sentiment_score': '0.038378', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'ABNB', 'relevance_score': '0.069798', 'ticker_sentiment_score': '0.0572', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'MSFT', 'relevance_score': '0.104531', 'ticker_sentiment_score': '0.112009', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'QCOM', 'relevance_score': '0.069798', 'ticker_sentiment_score': '0.013143', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'OSCR', 'relevance_score': '0.069798', 'ticker_sentiment_score': '0.172961', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'VVV', 'relevance_score': '0.069798', 'ticker_sentiment_score': '0.0572', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'ARM', 'relevance_score': '0.069798', 'ticker_sentiment_score': '0.0', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'ERJ', 'relevance_score': '0.069798', 'ticker_sentiment_score': '0.0572', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'PLTR', 'relevance_score': '0.069798', 'ticker_sentiment_score': '0.013143', 'ticker_sentiment_label': 'Neutral'}]}, {'title': \"Warren Buffett's Berkshire To Rake In $226M Passively In A Jiffy, Thanks To Its Core Holding Apple - Apple  ( NASDAQ:AAPL ) , American Express  ( NYSE:AXP ) \", 'url': 'https://www.benzinga.com/markets/equities/24/05/38615551/warren-buffetts-berkshire-to-rake-in-226m-passively-in-a-jiffy-thanks-to-its-core-holding-apple', 'time_published': '20240503T142345', 'authors': ['Shanthi Rexaline'], 'summary': \"Apple, Inc.'s AAPL benevolent shareholder returns have always proved to be a windfall for Warren Buffett-led Berkshire Hathaway, Inc. BRK BRK. With the tech giant's 4.2% dividend hike, the windfall is only going to get fatter.\", 'banner_image': 'https://cdn.benzinga.com/files/images/story/2024/05/03/Warren-Buffett-dividend-income.png?width=1200&height=800&fit=crop', 'source': 'Benzinga', 'category_within_source': 'Trading', 'source_domain': 'www.benzinga.com', 'topics': [{'topic': 'Financial Markets', 'relevance_score': '0.266143'}, {'topic': 'Earnings', 'relevance_score': '0.108179'}, {'topic': 'Technology', 'relevance_score': '0.5'}, {'topic': 'Finance', 'relevance_score': '0.5'}], 'overall_sentiment_score': 0.196746, 'overall_sentiment_label': 'Somewhat-Bullish', 'ticker_sentiment': [{'ticker': 'BAC', 'relevance_score': '0.193465', 'ticker_sentiment_score': '0.0', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'AAPL', 'relevance_score': '0.858272', 'ticker_sentiment_score': '0.393325', 'ticker_sentiment_label': 'Bullish'}, {'ticker': 'BRK-A', 'relevance_score': '0.097457', 'ticker_sentiment_score': '0.279473', 'ticker_sentiment_label': 'Somewhat-Bullish'}]}, {'title': '2 Tech Stocks For Your May 2024 Watchlist', 'url': 'https://stockmarket.com/featured/2-tech-stocks-for-your-may-2024-watchlist-2024-05-03', 'time_published': '20240503T141756', 'authors': ['Brett David'], 'summary': 'The technology sector is a dynamic component of the stock market, characterized by companies involved in electronics, software, computers, artificial intelligence, and other technological innovations. This sector is popular among investors due to its potential for high growth and ...', 'banner_image': 'https://u3j7m9h7.rocketcdn.me/wp-content/uploads/2020/06/aapl-stock.jpg', 'source': 'StockMarket.com', 'category_within_source': 'n/a', 'source_domain': 'stockmarket.com', 'topics': [{'topic': 'Retail & Wholesale', 'relevance_score': '0.5'}, {'topic': 'Financial Markets', 'relevance_score': '0.998962'}, {'topic': 'Earnings', 'relevance_score': '0.990893'}, {'topic': 'Technology', 'relevance_score': '0.5'}], 'overall_sentiment_score': 0.314665, 'overall_sentiment_label': 'Somewhat-Bullish', 'ticker_sentiment': [{'ticker': 'MSFT', 'relevance_score': '0.415699', 'ticker_sentiment_score': '0.226851', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'NFLX', 'relevance_score': '0.144708', 'ticker_sentiment_score': '0.27775', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'AAPL', 'relevance_score': '0.415699', 'ticker_sentiment_score': '0.440974', 'ticker_sentiment_label': 'Bullish'}, {'ticker': 'AMZN', 'relevance_score': '0.144708', 'ticker_sentiment_score': '0.27775', 'ticker_sentiment_label': 'Somewhat-Bullish'}]}, {'title': 'Amgen Posts Upbeat Results, Joins OneSpan, Paylocity Holding, MercadoLibre And Other Big Stocks Moving Higher On Friday - Amgen  ( NASDAQ:AMGN ) ', 'url': 'https://www.benzinga.com/news/24/05/38615271/amgen-posts-upbeat-results-joins-onespan-paylocity-holding-mercadolibre-and-other-big-stocks-moving', 'time_published': '20240503T141559', 'authors': ['Avi Kapoor'], 'summary': \"U.S. stocks were higher, with the Dow Jones index gaining over 400 points on Friday. Shares of Amgen Inc. AMGN rose sharply during Friday's session after the company reported better-than-expected first-quarter financial results. Also, the company announced a $50 million milestone payment from ...\", 'banner_image': 'https://cdn.benzinga.com/files/images/story/2024/05/03/Amgen-stock_0.jpeg?width=1200&height=800&fit=crop', 'source': 'Benzinga', 'category_within_source': 'Markets', 'source_domain': 'www.benzinga.com', 'topics': [{'topic': 'Life Sciences', 'relevance_score': '0.142857'}, {'topic': 'Energy & Transportation', 'relevance_score': '0.142857'}, {'topic': 'Technology', 'relevance_score': '0.142857'}, {'topic': 'Finance', 'relevance_score': '0.142857'}, {'topic': 'Retail & Wholesale', 'relevance_score': '0.142857'}, {'topic': 'Financial Markets', 'relevance_score': '0.108179'}, {'topic': 'Manufacturing', 'relevance_score': '0.142857'}, {'topic': 'Earnings', 'relevance_score': '1.0'}, {'topic': 'Real Estate & Construction', 'relevance_score': '0.142857'}], 'overall_sentiment_score': 0.49708, 'overall_sentiment_label': 'Bullish', 'ticker_sentiment': [{'ticker': 'ENPH', 'relevance_score': '0.116631', 'ticker_sentiment_score': '0.0', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'AAPL', 'relevance_score': '0.116631', 'ticker_sentiment_score': '0.338963', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'TWST', 'relevance_score': '0.116631', 'ticker_sentiment_score': '0.264502', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'OSPN', 'relevance_score': '0.116631', 'ticker_sentiment_score': '0.433897', 'ticker_sentiment_label': 'Bullish'}, {'ticker': 'LOCO', 'relevance_score': '0.116631', 'ticker_sentiment_score': '0.281457', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'AMGN', 'relevance_score': '0.230782', 'ticker_sentiment_score': '0.251095', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'IESC', 'relevance_score': '0.116631', 'ticker_sentiment_score': '0.275807', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'ATGE', 'relevance_score': '0.116631', 'ticker_sentiment_score': '0.281457', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'MODV', 'relevance_score': '0.116631', 'ticker_sentiment_score': '0.264502', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'MTZ', 'relevance_score': '0.116631', 'ticker_sentiment_score': '0.264502', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'RMAX', 'relevance_score': '0.116631', 'ticker_sentiment_score': '0.281457', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'WK', 'relevance_score': '0.116631', 'ticker_sentiment_score': '0.581252', 'ticker_sentiment_label': 'Bullish'}, {'ticker': 'XPO', 'relevance_score': '0.116631', 'ticker_sentiment_score': '0.474663', 'ticker_sentiment_label': 'Bullish'}, {'ticker': 'OLED', 'relevance_score': '0.116631', 'ticker_sentiment_score': '0.399451', 'ticker_sentiment_label': 'Bullish'}, {'ticker': 'ARDX', 'relevance_score': '0.116631', 'ticker_sentiment_score': '0.363669', 'ticker_sentiment_label': 'Bullish'}, {'ticker': 'CGON', 'relevance_score': '0.116631', 'ticker_sentiment_score': '0.384939', 'ticker_sentiment_label': 'Bullish'}, {'ticker': 'BKNG', 'relevance_score': '0.116631', 'ticker_sentiment_score': '0.399451', 'ticker_sentiment_label': 'Bullish'}, {'ticker': 'PCTY', 'relevance_score': '0.116631', 'ticker_sentiment_score': '0.362548', 'ticker_sentiment_label': 'Bullish'}, {'ticker': 'RPRX', 'relevance_score': '0.058472', 'ticker_sentiment_score': '0.12728', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'TNDM', 'relevance_score': '0.116631', 'ticker_sentiment_score': '0.264502', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'DBRG', 'relevance_score': '0.058472', 'ticker_sentiment_score': '0.096986', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'MELI', 'relevance_score': '0.116631', 'ticker_sentiment_score': '0.264502', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'ALHC', 'relevance_score': '0.116631', 'ticker_sentiment_score': '0.249799', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'CLFD', 'relevance_score': '0.116631', 'ticker_sentiment_score': '0.310807', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'IBRX', 'relevance_score': '0.116631', 'ticker_sentiment_score': '0.143501', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'WOW', 'relevance_score': '0.174167', 'ticker_sentiment_score': '0.135576', 'ticker_sentiment_label': 'Neutral'}]}, {'title': 'Market Clubhouse Morning Memo - May 3rd, 2024  ( Trade Strategy For SPY, QQQ, AAPL, MSFT, NVDA, GOOGL, META And TSLA )  - Invesco QQQ Trust, Series 1  ( NASDAQ:QQQ ) ', 'url': 'https://www.benzinga.com/24/05/38615142/market-clubhouse-morning-memo-may-3rd-2024-trade-strategy-for-spy-qqq-aapl-msft-nvda-googl-meta-and', 'time_published': '20240503T141226', 'authors': ['RIPS'], 'summary': \"Good Morning Traders! In today's Market Clubhouse Morning Memo, we will discuss SPY, QQQ, AAPL, MSFT, NVDA, GOOGL, META, and TSLA. Our proprietary formula, exclusive to Market Clubhouse, dictates these price levels. This dynamic equation takes into account price, volume, and options flow.\", 'banner_image': 'https://cdn.benzinga.com/files/market-clubhouse-morning-memo_217.png?width=1200&height=800&fit=crop', 'source': 'Benzinga', 'category_within_source': 'Markets', 'source_domain': 'www.benzinga.com', 'topics': [{'topic': 'Economy - Monetary', 'relevance_score': '0.576289'}, {'topic': 'Financial Markets', 'relevance_score': '0.658903'}, {'topic': 'Manufacturing', 'relevance_score': '0.5'}, {'topic': 'Earnings', 'relevance_score': '0.158519'}, {'topic': 'Technology', 'relevance_score': '0.5'}], 'overall_sentiment_score': 0.11814, 'overall_sentiment_label': 'Neutral', 'ticker_sentiment': [{'ticker': 'MSFT', 'relevance_score': '0.116772', 'ticker_sentiment_score': '0.027323', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'GOOG', 'relevance_score': '0.029292', 'ticker_sentiment_score': '0.074096', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'NVDA', 'relevance_score': '0.087717', 'ticker_sentiment_score': '0.082445', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'AAPL', 'relevance_score': '0.116772', 'ticker_sentiment_score': '0.03628', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'TSLA', 'relevance_score': '0.116772', 'ticker_sentiment_score': '0.009336', 'ticker_sentiment_label': 'Neutral'}]}, {'title': \"ETFs to Buy on Apple's Q2 Earnings Beat, Largest-Ever Buyback\", 'url': 'https://www.zacks.com/stock/news/2268018/etfs-to-buy-on-apples-q2-earnings-beat-largest-ever-buyback', 'time_published': '20240503T140000', 'authors': ['Sweta Killa'], 'summary': 'Apple beat estimates on both earnings and revenues, and unveiled a record share buyback program amid the steep decline in iPhone sales.', 'banner_image': 'https://staticx-tuner.zacks.com/images/articles/main/83/1163.jpg', 'source': 'Zacks Commentary', 'category_within_source': 'n/a', 'source_domain': 'www.zacks.com', 'topics': [{'topic': 'Earnings', 'relevance_score': '0.714479'}, {'topic': 'Technology', 'relevance_score': '1.0'}, {'topic': 'Financial Markets', 'relevance_score': '0.998287'}], 'overall_sentiment_score': 0.132197, 'overall_sentiment_label': 'Neutral', 'ticker_sentiment': [{'ticker': 'AAPL', 'relevance_score': '0.63414', 'ticker_sentiment_score': '0.162058', 'ticker_sentiment_label': 'Somewhat-Bullish'}]}, {'title': \"'Have You Used Siri Lately?' Elon Musk Answers MKBHD After Tim Cook Teases 'Exciting' AI Developments From Apple This Year - Apple  ( NASDAQ:AAPL ) \", 'url': 'https://www.benzinga.com/news/24/05/38614138/have-you-used-siri-lately-elon-musk-answers-mkbhd-after-tim-cook-teases-exciting-ai-developments-fro', 'time_published': '20240503T133217', 'authors': ['Rounak Jain'], 'summary': \"Tesla Inc. CEO and xAI founder Elon Musk answered tech YouTuber Marques Brownlee, also known as MKBHD's question about Apple Inc.'s AAPL virtual assistant, Siri. What Happened: Responding to MKBHD's post on X, formerly Twitter, Musk agreed with another user who expressed his dissatisfaction with ...\", 'banner_image': 'https://cdn.benzinga.com/files/images/story/2024/05/03/Elon-Musk-Apple-Siri.jpeg?width=1200&height=800&fit=crop', 'source': 'Benzinga', 'category_within_source': 'News', 'source_domain': 'www.benzinga.com', 'topics': [{'topic': 'Earnings', 'relevance_score': '0.158519'}, {'topic': 'Technology', 'relevance_score': '0.5'}, {'topic': 'Manufacturing', 'relevance_score': '0.5'}], 'overall_sentiment_score': 0.093002, 'overall_sentiment_label': 'Neutral', 'ticker_sentiment': [{'ticker': 'AAPL', 'relevance_score': '0.544495', 'ticker_sentiment_score': '0.168544', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'TSLA', 'relevance_score': '0.118647', 'ticker_sentiment_score': '0.0', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'CRYPTO:DOGE', 'relevance_score': '0.118647', 'ticker_sentiment_score': '-0.05853', 'ticker_sentiment_label': 'Neutral'}]}, {'title': 'AAPL: Apple  ( AAPL )  Quarterly Earnings Update: Buy or Wait?', 'url': 'https://stocknews.com/news/aapl-alot-vtkly-sekey-apple-aapl-quarterly-earnings-update-buy-or-wait/', 'time_published': '20240503T132712', 'authors': ['StockNews.com Staff'], 'summary': \"Tech behemoth Apple Inc. ( AAPL ) released its second-quarter earnings on April 30, 2024. Despite AAPL's efforts, its second-quarter results revealed a decline in sales, particularly in Greater China and iPhone revenue. However, the company managed to surpass analyst estimates for its EPS and ...\", 'banner_image': 'https://stocknews.com/wp-content/uploads/2022/08/Kritika-Sarmah_Image-3.jpg', 'source': 'Stocknews.com', 'category_within_source': 'n/a', 'source_domain': 'stocknews.com', 'topics': [{'topic': 'Earnings', 'relevance_score': '0.918141'}, {'topic': 'Technology', 'relevance_score': '1.0'}, {'topic': 'Financial Markets', 'relevance_score': '0.5855'}], 'overall_sentiment_score': 0.116589, 'overall_sentiment_label': 'Neutral', 'ticker_sentiment': [{'ticker': 'SEKEF', 'relevance_score': '0.054732', 'ticker_sentiment_score': '0.029', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'AAPL', 'relevance_score': '0.627847', 'ticker_sentiment_score': '0.204453', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'VTKLF', 'relevance_score': '0.054732', 'ticker_sentiment_score': '0.029', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'ALOT', 'relevance_score': '0.109206', 'ticker_sentiment_score': '0.032303', 'ticker_sentiment_label': 'Neutral'}]}, {'title': 'Apple Stock Is More Expensive Than You Think', 'url': 'https://www.fool.com/investing/2024/05/03/apple-stock-is-more-expensive-than-you-think/', 'time_published': '20240503T131205', 'authors': ['Neil Rozenbaum'], 'summary': \"Apple is one of the best businesses ever created, but that doesn't make it a cheap stock to buy.\", 'banner_image': 'https://g.foolcdn.com/editorial/images/775680/getty-investing-time-to-sell.jpg', 'source': 'Motley Fool', 'category_within_source': 'n/a', 'source_domain': 'www.fool.com', 'topics': [{'topic': 'Earnings', 'relevance_score': '0.310843'}, {'topic': 'Technology', 'relevance_score': '1.0'}, {'topic': 'Financial Markets', 'relevance_score': '0.161647'}], 'overall_sentiment_score': 0.078852, 'overall_sentiment_label': 'Neutral', 'ticker_sentiment': [{'ticker': 'AAPL', 'relevance_score': '0.975551', 'ticker_sentiment_score': '0.193659', 'ticker_sentiment_label': 'Somewhat-Bullish'}]}, {'title': 'Coinbase Global To Rally Around 22%? Here Are 10 Top Analyst Forecasts For Friday - Coinbase Glb  ( NASDAQ:COIN ) ', 'url': 'https://www.benzinga.com/news/24/05/38613204/coinbase-global-to-rally-around-22-here-are-10-top-analyst-forecasts-for-friday', 'time_published': '20240503T130208', 'authors': ['Avi Kapoor'], 'summary': 'Top Wall Street analysts changed their outlook on these top names. For a complete view of all analyst rating changes, including upgrades and downgrades, please see our analyst ratings page. Rosenblatt raised the price target for Apple Inc. AAPL from $189 to $196.', 'banner_image': 'https://cdn.benzinga.com/files/images/story/2024/05/03/Coinbase-Photo-by-Useacoin-on-Shuttersto.jpeg?width=1200&height=800&fit=crop', 'source': 'Benzinga', 'category_within_source': 'Trading', 'source_domain': 'www.benzinga.com', 'topics': [{'topic': 'Life Sciences', 'relevance_score': '0.2'}, {'topic': 'Retail & Wholesale', 'relevance_score': '0.2'}, {'topic': 'Financial Markets', 'relevance_score': '0.999982'}, {'topic': 'Energy & Transportation', 'relevance_score': '0.2'}, {'topic': 'Technology', 'relevance_score': '0.2'}, {'topic': 'Finance', 'relevance_score': '0.2'}], 'overall_sentiment_score': 0.265392, 'overall_sentiment_label': 'Somewhat-Bullish', 'ticker_sentiment': [{'ticker': 'WEN', 'relevance_score': '0.238913', 'ticker_sentiment_score': '0.303954', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'AAPL', 'relevance_score': '0.238913', 'ticker_sentiment_score': '0.103096', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'CYRX', 'relevance_score': '0.238913', 'ticker_sentiment_score': '0.187946', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'UNP', 'relevance_score': '0.238913', 'ticker_sentiment_score': '0.303954', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'LYV', 'relevance_score': '0.238913', 'ticker_sentiment_score': '0.061732', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'MCO', 'relevance_score': '0.238913', 'ticker_sentiment_score': '0.228333', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'XYL', 'relevance_score': '0.238913', 'ticker_sentiment_score': '0.193349', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'CCORF', 'relevance_score': '0.160633', 'ticker_sentiment_score': '0.156353', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'BKNG', 'relevance_score': '0.238913', 'ticker_sentiment_score': '0.044093', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'TXRH', 'relevance_score': '0.238913', 'ticker_sentiment_score': '0.20037', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'COIN', 'relevance_score': '0.238913', 'ticker_sentiment_score': '0.20913', 'ticker_sentiment_label': 'Somewhat-Bullish'}]}, {'title': \"Apple Delivered Much Better-Than-Feared 'Jalen-Brunson-Like' Quarter, Says Bullish Analyst: Why Betting Against Cupertino Is A 'Wrong Move' - Apple  ( NASDAQ:AAPL ) \", 'url': 'https://www.benzinga.com/24/05/38613198/apple-delivered-much-better-than-feared-jalen-brunson-like-quarter-says-bullish-analyst-why-betting', 'time_published': '20240503T130149', 'authors': ['Shanthi Rexaline'], 'summary': \"Apple, Inc.'s AAPL second-quarter earnings announcement catalyzed a strong upward move in the stock in premarket on Friday. A bullish analyst offered his take on the tech giant's quarterly results. Impressive Quarter: Wedbush analyst Daniel Ives was visibly impressed.\", 'banner_image': 'https://cdn.benzinga.com/files/images/story/2024/05/03/Apple_0.jpeg?width=1200&height=800&fit=crop', 'source': 'Benzinga', 'category_within_source': 'Trading', 'source_domain': 'www.benzinga.com', 'topics': [{'topic': 'Earnings', 'relevance_score': '0.158519'}, {'topic': 'Technology', 'relevance_score': '1.0'}, {'topic': 'Financial Markets', 'relevance_score': '0.214378'}], 'overall_sentiment_score': 0.107508, 'overall_sentiment_label': 'Neutral', 'ticker_sentiment': [{'ticker': 'AAPL', 'relevance_score': '0.735253', 'ticker_sentiment_score': '0.115921', 'ticker_sentiment_label': 'Neutral'}]}, {'title': \"3 Things Apple Isn't Shouting From the Rooftops\", 'url': 'https://www.fool.com/investing/2024/05/03/3-things-apple-isnt-shouting-from-the-rooftops/', 'time_published': '20240503T124614', 'authors': ['Rick Munarriz'], 'summary': 'The consumer tech giant impressed investors this quarter, but some hard realities are hiding in plain sight.', 'banner_image': 'https://g.foolcdn.com/image/?url=https%3A%2F%2Fg.foolcdn.com%2Feditorial%2Fimages%2F775653%2Fgettyimages-970395608.jpg&op=resize&w=700', 'source': 'Motley Fool', 'category_within_source': 'n/a', 'source_domain': 'www.fool.com', 'topics': [{'topic': 'Earnings', 'relevance_score': '0.993856'}, {'topic': 'Technology', 'relevance_score': '1.0'}, {'topic': 'Financial Markets', 'relevance_score': '0.161647'}], 'overall_sentiment_score': 0.188244, 'overall_sentiment_label': 'Somewhat-Bullish', 'ticker_sentiment': [{'ticker': 'AAPL', 'relevance_score': '0.59972', 'ticker_sentiment_score': '0.383927', 'ticker_sentiment_label': 'Bullish'}]}, {'title': \"3 Vanguard ETFs to Buy If You're Looking for Reliable Passive Income\", 'url': 'https://www.fool.com/investing/2024/05/03/3-vanguard-etfs-buy-generate-passive-income/', 'time_published': '20240503T124500', 'authors': ['Daniel Foelber'], 'summary': 'ETFs can be a great way to boost your passive income stream while also achieving diversification.', 'banner_image': 'https://g.foolcdn.com/image/?url=https%3A%2F%2Fg.foolcdn.com%2Feditorial%2Fimages%2F774101%2Fgettyimages-186803113-1201x801-d21b45d-2.jpg&op=resize&w=700', 'source': 'Motley Fool', 'category_within_source': 'n/a', 'source_domain': 'www.fool.com', 'topics': [{'topic': 'Life Sciences', 'relevance_score': '0.2'}, {'topic': 'Energy & Transportation', 'relevance_score': '0.2'}, {'topic': 'Technology', 'relevance_score': '0.2'}, {'topic': 'Finance', 'relevance_score': '0.2'}, {'topic': 'Retail & Wholesale', 'relevance_score': '0.2'}, {'topic': 'Financial Markets', 'relevance_score': '1.0'}, {'topic': 'Earnings', 'relevance_score': '0.310843'}], 'overall_sentiment_score': 0.3103, 'overall_sentiment_label': 'Somewhat-Bullish', 'ticker_sentiment': [{'ticker': 'MSFT', 'relevance_score': '0.188781', 'ticker_sentiment_score': '0.102078', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'CVS', 'relevance_score': '0.038101', 'ticker_sentiment_score': '0.043852', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'AAPL', 'relevance_score': '0.188781', 'ticker_sentiment_score': '0.102078', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'BAC', 'relevance_score': '0.038101', 'ticker_sentiment_score': '0.12002', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'MA', 'relevance_score': '0.076115', 'ticker_sentiment_score': '0.089961', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'V', 'relevance_score': '0.113956', 'ticker_sentiment_score': '0.098659', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'PG', 'relevance_score': '0.076115', 'ticker_sentiment_score': '0.033793', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'XOM', 'relevance_score': '0.038101', 'ticker_sentiment_score': '0.12002', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'DELL', 'relevance_score': '0.038101', 'ticker_sentiment_score': '0.043852', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'PANW', 'relevance_score': '0.038101', 'ticker_sentiment_score': '0.043852', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'WMT', 'relevance_score': '0.076115', 'ticker_sentiment_score': '0.125067', 'ticker_sentiment_label': 'Neutral'}]}, {'title': 'Earnings: Investors Eat Up Results From Chipotle, Tesla, and Alphabet', 'url': 'https://www.fool.com/investing/2024/05/03/earnings-investors-eat-up-results-from-chipotle-te/', 'time_published': '20240503T123300', 'authors': ['Motley Fool Staff'], 'summary': \"The market loves Alphabet's dividend and Tesla's mass market ambitions, but is less sold on Meta commingling AI and the metaverse.\", 'banner_image': 'https://g.foolcdn.com/editorial/images/775215/mfm_26.jpg', 'source': 'Motley Fool', 'category_within_source': 'n/a', 'source_domain': 'www.fool.com', 'topics': [{'topic': 'Technology', 'relevance_score': '0.25'}, {'topic': 'Finance', 'relevance_score': '0.25'}, {'topic': 'Economy - Monetary', 'relevance_score': '0.838487'}, {'topic': 'Retail & Wholesale', 'relevance_score': '0.25'}, {'topic': 'Financial Markets', 'relevance_score': '1.0'}, {'topic': 'Manufacturing', 'relevance_score': '0.25'}, {'topic': 'Earnings', 'relevance_score': '1.0'}], 'overall_sentiment_score': 0.1659, 'overall_sentiment_label': 'Somewhat-Bullish', 'ticker_sentiment': [{'ticker': 'NFLX', 'relevance_score': '0.022383', 'ticker_sentiment_score': '0.083053', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'GOOG', 'relevance_score': '0.044749', 'ticker_sentiment_score': '0.077412', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'ROKU', 'relevance_score': '0.078226', 'ticker_sentiment_score': '0.07168', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'AAPL', 'relevance_score': '0.005597', 'ticker_sentiment_score': '0.019211', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'MDNDF', 'relevance_score': '0.005597', 'ticker_sentiment_score': '0.019211', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'SPOT', 'relevance_score': '0.05592', 'ticker_sentiment_score': '0.095026', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'TYL', 'relevance_score': '0.027977', 'ticker_sentiment_score': '0.014873', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'COIN', 'relevance_score': '0.005597', 'ticker_sentiment_score': '0.019211', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'MSFT', 'relevance_score': '0.06708', 'ticker_sentiment_score': '0.103744', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'TSLA', 'relevance_score': '0.03357', 'ticker_sentiment_score': '0.048427', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'MA', 'relevance_score': '0.005597', 'ticker_sentiment_score': '0.120225', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'SBUX', 'relevance_score': '0.016788', 'ticker_sentiment_score': '0.080294', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'ETSY', 'relevance_score': '0.005597', 'ticker_sentiment_score': '0.048837', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'V', 'relevance_score': '0.044749', 'ticker_sentiment_score': '0.091216', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'RBLX', 'relevance_score': '0.005597', 'ticker_sentiment_score': '0.035107', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'CMG', 'relevance_score': '0.044749', 'ticker_sentiment_score': '0.11293', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'AMZN', 'relevance_score': '0.044749', 'ticker_sentiment_score': '0.074496', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'WMT', 'relevance_score': '0.005597', 'ticker_sentiment_score': '0.047298', 'ticker_sentiment_label': 'Neutral'}]}, {'title': \"Warren Buffett's Berkshire Hathaway Faces Apple Risk, Bearish Momentum Ahead Of Q1 Earnings - Apple  ( NASDAQ:AAPL ) \", 'url': 'https://www.benzinga.com/analyst-ratings/analyst-color/24/05/38612141/warren-buffetts-berkshire-hathaway-faces-apple-risk-bearish-momentum-ahead-of-q1-ea', 'time_published': '20240503T122547', 'authors': ['Surbhi Jain'], 'summary': 'Berkshire Hathaway Inc BRK BRK, the investment firm led by Warren Buffett, will be reporting first-quarter earnings on Saturday, May 4. Wall Street expects $4.57 in EPS and $80.41 billion in revenues as the company reports before market hours. The stock is up 21% over the past year, 12.07% up ...', 'banner_image': 'https://cdn.benzinga.com/files/images/story/2024/05/03/Berkshire-Hathaway-logo.jpeg?width=1200&height=800&fit=crop', 'source': 'Benzinga', 'category_within_source': 'Trading', 'source_domain': 'www.benzinga.com', 'topics': [{'topic': 'Financial Markets', 'relevance_score': '0.82617'}, {'topic': 'Earnings', 'relevance_score': '0.360215'}, {'topic': 'Technology', 'relevance_score': '0.5'}, {'topic': 'Finance', 'relevance_score': '0.5'}], 'overall_sentiment_score': -0.093533, 'overall_sentiment_label': 'Neutral', 'ticker_sentiment': [{'ticker': 'AAPL', 'relevance_score': '0.345674', 'ticker_sentiment_score': '-0.351056', 'ticker_sentiment_label': 'Bearish'}, {'ticker': 'BRK-A', 'relevance_score': '0.566715', 'ticker_sentiment_score': '0.105516', 'ticker_sentiment_label': 'Neutral'}]}, {'title': 'Streamers, Big Banks, and Cyberattacks', 'url': 'https://www.fool.com/investing/2024/05/03/streamers-big-banks-and-cyberattacks/', 'time_published': '20240503T122100', 'authors': ['Motley Fool Staff'], 'summary': 'The latest earnings season gives these investors a lot to talk about.', 'banner_image': 'https://g.foolcdn.com/editorial/images/773774/mfm_19.jpg', 'source': 'Motley Fool', 'category_within_source': 'n/a', 'source_domain': 'www.fool.com', 'topics': [{'topic': 'Technology', 'relevance_score': '0.25'}, {'topic': 'Finance', 'relevance_score': '0.25'}, {'topic': 'Economy - Monetary', 'relevance_score': '0.769861'}, {'topic': 'Retail & Wholesale', 'relevance_score': '0.25'}, {'topic': 'Financial Markets', 'relevance_score': '1.0'}, {'topic': 'Earnings', 'relevance_score': '1.0'}, {'topic': 'Real Estate & Construction', 'relevance_score': '0.25'}], 'overall_sentiment_score': 0.133577, 'overall_sentiment_label': 'Neutral', 'ticker_sentiment': [{'ticker': 'BLK', 'relevance_score': '0.005837', 'ticker_sentiment_score': '0.095482', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'NFLX', 'relevance_score': '0.040839', 'ticker_sentiment_score': '0.04237', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'TGT', 'relevance_score': '0.005837', 'ticker_sentiment_score': '0.058254', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'AAPL', 'relevance_score': '0.023344', 'ticker_sentiment_score': '0.055141', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'CRWD', 'relevance_score': '0.005837', 'ticker_sentiment_score': '-0.013053', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'BAC', 'relevance_score': '0.005837', 'ticker_sentiment_score': '0.141178', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'MS', 'relevance_score': '0.011673', 'ticker_sentiment_score': '0.156076', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'PLD', 'relevance_score': '0.040839', 'ticker_sentiment_score': '0.020786', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'GS', 'relevance_score': '0.005837', 'ticker_sentiment_score': '0.137899', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'UNH', 'relevance_score': '0.005837', 'ticker_sentiment_score': '-0.049173', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'MSFT', 'relevance_score': '0.005837', 'ticker_sentiment_score': '0.024082', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'FIVE', 'relevance_score': '0.035009', 'ticker_sentiment_score': '0.01952', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'FTNT', 'relevance_score': '0.017509', 'ticker_sentiment_score': '-0.014487', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'AMZN', 'relevance_score': '0.017509', 'ticker_sentiment_score': '0.024417', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'FOREX:USD', 'relevance_score': '0.005837', 'ticker_sentiment_score': '0.058254', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'CRYPTO:BTC', 'relevance_score': '0.005837', 'ticker_sentiment_score': '0.012385', 'ticker_sentiment_label': 'Neutral'}]}, {'title': 'Futures Rally Ahead Of Jobs Report', 'url': 'https://www.investors.com/market-trend/stock-market-today/dow-jones-futures-sp500-nasdaq-jobs-report-apple-aapl-earnings/', 'time_published': '20240503T120900', 'authors': ['SCOTT LEHTONEN', \"Investor's Business Daily\"], 'summary': \"Stock Market Today: Dow Jones Rallies Ahead Of Jobs Report. Apple Surges On Earnings Investor's Business Daily ...\", 'banner_image': 'https://www.investors.com/wp-content/uploads/2017/03/stock-wallstreet-031317-shutter.jpg', 'source': 'Investors Business Daily', 'category_within_source': 'n/a', 'source_domain': 'www.investors.com', 'topics': [{'topic': 'Retail & Wholesale', 'relevance_score': '0.25'}, {'topic': 'Financial Markets', 'relevance_score': '1.0'}, {'topic': 'Manufacturing', 'relevance_score': '0.25'}, {'topic': 'Technology', 'relevance_score': '0.25'}, {'topic': 'Finance', 'relevance_score': '0.25'}], 'overall_sentiment_score': 0.244945, 'overall_sentiment_label': 'Somewhat-Bullish', 'ticker_sentiment': [{'ticker': 'MSFT', 'relevance_score': '0.118064', 'ticker_sentiment_score': '0.228279', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'MELI', 'relevance_score': '0.039483', 'ticker_sentiment_score': '0.029877', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'NVDA', 'relevance_score': '0.118064', 'ticker_sentiment_score': '0.126755', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'AAPL', 'relevance_score': '0.15697', 'ticker_sentiment_score': '0.150155', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'TSLA', 'relevance_score': '0.118064', 'ticker_sentiment_score': '0.185084', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'NXPI', 'relevance_score': '0.118064', 'ticker_sentiment_score': '0.137699', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'AXP', 'relevance_score': '0.118064', 'ticker_sentiment_score': '0.207816', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'CMG', 'relevance_score': '0.07887', 'ticker_sentiment_score': '0.156581', 'ticker_sentiment_label': 'Somewhat-Bullish'}, {'ticker': 'IVZ', 'relevance_score': '0.039483', 'ticker_sentiment_score': '0.11352', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'NET', 'relevance_score': '0.039483', 'ticker_sentiment_score': '0.029877', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'TXRH', 'relevance_score': '0.15697', 'ticker_sentiment_score': '0.140979', 'ticker_sentiment_label': 'Neutral'}, {'ticker': 'COIN', 'relevance_score': '0.039483', 'ticker_sentiment_score': '0.029877', 'ticker_sentiment_label': 'Neutral'}]}]}\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "\n",
    "url = 'https://www.alphavantage.co/query?function=NEWS_SENTIMENT&tickers=AAPL&apikey=QDVUL9L3I646J5IR'\n",
    "r = requests.get(url)\n",
    "data = r.json()\n",
    "\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60554d28-392d-4874-bb17-83653b5410bc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9e174ce-2b46-4a44-968b-eb2847cd3ae3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}