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The Top 26 Stock Price Prediction Open Source Project

The App forecasts stock prices of the next seven days for any given stock under NASDAQ or NSE as input by the user. Predictions are made using three algorithms: ARIMA, LSTM, Linear Regression. The Web App combines the predicted prices of the next seven days with the sentiment analysis of tweets to give recommendation whether the price is going to rise or fal In the case of stock market it's a common practice to check historical stock prices and try to predict the future using different models. While this post does not cover the details of stock analysis, it does propose a way to solve the hard problem of real-time data analysis at scale, using open source tools in a highly scalable and extensible reference architecture GitHub - Pivotal-Open-Source-Hub/StockPrediction: Stock Prediction demo using Spring XD, Apache Geode and R. Presented at ApacheCon, IMCSummit and other conferences Stock-Prediction-Models, Gathers machine learning and deep learning models for Stock forecasting, included trading bots and simulations

The Top 86 Stock Market Open Source Project

Video: An Open Source Reference Architecture For Real-Time Stock

GitHub - Pivotal-Open-Source-Hub/StockPrediction: Stock

Awesome Open Source Stock Prediction Smart Algorithms to predict buying and selling of stocks on the basis of Mutual Funds Analysis, Stock Trends Analysis and Prediction, Portfolio Risk Factor, Stock and Finance Market News Sentiment Analysis and Selling profit ratio The orange color displays the forecast on the stocks price based on regression. The forecast predicted that there is likely downturn for Gold stock for rest of the months in 2019 Stock Prediction with Machine Learning. Summary. Stock Inference engine using Spring XD, Apache Geode / GemFire and Spark ML Lib. Requirements. Apache Geode (Incubating) or Pivotal GemFire. Spring XD 1.2+ Apache Spark 1.3.1. Apache Zeppelin (Incubating) 8GB+ RAM (recommended) Linux or OSX (Windows should be OK but instructions assume *nix shell

Real and predicted daily opening stock price of MSFT since 1999, basic network. It's hard to tell how well the algorithm is performing across this whole graph but you can definitely see a tighter fit across the train set, as we would expect. The Improvements. We could try to make our model more complex, and also increase the size of the dataset Stocks. Programs for stock prediction and evaluation. If you found this repo useful, you may want to consider buying me coffee using bitcoin : In order to use a Neural Network to predict the stock market, we will be utilizing prices from the SPDR S&P 500 (SPY). This will give us a general overview of the stock market and by using an RNN we might be able to figure out which direction the market is heading. Downloading Price Histor

Stock PredictionQuick look at our Data: Machine learning for Stocks and

A Stock Prediction System using open-source software Fred Melo fmelo@pivotal.io @fredmelo_br William Markito wmarkito@pivotal.io @william_markito . It's all about DATA Data Sources Look for patterns Prediction . Machine Learning is the answer Neural Networks Clustering Genetic Algorithms def predict(model, data): # retrieve the last sequence from data last_sequence = data[last_sequence][-N_STEPS:] # expand dimension last_sequence = np.expand_dims(last_sequence, axis=0) # get the prediction (scaled from 0 to 1) prediction = model.predict(last_sequence) # get the price (by inverting the scaling) if SCALE: predicted_price = data[column_scaler][adjclose].inverse_transform(prediction)[0][0] else: predicted_price = prediction[0][0] return predicted_pric A Stock Prediction System using open-source software Fred Melo fmelo@pivotal.io @fredmelo_br 1 Predict / Machine Learning Other Sources and Destinations JMS Streaming real-time analytics architecture. Transform Sink SpringXD Extensible Open-Source Fault-Tolerant Horizontally Scalable Cloud-Native HTTP Machine Learning Fast Dat Here you will train and predict stock price movements for several epochs and see whether the predictions get better or worse over time. You follow the following procedure. Define a test set of starting points (test_points_seq) on the time series to evaluate the model on; For each epoch. For full sequence length of training dat We offer forecasts on every popular Stock market that you might need and we are always open for further suggestions from our users. We feed our Machine Learning (AI based) forecast algorithm data from the most influential global exchanges. There are a number of existing AI-based platforms that try to predict the future of Stock markets

Stock Rnn is an open source software project. Predict stock market prices using RNN model with multilayer LSTM cells + optional multi-stock embeddings.. Open Source Lib Wall Street analysts seem overtly bullish on OPEN stock. According to the estimates compiled by CNN Business, the stock's median price target of $34 is a premium of over 106 percent With the recent volatility of the stock market due to t he COVID-19 pandemic, I thought it was a good idea to try and utilize machine learning to predict the near-future trends of the stock market. I'm fairly new to machine learning, and this is my first Medium article so I thought this would be a good project to start off with and showcase C:\Users\thund\Source\Repos\stock-prediction-deep-neural-learning > python download_market_data.py [*****100 %* *****] 1 of 1 completed Open High Low Close Adj Close Volume Date 2004-08-19 49.813286 51.835709 47.800831 49.982655 49.982655 44871300 2004-08-20 50.316402 54.336334 50.062355 53.952770 53.952770 22942800 2004-08-23 55.168217 56.528118 54.321388 54.495735 54.495735 18342800 2004-08. The first 2 predictions weren't exactly good but next 3 were (didn't check the remaining). Secondly, I agree that machine learning models aren't the only thing one can trust, years of experience & awareness about what's happening in the market can beat any ml/dl model when it comes to stock predictions

A Stock Prediction System using Open-Source Software 1. A Stock Prediction System using open-source software Fred Melo fmelo@pivotal.io @fredmelo_br William Markito wmarkito@pivotal.io @william_markito 2. It's all about DATA Data Sources Look for patterns Prediction 3 There's two ways to predict a stock, one is predicting the actual value into an x amount of time into the future, which is usually graphed and this is mainly what you'll see compared with the. Learn to predict stock prices using HMM in this article by Ankur Ankan, an open source enthusiast, and Abinash Panda, a data scientist who has worked at multiple start-ups. A Hidden Markov Model ( HMM ) is a specific case of the state space model in which the latent variables are discrete and multinomial variables

Stock Prediction Models - Find Open Source By Searching

We use cookies on Kaggle to deliver our services, analyze web traffic, and improve your experience on the site. By using Kaggle, you agree to our use of cookies Tech Stocks To Buy Based on Stock Prediction Algorithm: Returns up to 24.13% in 14 Days Small Cap Stocks Based on Deep-Learning: Returns up to 184.21% in 1 Year Energy Stocks Based on Stock Algorithm: Returns up to 89.56% in 1 Mont

Open Source Data Set for Price prediction. Hey Guys, I am a Machine Learning student and I am currently looking for a feature rich data set. The stock will be added at its full float-adjusted market capitalization before the open of trading on Dec. 21, the index provider said Stock market index prediction using artificial neural network Predicción del índice del mercado bursátil utilizando una red neuronal artificial. Author links open overlay panel Amin Hedayati Moghaddam a Moein Hedayati Moghaddam b Morteza Esfandyari c. Show more. Share. Cite Indonesia Stock Market Predicted To Open In The Green . Contributor. RTTNews.com RTTNews Published. May 30, 2021 10:03PM EDT (RTTNews) - The Indonesia. IBM Watson is a free, open-source AI software that provides authority to the companies to speed up the research and discovery, calculate disruptions, and improve interactions. Several businesses are taking advantage of this software to study their data, gather intellectual property, insights, and predict their future performance easily

Know more here.. Stock Price Prediction LSTM. About: This project is about using LSTM recurrent neural networks in open, high, low and closing prices of Apple Inc. stocks (OHLC Average Prediction).It includes two sequential LSTM layers that have been stacked together and one dense layer that is used to build the RNN model using Keras deep learning library Open a new Colab notebook (python 3). #Predict the stock price using the model pricePredict = mlpr.predict(dates) #Display the predicted reuslts agains the actual data mpl.plot(dates, prices) I am by no means a leading source of knowledge on this topic,. Share Market is an untidy place for predicting since there are no significant rules to estimate or predict the price of share in stock market. Many methods like technical analysis, fundamental analysis and statistical analysis etc. are all used to attempt to predict the stock price in the share market but none of these methods are proved as a consistently acceptable prediction/forecasting tool A Stock Prediction System using Open-Source Software. 1. A Stock Prediction System using open-source software Fred Melo fmelo@pivotal.io @fredmelo_br William Markito wmarkito@pivotal.io @william_markito. 2. It's all about DATA Data Sources Look for patterns Prediction. 3 We developed an NLP deep learning model using a one-dimensional convolutional neural network to predict future stock market performance of companies using Azure ML Workbench and Keras with open source for you to replicate

Build a stock market predictor Predict stock market trends using IBM Watson Studio and Watson Machine Learning. Save. Like. Get the code By Vanderlei Munhoz Pereira Build, train, and save a time series model from extracted data, using open-source Python libraries or the built-in graphical Modeler Flow in Watson Studio We're going to predict the closing price of the S&P 500 using a special type of recurrent neural network called an LSTM network. I'll explain why we use recu.. Machine learning has many applications, one of which is to forecast time series. One of the most interesting (or perhaps most profitable) time series to predict are, arguably, stock prices. Recently I read a blog post applying machine learning techniques to st o ck price prediction. You can read it here

STOCK MARKET PREDICTION USING NEURAL NETWORKS . An example for time-series prediction. by Dr. Valentin Steinhauer. Short description. Time series prediction plays a big role in economics. The stock market courses, as well as the consumption of energy can be predicted to be able to make decisions Chamath Palihapitiya's prediction about OPEN stock is bold, but perhaps realistic nonetheless By David Moadel , InvestorPlace Contributor Feb 9, 2021, 12:34 pm EDT February 9, 202 The Stock Exchange of Thailand now sits just shy of the 1,620-point plateau although it may see some profit taking on Wednesday. Lower Open Predicted For Thai Stock Market | Nasdaq Skip to main. In this article we will use Neural Network, specifically the LSTM model, to predict the behaviour of a Time-series data. The problem to be solved is the classic stock market prediction. All data.

stock-prediction · GitHub Topics · GitHu

  1. Stock Market Predictions. Volatility is easing, consumer spending growing, businesses reopening and stimulus is ready to surge into the economy. Are we really out of the stock market bubble/crash threat yet. Joe Biden's big tax increase along with inflation is sending the market plunging and the effect is chilling. The stock market forecast has been dimmed because a number of Democrat initiatives
  2. g languages, the readers are referred to the R Package Caret and to the sklearn.feature_selection module in Python (Scikit-Learn-Developers, 2014). Table 3
  3. Zocalo is a toolkit for building prediction markets, markets in securities that pay out depending on outcomes of future events.They provide estimates of the likelihood of specific outcomes that are more reliable than other sources of predictions. The Zocalo Project Lead is Chris Hibbert.. Visit the Zocalo project page at SourceForge
  4. Open: — This price of stock's opening price which means the very beginning price of particular trading day, but which is not be the same price of precious's day ending price. High: — This is the highest price of the stock on a particular trading day. Low: — This is the lowest stock price during trade day
  5. Find the latest Opendoor Technologies Inc (OPEN) stock quote, history, news and other vital information to help you with your stock trading and investing

TANAGRA is an open source project as every researcher can access to the source code, and add his own algorithms, as far as he agrees and conforms to the software distribution license.The main purpose of Tanagra project is to give researchers and students an easy-to-use data mining software, conforming to the present norms of the software development in this domain (especially in the design. Stock market prediction has always attracted a great deal of attention, both because of it's possible impact as well as the great difficulty it involves. With the advent of machine learning.

NOTE: The open source projects on this list are ordered by number of github stars. The number of mentions indicates repo mentiontions in the last 12 Months or since we started tracking (Dec 2020). The latest post mention was on 2021-06-06 Stonksmaster: Predict Stock prices using Python and ML - Part II # machinelearning # python # tutorial # programming. Rishav Raj Kumar Dec 10, 2020 ・7 min read. This is follow Built on Forem — the open source software that powers DEV and other inclusive communities Stock market prediction is difficult because there are too many factors at play, and creating models to consider such variances is almost impossible. However, recent advances in machine learning and computing have allowed machines to process large amounts of data. This will enable us to use past stock exchange data and analyze trends NIT Warangal Post Graduate Program in AI & Machine Learning with Edureka: https://www.edureka.co/nitw-ai-ml-pgpThis Edureka Stock Prediction using Machine..

In our latest entry under the Stock Price Prediction Series, let's learn how to predict Stock Prices with the help of XGBoost Model. In case you want to dig into the other approaches of Stock. Stock market is a complex and challenging system where people will either gain money or lose their entire life savings. In this work, an attempt is made for prediction of stock market trend. Two models are built one for daily prediction and the other one is for monthly prediction. Supervised machine learning algorithms are used to build the models

Stock forecast - Stock predictio

  1. Forecasting stock prices can be interpreted as a time series prediction problem, for which Long Short Term Memory (LSTM) neural networks are often used due to their architecture specifically built to solve such problems. In this paper, we consider the design of a trading strategy that performs portfolio optimization using the LSTM stock price prediction for four different companies. We then.
  2. e the future value of a company stock or other financial instrument traded on an exchange that follows such motion. Our goal for this project is to use open-source machine learning techniques and high performance computing tools (Hadoop with Mahout and pydoop&scikit-learn).
  3. g the S&P 500 with stocks that are under-perfor
  4. Higher Open Predicted For Indonesia Stock Market. (RTTNews) - The Indonesia stock market has finished lower in two straight sessions, sinking more than 35 points or 0.6 percent along the way. The.

Top Free Cloud, Open Source and Free Business Intelligence Software: The Original Review of Best of the Free Business Intelligence Software .What are the Best Free Cloud Business Intelligence Software: Sisense, Periscope Data, Google Data Studio, Cluvio, Tableau Public, Visualize Free, Databox Free Edition are some of the Top Free Cloud Business Intelligence Software.What are the Best Free. Big Data Analysis in Stock Market Prediction. Dept. of Computer Engineering, Institute of Technology, Nirma University, Abstract Big data analytics can be used in many domains for accurate prediction and analysis of the large amount of data. They facilitate the discovery of significant information from large data, which is hidden otherwise Stock Prediction-Intraday is one of the trading norms of the stock market, buy shares at the opening time of the market and then sell the same at the closing time of the same day. Today we are dealing with one of the data sets, based on daily data of seven years from 2014 to 2021. We are going to use a simple machine learning algorithm to. The stock market is very unstable and volatile due to several factors such as public sentiments, economic factors and more. Several Petabytes volumes of data are generated every second from different sources, which affect the stock market. A fair and efficient fusion of these data sources (factors) into intelligence is expected to offer better prediction accuracy on the stock market LIVE STOCK MARKET ACTION & PREDICTIONS -- April 7thAMC, GME, AAPL, TSLA, RMO & More Let's talk about GME, AMC, Tesla, Bitcoin & everything else going on in.

Honey Badger Hedge Fund: Hackers Predict Stock Market With Open Source Mojo Somewhere, out on the web, there's a secret Twitter account that's one hell of a stock picker Nowadays, the most significant challenges in the stock market is to predict the stock prices. The stock price data represents a financial time series data which becomes more difficult to predict due to its characteristics and dynamic nature. Support Vector Machines (SVM) and Artificial Neural Networks (ANN) are widely used for prediction of stock prices and its movements

Higher Open Predicted For Malaysia Stock Market. (RTTNews) - Ahead of the long weekend for Eid-ul-Fitr, the Malaysia stock market had ended the two-day slide in which it had stumbled almost 10. LIVE STOCK MARKET ACTION & PREDICTIONS -- April 9thAMC, GME, AAPL, TSLA, SOS, Bitcoin & More Let's close this week out on a positive note!Let's talk about G..

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Augur strives to bring prediction markets into the future. The tool, which Director of Operations Jeremy Gardner and Core Developer Joey Krug hope to make prediction markets more accurate than ever, comes with great precedent. But Augur believes, with the power of decentralization and open source technologies, theirs' is truly a paradigm shifting project Drupal-specific prediction markets. After working on the system for a bit I finally re-launched Open Prediction Marktets Beta site. The beta.openpredictionmarkets.org site has two goals: To serve as the place running the leading edge of my prediction market code. To help the Drupal community make more accurate predictions about its future Encog is an open source neural network framework released under The real trick in using neural networks for market prediction is representing the market data in a way that truly captures the essence of A black candle indicates a day where the closing price was lower than the opening price. The stock price decreased on a.

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In this demo I will try to predict the behavior of open price of stocks based on their historical data, meaning if a stock open price will go up or down. I am not a data scientist, but there are many examples online on how to do that (I took some code samples, fixed them, and adjusted them to work with COD) It implements a graphical environment for monitoring financial technical analysis of the main stocks and shares markets and currencies. I would also like to bring to your attention that, in its advanced version, Premium Markets also provides a Forecast machine learning engine based on neural networks Point and Figure. The latest update includes downloading data from AlphaVantage &Tiingo and some minor bug fixes. Also, the latest code is available via git. Point and figure is a stock charting technique used by technical analysts to predict stock prices. Point and figure charting plots price changes in direction by using a column of X's as. NIO Inc () Stock Market info Recommendations: Buy or sell NIO Inc - ADR stock? Wall Street Stock Market & Finance report, prediction for the future: You'll find the NIO Inc - ADR share forecasts, stock quote and buy / sell signals below.According to present data NIO Inc - ADR's NIO shares and potentially its market environment have been in a bullish cycle in the last 12 months (if exists)

AIStockFinder - Stock Forecast - Stock Predictio

Stockfish is open source (GPLv3 license). That means you can read the code, modify it, and contribute back. Stockfish on GitHub. Run Anywhere. You can use Stockfish on your computer or on your iOS or Android device. So you can get world-class chess analysis, wherever you are Higher Open Predicted For Thai Stock Market. (RTTNews) - The Thai stock market has finished lower in back-to-back trading days, tumbling more than 40 points or 2.7 percent along the way. The Stock. Jim Paulsen says a stock market correction is likely. Richard Drew/Associated Press. Jim Paulsen, the chief investment strategist of the Leuthold Group, sees a near-term stock correction as likley What is Apache PredictionIO®? Apache PredictionIO® is an open source Machine Learning Server built on top of a state-of-the-art open source stack for developers and data scientists to create predictive engines for any machine learning task. It lets you: quickly build and deploy an engine as a web service on production with customizable templates;.

Fannie Mae stock price prediction is an act of determining the future value of Fannie Mae shares using few different conventional methods such as EPS estimation, analyst consensus, or fundamental intrinsic valuation.The successful prediction of Fannie Mae stock future price could yield a significant profit Financial markets have a vital role in the development of modern society. They allow the deployment of economic resources. Changes in stock prices reflect changes in the market. In this study, we focus on predicting stock prices by deep learning model. This is a challenge task, because there is much noise and uncertainty in information that is related to stock prices. So this work uses sparse. The stock market is very complex and volatile. It is impacted by positive and negative sentiments which are based on media releases. The scope of the stock price analysis relies upon ability to recognise the stock movements. It is based on technical fundamentals and understanding the hidden trends which the market follows. Stock price prediction has consistently been an extremely dynamic field. Predict survival on the Titanic and get familiar with Machine Learning basics. 10k. House Prices. Predict sales prices and practice feature engineering, RFs, and gradient boosting. 4k. Predict Future Sales. Final project for How to win a data science competition Coursera course. 2k Mean Reversion. Martingales. The Search for Value. The Bottom Line. There are two prices that are critical for any investor to know: the current price of the investment they own or plan to own and.

Top 18 Predictive Analytics Free Software in 2021

In today's society, investment wealth management has become a mainstream of the contemporary era. Investment wealth management refers to the use of funds by investors to arrange funds reasonably, for example, savings, bank financial products, bonds, stocks, commodity spots, real estate, gold, art, and many others. Wealth management tools manage and assign families, individuals, enterprises. This paper proposed a method for stock prediction. In terms of feature extraction, we extract the features of stock-related news besides stock prices. We first select some seed words based on experience which are the symbols of good news and bad news. Then we propose an optimization method and calculate the positive polar of all words. After that, we construct the features of news based on the.

Stock Prediction - Find Open Source By Searching, Browsing

In this project I've approached this class of models trying to apply it to stock market prediction, combining stock prices with sentiment analysis. The implementation of the network has been made using TensorFlow, starting from the online tutorial. In this article, I will describe the following steps: dataset creation, CNN training and. Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on an exchange.The successful prediction of a stock's future price could yield significant profit. The efficient-market hypothesis suggests that stock prices reflect all currently available information and any price changes that are not based on newly revealed. Stock trend prediction refers to predicting future price trend of stocks for seeking profit maximum of stock investment. Although it has aroused broad attention in stock markets, it is still a tough task not only because the stock markets are complex and easily volatile but also because real short-term stock data is so limited that existing stock prediction models could be far from perfect. Based on a sample from January 1, 2009, to October 31, 2014, Li et al. developed an LSTM model that is based on investor sentiment extracted from internet stock message boards and market data to conduct out-of-sample forecasts for the open and closing prices of the CSI 300 index in the Chinese stock market Challenges of Stock Prediction: 10.4018/978-1-7998-1086-5.ch013: The challenge of the stock price forecast is the most crucial component for companies and equity traders to predict future revenues. A successful and accurat

Data Analysis & ML Algorithms for Stock Prediction - Mediu

Stock market. A stock or share (also known as a company's equity ) is a financial instrument that represents ownership in a company or corporation and represents a proportionate claim on its assets (what it owns) and earnings (what it generates in profits). — Investopedia. The stock market is a market that enables the seamless. What Is Bitcoin And Who Created It ? Bitcoin is a digital currency and a payment system invented by an unknown group or person by the name Satoshi Nakamoto , who published the invention in 2008 and released it as open source software in 2009. It is the first decentralized digital currency, meaning the system works without a single administrator or central bank, you can use them in every. In this study, the hourly directions of eight banking stocks in Borsa Istanbul were predicted using linear-based, deep-learning (LSTM) and ensemble learning (LightGBM) models. These models were trained with four different feature sets and their performances were evaluated in terms of accuracy and F-measure metrics. While the first experiments directly used the own stock features as the model. 5 things to know before the stock market opens Thursday. Published Thu, May 13 2021 7:30 AM EDT Updated Thu, May 13 2021 9:10 AM EDT. Yun Li @YunLi626. Source: NYSE. The blue-chip Dow. Many researchers both in academia and industry have long been interested in the stock market. Numerous approaches were developed to accurately predict future trends in stock prices. Recently, there has been a growing interest in utilizing graph-structured data in computer science research communities. Methods that use relational data for stock market prediction have been recently proposed, but.

GitHub - Pivotal-Open-Source-Hub/StockInference-Spark

Stock price prediction is an important issue in the financial world, as it contributes to the development of effective strategies for stock exchange transactions. In this paper, we propose a generic framework employing Long Short-Term Memory (LSTM) and convolutional neural network (CNN) for adversarial training to forecast high-frequency stock market In this tutorial, we'll build a Python deep learning model that will predict the future behavior of stock prices. We assume that the reader is familiar with the concepts of deep learning in Python, especially Long Short-Term Memory.. While predicting the actual price of a stock is an uphill climb, we can build a model that will predict whether the price will go up or down Stock market prediction has been identified as a very important practical problem in the economic field. However, the timely prediction of the market is generally regarded as one of the most challenging problems due to the stock market's characteristics of noise and volatility. To address these challenges, we propose a deep learning-based stock market prediction model that considers. Explore and run machine learning code with Kaggle Notebooks | Using data from S&P 500 stock dat

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Welcome to /r/StockMarket! Our objective is to provide short and mid term trade ideas, market analysis & commentary for active traders and investors. Posts about equities, options, forex, futures, analyst upgrades & downgrades, technical and fundamental analysis, and the stock market in general are all welcome. 1.5m Bull 2X Shares Stock Forecast, GUSH stock price prediction. Price target in 14 days: 117.943 USD. The best long-term & short-term Direxion Shares ETF Trust - Direxion Daily S&P Oil & Gas Exp. & Prod Digital Coin Price gives Stellar Lumens a slightly higher target of $1.70 by 2025, or a 347% gain. And finally, a blog called Primexbt puts the coin at $6 in the next five years. That's a whopping. 4. Model and validate data ¶. RNNs with basic, LSTM, GRU cells. In [9]: link. code. ## Basic Cell RNN in tensorflow index_in_epoch = 0; perm_array = np.arange(x_train.shape[0]) np.random.shuffle(perm_array) # function to get the next batch def get_next_batch(batch_size): global index_in_epoch, x_train, perm_array start = index_in_epoch index.

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