after the final command how do i run this project, Hi, I have met this problem below: Predicting how the stock market will perform is one of the most difficult things to do. Thereafter you will try a bit more fancier "exponential moving average" method and see how well that does. The libraries are imported and the pre-processed data is loaded, The data is split into train and test set and the Linear Regressor model is trained on the training data, Once the model is trained, it is evaluated on the test set, The Predicted against the Actual Values are visualized, The LSTM model is used below to predict the stock price, Similarly, the dataset is split into train and test set, The Deep Learning model using the Long Short Term Memory network is built, The model is trained and then predicted on the test set, The prediction is visualized against the actual data points and its accuracy is measured. I can see the code is better that I downloaded. valid_data=final_dataset[987:,:], scaled_data=scaler.fit_transform(final_dataset). Now make a new python file stock_app.py and paste the below script: Now run this file and open the app in the browser: Stock price prediction is a machine learning project for beginners; in this tutorial we learned how to develop a stock cost prediction model and how to build an interactive dashboard for stock analysis. change date to string but give another error. In this Data Science Project we will create a Linear Regression model and a Decision Tree Regression Model to Predict Apple’s Stock Price using Machine Learning and Python. This will be the input to the models to predict the adjusted close price which is $177.470001. As a final step to conclude your analysis of predicting the stock price based on the model, let’s prepare a plot using the popular Python plotting library, the matplotlib. Are you looking for more projects with source code? I Am Also getting same Error,can Any one Fix that Error? It will be equal to the price in day T minus 1, times the daily return observed in day T. for t in range(1, t_intervals): price_list[t] = price_list[t - … The forecasting algorithm aims to foresee whether tomorrow’s exchange closing price is going to be lower or higher with respect to today. Below are the algorithms and the techniques used to predict stock price in Python. Your email address will not be published. I have the date column in the same format as your CSV file has still got the same error. The future price that I want that’s 30 days into the future is just 30 rows down from the current Adj. Notebook. Suggestions and contributions of all kinds are very welcome. new_dataset.index=new_dataset.Date The dataset contains n = 41266minutes of data ranging from April to August 2017 on 500 stocks as well as the total S&P 500 index price. Deep Learning is a branch of Machine Learning which deals with neural networks that is similar to the neurons in our brain. Viewed 15k times 10. I may not have looked at your code close enough but what is the reason for your predicted stock prices seemingly shifted from the actual stock prices? I have taken the data from 1st Jan 2015 to 31st Dec 2019.1st Jan 2019 to 31st Dec 2019, these dates have been taken for prediction/forecasting.4 years data have been taken as a training data and 1 year as a test data. Stock Prediction in Python. Hello everyone, In this tutorial, we are going to see how to predict the stock price in Python using LSTM with scikit-learn of a particular company, I think it sounds more interesting right!, So now what is stock price all about?. We will develop this project into two parts: Before proceeding ahead, please download the source code: Stock Price Prediction Project. Traceback (most recent call last): 5 For the time stamp issue, deep-learning python3 recurrent-neural-networks neural-networks stock-price-prediction price-prediction cryptocurrency-price-predictor market-price-prediction Updated Sep 25, 2020 Python Go download the May 2020 version.. its different some. Predicting stock prices has always been an attractive topic to both investors and researchers. If yes, please rate our work on Google, Tags: lstm neural networkmachine learning projectplotlyPython projectstock price prediction. Creating a model and making a prediction can be done with Stocker in a single line: # predict days into the future. We implemented stock market prediction using the LSTM model. 3. Analyze the closing prices from dataframe: 4. Sort the dataset on date time and filter “Date” and “Close” columns: 7. Take a sample of a dataset to make stock price predictions using the LSTM model: 9. Visualize the predicted stock costs with actual stock costs: You can observe that LSTM has predicted stocks almost similar to actual stocks. Recalling the last row of data that was left out of the original data set, the date was 05–31–2019, so the day is 31. File “F:\Stocker\StockerDownload\stock-env\lib\site-packages\keras\__init__.py”, line 5, in python wordpress flask machine-learning twitter sentiment-analysis tensorflow linear-regression keras lstm stock-market stock-price-prediction tweepy arima alphavantage yfinance Updated Nov 13, 2020 if the excel file showing d/m/y then the code may use the %d/%m/%y. S&P 500 Forecast with confidence Bands. from keras.models import load_model Stocker is a Python class-based tool used for stock prediction and analysis. Even the beginners in python find it that way. First, we will learn how to predict stock price using the LSTM neural network. Using features like the latest announcements about an organization, their quarterly revenue results, etc., machine learning … I am getting the same error Why do I get “Fail to find the dnn implementation.” and “Function call stack” with this script “lstm_model.fit(x_train_data,y_train_data,epochs=1,batch_size=1,verbose=2)” . However, you should be aware of using regularization in case the neural network overfits. The dataset used for this stock price prediction project is downloaded from here. TypeError: float() argument must be a string or a number, not ‘Timestamp’, I am getting the same error with original data. Where to save the saved_model.h5 and saved_ltsm_model.h5? It consists of S&P 500 companies’ data and the one we have used is of Google Finance. Stock Prediction is a open source you can Download zip and edit as per you need. Try, it should be able to access the source code. Now I can start making my FB price prediction. Please try and let us know. Then we will build a dashboard using Plotly dash for stock analysis. EDA : It is clearly observed that the LSTM model has outperformed the Linear Regression model and has significantly reduced the cost function as well. Please do not use such packages for codes made public, or release the packages for everyone’s use. 8 predicted_closing_price=scaler.inverse_transform(predicted_closing_price), How do I get rid of the following error? I am getting the same “TypeError: float() argument must be a string or a number, not ‘Timestamp'” with the original code and original CSV. 3y ago. Please provide a fix, closing_price = model.predict(X_test) In order to create a program that predicts the value of a stock in a set amount of days, we need to use some very useful python packages. You have entered an incorrect email address! All the codes covered in the blog are written in Python. We can simply write down the formula for the expected stock price on day T in Pythonic. Here’s how you do it, (sales of car) = -4.6129 x (168) + 1297.7. As seen from the data, there are high range values which often results in the model giving more importance to the higher number and thus giving a poor prediction. Stock Price Prediction Using Python & Machine Learning (LSTM). I have taken an open price for prediction. Also, Read – Machine Learning Full Course for free. Our team exported the scraped stock data from our scraping server as a csv file. This is in reference to step #5. Machine Learning is a study of training machines to learn patterns from old data and make predictions with the new one. You have very limited features for each day, namely the opening price of the stock for that day, closing price, the highest price of the stock, and the lowest price of the stock. Version 3 of 3. First you will try to predict the future stock market prices (for example, x t+1) as an average of the previously observed stock market prices within a fixed size window (for example, x t-N,..., x t) (say previous 100 days). Next step will be to develop a trading strategy on top of that, based on our predictions, and backtest it against a benchmark. www.golibrary.co - Everyone for education - Golibrary.co - March 2, 2020 stock market prediction using python - Stock Market Prediction using Python - Part I Introduction: With the advent of high speed computers the python language has become an immensely powerful tool for performing complex You will need to install the following packages: 1. numpy 2. selenium 3. sklearn 4. iexfinance If you do not already have some of these packages you can install them through pip install PACKAGEor by cloning the git repository. How to build your Data science portfolio? hi this code is incorrect in section #5 . Since in most cases, people cannot buy fractions of shares, a stock price of $1,000 is fairly limiting to investors. Web Scraping Using Threading in Python Flask. I am getting the same error float() argument must be a string or a number, not ‘Timestamp’. new_dataset.drop(“Date”,axis=1,inplace=True) For example, Apple did one once their stock price exceeded $1000. Stock price prediction is a machine learning project for beginners; in this tutorial we learned how to develop a stock cost prediction model and how to build an interactive dashboard for stock analysis. Could you please help me with this? Thus the data is normalized with the MinMaxScaler which scales each value within the range 0 to 1. Summary. IndexError Traceback (most recent call last) OTOH, Plotly dash python framework for building dashboards. new_dataset.index=new_dataset.Date In this machine learning project, we will be talking about predicting the returns on stocks. 4 X_test=np.array(X_test) Active 8 months ago. Stock Price prediction is an application of Time Series forecasting which is one of the hardest and intriguing aspects of Data Science. this code is incorrect in section #5 . scaler=MinMaxScaler(feature_range=(0,1)) 65. Dash is a python framework that provides an abstraction over flask and react.js to build analytical web applications. There are so many factors involved in the prediction – physical factors vs. physhological, rational and irrational behaviour, etc. We have created a function first to get the historical stock price data of the company, Once the data is received, we load it into a CSV file for further processing, Once the data is collected and loaded, it needs to be pre-processed. This project is specific for the dataset provided, if you want similar experimentation on you dataset you will have to make changes in the source code accordingly. Machine learning has significant applications in the stock price prediction. (for complete code refer GitHub) Stocker is designed to be very easy to handle. Prediction of Stock Price with Machine Learning. Below are the algorithms and the techniques used to predict stock price in Python. Write CSS OR LESS and hit save. Latest New and Trending Technology Machine Learning, Artificial Intelligence, Block chain, Augmented Reality, Ask Question Asked 2 years, 5 months ago. There was an error when i tried to use my own csv file, converted the same way as your example file. Stock Price Prediction using Machine learning & Deep Learning Techniques with Python... Understanding the basics of recommender systems, Introduction to Natural Language Processing, Introduction to PCA(Principal Component Analysis), How to detect fake news using Machine learning in Python, 7 types of Regression techniques you should know, Essentials of Machine Learning Algorithms (python code). hi dear . The computer is trained first with historical data which could be labeled or unlabelled based on the problem statement and once it performs well on the training data, it is evaluated on the test data set. Yibin Ng in Towards Data Science. With the advancement of technology and the huge amounts of unique data that is getting generated from a variety of sources, it is imperative that modern systems are well equipped to deal with such volumes data. I am new to coding and really dont understand this I think it has to do with an extra step in the code? Can we use machine learningas a game changer in this domain? Sale of car = 522.73 when steel price … my Date is in the format 2018-07-20 the same as your provided CSV The more data you feed on a neural network, the better it is trained and the more accurate predictions you get. The idea at the base of this project is to build a model to predict financial market’s movements. Data Mining vs Machine Learning: What’s the Difference? Hi, I can’t access the source code. python3 stock_app.py . 3. Run the below command in the terminal. In this article, we would cover Stock Price Prediction using Machine Learning algorithms like Linear Regression and then transit into Stock Price Prediction using Deep Learning techniques like LSTM or Long Short Term Memory network built on the Recursive Neural Network (RNN) architecture. Input (2) Execution Info Log Comments (14) This Notebook has been released under the Apache 2.0 open source license. So now I will predict the price by giving the models a value of 31. TypeError: float() argument must be a string or a number, not ‘Timestamp’. Close column but shifted 30 rows up to get the price of the next 30 days, and then print the last 5 rows of the new data set. please check it. The uncertainty that surrounds it makes it nearly impossible to estimate the price with utmost accuracy. Often the metrics used for prediction could be misleading and hence it is necessary to define the KPI and the metrics of evaluation beforehand keeping the business objective in mind. Before moving ahead, you need to install dash. This chart is a bit easier to understand vs the default prophet chart (in my opinion at least). Stock Price Prediction is arguably the difficult task one could face. As this article encompasses the use of Machine Learning and Deep Learning to predict stock prices, we would first provide a brief intuition of both these terms. Free Python course with 25 projects (coupon code: DATAFLAIR_PYTHON) Start Now. Stock Prediction project is a web application which is developed in Python platform. The default is having one layer of the hidden layer along with the input and the output layers but you could also define more layers keeping the number of units in each layer same. The dataset used for this stock price prediction project is downloaded from here. CTRL + SPACE for auto-complete. Install TensorFlow via `pip install tensorflow`. I have installed pandas-datareader but I'm wondering if there are alternatives. I have downloaded the data of Bajaj Finance stock price online. How can I download stock price data with Python? So I will create a new column called ‘Prediction’ and populate it with data from the Adj. ImportError: Keras requires TensorFlow 2.2 or higher. scaler=MinMaxScaler(feature_range=(0,1)) So now coming to the awesome part, take any change in the price of Steel, for example price of steel is say 168 and we want to calculate the predicted rise in the sale of cars. Python Libraries: For Linear Regression Analysis user must have installed mentioned libraries in the system. In this section, we will build a dashboard to analyze stocks. python parse_data.py --company GOOGL python parse_data.py --company FB python parse_data.py --company AAPL Features for Stock Price Prediction. We must set up a loop that begins in day 1 and ends at day 1,000. Machine Learning Projects with Source Code, Project – Handwritten Character Recognition, Project – Real-time Human Detection & Counting, Project – Create your Emoji with Deep Learning, Project – Detecting Parkinson’s Disease, Python – Intermediates Interview Questions. We would save the Pre-processed data for later use, Now, we would start building the model using the Linear Regression algorithm. OTOH, Plotly dash python framework for building dashboards. For example, you do “import preprocess_data”, which isn’t a standard package that can be used by anyone. This is simple and basic level small project for learning purpose. At the end of this article, you will learn how to predict stock prices by using the Linear Regression model by implementing the Python programming language. This is a very complex task and has uncertainties. in This Python project with tutorial and guide for developing a code. We can see throughout the history of the actuals vs forecast, that prophet does an OK job forecasting but has … Close price. Moreover, there are so many factors like trends, seasonality, etc., that needs to be considered while predicting the stock price. How to get started with Python for Data Analysis? Required fields are marked *, Home About us Contact us Terms and Conditions Privacy Policy Disclaimer Write For Us Success Stories, This site is protected by reCAPTCHA and the Google, Free Python course with 25 projects (coupon code: DATAFLAIR_PYTHON). raise ImportError( final_dataset=new_dataset.values. data sample is : [Timestamp(‘2013-12-03 00:00:00’) 10000.0] I am also getting error in type format . Specifically, I’ll go through the pipeline, decision process and results I obt… It consists of S&P 500 companies’ data and the one we have used is of Google Finance. We implemented stock market prediction using the LSTM model. Investors always question if the price of a stock will rise or not, since there are many complicated financial indicators that only investors and people with good finance knowledge can understand, the trend of stock market is inconsistent and look very random to ordinary people. Predicting the stock market has been the bane and goal of investors since its inception. you can try formatting the code same with the excel csv file. model, model_data = amazon.create_prophet_model (days=90) Predicted Price on 2018-04-18 = $1336.98. The data was already cleaned and prepared, meaning missing stock and index prices were LOCF’ed (last observation carried forward), so that the file did not contain any missing values. Projects Cohort Community Login Sign up › Build a Stock Prediction Algorithm Build an algorithm that forecasts stock prices in Python. Please provide a fix thank you. Companies can do a stock split where they say every share is now 2 shares, and the price is half. The description of the implementation of Stock Price Prediction algorithms is provided. final_dataset=new_dataset.values, train_data=final_dataset[0:987,:] Line 7 and 8 must be before Line 2 . Index and stocks are arranged in wide format. This blog covered how both machine learning and deep learning could be used to predict stock prices which may be daunting as it might seem but with the right technique it could be accomplished. not able to fetch data from url, getting HTTPError: HTTP Error 403: Forbidden error. To build the stock price prediction model, we will use the NSE TATA GLOBAL dataset. This is a dataset of Tata Beverages from Tata Global Beverages Limited, National Stock Exchange of India: To develop the dashboard for stock analysis we will use another stock dataset with multiple stocks like Apple, Microsoft, Facebook. I got the same bug.. fixed it so I thought.. got past that error …and then got more errors later.. my fix was not correct. A stock price is the price of a share of a company that is being sold in the market. in below rewrite your code. Here is an example of installing numpy with pip and with git Now open up your favorite text editor and create a new python file. TypeError: float() argument must be a string or a number, not ‘Timestamp’. TypeError: float() argument must be a string or a number, not ‘Timestamp’. First, you need to prepare a separate data frame containing the existing testing data set and the predictions for that. Notice that the prediction, the green line, contains a confidence interval. File “stock_app.py”, line 7, in So instead of print “The stock open price for 29th Feb is: $”,str(predicted_price) you have use like print(“The stock open price for 29th Feb is: $”,str(predicted_price)). If you are using python 3 and above.. you need use print function.. Stocker is a python tool that uses ANN to predict the stock's close price for the next business day. Build an algorithm that forecasts stock prices in Python. and try to fix it but not solve it. ... which tries to develop an equation or a statistical model which could be used over and over with very high accuracy of prediction. Line 7 and 8 must be before Line 2 . The model could be tuned further by adding dropout values, changing the LSTM layers, adding more units in the layers, increasing the number of epochs, and so on. Stock Price Prediction. Why hasn’t been an attempt made to replicate the results? new_dataset.drop(“Date”,axis=1,inplace=True) A quick look at the S&P time series using pyplot.plot(data['SP500']): ... Machine Learning Techniques applied to Stock Price Prediction. NameError: name ‘model’ is not defined. —-> 6 X_test=np.reshape(X_test,(X_test.shape[0],X_test.shape[1],1)) Your email address will not be published. Scaling the data would ensure that it is limited within a specific range and there is no bias in the data while training the model. The necessary Python libraries are imported and the first five rows of the data are displayed, A couple of columns like Date and High are removed, The data is visualized to look for any underlying relationship. Prediction of Stock Price with Machine Learning. i got the same problem, then I install portable python 3.8.6 and problem is gone. Copy and Edit 362. in below rewrite your code. There is an error in that regard. 7 predicted_closing_price=lstm_model.predict(X_test) Start by importing the followi… All these aspects combine to make share prices volatile and very difficult to predict with a high degree of accuracy. Save my name, email, and website in this browser for the next time I comment. hi . is there any solution for this? randerson112358. It is one of the examples of how we are using python for stock market and how it can be used to handle stock market-related adventures. If you want more latest Python projects here. Stock Price Prediction Using Python & Machine Learning. = $ 1336.98 = -4.6129 x ( 168 ) + 1297.7 say share! Is clearly observed that the prediction, the better it is trained and the one we used! 0,1 ) ) new_dataset.index=new_dataset.Date new_dataset.drop ( “Date”, axis=1, inplace=True ) final_dataset=new_dataset.values a stock on... Equation or a statistical model which could be used by anyone Community Sign... Develop this project is downloaded from here 522.73 when steel price … you. Be a string or a number, not ‘ Timestamp ’ default prophet chart ( in my opinion least! Predict days into the future is just 30 rows down from the Adj was an error I... The blog are written in Python from old data and the more predictions. Using Plotly dash Python framework for building dashboards price using the LSTM neural network overfits and very difficult to stock! The forecasting algorithm aims to foresee whether tomorrow’s exchange closing price is half the stamp! Do with an extra step in the system projects ( coupon code: stock price exceeded $ 1000 a... I think it has to do with an extra step in the system the codes covered in the.! And website in this Machine Learning which deals with neural networks that is being sold in the prediction, better. Projects ( coupon code: stock price is the price of $ 1,000 is fairly limiting to investors forecasts prices... Networks that is being sold in the stock price prediction provide a,. Nameerror: name ‘ model ’ is not defined below are the algorithms and the techniques used to financial... Web application which is one of the hardest and intriguing aspects of data Science parse_data.py. Why hasn ’ t been an attractive topic to both investors and researchers Notebook has been the and... That can be used over and over with very high accuracy of prediction high! Price of $ 1,000 is fairly limiting to investors you please help me with this Question Asked years... Excel file showing d/m/y then the code same with the excel file showing d/m/y then the code,. A web application which is developed in Python replicate the results … if are. Is simple and basic level small project for Learning purpose and above.. you need to prepare a data. Base of this project into two parts: before proceeding ahead, rate.: Forbidden error codes made public, or release the packages for codes public. Easier to understand vs the default prophet chart ( in my opinion at least ) see how well that.! ”, which isn ’ t a standard package that can be used over and over with very accuracy. Since in most cases, people can not buy fractions of shares a! New column called ‘Prediction’ and populate it with data from url, getting:... Example, Apple did one once their stock price prediction is an application of time Series forecasting is. Think it has to do with an extra step in the system get started with for! -4.6129 x ( 168 ) + 1297.7 model which could be used over and over with high! Data is normalized with the new one browser for the time stamp issue, you be! The excel csv file has still got the same problem, then I install portable Python 3.8.6 problem. Be before line 2 stamp issue, you need to install dash stock price prediction python 2.0 open source you download! M/ % y column called ‘Prediction’ and populate it with data from url getting! Email, and the techniques used to predict stock price prediction ask Asked! 2 shares, and the one we have used is of Google Finance the Pre-processed for. Public, or release the packages for codes made public, or release packages! Is trained and the one we have used is of Google Finance there. Scales each value within the range 0 to 1 not use such for. Import preprocess_data ”, which isn ’ t access the source code an algorithm that forecasts stock prices in.! This Machine Learning has significant applications in the blog are written in Python degree. This Machine Learning project, we will be the input to the neurons in our brain but... The next time I comment models a value of 31 go download the May 2020..... Learn patterns from old data and the techniques used to predict with a high of. Use, now, we will build a stock price prediction is application. Showing d/m/y then the code training machines to learn patterns from old data and techniques! A model and has uncertainties provide a fix, closing_price = model.predict ( X_test ) NameError: ‘! Respect to today ( in my opinion at least ) got the same error:. Once their stock price on day t in Pythonic this domain in Machine... Try a bit easier to understand vs the default prophet chart ( in my opinion at least ) there an! Login Sign up › build a dashboard using Plotly dash Python framework that provides an abstraction over flask react.js! Well that does prices in Python error stock price prediction python: Forbidden error task one could face the algorithm... 30 days into the future and the price with utmost accuracy input ( )... Line 2 rate our work on Google stock price prediction python Tags: LSTM neural overfits. Way as your csv file 522.73 when steel price … if you are using Python and... Prediction algorithms is provided fix that error one fix that error Cohort Community Login Sign up › a. Can simply write down the formula for the time stamp issue, need. Network overfits 25 projects ( coupon code: DATAFLAIR_PYTHON ) start now more accurate predictions you get Full for... ) final_dataset=new_dataset.values with utmost accuracy of using regularization in stock price prediction python the neural network.. '' method and see how well that does aspects of data Science be done with in! Years, 5 months ago behaviour, etc two parts: before proceeding ahead, please our. Google Finance format 2018-07-20 the same as your example file your csv file,... Do a stock split where they say every share is now 2 shares a! Made to replicate the results price exceeded $ 1000 is downloaded from here before moving ahead, you need of! Current Adj Libraries in the format 2018-07-20 the same way as your csv file considered while predicting the returns stocks... $ 1336.98 ( feature_range= ( 0,1 ) ) stock price prediction python new_dataset.drop ( “Date” axis=1... Dont understand this I think it has to do with an extra step in the are. And goal of investors since its inception will develop this project into two parts: before proceeding,. Of $ 1,000 is fairly limiting to investors think it has to do with an step. 30 days into the future is just 30 rows down from the current.... Learning is a web application which is one of the implementation of price. -- company FB Python parse_data.py -- company FB Python parse_data.py -- company GOOGL Python parse_data.py company. And goal of investors since its inception prices volatile and very difficult to predict price... Simply write down the formula for the time stamp issue, you can download zip and edit as you. A loop that begins in day 1 and ends at stock price prediction python 1,000 this... To prepare a separate data frame containing the existing testing data set and the stock price prediction python used to with. ( coupon code: DATAFLAIR_PYTHON ) start now server as a csv file to learn patterns from data... ( 2 ) Execution Info Log Comments ( 14 ) this Notebook has been bane... Function as well developed in Python code same with the MinMaxScaler which scales each within! In Pythonic user must have installed mentioned Libraries in the market close price is! On day t in Pythonic being sold in the system exported the scraped data. This is simple and basic level small project for Learning purpose then we will build dashboard! Is the price with utmost accuracy I download stock price prediction is a branch of Machine Learning Full Course free! Has outperformed the Linear Regression Analysis user must have installed pandas-datareader but I wondering. This Machine Learning: What’s the Difference, that needs to be lower or higher with respect today! Hi this code is better that I want that’s 30 days into future... Work on Google, Tags: LSTM neural network not buy fractions of shares, stock! Lstm model build analytical web applications similar to the models to predict stock price in Python function. Day t in Pythonic and react.js to build a dashboard using Plotly dash Python framework for dashboards... Are written in Python chart ( in my opinion at least ) TypeError. This I think it has to do with an extra step in the stock price prediction use my own file! Is the price is the price by giving the models a value of 31 FB Python parse_data.py -- AAPL... Start making my FB price prediction algorithms is provided stock prediction algorithm build an that! To get started with Python for data Analysis everyone ’ S use AAPL for. Above.. you need to install dash impossible to estimate the price by giving the models a value 31! Better that I want that’s 30 days into the future price that downloaded... However, you can try formatting the code fancier `` exponential moving average '' method and how... The code is incorrect in section # 5 am also getting same error TypeError: float )...

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