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Journal articles on the topic 'Yfinance'

Author: Grafiati

Published: 5 June 2025

Last updated: 15 July 2025

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1

goud, B. Sai Dhanush. "Visualizing and Forecasting Stock Market Trends Using Dash and Machine Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem46474.

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Abstract: The financial market is a highly dynamic and nonlinear system where predicting stock prices remains a challenging task. In this paper, we present a web-based application built using Python's Dash framework for interactive stock data visualization and forecasting. The system utilizes real-time stock data from Yahoo Finance (via the yfinance API) and employs Long Short-Term Memory (LSTM) and Support Vector Regression (SVR) algorithms for price prediction. Furthermore, we incorporate the Moving Average Convergence Divergence (MACD) indicator to provide technical trend insights. Experime

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2

Yu, Yimiao. "LSTM-Based Time Series Prediction Model: A Case Study with YFinance Stock Data." ITM Web of Conferences 70 (2025): 03015. https://doi.org/10.1051/itmconf/20257003015.

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The goal of this study is to anticipate the time series of stock data that YFinance provides using a Long Short-Term Memory (LSTM) model, with a particular emphasis on the closing prices and daily returns of Apple Inc. (AAPL). The historical closing price data from January 1, 2010, to September 20, 2021, was used as the training set, while the data from September 21, 2021, to August 22, 2024, was employed as the validation set to test the model’s predictive capability. The experimental results demonstrate that the LSTM architecture performs excellently in handling data with long-term dependenc

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Gultom, Edra Arkananta, Kartika Dewi Sri Susilowati, and Anik Kusmintarti. "Design of a Stock Forecasting Dashboard using Python-Streamlit and FB Prophet with AI." Formosa Journal of Science and Technology 3, no. 11 (2024): 2445–64. https://doi.org/10.55927/fjst.v3i11.12216.

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This research aims to develop a stock price forecasting application using time series analysis with the Prophet model. The application retrieves historical stock data from Yahoo Finance (2015–present) for Indonesian stocks, which is then processed and analyzed to predict future prices. The study integrates yfinance for data collection, Prophet for forecasting, and Plotly for visualizing the results. The application allows users to select stocks and customize prediction periods (1–4 years). The findings indicate that while the model provides useful short-term predictions, its accuracy is limite

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Uhryn, D. I., Yu O. Ushenko, S. F. Shevchuk, et al. "Risk management and marketing in the IT industry for course analysis and forecasting of commodity money." Optoelectronic Information-Power Technologies 47, no. 1 (2024): 17–27. http://dx.doi.org/10.31649/1681-7893-2024-47-1-17-27.

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The article results in the development of an intelligent portal for analysing and forecasting the exchange rate of commodity money. This portal includes a comprehensive analysis of the commodity money market using advanced risk management and marketing technologies in the IT industry. The study used the scikit-learn, matplotlib, seaborn, yfinance, metrics libraries, as well as Prophet and Monte Carlo models. The choice of model is determined by the specific task and user requirements. Using the Prophet model allowed us to effectively predict the rate of a single asset, while the Monte Carlo mo

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Maulana, Rafli Iqbal, and Eka Angga Laksana. "Analisis Kinerja Pasar Saham Berbasis Business Intelligence secara Realtime." Jurnal Tekno Kompak 18, no. 1 (2024): 41. http://dx.doi.org/10.33365/jtk.v18i1.3266.

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Penelitian ini mengeksplorasi penggunaan widget Streamlit dan TradingView Python untuk membuat aplikasi dan dasbor pasar saham khusus. Streamlit adalah kerangka kerja sumber terbuka untuk tim Pembelajaran Mesin dan Ilmu Data, sedangkan TradingView adalah platform pembuatan grafik yang populer. Aplikasi ini memungkinkan pengguna untuk memilih simbol saham dan tanggal mulai untuk melihat harga saham dan indikator teknis. Aplikasi ini menggunakan modul yfinance dan ta untuk mengunduh harga saham dan menghitung indikator teknis. Aplikasi ini juga memungkinkan pengguna untuk mengunduh data harga sa

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Patel, Prof Rahulkumar, Devendra Joshi, Aniket Patil, Prajakta Yeole, and Dhanashri Wani. "Visualization and Forecasting of Stocks Using Python and ML." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 1814–20. http://dx.doi.org/10.22214/ijraset.2023.53954.

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Abstract: Stock trading is one of the most important activities in the world of finance. Market forecasting is the act of trying to determine the future price of other financial instruments traded on the financial exchange . This document explains the forecasting of the market using machine learning. Most stockbrokers use technical and fundamental or time series analysis when making stock forecasts. The programming language used to predict stock markets using machine learning is Python. In this paper, we propose a machine learning (ML) approach that will learn from the data available at yfinan

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M L, Dr Chayadevi. "StockForesight: A Dash-Based System for Visualization and Forecasting of Stock Prices." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 6096–102. https://doi.org/10.22214/ijraset.2025.69459.

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This study aims to provide a comprehensive solution for the analysis of stocks in response to the growing dynamism and complexity of financial markets. Through the incorporation of cutting-edge machine learning algorithms with the flexible features of the yfinance library, the project seeks to provide users with robust prediction capabilities. By using a model based on Support Vector Regression (SVR), the system improves the precision of stock predictions and offers insightful information on the dynamics of market sentiment. In addition, putting an interactive Dash application live on Heroku g

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Mandloi, Shlok, Aryaman Jalali, and Eugene Pinsky. "An Adaptive Hierarchical Tree-Based Clustering Approach to Outlier Detection in ETF-Focused Financial Time-Series." Machine Learning and Applications: An International Journal 12, no. 1 (2025): 131–46. https://doi.org/10.5121/mlaij.2025.12109.

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This paper introduces an adaptive framework for detecting outliers in financial time-series data, focusing on Exchange-Traded Funds (ETFs). The method integrates hierarchical clustering and binary tree analysis to identify unique ETF patterns while isolating anomalies. Using the yfinance API, daily returns for nine ETFs and the S&P 500 index were collected over 24 years. Regression analysis removed market influence, producing residuals that highlight ETF-specific behavior. Hierarchical clustering was applied to these residuals annually, with dendrograms converted into binary trees. Outlier

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9

Zhang, Suqin. "Stock price prediction based on the long short-term memory network." Applied and Computational Engineering 18, no. 1 (2023): 28–32. http://dx.doi.org/10.54254/2755-2721/18/20230958.

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Stock analysis is a challenging task that involves modelling complex and nonlinear dynamics of stock prices and volumes. Long Short-Term Memory (LSTM) is a type of recurrent neural network that can capture long-term dependencies and temporal patterns in time series data. In this paper, a stock analysis method based on LSTM is proposed that can predict future stock prices and transactions using historical data. Yfinance is used to obtain stock data of four technology companies (i.e. Apple, Google, Microsoft, and Amazon) and apply LSTM to extract features and forecast trends. Various techniques

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Sanskriti, Harmukh, Mishra Mansi, Jain Satyam, Chawda Archit, Prasad Kauleshwar, and Kumar Bhawnani Dinesh. "Forecasting Stock Market Index using Artificial Intelligence." Journal of Advances in Computational Intelligence Theory 4, no. 1 (2022): 1–7. https://doi.org/10.5281/zenodo.6500420.

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<em>In this project, we attempt to implement the most popular Deep Learning technique for Time Series Forecasting since they allow for making reliable predictions on time series in many different problems. Instead of dealing with the data points collected randomly, we are using Time Series model to work upon a sequence of data points at a particular time interval. We are using three major modules to forecast the data, and they are Streamlit, Yahoo Finance, and Facebook Prophet. The user can select the number of years according to their convenience for prediction. The data is collected by yfina

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ПЕЛЕЩАК, ІВАН, та ЮРІЙ ФУТРИК. "ПРОГНОЗУВАННЯ ЧАСОВИХ РЯДІВ ЗА ДОПОМОГОЮ НЕЙРОМЕРЕЖІ З ПОСЛІДОВНО З'ЄДНАНИМИ LSTM БЛОКАМИ". Herald of Khmelnytskyi National University. Technical sciences 347, № 1 (2025): 432–41. https://doi.org/10.31891/2307-5732-2025-347-59.

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Удосконалення методів прогнозування часових рядів є важливим завданням для багатьох галузей, таких як фінанси, виробництво, військова справа, медицина та енергетика. Особливо актуальним є використання рекурентних нейронних мереж з блоками LSTM, які дозволяють ефективно враховувати довготривалі залежності у даних. Проте оптимальні архітектури LSTM та параметри, такі як кількість блоків і рівень Dropout, залишаються недостатньо дослідженими. Розробка та оптимізація морфології рекурентної нейронної мережі для прогнозування часових рядів з використанням LSTM блоків, інтеграція EMA (exponential mov

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Bitto, Abu Kowshir, Imran Mahmud, Md Hasan Imam Bijoy, et al. "CryptoAR: scrutinizing the trend and market of cryptocurrency using machine learning approach on time series data." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 3 (2022): 1684. http://dx.doi.org/10.11591/ijeecs.v28.i3.pp1684-1696.

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<span>Cryptocurrencies are encrypted digital or virtual money used to avoid counterfeiting and double spending. The scope of this study is to evaluate cryptocurrencies and forecast their price in the context of the currency rate trends. A public survey was conducted to determine which cryptocurrency is the most well-known among Bangladeshi people. According to the survey respondents, Bitcoin is the most famous cryptocurrency among the eight digital currencies. After that, we'll explore the four most well-known cryptocurrencies: Bitcoin, Ethereum, Litecoin, and Tether token. The 'YFinance

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Bitto, Abu Kowshir, Imran Mahmud, Md. Hasan Imam Bijoy, et al. "CryptoAR: scrutinizing the trend and market of cryptocurrency using machine learning approach on time series data." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 3 (2022): 1684–96. https://doi.org/10.11591/ijeecs.v28.i3.pp1684-1696.

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Cryptocurrencies are encrypted digital or virtual money used to avoid counterfeiting and double spending. The scope of this study is to evaluate cryptocurrencies and forecast their price in the context of the currency rate trends. A public survey was conducted to determine which cryptocurrency is the most well-known among Bangladeshi people. According to the survey respondents, Bitcoin is the most famous cryptocurrency among the eight digital currencies. After that, we'll explore the four most well-known cryptocurrencies: Bitcoin, Ethereum, Litecoin, and Tether token. The 'YFinance&#39

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14

g, Abhishek, Abinav k, Akshitha r, and Mrs .rejitha r. "Stock market Price Prediction Using Machine Learning and Deep learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44240.

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This project presents a dynamic Stock Market Price Prediction Website that utilizes Machine Learning (ML) and Deep Learning (DL) techniques to forecast future stock prices based on real-time and historical data. The system is designed with a full-stack architecture, featuring a responsive frontend using HTML, CSS, and Bootstrap, and a robust backend powered by Python (Django) with data storage handled through SQLite. For data acquisition, the project integrates the Yfinance API to fetch live and historical stock market data and uses BeautifulSoup to scrape the latest financial news articles. T

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15

Pertsev, Y., and L. Korotka. "Comparative analysis of traditional statistical methods and the lstm neural network model." System technologies 1, no. 156 (2025): 65–77. https://doi.org/10.34185/1562-9945-1-156-2025-08.

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This paper presents a comparative analysis of traditional statistical methods (ARIMA, SARIMA) and a modern deep learning approach (LSTM) for financial time series forecasting. The study focuses on evaluating the efficiency of each model in predicting the closing price of Apple Inc. (NASDAQ: AAPL) stock. These models were selected due to their widespread use in financial analysis: ARIMA is suitable for stationary time series, SARIMA accounts for sea-sonal variations, and LSTM excels at capturing nonlinear dependencies and long-term trends. The study is based on historical closing price data of

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Kumar, Ankush. "Stock Price Predictions Using Machine Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48440.

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ABSRACT The goal of this research is to create a reliable ma- chine learning model that uses past market data to predict stock prices. This calls for the primary com- puter language to be Python and the use of special- ised tools like scikit-learn and yfinance. The inten- tion is to create a useful resource for those. Study finance or make stock market investments. This tool will assist them in making wise decisions. Accurately estimating the price of stocks is crucial for wise investment decisions. It lowers risks, assists investors in selecting the optimal stock combination, and may even inc

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Pramulia, Kevin, and Elis Sondang Dasawati. "Implementasi Sistem Pengambilan Keputusan Pembelian Saham di Indonesia berdasarkan Kinerja Berbasis Web." Jurnal Informatika dan Bisnis 14, no. 1 (2025): 15–27. https://doi.org/10.46806/jib.v14i1.1413.

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Stock purchases represent certificates of ownership in a company that are sold to the public. Making a stock purchase requires careful decision-making due to the large number of stock options available. One of the methods used is the trend following method, which can provide profit opportunities. However, calculations are still often done manually, which takes a lot of time to determine which stocks meet the criteria. Currently, there is no system that helps calculate the number of suitable stocks in Indonesia, which would otherwise save time and effort in identifying stocks that align with th

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Asmita Deshmukh. "FinAI: Gamified Finance Learning Platform." Journal of Information Systems Engineering and Management 10, no. 5s (2025): 572–83. https://doi.org/10.52783/jisem.v10i5s.685.

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Financial illiteracy in India is pervasive, leading to detrimental financial decisions and outcomes. In response, we developed FinAI, a software platform designed to simplify complex financial concepts through gamification. By offering accessible courses and clear explanations, FinAI empowers users, particularly beginners, to make informed financial decisions and capitalize on market opportunities. Our study addresses a pressing need for accessible and engaging financial education, aiming to mitigate the negative effects of financial illiteracy. Through FinAI’s gamified approach, users can nav

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Tripathi, Mr Durgesh. "WEALTH’S SECRET." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35276.

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In recent years, trading applications have revolutionized the way individuals engage with the financial markets, providing unprecedented access to real-time data and advanced trading strategies. This paper presents "Wealth Secret," a cutting-edge trading application designed to cater to the needs of both novice and experienced traders. The application leverages real-time stock and index data sourced from yfinance, focusing on the top 50 stocks of Nifty-50, leading stocks in the banking sector, and a comprehensive range of mutual funds including mid-cap, small-cap, and large-cap categories. "We

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Mulia Nur Anisa, Nunjila, and Asep Juarna. "Prediksi Harga Saham Bank BCA, BNI, dan BRI serta Komposisi Portofolio Maksimal Ketiga Saham Berbasis Regresi Linier dan Clustering." Explore 15, no. 1 (2025): 25–31. https://doi.org/10.35200/ex.v15i1.151.

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Harga saham sebuah perusahaan umumnya berubah dari hari ke hari, naik-turun, disebabkan oleh berbagai variabel ekonomi termasuk berbagai kebijakan pemerintah di bidang terkait dan juga sentimen masyarakat terhadap perusahaan tersebut. Perubahan harga saham sulit untuk dibuatkan formula matematikanya namun demikian nilai pendekatannya dapat dikomputasi jika tersedia satu set data historis harga saham tersebut, Portofolio saham adalah kumpulan saham yang dimiliki investor untuk mendapat keuntungan optimal, dalam hal ini tentu maksimal, dari investasi saham. Penelitian ini memprediksi harga tiga

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Amit, Kumar Yadav, Sharma Rohit, and Bainsla Swastik. "Evaluating Prediction of Stock Price using Machine Learning." International Journal of Innovative Science and Research Technology (IJISRT) 9, no. 11 (2024): 2644–54. https://doi.org/10.5281/zenodo.14413363.

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The extrapolation of stock prices is an essential and unresolved problem in the sphere of finance because the results of an accurate forecast can produce considerable economic consequences and the nature of the markets makes the task difficult. This research aims at applying the concept of machine learning in forecasting of stock price for Google shares using historical data of the company’s stock for the last20 years. The qualitative aspect of the research is the collection of data with the use of the yfinance API, data preprocessing with the handling of missing values and removal of ou

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Serhiienko, A. V., V. R. Bashkiser, D. V. Sushchevsky, and Ya V. Panferova. "Forecasting financial markets using the random forest algorithm." Reporter of the Priazovskyi State Technical University. Section: Technical sciences, no. 47 (December 28, 2023): 100–108. http://dx.doi.org/10.31498/2225-6733.47.2023.299987.

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The article provides material on the analysis of the financial market using the random forest algorithm. The general problem of forecasting financial markets and the role of modern technologies for accurate forecasts and automation of trading strategies are considered. An overview of existing forecasting models and the possibility of their application for financial markets was conducted. The latest studies and publications were analyzed, on the basis of which a research program was developed. The created program has a modular structure and represents a library that can be used for further rese

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Josey, Aaron, and Amrutha N. "Stock Market Prediction." Indian Journal of Data Mining 4, no. 1 (2024): 34–37. http://dx.doi.org/10.54105/ijdm.a1641.04010524.

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The prediction of stock market trends is a challenging yet critical task in the financial sector, given its significant implications for investors, traders, and financial institutions. This research leverages the Long Short-Term Memory (LSTM) algorithm, a type of recurrent neural network (RNN), to develop a robust model for forecasting stock prices. The study utilizes historical stock market data sourced from Yahoo Finance, accessed via the yfinance package in Python. The primary objectives are to preprocess the data, implement the LSTM model, and evaluate its performance against traditional m

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Aaron, Josey. "Stock Market Prediction." Indian Journal of Data Mining (IJDM) 4, no. 1 (2024): 34–37. https://doi.org/10.54105/ijdm.A1641.04010524.

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<strong>Abstract:</strong> The prediction of stock market trends is a challenging yet critical task in the financial sector, given its significant implications for investors, traders, and financial institutions. This research leverages the Long Short-Term Memory (LSTM) algorithm, a type of recurrent neural network (RNN), to develop a robust model for forecasting stock prices. The study utilizes historical stock market data sourced from Yahoo Finance, accessed via the yfinance package in Python. The primary objectives are to preprocess the data, implement the LSTM model, and evaluate its perfor

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Nandan S and Usha Sree R. "Cryptocurrency Price Prediction using Machine Learning." International Journal of Advanced Research in Science, Communication and Technology, November 30, 2024, 141–44. https://doi.org/10.48175/ijarsct-22530.

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Cryptocurrency price prediction is a complex task due to the volatile and dynamic nature of the market. To tackle this challenge, a study was conducted to compare the effectiveness of two popular machine learning models: Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). These models are designed to capture temporal dependencies in sequential data, making them suitable for predicting price movements based on historical trends. The study utilized historical price data, specifically focusing on the closing prices of various cryptocurrencies. The data was collected using the yfinance l

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"VISUALISING AND FORECASTING THE STOCK PRICE." International Journal For Innovative Engineering and Management Research, September 27, 2022, 80–89. http://dx.doi.org/10.48047/ijiemr/v11/i06/08.

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Stock investments provide one of the highest returns in the market. Even though they are volatile in nature, one can visualize share prices and other statistical factors which helps the keen investors carefully decide on which company they want to spend their earnings on. Developing this simple project idea using the Dash (a Python framework) to create web application, we can make dynamic plots of the financial data of a specific company by using the tabular data provided by yfinance python library and some machine learning models which will show company information and stock plots based on th

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Jovita Reejhsinghani, Mohit Gurnani, Aastha Makhija, Harshit Dhir, Sanjay Wankhade, and Vaishali Bodhale. "INTEGRATING AI AND MARKET ANALYTICS: A SMART FINANCIAL COMPANION." EPRA International Journal of Economic and Business Review, March 19, 2025, 38–41. https://doi.org/10.36713/epra20601.

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While financial management is an integral part of life today, many people find budgeting, expenditure control, and investment financing to be quite challenging. This paper presents FinanceGPT, a personal finance management assistant that helps with budgeting and monitoring of expenditures such as an interactive chatbot, real-time market feeds, and automated expense categorization. For automated market analysis, FinanceGPT uses yfinance, while mfapi.in is used for mutual fund data harvesting. Users are guided into financial planning using an intuitive and heuristic approach. To promote addition

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Saketha N and Dr. Chitra K. "FINSYNC AI: Stock Market Analysis." International Journal of Advanced Research in Science, Communication and Technology, November 30, 2024, 157–60. https://doi.org/10.48175/ijarsct-22534.

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The goal of the study "FINSYNC AI" is to enhance the accuracy and effectiveness of financial market forecasts by introducing a complex stock market prediction system. This work makes use of the frameworks Gradient Boosting Machine, Random Forest, Long Short-Term Memory (LSTM), and the proposed FINSYNC AI. The process involves acquiring historical stock market data, cleaning and normalizing it, and then feature engineering to produce new features like rolling means and trend components. These preprocessed data are then used to test and train several machine learning models in order to assess th

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Sudiatmika, I. Putu Gede Abdi, and I. Made Agus Wirahadi Putra. "Comparison of LSTM and GRU Models Performance in Forecasting Gold Prices: A Case Study Using Historical Data from Yahoo Finance." ARRUS Journal of Engineering and Technology 4, no. 1 (2024). https://doi.org/10.35877/jetech2760.

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This research aims to compare the performance of two types of recurrent neural network models, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), in forecasting gold prices based on historical closing price data. Historical gold price data from December 14, 2017, to March 14, 2024, was downloaded using the yfinance library. The data was normalized using MinMaxScaler and split into training and testing sets with an 80:20 ratio. LSTM and GRU models were constructed with two recurrent layers followed by a Dense layer for output. Both models were trained using the training data and eval

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Swati Khatri, Ankita Kasab, Shivani Dahake, and Namrata Hiwale. "Stock Price Prediction using Technical Analysis." International Journal of Advanced Research in Science, Communication and Technology, April 27, 2023, 308–15. http://dx.doi.org/10.48175/ijarsct-9510.

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Investors must have access to timely, accurate information in order to trade stocks effectively. Since many companies are traded on a stock exchange, a variety of factors affect the choice. In addition, it is difficult to foresee how stock prices will behave. The technique of predicting stock prices is crucial and difficult for the reasons mentioned above. Finding the predictive model with the lowest error rate and highest accuracy thus becomes a study topic. This work is our suggestion for solving the issue. In this work, we develop a model based on technical analysis which used Long Short-Te

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manu. "i like it." August 5, 2023. https://doi.org/10.9783/new.story.

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Financial and Economic Datasets in AI Training: Unveiling the Power of Data-driven Insights The rapid advancements in Artificial Intelligence (AI) and Machine Learning (ML) have transformed various industries, with finance and economics being no exception. The integration of financial and economic datasets in AI training has opened up a plethora of opportunities to gain valuable insights, optimize decision-making processes, and uncover hidden patterns. This article delves into the diverse uses of financial and economic datasets in AI training and their pivotal role in reshaping the financial l

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