{"id":1934,"date":"2023-09-22T07:37:37","date_gmt":"2023-09-22T07:37:37","guid":{"rendered":"https:\/\/eodhd.com\/financial-academy\/?p=1934"},"modified":"2025-02-05T12:38:50","modified_gmt":"2025-02-05T12:38:50","slug":"simplifying-stock-market-decision-making-with-live-data","status":"publish","type":"post","link":"https:\/\/eodhd.com\/financial-academy\/stocks-price-prediction-examples\/simplifying-stock-market-decision-making-with-live-data","title":{"rendered":"Simplifying Stock Market Decision-Making with Live Data"},"content":{"rendered":"\n<p>Have you ever thought about how you could make smarter decisions when it comes to investing in the stock market? I\u2019ve been on that very journey, and I\u2019ve come across a remarkable tool called Live (Delayed) Stock Prices API that provides almost real-time information about any stock, delayed by 15 minutes. This discovery got me thinking, and I decided to create my own personal stock market assistant. With live stock data at your fingertips, you can make more informed choices without relying on expensive platforms. What\u2019s more, you can develop your own investment strategy based on historical data to help you make the best decisions.<\/p>\n\n\n\n<p>I explored various ways to quickly access up-to-date stock prices and trends, and I found that most solutions fell short of my needs, except for this API that offers live stock data.<\/p>\n\n\n\n<p>Let\u2019s take a closer look at what this API can do. This is the output when the API is requested for TSLA stock:<\/p>\n\n\n\n            <div class=\"code__wrapper\">\n                <div class=\"code__content\">\n                    \n<pre class=\"wp-block-code has-white-color has-black-background-color has-text-color has-background\"><code lang=\"python\" class=\"language-python\">{'code': 'TSLA.US',<br> 'timestamp': 1693941000,<br> 'gmtoffset': 0,<br> 'open': 245,<br> 'high': 258,<br> 'low': 244.86,<br> 'close': 257.44,<br> 'volume': 112942754,<br> 'previousClose': 245.01,<br> 'change': 12.43,<br> 'change_p': 5.0733}<\/code><\/pre>\n\n                <\/div>\n                <div class=\"code__btns\">\n                    <button class=\"code__copy\" class=\"copy\" title=\"Copy url\">\n                        <svg class=\"code__copy__icon\" width=\"20\" height=\"20\" viewBox=\"0 0 20 20\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n                            <use xlink:href=\"\/img\/icons\/copy.svg#copy\"><\/use>\n                        <\/svg>\n                        <img decoding=\"async\" class=\"code__copy__approve\" alt=\"\" src=\"\/img\/approve_ico.svg\" loading=\"eager\">\n                    <\/button>\n                <\/div>\n            <\/div>\n        \n\n\n<p>This output provides all the information you need to create a successful investment strategy. Now, let\u2019s delve into how you can build a model to simplify your financial decision-making.<\/p>\n\n\n\n<p class=\"has-text-align-center\"><a class=\"maxbutton-1 maxbutton maxbutton-subscribe-to-api external-css btn\" href=\"https:\/\/eodhd.com\/register\"><span class='mb-text'>Register &amp; Get Data<\/span><\/a><\/p>\n\n\n\n\n\n\n<h1 class=\"wp-block-heading\">Python Implementation<\/h1>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>1. Importing the Necessary Packages<\/strong><\/h2>\n\n\n\n<p>Begin by importing some essential Python packages to support your project. These packages will assist you in handling data, training models, and more.<\/p>\n\n\n\n            <div class=\"code__wrapper\">\n                <div class=\"code__content\">\n                    \n<pre class=\"wp-block-code has-white-color has-black-background-color has-text-color has-background\"><code lang=\"python\" class=\"language-python\">import pandas as pd\nfrom eodhd import APIClient\nimport numpy as np\nfrom xgboost import XGBClassifier\nfrom sklearn.ensemble import IsolationForest\nfrom sklearn.ensemble import RandomForestClassifier<\/code><\/pre>\n\n                <\/div>\n                <div class=\"code__btns\">\n                    <button class=\"code__copy\" class=\"copy\" title=\"Copy url\">\n                        <svg class=\"code__copy__icon\" width=\"20\" height=\"20\" viewBox=\"0 0 20 20\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n                            <use xlink:href=\"\/img\/icons\/copy.svg#copy\"><\/use>\n                        <\/svg>\n                        <img decoding=\"async\" class=\"code__copy__approve\" alt=\"\" src=\"\/img\/approve_ico.svg\" loading=\"eager\">\n                    <\/button>\n                <\/div>\n            <\/div>\n        \n\n\n<p>Here\u2019s what each package does:<\/p>\n\n\n\n<p>Pandas: Helps with various data operations.<\/p>\n\n\n\n<p>Numpy: Used for mathematical operations in Python.<\/p>\n\n\n\n<p>Train_test_split: Splits your data into training and test sets.<\/p>\n\n\n\n<p>XGBoost, Random Forest, and Isolated Random Forest: These are classification models used for our task.<\/p>\n\n\n\n<p>eodhd: This is the official library of EODHD for accessing their APIs<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"a9f4\"><strong>2. API Key Activation<\/strong><\/h2>\n\n\n\n<p id=\"c710\">It is essential to register the EODHD API key with the package in order to use its functions. If you don\u2019t have an EODHD API key, firstly, head over to their&nbsp;<a href=\"https:\/\/eodhistoricaldata.com\/?utm_source=medium&amp;utm_medium=post&amp;utm_campaign=a_python_package_to_retrieve_financial_data_part2\" rel=\"noreferrer noopener\" target=\"_blank\">website<\/a>, then, finish the&nbsp;<a href=\"https:\/\/eodhistoricaldata.com\/register?utm_source=medium&amp;utm_medium=post&amp;utm_campaign=a_python_package_to_retrieve_financial_data_part2\" rel=\"noreferrer noopener\" target=\"_blank\">registration<\/a>&nbsp;process to create an EODHD account, and finally, navigate to the \u2018<a href=\"https:\/\/eodhistoricaldata.com\/cp\/settings?utm_source=medium&amp;utm_medium=post&amp;utm_campaign=a_python_package_to_retrieve_financial_data_part2\" rel=\"noreferrer noopener\" target=\"_blank\">Settings<\/a>\u2019 page where you could find your secret EODHD API key. It is important to ensure that this secret API key is not revealed to anyone. You can activate the API key by following this code:<\/p>\n\n\n\n            <div class=\"code__wrapper\">\n                <div class=\"code__content\">\n                    \n<pre class=\"wp-block-code has-white-color has-black-background-color has-text-color has-background\"><code lang=\"python\" class=\"language-python\">api_key = '&lt;YOUR API KEY&gt;'\nclient = APIClient(api_key)<\/code><\/pre>\n\n                <\/div>\n                <div class=\"code__btns\">\n                    <button class=\"code__copy\" class=\"copy\" title=\"Copy url\">\n                        <svg class=\"code__copy__icon\" width=\"20\" height=\"20\" viewBox=\"0 0 20 20\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n                            <use xlink:href=\"\/img\/icons\/copy.svg#copy\"><\/use>\n                        <\/svg>\n                        <img decoding=\"async\" class=\"code__copy__approve\" alt=\"\" src=\"\/img\/approve_ico.svg\" loading=\"eager\">\n                    <\/button>\n                <\/div>\n            <\/div>\n        \n\n\n<p id=\"fd3d\">The code is pretty simple. In the first line, we are storing the secret EODHD API key into the&nbsp;<code>api_key<\/code>&nbsp;and then in the second line, we are using the&nbsp;<code>APIClient<\/code>&nbsp;class provided by the&nbsp;<code>eodhd<\/code>&nbsp;package to activate the API key and stored the response in the&nbsp;<code>client<\/code>&nbsp;variable.<\/p>\n\n\n\n<p id=\"4634\">Note that you need to replace&nbsp;<code>&lt;YOUR API KEY&gt;<\/code>&nbsp;with your secret EODHD API key. Apart from directly storing the API key with text, there are other ways for better security such as utilizing environmental variables, and so on.<\/p>\n\n\n\n<p class=\"has-text-align-center\"><a class=\"maxbutton-1 maxbutton maxbutton-subscribe-to-api external-css btn\" href=\"https:\/\/eodhd.com\/register\"><span class='mb-text'>Register &amp; Get Data<\/span><\/a><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>3. Loading Historical Data<\/strong><\/h2>\n\n\n\n<p>Retrieve historical stock data for the period you\u2019re interested in. This data will be the foundation for training your model. We can easily extract the historical data of stocks using <a href=\"https:\/\/eodhd.com\/financial-apis\/intraday-historical-data-api\" target=\"_blank\" rel=\"noreferrer noopener\">EODHD\u2019s historical Intraday data API endpoint<\/a> via the eodhd package.<\/p>\n\n\n\n            <div class=\"code__wrapper\">\n                <div class=\"code__content\">\n                    \n<pre class=\"wp-block-code has-white-color has-black-background-color has-text-color has-background\"><code lang=\"python\" class=\"language-python\">def get_historical_data(ticker, start_date, end_date):\n    json_resp = client.get_historical_data(symbol = ticker, period = '5m', from_date = start_date, to_date = end_date, order = 'a')\n    df = pd.DataFrame(json_resp)\n    df = df.set_index('date')\n    df.index = pd.to_datetime(df.index)\n    return df\n\nTSLA = get_historical_data('TSLA', '2021-08-02', '2021-09-02')<\/code><\/pre>\n\n                <\/div>\n                <div class=\"code__btns\">\n                    <button class=\"code__copy\" class=\"copy\" title=\"Copy url\">\n                        <svg class=\"code__copy__icon\" width=\"20\" height=\"20\" viewBox=\"0 0 20 20\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n                            <use xlink:href=\"\/img\/icons\/copy.svg#copy\"><\/use>\n                        <\/svg>\n                        <img decoding=\"async\" class=\"code__copy__approve\" alt=\"\" src=\"\/img\/approve_ico.svg\" loading=\"eager\">\n                    <\/button>\n                <\/div>\n            <\/div>\n        \n\n\n<p class=\"has-text-align-center\">In the above code, we are using the&nbsp;<code>get_historical_data<\/code>&nbsp;function provided by the <code>eodhd<\/code> package to extract the split-adjusted historical stock data of Tesla. The function consists of the following parameters:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>the&nbsp;<code>ticker<\/code>&nbsp;parameter where the symbol of the stock we are interested in extracting the data should be mentioned<\/li>\n\n\n\n<li>the&nbsp;<code>period<\/code>&nbsp;refers to the time interval between each data point (5 minutes interval in our case).<\/li>\n\n\n\n<li>the&nbsp;<code>from_date<\/code>&nbsp;and&nbsp;<code>to_date<\/code>&nbsp;parameters which indicate the starting and ending date of the data respectively. The format of the input should be \u201cYYYY-MM-DD\u201d<\/li>\n\n\n\n<li>the&nbsp;<code>order<\/code>&nbsp;parameter which is an optional parameter that can be used to order the dataframe either in ascending (<code>a<\/code>) or descending (<code>d<\/code>). It is ordered based on the dates.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>4. Obtaining Live Data for Prediction<\/strong><\/h2>\n\n\n\n<p>Use <a href=\"https:\/\/eodhd.com\/financial-apis\/live-realtime-stocks-api\/\" target=\"_blank\" rel=\"noreferrer noopener\">EODHD\u2019s Live (Delayed) Stock Prices API<\/a> via the <code>eodhd<\/code> package to access live stock data, which is crucial for your decision-making process.<\/p>\n\n\n\n            <div class=\"code__wrapper\">\n                <div class=\"code__content\">\n                    \n<pre class=\"wp-block-code has-white-color has-black-background-color has-text-color has-background\"><code lang=\"python\" class=\"language-python\">def extract_intraday(symbol):\n    raw_df = client.get_live_stock_prices(ticker = symbol)\n    df = pd.DataFrame([raw_df])\n    return df\n\ntsla_intraday = extract_intraday('TSLA')<\/code><\/pre>\n\n                <\/div>\n                <div class=\"code__btns\">\n                    <button class=\"code__copy\" class=\"copy\" title=\"Copy url\">\n                        <svg class=\"code__copy__icon\" width=\"20\" height=\"20\" viewBox=\"0 0 20 20\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n                            <use xlink:href=\"\/img\/icons\/copy.svg#copy\"><\/use>\n                        <\/svg>\n                        <img decoding=\"async\" class=\"code__copy__approve\" alt=\"\" src=\"\/img\/approve_ico.svg\" loading=\"eager\">\n                    <\/button>\n                <\/div>\n            <\/div>\n        \n\n\n<p>This function takes a stock code as input, fetches live stock information from the API, converts the response into a Pandas dataframe, and returns it.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>5. Preprocessing the&nbsp;Data<\/strong><\/h2>\n\n\n\n<p>Before you can train a model to predict stock prices, you need to prepare and clean the data. This step ensures your predictions are as accurate as possible. Here\u2019s what you should do:<\/p>\n\n\n\n<p>Check for Class Imbalances: Sometimes, you might have more data for one class (e.g., \u201cbuy\u201d) than another (e.g., \u201csell\u201d). This can skew your model\u2019s predictions. To fix this, you can use two techniques:<\/p>\n\n\n\n<p>Oversampling: Creating more instances of the underrepresented class.<\/p>\n\n\n\n<p>Undersampling: Reducing the number of instances in the overrepresented class.<\/p>\n\n\n\n<p>Normalize the Data: Data normalization ensures that all your features have the same scale. This is important because some algorithms are sensitive to the scale of the input features. You can do this using techniques like:<\/p>\n\n\n\n<p>Standard Scaler: It scales your data to have a mean of 0 and a standard deviation of 1.<\/p>\n\n\n\n<p>MinMax Scaler: This scales your data to a specific range, usually between 0 and 1.<\/p>\n\n\n\n<p>Drop Unnecessary Columns: Sometimes, you have columns in your data that aren\u2019t relevant to your prediction task. It\u2019s a good idea to remove them to simplify your model and improve its performance.<\/p>\n\n\n\n<p>Here\u2019s an example:<\/p>\n\n\n\n            <div class=\"code__wrapper\">\n                <div class=\"code__content\">\n                    \n<pre class=\"wp-block-code has-white-color has-black-background-color has-text-color has-background\"><code lang=\"python\" class=\"language-python\">dataF = dataF.drop(['timestamp',  'gmtoffset',  'datetime'],axis =1)<br>tsla_intraday = tsla_intraday.drop(['code', 'timestamp', 'gmtoffset', 'previousClose', 'change', 'change_p'], axis=1)<\/code><\/pre>\n\n                <\/div>\n                <div class=\"code__btns\">\n                    <button class=\"code__copy\" class=\"copy\" title=\"Copy url\">\n                        <svg class=\"code__copy__icon\" width=\"20\" height=\"20\" viewBox=\"0 0 20 20\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n                            <use xlink:href=\"\/img\/icons\/copy.svg#copy\"><\/use>\n                        <\/svg>\n                        <img decoding=\"async\" class=\"code__copy__approve\" alt=\"\" src=\"\/img\/approve_ico.svg\" loading=\"eager\">\n                    <\/button>\n                <\/div>\n            <\/div>\n        \n\n\n<h2 class=\"wp-block-heading\"><strong>6. Forming a&nbsp;Strategy<\/strong><\/h2>\n\n\n\n<p>Next, you\u2019ll want to classify your training data based on your unique strategy. This helps your model understand how to make predictions. In my case, I used a simple strategy with three classes: \u201cwaiting\u201d (0), \u201cbuying\u201d (1), and \u201cselling\u201d (2).<\/p>\n\n\n\n<p>Here\u2019s how I did it:<\/p>\n\n\n\n            <div class=\"code__wrapper\">\n                <div class=\"code__content\">\n                    \n<pre class=\"wp-block-code has-white-color has-black-background-color has-text-color has-background\"><code lang=\"python\" class=\"language-python\">def signal_generator(df):<br>    open = df.Open.iloc[-1]<br>    close = df.Close.iloc[-1]<br>    previous_open = df.Open.iloc[-2]<br>    previous_close = df.Close.iloc[-2]<br><br>    if (open &gt; close and previous_open &lt; previous_close and close &lt; previous_open and open &gt;= previous_close):<br>        return 1  # Buying<br>    elif (open &lt; close and previous_open &gt; previous_close and close &gt; previous_open and open &lt;= previous_close):<br>        return 2  # Selling<br>    else:<br>        return 0  # Waiting<br><br>signal = [0]  # Initialize with \"waiting\"<br>for i in range(1, len(dataF)):<br>    df = dataF[i - 1:i + 1]<br>    signal.append(signal_generator(df))<br><br>dataF[\"signal\"] = signal<\/code><\/pre>\n\n                <\/div>\n                <div class=\"code__btns\">\n                    <button class=\"code__copy\" class=\"copy\" title=\"Copy url\">\n                        <svg class=\"code__copy__icon\" width=\"20\" height=\"20\" viewBox=\"0 0 20 20\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n                            <use xlink:href=\"\/img\/icons\/copy.svg#copy\"><\/use>\n                        <\/svg>\n                        <img decoding=\"async\" class=\"code__copy__approve\" alt=\"\" src=\"\/img\/approve_ico.svg\" loading=\"eager\">\n                    <\/button>\n                <\/div>\n            <\/div>\n        \n\n\n<h2 class=\"wp-block-heading\"><strong>7. Loading and Training the&nbsp;Models<\/strong><\/h2>\n\n\n\n<p>Now comes the exciting part\u200a\u2014\u200aselecting and training your model. In this tutorial, I used three different models: XGBoost, Isolated Random Forest, and Random Forest. Each has its unique strengths.<\/p>\n\n\n\n<p>XGBoost: a powerful boosting algorithm that\u2019s great at handling structured data like stock prices.<\/p>\n\n\n\n            <div class=\"code__wrapper\">\n                <div class=\"code__content\">\n                    \n<pre class=\"wp-block-code has-white-color has-black-background-color has-text-color has-background\"><code lang=\"python\" class=\"language-python\">from xgboost import XGBClassifier<br>model1 = XGBClassifier()<br>model1.fit(X_train, y_train)<\/code><\/pre>\n\n                <\/div>\n                <div class=\"code__btns\">\n                    <button class=\"code__copy\" class=\"copy\" title=\"Copy url\">\n                        <svg class=\"code__copy__icon\" width=\"20\" height=\"20\" viewBox=\"0 0 20 20\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n                            <use xlink:href=\"\/img\/icons\/copy.svg#copy\"><\/use>\n                        <\/svg>\n                        <img decoding=\"async\" class=\"code__copy__approve\" alt=\"\" src=\"\/img\/approve_ico.svg\" loading=\"eager\">\n                    <\/button>\n                <\/div>\n            <\/div>\n        \n\n\n<p>Isolated Random Forest: this model is excellent for detecting anomalies using binary trees.<\/p>\n\n\n\n            <div class=\"code__wrapper\">\n                <div class=\"code__content\">\n                    \n<pre class=\"wp-block-code has-white-color has-black-background-color has-text-color has-background\"><code lang=\"python\" class=\"language-python\">from sklearn.ensemble import IsolationForest<br><br>random_state = np.random.RandomState(42)<br>model=IsolationForest(n_estimators=100,max_samples='auto',contamination=float(0.2),random_state=random_state)<br><br>model.fit(X_train, y_train)<\/code><\/pre>\n\n                <\/div>\n                <div class=\"code__btns\">\n                    <button class=\"code__copy\" class=\"copy\" title=\"Copy url\">\n                        <svg class=\"code__copy__icon\" width=\"20\" height=\"20\" viewBox=\"0 0 20 20\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n                            <use xlink:href=\"\/img\/icons\/copy.svg#copy\"><\/use>\n                        <\/svg>\n                        <img decoding=\"async\" class=\"code__copy__approve\" alt=\"\" src=\"\/img\/approve_ico.svg\" loading=\"eager\">\n                    <\/button>\n                <\/div>\n            <\/div>\n        \n\n\n<p>Random Forest: a machine-learning algorithm that uses multiple decision trees to make predictions or classifications.<\/p>\n\n\n\n            <div class=\"code__wrapper\">\n                <div class=\"code__content\">\n                    \n<pre class=\"wp-block-code has-white-color has-black-background-color has-text-color has-background\"><code lang=\"python\" class=\"language-python\">from sklearn.ensemble import RandomForestClassifier<br><br>model1 = RandomForestClassifier()<br>model1.fit(X_train, y_train)<\/code><\/pre>\n\n                <\/div>\n                <div class=\"code__btns\">\n                    <button class=\"code__copy\" class=\"copy\" title=\"Copy url\">\n                        <svg class=\"code__copy__icon\" width=\"20\" height=\"20\" viewBox=\"0 0 20 20\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n                            <use xlink:href=\"\/img\/icons\/copy.svg#copy\"><\/use>\n                        <\/svg>\n                        <img decoding=\"async\" class=\"code__copy__approve\" alt=\"\" src=\"\/img\/approve_ico.svg\" loading=\"eager\">\n                    <\/button>\n                <\/div>\n            <\/div>\n        \n\n\n<h2 class=\"wp-block-heading\"><strong>8. Prediction on Live Data for Suggestions<\/strong><\/h2>\n\n\n\n<p>With your trained model, you can now make predictions on both test data and live data. Evaluate your predictions using metrics like precision, recall, accuracy, and F1 score. The F1 score is particularly useful when dealing with imbalanced data.<\/p>\n\n\n\n<p>Here\u2019s how you can do it:<\/p>\n\n\n\n            <div class=\"code__wrapper\">\n                <div class=\"code__content\">\n                    \n<pre class=\"wp-block-code has-white-color has-black-background-color has-text-color has-background\"><code lang=\"python\" class=\"language-python\"># Make predictions for test data<br>y_pred1 = model1.predict(X_test)<br>predictions1 = [round(value) for value in y_pred1]<br><br># Evaluate the predictions<br>from sklearn.metrics import confusion_matrix, recall_score, precision_score, f1_score, accuracy_score<br>cm = confusion_matrix(y_test, predictions)<br><br>rf_Recall = recall_score(y_test, predictions1, average='macro')<br>rf_Precision = precision_score(y_test, predictions1, average='macro')<br>rf_f1 = f1_score(y_test, predictions1, average='macro')<br>rf_accuracy = accuracy_score(y_test, predictions1)<\/code><\/pre>\n\n                <\/div>\n                <div class=\"code__btns\">\n                    <button class=\"code__copy\" class=\"copy\" title=\"Copy url\">\n                        <svg class=\"code__copy__icon\" width=\"20\" height=\"20\" viewBox=\"0 0 20 20\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n                            <use xlink:href=\"\/img\/icons\/copy.svg#copy\"><\/use>\n                        <\/svg>\n                        <img decoding=\"async\" class=\"code__copy__approve\" alt=\"\" src=\"\/img\/approve_ico.svg\" loading=\"eager\">\n                    <\/button>\n                <\/div>\n            <\/div>\n        \n\n\n<h1 class=\"wp-block-heading\"><strong>Where to Go from&nbsp;Here<\/strong><\/h1>\n\n\n\n<p>Now that you\u2019ve embarked on this journey, the possibilities are endless. You can fine-tune your model, explore different machine-learning algorithms, and analyze various stocks. With access to live stock data using Live (Delayed) Stock Prices API and the knowledge from this tutorial, you\u2019re well-equipped to make more informed decisions in the ever-changing world of stock trading.<\/p>\n\n\n\n<p class=\"has-text-align-center\"><a class=\"maxbutton-1 maxbutton maxbutton-subscribe-to-api external-css btn\" href=\"https:\/\/eodhd.com\/register\"><span class='mb-text'>Register &amp; Get Data<\/span><\/a><\/p>\n\n\n\n<p>Stay tuned for more insights and tips on improving your stock market strategies with data-driven solutions!<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Have you ever thought about how you could make smarter decisions when it comes to investing in the stock market? I\u2019ve been on that very journey, and I\u2019ve come across a remarkable tool called Live (Delayed) Stock Prices API that provides almost real-time information about any stock, delayed by 15 minutes. This discovery got me [&hellip;]<\/p>\n","protected":false},"author":18,"featured_media":1935,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[1,62,59],"tags":[],"coding-language":[30],"ready-to-go-solution":[],"qualification":[31,32],"financial-apis-category":[36],"financial-apis-manuals":[39,73,57],"class_list":["post-1934","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-fundamental-analysis-examples","category-stocks-price-prediction-examples","category-technical-analysis-examples","coding-language-python","qualification-experienced","qualification-guru","financial-apis-category-stock-market-prices","financial-apis-manuals-end-of-day","financial-apis-manuals-live-delayed-15-mins","financial-apis-manuals-real-time","has_thumb"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.9 (Yoast SEO v26.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Simplifying Stock Market Decision-Making with Live Data | EODHD APIs Academy<\/title>\n<meta name=\"description\" content=\"Discover a remarkable tool for smarter stock market decision making. 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