Amazon Stock Price Visualization Forecasting And Prediction Using Machine Learning With Python Gui

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Amazon Stock Price Visualization Forecasting And Prediction Using Machine Learning With Python Gui

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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-06-09

Amazon Stock Price Visualization Forecasting And Prediction Using Machine Learning With Python Gui written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-09 with Business & Economics categories.

Amazon is an American multinational technology company that is known for its e-commerce, cloud computing, digital streaming, and artificial intelligence services. It was founded by Jeff Bezos in 1994 and is headquartered in Seattle, Washington. Amazon's primary business is its online marketplace, where it offers a wide range of products, including books, electronics, household items, and more. The company has expanded its operations to various countries and is one of the largest online retailers globally. In addition to its e-commerce business, Amazon has ventured into other areas. It provides cloud computing services through Amazon Web Services (AWS), which offers on-demand computing power, storage, and other services to individuals, businesses, and governments. AWS has become a significant revenue source for Amazon. Amazon has also made a significant impact on the entertainment industry. It operates Amazon Prime Video, a streaming platform that offers a wide selection of movies, TV shows, and original content. As for Amazon's stock price, it has experienced substantial growth since the company went public in 1997. The stock has been highly valued by investors due to Amazon's consistent revenue growth, market dominance, and innovation. The stock price has seen both ups and downs over the years, reflecting market trends and investor sentiment. The dataset used in this project starts from 14-May-1997 and is updated till 27-Oct-2021. It contains 6155 rows and 7 columns. The columns in the dataset are Date, Open, High, Low, Close, Adj Close, and Volume. In this project, you will involve technical indicators such as daily returns, Moving Average Convergence-Divergence (MACD), Relative Strength Index (RSI), Simple Moving Average (SMA), lower and upper bands, and standard deviation. To perform forecasting based on regression on Adj Close price of Amazon stock price, you will use: Linear Regression, Random Forest regression, Decision Tree regression, Support Vector Machine regression, Naïve Bayes regression, K-Nearest Neighbor regression, Adaboost regression, Gradient Boosting regression, Extreme Gradient Boosting regression, Light Gradient Boosting regression, Catboost regression, MLP regression, Lasso regression, and Ridge regression. The machine learning models used predict Amazon stock daily returns as target variable are K-Nearest Neighbor classifier, Random Forest classifier, Naive Bayes classifier, Logistic Regression classifier, Decision Tree classifier, Support Vector Machine classifier, LGBM classifier, Gradient Boosting classifier, XGB classifier, MLP classifier, and Extra Trees classifier. Finally, you will develop GUI to plot boundary decision, distribution of features, feature importance, predicted values versus true values, confusion matrix, learning curve, performance of the model, and scalability of the model.

Google Stock Price Time Series Analysis Visualization Forecasting And Prediction Using Machine Learning With Python Gui

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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-06-11

Google Stock Price Time Series Analysis Visualization Forecasting And Prediction Using Machine Learning With Python Gui written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-11 with Computers categories.

Google, officially known as Alphabet Inc., is an American multinational technology company. It was founded in September 1998 by Larry Page and Sergey Brin while they were Ph.D. students at Stanford University. Initially, it started as a research project to develop a search engine, but it rapidly grew into one of the largest and most influential technology companies in the world. Google is primarily known for its internet-related services and products, with its search engine being its most well-known offering. It revolutionized the way people access information by providing a fast and efficient search engine that delivers highly relevant results. Over the years, Google expanded its portfolio to include a wide range of products and services, including Google Maps, Google Drive, Gmail, Google Docs, Google Photos, Google Chrome, YouTube, and many more. In addition to its internet services, Google ventured into hardware with products like the Google Pixel smartphones, Google Home smart speakers, and Google Nest smart home devices. It also developed its own operating system called Android, which has become the most widely used mobile operating system globally. Google's success can be attributed to its ability to monetize its services through online advertising. The company introduced Google AdWords, a highly successful online advertising program that enables businesses to display ads on Google's search engine and other websites through its AdSense program. Advertising contributes significantly to Google's revenue, along with other sources such as cloud services, app sales, and licensing fees. The dataset used in this project starts from 19-Aug-2004 and is updated till 11-Oct-2021. It contains 4317 rows and 7 columns. The columns in the dataset are Date, Open, High, Low, Close, Adj Close, and Volume. You can download the dataset from https://viviansiahaan.blogspot.com/2023/06/google-stock-price-time-series-analysis.html. In this project, you will involve technical indicators such as daily returns, Moving Average Convergence-Divergence (MACD), Relative Strength Index (RSI), Simple Moving Average (SMA), lower and upper bands, and standard deviation. In this book, you will learn how to perform forecasting based on regression on Adj Close price of Google stock price, you will use: Linear Regression, Random Forest regression, Decision Tree regression, Support Vector Machine regression, Naïve Bayes regression, K-Nearest Neighbor regression, Adaboost regression, Gradient Boosting regression, Extreme Gradient Boosting regression, Light Gradient Boosting regression, Catboost regression, MLP regression, Lasso regression, and Ridge regression. The machine learning models used to predict Google daily returns as target variable are K-Nearest Neighbor classifier, Random Forest classifier, Naive Bayes classifier, Logistic Regression classifier, Decision Tree classifier, Support Vector Machine classifier, LGBM classifier, Gradient Boosting classifier, XGB classifier, MLP classifier, and Extra Trees classifier. Finally, you will develop GUI to plot boundary decision, distribution of features, feature importance, predicted values versus true values, confusion matrix, learning curve, performance of the model, and scalability of the model.

Time Series Analysis Forecasting Stock Price Using Machine Learning With Python Gui

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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-07-02

Time Series Analysis Forecasting Stock Price Using Machine Learning With Python Gui written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-02 with Computers categories.

Stock trading and financial instrument markets offer significant opportunities for wealth creation. The ability to predict stock price movements has long intrigued researchers and investors alike. While some theories, like the Efficient Market Hypothesis, suggest that consistently beating the market is nearly impossible, others contest this viewpoint. Stock price prediction involves forecasting the future value of a given stock. In this project, we focus on the S&P 500 Index, which consists of 500 stocks from various sectors of the US economy and serves as a key indicator of US equities. To tackle this task, we utilize the Yahoo stock price history dataset, which contains 1825 rows and 7 columns including Date, High, Low, Open, Close, Volume, and Adj Close. To enhance our predictions, we incorporate technical indicators such as daily returns, Moving Average Convergence-Divergence (MACD), Relative Strength Index (RSI), Simple Moving Average (SMA), lower and upper bands, and standard deviation. In this book, for the forecasting task, we employ various regression algorithms including Linear Regression, Random Forest Regression, Decision Tree Regression, Support Vector Regression, Naïve Bayes Regression, K-Nearest Neighbor Regression, Adaboost Regression, Gradient Boosting Regression, Extreme Gradient Boosting Regression, Light Gradient Boosting Regression, Catboost Regression, MLP Regression, Lasso Regression, and Ridge Regression. These models aim to predict the future Adj Close price of the stock based on historical data. In addition to stock price prediction, we also delve into predicting stock daily returns using machine learning models. We utilize K-Nearest Neighbor Classifier, Random Forest Classifier, Naive Bayes Classifier, Logistic Regression Classifier, Decision Tree Classifier, Support Vector Machine Classifier, LGBM Classifier, Gradient Boosting Classifier, XGB Classifier, MLP Classifier, and Extra Trees Classifier. These models are trained to predict the direction of daily stock returns (positive or negative) based on various features and technical indicators. To assess the performance of these machine learning models, we evaluate several important metrics. Accuracy measures the overall correctness of the predictions, while recall quantifies the ability to correctly identify positive cases (upward daily returns). Precision evaluates the precision of positive predictions, and the F1 score provides a balanced measure of precision and recall. Additionally, we consider macro average, which calculates the average metric value across all classes, and weighted average, which provides a balanced representation considering class imbalances. To enhance the user experience and facilitate data exploration, we develop a graphical user interface (GUI). The GUI is built using PyQt and offers an interactive platform for users to visualize and interact with the data. It provides features such as plotting boundary decisions, visualizing feature distributions and importance, comparing predicted values with true values, displaying confusion matrices, learning curves, model performance, and scalability analysis. The GUI allows users to customize the analysis by selecting different models, time periods, or variables of interest, making it accessible and user-friendly for individuals without extensive programming knowledge. The combination of exploring the dataset, forecasting stock prices, predicting daily returns, and developing a GUI creates a comprehensive framework for analyzing and understanding stock market trends. By leveraging machine learning algorithms and evaluating performance metrics, we gain valuable insights into the accuracy and effectiveness of our predictions. The GUI further enhances the accessibility and usability of the analysis, enabling users to make data-driven decisions and explore the stock market with ease.

Stock Price Analysis Prediction And Forecasting Using Machine Learning And Deep Learning With Python

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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2022-05-27

Stock Price Analysis Prediction And Forecasting Using Machine Learning And Deep Learning With Python written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-27 with Computers categories.

This dataset is a playground for fundamental and technical analysis. It is said that 30% of traffic on stocks is already generated by machines, can trading be fully automated? If not, there is still a lot to learn from historical data. The dataset consists of data spans from 2010 to the end 2016, for companies new on stock market date range is shorter. To perform forecasting based on regression adjusted closing price of gold, you will use: Linear Regression, Random Forest regression, Decision Tree regression, Support Vector Machine regression, Naïve Bayes regression, K-Nearest Neighbor regression, Adaboost regression, Gradient Boosting regression, Extreme Gradient Boosting regression, Light Gradient Boosting regression, Catboost regression, MLP regression, and LSTM (Long-Short Term Memory) regression. The machine learning models used predict gold daily returns as target variable are K-Nearest Neighbor classifier, Random Forest classifier, Naive Bayes classifier, Logistic Regression classifier, Decision Tree classifier, Support Vector Machine classifier, LGBM classifier, Gradient Boosting classifier, XGB classifier, MLP classifier, Gaussian Mixture Model classifier, and Extra Trees classifier. Finally, you will plot boundary decision, distribution of features, feature importance, predicted values versus true values, confusion matrix, learning curve, performance of the model, and scalability of the model.

Hands On Ai Trading With Python Quantconnect And Aws

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Author : Jiri Pik
language : en
Publisher: John Wiley & Sons
Release Date : 2025-01-22

Hands On Ai Trading With Python Quantconnect And Aws written by Jiri Pik and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-22 with Business & Economics categories.

Master the art of AI-driven algorithmic trading strategies through hands-on examples, in-depth insights, and step-by-step guidance Hands-On AI Trading with Python, QuantConnect, and AWS explores real-world applications of AI technologies in algorithmic trading. It provides practical examples with complete code, allowing readers to understand and expand their AI toolbelt. Unlike other books, this one focuses on designing actual trading strategies rather than setting up backtesting infrastructure. It utilizes QuantConnect, providing access to key market data from Algoseek and others. Examples are available on the book's GitHub repository, written in Python, and include performance tearsheets or research Jupyter notebooks. The book starts with an overview of financial trading and QuantConnect's platform, organized by AI technology used: Examples include constructing portfolios with regression models, predicting dividend yields, and safeguarding against market volatility using machine learning packages like SKLearn and MLFinLab. Use principal component analysis to reduce model features, identify pairs for trading, and run statistical arbitrage with packages like LightGBM. Predict market volatility regimes and allocate funds accordingly. Predict daily returns of tech stocks using classifiers. Forecast Forex pairs' future prices using Support Vector Machines and wavelets. Predict trading day momentum or reversion risk using TensorFlow and temporal CNNs. Apply large language models (LLMs) for stock research analysis, including prompt engineering and building RAG applications. Perform sentiment analysis on real-time news feeds and train time-series forecasting models for portfolio optimization. Better Hedging by Reinforcement Learning and AI: Implement reinforcement learning models for hedging options and derivatives with PyTorch. AI for Risk Management and Optimization: Use corrective AI and conditional portfolio optimization techniques for risk management and capital allocation. Written by domain experts, including Jiri Pik, Ernest Chan, Philip Sun, Vivek Singh, and Jared Broad, this book is essential for hedge fund professionals, traders, asset managers, and finance students. Integrate AI into your next algorithmic trading strategy with Hands-On AI Trading with Python, QuantConnect, and AWS.

Cryptocurrency Price Analysis Prediction And Forecasting Using Machine Learning With Python

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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-07-21

Cryptocurrency Price Analysis Prediction And Forecasting Using Machine Learning With Python written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-21 with Computers categories.

In this project, we will be conducting a comprehensive analysis, prediction, and forecasting of cryptocurrency prices using machine learning with Python. The dataset we will be working with contains historical cryptocurrency price data, and our main objective is to build models that can accurately predict future price movements and daily returns. The first step of the project involves exploring the dataset to gain insights into the structure and contents of the data. We will examine the columns, data types, and any missing values present. After that, we will preprocess the data, handling any missing values and converting data types as needed. This will ensure that our data is clean and ready for analysis. Next, we will proceed with visualizing the dataset to understand the trends and patterns in cryptocurrency prices over time. We will create line plots, box plot, violin plot, and other visualizations to study price movements, trading volumes, and volatility across different cryptocurrencies. These visualizations will help us identify any apparent trends or seasonality in the data. To gain a deeper understanding of the time-series nature of the data, we will conduct time-series analysis year-wise and month-wise. This analysis will involve decomposing the time-series into its individual components like trend, seasonality, and noise. Additionally, we will look for patterns in price movements during specific months to identify any recurring seasonal effects. To enhance our predictions, we will also incorporate technical indicators into our analysis. Technical indicators, such as moving averages, Relative Strength Index (RSI), and Moving Average Convergence Divergence (MACD), provide valuable information about price momentum and market trends. These indicators can be used as additional features in our machine learning models. With a strong foundation of data exploration, visualization, and time-series analysis, we will now move on to building machine learning models for forecasting the closing price of cryptocurrencies. We will utilize algorithms like Linear Regression, Support Vector Regression, Random Forest Regression, Decision Tree Regression, K-Nearest Neighbors Regression, Adaboost Regression, Gradient Boosting Regression, Extreme Gradient Boosting Regression, Light Gradient Boosting Regression, Catboost Regression, Multi-Layer Perceptron Regression, Lasso Regression, and Ridge Regression to make forecasting. By training our models on historical data, they will learn to recognize patterns and make predictions for future price movements. As part of our machine learning efforts, we will also develop models for predicting daily returns of cryptocurrencies. Daily returns are essential indicators for investors and traders, as they reflect the percentage change in price from one day to the next. By using historical price data and technical indicators as input features, we can build models that forecast daily returns accurately. Throughout the project, we will perform extensive hyperparameter tuning using techniques like Grid Search and Random Search. This will help us identify the best combinations of hyperparameters for each model, optimizing their performance. To validate the accuracy and robustness of our models, we will use various evaluation metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared. These metrics will provide insights into the model's ability to predict cryptocurrency prices accurately. In conclusion, this project on cryptocurrency price analysis, prediction, and forecasting is a comprehensive exploration of using machine learning with Python to analyze and predict cryptocurrency price movements. By leveraging data visualization, time-series analysis, technical indicators, and machine learning algorithms, we aim to build accurate and reliable models for predicting future price movements and daily returns. The project's outcomes will be valuable for investors, traders, and analysts looking to make informed decisions in the highly volatile and dynamic world of cryptocurrencies. Through rigorous evaluation and validation, we strive to create robust models that can contribute to a better understanding of cryptocurrency market dynamics and support data-driven decision-making.

Stock Price Analysis Through Statistical And Data Science Tools An Overview

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Author : Vinaitheerthan Renganathan
language : en
Publisher: Vinaitheerthan Renganathan
Release Date : 2021-04-30

Stock Price Analysis Through Statistical And Data Science Tools An Overview written by Vinaitheerthan Renganathan and has been published by Vinaitheerthan Renganathan this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-30 with Business & Economics categories.

Stock price analysis involves different methods such as fundamental analysis and technical analysis which is based on data related to price movement of the stock in the past. Price of the stock is affected by various factors such as company’s performance, current status of economy and political factor. These factors play an important role in supply and demand of the stock which makes the price to be volatile in the short term. Investors and stock traders aim to book profit through buying and selling the stocks. There are different statistical and data science tools are being used to predict the stock price. Data Science and Statistical tools assume only the stock price’s historical data in predicting the future stock price. Statistical tools include measures such as Graph and Charts which depicts the general trend and time series tools such as Auto Regressive Integrated Moving Averages (ARIMA) and regression analysis. Data Science tools include models like Decision Tree, Support Vector Machine (SVM), Artificial Neural Network (ANN) and Long Term and Short Term Memory (LSTM) Models. Current methods include carrying out sentiment analysis of tweets, comments and other social media discussion to extract the hidden sentiment expressed by the users which indicate the positive or negative sentiment towards the stock price and the company. The book provides an overview of the analyzing and predicting stock price movements using statistical and data science tools using R open source software with hypothetical stock data sets. It provides a short introduction to R software to enable the user to understand analysis part in the later part. The book will not go into details of suggesting when to purchase a stock or what at price. The tools presented in the book can be used as a guiding tool in decision making while buying or selling the stock. Vinaitheerthan Renganathan www.vinaitheerthan.com/book.php

Stock Market Prediction And Efficiency Analysis Using Recurrent Neural Network

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Author : Joish Bosco
language : en
Publisher: GRIN Verlag
Release Date : 2018-09-18

Stock Market Prediction And Efficiency Analysis Using Recurrent Neural Network written by Joish Bosco and has been published by GRIN Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-18 with Computers categories.

Project Report from the year 2018 in the subject Computer Science - Technical Computer Science, , course: Computer Science, language: English, abstract: Modeling and Forecasting of the financial market have been an attractive topic to scholars and researchers from various academic fields. The financial market is an abstract concept where financial commodities such as stocks, bonds, and precious metals transactions happen between buyers and sellers. In the present scenario of the financial market world, especially in the stock market, forecasting the trend or the price of stocks using machine learning techniques and artificial neural networks are the most attractive issue to be investigated. As Giles explained, financial forecasting is an instance of signal processing problem which is difficult because of high noise, small sample size, non-stationary, and non-linearity. The noisy characteristics mean the incomplete information gap between past stock trading price and volume with a future price. The stock market is sensitive with the political and macroeconomic environment. However, these two kinds of information are too complex and unstable to gather. The above information that cannot be included in features are considered as noise. The sample size of financial data is determined by real-world transaction records. On one hand, a larger sample size refers a longer period of transaction records; on the other hand, large sample size increases the uncertainty of financial environment during the 2 sample period. In this project, we use stock data instead of daily data in order to reduce the probability of uncertain noise, and relatively increase the sample size within a certain period of time. By non-stationarity, one means that the distribution of stock data is various during time changing. Non-linearity implies that feature correlation of different individual stocks is various. Efficient Market Hypothesis was developed by Burton G. Malkiel in 1991.

Stock Prediction With Deep Learning

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Author : Ethan Shaotran
language : en
Publisher:
Release Date : 2018-06-10

Stock Prediction With Deep Learning written by Ethan Shaotran and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-10 with categories.

For centuries, human beings have tried to predict the future, whether it be NBA playoffs, weather, or elections. In this book, we tackle the common misconception that the stock market cannot be predicted, and build a stock prediction algorithm to beat the stock market, using Deep Learning, Data Analysis, and Natural Language Processing techniques.If you're new to Artificial Intelligence and Python, and are curious to learn more, this is a great book for you! Industry experts also have plenty to learn from the variety of methods and techniques used in data collection and manipulation.ABOUT THE AUTHOREthan Shaotran is an AI developer, researcher, and author of "Stock Prediction with Deep Learning". He is the founder of Energize.AI, where he built a financial stock prediction algorithm that outperformed the stock market in 2017. He is currently working on a thought experiment series to raise awareness on AI-related societal challenges within the AI community, regarding regulation and potential moral hazards, as well as autonomous vehicle driving software. Ethan has studied Economics and AI courses from Harvard, Stanford, and USF, is an affiliate with the Harvard Kennedy School's AI Initiative and is a member of the Association for Computing Machinery.